Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere (Oxford Biology)

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Author: Charles W. Fowler

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SYSTEMIC MANAGEMENT

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Systemic Management Sustainable Human Interactions with Ecosystems and the Biosphere Charles W. Fowler National Marine Mammal Laboratory Seattle, Washington

The views and opinions expressed herein are strictly those of the author and do not reflect policy of the National Marine Fisheries Service or the Department of Commerce.

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Great Clarendon Street, Oxford OX2 6DP Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York © Oxford University Press 2009 The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2009 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this book in any other binding or cover and you must impose the same condition on any acquirer British Library Cataloguing in Publication Data Data available Library of Congress Cataloging in Publication Data Data available Typeset by Newgen Imaging Systems (P) Ltd., Chennai, India Printed in Great Britain on acid-free paper by CPI Antony Rowe, Chippenham, Wiltshire ISBN 978–0–19–954096–9 10 9 8 7 6 5 4 3 2 1

About the cover

The art on the cover of this book was painted by Katherine Zecca, former graphic designer and scientific illustrator at the Alaska Fisheries Science Center (NOAA, NMFS) in Seattle, Washington, and now a freelance artist in Vermont (http://www.katherinezecca.com/). Typical of the excellence of her work, this piece shows some of the patterns and processes characteristic of ecosystems. Primary producers are found on the left with species from increasingly higher trophic categories toward the right. Species with small bodies are found at the bottom and the largest species are found at the top. Partially hidden in various locations are species that represent

processes and characteristics of species and ecosystems. These include extinction (a fish), mobility (a bat), and symbiotic interactions (a humming bird), and emerging diseases (an AIDS virus). Original requests for this art expressed the hope that a hidden representation of humans, to symbolize their influence on, and place in, nature, could be included. The difficulty of the task was rendered moot when readers pointed out that the ear of the elephant contains three different hidden images of human faces. These were totally unintentional, making their presence even more symbolic of the message of this book.

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Dedication

In our every deliberation we must consider the impact of our decisions on the next seven generations. Great law of the Hau de no Saunee*

This book is dedicated to (and in memory of) my father, Ervon W. Fowler, for his lessons in thought and to (and in memory of) my mother, Merna, who taught me the love of reality. It is also dedicated to Dr Gordon Orians whose incomparable insight regarding the natural world is reflected in my thinking, to my wonderful wife, Jean, for her enduring support and love, and finally, and most importantly, to the next seven generations of all species.

* Versions of the “law of the seventh generation” similar to that quoted here are frequently found in expressions of responsibility for our future. Some of its historical origins are documented conceptually in Fenton (1968), Parker (1916), Scott (1912), and Wallace (1946). As indicated by Hodgson (1989): “The law of the seven generations is found in the Bible and in the teachings of many cultures around the world. Among the First Nations, the Iroquois maintain it is a gift from our ancestors, whose overwhelming wisdom is inherent in its culture, political and philosophical definition. As Chiefs, as Leaders, we are to consider carefully, the effects of our decisions on the seven generations yet to come.”

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Preface

We can never solve our significant problems from the same level of thinking we were at when we created the problem. —Albert Einstein

Humans are now reawakening to an awareness of themselves as part of the vastly complex web of life. In part, we are realizing the enormous influence our species has and the extent to which we—in our hubris—have disrupted this web, risking extinction of our own species along with countless others. Can humanity find for itself a sustainable niche within the web of life? What serves to define that niche? These are the ultimate questions addressed by this book. Such questions were hardly so clear when this book was first conceived. In the mid-1980s, the United States’ National Marine Mammal Laboratory was reorganized, partly with the intent of converting from a species-oriented structure to a discipline-oriented structure. This change was related to decisions within the National Marine Fisheries Service, along with other government and nongovernmental organizations, to take seriously management as it would involve ecosystems. I became responsible for contributing to a scientific basis for management at the ecosystem level. I soon learned, however, that we only make a small step in the right direction when ecosystems are added to what we consider. It became clear that extending conventional concepts of management to ecosystems is causing more problems than are being solved. Full-scale change is needed, and a major part involves the way we think—our beliefs. One of the central themes of this book is that of identifying the information that most realistically provides guidance—a very select part of the information revealed by science.

I came to realize that we cannot control the nonhuman; control is limited primarily to human action. Reining in our incredible influential power serves as a form of management. The option of minimizing abnormality, to achieve health (at all levels) becomes real. We can limit our interactions with other species, with ecosystems, and with individuals (Christensen et al. 1996, Fowler 2005, Fowler and Hobbs 2002, Mangel et al. 1996). Thus, the option of managing our use of ecosystems to mimic other species emerged in my thinking as an option that has its parallels at all levels of biological organization. We, as individuals, and as a species, can manage our use of, interactions with, and influence on, other individuals, other species, ecosystems, and the biosphere. Our species is just one part of the picture. This reality requires us to face questions about the constraints and actions our species should require of itself. We need to recognize that we are part of various systems that include other species faced with the same reality and complexity that we face. Fitting in means being a normal part. Early in my work on this book, it seemed to me that the problems that confront us could be dealt with by further consideration, acceptance, and integration of the concept of extinction—especially selective extinction and speciation. However, it soon became clear that this was not going to suffice for management either. Like other fields of science, it provides critical understanding, but could never guide decision-making by itself. Whole systems thinking also greatly influenced my own thinking. All systems are finite while complexity and

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reality are not; there had to be an approach that was not confined to any one system or level in the hierarchical structure of life. All had to be taken into account. A different perspective began to take form. The form of management that emerged was systemic management to minimize human abnormality. Realizing the potential was one of the most gratifying experiences of my career. Systemic management builds on the variety of ways we understand reality. With a major reassignment of the role of stakeholders, it conforms to the requirements of management as laid out in the literature. In my attempt to digest the literature on management at the ecosystem level, I found frequent mention of the need for “unifying” perspectives; holism was needed. As I proceeded, the needs and the potential seemed to match. What we study are the parts of reality in its unified form: holism is found in reality; informative natural patterns emerge in this reality, integrating through emergence. This book, then, is intended to open the door to a conversion in thinking, similar to what I experienced in the process of writing it. I see a need for the recognition and application of information more directly related to reality. All of the things that science studies are combined in nature—the reality we want to be part of in finding sustainability. That combination is what all sciences would account for if their disciplines could be united. My hope is that humans can find sustainability in this context.

I expect controversy—controversy stems naturally from change. Most controversial, perhaps, will be the conclusion that the role of values and opinions needs to be replaced. They take on an entirely different role in systemic management: motivation for asking clear management questions. I identify problems in the conventional use of science. This is not to be misinterpreted; research and scientists are not to be branded as wrong or ill-intended. Rather than demonize science, my message concerns the identification and use of the products of science that best meet our needs for guidance. I wrote this book for the benefit of the biota of the earth, focusing on action we humans can take, primarily as a species. As parents discover, we are not born with user guides: one title suggested for the book was “How to be a successful species.” This book is part of my contribution to human success through my own individual action. If the messages and implications are troublesome, we must be mindful that benefits at the species level often require sacrifice at the individual level, sacrifices that become more extreme when ecosystems and the biosphere are included. In the spectrum of time scales, short-term benefits involve long-term costs and, conversely, short-term efforts involve longterm gains. This book is my best effort toward thinking of the welfare of future generations of the human species, and all those species that accompany and support us in life. I hope it will lead to substantive progress.

Acknowledgments

I could never have produced this book alone. I benefitted beyond measure from the support, challenge, and insight, from the complements and criticisms, and from the advice and difference of opinion from others—all immeasurable contributions to an improved product. My phenomenal wife, Jean, supported me through the entire process, tolerating extensive hours spent working at home, and enduring diversions that true passions entail. There can be no adequate expression of my thanks to her! Howard Braham, R.V. Miller, Doug DeMaster, Sue Moore and John Bengtson, Peter Boveng, Phil Clapham, Bob DeLong, Tom Gelatt, Tom Loughlin, and Paul Wade were supervisors and fellow program leaders at the National Marine Mammal Laboratory during my work on this book. These understanding people sacrificed time on my behalf and provided much needed support and encouragement. If there are others who deserve co-authorship in this work, they would clearly include Jason Baker, Larry Hobbs and Dorothy Craig. Jason provided invaluable help in editing various versions of the book, wrestling with consistency, and endless discussions regarding the weaving of concepts into an intelligible whole. Larry has been absolutely invaluable and a constant source of support and wisdom, ideas and thinking throughout the process. The importance of his contributions is beyond measure. Dorothy brought both a passion and understanding of my message as well as a sharp editorial mind. I have learned as much in working with these three as I have in any other aspect of this project. In a similarly personal way, I want to thank Shannon McCluskey and Cynthia Hedlund for reminding me of myself—easily lost in the mission and passions behind this book.

I thank both Alec MacCall, and Andrea Belgrano for their encouragement, interest, and suggestions for getting this book published. Alec provided more reviews of this book than any other person; his contributions are deeply appreciated. Andrea’s suggestion that Oxford University Press should be the publisher was pivotal in getting to this point. I thank my colleagues at the Alaska Fisheries Science Center in Seattle, especially those who contributed to the final review of the manuscript: Phil Clapham, Bob DeLong, Sharon Melin, Susan Picquelle, Jay VerHoef, and Paul Wade. Over the years, the immense task of getting to this point has involved valuable reviews (including those of related foundational papers), many discussions, criticisms, and challenges, moral support, altered schedules, art and graphs, suggested references, information, and data extracted from a wide variety of sources. Numerous interns and students have been invaluable in searching literature, finding data, and writing reports. In seeking publication, several editors have provided invaluable comments and suggestions as did reviewers from whom they sought advice. I requested reviews from a variety of fields of science, from various friends and from many colleagues—all of them were invaluable in their help. Earlier drafts of this book were used in university-level courses at several universities, and I appreciate the criticisms, comments, and suggestions that were generated. Collectively they are too numerous to list, but I extend my heartfelt appreciation to each and every one. In addition to those listed above, Jim Anderson, Eric Charnov, Jerry Cufley, Alec MacCall, Jim MacMahon, Marty Nelson, Mike Perez, Tim Ragen, Tim Smith, and Paula White have been a continuing xi

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source of inspiration and our many thought-provoking conversations have been invaluable to me. Special thanks go to Bob Francis and Marc Mangel who coordinated two of the upper-division university courses that used the manuscript for this book as a text. The kindness of Jim Brown, Robert Furness, Jane Masterson, Melanie Moses, Mark Pagel, Robert Peters, Robin Phillips, and Douglas Kelt for their efforts and time in providing data and graphic material is gratefully acknowledged. Various communities of people have contributed to foundations upon which this book rests. These include those who have developed (and continue to develop) the various fields of science—especially those focusing on selective extinction/speciation (see Okasha 2006), ecology, and macroecology. The time, money, and personal expense behind revealing pattern-based information is immense and cannot go without being acknowledged. Another group includes those who have spent considerable time and effort in developing the concepts of management (e.g., see the references of Appendices 4.1 and 4.3 and workers to whom these references lead). This extends to those who have delved into

epistemology and the role of human thought in psychology, values, decision making, and control. Here, people like Gregory Bateson count heavily in my mind. I am grateful to all the people who have contributed to thinking about management. There is a clear precedence to the message that we can learn from nature in the works of people like Aldo Leopold, John Muir and Wendell Berry—an innumerable group of thinkers (including many from the world’s ancient wisdom traditions) whose contributions I hope I have at least partially translated to a scientifically based way of proceeding. To those whom I have overlooked in the many hours of searching the relevant literature, I apologize. I thank them all! The editorial help of Dorothy Craig, Gary Duker and Jim Lee was invaluable. My sincere gratitude to the editors and staff at Oxford University Press, especially Ian Sherman, Helen Eaton, Carol Bestley, Caroline Broughton, and Kathy Lahav—tireless dedicated people. This book is what it is, in part, as a result of everyone’s contribution. It remains a first step, however, and only the future can reveal how blind I remain in my human view of that future and the reality to be faced in being part of it.

Contents

1 Systemic management—what and why? 1.1 Tenets of management 1.2 Motivation for change 1.3 Introducing pattern-based management: systemic management in the eastern Bering Sea 1.4 Summary and preview

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2 Patterns among species: information 2.1 Patterns among species 2.2 The eastern Bering Sea example 2.3 Summary and preview

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3 Selective extinction and speciation 3.1 Natural selection among individuals and species 3.2 Understanding extinction and speciation 3.3 Interaction of evolutionary processes 3.4 Influence of environment on extinction, speciation, and selectivity 3.5 Historical perspective 3.6 Implications for management 3.7 The eastern Bering Sea example 3.8 Summary and preview

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4 Why conventional management does not work 4.1 Transitive management in relation to identified criteria for management 4.2 Other recognized drawbacks of conventional management 4.3 Inadequacy of current approaches to management by focal level 4.4 Fatal flaws in conventional management by focal level 4.5 Including ecosystems and the biosphere in management 4.6 The case for systemic management 4.7 The eastern Bering Sea example 4.8 Summary and preview

78 78 84 93 108 111 116 117 120

5 Why systemic management works 5.1 Systemic management adheres to the tenets of management 5.2 Limitations of systemic management 5.3 Why extinction should be a management issue

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68 70 74 75 77

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5.4 A protocol for systemic management 5.5 The eastern Bering Sea example 5.6 Summary and preview

148 153 157

6 Humans: a species beyond limits 6.1 Limits to natural variation as limits to sustainability 6.2 Consideration of hierarchy 6.3 The eastern Bering Sea example 6.4 Summary and preview

159 159 190 196 203

Epilogue Notes List of Appendices References Author Index Subject Index

204 227 251 253 279 285

CHAPTER 1

Systemic management—what and why?

When we try to pick out anything by itself, we find it hitched to everything else in the Universe. —John Muir

We are faced with an uncertain future. Species are disappearing. Deforestation, agriculture, and fishing have changed ecosystems. Our polluted Earth is warming, the pH of our oceans is declining, and introduced species are altering their habitat. Scientists discover more problems every day. We may have evolved to become an extinction-prone species. Can humans manage other species, ecosystems, or the Earth in response to this information? No. Because of the complexity and interconnected nature of reality, management that seeks to control, dominate, or design such systems is doomed to ultimate failure. Managing the nonhuman usually causes more problems than it solves. Within limits, what we can manage is ourselves— in essence, reinventing ourselves as a species (Berry 1999). We can manage our interactions, influence, and relationships with each other and with the nonhuman. Managing to find an appropriate fit or place in our universe (“systemic management”, Fowler 2003) requires self-control. This book explores systemic management, its applications, and foundation. Such management has a broad set of objectives, with the primary goal being sustainability for both humans and the nonhuman.1 This means striving toward a future in which ecosystems and the biosphere can support a diversity of life, including humans. Ecosystems and other living systems respond to human influence and will respond to a sustainable human presence. One of the conclusions of this book is that immense change will be required; current

efforts are exposed as largely artificial, superficial, and fallacious. This book has two primary objectives: 1. To propose systemic management as a replacement for conventional forms of management. Systemic management emerges from the tenets developed for management in the last several decades. These include our understanding of ourselves (e.g., as individuals, societies, and a species), our thinking and our belief systems. Systemic management is realitybased management with a variety of components that include ecosystem-based management. In its application, systemic management fully accounts for the complexity of nature and avoids the errors of conventional management. 2. To describe an empirical objective methodology that provides guidance for achieving sustainable relationships between humans and the nonhuman. Carefully using natural patterns (Belgrano and Fowler 2008, Fowler 2003, Fowler and Hobbs 2002) to guide human endeavor achieves objectivity not found in conventional management. The selection of patterns that will meet our needs requires that we ask our management questions with extreme care. Science then reveals patterns that match these questions and provide answers that prevent the misguidance of conventional management. Such science best meets the needs of management. Although systemic management applies generally, the examples in this book focus primarily on sustainability among species. Empirical 1

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(e.g., macroecological) patterns reveal what it means to be sustainable as a species. Species reflect the numerous and often very powerful forces of nature. Our species has the choice of living within the limits of nature or facing the consequences of being anomalous—one of which is an untimely extinction. Defining what it takes and then being a successful species are both complex, nontrivial matters. Complicated relationships with our environment include countless interactions with other species, ecosystems, and the biosphere (e.g., our use of water, consumption of food, and production of CO2). Management involves social, religious, ethnic, political, and economic elements, exemplified by decision making, laws, policy, and action. Systemic management involves actions and decisions by individuals, institutions, and society—all carried out consistently (Hobbs and Fowler 2008). Being in accord with the laws of nature by mimicking empirical examples of sustainability results in consistency and objectivity. Because empirical examples reflect the complexity of their emergence, systemic management involves context and the complexity of the biological systems of which we are a part. It involves time scales we ordinarily ignore. It involves thinking about our thinking so as to embrace a basis for management that replaces thinking. It means abandoning the artificial and adopting the real. It means embracing the holistic of reality (Appendix 1.1). This chapter summarizes the underlying concepts of the book including: An introduction to systemic management as it emerges from published tenets of management, derived in part from the recognized need for ecosystem applications (Fowler 2003). A review of the motivation to reject current management and accept a preferable, more objective, alternative. An introduction to some of the steps involved in systemic management as applied to the eastern Bering Sea, an example of the ecosystem component of systemic management that will be developed at the end of each subsequent chapter. A summary and preview of the progression of concepts found in the chapters ahead.

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1.1 Tenets of management Management includes our use of natural resources, our interaction with other species, ecosystems, and the biosphere. Great importance was placed on defining the principles of such management in the late 20th century (Rockford et al. 2008). Much of this effort was motivated by the need for management that applies to ecosystems (e.g., Christensen et al. 1996, McCormick 1999). The resulting work has been synthesized on numerous occasions (e.g., Arkema et al. 2006, Fowler 2003, Francis et al. 2007, Lackey 1998). With one primary exception, these tenets are more completely embodied in systemic management than any other approach. That exception involves the role of stakeholders (Fig. 1.1); it is a major exception. In conventional management, stakeholders are involved in setting objectives and making decisions. In systemic management, the role of stakeholders is confined to asking clear management and science questions, followed by carrying out management with objectives inherent to the information produced by science. Objectivity is achieved in this shift—systemic management is pattern-based rather than opinion-based management (Belgrano and Fowler 2008). The science behind systemic management provides information that needs no conversion; the translation (Brosnan and Groom 2006) inherent to current management becomes unnecessary in answering management questions systemically. Nine primary tenets of management are introduced below. Following each tenet is a very brief description of how systemic management simultaneously and consistently adheres to the principles involved. Management Tenet 1: Management must be based on an understanding of humans as part of complex biological systems (Christensen et al. 1996, Mangel et al. 1996, NRC 1999). Systemically, humans are not separate from, unaffected by, or free of our own limits, or the limits imposed by the systems we influence and of which we are a part. Management includes learning to function sustainably as components of not only ecosystems and the biosphere but of the interconnected universe in which we find ourselves. Precluding human existence is not an objective. Management Tenet 2: Management must recognize that control over other species and ecosystems is impos-

SYS T EM I C M A N AG EM EN T— W H AT A N D W H Y?

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Problem or management issue

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Stakeholders Systemic management Figure 1.1 Schematic comparison of conventional and systemic management showing the difference in the role of stakeholders (Belgrano and Fowler 2008).

sible (Christensen et al. 1996, Holling and Meffe 1996, Mangel, et al. 1996, NRC 1999). The most realistic option for control is control of human action (intransitive management, Fowler 2003) and decision making—as limited as that itself may be. The self-control of systemic management acknowledges that we can control our influence on the nonhuman (e.g., fishing with all its effects on fish, and their ecosystem) much more effectively than we can control the nonhuman (e.g., other species, their ecosystems, and habitat). It is axiomatic that all human influence (like the influence of all species) has its consequences, whether direct or indirect—a fact over which we have no control. Management Tenet 3: Management must account for the complexities of reality, including the various scales of time, space, and biological organization (Christensen et al. 1996, Costanza et al. 1992, Mangel et al. 1996, Moote et al. 1994). Systemic management is based, in part, on the reality of emergence. 2 Through emergence, natural patterns are infinitely integrative and replace human institutions in objectively and holistically accounting for complexity in the interconnectedness of nature (Belgrano and Fowler

2008). Included in this complexity is the reality of numerous human influences. Human limitations are real—there are things we will never know. Management Tenet 4: Management must be consistent in its applications, particularly in simultaneous application at the various levels of biological organization (Christensen et al. 1996, Costanza et al. 1992, Ecosystem Principles Advisory Panel 1998, Hobbs and Fowler 2008, Moote et al. 1994). In systemic management, the thinking and action of individuals, governments, and institutions are consistent. Human influence on nonhuman populations or species is consistent with our influence on ecosystems or the biosphere. There is consistency between management questions and guiding information produced by science. Management Tenet 5: Management must strive to avoid the abnormal in processes, relationships, individual organisms, species, and ecosystems (Belgrano and Fowler 2008, Christensen et al. 1996, Ecosystem Principles Advisory Panel 1998, Fowler and Hobbs 2002, Mangel et al. 1996). Abnormal conditions of any kind are the basis for taking the responsibility to find and correct underlying human

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abnormalities. Healthy1 systems are an important objective of systemic management—with consistency (Management Tenet 4) involving individuals, species, ecosystems, and the biosphere. This tenet promotes the objectivity of systemic management achieved by confining the role of stakeholders to that of asking clear questions (Fig. 1.1) followed by carrying out management action. Management Tenet 6: Management must be risk averse and strive to achieve sustainability (Christensen et al. 1996, Francis et al. 2007, Holt and Talbot 1978, Mangel et al. 1996, NRC 1999). Sustainability is, by definition, not achieved by any form of management that generates abnormal levels of risk. In adhering to Tenets 3 and 4, systemic management treats risk in its aggregate so that consistency is achieved. Extinction is a risk accounted for in the emergence of patterns. Sustainability is achieved by avoiding the abnormal (Management Tenet 5) as revealed in such patterns. Management Tenet 7: Management must be based on information (Christensen et al. 1996, Interagency Ecosystem Management Task Force 1995, Malone 1995, Mangel et al. 1996, NRC 2004). Information from integrative patterns is at the core of systemic management (Belgrano and Fowler 2008, Fowler 2003, 2008). This information, at least partially cybernetic in nature, provides managers with objective, meaningful, measurable, and reasonable goals and standards (Management Tenet 9) of which maximizing information and biodiversity (Fowler 2008) is an option. Management Tenet 8: Management must include science and scientific information (Christensen et al. 1996, Francis et al. 2007, Interagency Ecosystem Management Task Force 1995, Malone 1995, Mangel et al. 1996). Both conventional and systemic management rely on carefully conducted science. Systemic management, however, recognizes a difference between the products of science and the scientific process to define the best scientific information for management: there must be consonance between management questions and the scientifically revealed natural patterns used to address them (Management Tenet 4) in implementing Management Tenet 5. Management Tenet 9: Management must have clearly defined, measurable goals and objectives (Christensen

et al. 1996, NRC 1999). Integrative natural patterns provide a basis for detecting and measuring the abnormal. The magnitude of problems before us becomes clear. Fixed-point goals are not part of systemic management insofar as patterns in variation of variation make adaptability part of the process. Systemic management provides consistent (Management Tenet 4) guidelines, criteria, and standards of reference for action (e.g., to maximize biodiversity; Belgrano and Fowler 2008). Notable in the list above is the confined role of stakeholders; this is consistent (Management Tenet 4) with the requirement of objectivity inherent to Management Tenets 7 and 8. The importance of involving stakeholders is widely recognized (e.g., Arkema, et al. 2006, Francis et al. 2007, Moote et al. 1994, Mangel et al. 1996, Phillips and Randolph 2000). However, this is often equated with making human values other than that of systemic sustainability (avoiding the abnormal) the basis for the decision-making process (Franklin 1997, Grumbine 1997, Hull 2006, Mangel et al. 1996, Moote et al. 1994, NRC 2006, Norton 1987, Sagoff 1992, Salwasser et al. 1993) rather than simply knowing that such values count as components of the complex systems of which we are a part. Intentional use of such values in decision making effectively precludes from the tenets found in the literature a tenet specifically expressing demands for objectivity (e.g., solution to the polarity between the biocentric and anthropocentric; Stanley 1995); some claim that valueneutral approaches are a myth (Norton 2005). Systemic management acknowledges all stakeholders (managers, scientists, government leaders, organizations and institutions, indigenous societies, and individuals) and their values as parts of systems important to management (Management Tenet 1). In conventional management, stakeholders are involved in decision making (converting information to objectives and the intended course of action; Brosnan and Groom 2006) making management subjective. In systemic management, this role is replaced by informative integrative patterns (Fig. 1.1) produced through research designed in response to good management questions, posed with the help of all stakeholders. Human limitations do not enter into the process of converting

SYS T EM I C M A N AG EM EN T— W H AT A N D W H Y?

information to objectives, thus dealing with the reality (Management Tenet 3) of human limits. This achieves a degree of objectivity not possible in conventional management (Belgrano and Fowler 2008). Each carefully posed management question then defines the science needed to reveal the matching pattern; the science that best meets the needs of management. Defining such science has not heretofore been possible (NRC 2004).

1.2 Motivation for change There are a number of reasons to consider alternatives to conventional management. The need for change is seen on many fronts and falls into six categories: Deterioration (abnormal state) of many of the world’s ecosystems, the biosphere, and the environment in general—failures of conventional management. Recognized inherent inadequacies that contribute to the failures of conventional management. Legal mandates in the form of legislation and international agreements that require managers to account for more complexity (e.g., ecosystems and other broader bioregional and global scales, evolutionary and geological time scales). Flaws in the traditional use of science in management. Lack of standards, criteria, reference points, and normative information for systems such as ecosystems. Lack of clearly defined, broadly accepted ecosystem-level management.

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Earth’s ecosystems and biosphere (MEA 2005a, b); much of this change is anthropogenic (Moran 2006). The magnitude and pervasive nature of anthropogenic influence on our planet has reached such extremes that scientists now refer to this period of time in Earth’s history as the Anthropocene Era, partly in response to the geophysical nature of some of the impacts we are having (Steffen et al. 2004). In many cases, scientists think of the current states of ecosystems and the biosphere as abnormal. The abnormal concentration of CO2 currently in our atmosphere is an example (Fig. 1.2), bringing with it global warming, oceanic acidification, redistribution of water/ice, and other associated changes and ramifications yet to be recognized by science. We have cleared forests for agricultural use and harvested timber so extensively as to cause

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CO2 concentration (ppm)

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One of the roles of science is to reveal problems. Science has revealed a great deal of change in the

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1.2.1 Abnormal state of ecosystems and the biosphere

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Most, or all, of the above involve human values that are brought to the task of change—change developed in this book as critically necessary. This change involves the processes of decision making, policy setting, and finding objectives. It avoids using values (other than sustainability), emotions, politics, and opinion as the basis for policy.

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Figure 1.2 The historical record (as reconstructed from ice cores) of atmospheric CO2 concentrations for the past 400,000 years (ppm, top panel) and the resulting pattern of variation showing the abnormality of currently observed levels (bottom panel; Dr Pieter Tans, NOAA/ESRL [www.cmdl.noaa.gov/gmd/ccgg/trends]).

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Figure 1.3 The forest cover of New Zealand (shown in black) in its decline from about 1000 AD (prior to human arrival) to 1840 (prior to European settlement) and the mid-1990s (updated from Mark and McSweeney 1990, after Froude et al. 1985, with maps provided by R. Philips).

dramatic declines in the area of the Earth covered by forests (e.g., Fig. 1.3). Even greater changes have occurred in grassland ecosystems (Thomas 1956). The world’s commercial fisheries are largely overharvested (even by conventional standards, Francis et al. 2007, Myers and Worm 2003, NRC 1999, Rosenberg et al. 1993) and many freshwater fisheries have disappeared (Karr 1994). Bats, bees, and other pollinating species are exhibiting population declines (NRC 2007). Many species are threatened; amphibian extinction is a small part of an overall extinction crisis (Groom et al. 2006, Raven and Cracraft 1999). Related ecosystem changes include: reduced food chain length (mean trophic level) and reduced mean body size in many species, increased predominance of pests and diseases, and altered species composition, distribution of biomass, productivity, decomposition, cycling of nutrients and biomass, community respiration levels, and net production. Although many problems are obvious, there is concern that we have little information about the normal states of ecosystems against

which to objectively measure the abnormality (Costanza et al. 1992a). The consequences of altered ecosystems to humans—especially future generations—are increasingly being recognized. One ultimate consequence is the increased risk of human extinction.3 Short of extinction is feedback (e.g., a global pandemic, Garrett 1994) resulting in the risk of an abnormally low human population. As indicated by Rapport (1992), altered ecosystems often include the loss of goods, services in support of humans,4 increasingly widespread disease and starvation (Pimentel et al. 2007), and altered coevolutionary interactions. In the larger perspective (systemically), the loss of goods and services to all species is of concern. Although human values may not be objective, they do serve to bring our attention to factors relevant to asking good management questions. Our emotional reactions to the changes we see around us serve well to help focus on specific interconnections and relationships between humans and the

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nonhuman. The various values we bring to decision making can be used to focus management questions. Among these values are those we place on healthy ecosystems. These fall within a spectrum between the utilitarian-materialistic and intangibleaesthetic. Sagoff (1992) considers these values in three categories: instrumental, aesthetic, and moral. These values have resulted in action— management that has contributed to the patterns we observe today. Current management fails to address the extent to which instrumental, aesthetic, and moral benefits can be sustainably provided by the earth’s ecosystems. These benefits count among the complete set of services provided by ecosystems—important not only to humans, but to other species as well. In other words, the goods and services recognized as important to humans are important to all species. Every species depends on its ecosystem for survival. The related services include producing oxygen, absorbing carbon dioxide, regulating climate, recycling wastes (including detoxifying pollutants), controlling pests and disease (including resistance to invasion by exotic species), pollinating plants, preventing erosion, storing nutrients, and maintaining diversity (genetic potential for the future). The loss of such services for all species counts among the risks to be accounted for in management (Management Tenet 6). Economists find little challenge in placing monetary value on material products and many ecosystem services (e.g., see World Conservation Monitoring Centre 1992), and translate the loss of products from disturbed ecosystems to economic losses (e.g., Page 1992), whether it is a realistic translation or not. Stakeholders use these values to sway management decisions in current forms of management. As such, and owing to our part in nature (Management Tenet 1), they count among the factors (realities) reflected in the patterns we see today; these patterns are, in part, products of decision making based on the consideration of economic interests. Less tangible and quantifiable values are behind the aesthetic and recreational uses humans make of ecosystems. Many people enjoy foraging for berries, nuts, and mushrooms, and hunting and

7

fishing are a source of food and pleasure. Others enjoy just being in natural settings, supporting a huge ecotourism industry, backpacking/camping, nature-based healing (e.g., vision questing/fasting, and Outward Bound programs), and vacations in national parks. The value humans place on nature for nonconsumptive uses often declines to the extent it has been altered by human action. In North America, increasing value has been placed on wilderness—ecosystems in their wild state (Nash 1982). Visits by tourists to the natural settings of ecosystems seen as relatively free of human disturbance (ecotourism) have a growing economic importance for host countries and communities (World Conservation Monitoring Centre 1992). Aesthetic value is also found in the spiritual and religious aspects of our experience of nature and seen as fundamental to what we are as a species (Nash 1982, Plotkin 2008, Roszak et al. 1995). These include the psychological effects and insight from firsthand experiences (part of the focus of the field of ecopsychology, and nature-based therapy). These effects can be lost in degraded ecosystems (Appendix 1.2) contributing to reactions in the category of “nature-deficit disorder” recognized as a lack of exposure to the nonhuman in natural systems (Louv 2005). Many would consider these spiritual/cultural losses of equal importance to the loss of more materialistic services. More objectively, such services count among the set of reciprocal relationships we have with our environment. To one extent or another, being deprived of normally functioning ecosystems and natural environments precludes our experience of normal human–nature interactions. To the extent that this happens, dysfunctional (abnormal) individual, family, or social systems may result (Gore 1992, Roszak et al. 1995). In such situations, the effect on humans of being deprived of the experience of normal forms of natural systems is analogous to the reactive attachment disorder in individual infants who are deprived of interaction with loving parents (failure to thrive or hospitalism; American Psychiatric Association 1980). 5 Part of what is involved here is our dependence on a normal state of nature to our understanding6 of life in general (Bateson 1979), or life in all realms, from

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cells to the biosphere, including the psychological and psychiatric (Dell and Goolishiam 1981, Roszak et al. 1995). To varying degrees, we are a species with unrealistic belief systems and socially dysfunctional patterns, in part as reflective of the abnormal in our relationships with the nonhuman. In the reciprocity of the interconnected aspects of complex systems, then, these are factors inherent to patterns observed in both society and our environment today. In this regard, the abnormal of both the human and the nonhuman is involved; they are reciprocally intertwined and part of the complexity important to management (Management Tenet 3).

1.2.2 Inadequacy of conventional management Conventional management suffers a general lack of objectivity. In the minds of many, this might be seen as the primary problem we face in management today; in addition to objectivity as an inherent demand of the tenets of management listed above, many would list a tenet specific to this need: Management should be objective. But the inadequacies of traditional management involve much more. There is a general failure to adhere to other tenets of management. Perhaps the main problem in conventional management is its erroneous logic (Belgrano and Fowler 2008, Hobbs and Fowler 2008). We use information of one kind to make management decisions about matters of a different kind (there is a mismatch between information and policy—knowing that the pH of oceans is changing does not tell us how much CO2 can be produced sustainably by our species). Conventional management fails to account for complexity. Current approaches are largely transitive without adequate consideration of the consequences of our attempts to control nature. For example, problems in our continued use of conventional single-species approaches to manage natural resources are increasingly well recognized (e.g., Cairns 1986, Fowler 2003, Fowler and Smith 2004, Ludwig et al. 1993, Magnuson 1986, NRC 1999, Safina 1995, Schaeffer et al. 1988, Wagner 1977). Among these problems is the failure of conventional management to account for genetic effects

(see Conover and Munch 2002, Fenberg and Roy 2008, Law et al. 1993, Stokes and Law 2000, Swain et al. 2007, Thompson 2005), a factor recognized in coevolutionary predator-prey responses (Kerfoot and Sih 1989). Many have called for an evolutionarily enlightened form of management in view of the coevolutionary interactions (only one example of the ways things are interconnected; Swain et al. 2007, Thompson 2005). Management Tenet 3 requires that genetic and evolutionary factors be taken into account. We fail to account for evolution and complexity in the conventional use of models in management. Most single-species management models assume constant parameters, while in reality such parameters change constantly, especially over long time scales (Orians 1974, 1975, Pimentel 1966). Predatorprey interactions are under-represented and even ecosystem models fail to provide holistic/realistic guidance in our use of individual resource species (e.g., Christensen et al. 1996, Ehrlich 1989, Peters 1991).7 The uncertainty involved is too extensive to overcome completely and predictions are unreliable (Ludwig et al. 1993, Pilkey and Pilkey-Jarvis 2007). When reliable predictions are possible, most demand human evaluation and translation to management goals, which prevents objectivity in decision making. Current forms of management all too often fail to ask clear management questions (top row, Fig. 1.1). Ill-defined management questions are posed to experts (scientific committees, advisory boards, stakeholders) who are allowed to (required to) select and use information for decision making—often making unrealistic conversions (e.g., information on production as used in finding sustainable consumption in the management of fisheries; Fowler and Smith 2004). As a result, human limitations are, by design, inherent to the process and economic factors, politics, religious beliefs, and human values serve as the basis for decisions (Hobbs and Fowler 2008). The lack of objectivity in conventional management has, as part of its origins, poorly posed management questions—specifically, questions that are referred to stakeholders rather than being used to direct science toward the research necessary to obtain objective answers.

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Recognition of the flaws and shortcomings of conventional management is another step toward realizing the need to seriously consider alternative forms of management—different paradigms (Lavigne et al. 2006).

1.2.3 Legal mandates to extend management to include ecosystems The seriousness of recognized environmental problems and the inadequacy of conventional approaches, have led to a variety of legal mandates worldwide.8 Many of these require undertaking “management at the ecosystem level” or “ecosystem-based management” despite the absence of any clear, widely accepted definition for just what such management is. In the United States, the Marine Mammal Protection Act (MMPA) emphasizes the importance of the “ . . . health and stability of the marine ecosystem . . . .” To comply with such a mandate, we need to know what the “health”9 of an ecosystem is, how to measure it, and what we can do to practically apply such knowledge. As reviewed in Thorne-Miller and Catena (1991), the U.S. Marine Protection, Research, and Sanctuaries Act requires consideration of the maintenance of ecosystem structure and prohibits the degrading of ecological systems. Goals of the U.S. Fish and Wildlife Service’s National Refuge System include the preservation, restoration, and enhancement of the natural ecosystems of all endangered or threatened species. The U.S. Endangered Species Act, the Magnuson Fishery Conservation, and Management Act (US Public Law 101–627) require ecosystem consideration in management. The preservation or protection of particular ecosystems is required in the management of U.S. National Parks and Refuges by a variety of statutory mandates (Thorne-Miller and Catena 1991). Other nations have their own environmental laws and regulations, many of which require ecosystem considerations. In Europe, these involve a number of international regulations, many of which involve nations outside the European Union (Mitchell 2003).

9

International mandates, in general, are exemplified by the agreement to study and manage the resources of the Antarctic. The Convention for the Conservation of Antarctic Marine Living Resources explicitly requires an ecosystem perspective in obligating “ . . . prevention of changes or minimization of the risk of changes in the marine ecosystem which are not potentially reversible over two or three decades . . . .”10 Other international efforts to achieve ecosystem approaches to management are found in both government and nongovernmental programs. The United Nations Environment Program places particular attention on the effects of pollution on ecosystems. The International Union for the Conservation for Nature and Natural Resources, the Biosphere Reserve Program (sponsored by the UN’s Educational, Scientific, and Cultural Organization), and The Nature Conservancy all have ecosystem-oriented objectives for conservation. Many existing international agreements and treaties regarding wildlife and the environment explicitly recognize or require consideration of ecosystems in management.11 Complexity is important in management (Management Tenet 3). The emphasis on ecosystems in legislation and the management literature is a step toward including more complexity. But it is only a step; it recognizes the need to extend the scope of management from individuals or individual species to include ecosystems. Beyond ecosystems, we now recognize the need to include the biosphere (Fuentes 1993, Huntley et al. 1991, Lubchenco et al. 1991, Myers 1989, Vallentyne 1993). We are beginning to appreciate the importance of accounting for the complexity of all levels of biological organization from cells to the biosphere (Management Tenet 4). Accounting for ecosystems, and the legal mandates to do so, are steps in that direction. The end point of efforts to list what should be taken into account would be the full complexity of reality (Appendix 1.1)—a list that is humanly impossible to achieve in conventional thinking. Finally, legislation in the United States requires the use of the best available scientific information in the process of management (NRC 2004). When values other than that of systemic sustainability are brought to the task of choosing information for use in management (e.g., what stakeholders

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consider to be “sustainable development,” “sustainable growth,” or “sustainable economy,” Step 4, top row of Fig. 1.1), management is vulnerable to error. The erroneous nature of conventional management (Belgrano and Fowler 2008) can be viewed as just one of many major problems we currently face.

1.2.4 Flaws in our use of science Philosophers of science have debated the utility and nature of science extensively. The limitations and imperfections of science have become a matter of principle (e.g., Christensen et al. 1996, Fowler 2003, Fowler and Hobbs 2002, Francis et al. 2007, Holt and Talbot 1978, NRC 1999). One of the main limitations of science is our inability to account for the unknown, especially in the construction of simulation models.7 It is one thing to know we leave out crucial information (omission of important processes, Pilkey and Pilkey-Jarvis 2007), it is yet another to know that there are things we cannot know to include. Another limitation of science is the inability to assign objective relative importance to the various subjects of scientific study (e.g., the never-ending nature/nurture debate). We have historically taken the subjective path by relying on human judgment (e.g., the politics involved in the last step in conventional management as depicted in Fig. 1.1) to account for the unknown in science and management. We scientists find it impossible to combine our information into a coherent synthesis free of subjective judgment and almost always ignore the unknown (the Humpty-Dumpty syndrome, Fowler 2003). Scientific inquiry often proceeds with the assumption that things that are statistically insignificant are unimportant, yet we acknowledge the importance of such factors on longer time scales in the phenomenon referred to as the “butterfly effect”(Gleick 1987), or the effects of initial conditions (Bateson 1979, Brown et al. 1996, Koehl 1989, Merton 1936, Pennycuick 1992, Williams 1992). Many things are too subtle to be detected by conventional scientific methodology. Science is confined to the reductionistic; it involves human limitations, particularly the limits of the human mind. This is a reality to

be accounted for in management (Management Tenet 3). Conventional decision making and management fail to find a way in which reductionism provides a path forward. There is very often a mismatch between the information used and the management question being addressed (Hobbs and Fowler 2008). A prime example is the case of using information on CO2 concentrations in the atmosphere to promulgate regulations on CO2 production; abundance and production are two different things. Thus, when faced with information of one kind and a management question of a different kind, conversion by stakeholders is required (step 4, Fig. 1.1) rather than avoided. This process is inherently subjective and when we choose to continue with conventional approaches, we are choosing to avoid more objective options. Taken as a whole, science has shown us that things are interconnected (Plate 1.1) and complicated. Science helps us explain and predict; we know that there is explanation and pattern. Science has documented and explored the emergence of things that we observe. Science has discovered, named, categorized, and characterized many things. It also exposes problems. The products of science are dumped in the hands of stakeholders, managers, and teams of scientists who are asked to make sense of it all as part of making a decision (the information supplied to decision makers, step 3 in the top row of Fig. 1.1). We know that we humans are limited, yet that knowledge does not stop us from making decisions in defiance of the HumptyDumpty syndrome (converting and combining information selected from libraries full of available information to guide management—step 4 in the top row of Fig. 1.1). Conventional management has not found a way to use reductionistic science in a way that circumvents the problems of conventional decision making.

1.2.5 Lack of standards for ecosystems and the biosphere Both the magnitude and number of environmental changes occurring around us are of growing concern (MEA 2005, Moran 2006, Steffen et al. 2004). There are exceptions (e.g., Fig. 1.2, and more qualitatively, Fig. 1.3), but in many cases there is limited

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basis for scientifically proving that we are seeing anything abnormal or for measuring abnormality (Management Tenet 5). We need much more information regarding normal variation for characteristics of ecosystems, especially over the long time, and large spatial scales relevant to such systems (Ricklefs 1989). Does the diversity, species composition, mean trophic level, or carbon content of (and flow through) ecosystems vary so much that ecosystems today can be considered normal? Or is such variation small enough that the changes we have caused make today’s ecosystems anomalous? Identifying, measuring, and interpreting ecosystem-level change are not well-developed skills. As Page (1992) puts it: “There are no constant reference points from which functions and norms can be defined.” Regardless of how we undertake management, it is important to have benchmarks for evaluating systems (including ecosystems) for two reasons. We need to be able to identify problems whether or not we can solve them directly, and we need to know if whatever we do in management is resulting in progress. Objective assessment is a crucial element of management and there has to be a scientifically valid way of providing information on both fronts (Management Tenets 7, 8, and 9). Standards, guidelines, and reference points emerge from information on the normal range of natural variation (Management Tenet 5), but these are quite rare for ecosystems and the biosphere compared to those that are used in human and veterinary medicine for individual organisms. As the changes occur in ecosystems and the biosphere, there is more need for criteria to evaluate them. The normal “state” of ecosystems is one thing, the normal “state” for species is another. There is growing attention to macroecology (as will be fleshed out in Chapter 2). In spite of what we have before us, there is a general lack of normative information about the influence other species have on their ecosystems and the biosphere. This impedes the comparative evaluation of human influence within such systems (Management Tenet 1), whether we currently fit in sustainably or not. Criteria are needed not only for defining and evaluating the health of ecosystems, but also for regulating our influence on ecosystems so that they achieve and

11

experience their own long term sustainability (i.e., are not abnormal owing to human influence). Sustainability in the long term, however, involves change—both human and nonhuman. Developing a realistic appraisal of ecosystems depends on information for ecosystems that have characteristics and properties representative of prevailing conditions. The current state of ecosystems reflect, among other things, human influence. To achieve long-term sustainability will require systems free of abnormal human influence; we need systems with structure, function, and variability typical of normal circumstances to serve as points of reference (Norton 1987, Management Tenet 3). Natural ecosystem change dictates that we need information on variation caused by factors such as season, climate, solar dynamics, and the coming and going of ice ages—the same as needed for individuals, species, and the biosphere. A great deal of recovery time will be required for ecosystems after they are relieved of abnormal human influence. In the mean time, current information is reflective of current circumstances—including human influence, belief systems, and values.

1.2.6 Lack of definition for management involving ecosystems Including ecosystems is part of the effort to account for greater complexity in management. A great deal of literature deals with this issue but most present principles or guidelines in indirect, vague, or general terms (e.g., Christensen et al. 1996, Francis et al. 2007, Guerry 2005, Mangel et al. 1996 and the references therein). Definitions are often expressed in terms of desirable qualities rather than operational prescriptions for action (Haeuber and Franklin 1996). We are faced with a variety of tenets such as the nine listed above. Historical work has resulted in a useful list of issues, principles, concepts, or processes that can be used to evaluate alternative management processes. Conventional management clearly falls short of full and consistent adherence to these standards. The transitive and subjective aspects of the thinking behind conventional management is a major factor in preventing a good definition of ecosystem (or ecosystem-based) management. We

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can only manage our interactions and relationships with ecosystems. We do not fully adhere to Management Tenet 2. However, the lack of clarity we have in defining management at the ecosystem level involves more. It involves a lack of full simultaneous, consistent, and objective adherence to all the tenets of management to include those beyond Management Tenet 2. This involves tenacious clinging to the belief that the current role of stakeholders is a viable option. In conventional management, this belief is held paramount to other tenets, especially Management Tenet 5. The need to account for or consider anything (factors, processes, principles) is translated to a human undertaking that involves thought, opinion, or nonconsonant artificial representations of reality (e.g., see Francis et al. 2007). Collaborative decision making, meetings of experts, and other institutional processes are used to convert scientific information to management (Fig. 1.1—a process vulnerable to politics and other human values). The fallacious inconsistency (Fowler and Smith 2004, Hobbs and Fowler 2008) inherent to this belief has contributed to the problems we face today and is a major component of our inability to define management at the ecosystem level. Anthropocentric values supersede objective consideration of the complex systems of which we are a part, of which we are composed, and of which we represent. Our central concern is how to go about integrating the combination of information we would like to bring to bear in management. Changing the approach (Botkin 1990, Lawton 1974, Lavigne et al. 2006, McGowan 1990, Orians and Paine 1983, Roughgarden 1989) is less frequently seen as necessary than are improvements (better application and further development; Wilson 1998) of existing approaches. This perpetuates the problem.

1.3 Introducing pattern-based management: systemic management in the eastern Bering Sea Systemic management relies heavily on using empirical information from other species12 as guidance for human self-control at the species level (Hobbs and Fowler 2008). This section introduces the basic methodology of using patterns (probabil-

ity distributions, empirically derived species frequency distributions, or macroecological patterns, Fowler and Perez 1999) to replace the decision making of conventional management. Examples from the eastern Bering Sea ecosystem provide an example of full ecosystem-based management—to include management of the suite of human influences on ecosystems. The eastern Bering Sea example is developed in each subsequent chapter to illustrate not only the part of systemic management involving ecosystems but also other parts, including those involving individual species and multispecies groups or communities. Patterns are part of the foundation for systemic management (Belgrano and Fowler 2008). Through the process of emergence,2 they provide integrated objective information that automatically accounts for the complexity we are unable to consider in conventional practices. A probability distribution like the standard “bell shaped curve” shown in Figure 1.4 is a function (f, that varies from pattern to pattern) of all factors (e i) that contribute to its formation (Belgrano and Fowler 2008, Fowler and Crawford 2004). Patterns integrate and account for complexity (the infinite in both panels of Fig. 1.4, or reality; Appendix 1.1) and all its elements in direct proportion to their relative importance. This includes all interactions and interrelationships—it specifically includes ecosystems. We don’t need to know the ei, or the function “f” (top panel, Fig. 1.4) or every contributing factor (arrows, bottom panel, Fig. 1.4), to use the probability distribution. It is information that represents a complete, integrated accounting of all factors through its emergence. Subjective assignment of the relative importance of various factors to be weighed in conventional decision making (Canter 1996) is replaced by the objective weights realized in nature to account for their actual relative importance. Patterns replace humans in accounting for complexity (Fig. 1.1). Graphs of species-level patterns (species frequency distributions; Fowler and Perez 1999) are scientific representations of empirical probability distributions for various measures of species (e.g., energy consumption, population size, trophic level, geographic range size, and mean adult body size). Species are concentrated in some parts of

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such patterns more than others. The means, modes, and medians of the distributions indicate central tendencies that characterize the group of species represented; these are statistical characteristics of the group. The spread or dispersal of species across measures of a species-level characteristic reveals the variability among species. The limits to that natural variation are represented in the tails on either side of the curve and are measured in terms of statistical confidence limits that are also characteristic of the pattern (Fowler and Hobbs 2002). Abnormality can be identified among outlying observations.

1.3.1 Illustrating the methodology The species with populations in ecosystems (specified geographic areas), or any other species assemblage occur in macroecological patterns (Belgrano

8

Figure 1.4 Patterns as they are products of, and account for, complexity. A. Representation of typical probability distribution as a function (f) of all factors (ei) that contribute to its formation; species frequency distributions are empirically based graphs to which a probability curve can be applied. B. Every pattern (e.g., P, in the large dark circle) is a product of, and accounts for, all factors that contribute to its formation (the infinite set of interacting factors represented by the arrows depicting reciprocity by their being double-headed).

and Fowler 2008, Fowler and Perez 1999) as emergent structure. The full Bering Sea is a marine ecosystem located north of the Aleutian Islands, between Russia on the west and mainland Alaska on the east. For our purposes, a subsection of the ecosystem, the eastern Bering Sea, is somewhat arbitrarily defined as the area east of a line from Attu Island (western Aleutian Islands) in the south to the center of the Bering Strait in the north (Perez and McAlister 1993, see map in Fig. 1.5)—this is the ecosystem revisited in subsequent chapters. The area and its history are described in reports such as that of the NRC (1996). The eastern Bering Sea ecosystem is of great value to humans; it provides ecosystem services to all species involved. Science has documented changes of concern. Recent commercial harvests of fish have taken millions of tons annually from a variety of resource species, and several species of

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Chukchi sea Bering Strait

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Figure 1.5 Map of the Bering Sea showing the demarcation between the eastern Bering Sea (east of the straight line) and the western Bering Sea (Perez and McAlister 1993).

marine mammals have been harvested intensively. Changes have been observed in the composition of fish species. The population of walleye pollock (Theragra chalcogramma) has been observed to be declining in the last several years (Ianellli et al. 2006). Marine mammal populations have declined and one species, the Steller’s sea cow, went extinct in recorded history. These and other impacts of human activities (e.g., pollution, oceanic acidification, global warming) have generated a great deal of concern. Several species of marine mammals are subject to legal protection owing to their low numbers. The patterns shown in Figure 1.6 for the eastern Bering Sea (based on Perez and McAlister 1993) characterize only one of the many facets of any ecosystem. This first example demonstrates variabil-

ity among the population sizes of marine mammal species based on numbers of individuals in thousands (top panel, and log10 raw numbers bottom panel), averaged over seasons. Figure 1.6 also demonstrates limits to natural variation in population size among species, especially as exhibited in the lower panel. Figure 1.7 shows a different pattern with its variability and limits. It involves the same set of 20 marine mammal species as represented in Figure 1.6. In this case, we see the biomass consumed annually as estimated for the populations of these species. This graph also shows options for expressing measures of consumption. In this graph, consumption is represented from individual species (walleye pollock, top row), finfish (a group of species, second row), and the entire eastern Bering Sea

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(an ecosystem, bottom row). It demonstrates two modes of presentation (raw measures and log transformed values), and gives measures of consumption rates by humans (harvests, in the right column) for comparison. Importantly, this graph introduces the kinds of data useful to the combination of singlespecies, multispecies, and ecosystem components of systemic management (Fowler 2002). Fowler and Hobbs (2002) review published accounts of the basis for Management Tenet 5— the need to do what can be done to avoid abnormality. Management at the individual level is based on this tenet when action is taken to ensure that characteristics such as body temperature, blood pressure, respiration rate, food consumption, and heart rate fall within the normal range of natural variation in the practice of medicine. Applying this tenet to the harvests of walleye pollock (Fig. 1.7), a

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single-species application of systemic management in the eastern Bering Sea, would mean reducing takes. The multispecies and ecosystem applications are seen in the changes indicated by the second and third rows of Figure 1.7. We begin to see the extent of both the problems and needed change: there are orders of magnitude in the differences between consumption rates by humans and the mean of consumption rates among other species. This challenge is further complicated by the need to comply with the other tenets of management. There are other management questions to be addressed. What would sustainable harvest rates be in the absence of abnormal anthropogenic influence? Such influence (e.g., harvesting, global warming, pollution) on nonhuman species may have resulted in reduced populations. Patterns based on data for species subject to such influence would lead to indications of what is sustainable now, but may exaggerate measures of human abnormality for circumstances free of abnormal human influence. Figure 1.8, for example, shows that the biomass of cetaceans in the entire Bering Sea ecosystem in the 1980s/1990s was estimated to be about 20% of levels found there in the mid-1940s (Sobolevsky and Mathisen 1996). The same study determined that corresponding declines in population numbers (total for all cetaceans) resulted in populations in the 1990s that were about 64% of earlier levels. The most significant changes in the Bering Sea involve the extinction of the Steller’s sea cow and marked reductions in sei and blue whales (the latter assumed to be represented by only small numbers of individuals in Fig. 1.8). Figure 1.8 exemplifies changes in population size and increases in the variation among species similar to changes observed in other disturbed ecosystems. These changes are part of what we take into account when we use other species as empirical examples of sustainability to adhere to Management Tenet 5. Estimates of consumption rates based on data from the 1940s, although still biased by human influence, would clearly serve as a better frame of reference for sustainable harvests from an ecosystem free of abnormal human influence—something we might achieve decades down the road. Figure 1.7 reflects human influence (e.g., values or belief systems, harvesting, pollution, global warming, politics)

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2 3 4 5 6 7 8 9 10 log10 (biomass consumed, t/yr)

Figure 1.7 Species frequency distributions representing patterns in the eastern Bering Sea, showing variability among marine mammal species as distributed over their estimated annual biomass consumption within this region across seasons (hundred thousand metric tons, left column; log10 metric tons, right column) for walleye pollock (consumption by six species of marine mammals, top row), finfish (by 20 species of marine mammals, second row) and the eastern Bering Sea (by 20 species of marine mammals, bottom row). These graphs are based on information from Fowler and Perez (1999), Livingston (1993), Perez and McAlister (1993). See Appendix 1.3 for details.

along with the influence of the nonhuman (current environmental and evolutionary circumstances). This information reflects sustainability today, accounting for abnormal human influence. Free of abnormal human influence, the system may be able to regain its health to exhibit the corresponding sustainability.

1.3.2 Posing management questions Management applied to ecosystems requires the ability to ask appropriate, but very specific, questions (Belgrano and Fowler 2008; Fig. 1.1). For the eastern Bering Sea example, we can start with questions regarding sustainability at the ecosystem level. These include: How much biomass can sustainably be removed by humans? How much biomass should be left for other species?

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How many species can sustainably be harvested as resources? What is the appropriate allocation of biomass removal over the various trophic levels (what portion of the harvested biomass would come from each trophic level)?

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Within the ecosystem, other questions apply to harvests of individual species: How much biomass (or how many individual organisms) can sustainably be harvested from any particular resource species’ population? What is an appropriate size or age composition for the harvest of any particular species (how should the harvest be allocated over size or age)?

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●

Other questions relate to other factors both scientists and other stakeholders believe are important: How do we account for change and the fact that humans have caused change?

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SYS T EM I C M A N AG EM EN T— W H AT A N D W H Y?

Portion of species

0.6

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0.4 0.3 0.2 0.1 0.0

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7

8

Figure 1.8 A species frequency distribution representing the entire Bering Sea, showing variability in population biomass among cetacean (whale) species (averaged across seasons, from Sobolevsky and Mathisen 1996; see Appendix 1.3 for details). This figure compares distributions for the late 1940s (top panel) and the late 1980s and early 1990s (bottom panel).

How do we account for evolutionary impacts of our harvesting on each individual resource species? How do we account for the trophic level, body size, or metabolic rate of humans? How do we account for broad temporal and spatial scales without discounting or failing to consider the finer resolution involved in consideration of individuals, molecules, elements, behavior, habitat heterogeneity, and energy dynamics (Carpenter 2002)? How do we account for species-level dynamics like that involved in extinction and speciation?

●

●

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●

Still other questions relate to human institutions and constructs in the context of the laws of nature: How do we account for economics in decision making?

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How do we resolve differences in management advice stemming from different sciences as conventionally applied? (For example, widely different quotas for harvests are suggested from the perspectives of population dynamics, behavior, evolution, and oceanography.) Does one form of science result in more reliable advice than another? How do we avoid ignoring or discounting any of the relevant sciences that might provide relevant information to management?

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0.5

0.0

17

In the chapters ahead, we address questions like these. Such questions serve as effective starting points for the process of refinement; management questions are refined so that relevant issues are accounted for directly. The way we ask both management questions and science questions is very important—there must be a match or isomorphism (consonance,13 Belgrano and Fowler 2008) between them. Initial questions must be refined to directly account for complexity. Clear questions lead to science that reveals the pattern on which management can be based. The next step is, of course, implementing what we learn from those patterns. This is where one of the many differences between conventional management and systemic management becomes central to understanding the changes we need to make. The eastern Bering Sea again provides examples. In this ecosystem, one of the main institutions involved in management is the North Pacific Fishery Management Council. Fisheries and marine mammals are also managed under the terms established by legislation and institutions such as the International Whaling Commission. Groups of scientists provide information. Other nongovernmental organizations are involved; these include the World Wildlife Fund, the Nature Conservancy, various indigenous peoples, and groups representing the fishing industry. There are political and economic forces represented in each case. Fish are consumed by the people involved. These groups, their values and consumption are part of the eastern Bering Sea ecosystem (Management Tenet 1). In conventional management, the debate among organizations, institutions, and individuals, such as those listed above, is a process of selecting and

18

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digesting scientific, economic, and social information to define management objectives (step 4, top row, Fig. 1.1) rather than to ask a clear management question (or questions, step 2, bottom row, Fig. 1.1). Stakeholders are doing what they genuinely believe is the best job possible in established ways of accounting for the complexity of factors needed to make a decision. Legislation allows (in many ways encourages or even requires) stakeholders to put economic values ahead of sustainability or to place primacy on economic sustainability. Such debate, evaluations, and conversion of information involves a recombination of fragmented information that is not only vulnerable to error, but ultimately impossible (involving what has elsewhere been called the Humpty-Dumpty syndrome, Fowler 2003). In the process, the bulk of reality gets ignored or misinterpreted; piecemeal tidbits of information that science provides are selectively converted to management advice. Human limitations prevent fully adhering to Management Tenet 3. Systemically, this management process is one of the factors contributing to the patterns we see; humans are part of the system (Management Tenet 1). The abnormal take of fish in the eastern Bering Sea (Fig. 1.7) is a direct product of conventional management policy—a harvest certified as sustainable by the Marine Stewardship Council (another human institution) in spite of the pathology involved in its being abnormal in comparison to that of other species. The pattern of human abnormality can be directly tied to such actions as contributing factors; denial of problems contributes to their being problems. Systemic management puts stakeholders in an entirely different role regarding policy. Stakeholder involvement remains critical but is confined to the posing of clear management questions so that science can proceed to its task of revealing objective patterns matching the management questions (Fig. 1.1, Belgrano and Fowler 2008). Politics, economics, and human values are given objective consideration in the decision-making process based on these patterns—such factors are inherent to the patterns by being contributing factors in their emergence (Fig. 1.4). Thus, patterns account for the effects of anthropogenic factors along with the remaining set of factors important to manage-

ment (the infinite of Fig. 1.4—reality). Included are evolutionary and coevolutionary forces, and other elements of the set of realities involved in the emergence of the patterns we see. The sample patterns presented for the eastern Bering Sea in this chapter provide tantalizing insight into how systemic management works. Before delving into more detail, it is necessary to develop more about the foundation on which systemic management is based. It is important to appreciate the variety among, and within, specieslevel patterns and how they represent complexity. We need to understand better how patterns are natural phenomena that emerge from and reflect complexity—how they represent cybernetic information that accounts for complexity (Plate 1.2) so that stakeholders, scientists, and managers confine their roles to those in the bottom row of Figure 1.1. Then we can proceed toward a more thorough understanding of how patterns serve as guidance for systemic management. We need a deeper appreciation of the ways patterns account for human influence. With this understanding we can then explore how species-level patterns provide answers to questions such as those raised above, as well as ways to analyze patterns and choose appropriate sets of species to be most informative.

1.4 Summary and preview 1.4.1 Summary This book draws upon work accomplished in various fields of science and integrates the work of ecologists with that of paleontologists, theoretical statisticians, complex or whole systems theorists, and philosophers of science—not to make management decisions, but to find a way in which objective guidance can be found. It introduces the practice of systemic management based on the tenets of management synthesized from the decades-long attempt to define principles for management. Conventional management has been largely transitive management of things like resources, wildlife, ecosystems, pests; systemic management is intransitive management of our interactions with all other systems (Management Tenet 2; Fowler 2003). Our management of human interactions

SYS T EM I C M A N AG EM EN T— W H AT A N D W H Y?

with ecosystems is the ecosystem-based component of systemic management. In systemic management, empirical examples of sustainability are the source of guidance, particularly as they fall into informative integrative patterns. Human concepts or constructs are confined to those most directly relevant to observing, representing, and analyzing natural patterns. The representation of guiding information is exemplified by frequency (or probability) distributions, but, for management, only those directly matching specific management questions. Patterns and questions must be consonant (Belgrano and Fowler 2008, Fowler and Smith 2004) to be consistent (Management Tenet 4). Patterns illustrate variation and its limits, completely accounting for all complexity (Fig. 1.4, Belgrano and Fowler 2008, Fowler 2003, Fowler and Crawford 2004, Fowler and Hobbs 2002). Frequency distributions for various species-level characteristics incorporate direct consideration of natural processes such as selective extinction and speciation (Fowler and MacMahon 1982). The examples provided by other species collectively result in a measure of what sustainability looks like (for each management question) so that we not only learn from nature, but also identify problems to be solved in management. These examples show how unsustainable the human species has become in our participation and interaction with other biological systems (e.g., other species, multispecies groups, ecosystems, and the biosphere). In the practical applications that emerge, systemic management is demonstrated as a path toward sustainable human existence. In the end we are left with the question: can we, as a species, make the changes necessary to function sustainably?14 Among humans, a species-level self consciousness is just now beginning to emerge. Comparing ourselves with other species has the potential of awakening in humanity both a sense of what we need to do as a species and the ability to do it.

1.4.2 Preview The structure of the book builds toward practical application. It first sets the stage by introducing a variety of patterns and how they are formed,

19

especially as the selectivity of extinction and speciation contribute to their emergence. More detail concerning the flaws of conventional management are followed by in-depth consideration of systemic management as a preferable alternative, especially in terms of setting objectives. The epilogue considers systemic management with a measure of philosophy, in part addressing alternatives for action to achieve objectives. The specific example for the eastern Bering Sea, introduced above and developed further at the end of each chapter, illustrates ecosystem-based management as one part of systemic management. Single-species approaches are also part of systemic management and they too are exemplified in examples from the eastern Bering Sea as are multispecies applications. Other examples will include direct application of systemic management to the genetic effects of commercial fishing and in regard to the use of marine protected areas. One of the underlying assumptions of this book is that human constructs show varying degrees of reliability in providing guidance for management. Among the best of what we produce are representations of empirical examples of sustainability directly matched with (consonant with) specific management questions. The more models (opinions and values) are restricted to partial indirectly related information, the more misleading they are. The most reliable guidance comes from direct observation of nature itself—as long as the observations are confined to the issue, dimensions, and logical type of the management question being asked (Belgrano and Fowler 2008).15 This assumption is fundamental to the contrast between systemic management and conventional management; in the latter, human limitations (amplified in Chapter 4) give rise to erroneous management. The paragraphs below summarize the focus of each of the following chapters. Chapter 2 presents the study of variation and its limits as demonstrated in patterns among species (see also, Fowler and Hobbs 2002). Macroevolutionary patterns are introduced as emergent from the combined forces of nature, including not only human influence, but very importantly, both nonevolutionary and evolutionary processes. They demonstrate that ecosystems

20

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(or any other species assemblage) have boundaries, limits, and form—elements of structure or order with genotypic as well as phenotypic nature. These patterns are natural phenomena that represent the evolved nature of ecosystems and other natural systems to which nonevolutionary factors also contribute (Fig. 1.4); they are emergent.2 As such, these patterns reflect the complexity that must be accounted for in management, and illustrate sustainability at the species level so as to provide guidance for human action. Chapter 3 explores selective extinction and speciation as contributing factors in the formation (emergence) of species frequency distributions— factors that cannot be ignored in considering the complexity of reality (Management Tenet 3, Appendix 1.1). Historical consideration includes a comparison of the dynamics of selectivity among species and similar dynamics among individuals in the analogous processes of natural selection at each level, including the environmental factors that elicit selectivity. The risk of our own extinction is considered in this context. The appendices to Chapter 3 describe the mechanics of selective extinction and speciation, in combination with evolution through natural selection at other levels, to illustrate how they count among the factors contributing to the emergence of patterns in the composition of species within ecosystems and the biosphere—the characteristics of such systems. Chapter 4 presents a detailed accounting of the problems we encounter in conventional thinking and management. Such management falls short of adhering to most of the tenets of management individually, and fails completely to adhere to all of them simultaneously, consistently, or objectively. We fail to account for complexity and we ignore the human limits that lead to this failure. Comparisons are drawn between conventional and systemic management at various levels of biological organization, and corresponding lessons, learned historically, concerning what can and cannot be done. Chapter 5 provides more detail regarding the ways systemic management adheres to the tenets of management, objectively, simultaneously, and consistently. Pattern-based management accounts for the infinite of complexity—reality. Systemic management is reality-based management. The

implications are considered insofar as they are challenges we humans face as a species and how the potential can be evaluated in regard to what we are, and how we function, as a species. Our ways of thinking are inherent to the differences between conventional and systemic management. Thus, the chapter also looks toward the future with a brief consideration of some of the human complexity involved in changes needed for sustainability. Guidance is found by refining questions systemically to guide the science needed to provide information-based goals needing no translation or conversion. Objectivity is achieved by converting the role of stakeholder from that of information conversion in conventional management, to asking management questions in systemic management (Fig. 1.1). Chapter 6 deals with species-level patterns as sources of information for practical application (Management Tenets 5 and 7), considering humans, along with other species, as parts of ecosystems and the biosphere (Management Tenet 1). This simultaneously allows for and requires an evaluation of ourselves and our needs as a species, by accounting for the probability of our own extinction. This chapter stresses the likelihood that conventional thinking may be one of the world’s biggest problems. It is behind both a variety of risks to our own species as well as those to the world’s ecosystems and the biosphere. It involves the magnitude of the human population. The related problems are numerous—for example, overconsumption, habitat destruction, extinction, and CO2 production. If we are to avoid the abnormal or pathological through human change, it must include the systemic of human change, including our thinking on a global scale. The book’s Epilogue provides a more subjective look at the challenges of implementing systemic management and the responsibilities to be shared among humans: organizations, scientists, political and religious leaders, and all of us as individuals. While there is hope that it can occur, there is substantive basis for suggesting that it is beyond human adaptability; scientists may continue more in the role of documenting and observing the results of our management than in guiding human endeavor. On the other hand, it is possible that we

SYS T EM I C M A N AG EM EN T— W H AT A N D W H Y?

have evolved to be sufficiently unique in our capacity to embrace the wisdom of experience and empirical evidence of what works to make a difference and actually achieve sustainability. Readers who want to know conclusions first, and then develop an understanding of their foundation, may choose to read Chapters 5 and 6 before the others. The mathematically inclined will find value in the models of selective extinction and

21

speciation in the appendices to Chapter 3; personal experience with building and exploring such models is highly recommended. The visually inclined will appreciate the graphic illustration of empirically observed information in Chapter 2, corresponding to model-generated probability distributions in Chapter 3 and elsewhere, as well as the measures of human abnormality in Chapter 6.

CHAPTER 2

Patterns among species: information

But ask now the beasts, and they shall teach thee; and the fowls of the air, and they shall teach thee; or speak to the earth and it shall teach thee; and the fishes of the sea shall declare unto thee. — Job 12:7–8

This chapter presents a variety of patterns found among species. Some represent ecosystem structure while others depict ecosystem function. Among the latter are patterns in relationships. Such relationships involve interactions among species as well as between species and systems such as ecosystems and the biosphere—systems of which they are a part. Each pattern exemplifies the kinds of things science discovers, then attempts to characterize, explain, understand, and use in making predictions. The objectives of this chapter include: Establishing the fact that ecosystems and the biosphere have structure and function seen in macroecological patterns, many of which have yet to be discovered and characterized. Illustrating how macroecological patterns provide an empirical basis for evaluating and measuring the abnormality among species. Developing the concept of emergence behind the formation of patterns as the basis for understanding patterns as integrative of the complexity we need to account for in management. Defining the best available scientific information for management: patterns that match the management questions (i.e., are consonant1 with management questions). ●

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A secondary objective is to define the best kind of science in the service of management as the science that discovers, characterizes, and analyzes patterns that are consonant with management questions. In the paragraphs ahead, we will see that the value of a pattern lies in its information (Fowler 22

2008); each pattern embodies limits to natural variation reflective of, and accounting for, the complexity behind their formation (Fig. 1.4; emergence, Belgrano and Fowler 2008). By using this information we can identify the abnormal. Species, like everything else, show variation, but always variation confined by limits. Variation among species occurs within any set of species whether it is a taxonomic group, a community, a guild, a species within a given ecosystem, or species of a particular body size. Linking information with management is of crucial importance (Management Tenet 7, Chapter 1). This involves finding patterns consonant with management questions (Belgrano and Fowler 2008; Fowler 2003, 2008)—a step in management introduced with examples for the eastern Bering Sea in Chapter 1. Patterns can be used to guide human action to avoid abnormality in achieving systemic health (Belgrano and Fowler 2008; Management Tenet 5, Chapter 1). Following a brief introduction to patterns involving single species-level characteristics, this chapter illustrates variation within limits for two, three, and four or more species-level dimensions as they relate to each other. Correlative multidimensional patterns provide for overt treatment of factors identified in management questions—identified in Step 2 of Systemic Management, Figure 1.1, as questions get refined (made more specific). The chapter concludes with a return to the eastern Bering Sea example after a summary of some of the processes known to contribute to the formation

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

2.1 Patterns among species Patterns are central to adhering to Management Tenet 5, Chapter 1. Limits cannot be ignored; limits help identify abnormality. Within species assemblies, patterns materialize, in part, because variation is limited (Fowler and Hobbs 2002)— inherently accounting for all factors that contribute to variation and its limits. Form, shape, and structure are characteristic of patterns bounded by limits. These are especially prominent in relationships found in covariation or correlation among two or more measures or species. Although not a major focus of this book, patterns also occur among species assemblies. In other words, there are similarities from group to group just as there are patterns within various sets of species. Systems such as ecosystems can be assessed using information regarding patterns among groups of species—patterns (among patterns) that promote the identification of abnormality among ecosystems. Macroecological patterns are receiving increasing attention in the scientific literature (e.g., Brown 1995, Gaston and Blackburn 2000, Lawton 1999, Rosenzweig 1995, Appendix 2.1). The sample of species-level attributes considered in this chapter, and the general literature on macroecology, point toward a broader set of measures yet to be tapped and in need of attention—especially as patterns matching management questions. Science in search of explanation often emphasizes ecological mechanics and the flow of materials and energy (thermodynamics). Their importance is recognized in combination with evolutionary forces—all contributing to the emergence of macroecological patterns. Following advances made in recent decades, more attention is being paid to the effects of natural selection at the species level as it contributes to macroecological patterns, as will be seen in Chapter 3, especially in terms of the structure and function of ecosystems and the biosphere—ecosystem- and biosphere-level properties.

2.1.1 Single species-level characteristics This section uses patterns among species to illustrate limits to natural variation in a variety of individual species-level characteristics ranging from body size and trophic level to predation rates, consumption, geographic range, population size, and density dependence. Brief consideration is given to the explanatory role of science and the matter of finding consonance between patterns and management questions wherein each question is matched with a specific pattern. 2.1.1.1 Body size Expressed in raw numbers, small species are extremely prevalent worldwide (May 1978, 1986). Over 99% (probably over 99.9%) of all species fall within the lower 0.1% of the known size range.2 Viruses, for example, are about 24 orders of magnitude smaller than blue whales and about 21 orders of magnitude smaller than humans. Even insects, such as the honey bee, are about nine orders of magnitude smaller than blue whales. Nearly all known species are smaller than humans. Examples 3 of patterns in body size usually involve samples from specific taxonomic groups and environments. Most show forms similar to that seen in Figures 2.1 and 2.2. Figure 2.2 demonstrates how patterns can vary among dissimilar environments—patterns are contingent on such circumstances. Although the distribution of body size is obviously different in comparing patterns between 0.3 Portion of species

of patterns—processes that are accounted for by patterns when used in management to avoid the abnormal.

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Figure 2.1 The pattern of body size among North American mammals (mass in grams in log10 scale, modified from Brown and Nicoletto 1991).

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marine and terrestrial habitats, there is a similarity to the shape of such distributions. Existing data show such similarity across a wide variety of taxa. As emphasized by Brown and Nicoletto (1991), the mode of frequency distributions for body size among animals is at the lower end of the distribution with a long upper tail and a short or truncated lower tail. Some patterns have more than one mode as seen in both Figures 2.1 and 2.2. The means of body size distributions vary with taxonomic group, environment, and other factors. Plants also show patterns in body size much like those observed for animals. As with animals, most species of plants are small (Aarssen et al. 2006, Whitmore 1980) making the overall pattern one that crosses trophic categories. Within the overall pattern there are contributions to observed variation specific to a variety of factors, including trophic level. Discovery, observation, and characterization of patterns (e.g., Figs 2.1 and 2.2) are among the kinds

Portion of species

(A) 0.4 0.3 0.2 0.1 0.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 log10 (body mass, kg)

Portion of species

(B) 0.4 0.3 0.2 0.1 0.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 log10 (body mass, kg) Figure 2.2 A comparison of patterns in body mass among (A) marine mammals and (B) terrestrial mammals (Damuth 1987, Fowler and Perez 1999).

of things done in the practice of science—often defined as the goals of science. Similarities, as well as differences, among patterns raise curiosity. Why are species frequency distributions involving body size of the shape shown in Figures 2.1 and 2.2? Why are there not more large species? Such questions involve explanation—another of the roles of science in understanding nature. Evolutionary forces predominate in the minds of many; species of large body size are probably more prone to extinction than are their smaller counterparts (Chapter 3, Aarssen et al. 2006). Most of the research following the original discovery and publication of empirically observed patterns has been explanatory. However, complexity and human limitations preclude complete exhaustively detailed explanation and understanding of patterns—a full explanation is impossible. From a systemic point of view, patterns observed in nature are understood as products of all of the factors involved in their origin (Belgrano and Fowler 2008), including natural selection at all levels. This involves the entire suite of factors that make up the complexity of reality; patterns are emergent from this reality (the infinite in Fig. 1.4; Belgrano and Fowler 2008, Fowler and Crawford 2004). This is true whether or not we can identify, study, name, or assign their relative importance to all contributing factors. The higher risk of extinction among larger bodied species emphasizes the importance of asking management questions in such a way that body size is taken into account (especially owing to the fact that we humans are large bodied; Boulter 2002). For example, what is a sustainable harvest rate from a resource species that has a mean adult body mass of 200 kg? As humans, with an adult body mass of approximately 68 kg, what is a sustainable consumption rate of resources from the biosphere for species of that size? 2.1.1.2 Trophic level Within ecosystems, primary consumers tend to outnumber primary producers and secondary consumers; the number of species tends to drop on either side of the trophic level represented by primary consumers. The decline from this mode through higher trophic levels has been shown in the majority of sets of species that have been

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

examined. There are few, if any, species at very high trophic levels; food chains longer than 15 species are rare if they exist at all (Cohen et al. 1986). Thus, the pyramid of numbers of individuals (fewer at higher trophic levels) is accompanied by a pyramid of numbers of species (Anderson and Kikkawa 1986), usually on a narrow base of primary producers. This is clearly illustrated by data from Schoenly et al. (1991), as shown in Figure 2.3. These data, from 95 insect-dominated food webs, show the frequency distribution of species within these webs according to trophic level. In parallel with other studies, the bulk (over 95%) of the species from these webs are in the lower four trophic levels. The drop in species numbers above the trophic level occupied by primary consumers is nearly ubiquitous. Among terrestrial mammals of the southwestern United States, Patterson (1984) showed that herbivorous species outnumber insectivores and carnivores combined. Among the terrestrial mammals of North America, almost 200 (~74%) are herbivores and about 70 (~26%) are carnivores (Brown 1981). Other samples of mammalian species show the same pattern (e.g., Kelt and Van Vuren 2001) as is the case for other studies involving insects (Erwin 1982, 1983; May 1990). Such patterns are frequently observed in the literature on food webs.4 The explanatory aspect of science views evolutionary history as a primary component in

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the origin of the distribution of species across trophic level. Also, contributing to these patterns, of course, are the effects of energy depletion and availability, especially in setting upper limits to trophic level. Superimposed on this are the effects of increasing risks of extinction at higher trophic levels (Fowler and MacMahon 1982). These begin the list of factors contributing to the origin of patterns in trophic level—patterns observed and characterized by science. How can we account for trophic level in management decisions? Management needs to ask questions such as: What is the sustainable harvest of resource species found at the fourth trophic level? How should our consumption of resources be allocated over resources at various trophic levels? How many different trophic levels should be involved in our consumption of resources? 2.1.1.3 Number of consumers Another focus of science has been to count the number of consumer species for which a species serves as a resource. Figure 2.4 demonstrates limits to the variation in such counts. In this case, the number of predators is very limited, possibly because this sample is restricted geographically and treats only the macro-predators (i.e., pathogens and parasites are not included). Does the number of consumers per prey species change (increase or decrease) with body size? Potentially, the number of predators decreases and the number of parasites

0.7 0.6 Portion of species

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Figure 2.3 The distribution of species across trophic level for 95 insect-dominated food webs from Schoenly et al. (1991).

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Figure 2.4 Frequency distribution of species according to the number of predators that consume them based on data for 95 insect-dominated foodwebs from Schoenly et al. (1991).

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and pathogens increases. For example, there are at least 36 species for which the northern fur seal serves as a resource; only about three species are predators while there are 16 species of bacteria that occur internally and 17 species of parasites (Fowler 1998). The findings of food web analysis indicate that the number of predator species per prey species is less than would be expected if predator–prey relationships were assigned randomly within a community. The number of consumer species feeding upon each resource species is a form of interaction measured as part of connectivity. The results of food web research consistently show that consumer–resource interactions are only a very small subset of the total possible interactions within any set of species. The number of predatory consumer species per resource species appears to be roughly constant regardless of the number of species in the sample or the number of species in the assemblages themselves (Cohen and Newman 1988, May 1983, McNaughton 1978, Paine 1988, Pimm 1982, Pimm and Kitching 1988, Sugihara et al. 1989, Yodzis 1980). Science may be able to partially explain patterns such as those in numbers of predatory species per resource species, but are the explanations sufficient to address management questions? What is the sustainable harvest of a resource species already serving as a resource for eight other consumers? What is the sustainable harvest from a predatory resource species in competition with five other predators that consume a common resource species? 2.1.1.4 Interaction strength The influence of one species on others can be measured in a variety of ways, one of which is shown in Figure 2.5 (Bascompte et al. 2005, similar to the work of Paine 1992, Appendix Fig. 2.1.4). The work of Bascompte et al. (2005), like many other cases, involves numerous cases wherein interactions involve groups of species treated collectively (as though the group is assumed to be a species). However, there are also cases of individual species interacting with other individual species. The overall pattern, despite this inconsistency, is that expected for strictly species-by-species

measurements, although its shape and the range of variation would be expected to change when interaction strength is measured for an entire species (rather than simply for individuals). It is highly likely that the pattern of interaction strength shown in Figure 2.5 is a general pattern (similar to a normal distribution) with an intermediate maximum surrounded by fewer species toward the extremes—natural variability (Power et al. 1996) within bounds. When we humans influence other species, at what level can we influence them sustainably? Clearly, this will depend on the kind of impact (e.g., competition, pollution, consumption, or habitat modification). Too much impact could result in reduced resources with a variety of ecosystem-level effects. When the impacts involve services upon which we depend, too little could result in an unsustainably low human population. When we ask such questions, the patterns used to address them must involve interaction measured in the same units to achieve the consistency required by Management Tenet 4, Chapter 1. Individual humans as well as our species depend on resources for their existence, and both individuals and our species have influence on other species. Here, we encounter the importance of distinguishing measures for individuals and those for species—two different logical types (individuals are parts of species).

0.20 Portion of “species”

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0.15 0.10 0.05 0.00

–8 –7 –6 –5 –4 –3 –2 –1 Interaction strength

0

1

Figure 2.5 The pattern among 249 “species/trophic groups” in the strength of their interactions (log portion of prey biomass consumed per capita, n = 3313) with other species/groups within a Caribbean marine ecosystem (Bascompte et al. 2005).

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2.1.1.5 Predation/consumption rates One specific measure of interaction between species is the rates at which predators consume their resource species. Figure 2.6 shows patterns (as frequency distributions) in the rates at which nonhuman vertebrate species consume walleye pollock (T. chalcogramma) in the eastern Bering Sea. In this case, the interaction is measured as the fraction of the standing stock that they consumed in the 1980s (Fowler et al. 1999, Livingston 1993, and Livingston personal comm., 1994). Note that in this section, the measure is that of consumption rate per species, rather than the per capita consumption shown in Figure 2.5; this is an important difference in units as it involves an important element of consonance—logical typing (the distinction between a part and whole, here individual compared to species). The top panel of Figure 2.6 shows the distribution of population-level consumption rates as measured for each species according to biomass

and the lower panel according to numbers of individuals consumed. These data do not include the numerous parasites and disease organisms for which walleye pollock also serve as a resource species, but include more than the six species of marine mammals represented in the top row of Figure 1.7. Also, these data represent conditions across only part of the geographic range of walleye pollock, an ecosystem; as such, measures of consumption apply to populations of both consumers and resources measured for these populations on a species-by-species basis. The consuming species represented in Figure 2.6 are those that are classically defined as predators on walleye pollock, that is, species that ingest complete or large parts of the bodies of individual walleye pollock. For these species, the mode in their consumption rates occurs at quite low levels (less than 0.4% based on biomass, and 10% for numbers). This is in spite of the fact that the data for this figure

0.4 Pollock biomass

0.4 0.3 0.2 0.1 0.0

Portion of species

Portion of species

0.6 0.5

Pollock numbers

0.1

0.0 0.1 0.2 0.3 0.4 0.5 Portion of standing stock consumed (numbers)

Portion of species

Portion of species

0.2 0.1

–7.0 –6.0 –5.0 –4.0 –3.0 –2.0 –1.0 0.0 log10 (portion biomass consumed)

0.4

0.2

0.0

0.3

0.0

0.002 0.006 0.010 0.014 0.018 0.022 Portion of standing stock consumed (biomass)

0.4 0.3

27

0.3 0.2 0.1 0.0

–4.0 –3.0 –2.0 –1.0 0.0 1.0 log10 (portion numbers consumed)

Figure 2.6 Patterns in the annual consumption rates among nonhuman vertebrate species that consumed walleye pollock ( Theragra chalcogramma) in the eastern Bering Sea in the 1980s. Annual consumption rates are expressed as fraction of standing stock of walleye pollock biomass (top row) and numbers consumed (bottom row). The left column is for raw measures (one consumption rate of 1.4 is omitted for numbers) and the right column is in log10 scale (from Livingston 1993 and pers. comm.).

SYSTEMIC MANAGEMENT

example, shows patterns in consumption for the northwest Atlantic for a group of fish species (A) and for an ecosystem (B)—another set of examples crossing the hierarchical structure of natural systems. The consumption rates for four individual prey (or resource) species in this ecosystem are shown in Appendix Figure 2.1.5. Note that the top panel in Figure 2.7 represents consumption by all vertebrates (fish, birds, and mammals that feed on the four resource species), while the bottom panel represents consumption (from the entire ecosystem) by 24 species of homeotherms (i.e., marine mammals and seabirds). Figure 2.7 raises a question about diet composition (see also Appendix Figs 2.1.5 and 2.1.6B). What fraction of the diet of each predator is composed of each individual resource species? The allocation of consumption over alternative resource species also falls into patterns so as to exhibit limits, as with

(A) 0.20 Portion of species

are determined as a fraction of the harvestablesized pollock, which are only a part of the overall population.5 Consumption rates as represented in Figures 1.7 and 2.6 fall into such patterns within any ecosystem. For example, Appendix Figure 2.1.5 illustrates the patterns in consumption rates among nonhuman vertebrate predatory species in the northwestern Atlantic Ocean in their take of four prey species. Figures 1.7 and 2.6 also exemplify the whole/part (hierarchical) structure of consumption ranging from that experienced by individual species, through consumption from species groups, to consumption within ecosystems. The explanation of observed consumption rates must include evolved characteristics such as life history traits of both the predators and the prey. Consumption rates such as those shown in Figure 2.6 represent part of a pattern in which intrinsic rates of increase (rmax, the maximum rate at which populations of the prey species can increase) contribute to limiting predation rates. Predators on any resource population cannot sustainably consume more than is produced. Production is often less than 0.3, or 30%, per year for large mammals—production which, when consumed, would be shared by the full set of predators. Predators on species with high r values (e.g., many small bodied species) would be expected to consume at higher rates—higher turnover resulting in predation rates that are orders of magnitude larger. Enright (1969) found that zooplankton consumed as much as 50% of their phytoplankton resource base per day. This larger pattern will be treated correlatively in Chapter 5. When consumption is of body parts (e.g., leaves, fins, blood, berries), rather than of entire organisms, consumption rates appear to be low. Pimentel et al. (1975) found that on average only 10% of primary production is consumed by primary consumers. Based on a count (minimum) of the number of species from a sampling of the primary sources (nine of 18 cases) there was a mean of 7.5 species per group of consumers. This implies a mean consumption rate of 1.3% expressed as a percentage of the primary production consumed annually per consuming species. Every ecosystem exhibits patterns like those represented by Figures 1.7 and 2.6. Figure 2.7, for

0.15 0.10 0.05 0.00 2.0

3.0 4.0 5.0 6.0 log10 (biomass consumed, t/yr)

7.0

2.0 3.0 4.0 5.0 6.0 log10 (biomass consumed, t/yr)

7.0

(B) 0.40 Portion of species

28

0.30 0.20 0.10 0.00 1.0

Figure 2.7 Patterns in consumption rates within the northwest Atlantic ecosystem showing (A) consumption of the four species shown in Appendix Figure 2.1.5 by 16 species of vertebrates and (B) total consumption within the Georges Bank ecosystem by 24 species of marine mammals and birds (from Backus and Bourne 1986).

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

harvest of biomass from the eastern Bering Sea, the Benguela ecosystem, or the Georges Bank ecosystem? What is a sustainable harvest of biomass from the biosphere? There is more to consonance than the matter of pattern and question both involving consumption. Consonance between guiding pattern and management question also involves a match in the kind of system with which we are interacting. The management questions just posed involve interactions with identified systems—steps toward consonance. Further consonance is achieved when we account for human characteristics. To explicitly account for our body size, for example, consonance is involved in the pattern shown in Figure 2.10—it

0.3 Portion of species

all patterns. This is exemplified for the portions of the diets of marine mammal consumers in the northwest Atlantic that are composed of mackerel as illustrated in Figure 2.8. Such allocations can be determined for each individual resource species within the diets of any set of consuming species (Fowler 1999a). Consumption (whether of biomass or numbers), of course, is not restricted to individual species (Fig. 2.6 and Appendix Figs 2.1.5, 2.1.6A), groups of species (Appendix Figs 2.1.6B, 2.1.7A), or ecosystems (Appendix Figs 2.1.6C, 2.1.7B). Consumption from the biosphere (hierarchically inclusive of ecosystems, or of which ecosystems are parts) also shows patterns similar to those for ecosystems. Such consumption is illustrated for biomass in Figure 2.9, which is an illustration of consumption by 55 species of marine mammals, and in Figure 2.10 as consumption by 16 species of cetaceans of near-human body size. Using the patterns of Figures 2.6–2.10 we have examples of information that can guide human interactions with the nonhuman whether it be other species, species groups, ecosystems, or the biosphere. When management questions involve sustainable levels of consumption (as a specific kind of interaction) there is consonance in the patterns (information) revealed by science when those patterns involve consumption. However, we begin to see also the option of developing management questions regarding our (human) interactions with anything in our environment. What is a sustainable

0.1

1

2

3 4 5 6 7 8 log10 (biomass consumed, t/yr)

9

10

Figure 2.9 Pattern in biomass consumption from the Earth’s biosphere as observed for 55 species of marine mammals (from Fowler and Perez 1999).

0.3 Portion of species

Portion of species

0.2

0.0

0.4 0.3 0.2 0.1 0.0 –1.5

29

–0.5 0.0 0.5 –1.0 log10 (portion of annual diet)

1.0

Figure 2.8 Pattern in the fraction of the annual diets of marine mammal species in the northwest Atlantic composed of mackerel (from Overholtz et al. 1991).

0.2

0.1

0.0

1

2

3 4 5 6 7 8 log10 (biomass consumed, t/yr)

9

10

Figure 2.10 Pattern in biomass consumption from the Earth’s biosphere as observed for 16 species of cetaceans within an order of magnitude of human body size (from Tamura and Ohsumi 1999).

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2.1.1.6 Consumption of energy/production of CO2 Closely related to the consumption of biomass are the consumption of energy and the production of carbon dioxide (CO2) which also show patterns in nature. Figure 2.11 illustrates the consumption of energy per unit area for 368 species of terrestrial mammals, excluding humans. Figure 2.12 shows the production of CO2 for 63 species of mammals (21 marine and 42 terrestrial). The total annual consumption of energy for such species (Fowler 2008) has, as would be expected, an almost identical probability distribution (similarly shaped pattern) to that of CO2 production. With even a mere suspicion that CO2 production by humans is involved in global warming, we are faced with a management question regarding sustainable levels for the production of this gas. Part of the concept of consonance among patterns, science, and management involves circumstances

0.20

Portion of species

represents species of human body size. It also accounts for our common taxonomic category—all species are mammals. Thus, the patterns in this section involve identifiable consonance with the management questions raised in the last paragraph. They relate directly to the matter of our harvest of natural resources. But do these patterns account for behavioral factors, physiological processes, and coevolutionary interactions to fulfil the requirements of Management Tenet 3 (Chapter 1)? As with all patterns, explanatory factors are accounted for in the nature of the pattern. Complexity and human limitations preclude complete detailed explanation but each pattern is a product of the entire suite of contributing factors whether or not we can find them, study them, or understand them all. Patterns are integrative (Fig. 1.4, Belgrano and Fowler 2008) of all such factors, including those we cannot know. Included among these factors is the full set of human influence, both historical as well as those in today’s world where we are beginning to recognize the extent and abnormal nature of our impact. Included among such factors as they impact the patterns we observe is our consumption of energy and production of carbon dioxide resulting in influence of the kind that all species have. These factors themselves fall into patterns as seen in the next section.

0.15

0.10

0.05

0.00

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 log10 (million joules per km2 per day)

Figure 2.11 The frequency distribution of estimated energy consumption per unit area for 368 species of terrestrial mammals (from Damuth 1987, and Fowler and Perez 1999).

0.20

Portion of species

30

0.15

0.10

0.05

0.00 –6.0 –5.0 –4.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 log10 (million metric tons CO2)

3.0

Figure 2.12 Limits observed in the variation of estimated total CO2 production among 63 species of nonhuman mammals (from Fowler and Perez 1999).

that are common to the management question and the informative integrative pattern (Belgrano and Fowler 2008). Any question about sustainability for our species involves the need for overt consideration of the fact that humans are mammals. The patterns shown in Figures 2.11 and 2.12 involve CO2 production for mammals, thus achieving consonance in this regard. In starting with the vague management questions of conventional management (e.g., “What should we do about global warming?”, top row, Fig. 1.1) refinement in systemic management (bottom row of Fig. 1.1) is a process of

2.1.1.8 Geographic range The spatial distribution of consumption leads naturally to the topic of geographic range. Consumption of resources will be distributed over regions within the geographic range of a consuming species; areas outside the geographic range are not subject to the direct effects of consumption. Samples of data available for the geographic range size of mammals in North America (excluding marine mammals) show a pattern illustrated in Figure 2.14 for nonhuman species. In this figure, species-level geographic range size is shown as both raw measures and in log scale to show that most species are concentrated around a mode at the lower end of the range of the distribution. Brown and Nicoletto (1991) present further analysis of data for North American mammals that show the same pattern. The geographic ranges of species overlap with each other as well as with areas that we consider to be ecosystems. The extent of the overlap can be

Portion of species Portion of species

2.1.1.7 Allocation of consumption over space Consumption affects both prey species and ecosystems heterogeneously over space. Consumption is allocated over space just as it is over alternative resource species, again showing variation within limits. Figure 2.13 illustrates this for the spatial allocation of resource consumption from various geographic sections of the Benguela ecosystem. Although not a trivial scientific exercise, this kind of pattern can be characterized for any ecosystem as well as the biosphere. When our management question involves spatial distribution of harvests or consumption, a consonant empirical pattern will have information with units involving distribution. How should we allocate our harvests of biomass over different areas within an ecosystem? Figure 2.13 involves units that are consonant with this question, but only insofar as it involves the spatial distribution of consumption. The management question, however, involved human action; because humans are mammals, the consonance is partial.

Portion of species

incorporating such factors directly. Contextual circumstances involve latitude. What is the sustainable level of total CO2 production for a mammalian species of human body size at the equator?

Portion of species

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

31

0.5

Northern Namibia

0.4

Mean = 30.1%

0.3 0.2 0.1 0.0

0

4

15

32 51 71 Percent of total

87

97

1.0

Southern Namibia

0.8

Mean = 6.8%

100

0.6 0.4 0.2 0.0

0

4

15

32 51 71 Percent of total

87

97

100

0.5

Western South Africa

0.4

Mean = 30.5%

0.3 0.2 0.1 0.0

0

4

15

32 51 71 Percent of total

87

97

100

0.5

Southern South Africa

0.4

Mean = 32.6%

0.3 0.2 0.1 0.0

0

4

15

32 51 71 Percent of total

87

97

100

Figure 2.13 The frequency distribution of consumption (arcsinscaled percent of total annual consumption) as allocated among four areas by 33 species of seabirds off the coast of Namibia and western and southern South Africa (from Crawford et al. 1991).

measured (varying from no overlap to complete overlap as portions from 0.0 to 1.0). Figure 2.15 shows the frequency distribution of overlaps in the geographic ranges of 21 species of marine mammals with the eastern Bering Sea ecosystem measured as portions of the eastern Bering Sea that fall within their geographic ranges. In all cases the full

SYSTEMIC MANAGEMENT

Portion of species

Portion of species

32

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Portion of North America

0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 –5.75

–4.75 –3.75 –2.75 –1.75 –0.75 log10 (portion of North America)

0.25

Figure 2.14 The pattern in geographic range size for 523 North American terrestrial mammal species expressed as both the portion of North American land area occupied (top panel) and in log scale (bottom panel) (modified from Pagel et al. 1991a with data provided by M. Pagel).

Portion of species

0.3

0.2

0.1

0.0

0.0

0.2 0.4 0.6 0.8 Portion of eastern Bering Sea

1.0

Figure 2.15 The frequency distribution for geographic range overlap with the eastern Bering Sea ecosystem by 21 species of marine mammals (from Angliss and Lodge 2004, with maps and data provided by R. Outlaw and L. Johnson).

geographic ranges of individual species include areas outside of this ecosystem (ecosystems are interconnected; Guerry 2005, Plate 2.1). There are four species whose geographic ranges include the entire eastern Bering Sea (northern fur seals, Callorhinus ursinus; Steller sea lions, Eumetopias jubata; fin whales, Balaenoptera physalus; and right whales, Balaena glacialis). Thus, the pattern shown in Figure 2.15, less one species, would apply to the overlap with the geographic ranges of all four of these species. Total geographic ranges have been estimated for many species, including the 9505 species of birds in the study by Orme et al. (2006). These data, as well as data presented by Bock and Ricklefs (1983) for birds of North America show the same general pattern observed in Figure 2.14; most species are confined to small geographic ranges. Brown (1995), Rosenzweig (1995), and Gaston and Blackburn (2000) present a broad variety of patterns involving geographic range (including graphic examples) with extensive consideration of explanatory factors and primary references. The work of Orme et al. (2006) and Ruggiero and Hawkins (2006) exemplifies studies of pattern—based on global geographic factors. As with all patterns, however, an exhaustive or complete detailed explanation will remain impossible owing to the complexity of factors involved. Science has made a great deal of progress, but, owing to human limitations, a full explanation cannot be achieved. From a management perspective, however, the emergence of these patterns ensures that all explanatory factors, known or unknown, are accounted for in the information they represent (Fig. 1.4). What is the size of a sustainable geographic range? What portion of an ecosystem can sustainably be subjected to the harvest or consumption of resources? Patterns such as that in Figure 2.15 provide consonance with the latter question in representing the ecosystem in question and as information for mammalian species. The measure is of area occupied (and subject to consumption of resources)—the characteristic specified by the question. What portion of the eastern Bering Sea should be set aside in areas free of commercial fishing? To address this question, measures of area free of the direct effects of consumption would

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

be used; the consonant pattern would be that of the portion of the ecosystem that falls outside the geographic range of each species. The two questions (how much to occupy vs. how much to leave unoccupied) would be addressed with consistent information (Hobbs and Fowler 2008). 2.1.1.9 Population density Damuth (1987) and Schmid et al. (2001) present data on population density (numbers of individuals per unit area). Figure 2.16 illustrates the pattern in population density for 368 species of nonmarine mammalian herbivores (from Damuth 1987). In panel A of Figure 2.16, density is expressed in raw measurements and heavily skewed to the right (i.e., has a long right tail or is positively skewed). This is in contrast to panel B where the distribution is shown in log scale. Clearly, most species occur at the lower end of the distribution but species with

Portion of species

(A) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0–2

4–6 8–10 12–14 16–18 20–22 24–26 28–30 Density (1000s/k2)

(B) 0.16 Portion of species

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 –1.5

–0.5

0.5 1.5 2.5 3.5 log10 (density, individuals/k2)

4.5

Figure 2.16 The frequency distribution of 368 mammalian herbivore species over population density, (A) expressed as numbers (in thousands) per square km, and (B) expressed as log10 of numbers per square km (data from Damuth 1987).

33

extremely low densities are rare (panel B). Related studies stress the consistency of the pattern of Figure 2.16 within taxa and various interpretations and explanations of the pattern.6 The distribution of a large sample of species (such as those represented in a complete ecosystem, or the biosphere) would be different from that confined to mammals. The distribution would have roughly the same shape, but would appear shifted to the right because mammals, on average, occur at lower densities than other more common, smaller-bodied species such as bacteria (Peters 1983). Gaston and Blackburn (2000) present a detailed consideration of patterns in density, especially as related to birds to show some of the variability among patterns as would be involved in the pattern for all species. Patterns in density match management questions regarding density, at least insofar as density is concerned. If raising domestic animals is a sustainable endeavor, what is a sustainable density for sheep (measured, e.g., as sheep per square kilometer)? This question is asked in regard to domestic herbivorous mammals, and we see elements of consonance between the question and the patterns in this section—patterns involving herbivores. Other elements of consonance would be brought to the task in consideration of latitude, habitat type, soil, and seasonal precipitation. 2.1.1.10 Population size It is well recognized that small populations are vulnerable to extinction. This fact, in combination with the information from the previous two sections suggests that we should expect to observe few species with small total populations. There are, in fact, fewer species with small populations than species with larger populations. This is seen in a compilation of estimated total population size for species of nonhuman mammals of roughly human size (Fowler and Perez 1999, Fig. 2.17).7 Few species occur at either extreme. This sample, of course, applies only to large bodied mammalian species. Much larger total populations occur among species with smaller bodies (e.g., bacteria). Unfortunately, there are no published extensive compilations of species numbers with direct measurements of simple total population size; monitoring and assessing population size are extremely challenging

34

SYSTEMIC MANAGEMENT

Portion of species

(A) 0.20 0.15 0.10 0.05 0.00 1.0

3.0

5.0 7.0 9.0 log10 (population size)

11.0

3.0

5.0 7.0 9.0 log10 (population size)

11.0

Portion of species

(B) 0.20 0.15 0.10 0.05 0.00 1.0

Portion of species

(C) 0.20 0.15 0.10 0.05 0.00 1.0

3.0

5.0 7.0 9.0 log10 (population size)

11.0

Figure 2.17 The frequency distribution of 63 species of mammals of approximately human body size as distributed over population size expressed as the logarithm of total numbers. (A) 21 species of marine mammals, (B) 42 species of terrestrial mammals, and (C) the combination of marine and terrestrial mammals in A and B (Fowler and Perez 1999, Nowak 1991, and Ridgway and Harrison 1981–1999).

scientific exercises. There is an important need for research in this regard to frame and address management and scientific questions about sustainability regarding total population size, especially for humans as will be seen in Chapter 6.

What is a sustainable population for large mammals? For any ecosystem or the biosphere, what portion of species would we normally expect to find in either tail of distributions such as that shown in Figure 2.17? The concern over endangered species leads to questions regarding the kinds of influence we have in contributing to the risks of extinction (e.g., portion of primary production monopolized, geographic range, consumption rates, CO2 production, . . . .—the beginning of an essentially unending list). Implementing Management Tenet 2 (Chapter 1), “What is a sustainable population for humans?” This question will be addressed in detail in Chapter 6, exemplifying the matter of achieving specificity while maintaining consonance in the process of refining questions. The factors that limit population size include resource availability within ecosystems in combination with the effects of predation and diseases. Much habitat is unsuitable and geographic ranges are limited. The dynamic balance that emerges from the combination of all such factors is the carrying capacity of such systems for populations. Thus, as with all patterns, patterns in population size show upper limits, and few species are expected to exhibit populations beyond such limits for any length of time—leading to the matter of variation in population size. 2.1.1.11 Population variability Most species show little population variability compared to the maximum variability observed. This is of little surprise based on knowledge of the elevated risk of extinction associated with high population variability. However, no species is completely resistant to the effects of its environment and all exhibit some degree of population variation. Patterns emerge as shown for population variation among fish; there is a lack of species at both extremes (Fig. 2.18). Studies of vertebrates (Fig. 2.19A) and arthropods (Fig. 2.19B) have shown a lack of species exhibiting high population variation. This is clear in Figure 2.20 for a sample of species across a variety of taxa. Although no species can withstand environmental influence to show no population variation, this is not always obvious in the ways information is presented. Choice of scale

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

(A) 0.6 Portion of species

Portion of species

(A) 0.3

0.2

0.1

0.0

35

0.5 0.4 0.3 0.2 0.1 0.0

0.0

0.2

0.4 0.6 0.8 1.0 1.2 Population variation (CV)

1.4

0.0–0.2

0.4–0.6

0.8–1.0 Variability

1.2–1.4

1.6–1.8

0.0–0.2

0.4–0.6

0.8–1.0 Variability

1.2–1.4

1.6–1.8

(B) 0.6 Portion of species

Portion of species

(B) 0.3

0.2

0.1

0.5 0.4 0.3 0.2 0.1 0.0

0.0 –1.0

–0.8

–0.6

–0.4 –0.2 0.0 log10 (CV)

0.2

0.4

Figure 2.18 Pattern in population variation (coefficient of variation, CV) expressed in raw form in panel A and in log scale in panel B for 21 species of marine fish (from Fowler and Perez 1999, as based on Spencer and Collie 1997).

is important in observing full patterns (compare Appendix Figs 2.1.8, 2.1.7, Figs 2.18, 2.19, and 2.20 to see cases where the lack of species with very low population variability is not obvious). One meaningful measure of population variability is the coefficient of variation because of its scaled nature. However, patterns regarding population variation based on other measures are found in the literature as shown above. The magnitude of variability by some measures is related to the time over which measurements are made (e.g., longer time frames may capture more extremes, Inchausti and Halley 2001). As in all cases, observed population variation among nonhuman species reflects human influence (Anderson et al. 2008, Apollonio 1994, Gray 1989, Rapport 1989b, 1992, Rapport et al. 1985, Regier 1973, Regier and Hartman 1973, Rosenzweig 1971, Woodwell 1970, Yan and

Figure 2.19 The frequency distribution of a sample of vertebrates (A) and arthropods (B) by population variability measured as the standard deviation of the logarithm of population sizes (from Hanski 1990).

Welbourn 1990). Observed patterns account for such influence among the many other factors involved in their origins. Patterns in this section relate to one reaction of ecosystems to disturbance: increased population variation. How much variation is too much? How do we use abnormal variation to lead to meaningful management questions that conform to the tenets of management laid out in Chapter 1? In part, the interconnected nature of systems leads us to ask questions about human influence (Management Tenet 2, Chapter 1) and its sustainability— influence contributing to abnormal variation even if only by way of indirect ecosystem effects. We know that fishing contributes to population variation (Anderson et al. 2008): “At what rates should we harvest the populations of species that serve as resources?” Should the harvest of a fish species that shows high population variation be different from one that shows low population variation?

36

SYSTEMIC MANAGEMENT

(A) 0.5

Portion of species

Portion of species

0.4 0.3 0.2

0.4 0.3 0.2 0.1

0.1

0.0

0.20

0.40

0.60 0.80 1.00 Variability

1.20 >1.21

Figure 2.20 The frequency distribution of a sample of 104 species of a variety of taxa (plants, insects, invertebrates, parasites, birds, and mammals) over population variability measured as the standard deviation of the logarithm of population sizes (from Connell and Sousa 1983).

2.1.1.12 Density dependence Limits to population variation are related to population regulation and density dependence—the tendency of a population to neither go extinct nor monopolize the energy and resources of an ecosystem. Figure 2.21 illustrates the distribution of several species based on the density dependence they exhibit. Density dependence in this sample involved 190 populations of 20 species of moths and aphids (Hanski 1990) measured using a method developed by Bulmer (1975). Because the variability for individual species was not presented separately from variability between sub-populations, these figures represent a combination of within-species variability as well as the among-species variability (the latter being more the focus of this chapter). Nevertheless, it is clear that most species show density dependence intermediate to the extremes. The lack of species with high population variability is consistent with the lack of species with extreme density dependence. Extreme population fluctuations would often result from extremely positive density dependence (over-compensation, chaos, Appendix Fig. 2.1.8) with its concomitant risk of extinction. On the other extreme, lack of density dependence also carries a risk of extinction; lack of recovery from low population levels leads to species-level vulnerability. Thus, risk of extinction counts among the explanatory factors

1.25 1.00 0.75 0.50 0.25 0.00 –0.25 –0.50 –0.75 –1.00 Index of density dependence

(B) 0.8 0.7 Portion of species

0.0

0.6 0.5 0.4 0.3 0.2 0.1 0.0

1.25 1.00 0.75 0.50 0.25 0.00 –0.25 –0.50 –0.75 –1.00 Index of density dependence

Figure 2.21 The frequency distribution of a collection of species of moths (A) and aphids (B) by level of density dependence, from Hanski (1990). Strength of density dependence increases toward larger negative numbers (reversed from usual order in these graphs to show increasing density dependence from left to right).

contributing to the origin of patterns in density dependence. What is a normal level of density dependence? For nonhuman species, this is a question for science, rather than management, as we cannot control the density dependence of other species (Management Tenet 2, Chapter 1). Among the management questions generated by abnormal density dependence would be those posed in regard to human contributions through factors which are under our control. Explicitly accounting for density dependence would be exemplified by management questions such as: What is an appropriate size composition for the harvest of individuals from a species with 0.5 as an index of density dependence (in units of Fig. 2.21)? This question defines the need for patterns involving both selectivity and density dependence to reveal what is normal and what is abnormal.

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

2.1.1.14 Other factors 2.1.1.14.1 Rate of population increase Rates of population increase are related to a number of life history factors. These include fecundity, life span, and age at first reproduction. Fecund species with short life spans and early maturation do tend to be more numerous than less fecund longer lived, and late maturing species (Marzluff and Dial 1991). Maximum intrinsic rates of increase are related to body size (Blueweiss et al. 1978, Peters 1983, Sinclair 1996), such that the distribution of species by body size (Figs 2.1, 2.2) also approximates the distribution for the corresponding rates of increase. Figure 2.22 shows the distribution for 43 species of mammals as a taxonomic group. The bimodality of this

distribution is notable, compared to that of body size for terrestrial species in Figure 2.2. 2.1.1.14.2 Biomass density Figure 2.23 is an approximation of the frequency distribution pertaining to biomass per unit area for nonmarine mammal species (data from Damuth 1987). Biomass per unit area was estimated for Figure 2.23 by multiplying observed adult body size by observed density.10 Although such an approach does not account for changes in size with age, the

0.28 Portion of species

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–2.0 –1.6 –1.2 –0.8 –0.4 0.0 log10 (rmax)

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Figure 2.22 A frequency distribution for the log10 of the maximum intrinsic rate of increase as observed for 61 populations of 43 species of mammals, from Sinclair (1996).

0.16 Portion of species

2.1.1.13 Mode of reproduction One of the most basic examples of species-level patterns involves whether a species reproduces sexually or asexually. The observed pattern is clear: species that produce asexually are a small portion of the total, especially among animals.8 A bar graph like other frequency distributions in this chapter would show two bars with the asexual category as a much smaller portion of the total than that for sexually reproductive species. However, determining the incidence of sexual reproduction is complicated by definition of sexual reproduction and the fact that some species (e.g., many plants) reproduce both ways.9 What is a sustainable level of resource consumption that accounts for the mode of reproduction by the resource species? Mode of reproduction is one of the factors that contribute to the emergence of patterns and is therefore inherent to the information embodied in patterns. Figure 2.6, for example, involves sexually reproducing species so as to be taken into account overtly in this and other patterns restricted to sexually reproducing species. Likewise, the predators involved in this pattern are sexually reproducing species, as are humans—further specificity without loss of consonance. In patterns that are not confined to sexually reproducing species, mode of reproduction is part of what contributes to their formation to be an inherent part of the information embodied— often something that can be teased out explicitly in correlative analysis.

37

0.12 0.08 0.04 0.00 –0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 log10 (kg/km2)

Figure 2.23 The frequency distribution of 368 mammal species according to an index of their biomass per unit area based on multiplying adult body size by the density for the species in the data set, from Damuth (1987). The frequency distribution for metabolic rate per unit area is expected to be very similar (see Figs 2.11 and 2.12).

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frequency distributions regarding biomass per unit area are expected to show the general character depicted in this graph. As with numerical density, patterns such as that of Figure 2.23 provide information regarding advisable density for domestic livestock. Assuming that agriculture is itself sustainable, how many tons of sheep per square kilometer represent a sustainable grazing pressure in the areas of Australia that receive 20 cm of rainfall per year? As is hopefully obvious, the answer determined through the application of information from patterns like Figure 2.23 will be consistent with the advisable density in numbers of sheep per square kilometer as based on patterns such as that in Figure 2.16. To deal overtly with the issue of precipitation, either a subset of species under such circumstances would be chosen to answer the question, or correlative patterns within the overall pattern, as related to precipitation, would provide the needed information to answer the question with consonance. 2.1.1.14.3 Age/size composition of consumed resources The biomass or numbers (e.g., Fig. 2.6) of consumed resources are not the only aspects of consumption or predator–prey interactions that can be measured for consumer species. Nor are they the only issues of importance in management of resource use by humans. The allocation of resource consumption is also important as exemplified by allocation over

space (Fig. 2.13), time (Fowler and Crawford 2004), and alternative resource species (Fowler 1999a). Allocation over the different age and size categories of an individual resource species is also a matter for management. Patterns with units defined by relevant management questions are exemplified by Appendix Figure 2.1.11 which shows how 12 species of seabirds allocate their consumption of sand eel near the Shetland Isles, across the various size classes of this resource species. To address real management questions (questions involving what humans should do) the patterns needed for the consonance necessary to be realistic would be based on similar information for marine mammals. This assumes that harvesting in marine habitats is sustainable for humans—a break from true consonance. Facing this assumption directly, a new (or different) question can be asked: “What is the normal ratio of energy consumed to energy expended in consumption processes?” Allocation over size (sex, space, age, or any phenotypic character) involves selectivity and all of the genetic/evolutionary consequences. One index of selectivity by size involves the mean size of individuals harvested/consumed. Figure 2.24 shows the pattern in mean size of prey in the diets of marine mammals in their consumption of fish and cephalopods (Etnier and Fowler 2005). As humans (i.e, as a mammalian species), what is the appropriate mean size for our catch of fish or cephalopods? The pattern in Figure 2.24 involves consonance with this management question not

0.25

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0.00 0–5

10–15 20–25 30–35 40–45 50–55 60–65 70–75 80–85 90–95 Mean length of prey (cm)

Figure 2.24 Pattern in the size of prey taken by marine mammals worldwide (Etnier and Fowler 2005). The frequency is measured among studies (cases, N = 726) wherein individual species of marine mammals are often represented several times.

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39

found in the pattern for birds shown in Appendix Figure 2.1.11.

the appropriate population density for humans, in view of our rate of increase per generation?”

2.1.1.14.4 Rate of increase per generation time The maximum rates of population increase (r) and generation times (T) displayed above can be combined (multiplied) to show the rate of increase per generation as displayed in Figure 2.25 for 61 populations of 43 species. Note the lack of bimodality in this pattern in spite of its presence in the pattern involving generation time (e.g., Appendix Fig. 2.1.12). Management questions would be generated by any observation of abnormal rates of increase for other species. Such questions would involve any potential contributing factor stemming from human influence. Given the potential for genetic effects of harvesting: “What is an advisable allocation of harvested individuals over body size (sex, reproductive status, depth, latitude)?” If the human rate of increase per generation is abnormal, which of the components are abnormal (e.g., mean age at first reproduction, age specific birth rates, or death rates) and in need of change? We are faced with an important scientific question: “Does rT contribute to any of the variation in observed population density?” If rT explains any of the variation in population density, it is important to be asking management questions such as: “What is

2.1.1.14.5 Lifetime reproductive effort Charnov et al. (2007) calculated estimates of what they call lifetime reproductive effort (LRE) for both mammals and lizards. These two groups of species show similar patterns for this characteristic (Fig. 2.26). Are these patterns reflective of what we would observe for other groups of species? Specifically, is this pattern independent of body size as would be indicated by these data? What are the factors involved in the origin of these patterns; how are these patterns explained? Are there correlative components to the explanation of the observed variation beyond errors of estimation (e.g., is the rate of increase per generation time correlated with LRE; Fig. 2.25)? These are scientific

Portion of species

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log10 (rT) Figure 2.25 The pattern for rate of increase per generation (rT, where r is the intrinsic rate of increase and T the generation time) representing data for 61 populations of 43 species of mammals, presented by Sinclair (1996).

0.0

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Figure 2.26 The pattern in LRE for 40 species of mammals and 54 species of lizards (Charnov et al. 2007, with data provided by M. Moses).

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questions regarding characterization and explanation of such patterns. In terms of management, is the LRE for any species abnormally high or low as a result of human influence; in particular are humans any different from what is observed for other mammals? If so, what elements of human life history contribute to any observed abnormality? Does the sustainable harvest rate differ for resource species of differing LRE? What is the sustainable size or age selectivity for harvests of species with different LRE? 2.1.1.15 Patterns and management questions Many of the patterns above (and in Appendix 2.1) involve characteristics, often largely evolved characteristics. Attempts to control such factors have consequences over which we have little or no control in management (Management Tenet 2, Chapter 1). However, there are a few that involve interactions (relationships) with other species, ecosystems, and the biosphere wherein there is the option of management when such interactions involve humans. All patterns involve factors that are accounted for in their contribution to the patterns that are informative to management where action by humans can be taken to regain normalcy. There are interrelationships among patterns (Fig. 1.4). Thus, patterns regarding individual species-level characteristics such as mean body size, trophic level, and elements of life history strategy (e.g., generation time) represent phenomena that are important to weave into management questions regarding our interaction with nonhuman species (e.g., What is the sustainable harvest of species with a generation time of 12 years?). On their own, patterns such as that for increase per generation time (Fig. 2.25) would only be consonant with management questions such as “What is the most sustainable rate of increase per generation for humans?”—something over which we have some, but limited, control. However, like body size, they would be the basis for refining existing management questions and asking new management questions. In other words, if there is abnormality observed for nonhuman systems (e.g., another species), and especially if it is known that it is the result of abnormal human influence, factors such as body size or rate of increase per generation can be used to frame

management questions. Thus, indirect relevance is retained in asking questions such as: “What is the sustainable consumption of energy for a species with a rate of increase per generation characteristic of humans?”, or “What is the sustainable geographic range size for a species with a rate of increase per generation time characteristic of humans?” Thus, beyond those patterns involving factors over which we have little control, there are many patterns which provide measures with units of varying degrees of consonance with pressing management questions. Important among such questions is the quantification (Management Tenet 9, Chapter 1) of sustainable interactions with other species, ecosystems, and the biosphere. Consonant patterns are exemplified in Figures 2.4 (for numbers of prey species and the question of how many species we should consume), Figures 2.6–2.11 (regarding the extent and nature of our consumption of resources), and Figures 2.14 and 2.15 (regarding areas we should occupy vs. leave protected from direct human influence, Hobbs and Fowler 2008). Posing management questions by weaving in factors such as body size, generation time, and rate of population increase, involves the effect of such factors in otherwise consonant patterns. Refining management questions leads to accounting for such effects overtly. It involves knowing about correlative relationships—the combination of two or more factors within a pattern. The next sections treat this kind of complexity.

2.1.2 Two species-level characteristics Single species-level characteristics like those examined in the previous section are not independent of other characteristics—a fact impossible to escape in treating examples of explanation in the patterns above. Often, but not always, much of the variation exhibited within a pattern is explained by other variables—correlative patterns. For example, patterns often involve obvious correlations with body size (e.g., Blueweiss et al. 1978, Brown 1995, Calder 1984, Damuth 1981, 1987, 1991, Gaston and Blackburn 2000, Kelt and Van Vuren 2001, Peters 1983, Rosenzweig 1995, Schmid et al. 2001, Schmidt-Nielsen 1984, Sinclair 1996). Fowler (1988), Charnov (1993), and Sibley et al. (2005) consider a

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

number of correlated species-level characteristics, including some that are not related to body size. Research involving correlative variation is part of the science devoted to explanation and includes characterization of patterns-within-patterns. Such information allows for the refinement of management questions and is part of the role of scientists as stakeholders in systemic management (bottom row of Fig. 1.1). The refinement of management questions is one of the ways systemic management accounts for complexity. All patterns involve a collection of correlative interactions. Table 2.1 shows the potential for research regarding combinations of two and three characteristics (second and third columns). The fourth column shows the total of all single and multifactor combinations of potential importance in a given pattern, keeping in mind that, in reality, the number of factors involved (n of Fig. 1.4), and their synergistic interactions, are impossible to completely understand. None of the published

patterns represent an entire set of species making up a complete community or ecosystem; furthermore, studies such as that of Bascompte et al. (2005) always involve groups of species (higher level taxa, or trophic groups) treated as a “species” in addition to the set of species that are treated individually. The resulting graphs represent correlations better than they do the density of species within the correlative relationships (this density and its distribution being critical to defining the normal compared to the abnormal). 2.1.2.1 Body size and other species-level characteristics Observed correlations between body size and other species-level characteristics include geographic and home range size, rate of increase, generation time, population density and size, trophic level, and metabolism (e.g., consumption and respiration). Other examples of characteristics related to body size include rates of movement, ingestion,

Table 2.1 Numbers of possible combinations of characteristics for which patterns involving species frequency distributions could be produced Number of characteristics

Interactions between two characteristics

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 3 6 10 15 21 28 36 45 55 66 78 91 105 120 136 153 171 190

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Interactions among three characteristics — 1 4 10 20 35 56 84 120 165 220 286 364 455 560 680 816 969 1140

Total number of interactions 1 4 11 26 57 120 247 502 1,013 2,036 4,083 8,178 16,369 32,752 65,519 131,054 262,125 524,268 1,048,555

These are theoretical in the sense that research to actually demonstrate such interactions cannot be exhaustive, especially when numerous factors are involved as is almost always the case.

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and reproduction, litter size, survival, and age of first reproduction (Peters 1983). The well-known relationship between body size and metabolism is one of the best known examples (Kleiber’s rule, Kleiber 1961). Several of these are illustrated in this section (and Appendix 2.1).

to the observed pattern). A fitted continuous surface representing species numbers over body size and geographic range would be possible and could represent an entire ecosystem if all species were involved (see Appendix Fig. 2.1.15). The shape, but not necessarily the position, of such distributions may be expected to remain consistent from habitat to habitat (e.g., marine to terrestrial). The combination of geographic range and body size have been the subject of numerous studies (e.g., Anderson 1977, Brown 1995, Gaston and Blackburn 2000, Gaston and Lawton 1988a,b, Hugueny 1990, May 1988, and Rosenzweig 1995). As with other patterns in this chapter, we see the increasing potential for refining management questions. What portion of the geographic range of a species with a range size of 50,000 km2 and body size of 50 kg should be protected by spatial limits in the harvest of one of its resource species?

2.1.2.1.1 Body size and geographic range An example of empirical information on body size and geographic range size is shown in Figure 2.27 (from Brown and Nicoletto 1991) with the density of species represented by bars arranged in rows and columns. Each column of bars shows the portion of species in the total sample as distributed over geographic range for a selected category (span or bin) of body size. Likewise, for a given category of geographic range (row) there is a corresponding distribution of species over body size. Thus, the general patterns described for both body size (Fig. 2.1) and geographic range (Fig. 2.14) on their own are consistent with the internal patterns seen in their combination (recall the consistency required of Management Tenet 4, Chapter 1). Figure 2.27 shows an increase in mean geographic range with increasing body size. From the other perspective, the mean body size of species decreases as geographic range size decreases. Figure 2.28 presents this pattern in different graphic form. Note the lack of cases in the lower right corner—where the combined effects of large body size and small range size result in elevated risks of extinction to prevent the accumulation of species (as at least one set of factors contributing

2.1.2.1.2 Body size and home range size The home range size of mammal species is correlated with body size (Fig. 2.29). This overall pattern (e.g., Appendix Fig. 2.1.13) can be broken into subpatterns confined to particular ranges of body size (Appendix Fig. 2.1.16). Different patterns would also be seen for body size for different segments of home range size. Each such pattern is similar to those in the first part of this chapter, dealing with individual species-level traits. Similarly, each pattern demonstrates limits to variation—in this case, as related to the two correlated variables of body size and home range size. Gaston and Blackburn

Portion of species

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Figure 2.27 The correlative pattern between geographic range and body size for North American mammal species shown as a two-dimensional frequency distribution (from Brown and Nicoletto 1991).

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8

log area (km2)

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log10 area (km2)

Figure 2.28 The pattern in the relationship between geographic range and body size for North American mammal species based on Brown (1981). The concentration of data points shows relative species frequency. The upper edge of the collection of points represents limits set by the size of the continent.

7 6 5 4 3 2 1 0 –1 –2 –3 –4

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log10 (body mass, g)

Figure 2.29 The bivariate distribution of 280 mammal species in their variation over body mass and home range size as based on data from Kelt and Van Vuren (2001, with data provided by D.A. Kelt).

(2000) treat the relationships between these two species-level characteristics for birds, again showing patterns that exhibit correlations and limits to natural variation similar to those for mammals. Management questions can explicitly include home range size. An example would be: “What is the sustainable harvest of biomass from resource species with an average adult body size of 70 kg, fed upon by four species of predators with home ranges averaging 20 km 2?” What portion of the geographic range of a species with a home range of 400 km2 should be protected from the harvest of

43

primary producers in its ecosystem? What should we do if we find a species whose individuals exhibit a mean home range that is abnormal in comparison to the pattern shown in Figure 2.29? The latter question is too vague to provide management advice in regard to our interactions with other species or the ecosystem, but it directly relates to the management of the decision-making process. The answer is: “Ask management questions regarding interactions with the focal species, other species, the ecosystem, and the biosphere as systems in which the problem is observed.” 2.1.2.1.3 Body size and population variability A voluminous literature treats the joint distribution of species by body size and population variability. Related research examines variability among these patterns, their interpretation, and factors thought to contribute (e.g., Brown 1995, Connell and Sousa 1983, Gaston and Lawton 1988a,b, Pagel et al. 1991a,b, Patterson 1984). With only few exceptions, this literature supports the conclusion that species with large body size do not show as much population variability as smaller species (e.g., Gaston 1988, Gaston and Lawton 1988a,b, Hanski 1990, Lawton 1989a, 1990, Pagel et al. 1991b, Pimm 1991, Pimm and Gilpin 1989, Pimm and Redfearn 1988, Schoener 1985, Sinclair 1996, Southwood 1977, Taylor and Woiwod 1980, Zeveloff and Boyce 1988; Appendix 2.1). The data from Sinclair (1996) clearly demonstrate this relationship between body size and population variation (Fig. 2.30). In view of the patterns for body size alone (Figs 2.1 and 2.2), and for population variation alone (e.g., Fig. 2.19), most species within patterns such as that shown in Figure 2.30 (e.g., the entire set of species for a specific ecosystem, or even the biosphere) would tend to be found in the upper left. The density of species within the relationship shown in Figure 2.30 is more a product of selective research than a representation of what would be seen in nature. In other words, more species are expected to be small bodied species with higher levels of population variation than larger bodied species with low population variation—a pattern different from Figure 2.30 in the concentration of species numbers but not in the correlative relationship.

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4

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3 2 1 0 –1 –2 –3

–1.6

–1.2

–0.8

–0.4

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log10 (body mass, kg)

Figure 2.30 The standard deviation of observed rates of change (in log10 scale) as related to log10 body mass (kg) for 79 populations of 55 species of mammals, from Sinclair (1996).

Management questions in which body size is explicitly included also account for population variation because of the correlation between body size and population variation but not necessarily with the precision or accuracy to be achieved with information about population variability itself. Variance within the correlation between population variability and body size involves other factors; population variability is also a product of factors other than body size. The more such factors are woven into the management question explicitly (followed by research to reveal the consonant pattern), the more we achieve in accounting for complexity. 2.1.2.1.4 Body size and population density/size The decline in the correlative relationship between population density and increasing body size for herbivorous terrestrial and freshwater mammals from Damuth’s (1987) data is shown in Figure 2.31. Similar patterns are observed for plants (Belgrano et al. 2002, Enquist and Niklas 2002). Explaining this pattern in its combination of species-level traits has been the subject of a great deal of research (e.g., Basset and Kitching 1991, Blackburn et al. 1993, Brown 1995, Brown and Maurer 1987, Cristoffer 1990, Damuth 1991, Duarte et al. 1987, Eisenberg 1981, Gaston and Lawton 1988a, Gaston and Blackburn 2000, Juanes 1986, Lawton 1989a, 1990, Lawton et al. 1994, Marquet et al. 1990, Morse et al. 1985, 1988, Pagel et al. 1991a,b, Peters 1983, Peters

and Wassenberg 1983, Robinson and Redford 1986, Schmid et al. 2001). What is the sustainable density for herbivorous mammals with a mean adult body mass of 150 kg? This question (not a management question as it does not involve human action; Management Tenets 2 and 5, Chapter 1) is an example of refining a question about sustainable density to include explicit consideration of body size. Consonance with this question is seen in the pattern represented in Figure 2.31 where the species involved are mammalian herbivores measured in terms of their density. The question could have been made even more specific (refined further) if it had specified consideration of mammals in terrestrial environments—the environment represented by the data in Figure 2.31. It would have been a management question (i.e., would involve an objective for human action) if it had been: “In terrestrial systems, and assuming humans are herbivores, what is a sustainable density for humans as a mammalian species with adult body mass of 68 kg?”. 2.1.2.2 Other patterns involving two dimensions A variety of other patterns has been recognized in correlative, two-dimensional relationships among species-level characteristics. In addition to those reviewed below there are the pair-wise correlative relationships involving geographic range and density, geographic range and population size, and geographic range and population variation (among others, Appendix 2.1). Each continues to add to the options for asking and refining management questions; each is an integrative informative pattern. 2.1.2.2.1 Intrinsic rate of increase and generation time A clear relationship between the intrinsic rate of increase (r) and generation time (T) is to be expected in view of the relationships between these two variables and body size (Appendix Figs 2.1.18 and 2.1.19). Figure 2.32 shows this relationship for 61 species of mammals. The slope of the regression line fit to these data (geometric mean regression) is −1.001 ≈ −1 (related to the fact that their relationship with body size is equal but opposite, as well as the fact that the product rT is independent of body size, Fowler 1988, Sinclair 1996).

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To the extent that both of these characteristics are related to body size, they are both (at least partially) accounted for in management based on patterns that involve body size as defined by the questions (e.g., What is the sustainable density of a mammalian species with a body size of 68 kg?). Direct measures of density accompanied by direct measures of rate of increase, generation time, and body size avoid the potential errors in correlative relationships and can be used to address questions where all three are specified. 2.1.2.2.2 Shape of productivity curves and rate of increase per generation Populations often show growth represented by the well known “S”-shaped (sigmoid) curves, growing slowly at first, rapidly at intermediate population levels and leveling off at higher levels. Species can be characterized by R, the population level

Figure 2.31 The bivariate relationship between population density and body size for 368 mammalian herbivore species: the top panel shows data plotted in a scatter plot and the bottom panel shows the corresponding threedimensional bar graph (or frequency distribution with data from Damuth 1987; units of density on the left, and body size from left to right).

corresponding to maximum growth in their populations expressed in terms relative to normal (or mean) population levels (carrying capacity). Thus, R varies in values between 0.0 and 1.0. Also, life history characteristics of species can be represented by numerous metrics, one of which is the rate of increase per generation time (rT, see Fig. 2.25). Figure 2.33 shows the pattern in the relationship between R and rT. As mentioned above, both r and T are related to body size. The correlative relationships are opposite (as equations), however, and the effect of body size is removed (cancels) in their product (rT). Thus, rT is expected to show no relationship with body size (Fowler 1988). There is, therefore, no surprise in finding a lack of relationship between the shape of productivity curves and body size when we look at empirical information (Fowler 1988, Sibley et al. 2005, Sinclair 1996). In terms of

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2008). In the shift from the top row to the second row of Fig. 1.1, we are faced with asking management questions such as: “What is the sustainable harvest rate for species with a rate of increase per generation of 5.0 (an otherwise dimensionless number, Charnov 1993)?” The informative consonant pattern is that involving consumption rates by predators feeding on species with that rate of increase per generation time.

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2.1.3 Three species-level characteristics -1

Inflection point as portion of K = R

Figure 2.32 The correlation between rate of increase (rmax, yr ) and generation time (yr) as based on 61 species of mammals, with data from Sinclair (1996).

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Figure 2.33 Relationship between the position of the inflection point (R) of the growth curve for populations and the rate of increase (r) per generation time ( T) (ln transformed, ln(rT )), from Fowler (1988).

the distribution of species within this relationship, most species would be expected to occur between the extremes, corresponding to intermediate values of rT (Fig. 2.25) and the corresponding linearity in density dependence (R ≈ 0.5). Historically, management placed the maximum sustainable yield of resource species at the peak of their productivity curves. Populations were reduced to correspond to R (below 0.5 for many fish species, and above 0.5 for many large bodied species). This strategy is now recognized as a fallacious management practice (Belgrano and Fowler 2008, Fowler and Smith 2004, Hobbs and Fowler

This section treats a few examples of patterns among species that involve three species-level attributes (see Appendix 2.1 for others). Graphic representation becomes increasingly difficult in considering more than one species-level characteristic. For three characteristics, one option is to plot points in a three-dimensional graph with the density of points representing the frequency of species, especially in stereograms (e.g., Fig. 2.34 as a hypothetical example, Fowler and Perez 1999).11 Another option is to graphically present serial slices through one of the three dimensions with the density of points representative of the frequency of species. In all cases, the continuing points of importance are that variation is always limited, observed patterns illustrate such limitation, patterns represent an integration of information, and, with patterns, abnormality can be measured to address consonant management questions. As the more generic single-attribute patterns are broken down into their correlative sub-patterns, we are proceeding through a process of analysis of variance (in statistical terms) at the same time we are refining management questions to achieve greater specificity. As such, it is not unusual, nor surprising, to see the upper and lower limits to variation approach each other in specific parts of the more generic patterns—the range of variation narrows. A small set of empirical data does exist to represent part of the relationship shown in Figure 2.34 (the pattern represented as might be hypothesized for a full ecosystem). The empirical data come from the species in common to two sets of published data. The 14 species shown in Figure 2.35 represent those that have measures of all three of these species-level characteristics: population

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Figure 2.34 A cluster of species (heavy points in the top panel) distributed in three-dimensional space (shown projected in each two-dimensional combination on the walls and floor of the top panel and as a stereogram in the bottom panel) much as might be expected for population variability, body size, and population density (the latter two variables shown in log scale).

variation (log10 standard deviation of r, Sinclair 1996), body mass, and population density (Damuth 1987). The orientation of this graph is different from that of Figure 2.34 but it shows the same correlations. The density of (and very likely the variation among) species can be misleading in this illustration because many small species with high population density (i.e., the extension of the relation of Fig. 2.31 to include species with the body size of insects and bacteria) and variation (i.e., the extension of the pattern in Fig. 2.30 to include very small bodied species) are missing. Nevertheless, some of

Figure 2.35 The relationship observed between population variation (log10 standard deviation of rate of change), density (log10 numbers per km2) and body size (log10 kg) for 14 species of mammals from the combined data sets of Damuth (1987) and Sinclair (1996).

the nature of the relationship in three-dimensional space is clear. Management questions can involve an explicit recognition of any one of the three variables/ characteristics within this relationship (population variation, body mass, and population density). An example would be: “What is a sustainable harvest of a mammalian species with a body mass of 100 kg?” Such questions (involving body mass) automatically achieve progress in accounting for population variation and population density. As in all cases, however, direct information (actual data) for consumption rates corresponding to measures for population variation and density provides greater insight into sustainability. 2.1.3.1 Population density, body size, trophic level Figure 2.36 illustrates a portion of the threedimensional pattern for mammalian species in their simultaneous distribution over body size, trophic level, and population density—part of what would be seen among the full set of species within an ecosystem. The bottom panels show both herbivores and carnivores that have a body size close to that of humans (34–102 kg). There are fewer carnivores (Fig. 2.36 left panels) than herbivores (right panels). Among both carnivores and herbivores there are fewer species with the body size of humans

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(Fig. 2.36, bottom panels). In both cases, the largerbodied species have populations with fewer individuals per square kilometer. Carnivores tend to show lower population densities than herbivores of similar body size (Marquet 2002). These characteristics are all consistent with patterns shown earlier in this chapter. With information on increasingly more species-level characteristics we are in position to ask increasingly more refined questions—both in management and in research. For the three characteristics just covered, we can evaluate other species: “What population densities are sustainable for a strict carnivore with an adult body mass of 160 kg?” For others we might ask: “What management action is appropriate if we find that we are abnormally restricting the geographic range of an endangered mammalian carnivore with an adult body mass of 100 kg, and density of four per square

kilometer?” The vague nature of this question would lead to better management questions such as: “What portion of the continent occupied by the species (characterized as an endangered mammalian carnivore with an adult body mass of 100 kg, and density of four per square kilometer) should be left unoccupied by humans?”

2.1.4 More than three species-level characteristics The numbers of combinations for more than three species-level characteristics provide a superficial introduction to the complexity of real-world biological systems (Table 2.1). Table 2.2 shows a sample of 14 species of mammals for which there are six measures, representing the species common to three sets of data from the literature. Here, the six measures are body size, generation time, maximum

0.15 Mammalian herbivores

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Figure 2.36 The distribution of 410 species of mammals according to trophic level, and body size as related to population density (369 herbivores, and 42 species of carnivores, from Damuth 1987). The bottom panels show 29 species of herbivores and seven species of carnivores of a body size similar to that of humans (50–150% of human body size).

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rate of increase, variation in population size, home range size, and density. Owing to the near impossibility of graphic demonstration of more than three characteristics, no figures representing the data in Table 2.2 for four or more interactions are included in this section. This small set of data alone allows for the presentation of 15 graphs in two dimensions, and 20 in three—some of which were presented above. However, there are 15 four-, six five-, and one six-dimensional patterns for this set of data for a total of 63 different ways of viewing these data if it were graphically possible to include them all.

2.1.5 Processes contributing to patterns among species Tribute must be paid to the progress made in studying patterns among the many species-level attributes. One of the main points of this chapter is the fact that science includes the discovery of patterns. Following discovery, scientists find themselves involved in characterization, description, analysis, and explanation. Characterization

49

provides information on the limits in natural variation among species. Another related point is that patterns (in relationships involving one, two, three or more dimensions) define a “morphology” for the groups of species involved (as in Figure 2.34).12 Such patterns are of particular interest in studying ecosystems, where the geographic ranges of various sets of species overlap and each ecosystem has its own set of species with patterns to be observed and subjected to research. However, owing to the complexity of our world, the study of such patterns as far as it has progressed has barely scratched the surface. This point is made by the tiny fraction of the possible combinations (Table 2.1) reviewed in this chapter and represented in the scientific literature beyond what is covered here. Over 30 characteristics are presented in this chapter (and Appendix 2.1). With this many measurable attributes, the entire collection of species in an ecosystem could be represented by 1.074 × 109 distributions of the types reviewed above. For the collection of patterns for three characteristics (4060 combinations among the 30 characteristics) there would be correlative relationships exemplified

Table 2.2 A list of the species common to the sets of data from Damuth (1987), Sinclair (1996), and Kelt and Van Vuren (2001) showing the corresponding measures for each of the 14 species Species name

log10(generation time, years)

log10 (intrinsic rate of increase, rmax)

Antilocarpa americana Bison bison Cervus elephus Giraffa camelopardalis Lepus americanus Loxodonta africana Microtus agrestis Mustela erminea Mustela nivalis Odocoileus hemionus Ovis canadensis Rangifer tarandus Syncerus caffer Vulpes vulpes

0.699 1.000 0.778 1.114 0.176 1.398 −0.482 0.000 −0.301 0.699 0.699 0.845 1.000 0.342

0.352 0.488 0.292 0.260 0.517 −0.019 0.246 0.619 0.573 0.047 0.243 0.288 0.462 0.563

log10 (standard deviation, observed rate of change, r) −0.654 −0.606 −1.051 −0.721 0.012 −1.071 0.468 0.408 0.492 −0.910 −0.731 −0.814 −0.724 −0.186

log10 (home range, km2)

3.005 4.425 3.874 4.135 0.515 5.244 –1.155 2.122 1.812 3.545 3.440 5.550 3.912 2.692

log10 (density, N/ km2)

log10 (body mass, kg)

−0.032 −0.495 0.739 −0.028 2.149 0.037 3.342 1.021 1.556 1.000 0.228 0.459 0.580 0.204

1.778 2.845 2.279 2.903 0.176 3.477 −1.602 −0.824 −1.523 1.699 1.845 2.176 2.653 0.845

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in Figure 2.34 to be seen as stereograms to better appreciate the patterns. The scientific questions to be addressed suggest the progress yet to be made, for example: What patterns are revealed by various threedimensional relationships among species-level measures expanded beyond those represented in existing literature? How extensive is the list of dimensions over which species can be measured? How do patterns and relationships vary with environmental conditions (varying habitat or space: latitude, marine vs. terrestrial, etc.)? How do such patterns and relationships vary over time (e.g., season, climate)?

●

●

●

●

However, science does not linger on discovery or description, and explanation is never far from the minds of scientists who study relationships and patterns (e.g., Gaston and Blackburn 2000, Hubbell 2001). Patterns are natural phenomena, but what are their origins? The primary processes and dynamics that have been identified as contributing to the production of species frequency distributions are: ecological mechanics, evolutionary processes of natural selection at the individual level as well as at the species-level, primarily through selective extinction and speciation. These three sets of processes are interrelated and all operate within the context of environments as a set of conditions that contribute their own effects. The combination thus involves four domains for research13 —unlimited science of which we already see tantalizing results. Evolutionary processes are considered in detail in Chapter 3, with emphasis on species-level processes of speciation and extinction. In Chapter 3, it is argued that speciation and extinction are more important in the formation of species frequency distributions than natural selection at the individual level, and that both are more important than the nonevolutionary factors of ecological mechanics. All operate in the context of environmental circumstances, which is increasingly recognized as subject to significant human influence. In the final analysis, all contributing factors are involved in the origin of patterns, known or unknown and regardless of the category in which they fall and regardless of the importance we think they have.

Of primary importance to the message here, however, is the conclusion that the patterns in species frequency distributions emerge14 from the complexity of reality (Belgrano and Fowler 2008, Fowler and Crawford 2004). As such, all factors are accounted for through their contributions to observed patterns whether or not we can list them, study them, or know about them. It will be argued in Chapters 5 and 6 that while establishing the relative importance of such factors may be important in scientific debate, it is not necessary for management. This is because pattern-based management automatically accounts for the relative importance of individual factors through their contribution to the emergence of patterns (bottom row of Fig. 1.1, Fig. 1.4; Belgrano and Fowler 2008). 2.1.5.1 Ecological mechanics Ecological mechanics (the complex of biological, chemical, and physical processes and dynamics, or the non-evolutionary processes), have been the central focus of much of conventional ecology. Ecological mechanics includes the components of which species are comprised and the interactions within and among them: populations, individuals, organs, tissues, cells, chemical compounds, and elements. Examples of these processes are predator/prey and behavioral interactions (Plate 2.2), common resource use (competition), the influence of environmental variability, pheromones, nutrient dynamics and availability, and energy flow and availability. Such dynamics and relationships involve sound (hearing), light (sight), gravity, electrical fields, and related behavioral responses. Ecological mechanics encompass many of the factors that come into play in population dynamics, all the limitations and stimuli provided by the chemical environment, and the complete temperature regime (including variation and its patterns), plus those of humidity and radiation with their variation and variety of effects. All such factors influence the position of each species in such patterns, where each pattern represents a different expression and a different accounting of those factors (Fig. 1.4). Nonevolutionary factors help define both variation and its limits within patterns (Fowler and Hobbs 2002) and so are taken into account in management that is

PAT T ER NS A M O N G SP ECI E S: I N F O R M AT I O N

based on such patterns by avoiding the abnormal (Management Tenets 3, 5, Chapter 1; Fowler 2003, Belgrano and Fowler 2008). Virtually all of the patterns presented above have resulted in examples of explanatory science following their original publication, some more than others. As seen above, it is possible to conduct an analysis of variance to decompose patterns into explanatory correlative sub-patterns. When correlative relationships involve any of the contributing factors, we can explicitly or overtly account for those factors if we have information to characterize the relationship. Such information allows for the refinement of management questions to include such factors so as to match the consonant integrative pattern (Belgrano and Fowler 2008). This includes the matter of environmental conditions. For example, Orme et al. (2006) show patterns in geographic range size as it varies globally among birds; questions regarding an expected range size can overtly include geographic location. The matter of ecological mechanics is the subject of the bulk of extant ecological literature. As such, it will not be treated in as much detail as natural selection in Chapter 3. 2.1.5.2 Natural selection at the individual level What a species is (genetically—the combination of phenotypes seen among individuals) clearly contributes to where it falls in the distribution of species within patterns. The evolutionary process of natural selection at the individual level (and among genes and combinations of genes) is instrumental in determining the genetic makeup of every species. Evolutionary dynamics involve a focus on the genetic information that overlaps ecological mechanics (natural selection often removes individuals or gene combination on the basis of related mechanical processes that don’t work). Thus, phenotype or genetic design influences much of what is observed in ecological mechanics,15 both among individuals and among species. Mechanical processes are also constrained by the raw materials making up the building blocks of life while genetic information contributes to patterns within these constraints. The constraint of context is a reality—including the context of availability of raw materials as well

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as the influence of environmental variation, and a wealth of other factors. Evolutionary dynamics have not been described to the satisfaction of everyone, partly as a result of the complexity involved. However, the predominant process is natural selection at the individual level, as we understand it through the field of evolutionary biology. Natural selection may change (or maintain) gene frequencies among individuals, and thus change (or maintain) the character and attributes of species. The resulting genetic code, the genome of every species, is a major factor contributing to what a species is and does, and where it falls in a species frequency distribution. Evolution through natural selection operating on individuals (and the genes they carry) is linked in various ways to selective extinction and speciation, summarized below and amplified in Chapter 3—as selection operating at the species level. Part of what happens in natural selection acting on the gene frequencies within species is genetic drift (i.e., change or recombination of DNA, often characterized as random). This physical/chemical phenomenon exemplifies elements of complexity that cannot be predicted but have species-level consequences—physical–chemical processes within individuals within species. However, among species, genetic drift also provides an element of diffusion in species-level dynamics that contributes to the formation of variation and pattern among species. 2.1.5.3 Selection by extinction and speciation Natural selection at the species level is another major influence on the formation of patterns among species (Aarssen et al. 2006, Arnold and Fristrup 1982, Fowler and MacMahon 1982, Gaston and Blackburn 2000, Slatkin 1981). A specific example of species-level selection as a pattern-shaping factor is found in the work of Damuth (2007). In this research species-level selection is examined in regard to a well-recognized pattern—a pattern in which energy use per unit area shows no correlation with body size. In simplistic terms, selection among species occurs because different species face different risks (chances or rates) of extinction and experience different rates of speciation. In the formation of patterns, the dynamics of extinction and speciation often override those of

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natural selection among individuals and ecological mechanics, both of which can contribute to the formation of characteristics that make a species extinction prone (Chapter 3). Discussion and debate regarding the role of selectivity in extinction and speciation in the formation of many species-level patterns like those presented in this chapter are common in the scientific literature. Such efforts are part of the growing science of macroecology (e.g., Blackburn and Gaston 2006, Brown 1995, Gaston and Blackburn 2000). Everything is part of something else (with the possible exception of reality as a whole, Appendix 1.1; Wilber 1995); every system is part of a larger system. The environmental context of species and the ecosystems within which they occur involves a powerful set of factors that contribute to what we see. Even the forces of extinction involve elements such as climate, island size, latitude, and continental drift. An excellent example of implementing this principle in macroecological research is that of Orme et al. (2006) wherein it is shown that geographic range size and geographic location show pattern. The geographic range size of species is not independent of the context provided by the earth, its continents, climate, and weather. Island biogeographic research is founded on such principles—the numbers of species in a particular area is influenced by numerous factors. These factors include the size of the area, and, as is increasingly recognized, major anthropogenic impacts. Although other factors are undoubtedly involved in the formation of species frequency distributions, it seems clear that among the primary influences are ecological mechanics, evolutionary processes of natural selection at both the individual and species-level, and the context provided by systems in which species occur. Regardless of their relative importance, none of them can be ignored, especially in management (Management Tenet 3, Chapter 1), including those things not included in our current perceptions. In all cases, human influence counts among the factors involved. 2.1.5.4 Context: environmental factors Environmental circumstances count heavily in contributing to the formation of patterns such as those illustrated in this chapter. Latitude, climate,

weather, temperature, precipitation, CO2 levels, pollution levels, and radiation are among the many explanatory factors in the origin of such patterns. Such factors not only affect patterns through the processes of natural selection, but also in their roles in ecological mechanics. The science of macroecology includes looking both at the correlative nature of patterns among species as they relate to environmental factors, but also patterns among assemblies of species. Island biogeographic research spawned major contributions to the understanding and nature of species–area relationships. The relationships between geographic range size and global location exemplify such larger-scale patterns (e.g., Orme et al. 2006)—patterns that can be used to refine management questions to account for what is observed by scientists.

2.2 The eastern Bering Sea example The patterns among species displayed in this chapter illustrate measurable characteristics of any species assemblage. For example, the species frequency distribution for the numbers of species at each trophic level for the eastern Bering Sea would include every species in the ecosystem—none would be excluded. The mean trophic level, based on such data, represents an ecosystem characteristic as would the distribution of species across the various trophic levels. Thus, when expressed as portions of the species represented within the system (as described in Appendix 1.3), the distribution over trophic level (and other species-level characteristics) represents frequency, or probability, as an ecosystem characteristic. Graphic representations of the set of species with populations in each ecosystem can include all the species characteristics represented above. Such initial steps toward dealing with complexity can be extended to include combinations of characteristics, and characteristics either ignored in this chapter or yet to be discovered. Scientists recognize characteristics such as mean age at first reproduction, populationsuppressing effects of predators on prey species, contributions to nutrient turnover, mean mortality rates, and mobility. The complexity of ecosystems includes the variety of ways species can be measured, including those we have not yet discovered.

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Several species frequency distributions in Chapter 1 directly pertain to the eastern Bering Sea. Another, introduced in Figure 2.6, adds further information about consumption rates among the predators that feed on walleye pollock, a fish species with a population in the eastern Bering Sea. Figure 2.24 introduces patterns regarding spatial distribution of species in this ecosystem (and their direct influence on other species within their geographic ranges). The patterns illustrated for other ecosystems (e.g., Figs 2.7, 2.13, and Appendix Fig. 2.1.6) are the kinds of patterns that also exist in the eastern Bering Sea. Such patterns can be described for any ecosystem, notwithstanding the considerable logistic difficulty in the research required to do so. As mentioned briefly in connection with some of the patterns above we can take further steps in refining management questions. What is a sustainable harvest of walleye pollock for our species as a large mammal functioning as a predator in the eastern Bering Sea? This is a management question. Although relevant, especially as they involve factors that are relevant, most of the patterns earlier in this chapter fail to match this question. Consonance between management question and integrative informative pattern must involve similar circumstances. Out of all of the figures reviewed above, only Figure 2.6 is for consumption of pollock in the eastern Bering Sea. It fails to be fully consonant with the management question, however, in that it includes estimates of consumption rates for birds, whereas we are mammals. Consonance between question and pattern must involve identical units. It must involve the same categories as in the case of dimensionless numbers like the rate of increase per generation time (Fig. 2.25). If commercial harvests are measured in tons of biomass per year, Figure 2.9 does not provide what we are after because it involves the measure of numbers or portions of standing stock biomass. The information in Figure 2.9 does involve consumption, however, and our management of harvests measured in mass must be consistent with this information (Management Tenet 4, Chapter 1). How do we account for the body size of prey? Figure 2.6 involves walleye pollock themselves, prey size is inherent to the pattern.

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What is a sustainable harvest of walleye pollock that accounts for our species as a large mammal functioning as a predator in the eastern Bering Sea? What should that harvest be to account for global warming, pollution, a history of commercial whaling, and the effects of commercial fishing on other species in this system? Figure 2.6 represents data for species with positions within the pattern as products of the effects of such factors. Such effects are inherent to the pattern. There may be continued reactions to such factors, and patterns reflective of more recent conditions may better account for what is currently sustainable. As the systems change and react, sometimes with considerable time lags, to environmental circumstances, different patterns may emerge. Although there may not be an explicit representation of the effects of past human activities, these factors are accounted for by the observed pattern; the species involved have shown response to such factors. Ecosystems such as the eastern Bering Sea can be characterized in all their complexity by the species-level patterns within them, dimension by dimension, much as we characterize individual humans by measures such as cholesterol levels, body mass, heart rate, intraocular pressure, blood pressure, and body temperature. Patterns among species demonstrate limits to natural variation. Collections of means, or other measures of central tendencies, among these patterns are the basis for characterizing variation among ecosystems or within any ecosystem over time. This, in turn, helps characterize patterns at the ecosystem level enabling ecosystem assessment as suggested by Rapport (1989a, b) and others. In implementing Management Tenet 2, Chapter 1, however, we can’t fool ourselves into thinking that we can control ecosystems. Ecosystems (including the fact that ecosystems interact among themselves, Guerry 2005), involve too much complexity to avoid the unintended consequences of attempts to control them. When we observe lack of health in an ecosystem (e.g., an ecosystem that shows a trait outside the normal range of natural variation among ecosystems) we can ask management questions regarding the various ways we (humans) might be contributing. If we are involved in abnormal influence in our relationships with the

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biosphere, any ecosystems, or their components, we can then relieve those systems of the anthropogenic problem(s) as our contribution to solving any observed problems.

2.3 Summary and preview Management is human action so as to retain its intransitive quality, as introduced in Chapter 1. In this chapter, we have seen that there are patterns among species representing ecosystem structure and function reflective of the complexity we want to account for in establishing goals for management. We can measure the abnormal among species, based on these patterns, and we can measure the abnormal among ecosystems in comparing patterns. When humans are the abnormal among species, there is basis for management action, especially when it involves our interactions and relationships with other species, ecosystems, or the biosphere. With this foundation for management, it is clear that the best available scientific information for setting goals in management are the patterns that match management questions so that action can be taken to avoid the abnormal (Management Tenet 5, Chapter 1). The science that best serves management, then, is research that brings information about those patterns to managers and other stakeholders. These are people—people involved in asking the management questions (bottom row of Fig. 1.1). The questions they ask define the kind of information patterns must contain. In this chapter, the asking of such questions was introduced (and will be exemplified in more detail in Chapter 6).

Science does more than discover and characterize patterns, however; it also involves explanation. This aspect of science develops an understanding of some of the elements of emergence behind patterns. More holistically, this aspect of science leads to the understanding that when patterns consonant with management questions are used to guide management, the elements of emergence (of reality, Fig. 1.4) are taken into account. Of the processes contributing to patterns, nonevolutionary factors and natural selection acting on individuals are relatively well understood. We know that the environment provides contextual constraint and influence on such factors—acting to prevent the abnormal. However, only in the last several decades have scientists paid much attention to the influence of selective extinction and speciation in the formation of macroecological patterns— especially in preventing the accumulation of examples of species beyond the extremes revealed by patterns. In conventional management, extinction and speciation are largely ignored, especially insofar as they are selective; when they are considered it is through the error prone processes of conventional management (top row, Fig. 1.1). In this context, Chapter 3 considers extinction and speciation in more detail, particularly regarding the limits made obvious in patterns—limits with which we can recognize the abnormal or pathological. Risks and limits are part of what we are required to consider in systemic management (Management Tenet 6, Chapter 1). Understanding them emphasizes that extinction is a risk faced by our own species.

CHAPTER 3

Selective extinction and speciation

What has survival value for the individual may be lethal for the population. . . . —Gregory Bateson

This chapter explores one of the primary contributors to the emergence of macroecological patterns— natural selection at the species level. It considers the definition of the processes of selective extinction and speciation and provides a brief history of our understanding of them. The main focus of the chapter is on what natural selection involves. This includes opposing forces, exemplified by natural selection at the individual/gene level that can lead to extinction—a situation in which we humans likely find ourselves. The appendices focus more on the way natural selection operates at the species level. This involves how selectivity influences natural patterns. Extinction and speciation are parts of the complexity that must be taken into account in management (Management Tenet 3). Evolutionary enlightenment (Brown and Parman 1993) is crucial to management. Management must incorporate the universal and ubiquitous nature of selective systems failure (Morowitz 2002, Ormerod, 2006). Conventional management fails to account for these realities almost entirely;1 in the rare occasions when they are taken into account, they are woven into the decision-making process inconsistently and superficially. One of the objectives of this chapter is to rectify that problem. When management is based on patterns, all evolutionary processes contributing to the pattern are taken into account (Fig. 1.4). Management Tenet 6 (Chapter 1) requires that risks be taken into account—this includes the risk of extinction by any or all species. It is important, therefore, to understand how the pattern-based nature of systemic management takes into account these risks in a balanced, objective and consistent manner. Central to this process, of course, is the avoidance of extremes

and their associated risks, including that of extinction (Management Tenet 5, Chapter 1). Natural selection at the species level involves consequences for ecosystems as well as other levels of biological organization. That is, selective extinction and speciation contribute to the development and characteristics of systems in which species are represented—systems such as ecosystems.2 Evolution and coevolution at all levels contribute to the characteristics of such systems; evolution and coevolution are part of the history and explanation of the nature and character of ecosystems and the biosphere. The interactions among all systems and their components are part of the emergence of observed patterns (Fig. 1.4). Ecosystems and the processes involved contribute to the formation or explanation of macroecological patterns. The biosphere and all of its components and processes contribute to the formation or explanation of macroecological patterns. The pattern-based nature of systemic management accounts for the complexity of reality3 including ecosystems4 so that it is “ecosystem-based” with all of the evolutionary and coevolutionary forces involved. This chapter begins with a comparison of natural selection among individuals and species to better understand the latter. This is followed by a more detailed description of selective extinction and speciation. A tabular matrix is used to explore how natural selection among individuals (microevolution) interacts with selective extinction and speciation to change the numbers and mix of species in an ecosystem or other species assemblies (macroevolution). Appendices provide modelbased exploration of selectivity to help grasp the 55

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combined effects of evolutionary processes. The overarching evolutionary influence of the environment is then considered, followed by a historical review of the study of selective extinction and speciation over the past 150 years, and a return to the eastern Bering Sea.

3.1 Natural selection among individuals and species Selective extinction and speciation are part of natural selection involving any level of biological organization (Arnold and Fristrup 1982, Jablonski 2007, Lewontin 1970, Okasha 2006, Williams 1992). The best-studied form of natural selection is selection among individual organisms, and the genes or gene-combinations that individuals carry, often focusing on the resulting characteristics and their change. Comprising individuals, the species evolves as a result. Speciation occurs through the effects of natural selection acting on individual organisms as carriers of genetic code selected through selective mortality and reproduction. In contrast, it is groups (sets) of species that change (evolve) through selective extinction and speciation—groups (sets) such as those involved in ecosystems.5 The evolution of species within ecosystems influences the structure and function of ecosystems as does extinction (Fowler and MacMahon 1982). The characteristics of macroecological patterns such as those exemplified in Chapter 2 are, in part, the product of selective extinction (Jablonski 2007). Natural selection at the individual level, having been well studied and documented, is a good analogy to aid in understanding the nature of selective extinction and speciation. Similarities and differences between natural selection of individuals and of species are described in this section.

3.1.1 Similarities Endpoints are part of the picture for the natural selection of both individuals and species. Endpoints— death in the case of individuals and extinction in the case of species—are reached by the individual or species which carries the genetic code that is subject to selection. Individual organisms that die

lose all future chances to pass on the characteristics embodied in their genetic makeup; individuals that die before they reproduce lose any chance to pass on their genes. Individuals that live longer often have greater potential for reproduction. A species that suffers extinction cannot pass on its genome and associated characteristics. Speciation cannot occur after extinction. Nonrandom removal is achieved among individuals by selective mortality and among species by selective extinction. The chances of mortality or extinction are influenced by the characteristics of the individual or the species, and, in both cases, by environmental conditions. Figure 3.1 illustrates this comparison in an over-simplified example to illustrate the point. In this example, there are two groups: one is a group of individuals (a species, right side); the other is a group of species (such as those represented by populations in an ecosystem, left side). If the group of individuals is faced with predation, where speed is critical to escape, the chance of predation-induced mortality is greater for the slower individuals. If the group of species is faced with environmental changes (e.g., increasing water temperature) the chance of extinction is greater for species with slow evolutionary rates because these species are less capable of adapting to the changing circumstances. Selective extinction within groups of species results in change just as selective mortality does for a group of individuals. After the change, the collection of species in Figure 3.1 (left ordinate) has an increased mean potential evolutionary rate. In comparison, the outcome of natural selection acting on the group of individuals (Fig. 3.1, right ordinate) is a sub-set with a mean potential running speed that is greater than that of the original group. In this example, a change in the characteristics of each group occurs as a result of natural selection.6 Through classical Darwinian insight, evolutionary changes occur within species; characteristics of the surviving individuals have a different composition (different pattern or frequency distribution). The genetic component of this change in composition is an altered gene frequency. Changes also occur in the set of species because the surviving species also exhibit a new composition based on species-level characteristics. This change also has a

SEL EC T I V E E X T I N C T I O N A N D SP ECI AT I O N

Set of species Portion of species

Portion of individuals

Species

Evolutionary rate

Running speed

Birth Portion of individuals

Reproduction

Portion of species

Speciation

Mortality Portion of individuals

Selective removal

Portion of species

Extinction

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genetic component in the composition of genomedetermined characteristics (the genome being the entire distinctive collection of genetic material for each species, a distinctive set of genes). Thus, any set of species, such as the group of species with populations in an ecosystem, has genetically influenced characteristics in the same sense that any individual species does. A common feature of the two processes in the preceding example is the selective removal of units from the group, represented by the shaded curve in the middle of Figure 3.1 in both cases. Some units—the slower-moving individuals or slowerevolving species—are removed with higher probability. In addition to the nature of selective forces (e.g., predation or climate), the chance of dying or extinction depends on the characteristics of the unit of selection. Because individuals differ within species, and species differ within sets of species, these chances differ from individual to individual (for evolution of the species) and from species to

Figure 3.1 The similarity in effects of natural selection among individuals and species. The shaded area of the frequency distribution (pattern) represents the removal of either individuals from a species or species from the biosphere. The net change in the characteristics of the two groups is seen in the shift of the mean following reproduction (birth for individuals in a species and nonselective speciation for a set of species) to restore total numbers. In both cases, of course, it is the genetic code (frequency) behind such characteristics that is changed (modified from Fowler and MacMahon 1982).

species (for change in a species frequency distribution as evolution of the set of species). Note that in both cases, potential is an important element in selectivity. It is the characteristic over which selection is occurring. Individuals do not spend all their lives running at maximum speed; species do not spend all their existence evolving as fast as possible. Environmental factors play crucial roles in the manifestation of selectivity. The analogy extends to reproduction and speciation.7 Comparisons can be made between the potential for a species to split, creating two or more discrete species (cladogenesis), and the analogous process of birth for individuals within a species. Fitness results from the combined effects of selfreplication (birth within species and cladogenesis of species within a macroecological pattern) and demise (mortality within species and extinction of species). Fitness involves the probability that descendants will be present in future reproductive groups. Within species the descendants

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are individuals; within an assembly or group of species the descendants are species. Complexity expressed through randomness in the elements of drift and mutation lend to another similarity in the evolutionary processes acting on individuals and species. To the extent that genetic drift reflects complexity in the dynamics of specieslevel evolution, it has a counterpart in mutation. The rates at which these phenomena occur may be related to the characteristics of the carriers of the genetic units involved (individuals for mutation and species for drift). However, the type of change reflects complexity (a variety of factors) more than any one specific driving force.

3.1.2 Differences The differences between selection at the individual and species levels are also important to recognize. In the classical Darwinian understanding of evolution, variety among the individuals upon which natural selection operates is a prerequisite. In sexually reproducing species, variety among individual organisms results from recombination of genetic material and the origin of new genetic material through mutations. Although the predictability of these processes is usually limited, both involve the effects of complexity (often seen as “random”). By contrast, in selective extinction and speciation, the evolution of a species to form an entirely new species (anagenesis, somewhat analogous to mutation) contributes to the variety upon which selective processes operate. Anagenic change among species may be more predictable than are mutations or genetic recombination. Species that evolve with identifiable trends (e.g., toward larger body size, increased specialization, or reduced population variation) experience such change. A distinction must be made between variation in the direction of evolutionary change and nonselective speciation with regard to species characteristics. The variance involved in the first case means that the direction in which any particular species evolves may be largely unpredictable, as in genetic drift. In contrast, nonselective speciation is the opposite of selective speciation; there is no difference in the chances of experiencing speciation

across the spectrum of variability among species. Thus, directional evolution can be either selective or nonselective. As the term will be used here, nonselective speciation occurs independently of any particular species characteristic—if such a dynamic is possible. Directional speciation shows its own variability; it is not uniform among species. In other words, directional tendencies will have specific exceptions.8 As will be seen shortly, the evolution of larger body size provides an example of this phenomenon. As mentioned above, the direction of speciation (specifically anagenic change) is often seen as unpredictable, and therefore resembles the unpredictable nature of mutation. Those who argue that this is random emphasize the lack of evidence for directional mutation/evolution.9 Others have a contrasting perspective. For example, the concept of evolutionary stable strategies (e.g., Williams 1992) embodies the notion that evolution can tend toward a particular (but not necessarily predictable) state, often dynamic with opposite and predictable directional evolution on either side of that state. If such evolutionary patterns correspond with each other among species, there would be a higher-level stable strategy arising from anagenic forces. This might be the case for evolutionary forces acting above and below certain optimal levels of density dependence (Fig. 2.21, Fowler 1995). The evolution of larger body size is often considered an example of directional change in phyletic evolution. Directionality has been observed frequently enough for body size to be embodied in Cope’s law.10 The evolutionary increase in the size of the horse is often cited as an example (see other examples in Newell 1949). Examples of evolution toward smaller body size (Futuyma 1986a, LaBarbera 1989, Newell 1949, Stanley 1973) emphasize the phenomena behind Cope’s law as a tendency with variation—a pattern. As such, directional evolution as a tendency may be so variable as to be conspicuous only in large samples (as in clades, Gillman 2007) or in particular cases. Directional change is not restricted to body size. Specialization, for example, involves specific diets, adaptation to narrow ranges of environmental conditions, or intraspecific dependence for dispersal or pollination.11

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Thus, there may be a significant component of variation to the direction that species evolve but it is not without limits. Based on the evidence, much evolutionary change may be without identifiable trends, especially for some specific species-level traits.12 This does not mean that it occurs without limits. For at least some species-level characteristics there is evidence of directionality in anagenic speciation (LaBarbera 198913). The point here is not that one or the other is more important, but that both “randomness” and directionality are to varying degrees part of the evolutionary dynamics among species. The microevolution of species (i.e., within a species—the evolution of individual species), regardless of the extent of any randomness, occurs as part of processes involving selective extinction and speciation. The degree of directionality in species-level evolution may be greater than in the mutation or recombination of individual genes. Actual relative importance is inherent to patterns, regardless of the outcome of debate over this issue.

3.1.3 Conditions for natural selection Lewontin (1970) specified the underlying conditions necessary for the operation of natural selection, regardless of the level of biological organization to which they apply. Arnold and Fristrup (1982) extended the concept along similar lines. The basic elements of the idea14 as presented in both cases include: 1. Variable characteristics must occur among the units making up a level of biological organization. Phenotypic variation with a genetic component to the variation is necessary. In selective extinction and speciation, this means phenotypic variability among groups of species, such as those represented in any ecosystem. This variability is embodied in species-level patterns or frequency distributions among species as a form of diversity. 2. Variability must occur in rates of survival and reproduction by these units in different environments. This is variability in species-level fitness and pertains to the relative probabilities of being represented in the species pool from which future ecosystems will be assembled. For species

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this combines the concept of variability both in avoiding extinction (species-level survival) and undergoing speciation and reproduction (through cladogenesis) in relation to their environment. 3. Correlation must exist between the qualities of parental units and descendant units, including potential for contribution to future generations (fitness is heritable). Among the traits that are passed from one species to succeeding species are those that selective extinction and speciation act upon. As mentioned above, and by Arnold and Fristrup (1982), the third condition may be considered as a combination of two component parts: (1) the relationship between parents (parental species) and offspring (newly evolved species) in their heritable attributes, and (2) the property-dependent15 interaction of units of selection with the environment that determines fitness. Thus, the pertinent set of species changes composition (the group evolves) in adaptation to its environment through the processes of selective extinction and speciation. This happens in parallel with changes in the species themselves through natural selection at the individual level, since species are groups of individuals. Sets of species are often similar in similar physical habitats (the abiotic environment including climate) and different in different habitats.16 Environmental influences are discussed in greater detail later in this chapter.

3.2 Understanding extinction and speciation In this section, the processes of extinction and speciation are described more fully. The existence of a species ends naturally through the process of extinction. Conversely, new species originate through the process of speciation. Extinction and speciation are two basic processes in the evolution of any group of species.17 The basic ideas developed in this chapter are: These processes are influenced by the qualities or attributes of species; that is, the chances of extinction or speciation depend, in part, on what species are and what they do. Different kinds, forms, and extents of selectivity occur in different environments (Appendix 3.1).

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Species-level patterns, patterns among species, macroecological patterns, or species frequency distributions are heavily influenced by these dynamics. The normal ranges of variation among species are evolved characteristics of various species assemblages, such as those represented in ecosystems. The trial-and-error nature of natural selection among species contributes to the basis for using macroecological patterns as guidance in management (in implementing Tenet 5, Chapter 1). As a species, we humans are subject to specieslevel risks simply by virtue of being a species. This includes extinction,18 which would be the polar opposite of sustainability.

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3.2.1 Extinction It has long been recognized that the existence of a species almost always ends in extinction, especially over geological time scales. Almost all species that have ever existed are now extinct (Brown 1995, Lewontin 1970, Raup 1986a). Numerous species have gone extinct in recent history (measured in time scales of only a few human lifespans; for example, the heath hen, dodo, passenger pigeon, Steller’s sea cow, and Caribbean monk seal as well recognized species; Day 1981). Most recorded extinctions pertain to species that were deemed to be of sufficient importance or were sufficiently visible to merit notice; extinctions among microbes, many plants, tropical insects, or marine zooplankton go unnoticed by comparison. The geological record is replete with examples of species (e.g., most of the dinosaurs and many marine mollusks) that have disappeared to leave no descendants. Even the species observed in the fossil record, however, are only a small fraction of the species that are either going or have gone extinct. Extinction must be distinguished from another process that also places temporal boundaries on the existence of species: the conversion of one species to another through evolutionary change. Many lineages in the fossil record represent a continuous series of consecutive groups we define as species. The species we see today represent the few such lineages that have not resulted in extinction. Anagenesis is the evolutionary process by which

a species in a single lineage changes to become a new species without the lineage going extinct (White 1978). How does this relate to patterns in the diversity of species illustrated in macroecological studies— particularly frequency distributions such as those shown in Chapter 2? If we place all species in categories according to mean adult body size (as in Figs 2.1 or 2.2), it is possible, through evolutionary change, for any particular species to leave one category and join another. Thus, the original category loses a member, a loss for which the term “pseudoextinction” can be used. However, the overall set of species represented by a frequency distribution does not lose the species; there was no true extinction (the original species simply has a larger mean body size in this example). Henceforth, graduation from one category to another will be referred to as anagenesis regardless of whether or not it would be called a new species by taxonomists.19 Thus, anagenesis and pseudo-extinction are ecologically equivalent with regard to the characteristic(s) that change(s).20 To understand selective extinction and speciation, particularly their influence in the formation of patterns among species, it is important that the principal distinction between the pseudo-extinction of anagenesis and terminal extinction be quite clear. Species cease to exist in both cases, either by conversion to a new species through evolution (anagenesis) or after suffering terminal extinction. The important distinction is the loss of future potential when terminal extinction occurs. Those species that go extinct (terminal extinction) are end points and cannot produce more similar species. In anagenesis, a similar21 species replaces the original and has potential for splitting into two or more species (cladogenic speciation) to produce others resembling it. For clarity, the term extinction will be used to denote the terminal variety that is the focus of this section of this chapter. As in the study of mortality rates within populations, the rate at which extinction occurs (historically and currently) among the Earth’s collection of species is important. This information helps evaluate extinction rates of anthropogenic origin, a point of considerable discussion in the field of conservation biology (e.g., Ceballos and Ehrlich

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2002, Cracraft and Grifo 1999, Davies et al. 2006, Ehrlich and Ehrlich 1981, Lawton and May 1995, Myers and Worm 2003, Raup 1984, Simberloff 1986a, Soulé and Wilcox 1980, Thomas et al. 2004, Western and Pearl 1989). Rates of extinction in the absence of abnormal human contributions serve as reference points for comparison and criteria for action. The motivation for taking action increases as anthropogenic causes of extinction rise significantly above background levels (e.g., by a thousand-fold, May et al. 1995), especially in comparison to rates of extinction caused by other species (Management Tenet 5, see Chapter 6 for more detail and comparison). To take full advantage of information about extinction rates, we also need to know about their variability. Although the long-term worldwide mean rate of extinction is useful, many studies have shown that such rates have varied greatly over time.22 It would be misleading to conclude that anthropogenic contributions to extinction are normal if comparison is being made with abnormal contributions by other species. The degree to which extinction rates vary and the factors that contribute to their variability must be considered. For the objectives of this book, it is important to understand that variability (Plate 3.1) in rates of extinction includes those across various ecological categories of species. Across the spectrum of variability for any species-level attribute, the chances of extinction can change. Such change is thus not so dependent on the taxonomic definition, identity, or genealogical relationships among species as it depends on their characteristics and the roles they play in relation to other species, communities, ecosystems, or the biosphere. That is, species experience different rates of extinction in relation to factors exemplified by factors such as their trophic level, population variability, and resource consumption rates (as shown in the patterns displayed in Chapter 2). These differences count among factors important in shaping species-level patterns (Fig. 1.4). Such patterns involve the composition of sets of species represented in ecosystems, and thereby the characteristics of ecosystems themselves (Fowler and MacMahon 1982, Hubbell 2001, Petchey et al. 2004, Solan et al. 2004).

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3.2.2 Speciation Just as birth helps prevent a population from succumbing to extinction caused by death, speciation helps prevent the Earth’s set of species from being drained by extinction (i.e., the termination of all life as an “extinction” at a different level—the death of the biosphere). The supply of new species is produced by the well-recognized and intensely studied process of speciation involving, in large part, evolution through natural selection, especially among individuals (Williams 1992). There are two types of speciation. The first, as discussed above, is anagenesis in which individual organisms within a species nonrandomly replicate and die such that species evolve to become new species. In some of these cases the species simply moves from one category of a frequency distribution to another without becoming (being named) a new species, though it can be defined as pseudoextinct as mentioned above. Cladogenesis is the second form of speciation, and can also be involved selectively. Cladogenesis is the splitting of a single species to create two or more discrete species (White 1978). Unlike anagenesis, it results in an increase in species numbers, and like anagenesis can also change the position of species within one or more pattern or frequency distribution. The dynamic balance between cladogenic speciation rates and extinction rates has its parallel at the population level. Just as the number of individuals in a population is the result of a balance between births and deaths, the numbers of species (e.g., in a geographically defined set of species) results, in part, from the balance of cladogenesis and extinction 23 —other contributions involving changes in geographic distributions. This balance, through dynamics that are similar to those contributing to the balance within populations, prevents the formation of an infinite number of species as well as the decline of species numbers to zero. It regulates species numbers while promoting variation between upper and lower limits in diversity dependence (Rosenzweig 1975, 1995). One of the key points of this chapter (see Appendices 3.2–3.5), however, is that the continuing process of speciation provides the raw material from which

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selective extinction carves away (prunes) species, leaving behind species observed in their limited variation within frequency distributions such as the patterns shown in Chapter 2. Within such distributions, species types that undergo cladogenic speciation contribute to the accumulation of similar species. The need to account for a supply of species to counterbalance the drain of extinction was part of what prompted the work of Wallace and Darwin. Scientists of their time had strong palaeontological documentation of the demise of species through extinction but needed to account for their replacement. Having addressed the question of the origin of species, Wallace, Darwin, and others launched the concept of evolution as what is now considered the primary contextual paradigm for modern biology, with an historical focus at the individual and species level (Kuhn 1962, Mayr 1982). This paradigm has, among its origins, many palaeontological roots. Today the growing horizons of ecology are profiting from a similar consideration of the palaeontological roots of selective extinction and speciation with implications for hierarchical realms of biological complexity (Jablonski 2007) exemplified by ecosystems. Selective cladogenesis occurs with varying probabilities related to the differing attributes of species. As with extinction, an important aspect of this process is that the probability of speciation varies across the variation of individual specieslevel attributes. Thus, species with one measure of a particular attribute (e.g., a specific measure of evolutionary plasticity, body size, or trophic level) may exhibit a different probability of undergoing speciation than species that exhibit less, or more, of the trait. Understanding speciation rates is important from several points of view. Considering the recent high rates of extinction occurring as a result of human activities, it is important to know whether or not the newly extinct species are being replaced with new species. This contribution to species numbers occurs as a background rate that is also influenced by conditions in the physical environment.24 Speciation rates are also influenced by the biotic environment.25 At the risk of under-emphasizing the importance of the physical environment in this

regard (it is involved, after all, as important elements of the ei in Fig. 1.4), the important point here, and as it relates to the matter of patterns important to systemic management, is the degree to which speciation rates depend upon the characteristics of species themselves. Nevertheless, the ways such rates differ from environment to environment helps explain how ecosystems differ from each other. Like extinction rates, speciation rates vary over time. A topic of intense interest to paleontologists in recent decades is the pattern of “punctuated equilibrium”26 —periods of time during which little speciation occurs, interrupted by pulses of relatively high speciation rates that are often associated with high extinction rates (Stanley 1985, 1990a,b).

3.2.3 Differences among species Species have a variety of distinguishing characteristics, a tiny sample of which was seen in the last chapter.27 The differences we usually think of are inherent in the definitions and taxonomy of species. Some involve differences, such as morphological distinctions, that we see in distinguishing the individuals of one species from those of another species, as well as distinguishing individuals within a species. Others are more emergent28 species-level characteristics (such as evolutionary plasticity or population variability) that distinguish species but not individuals within species. We are presently concerned with the differences that determine probabilities of extinction and speciation, and thereby contribute to the formation of species frequency distributions as examples of macroecological patterns. To go beyond classical views of extinction and speciation founded on taxonomic and genealogical paradigms, species must also be considered in terms of their ecological roles—a point of considerable importance if we are to include evolutionary enlightenment in ecosystem-based management. Although morphological differences often contribute to functional differences, the latter involves such things as the flow of energy and materials, population dynamics, distribution, and abundance. For some characteristics, species fall into fairly distinct categories, such as those species with wings or without wings, or species with eyes or

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without eyes. The chances of extinction may differ among such discrete groups. In contrast, characteristics such as population variability, potential evolutionary rate, or generation times are continuous variables. The chances of extinction or speciation may also vary continuously along such gradients. The relationships between the chances of extinction and speciation and these characteristics are critical in comparing species and in understanding the role of selective extinction and speciation in the formation of species frequency distributions and other macroecological patterns.

3.2.4 Basic postulates of selective extinction and speciation Seven basic postulates of selective extinction and speciation consider species as units of biological organization (see, e.g., Arnold and Fristrup 1982, Fowler and MacMahon 1982, Lewontin 1970). These postulates are listed below, emphasizing the ways in which selective extinction and speciation contribute to the formation of patterns among species. 1. Species experience a form of terminated existence, or mortality, through extinction. This removes a portion of the species from any or all categories in a species frequency distribution. The risks (probability) of extinction experienced by every individual species translate to rates of extinction experienced by each group of species within a macroecological pattern. 2. Species undergo speciation, or the birth of new species, resulting in more than one similar species (cladogenesis). This adds species to a particular category within a pattern (species frequency distribution), usually the same as, or similar to, that of the parent species. The change to qualify as a new species may involve a different characteristic or dimension than that of a specific pattern. All species have potential for such replication and, within groups, speciation occurs as rates wherein a fraction of the group changes each unit of time. 3. Species undergo forms of self-continuation (temporal replication without subdividing into more species) in which some species change over periods of time without becoming a new species (anagenesis). A species that experiences anageneic

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change may remain in one category of a singlecharacter species frequency distribution or may switch categories. The probability of anagenic change varies from species to species, depending in part on their characteristics. This includes the probability that species will evolve in one direction more than another for specific traits (e.g., toward larger as compared to smaller body size). 4. Species differ from one another in character, attributes, properties, and ecological relationships. These differences arise, in part, from the evolutionary processes of speciation. This diversity is expressed in the range of variability seen in the various patterns among species. 5. The probability of experiencing extinction varies from species to species, depending on their characteristics. For measures of species-level characteristics where this occurs, the probability of extinction changes across the spectrum of variation within observed patterns of diversity. 6. The probability of experiencing or realizing either cladogenic or anagenic speciation varies from species to species, depending on their characteristics. As with extinction, this probability changes across the spectrum of diversity within a species frequency distribution for many specieslevel characteristics. 7. For both extinction and speciation, selectivity is also influenced by the nature of the environment (biotic and abiotic) to which species are exposed. The species frequency distributions for sets of species in different environments are influenced differently by those environments. At this point in the history of biological sciences, some of the seven postulates of selective extinction and speciation seem obvious. For example, the second and third postulates (species maintain their identity through self-replication, and have a form of birth, through speciation) might seem obvious or trivial. However, it must be recognized that these points imply species-level inheritance of traits through their respective genomes and these genomes represent information about the nature of species and their fitness (sustainability). This is important to management (Fowler 2008). New species formed in the processes of evolution are different enough from sibling species (cladogenesis) or the parent species (anagenesis)

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to qualify for new names according to taxonomic standards. Even the changes that do not qualify for new taxonomic status, however, give rise to variation—variability among species. To the extent such changes reflect complexity through randomness, they are roughly analogous to the process of mutation among genes for selection at the individual level. Regardless of whether new names are warranted (a matter of human judgment), species retain enough characteristics to be described as having inherited them. Specifically, there are basic attributes of a species that contribute to its species-level fitness, its chances of persisting in the future, or having descendent species. At least some of these change little between a parent species and its immediate descendent(s). Species-level fitness is inherited for those characteristics that are conserved, and to the extent they relate to the probabilities of extinction and speciation and the matter of sustainability. However, variation in character does occur in the speciation process. Over time scales much longer than required for the origin of one new species, variety develops among species. As indicated by Vrba (1984), there is interplay between heritability and differentiation; neither is absolute or exclusive of the other. Species as different as bacteria, kelp, insects, and elephants arise, but only across many “generations” of species, because each successive species inherits most of its predecessor’s traits. Thus, in the accumulation of the smaller changes that preserve most characteristics and similarity

from one species to the next, a great deal of variety among species develops over long time scales.

3.3 Interaction of evolutionary processes Patterns among species are, in part, products of the combined evolutionary effects of natural selection among individuals, and selective extinction and selective speciation.29 These forces can interact in a variety of ways, summarized in Table 3.1. Cladogenesis is the species-level processes of replication, birth, or recruitment. Anagenesis is the change of a species lineage over time. The processes of anagenesis and cladogenesis reflect natural selection acting primarily on individual organisms to result in changes that may alter various categories in the frequency distributions representing macroecological patterns. In contrast, extinction acts as selection among species to remove them differentially. Clearly, anagenesis and selective extinction can either reinforce or oppose each other. In Combinations 1 and 2 they reinforce each other in one direction and in Combinations 3 and 4 they reinforce in the other direction. Two other pairs of combinations (5 and 6, compared to 7 and 8) create emergent balances resulting from the opposition of the two forces. There are eight combinations when the effects of selective cladogenesis are included. Only two of these have simple and predictable results

Table 3.1 Eight combinations for the contributions of anagenesis, cladogenesis, and extinction Contributing Process

Combination 1

2

3

4

5

6

7

8

Effect of Contribution (+ = relative addition, − = relative removal) Anagenesis Cladogenesis Extinction

+ + +

+ − +

− + −

− − −

+ + −

+ − −

− + +

− − +

Depending on whether they are contributing (+) to species numbers or removing (–) species from a particular category within a species frequency distribution. Cladogenesis cannot remove species but can add species in other categories faster, to result in the relative reduction of a specific category. Extinction cannot add species but can remove them from neighboring categories faster thereby resulting in relative increase—just the reverse of the effect of cladogenesis.

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because of the uniformity of the three contributing forces (Combinations 1 and 4). In each case the category of species either clearly gains species (Combination 1) or loses species (Combination 4) relative to other categories. However, we must remain mindful of the fact that the factors listed in Table 3.1 are not the only contributing factors, and the categories in this table often involve opposing forces. Opposing forces involve evolutionary factors as well as ecological mechanics. The counterbalancing of the opposing forces of Table 3.1, however, is the main source of pattern in species frequency distribution insofar as selective speciation and extinction are concerned. For example, the effects of cladogenesis may be quite influential in tipping balances when extinction and anagenesis are in opposition (Combinations 5 through 8), and extinction may prevail when anagenesis and cladogenesis are in opposition (Combinations 2, 6, and 7). In all cases, the relative strengths of the effects of the combined processes determine the contribution made by selective extinction and speciation to the formation of species frequency distributions. The following sections contain a more detailed consideration of such factors.

3.3.1 Natural selection at the individual level in concert with extinction In some situations, natural selection at the individual level and extinction at the species level may be thought of as working in concert, resulting in either an accumulation or reduction of species. On one hand, the evolution of species may lead to the development of characteristics that reduce the threat of extinction (Combinations 1 and 2 of Table 3.1). In such cases, natural selection at the individual level and selective extinction at the species level may be thought of as working together to result in an accumulation of species. For example, any evolution of homeostatic density dependence through natural selection at the individual level (Fowler 1995) leads to decreased risk of extinction from both population variability and from low population levels. This would result in an accumulation of species that exhibit intermediate density dependence (see pattern in density dependence as

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shown in Fig. 2.21). Selective cladogenesis can work in conjunction with (Combination 1), or in opposition to (Combination 2) these forces to decrease or reinforce (respectively) their combined effects. Opposition occurs through the cladogenic formation of species at higher rates in other categories, to result in relative decrease in a reference group. On the other hand, both evolutionary trends and selective extinction may act to remove species from a particular category (Combinations 3 and 4). In such cases, natural selection at the individual level and selective extinction at the species level may be thought of as working together to reduce species numbers. Species with such characteristics would be faced with selective pressures to change and thereby experience pseudo-extinction. They would also likely tend to experience terminal extinction. An example might be population variability in which the evolution of density dependence combined with extinction might contribute to reducing species numbers among those that exhibit high levels of population fluctuation. Both evolutionary trends (including the anagenesis in cladogenic splitting) and extinction remove species from categories with such characteristics. Again, selective cladogenesis can work in opposition to (Combination 3), or in conjunction with (Combination 4), these forces to reduce or reinforce their combined effects.

3.3.2 Natural selection at individual level in opposition to extinction In other situations, natural selection at the individual level and extinction at the species level may be thought of as working in opposition. The opposition can work both ways (Grantham 1995): as shown in Table 3.1, anagenesis can contribute to accumulations, as shown in Combinations 5 and 6, or removal of species, as shown in Combinations 7 and 8. As Fowler and MacMahon (1982) point out, the frequency distributions of species along a particular dimension is the result of a dynamic balance between these two forces if unaffected by other factors such as cladogenesis, or ecological factors less directly involved in the processes of extinction or speciation. Within the realm of higher-level selectivity, selective cladogenesis may

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be quite influential if its effects are reinforced by either anagenesis or extinction. In one category of opposition between anagenesis and extinction, the evolution of species results in the acquisition of characteristics that are detrimental at the species level (Combinations 5 and 6). Such changes increase the risk of extinction (Dobzhansky 1958) as an expression of the “selfish” nature of genes (Dawkins 1976). Measured in terms of fitness for individual organisms, such characteristics are advantageous. However, at the species level they result in an increased threat of extinction (“evolutionary suicide,” “evolution to extinction,” or “Darwinian extinction”; Parvinen 2005, Rankin and López-Sepulcre 2005). In this case, individual fitness translates to poor fitness for the species as a “fatal flaw” (Potter 1990) or what is commonly called an evolutionary dead end—at least in terms of risk or probability. An example of this might be the evolution of larger body size (e.g., Valkenburgh et al. 2004). It may be useful (advantageous) to individuals as an adaptation to variable environmental conditions or as an escape from the risk of predation. At the same time large body size brings with it longer generation time and reduced evolutionary plasticity (Appendix 3.1) and other disadvantages at the species level. Selective cladogenesis can work to oppose or reinforce the effects of either anagenesis or extinction. In Combination 5 it reinforces anagenesis and opposes extinction, just the reverse of what happens in Combination 6. Here we must be mindful of ways that humans may have evolved to be an extinction prone species. Our intelligence (at least the ways we commonly use it) may benefit individuals (or even some groups of individuals) but also increase our risk of extinction—a matter of serious concern in any evolutionarily enlightened form of management. In a second category of opposition between anagenesis and selective extinction, traits advantageous at the species level are disadvantageous to individuals (Combinations 7 and 8). Such traits may arise as species-level characteristics through rare circumstances in the evolution of species. Evolution could occur at such low rates that selective cladogenic speciation (involving other traits) produces species with these characteristics faster than pseudo-extinction removes them. An example

might be sexual reproduction in which there are disadvantages for individuals but advantages for species.30 In this case, sexual reproduction may involve color patterns and behaviors advantageous for mating but also attract predators. Meanwhile it may promote evolutionary plasticity to reduce extinction rates through flexibility provided by recombination. A variety of such seeming enigmas in evolution fit this category (e.g., Zahavi’s handicap principles, Zahavi and Zahavi 1999). As above, selective cladogenesis can work to resist or reinforce the effects of either anagenesis or extinction. In Combination 7 it reinforces extinction and opposes anagenesis; in Combination 8 the opposite occurs. In the case of sexual reproduction, for example, cladogenesis might overpower the effects of anagenesis to act in concert with reduced risk of extinction.

3.3.3 Patterns The mix of evolutionary dynamics changes along the spectrum of a species-level characteristic to be among the contributing factors behind observed patterns—in their history or explanation. All are at the core of how selective extinction and speciation combine with ordinary evolutionary processes to contribute to determining the nature of various sets of species and thereby the structure of patterns among species. In most species, microevolution produces characteristics described by Combination 1 of Table 3.1, where all forces act in concert to result in the accumulation of species. These characteristics are not removed by extinction, or alternatives are removed faster, and such species show peak levels of cladogenic replication. Species with characteristics that fall in Combinations 3 and 4 are expected to be rare, particularly if cladogenesis is more rapid for other types of species (Combination 4). In the extreme, neither microevolution nor cladogenesis produce them very often, and any that do arise go extinct. Within Combinations 2, 3, and 5 through 8, dynamic (and often delicate) balances of various forms are struck in the opposing forces. In Combinations 5 through 8, cladogenesis may be more important than in Combinations 1 through 4.

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Similarly, anagenesis is reinforced in Combinations 2, 3, 5, and 8 and extinction in Combinations 2, 3, 6, and 7 wherein there is opposition between the other two contributing factors. Species types for which selective cladogenesis acts to reinforce the production of species (Combination 5) may often be represented by an accumulation of species. Except for Combinations 1 and 4, the degree of balance (relative effects of each of the three contributing processes) achieved in the relative contribution is important in establishing emergent patterns. The nature of these balances leaves them vulnerable to influence and underscores the importance of the call for evolutionarily enlightened management (e.g., Brown and Parman 1993, Thompson 2005). The importance of extinction and speciation in determining the nature of species frequency distributions and the influence of the directional evolution of species can be expected to vary according to characteristic (e.g., among trophic level, population density, density dependence). For many characteristics the main influence may be those of the selectivity of extinction and speciation. Part of the basis for such an argument is found in the contention that the direction of speciation is largely unpredictable for such characteristics. This view is supported, as reviewed above, by the perception of the process as random (as from genetic drift), the lack of empirical evidence showing it to be directional, and the empirical evidence of exceptions to directional speciation. In contrast, other patterns may result more from evolutionary stable strategies with differing directional evolution in neighboring sections of a species frequency distribution. Variability can be expected in the relative importance of selective extinction compared to the influence of selective speciation or anagenesis when compared among the various species frequency distributions. As is apparent from the literature (e.g., see Appendix 3.1), the majority of available examples seem to pertain to extinction. Thus, for certain characteristics, the nature of species frequency distributions may be more a product of selective extinction than of selective speciation. Again, any debate about such issues is irrelevant in management based on patterns in that the relative importance is reflected in patterns for no more, and no less, than what it is—reality.

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3.3.4 Limits to microevolutionary explanations The potential for opposing trends in the species dynamics outlined above demonstrates one of the limitations of historical approaches to ecosystem science. Related sciences largely draw upon ecological mechanics whereas evolutionary forces are major contributors to what we see. Within the evolutionary sciences, however, there is the tendency to neglect extinction (Jablonski 2007). Although it is a critical part, the neo-Darwinian evolutionary paradigm alone cannot explain ecosystem structure and function. There are many examples in which evolutionary changes carry species toward increasing probability of extinction31 rather than simply providing the supply of species within groups of species from which ecosystems are assembled. Examples of evolutionary changes that would seem to result in dead-ends include coevolutionary dependence, specialized feeding habits, occupation of higher trophic levels, large body size, occupation of restricted areas of specialized habitat, and any evolutionary sacrifice of sexual reproduction. In such cases, the evolution of species would end in extinction through the result of improved fitness at the individual level and reduced fitness at the species level. Thus, the evolution of species alone is inadequate to explain the nature of species frequency distributions and thereby many aspects of ecosystem structure and function. As pointed out by Alexander and Borgia (1978), differential species extinction and speciation may be confused with group selection. In spite of the similarities, however, the distinction between group selection and selective extinction and speciation is clear. Group selection is involved in the evolution of species to influence the nature of species, and thereby species frequency distributions. In contrast, selective extinction and speciation affect the evolution of sets of species, with direct influence restricted to patterns among species—species frequency distributions. Group selection pertains to changes within a species, as genetically different portions or groups within a species experience different probabilities of mortality or reproduction.32 Selective extinction entails differing chances of the death or disappearance of entire species. These

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are probabilities that differ between or among species, not within species—a distinction of logical type and hierarchical level. In the context of this book, patterns among species are understood as shaped to a significant degree by selective extinction and speciation. This influence, in turn, affects the structure and function of any collection of species (e.g., clades, guilds, communities, ecosystems, the biosphere).

3.4 Influence of environment on extinction, speciation, and selectivity 3.4.1 Extinction and environmental factors The primary focus of the preceding sections was the dependence of extinction and speciation on species-level attributes. However, extrinsic or exogenous factors are also important in determin-ing which species go extinct. This is fundamental to understanding differences among sets of species exposed to different physical conditions. Successive events leading to extinction may include meteor impacts, volcanic eruptions, climate change, and ice ages, and, more recently, overharvesting, extreme climate change, habitat destruction, and pollution by humans. Consider, for example, evolutionary changes among primary producers and habitat fragmentation as two separate causes of extinction. Some species are lost from both causes. Other species would go extinct because of the risks of only one or the other of the two causes; in such cases the differences would likely be related to species-level characteristics. In each case the composition of the extinct species and the composition of the remaining species would be different (Fig. 3.1). Furthermore the magnitude of extinction may differ according to cause. The need to evaluate the effects of anthropogenic sources of risks relates to both. The factors that lead to extinction include various combinations of abiotic and biotic elements and extinction can be combination-dependent— synergism among causes, between cause and riskproducing species-level characteristics, and among species-level characteristics. Changes in the physical environmental are particularly important in causing extinction (Cracraft 1985a, Eldredge 1991,

Knoll 1989). These include changes in temperature (Cracraft 1985a, Eldredge 1991, Stanley 1984), changes in oxygen or carbon dioxide levels (Knoll 1989, Stanley 1984), sea level change (Hallam 1989), bolide impact, climate (Knoll 1989), volcanism (Knoll 1989), oceanographic circulation, tectonic activity, and geography (Knoll 1989). The degree to which these factors operate selectively has yet to be clearly determined. A variety of biotic factors is believed to contribute to risks of extinction (Maynard Smith 1989). Among those are the effects of predation, parasitism, or disease (Bakker 1983, Martin 1984a,b, Maynard Smith 1989, Roughgarden 1983, Stanley et al. 1983); biotic habitat destruction (Maynard Smith 1989, Tilman et al. 1994); and dependence on other species (e.g., symbiotic interactions, pollination, dispersal, Bakker 1983, Fowler and MacMahon 1982, Janzen and Martin 1982, Roughgarden 1983). Causes of extinction vary in both time and space. Among the biotic elements listed above, the effects contributing to extinction can be expressed at any time scale. Predators that invade a new habitat may result in the extinction of certain prey species within decades or less. Similarly, a predator that evolves more efficient strategies may decimate its prey, but over a much longer time scale (depending on the speed of evolutionary changes). Coevolution may also lead to extinction over long time scales (e.g., pollination, see Futuyma and Slatkin 1983a,b, Van Valen 1973b) as another example of natural selection at the individual level leading to extinction (individual-level selection in the context of reactions to the effects of other species which are themselves reciprocally undergoing the same kind of evolution). In the spatial context, causes of extinction in tropical locations differ from those at higher latitudes. They also differ between deserts and intertidal areas, and between mountain tops and ocean bottoms. The variance of selectivity across habitats is important to selective extinction and speciation. For example, specialist species may be more vulnerable to one cause of extinction and immobile species more at risk to another. Such cases lend uniformity among the sets of species associated with certain habitat types (e.g., tropical rain forests, deserts, and alpine tundra) in different locations.

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When one species goes extinct, extinctions of other ecologically related species can result.33 For example, a specialized parasite goes extinct if it loses its singular host species. Any microbial species dependant on the parasite also disappear. The connection of one extinction to another need not be so direct. For example, the extinction of one species might result in the extinction of another through a complex web of relationships connecting it and other species (e.g., via a predator, a parasite, and a disease). In such cases we are dealing with indirect, or higher order, relationships wherein one extinction contributes to another. Such reverberations through the ecosystem have been referred to by a variety of names but are often called domino effects (e.g., Stanley 1984). Examples are well defined, described, and documented in the literature.34 Of considerable practical importance is the fact that biotic pathways resulting in extinction can entail extensive time lags. Secondary extinctions may not occur until decades or centuries after the initial domino falls. Although not accepted by all experts, a good example may be provided by the tree (Calvaria major) on the island of Mauritius. It is thought to have depended entirely (or at least largely) on the dodo for seed dispersal and germination (Temple 1977). It was not until almost 300 years after the extinction of the dodo that the lack of recruitment of young trees was noticed. Because this is a long-lived tree, even more time would have passed before the last adult tree would have died, resulting in extinction. Further, and more defensible, examples are probably to be found among the loss of bats as pollinators in many ecosystems around the world; it is impossible to predict the number, extent, and nature of such delayed effects (nor can we know with certainty whether or not the extinctions will include humans). Thus, many (possibly most) of the ecosystem-level effects caused by perturbations of any kind may not be noticeable in time scales of human lives.

3.4.2 Speciation and environmental factors Speciation, like extinction, can be considered a process related to both physical and biotic factors (Allmon and Ross 1990). Classical views of speciation emphasized barriers in the physical

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environment as a driving force (Mayr 1982). The emergence of mountain ranges or splitting of land or water masses, to divide the geographic range of a single species, are classic examples. Anything that disrupts the gene flow necessary for continued existence as a single species increases the chances of speciation. Differences in the environment between two areas or genetic drift would result in eventual cladogenic speciation. Changes in environmental conditions also result in anagenic evolution to form new species. Similar forces have been identified for biotic components of the environment (Mayr 1982). The separation of a single species into two sub-populations, for example, can occur as a result of disease killing individuals in a space between areas occupied by remaining populations; with enough time, the two remaining populations can become distinct species (Plate 3.2). Physical factors continue to be seen as influential in promoting speciation, particularly through changes in the Earth’s geomorphological features and climate change.35 Specific kinds of events in the physical environment that promote speciation are also often related to extinction, exemplified by changes in sea level, oceanographic circulation, tectonic activity, and geographic and atmospheric composition. Qualities of the physical environment may also relate to speciation rates. These include diverse topography (Miller 1956) and environments that are stressful as compared to constant, uniform environments (Parsons 1991a,b, Petraits et al. 1989). As with extinction, the selectivity of such factors and processes is not well understood. Coevolutionary ecology, as one set of studies of biotic factors, is a well developed field. Many studies consider the variety of ecological interactions, such as predator–prey relationships, symbiosis, mimicry, or parasitism (Thompson 1982, 1994, 2005) in an evolutionary context. Evolutionary changes in one species give rise to a different biotic environment and related selective factors to which the other species are exposed. Coevolutionary interac-tions are often reciprocal. Such processes fall within the realm of the Red Queen model of evolution.36 Changes in a species resulting from its evolutionary response to co-occurring species undoubtedly leads to anagenic speciation on a regular basis in evolutionary time scales.

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In cladogenic speciation, an existing species must be split. This may occur, for example, when the geographic ranges of interacting species partially overlap. In such situations, selective forces differ between the area of overlap and the remaining range. This difference may result in the formation of two separate species, one with a geographic range that corresponds roughly to the area of overlap. Such biotic shear forces are probably numerous. Other interactions believed to be potential biotic stimulants to speciation include parasite/host interactions, seed dispersal (or facilitated mobility in general), species that form physical substrate for others, habitat alteration, and specialization.37

3.4.3 Selectivity in different environments In the last two sections it was argued that both the abiotic and biotic environments affect extinction and speciation. The abiotic environment influences species frequency distributions through two direct effects: on the evolution of individual species and on the expression of selectivity in both extinction and speciation. There are also indirect effects; evolutionary forces are systemic in permeating entire systems. The direct impact of the physical environment on both individual species and sets of species results in formation of the biotic environment important to the evolution of both. The biotic factors of importance are expected to be different in each physical environment. Thus, each set of species is adapted to its specific physical (and accompanying biotic) environment. This adaptation reflects selection among species manifested directly by the physical environment and indirectly through biotic pathways. As observed in nature, the sets of species (and the ecosystems assembled from these sets) in different physical environments are different from each other. The more the physical environments differ from each other, the more the sets of associated species differ.38 As an example of the effects of physical environment on species frequency distribution, compare Figure 2.2A and B.

and evolutionary theory. Its origins extend back to Darwin and Wallace. Most progress, however, occurred in the last several decades of the 20th century (Okasha 2006). Today, selectivity among species (especially in extinction) is often taken for granted among paleontologists, who have published numerous examples. Many scientists in the field of macroecology also assume such selectivity counts among explanatory factors behind macroecological patterns. It remains, however, to be given attention anything like that given to microevolutionary processes (Jablonski 2007). Important to the mission of this book, however, is the even more superficial role of the principles underlying selective extinction in the ways policies are established and decisions are made in conventional management. In systemic management this problem vanishes; such factors are inherent to decision making based on patterns which integrate all contributing factors to include selective extinction, selective speciation, and all other forms of natural selection.

3.5.1 Darwin and Wallace As early as 1855, a description of the primary elements of the process of selective extinction and speciation appear in the writings of A. R. Wallace (Appleman 1970, Bateson 1979, Brooks 1972, Fowler and MacMahon 1982). For example, in a letter to Darwin, Wallace wrote: The action of this principle is exactly like that of the centrifugal governor of the steam engine, which checks and corrects any irregularities almost before they become evident; and in like manner no unbalanced deficiency in the animal kingdom can ever reach any conspicuous magnitude because it would make itself felt at the very first step, by rendering existence difficult and extinction almost sure to follow. (From Appleman 1970, Bateson 1979).

In another context, Wallace wrote:

3.5 Historical perspective

In the long series of changes the earth has undergone, the process of peopling it with organic beings has been continually going on, and whenever any of the higher groups have become nearly or quite extinct, the lower forms which have better resisted the modified physical conditions have served as the antitypes of which to found the new races. (From Brooks 1972)

The concept of selective extinction and speciation has its roots early in the history of modern biology

Darwin’s thoughts regarding the concept of selective extinction and speciation have been reviewed

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or mentioned repeatedly (e.g., Grant 1989, Rensch 1959, Stanley 1979). His approach to natural selection is much broader than represented in NeoDarwinism. The following are quotes from “The Origin of Species” (Darwin 1896): . . . Natural Selection almost inevitably causes much Extinction of the less improved forms of life. . . . This great fact of the parallel succession of the forms of life throughout the world, is explicable on the theory of natural selection. New species are formed by having some advantage over older forms; and the forms, which are already dominant, or have some advantage over the other forms in their own country, give birth to the greatest number of new varieties or incipient species. The extinction of species, and of whole groups of species, which has played so conspicuous a part in the history of the organic world, almost inevitably follows from the principle of natural selection. . . . Natural selection, as has just been remarked, leads to divergence of character and to much extinction of the less improved and intermediate forms of life. Hence, the more common forms, in the race for life, will tend to beat and supplant the less common forms, for these will be more slowly modified and improved. It is the same principle which, as I believe, accounts for the common species in each country . . . presenting on an average a greater number of well-marked varieties than do the rarer species. Extinction has only defined the groups: it has by no means made them. . . . . . . species which are the most numerous in individuals have the best chance of producing favorable variations within any given period.

Chapter IV of Darwin’s Origin contains a general treatment of the process, and an entire section entitled “Extinction” includes a strong flavor of differential extinction probabilities dependent on the characteristics of species. The opposing forces of natural selection at the level of the individual and that for species are recognized. Darwin clearly understood the concept of patterns among species as being a product of selective extinction and speciation. He also considered speciation as the production of the raw material upon which extinction operates. As reviewed by Brooks (1972), the work of Lyell in his Principles of Geology (published between 1830 and 1833) demonstrated that extinction was important in the dynamics of the numbers of species. Wallace’s and Darwin’s work added the concept of

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the origin of species to supply the raw materials upon which extinction could act and to prevent extinction from obliterating life. The early evolutionists, however, emphasized taxonomic groupings of species, and were less concerned with patterns of significance for sets of species such as those from which ecosystems are assembled. Wallace and Darwin did little to develop explanations for the form and function of groups of interacting species or ecosystems other than for the nature of the species themselves. The main focus for both men was the manner in which species changed and arose, especially regarding taxonomic affinity, an emphasis that still dominates much of biological thought.

3.5.2 From Darwin and Wallace to 1980 In modern ecosystem-level biology, less progress has been made in incorporating the concept of selective extinction and speciation than in evolutionary biology and paleontology (where there also remains considerable room for progress: Jablonski 2007). The inertia of this situation has its roots in accomplishments prior to 1980. Recent reviews of material related to selective extinction and speciation point to a great deal of scientific literature prior to 1980. A brief introduction to the progress and related literature is helpful. Useful accounts of the developments of this period are found in Arnold and Fristrup (1982), Brooks (1972), Eldredge (1985), Fowler and MacMahon (1982), Grant (1989),39 Okasha (2006), Salthe (1985), Slatkin (1981), Stanley (1979),40 and Williams (1992). A critique of macroevolution and its history by Hoffman (1989c) is also a valuable source of information. Of course, the literature prior to 1980 also contains useful historical information (e.g., Bateson 1972, 1979, Gould and Eldredge 1977, Hull 1976, Lewontin 1970, Stanley 1979, Wynne-Edwards 1962). Early in the last century, mention of selection at the species level is found in De Vries (1905). The evolution of ecosystems through natural selection operating among a variety of levels of biological organization, including species, is a concept appearing at least as early as 1949 (Allee et al. 1949). The first appearance of selective extinction and speciation in general textbooks may have been the brief mention of interspecific selection in Simpson

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and Beck (1965). The next general text to include the concept may have been that of Futuyma (1979) who states: “ . . . group selection in the form of species selection must certainly operate; a community contains only predator species that are not so efficient that they eliminate their prey and thereby themselves.” (Note that species selection is equated with group selection.) The late 1970s may be characterized as a period during which acceptance and recognition of selective extinction and speciation became established, if no more than as a debatable concept (e.g., Eldredge and Gould 1972, Gould and Eldredge 1977, Stanley 1975a, 1979, Vrba 1980). Lewontin (1970) and Hull (1976) provide early descriptions of the general process of natural selection and mention its applicability to species as the units subject to differential survival and reproduction.41 The early development of the field emphasized the selectivity of extinction more than speciation (Arnold and Fristrup 1982). Lewontin (1970) and others presented elements of the idea in conceptual form and certainly allowed for the speciation component. However, people working with empirical data, and paleontologists in particular, tended to dwell on extinction (e.g., Eldredge and Gould 1972). By the mid 1970s this began to change (e.g., Arnold and Fristrup 1982, Gould and Eldredge 1977, Stanley 1979). Stanley (1975a) clearly included speciation, stating that the process “ . . . favors species that speciate at high rates. . . . ”. Today the speciation component is clearly defined, and understood to be a primary component as described above.42 It should be clear that this is a matter of conceptual history and may have little bearing on the relative effects of selective extinction in comparison to selective speciation in determining the characteristics of species-level patterns in reality. The palaeontological and, to a large degree, the evolutionary literature dwell on genealogy or taxonomy in the treatment of selective extinction and speciation rather than on its implications for ecosystems or biosphere. Macroevolution, primarily a product of palaeontological work, is represented in the literature essentially as selective extinction and speciation applied to phyletic groups. As such, the sets of species to which selective extinction

and speciation apply in macroevolution are taxonomically related groups of species, or clades (Cracraft 1982, 1985b, Damuth 1985, Greenwood 1979, Levinton 1983, Vrba 1984). Macroevolution has been defined as “ . . . evolution above the species level, evolution of the higher taxa, or evolution as studied by paleontologists and comparative anatomists. . . . ” (Mayr 1982). Stanley (1975a) says: “ . . . species selection operates upon species within higher taxa. . . . ”. As Gilinsky (1986) expresses it: “Species selection . . . is a theory for explaining patterns of differential representation of species of certain kinds within clades; that is, it is a theory for explaining taxic trends”. Ecosystem-level implications of selective extinction and speciation received little attention prior to 1980. Ecosystems have been rarely considered, in the history of ecosystem science, as units affected by the evolution of the sets of species from which they are assembled, especially prior to 1980. Golley (1993), Hagen (1992), and McIntosh (1985), for example, presented histories of the ecosystem sciences in which essentially no mention is made of selective extinction and speciation. The concept has been given little or no credit for contributions to understanding or providing a basis for practical management in regard to ecosystems—a situation that continues. The bulk of ecosystem science has been, and continues to be, descriptive in this regard. The identification of species level characteristics of importance to selective extinction and speciation has been the critical, but largely unwitting, contribution of this work. The lack of integration of selective extinction and speciation into ecosystem science did not preclude the development of its specific elements in more theoretical ecological settings. Island biogeographic theory is a prime example. Early work in this field (e.g., MacArthur 1972, MacArthur and Wilson 1967) is directly relevant to selective extinction and speciation in the examination of extinction as related to geographic range size. As a result of these efforts, species-specific geographic range size is one of the most studied and well-understood dimensions in its effects on risk of extinction. In the 1980s and 1990s much useful research in the field of island biogeography contributed to the more general concept of selective extinction

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and speciation. Island biogeographic work was expanded to include consideration of a number of species characteristics that influence the probabilities of extinction. For example J. Brown (1971, 1981) suggests that in addition to geographic range size, factors such as trophic level, body size, and specialization play roles in differential extinction. The treatments of Brown (1995) and Rosenzweig (1995) exemplify the expansion and growth of island biogeographic work.

3.5.3 1980 to the present The recent history of the development of selective extinction and speciation is far too voluminous to adequately present in the confines of a chapter (see the account in Okasha 2006). Since 1980, two major groundswells of work seem to be merging. This is the merger of the evolutionary and the conventional approaches to the study of ecosystems; studies are no longer confined to a focus on ecological mechanics (although the same cannot be said about the role of the related science in policy and management). The merger of evolutionary and ecological dynamics in science has advanced substantially through addition of natural selection at the species level to the evolutionary component. Many conventional life sciences serve to identify characteristics of species over which natural selection among species operates. The results are information-based approaches exemplified by Eldredge (1985), Salthe (1985), and Williams (1992). Conventional ecosystem sciences are described in historical reviews by Hagen (1992) and Golley (1993). Evidence of the merger of conventional and evolutionary views is found in the work on both approaches. In conventional ecosystem sciences, for example, the processes of natural selection at the species level are discussed by Allen and Hoekstra (1992) and Allen and Starr (1982). In paleontology, the work of Eldredge (1985) is a good example wherein the merger exhibits significant development. The bridging of these approaches, for example by considering selective extinction and speciation as contributing factors in the evolution of biological communities and ecosystems, is a critical step leading to the formulation of systemic management (Fowler 2003, Fowler and

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MacMahon 1982). Philosophical consideration of this merger is also found in related literature (e.g., Salthe 1985). From Wallace and Darwin to the present, there has been an increasing tendency to focus on specific elements of selective extinction and speciation. Such specificity is often reflected in the terminology (Appendix 3.6). Much of this work is done, and the terms are used, without mention of the broader context. This seems particularly obvious in the separation of the fields of paleontology and ecology, a separation that is less prominent in work exemplifying the union between ecology and evolutionary studies involving natural selection at the species level realized in recent decades as mentioned above. Consideration of the species attributes that result in variability in extinction and speciation rates are found in both ecological and palaeontological literature. Examples with palaeontological focus are numerous (see references in Appendix 3.1).43 These are directly tied to the extinction and speciation processes. Ecological and ecosystem research, meanwhile, has resulted in the accumulation of species-level patterns with an increasing tendency to mention the link to selective extinction and speciation. The work of Gaston and Blackburn (2000) exemplifies this trend. One of the examples of direct and overt implementation of the concepts of selective extinction is that of Damuth (2007) who considered the lack of correlation between body size and energy consumed per unit area. Part of the progress seen in the merger of studies of ecological mechanics and evolutionary paradigms in recent years is the recognition that the character of various sets of species develops over time (i.e., have their own form of ontogeny). Damuth (1985) and Grantham (1995) point out that, historically, the unit that evolves through selective extinction and speciation has received relatively little attention. Fowler and MacMahon (1982) identify such units among the sets of species from which communities and ecosystem are assembled. This step, however, is only part of the larger context wherein progress in recent years has linked the variety of hierarchical levels of units that evolve and units of selection (Williams 1992). Progress at the ecosystem level is an example of the linkages

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between the material- and information-based views of biological systems.44 Modeling has been central to conventional studies of the ecological mechanics of ecosystems (Golley 1993, Hagen 1992). These efforts have largely been attempts to mimic ecosystem characteristics, but usually fall prey to the fallacy of composition where it has been assumed they are adequately explanatory.45 By design, they involve omission of important processes (Pilkey and PilkeyJarvis 2007) by omitting evolutionary dynamics and interrelationships. Alternative models have demonstrated explanatory potential and see ecosystems as assemblies of populations from sets of species with frequency distributions influenced by selective extinction and speciation. Such efforts have occurred in recent years amidst steps to include more of the various elements of selectivity. Slatkin’s (1981) work is one example. Extinction and speciation were included in his model “by assuming some dependence of either speciation rates or extinction rates on the phenotypic character”. This model also included directional speciation as a bias in phenotypic change. The larger concept of selective extinction and speciation is emerging in the recognition of the simultaneous realities of both ecological mechanics and evolutionary processes operating to influence various sets of species. In essence, for ecosystem modeling, this is an amalgamation of historical dynamic systems approaches and the information-based approaches embodied in models like Slatkin’s (1981). Progress in the merger of mechanical and evolutionary process is evident in the work of both paleontologists and ecologists (e.g., Allen and Hoekstra 1992, Allen and Starr 1982, Brown 1995, Damuth 2007, Eldredge 1985, Gaston and Blackburn 2000, Jablonski 2007, Kitchell 1985, Rosenzweig 1995)46. But general textbooks seem to lag behind in this regard. In the second edition of Futuyma’s book on evolutionary biology (Futuyma 1986a), the term “species selection” is defined to occur when “speciation rates or extinction rates of whole species are effected by whether or not they possess a particular characteristic; consequently, the proportion of species in a higher taxon that bear one trait or another might change”. Note here that the process

is conceptually related to taxa as the pertinent set of species. Begon et al. (1990), in a general ecology text, briefly mention the concept of selective extinction and speciation. In an otherwise predominantly descriptive approach to the topic, the authors of this text draw a connection among ecosystem properties, species frequency distributions (composition), and selective extinction and speciation. Enger and Smith (2000) provide a slightly more detailed account of selectivity and present a short list of characteristics that lend to extinction risk. This text, like most, however, does not link selectivity to the evolution of the sets of species making up ecosystems. Maynard Smith (1983) reduces selective extinction and speciation to a single sentence in saying “ . . . species will differ in their likelihood of extinction, or of further splitting, and these likelihoods will depend on their characteristics”. This form of natural selection is occasionally seen as part of natural selection as it operates at all levels (Jablonski 2007, Okasha 2006). The resulting frequency distributions of species are seen by Fowler and MacMahon (1982) as ecosystem properties in the form of structure and function acquired through selective extinction and speciation operating on the species from which ecosystems are assembled. The characteristics acquired through these processes are influenced by the nature of the abiotic environment. This is a role of selective extinction and speciation that contributes to the explanation of why ecosystems differ from one physical environment to another. The concept that sets of species adapt to their abiotic environments as an aspect of selective extinction and speciation is yet to be developed in a useful (applicable) way.47

3.6 Implications for management What does all of this mean for management? From the practical perspective, patterns among species embody the effects of evolutionary factors, including selectivity at the species level. As sets of information, the patterns themselves account for such processes along with all other contributing factors. This is the importance of Figure 1.4. In the voluminous literature on management, strong emphasis is placed on the importance of accounting

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for a particular factor, or set of factors (such as coevolution, extinction, nutrient flow, behavior, and risk—the n factors of Fig. 1.4). In conventional approaches this is done in Step 4 of the top row of Figure 1.1. It is largely a thought process48 subject to our inability to think of everything, subject to our value systems, and subject to our inability to realistically combine what we think we know. In systemic management this entire process is avoided. It is replaced by using empirical patterns. Accounting for all such factors (i.e., accounting for complexity) happens either automatically in the nature of the pattern, or in scientific endeavor dedicated to characterizing correlative sub-patterns. This is because of the integration inherent to patterns used to set goals and policy (step 4, bottom row, Fig. 1.1; Belgrano and Fowler 2008). This relieves the management process of a great deal of human error and introduces a level of objectivity impossible to find in conventional management. Specifically, the risk of extinction is accounted for when we make management decisions based on species-level patterns when we use them to address management questions regarding the human species in our interactions and relationships with other systems. Because all elements of reality are accounted for (the infinite of Fig. 1.4), included are the process of speciation, the interaction of micro- with macroevolution, and the synergism of all evolutionary processes with all forms of ecological mechanics and environmental influence. Within almost all patterns, however, we find sub-patterns involving correlative relationships. We know about risk of extinction and its relationship with body size; such insight is one of the early products of science dealing with species-level selectivity. This emphasizes the importance of refining management questions to directly account for body size. What is a sustainable population density for humans as a species with an adult body mass of 68 kg? This refinement continues; the taxonomic aspects of selective speciation and extinction cannot be neglected. What is a sustainable population density for humans as a mammalian species with adult body mass of about 68 kg? Some patterns are not correlated with body size; energy consumption per unit area is an example. This does not relieve us of the responsibility for dealing

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with other correlative sub-patterns and refined management questions. Trophic level is implicated in species-level selectivity. How many joules per km2 can sustainably be consumed by humans in our occupancy of a trophic level between primary consumers and strict carnivores? Sub-patterns also involve the environment. Deserts, coral reefs, rainforests, continental shelves, alpine tundra, rivers, and lakes all represent different ecosystems. Each involves, in part, a different set of species influenced by selectivity in extinction and speciation. The patterns we observe among these sets of species is influence by the reality of context provided by the environment. As such, our management questions must include specificity regarding contextual factors. How many joules of energy per km2 can be consumed sustainably each year in a tropical rain forest (low latitude desert, North American grasslands, or Antarctic ice sheet) by humans as a mammalian species with an adult body size of 68 kg? What is a sustainable density for humans as a mammalian species with an adult body size of 68 kg in areas of precipitation between 20 and 45 cm per year?

3.7 The eastern Bering Sea example In this chapter, extinction and speciation are emphasized as processes acting on species, or sets of species, particularly those represented in ecosystems (i.e., with geographic ranges partly or entirely within a particular ecosystem). Thus, any ecosystem, such as that of the eastern Bering Sea, is not just a system of material and energy flow, not simply a system of interacting populations, not merely a system affected by climate or weather (ecological mechanics). It is not merely a system within which species evolve and have evolved. It is not simply a system of coevolutionary interactions wherein species have evolutionary influences on each other, directly and indirectly. It is not the only system impacted by global warming, oceanic acidification, pollution, and the harvesting of resources for human consumption. It is a system that includes all such things and much more. The eastern Bering Sea is also a system with design and structure influenced by species-level dynamics in which species experience different rates of extinction, speciation

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and evolutionary changes depending on their evolutionary, ecological, and physiological characteristics. The design and structure are what we see in species-level patterns, or frequency distributions, when we observe their shape, positions and interrelationships as exemplified in Chapter 2.49 These emergent properties (Fig. 1.4) form the characteristics of ecosystems and are exemplified for the eastern Bering Sea in Figures 1.6–1.8 and 2.6. One of the main points of this chapter is that these characteristics are, in part, evolved characteristics. In a very real sense, ecosystems have characteristics that involve a phenotypic component. Just as the mean body weight of adult prairie dogs is, in part, an evolved characteristic of that species, the mean trophic level of the species of the eastern Bering Sea ecosystem is, in part, an evolved characteristic of that ecosystem, as for any ecosystem. This knowledge makes assessment of ecosystems a more reasonable objective. However, evolution and genetics are not the only factors involved for either prairie dogs or the eastern Bering Sea. The effects of the environment (including human influences, on shorter time scales) give rise to some of the variation about the central tendencies within patterns. The intrinsic elements of mechanical relationships also come into play. Any other such factors and effects cannot be ignored as contributing factors. Selective extinction and speciation are among the trial-and-error processes of evolution (selective systems failure) that lead to understanding species as examples of sustainability. Their existence as species is information about sustainability as species (Fowler 2008). It is within the normal ranges of natural variation that we find guidance for systemic management (to adhere to Management Tenet 5, Fowler and Hobbs 2002): avoiding the abnormal. This information can be used, for example, to manage fisheries so human consumption of fish is not abnormal (e.g., within statistical confidence limits) of natural variation of consumption by other predators. As revealed in distributions such as that shown in Figure 2.6, recent harvests of pollock by humans are over 25 times greater than the mean consumption of this species by marine mammals in the eastern Bering Sea and North Pacific (Fowler 2008). The growing clarity concerning the

need to make such dramatic changes to achieve sustainability underscores the need to be sure that the intricacies of systemic management are clearly understood. The series of questions posed in Chapters 1 and 2 remain to be fully answered. Now, however, species frequency distributions can be appreciated as helpful in two ways. First, the variation among them helps establish normal ranges of natural variation among ecosystems (or any other category of sets of species). When such variation is appreciated and when we have data for ecosystems in general, the eastern Bering Sea can be compared to such variation and evaluated or assessed, asking such questions as: Is the mean abundance among the cetaceans (Fig. 1.8) normal or abnormal? Is the composition of the fish community normal or abnormal? Is the distribution of species in their geographic ranges normal or abnormal? Is the mean trophic level normal or abnormal? Is the species diversity normal or abnormal?

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Second, the variation within species frequency distributions is helpful in establishing the normal ranges of natural variation among species. Related questions are: Are individual species, such as the threatened or endangered Steller sea lion, bowhead whale, or blue whale, at population levels outside the normal range of natural variation for such species? To address management questions related to relationships between the human and the nonhuman, can we account for all species, or all interrelationships, by any means other than using empirical patterns—specifically macroecological patterns?

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Most importantly, from the point of view of what humans have as options for action (Management Tenet 2, Chapter 1), are questions related to our involvement in the eastern Bering Sea. What patterns among species are of relevance to the questions facing managers with responsibility for accounting for ecosystems, complexity, long time scales, evolution, and a variety of sciences that study such phenomena?

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How does paleontology get included in the list of sciences upon which management decisions are made? To what extent are we humans facing risks of extinction brought on by our evolution (Combination 6, Table 3.1)—including risks that involve our effects on ecosystems and the biosphere? The list of factors of concern is large and now the elements of evolution, extinction, and other components of species-level dynamics cannot be ignored. How do we assure that they are taken into account in our management?

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3.8 Summary and preview This chapter has examined some of the basic elements of selective extinction and speciation through a definition of concepts and a presentation of basic elements and history. Selective speciation can be seen in contrast to the role of selective extinction. Both selective extinction and speciation contribute to the nature of species-level patterns as patterns that characterize systems such as ecosystems. NeoDarwinian or microevolutionary forces are seen as operating within the larger realm of natural selection (natural selection at all levels; Jablonski 2007, Okasha 2006) that includes selective extinction and speciation. Parallel processes, one within the other, create a hierarchical structure with fractal-like repetition. As concepts, selective extinction and speciation focus on sets of species, exemplified by clades or other taxonomic units, but are not restricted to any one set; they include the sets represented in ecosystems and the biosphere. Similarly, the concept of evolution of species has broader application than the survival/reproduction of related individuals within genealogical family50 units making up species. Processes of selective extinction and speciation apply to assemblages of species, including those represented in ecosystems. Through a few preliminary examples, selective extinction

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and speciation have been presented as important among the many elements that contribute to the formation of patterns in the frequency distributions of such groups of species. Selective extinction and speciation are analogous to their component process—the much better understood process of natural selection at the level of the individual. In this analogy, anagenesis contributes to variation in its parallel to mutation or recombination. Thus, the factors that contribute to the formation, explanation, history, and emergence of patterns (Fig. 1.4) include selectivity in extinction, speciation, mortality, and reproduction. Evolutionary forces are accounted for when we use patterns to guide management. Because such factors are accounted for in this way, systemic management is an evolutionarily enlightened form of management. The reality (complexity) accounted for includes all evolutionary forces, factors, dynamics, and processes. Such factors are parts of reality (Appendix 1.1). The remaining chapters begin to answer the management questions posed above and in previous chapters (and specifically for the eastern Bering Sea). These chapters delve into more detail and examples of applying the information contained in species-level patterns so as to adhere to all nine Management Tenets as presented in Chapter 1. Chapter 4 is a critique of conventional management to expose faults that contribute to the abnormalities revealed by comparing humans with other species as well as the plethora of global problems confronting us. Chapter 5 looks more closely at systemic management as a replacement for currently failing forms of management to more effectively guide human behavior in a sustainable direction so as to reduce the risk of our own extinction. Chapter 6 is an evaluation of the human species in comparison to other species to identify problems to be solved and objectives to be achieved so that we and the systems upon which we depend can be sustainable.

CHAPTER 4

Why conventional management does not work

Reasonable people adapt themselves to the world. Unreasonable people attempt to adapt the world to themselves. All progress, therefore, depends on unreasonable people. —George Bernard Shaw

The preceding chapters introduce the phenomenon of limited variation among species. They explain some of the processes that contribute to both the variation and its limits as they result in species-level patterns. They also present some of the initial stages in practical application of the information in such patterns. Like all patterns, patterns observed among species define what is normal and what is abnormal for species. This chapter examines different limits—the problems, failings, and errors of conventional management—to set the stage for understanding how systemic management provides solutions by carefully choosing and using patterns such as those seen in the preceding chapters. This chapter shows conventional management to lack objectivity, fail to account for complexity, and involve erroneous beliefs/thinking—all problems largely avoided in systemic management. The context for the topic of this chapter is the set of problems confronting mankind, primarily environmental, that are often seen as catastrophic and getting worse. One of the products of science is that of documenting and describing problems so that they can be drawn to everyone’s attention. Scientific effort has been remarkably successful at listing the problems of anthropogenic origin: global warming, oceanic acidification, loss of topsoil, abnormal extinction rates, pollution/toxic waste, habitat degradation . . . . and the list goes on (Christensen et al. 1996, Colborn et al. 1997, MEA 2005a, b, Moran 2006, Pauley et al. 1998, Silver and DeFries 1990, Steffen et al. 2004, Turner et al. 1990, Woodwell 1990; World Conservation Monitoring Centre 1992; Vitousek et al. 1997). If current management were working, 78

we would not find ourselves in this situation. We are faced with having to take a critical look at the conventional management process—the topic of this chapter (and shown in the top row of Fig. 1.1). Conventional management, as a process, can be assessed in several ways. This chapter begins by examining in some detail the failure of past approaches to meet the nine management tenets laid out in Chapter 1. Following this, a number of drawbacks to conventional management are discussed as they involve factors other than those covered by the nine tenets. A final appraisal looks critically at conventional management at four focal levels of biological organization (individual, species, ecosystem, and biosphere), with particular attention to tradeoffs in risks and benefits for any management action to demonstrate the vulnerability of conventional management to human limitations and values. The advantages of systemic management are introduced, showing how it can be applied at all focal levels simultaneously, particularly at the focal levels of ecosystems and the biosphere. The chapter concludes with a return to the example of the eastern Bering Sea to illustrate the pitfalls of conventional management and the potential benefits of systemic management applied to a specific marine ecosystem.

4.1 Transitive management in relation to identified criteria for management When conventional approaches to environmental/ resource management fail, managers and

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management scientists typically judge them to be insufficiently developed rather than in need of replacement. Full recognition and acceptance of fundamental flaws force consideration of completely different alternatives. Instead, it has been common practice to prescribe improvement to existing approaches—better models, more precise data, and training of specialists to carry on with conventional thinking. Arguments are often presented for the claim that what we are doing is acceptable. Such opinions contribute to the problems we observe; they are among the explanatory factors behind observed patterns (Fig. 1.4). Retaining an inadequate approach will not compensate for basic or fundamental irreparable flaws. Given the current state of affairs and the risks facing us, we must be open to completely replacing all failing parts of existing approaches. We need to stop using those elements of current management processes that do not work. Part of this effort involves changing the belief systems that perpetuate the use of failing management systems. The requirements of Management Tenets 1–9 (Chapter 1) are not met, in part, because of the transitive aspect of conventional management. This is in direct violation of Management Tenet 2 (Fowler 2003). Much of current management presumes an ability to control (design, engineer, or regulate) the nonhuman world. This involves stakeholders (e.g., scientists, managers, representatives of the public, politicians) attached to the role of translating scientific information to management objectives (step 4, top row, Fig. 1.1; Belgrano and Fowler 2008). Such management often leads to unresolvable conflict and creates more problems than are solved—unresolved paradox abounds. Unbridled “management” of nonhuman focal units (e.g., a single species of game, commercial fish, pest, or disease) does not adequately deal with complexity. Our limitations prevent us from ever being able to identify or understand all the consequences of any purposeful action, transitive or intransitive. Some consequences are beneficial, others are harmful, and some are both (and in all cases, these categories involve the application of humans values). Only a few consequences can be predicted, if at all (Pilkey and Pilkey-Jarvis 2007); timing is especially difficult to estimate. Harmful consequences can be

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particularly troublesome when action is directed at the nonhuman. Whether aimed at individuals, species, communities, ecosystems, or the entire biosphere,1 management involves long-term consequences for all. In dealing with complexity, it is important to know that our actions have broad consequences owing to our being part of an interconnected complex natural system. We are left with three important questions. What are the failures of conventional management that lead to observed problems? What aspects of conventional management need to be retained, and what needs to be replaced? The following leads to answers to these questions—answers that are more fully developed in the next chapter.

4.1.1 Management Tenet 1: Including humans—management must be based on an understanding of humans as part of complex biological systems On the surface, it is clear that one important goal is to ensure that humans remain a viable component of reality. Purposeful self-imposed extinction is not an option that many would entertain as an example of sustainability. However, being a part of, being composed of, and being subject to the reactions of complex systems is much more complicated than mere existence. Human nature and the roles we play in complex systems or reality are not adequately dealt with in current management. Current approaches to management fail to give complete consideration to humans. We do not adequately question the sustainability of our influence on nonhuman systems. We fail to fully consider the consequences of our actions on other elements of the systems of which we are a part. And we fail to adequately address the reactions of those components to our influence, especially in ways that result in important consequences for us (feedback on all spatial and temporal scales). The complexity of human systems is inadequately accounted for in conventional management. Neither models nor lists of factors represent a complete consideration of all factors involved in complexity (thus failing to adhere to Management Tenet 3). These failings are not a matter of malice; they are the result of being finite—something over which we have no control.

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As such, there is much more to considering humans as parts of complex systems than simply our needs for food, fiber, and services (Appendix 4.1). In management, humans must be included in systems as components subject to the laws of nature the same as other components. It is important to ensure that our needs are of a magnitude that can be sustainably met by the systems upon which we depend. It is important that sustainability involve the other elements of complex systems—no more, nor no less, than it applies to humans. Considering humans as part of reality means that the reciprocity of influence and feedback is complicated by human nature. In conventional management, and in society at large, economic factors are often assigned a level of importance in decision-making that leads to the relative exclusion of environmental factors as well as our own long-term sustainability. To what extent have economic considerations gotten us into the problems that we now face (Appendix 4.2)? To what extent have the laws we have erected for ourselves contributed to current problems? What level of human needs can be met sustainably? How can we account for the complexity of human nature? Conventional approaches do not have answers to these questions, at least not in ways that simultaneously meet the requirements of the management tenets, especially Management Tenet 3 (the need to account for reality). Both human nature and our interactions with our environment are parts of the complexity that is to be accounted for in applying Management Tenet 3. In conventional approaches, there is no way to objectively weigh the relative importance of human and nonhuman factors (e.g., economic considerations versus environmental issues, or resource use versus the risk of extinction). The tradeoff among such factors is weighed, evaluated, and debated among stakeholders in conventional forms of management—not an objective process. Management Tenet 1 requires that short-term human needs cannot be given priority over other issues, including long-term human needs and the risk of our own extinction. The other tenets of management require that we place importance on such issues so that human needs are weighed objectively in relationship to other issues over a broad range of scales in time, space, and complexity. This

tenet closes the door to treating human needs as the primary issue—human needs are crucial along with those of other species, ecosystems, and the biosphere. It implies the need to consider humans as subject to management in ways not dealt with in conventional approaches.

4.1.2 Management Tenet 2: Limited control— management must recognize that control over other species and ecosystems is impossible We can influence both the human and the nonhuman. However, there are always side effects (e.g., higher order effects). Such ripple effects are natural consequences of the interconnectedness found among the various elements of reality (Fig. 1.4). This second tenet is based on the principle that there are innumerable ways that influence of any kind has ripple effects through this interconnectedness. It includes feedback of direct importance to humans on various time scales. Although we have tried, we cannot control things to avoid the laws of nature. These laws guarantee that there are repercussions from influence, whether it is ours or that of other species. Conventional management tends to take control, to manipulate without full consideration of the myriad ways interrelated systems will respond. Pest “control” and predator “control” are extreme examples; the “control” of a fish population under management to achieve maximum sustainable yield (MSY) is another. We “control” diseases, rivers, floods, and crime. Most current management is based on short-sighted objectives of meeting human needs without adequately accounting for its ramifications, especially long-term consequences. These ramifications involve both other species and humans (i.e., we humans also experience feedback from our actions; Redman 1999). It is impossible to control systems of which we are a part.2 Managing ecosystems transitively is not an option. We can manage our influence on, but not manage, the biosphere. Inclusive systems place more control or limits on their components than the other way around. Yet the use of the terms “ecosystem management” and “ecological engineering” (e.g., Jørgensen and Mitsch 2000, Kangas 2004) are indications that conventional thinking is being carried forward in many current forms

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of management with the risk of even greater failures and problems as we approach them through transitive action. Conflicts that arise from conventional transitive management occur in a variety of ways, but especially when control is attempted simultaneously at various levels of biological organization (see Management Tenet 4 below).

4.1.3 Management Tenet 3: Complexity and interconnectedness—management approaches must account for reality in its complexity over the various scales of time, space, and biological organization Complexity and uncertainty plague conventional management of our use of resources (conventionally referred to transitively as “resource management” or “management of resources”). Statistical uncertainty (Ludwig et al. 1993) is compounded by natural variability, which further complicates management. Numerous time scales, processes, variables, elements, factors, and parameters contribute to complexity to make conventional methods not only inadequate, but often misleading.3 Our ignorance of the factors that determine ecosystem structure and function adds to this uncertainty. Following conventional approaches, there is very clearly no way to realistically account for things we do not, and cannot, know—regardless of how many specialists and stakeholders are involved. The meetings of panels of experts involve a combination of their information and expertise but also their values, biases, and ignorance.4 Using conventional approaches, it is humanly impossible to recombine information produced by reductionistic science to adequately represent reality for management purposes—reality is too complex (Appendix 1.1). Owing to this complexity, we find it impossible to assemble adequate models with the parts of which we are aware (the Humpty Dumpty syndrome: Dunstan and Jope 1993, Fowler 2003, Fowler and Hobbs 2002, Horgan 1999, Nixon and Kremer 1977, Regal 1996). Complexity (reality) involves, among other things, hierarchical structure. This structure includes all biotic systems (from the sub-cellular, through individuals, and ecosystems, to the biosphere).

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There are conventional approaches to management that achieve progress in dealing with individuals (e.g., medicine) but comparable achievements for ecosystems and the biosphere have not been made part of management—especially to account for the problems created by medicine and agriculture as currently practiced. Most clear is the lack of an approach that adheres to all nine management tenets listed here. Full objective consideration of reality is impossible in current ways of managing owing to the reliance on stakeholders (with their special interests, human limitations, bias, and values) to accomplish the task. It is impossible to make models or lists of factors that include all of them in making decisions. The infinite cannot be represented in a model.

4.1.4 Management Tenet 4: Simultaneous consistency—management must be applied consistently in all its applications and must apply simultaneously at the various levels of biological organization Conventional management does not regulate our production of CO2 in a way that is consistent with management of our consumption of energy. Of the production of biomass within an ecosystem, by an individual species, or within the biosphere, what portion should we consume or harvest? What portion should we leave for other species? What portion should we leave for ecosystems and the biosphere? Conventional management fails to find consistency in answers to these kinds of questions. What portion should be left so that there is sustainability at all levels? Even though everything is characterized by numerous interconnections, there is no consistency defined for management of pests, resources, land, or medicine in current management practices. Current management fails to simultaneously and consistently account for genetic impacts of resource use and the effects of establishing protected areas. Consistency in conventional management is lacking in its various applications. This failure has no solution in the current use of models, existing legal mandates, or habitual thought processes. It is business as usual when we hold meetings or develop matrices with lists of factors (as done in the US NEPA

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process, Cantor 1996) in extremely superficial attempts to modify conventional management rather than replace its failing parts. This is especially true in dealing with the various levels of biological organization. The objectives of medicine and agriculture are often at odds with efforts to deal with overpopulation as a cause of ecosystem-level problems; the same is true for medical effects on the genetic attributes of our species. Attempts to control other species can be totally inconsistent with objectives for ecosystems. For example, current advice for an ecosystem approach to management is to reduce commercial harvests of fish (NRC 1999). This is in contrast to single-species approaches used for current harvest practices based on “controlling” a population to stimulate production (without considering all the consequences that are unknown and/or beyond control). Thus, recommended harvests of marine fishes are based largely on conventional approaches and exceed those recommended in consideration of ecosystems. There is little, if any, basis for overcoming such inconsistencies in conventional management (especially its transitive forms).

4.1.5 Management Tenet 5: Avoiding the abnormal—management must undertake to ensure that processes, relationships, individuals, species, and ecosystems are within (or will return to be within) normal ranges of natural variation Applied to individual humans, we follow this principle when we take action to keep body temperature, blood pressure, heart rate, or body weight close to normal, where normal is near the mean for otherwise comparable individuals (e.g., same sex and age, and similar circumstances). Applied transitively to other species (i.e., control of another species achieved through human action), Management Tenet 5 would require maintaining individual species within their normal ranges of natural variability among species. Applied transitively to ecosystems as parts of the biosphere, this would mean attempting to manipulate ecosystems to alleviate ecosystem-level abnormality—based on the few ecosystem-level characteristics scientists have identified. However, as seen above, transitive

approaches usually (probably always) cause more problems than they solve, largely because they are based on inadequate consideration of reality. Nevertheless, it remains important that systems at all levels show a minimum of abnormality, taking action where we can (Management Tenet 2; keeping in mind that things like death and extinction are natural processes that can themselves exhibit abnormality). Circumstances that have been identified as problems for the earth’s ecosystems and biosphere (e.g., Christensen et al. 1996, Colborn et al. 1997, MEA 2005a,b, Moran 2006, Pauley et al. 1998, Silver and DeFries 1990, Steffen et al. 2004, Turner et al. 1990, Woodwell 1990; World Conservation Monitoring Centre 1992; Vitousek et al. 1997) are evidence of the failure of conventional approaches to keep biotic systems within the normal range of natural variation. Fisheries management that aims to “control” a population at 40% of its virgin biomass is specifically designed to achieve the abnormal, as are the practices of agriculture and aquaculture wherein we maintain monocultures that are abnormal in the context of environmental circumstances. Much of conventional management is aimed at achieving the abnormal. We find our species to be abnormal in numerous ways (Fowler 2008, Fowler and Hobbs 2002, 2003) largely as a result of the unintended consequences of action based on what are considered good human values (e.g., medicine, agriculture, and a variety of global humanitarian efforts). What science has observed to be abnormal or pathological (e.g., Figs 1.2 and 1.7, Fowler and Hobbs 2003) proves that current management is not working.

4.1.6 Management Tenet 6: Sustainability and risk—management must be risk averse and exercise precaution in achieving sustainability It is doubtful that any action taken in conventional management is based on thorough consideration of the risk of human extinction. It is safe to assert that no action is taken through current management practices in a way that simultaneously, consistently, and fully accounts for the full suite of risks we face, especially in a way that weighs their relative importance objectively. Risks are part of

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complexity and it is impossible to include them all explicitly in models or lists. Accepting no alternative, conventional management proceeds with the assumption that we (stakeholders) are doing the best we can—largely trapped in the top row of Figure 1.1 where minimizing one risk contributes to others (e.g., minimizing the risk of death contributes to overpopulation and increased risk of extinction). In conventional management, it is assumed that we have no option other than to ignore those risks that we know nothing about. We tend to deliberately ignore those that are deemed to be statistically insignificant even though, collectively, such risks may be more important than any one of those we now recognize. Many risks involve butterfly effects (Gleick 1987) to be of long-term consequence (e.g., the long-term effects of low levels of endocrine disrupters); most minor factors deemed of little short-term consequence are ignored. Such ignorance is not dealt with in conventional management; again, this not malice, but because of the finite nature of being human. Through conventional management practices, there is no basis for defining sustainability that fully accounts for complexity to simultaneously, and consistently, include all risks with an objective weighing of each.

4.1.7 Management Tenet 7: Knowledge and information—management must be based on information Informed decisions are clearly better than outright guesses. Current forms of management fail to find information that conforms to all nine tenets of management, and distinguish the pieces of information that are directly useful, beneficial or constructive from those that are indirectly related, misleading, deleterious or harmful.

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Current management uses piecemeal information and proceeds in spite of the fact that we know those pieces are being used in ways that do not involve complete and consistent consideration of other pieces of information. The choices, translation, and evaluation of these tidbits of information is in the hands of stakeholders (top row, Fig. 1.1)

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as the Achilles heel of conventional management (Belgrano and Fowler 2008). Thus, fisheries management is based on the concept of MSY (or related approaches), knowing that there is no consideration of the genetic effects of harvesting, effects of reducing a population on other species, or effects of harvesting on the ecosystem. Even when such information is given superficial consideration (primarily through debate, opinion, and expression of special interests), our combination of the piecemeal elements of information is woefully inadequate. The pattern involved in the sigmoid population growth curve is a representation of how reduced populations grow, not a representation of sustainable yields, as currently interpreted in fisheries management (Fowler and Smith 2004; see T. Smith 1994 for a history of this thinking). It says very little if anything about the sustainability of reduced populations or the sustainability of harvests that maintain reduced populations, let along the sustainability of ecosystems within which the reductions occur. Current management largely ignores the information content of genes and genomes in spite of numerous calls for evolutionarily enlightened management to incorporate what we know and understand about genetics, evolution, and coevolution into our management processes, decision-making, and policies (Conover and Munch 2002, Fenberg and Roy 2008, Law et al. 1993, Stokes and Law 2000, Swain et al. 2007, Thompson 2005). The minimal progress that has been made in this regard has been confined largely to the debate, consideration, meetings, and panels of stakeholders in their role choosing and interpreting information in step 4 of the top row of Figure 1.1, where conventional management is most vulnerable to human nature.

4.1.8 Management Tenet 8: Including science—management must include scientific methods and principles in research, monitoring, and assessment Information produced by scientists is used in current management. Scientists monitor change and the reactions by systems in response to management action. Efforts within the field of science do an admirable job of drawing our attention to

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problems and explaining what we see. Science is one of the best forms of objective observation. However, there are problems. One problem is found in management that is based on what we understand from one form (branch or field) of science without consideration of the others—especially those yet to emerge in being increasingly aware of the complexity of reality. Regardless of progress, however, we are not dealing with complexity—partly because of the endless nature of the path of becoming aware of reality. We fail to fully apply Management Tenet 3. Another problem involves logical alchemy; scientific information of one kind is converted to another (e.g, in fisheries, information about production is converted to advice about consumption; Fowler and Smith 2004, Hobbs and Fowler 2008). Current management is based on the assumption that reductionistic scientific information that does not match a management question can be converted directly to useful advice. We fail to realize that pieces of scientific information relevant to, but not consonant with, management questions mislead management when used on their own (Belgrano and Fowler 2008). Thus, medicine is based primarily on physiology and morphology without fully incorporating information from other sciences such as genetics, paleontology, ecology, and population dynamics. Fisheries management is based on population dynamics and stock assessments but genetics, ecology, evolutionary biology, paleontology, behavioral sciences, and chemistry are not fully considered in decision making. Again, a complete accounting of complexity is lacking. There are still other sets of problems: Scientists from the various disciplines lack a way to contribute objectively the information they produce in proportion to the actual (real) importance of information from their fields. Nature involves a huge set of factors scientists have yet to identify, but which are not considered in management today because of our ignorance. By definition, scientists will never be able to account for anything that is unknowable.

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Current management has not found a way in which science can be used to produce guiding information in a way that accounts for

complexity—acknowledging and taking advantage of both the limits and value of reductionism.

4.1.9 Management Tenet 9: Goals and objectives—management must have clearly defined, measurable goals and objectives Limited progress in ecosystem considerations has resulted in the suggestion that commercial fishing should be reduced (NRC 1999). The extent of reduction(s) thought to be necessary has yet to be clearly specified. This is only one of many examples of how conventional management fails to define clear, measurable goals and objectives. As mentioned above, the suggestion to reduce commercial fishing in marine ecosystems is inconsistent with management advice based on the assumption that fishing to obtain MSY is, as implied by its name, sustainable. Current forms of management do not have advice regarding the sustainable population level for humans, the amount of water we can sustainably use, or the amount of carbon dioxide we can sustainably produce. Concerns about such issues raise management questions, but political, cultural, religious, or scientific information that specifies sustainability in an objective, consistent way has not been part of conventional management. Such management does not set goals that can be achieved while applying Management Tenet 4—making each goal consistent with the others.

4.2 Other recognized drawbacks of conventional management The following section considers other aspects of the inadequacy of current approaches to managing (especially in regard to our use of living resources), and continues developing the rationale for completely different alternatives to major parts of conventional management. The problems include: Lack of a common definition for “ecosystem management.” Lack of criteria for evaluating ecosystems. Neglect of extinction, speciation, and other longterm processes. Reliance on quantitative models representing information confined to that of indirect relevance.

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Resorting to transitive mitigation (especially symptomatic relief) rather than problem solving. Failure to reverse the burden of proof. Identifiable faulty assumptions and beliefs.

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4.2.1 Lack of a common definition for “ecosystem management” “Ecosystem management” has no universally accepted definition (Appendix 4.1, Agee and Johnson 1988, Christensen et al. 1996, Malone 1995, Stanley 1995), and we are only beginning to conceive of the option of management related to the biosphere. Some progress toward adhering to Management Tenet 2 is achieved by the adoption of “ecosystem-based management” as the objective. There is a history and foundation for dealing with greater complexity (Lavigne 2006, Rockford et al. 2008). However, conventional management does not deal with Management Tenet 3 that requires us to simultaneously (and consistently—Management Tenet 4) have “biosphere-based” management in addition to “ecosystem-based management.” Experts and specialists disagree about what the specific goals of management at the ecosystem level should be, especially in terms of transitive management. Current attempts at ecosystem management often consist of managing single-species populations, considering5 only a select few of their ecological relationships with their environment, including other species. Simultaneous management of a number of individual species may be a step in the right direction, but does not constitute management that includes ecosystem-level objectives (e.g., normal ecosystems, leaving portions of ecosystem productivity for sustainable ecosystem function) nor responsibility for doing what is necessary to attain them.6 The way information is used for decision making often fails to account for properties of the complete ecosystem or the biosphere and their reactions to human activities.7 The finite nature of being human prevents the full complexity of reality (Appendix 1.1) from ever being taken into account. In conventional thinking, we involve stakeholders as best we can, but fail to prevent the inherent errors.

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Definitions of management as applied to ecosystems are usually more descriptive than prescriptive (Haeuber and Franklin 1996). “Commandments”, “pillars”, and “tenets” for management to embrace greater complexity abound in the literature (Arkema et al. 2006, Fowler 2003, Francis et al. 2007, Lackey 1998, Appendix 4.3) but superficial or incomplete attention to (and implementation of) such advice does not constitute good management. When prescriptive, such advice often applies to only a few isolated aspects of management. Most importantly, most definitions are primarily lists of factors that need to be taken into account, but without means to find balance in the face of conflict; (e.g., combined consideration of endangered species and economic issues, two or more endangered species, or endangered species and “ecosystems”). The impossibility of ever completing a list of what should be taken into account underscores the importance of finding a different way to account for complexity, as required by Management Tenets 3 and 4. Other definitions dwell more on who should be involved, again without basis for balance among competing agendas. Human limitations are not taken into account. When a form of management emerges that has the desired qualities we will be able to recognize it (Haeuber and Franklin 1996), but so far no prescriptive definition has been generally accepted. Described below are several of the most common concepts of “ecosystem management”. They relate to extractive potential, ecological mechanics, human influence, ecosystem health, and multispecies subsystems. Each represents a focus that precludes objective consideration of the others. 4.2.1.1 Definitions related to extraction potential Some definitions of “ecosystem management” stress extraction: harvesting a crop (timber), removing products (oil, medicinal plants), or utilizing materials (water, nitrogen, oxygen) to meet human needs without addressing the question of whether or not the needs should be adjusted to match what can be sustainably met. Ecosystem management, as currently practiced, is intended to meet very real short-term human needs for products such as food, fuel, and fiber, but is not guided by information that fully accounts for complexity. The services

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provided by ecosystems are seen as crucial to humans without adequately addressing the matter of whether or not the needs by other species for the same services are being sustainably met. Some of the immediate direct effects of extraction are considered, while long-term (e.g., evolutionary) and indirect effects are largely ignored. Attempts to account for long-term consequences of “ecosystem management” are usually framed in terms of protection or conservation; however, they are not based on objectively determined constraints, and are not tied directly to complexity. There are few if any quantifiable objectives involved. At their extremes, short-term and long-term considerations are usually seen as in conflict, and there is little basis for finding a balance or reasonable middle ground (Stanley 1995). The dilemmas involved have not been resolved. A few of the effects of extractive activities on ecosystems are recognized, but are usually dealt with in terms of transitive management, assuming humans can control them. For example, Allen and Hoekstra (1992) state that ” . . . management itself explicitly holds the system away from equilibrium”. In such perspectives, as with prevailing single-species approaches to the management of our use of natural resources, we assume that if human influence were removed the effects would dissipate and the system would either approximate some previous condition or assume a new but similar form. To compensate for change, Allen and Hoekstra (1992) include an active role for management: ” . . . the central concept is subsidy: subsidy of the managed system in recompense for the destroyed context. . . . ”. This puts the influence into a different mode or realm, involving other elements of complexity, with long-term consequences of their own about which we know little or nothing—the problem with all mitigation. With little basis for claiming that they are sustainable over evolutionary time scales, silviculture, agriculture, and aquaculture8 are among the most extreme forms of transitive “ecosystem management” practiced by humans. They entail maintenance of ecosystems in states that are conducive to the production and extraction of products for human use from very few species (especially in any one area), usually domesticated or subjected

to transitive genetic management. Agricultural practices constitute a large portion of the experience gained in manipulating (engineering; e.g., Kangas 2003) ecosystems. Manipulation of ecosystems may be less of the focus in forestry, grazing of livestock on public lands, and fisheries, but all concentrate on the extraction of products. In both agricultural and nonagricultural arenas, attempts to control predators, disease, weeds, or other species considered to be pests clearly fall in the category of transitive management. Other kinds of purposeful manipulation, such as fertilization in marine and freshwater systems, genetic engineering, and extracting CO2 from the atmosphere are being actively explored—all part of conventional management. 4.2.1.2 Definitions based on ecological mechanics Nonevolutionary mechanics are often considered as the primary basis for management. Materials/ energy or ecosystem/physiological approaches are apparent in many objectives of current “ecosystem management”. For example, Allen and Hoekstra (1992) offer as one objective to ” . . . maximize the natural contributions of energy to the functioning of the managed system, while minimizing artificial energy subsidies”. Such definitions have not precluded recognition of the need to proceed on the basis of holistic properties of ecosystems. As with the bulk of others who have dealt with the concept, Allen and Hoekstra (1992) recognize the need for a more inclusive basis for ecosystem management: ” . . . complexity is hard to manage. . . . because of our limitations in conceiving the whole”. Given our finite nature, it is impossible in conventional approaches. Management suggested for fisheries based on the sigmoid population growth curve, habitat protection based on risk of extinction, agriculture based on biodiversity, and pest control based on chemistry are all examples of management grounded on the knowledge that there are the respective mechanics involved. All are carried out in a way that is similar to medicine based on knowledge of the risk of mortality. However, in each case, complexity in its fullest is ignored. We are conscious of these factors and proceed purposefully (the “conscious purpose” of Bateson 1972).

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4.2.1.3 Definitions related to human influence Normative properties of ecosystems are an issue in defining “ecosystem management” and often involve conflicts with human values, such as preserving habitat for an endangered species at the cost of reduced employment or food production. Attempts have been made to define ecosystem properties relative to human influence. Anderson (1991), for example, defines “naturalness” in terms of system qualities that would prevail in the absence of human influence. In other formulations of ecosystem management, natural states include humans as a component.9 Conventional approaches to assessment could be based on measures of the departure from normal states, such as the amount of energy required to maintain altered states (e.g., altered states of species richness, resilience, mean trophic level, or other ecosystem-level dimension of which many are recognized, e.g., Link et al. 2002). Unaltered states, in Anderson’s view, include species composition as well as ecosystem functioning so that “ . . . critical ecosystem components are present and structured in such a way that processes function within normal limits . . . over the long term”. Furthermore, “Integrity in a natural system further requires that it have the capacity for self-repair when perturbed . . . and that it be self-sustaining and self-regulating without human intervention”. 4.2.1.4 Definitions based on ecosystem health Often, the concept of ecosystem health is raised in attempts to define “ecosystem management”.10 For example, ecosystem health is a required management objective of the U.S. Marine Mammal Protection Act. While this may be a worthy objective, ecosystem health is an elusive concept primarily as a result of the vague nature of the term “health”. The criteria for assessing the need for and judging the success of management action to achieve ecosystem health are often unaddressed (see Karr 1987, 1990, 1991 for examples of measures that can be used). Health is often defined as absence of disease which may be helpful but still lacks specificity. Having no clear definition of ecosystem health emphasizes the need for standards to judge ecosystems that are altered from normal conditions as would be the case for ecosystems outside

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the normal range of natural variation among such systems. Ecosystems have a number of dimensions over which comparative studies have the potential of establishing what is normal and what is not (e.g., linkage density, species richness, mean trophic level, total biomass; Link et al. 2002). Of course, it is possible for ecosystems to be unhealthy owing to the abnormal or pathological effects of component species. In all cases, conflict is inherent. The maintenance of viable populations of other species, or more normal states for ecosystems and the biosphere, is often at odds with short-term human interests. How do decision-makers take into account these opposing values in view of the number of such conflicts and the complexity of issues involved in each? How do managers deal with conflicting or widely diverse advice from scientists in different fields of science (or stakeholders in general)? In conventional management decisions are made with the advice of stakeholders—all with a heavy bias toward favoring human interests. 4.2.1.5 Definitions based on multispecies subsystems If we were to manage ecosystems as we have individuals and species, the term “ecosystem management” would be reserved for cases in which an entire ecosystem is the principal focus. However, various forms of multispecies transitive management are often considered ecosystem management, although the assembly of managed species is only a minor part of the overall ecosystem. Examples include management of a predator and its prey such as wolves and elk, and management of a guild or taxonomic group such as viral diseases. If only the properties and dynamics of harvested and directly impacted species are considered, this subsystem of species is being managed, not the ecosystem. Managed intransitively (controlling human participation in ecological relationships with ecosystems, guilds, or biological communities), the extraction of goods from a subsystem necessarily involves the overall ecosystem. While conventional subsystem management views the species of such subsystems as separate parts of the larger system, management applied to an entire ecosystem would consider the ecosystem as a whole.11 Transitive management applied directly to ecosystems, inevitably fails to

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fully consider the consequences. Systemic management (intransitive management, Fowler 2003) applies to human interactions with ecosystems (and all other levels of biological organization and the physical environment, Fowler 2002), taking into account all the associated complexity (Fig. 1.4).

4.2.2 Lack of criteria for evaluating ecosystems Management action must consider measurable properties of ecosystems so ecosystems can be assessed and ecosystem-level problems identified and taken into account. Standards of reference, normative information (e.g., means and variances that establish normal ranges of natural variability), and clearly defined objectives are needed (Management Tenet 9).12 Means of accounting for risks and benefits of alternative courses of action are crucial in setting objectives and in assessing success or failure in management. Conventional ecosystem sciences have not provided a well-founded basis for evaluation of ecosystems. A few potentially useful properties of ecosystems have been identified (e.g., Link et al. 2002), but the limits to natural variation of such properties are not well known and normative approaches are underdeveloped. Thanks to historical ecological research, we are beginning to note the changes that have occurred in ecosystems influenced by humans, especially through abnormal anthropogenic influence. However, it is difficult to evaluate change without knowledge of normative ecosystem properties, including variation over evolutionary time scales, and whether change is desirable, as assumed in ecosystem restoration (Berger 1990, Bratton 1992, Jordan et al. 1990), or is a reaction to environmental change, the influence of biotic components, or stress from anthropogenic or other causes. We have yet to fully understand the implications of changes such as reduced species numbers, increased mean population variability, reduced mean body size, or abnormal total metabolic rate. We know about the existence of these properties, but have only a vague qualitative notion about some of the risks of changing them. We do not know if the changes we are observing fit within normal variability, including variability over evolutionary time scales, or exceed it.

Our ignorance is largely due to the paucity of ecosystem monitoring. There are few sets of data with which we can characterize complete ecosystems. There are even fewer data sets collected over time, that constitute true monitoring. Temporal and spatial variability in ecosystems and their properties remain poorly characterized. Monitoring has been a very expensive and nearly impossible task for many of the ecosystem metrics we have succeeded in observing over the short time frames of current research.

4.2.3 Neglect of extinction, speciation, and other long-term processes Concern about endangered species aside (most endangered species count as further examples of failures in conventional management), conventional approaches to management have not adequately considered long-term species dynamics. Selective extinction and speciation are among the factors that contribute to the formation of species-level patterns (Chapter 3). Because ecosystems include populations assembled from unique sets of species, the properties of ecosystems, including their structure and function, are significantly affected by natural selection at the species level (Arnold and Fristrup 1982, Fowler and MacMahon 1982, Gaston and Blackburn 2000, Levinton 1988). Over comparably long time scales, coevolutionary changes occur in altered ecosystems, some in direct reaction to human influence, others as higher-order ripple effects. These processes, and the degree to which they are abnormal, have not been addressed in conventional management. Only in recent decades has the issue of extinction been thought of as important in management by way of the contributions from fields such as conservation biology. Although human extinction has been of concern (Chapter 1, endnote 3), it has not counted in the factors and risks brought to bear in management.

4.2.4 Reliance on misleading quantitative models Current management of harvests from individual resource species often proceeds with quantitative models of their population dynamics. This treats

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species as abstract objects that are largely separated from their environment. Factors that are intrinsic or endogenous to species (e.g., individuals which are themselves systems) are largely ignored. Single-species resource management historically has used the concept of MSY, or some variation on that theme (e.g., as in fisheries management, Punt and Smith 2001, Rosenberg et al. 1993). Changes such as population reduction or restructuring (e.g., altered age or size composition) are often the intended results of management. The benefit (evaluated in human terms) of increased production is recognized, but the associated risks are usually unknown, especially in regard to long-term direct effects and either short- or long-term indirect effects on other components of the system, including the feedback we experience. The portion of the production of a resource species that should be left to sustain other species is largely a matter left unaddressed. The unknown risks are usually assumed worth taking, given the value of the product obtained, usually a short-term economic value. In the mathematical shorthand of resource models, characteristics of species are represented by parameters, the majority of which are usually assumed to remain unchanged. However, in time scales of days to only a few years, harvesting can alter the genetic nature of exploited species (e.g., Conover and Munch 2002, Law 2001). Initially projected sustainability may lead to unforeseen problems, even overharvesting, even as defined conventionally, due to genetic alteration of the population. For example, genetic changes in age at first reproduction could involve changes that would require a new population model to calculate MSY (Conover and Munch 2002, Fowler 1995, Law et al. 1993). Ecosystems can be only partially understood by attempting to reconstruct them in models.13 We will never be able to list all parts of an ecosystem, or all the processes in which they are involved, much less understand all the interactions among them. The context of systems (systems of which ecosystems are a part) are severely underrepresented if they are represented at all. Again, our finite nature makes conventional management seriously vulnerable where we use stakeholders in unrealistic attempts to account for complexity

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(top row, Fig. 1.1). Efforts to develop lists or models cannot fully account for complexity (in violation of Management Tenets 3 and 4). These inadequacies are of crucial importance to management and lead to causing rather than solving problems, especially in the transitive form of management where we try to control, engineer, or design ecosystems (Holling and Meffe 1996). Clearly, we need to manage our actions to account for ecosystems not only as complete units,14 but also as part of more inclusive systems and as made up of subsystems and component processes that include humans.

4.2.5 Transitive mitigation exchanges one set of symptomatic problems for another It is a common managerial strategy to require mitigating action. However, the complexity and interconnectedness of reality presents a situation in which mitigation almost always creates a different set of problems. The problem, rather than being solved, is moved to a different realm. Aquaculture, as a means of reducing harvest of wild fish, produces waste products, including biocides (antibiotics), appropriates energy, and uses water and fuel for distribution, and materials for the facilities. If we, as a species, are already abnormal in regard to the limits to natural variation for any such factors, the option is not advisable. The actual problem is not addressed. In the case of agriculture, silviculture, and aquaculture, mitigation also does not address the question of how much food we should be eating (or not eating) in order to achieve sustainability for all species and ecosystems. The prospect of undertaking aquaculture to mitigate overharvesting the oceans should force us to ask such related management questions. What portion of the surface of the earth is most sustainably occupied by the human species? If we are already occupying an unsustainably large portion of the earth, occupying more space in the practice of aquaculture or more agriculture is not advisable. Further consideration of aquaculture raises other questions in expanded form. How much food can be sustainably consumed by one species in an ecosystem or in the biosphere? How many people can sustainably participate in ecosystems

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or the biosphere in their consumption of food, and all other aspects of the complexity of being part of such systems? The systemic view of conventional mitigating action recognizes that we are transferring the symptomatic expression of a problem to a different realm (or a different time or location) rather than dealing with the problem directly. If we react emotionally to these circumstances by feeling boxed in, we are directly experiencing what it means to be finite within finite systems in an infinite reality. As mentioned in Chapter 3, mitigation can involve intransitive action based on empirical patterns observed to occur in the tradeoffs among natural forces. Such would be the case if we were to attempt solving the problem of over-consumption by reducing the human trophic level. Such action would be based on the assumption that consumption rates decline with increasing trophic level, as observed in natural systems. Doing so, however, would be insufficient to solve the problems of excessive consumption, and, as seen in Chapter 6, requires further mitigation through reducing the human population. There is a correlation between population level and consumption rates.

4.2.6 Failure to reverse the burden of proof Science, as mentioned earlier, does a remarkable job of bringing to our attention the fact that there are problems in our world. We know about global warming, we know about oceanic acidification, we know about the loss of topsoil, and we know about the loss of pollinating species. A typical reaction to such news, in conventional management, is the call for more research, when it was research that exposed the problem at the outset—the observational aspect of science is crucial. The listing of an endangered species (or the listing process) is often an event met with calls for more research. Behind such reactions is the opinion on the part of some that humans are responsible—humans played a role in causing the problem. Opposition and polarization emerge, with stakeholders entrenched in taking inconsistent positions (top row of Fig. 1.1). Some point out that there is no proof that humans have anything to do with the problem. Everyone knows that there is a great deal of complexity

behind what we observe (Fig. 1.4) and there can be (and undoubtedly are) many contributing factors to the problems that we observe. Among these, humans may or may not be a significant factor. These circumstances lead to much debate, often quite vitriolic and passionate. Under such circumstances, a typical response (whether political or among special interest groups, including scientists) is to call for research to see if there is a connection between observed problems and human activities. Senators in the United States, for example, can take advantage of such situations to call for more research, knowing that it will stimulate the economy of their state. Employment is enhanced among their constituents. With these tactics, the day when actual responsible management action has to be taken can be delayed. We see such behavior in numerous cases around the world involving pollution, endangered species, and loss in biodiversity. The effort to establish a human connection to global warming was an international undertaking involving a huge cost in money, time, and expertise. The loss of biodiversity is now a matter of concern in regard to the risk of our own extinction. Rather than assuming connections between current management and the risk of human extinction, demands will be made for research that convincingly demonstrates a connection. This is a direct rejection of one of the primary conclusions of science: interconnectedness characterizes complex systems. In spite of the probability that we are contributing to such risks, calls for more convincing research are one of the most common reactions to the observation of a problem. Scientists have a vested interest in such reactions even though one of the experiences of science is that we can disprove a great many things but regularly fail to prove anything (Bateson 1979). However, more research is conducted and scientific information is produced. They contribute to the libraries full of information from which scientists, managers, and other stakeholders select and convert specific pieces (top row, Fig. 1.1) in order to derive management advice. The information is interpreted differently by different people and limited progress is made toward solving real problems. Part of the process of interpretation/translation

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involves a variety of emotions, primarily fear. Human values, often economic values, become heavily involved in the decision-making. Objectivity is largely elusive and human values, emotions, biases, limitations, and short-term interests become heavily involved in the management process. These, in turn, become inherent to the problems we see— the problems indicative that conventional management is not working. Reversing the burden of proof is a fundamental element to more realistic management in the minds of many (Corkett 2005, Ludwig et al. 2001, Mangel et al. 1996), although it may not be as clear in its actual application as many think (McNeely 1999). In taking a precautionary approach (dealing with risks—Management Tenet 6), reversing the burden of proof is often seen as crucial. Taking the responsibility that there is even a remote possibility that there are human causes behind the problems we observe is certainly not a uniform element of conventional management. Given the complexity and interconnected nature of our universe (Management Tenet 3), the failure to reverse the burden of proof is another of the failings of conventional management.

4.2.7 Faulty assumptions and beliefs Conventional approaches to management commonly make several assumptions, all of which are invalid and contribute to causing the problems we face—both those we know about and others we have not yet identified. Such beliefs count among the factors that are involved in the complexity of reality as they contribute to the problems before us (Fig. 1.4). These assumptions are: Human limitations are inconsequential. Things that appear unimportant are unimportant. Cumulative (statistically) insignificant effects can be disregarded.

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4.2.7.1 Human limitations are inconsequential Human limitations are not taken into account adequately in the ways we establish management goals in conventional management (top row, Fig. 1.1). These limitations involve our choices of concepts, theories and ideas to use in management.

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They encompass our choices of information to be used in setting management objectives. They are involved in our belief that stakeholders can convert information (Brosnan and Groom 2006) in the interface between science and management. Both our strengths and weaknesses are important to consider; it seems that conventional management has achieved very limited success. Even when human limitations are recognized, the extent and effects of our assumptions cannot be measured. It is often assumed that bringing more people into the decision-making process will be helpful; however, this ignores the compounding effect of ignorance. Experts are brought into these exercises, often on “blue ribbon panels,” in meetings, advisory committees, or special congresses, but there is no means of overcoming the Humpty-Dumpty phenomenon—the inability to combine information in an objective manner (Belgrano and Fowler 2008, Fowler and Hobbs 2002). Everyone has an opinion, but each opinion places different weight or value on the various factors that should be considered important (Plate 4.1). Conventional forms of management have no objective means to account for all of complexity or reality in a way that considers the relative importance of all relevant factors simultaneously. The human mind is incapable of such an exercise and simulation models (another way we attempt to deal with complexity) suffer from the same “incompleteness” that is characteristic of mathematics itself (Gödel’s theorem; Gödel 1931, Makous 2000). Models always make use of subsets of reality (Caughley 1981, Pilkey and Pilkey-Jarvis 2007). In general, representations of any reality are always abstractions; maps are never the territory (Bateson 1972); statues are not the person; history books are not what happened; the concepts in our minds are not the reality we are trying to account for. Bringing a group of people together can result in agreement on these principles, but combining concepts still involves the risk of falling prey to the fallacy of composition (Johnson 1987). Not only can we not think of all of the factors to consider, we cannot place realistic relative importance on those we do recognize, and most combinations of partial information are very likely to be grossly misleading.

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History teaches us that the things not considered in decision-making are usually at the roots of mistakes recognized in retrospect. The decisions made by prehistoric societies undoubtedly seemed logical at the time they were made, even though they so often ultimately resulted in disasters (Costanza et al. 2007, Diamond 2004, Redman 1999). The lessons of such experience emphasize the importance of trial-and-error approaches and the learning process involved. The things that our minds are incapable of considering are important to one degree or another. Evolution, adaptive management (Christensen et al. 1996, Grumbine 1997, Mangel et al. 1996, Moote et al. 1994, Walters 1986, Walters 1992) and benchmarking (Bogan and English 1994, Boxwell 1994, Camp 1995, Spendolini 1992) in business management are all trial-anderror approaches to finding ways forward in the face of complexity. Conventional management inconsistently takes advantage of what has worked out in trial-and-error processes, and fails entirely at the higher levels of biological organization. 4.2.7.2 Things that appear unimportant are unimportant Sometimes the things we think are unimportant turn out to be quite important. Believing otherwise is a failure to adhere to Management Tenets 3 and 4. Butterfly effects (Gleick 1987) are an example, and no one has found a way to account for such factors in conventional management. The effects of initial conditions are well known, documented, and experienced in the realm of complex systems simulation. A baby exposed to a minimal amount of an endocrine disrupting substance may not experience developmental problems until adolescence. We might all agree that the moon is an unimportant factor in considering the appropriate geographic range size for the human species, whereas, in reality, it might turn out to be very important in gravitational processes involved in plate tectonics that determine continental shape and size that are so important to geographic range. The roles of molecules, atoms, and subatomic particles are never taken into account in management decisions regarding fisheries, forestry, or economics, yet no ecosystem is without such components.

The butterfly effect means that an apparently minor event may have dramatically large consequences in the future. Synchronicity, nonlinearity, and the synergistic compounding of such effects in the complexity of reality are beyond the realm of conventional approaches to management. 4.2.7.3 Cumulative insignificanteffects can be disregarded Conventional management also has no means of accounting for the possibility that the combined importance of “minor” effects may far outweigh one or more of what we recognize as primary factors, even without butterfly effects. Reality is so complex that such situations are probably more common than not. This is exemplified by the equation: ex =

∞

xn

∑ n! . n=0

For x = 20 the combination of the six largest (“most important”) elements in the series contribute less than all the others combined—an infinite number, most of which are tiny. Indirect (or higher order) factors are often considered sufficiently inconsequential to be ignored. Smith (T. Smith 1994) documented an example of the assumption that higher order factors can be ignored collectively, at least temporarily. This example involved the history of “resource management” wherein Schaefer’s (1956) graphic concept of complexity led to postponing consideration of a great deal of reality. Schaeffer ranked the various factors involved in real-world systems into levels, with the primary level (direct effects) being considered the most important for investigation. People following Schaeffer’s line of thinking made the decision to confine consideration to the primary factors—assuming this path to be adequate. That decision came to influence heavily the nature of fisheries management, even as practiced at the present. Without seeing either the need or a way to deal with complexity, it was decided to deal with factors that were considered most important and leave until later the treatment of other aspects of complexity. The danger of such decisions lies in the probability that considering only a select few

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important factors as “adequate” will overlook many other more important factors. As shown in the formula above, the primary elements of the model of the value of ex often account for only small parts of the desired result—an accurate picture of reality. In the complexity of reality, it is highly likely that such circumstances are very common; the combination of what we think are “minor factors” is more important than any one of the individual elements we think of as most important. The genetic effects of harvesting continue to be largely ignored in fisheries management (Conover and Munch 2002, Law 2001) as another example of secondary effects that, in the end, may be more important than any of the factors we believe to be so important today. Genetic effects are to be combined with the consequences of reducing resource populations on other species that use the same resources, restructuring the ecosystem, redistribution of resource populations—the list is endless.

4.3 Inadequacy of current approaches to management by focal level Individuals (humans or other organisms) are often isolated for purposes of management, as are individual species, ecosystems, or even the biosphere itself, with drawbacks summarized below. Transitive management that focuses on individuals of any species as the unit of management places disproportionate value or importance on individuals to the relative exclusion of other levels of biological organization (e.g., species, ecosystems, or the biosphere). Transitive management of species focuses on one or more species and makes individuals, ecosystems, or the biosphere secondary. Transitive management of ecosystems focuses on one or more ecosystems and makes both individuals and species secondary. Furthermore, transitive ecosystem management more often than not aims to alter or influence ecosystems to meet human needs without ensuring the needs of other species, ecosystems, and the biosphere are met.

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In comparing management at various levels, it becomes clear that dilemmas arise when attempting to simultaneously manage individuals, species,

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ecosystems, and the biosphere. It is important to accept the frustration of these dilemmas as real; an experience of the impossibility of resolving such dilemmas through conventional management (in violation of Management Tenet 4). From such a critique, we can begin to appreciate the viability of intransitive alternatives, and particularly systemic management, based on the complexity of reality. The success of the intransitive component of management at each level emerges as an instructive pattern. This section looks at management as applied to the focal levels of biological organization (e.g., Haskell et al. 1992). Such a comparison clearly demonstrates that “ecosystem management” cannot be achieved in isolation from management that simultaneously takes into account individuals, species, and the biosphere. Transitive isolation of nonhuman focal levels is not an option, but we can control our interactions with them—all of them. The common patterns, problems, and successes that emerge from the analysis below help define an approach applicable at all levels. Intransitive approaches, including the self-control of our species as a whole, emerge as part of systemic management. This appears to be the only viable option applicable to management at all levels. In this critique, it is helpful to acknowledge at the outset that conventional management is done by managers—individual people responsible for managing, usually at a particular focal level. Supervisors, coaches, medical doctors, veterinarians, farmers, and game ranchers are often seen as the managers for individual organisms such as employees, athletes, patients, dogs and cats, cattle, or a panda. Disease control specialists, pest control professionals, United Nations officials, foresters, fisheries managers, and legislators regularly manage in regard to species such as the HIV virus, Douglas fir, tuna, or minke whales. US fishery management councils, the UN Food and Agricultural Organization, the UN Environmental Program, the Greater Yellowstone Coalition (Clark et al. 1991), and legislators establish management practices involving ecosystems such as the Bering Sea, game parks, the Antarctic, forests, marine reserves, and the Great Barrier Reef. All involve humans.

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4.3.1 Elements of management by focal level Management at all focal levels involves common elements. In this section, these are listed with very brief descriptions. They are then considered in more detail to clarify some of the impasses and other difficulties that are encountered in conventional management. Following this, the underlying problems that are exposed are summarized as fatal flaws characteristic of current forms of management. (a) Setting goals and objectives. Setting goals is necessary in management whether applied to individuals, species, ecosystems, or the biosphere. These goals include acquiring products and sustained production. Other goals include influencing the system to achieve specified states or products and services as well as to identify a more scientific basis for management. Applied transitively, management goals put primary interest on the focal level (individual, species, ecosystem) of management to the relative rejection of interest in other levels. (b) Making tradeoffs among goals: goods and services. The objectives of conventional management are biased toward obtaining goods and services of value to humans. In addition, varying and often unrealistic value is placed on other species, ecosystems, or individuals. We have no means of resolving conflicts when these values lead us in different directions. Objective resolution of conflict is impossible when action at one level causes problems at another in conventional management. (c) Monitoring. Components of any system (individuals, species, ecosystem) may be monitored (e.g., see Davis and Simon 1994b regarding ecosystems). However, conventional management pays uneven attention to monitoring systems of which the focal system is a part. The degree to which action seems necessary is based, in part, on comparing information collected in monitoring with normative information, typical states or other standards of reference. Genetically-based characteristics are, to varying degrees, involved in normative standards; they are more frequently ignored for species than for individuals. The concept of selective extinction and speciation opens the option for comparable consideration for various sets of species and

particularly those represented in ecosystems. Necessity for action is partly based on knowledge of the risks of both action and inaction. (d) Evaluating for comparison with norms. Avoiding abnormality applies at all levels. It is recognized that natural variability and its limits have both genetic (inherited) and environmental components. In conventional management, loss of the focal system (e.g., death of individuals or extinction of species) is a risk given higher priority than would be the case if they were treated, along with other risks, as natural processes. When such ecosystem- or biosphere-level processes occur at abnormal rates there is cause for concern. (e) Evaluating relative risks and benefits. Conventional decision-making cannot account for all risks (e.g., death and extinction) and benefits, nor can it provide an objective evaluation of the tradeoff among them. Perhaps most important is the inability of conventional management to solve the dilemmas that arise in attempts to balance the tradeoffs when conflicts emerge. (f) Identifying the cause(s) of problems. Managers want to know the causes of observed changes in a focal system, whether the changes are desired or undesired. In conventional management, information about cause-and-effect relationships is usually demanded in the form of proof or convincing scientific information. (g) Assessing management options. Options for action are assessed in light of identifiable costs, benefits, and likelihood of success, including effects at other levels, and whether or not they are within our power to accomplish. However, many of these are unknown. It is particularly important to identify the appropriate level of biological organization at which action should be directed. In current practice, management action is often designed for symptomatic relief when the root cause of unwanted change is difficult to identify or change. It is critically important to define when, where, and how control is an option. Control is usually maximized by controlling our influence on the nonhuman rather than controlling the nonhuman. (h) Taking management action . In many cases, action is taken following the considerations of the elements of management listed above.

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The following is a more detailed consideration of each of these elements of management with attention to the failings of conventional management, emphasizing the difficulty of simultaneously applying it to individuals, species, ecosystems, and the biosphere.

4.3.2 Setting goals and objectives Generally, management actions are directed towards a set of goals and objectives. For management of individual organisms and species, these typically include: Obtaining goods, services, or performance, and meeting the needs of the focal unit. Detecting problems through monitoring. Maintaining the focal unit in a phenotypic state capable of providing goods and services,15 often making use of genotypic standards or reference points. Protecting, maintaining, or even producing a genotype compatible with both the environment and the other objectives.16

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Note that attaining these goals depends on scientific underpinnings in the form of knowledge of the attributes (including needs) of individuals, species, and the species sets represented in ecosystems, plus their interactions with their environment. Therefore, another goal of management is to provide the scientific information necessary to achieve objectives like those listed above. In conventional approaches, goals related to other focal levels are often secondary and are pursued only if the solutions are also advantageous to the identified focal level. Often goals for other levels are sacrificed for the benefit of individuals, especially individual humans. This produces conflict in the human experience of management because different values are placed on different focal units or levels. This ignores the fact that what is good for the part can be to the detriment of the whole (combinations 5 and 6, Table 3.1). This inconsistency violates Management Tenet 4. Following the pattern for individuals and species, conventional “ecosystem management” or “biosphere management” would have the same categories of objectives. Ecosystem or biosphere

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properties would be maintained so as to ensure the capability of providing goods and services sustainably—not just for humans but for all species. This would be accomplished while using standards of reference for the nature of ecosystems based on a phenotypic view of ecosystem structure and function (i.e., based on the concept of selective extinction and speciation). An ecosystemlevel genotype compatible with the environment, human needs that can be met sustainably, and a normal risk of extinction for any species (including humans) would be an important achievement. As management is currently practiced, maintaining the integrity of ecosystems or the biosphere is much less important than maintaining the integrity of species or individuals at the other levels of biological organization. In spite of the potential for problems, incorporating the concept of selective extinction within conventional sciences provides a scientific basis for transitive management of ecosystems in parallel with the conventional approaches at the individual and species level. The characteristics of ecosystems (e.g., metrics such as mean trophic levels, mean variability in population size, mean potential evolutionary rates of the sets of species represented in them) emerge from mechanical and selection processes and have a genetic component. However, these concepts are not as well accepted for ecosystems as they are for individuals and species, to the extent that the concept that ecosystems have evolved characteristics is occasionally rejected (e.g., Golley 1993). The conflicting goals that arise in simultaneous consideration of transitive management applied to individuals, species, and ecosystems remain unresolved. What are perceived as goals for maintaining ecosystem health (systems within the normal ranges of natural variability) often run counter to the goals we commonly seek for the human species and especially those we seek for individual humans. These conflicts become even more obvious in “ecosystem management” carried out in parallel to conventional management as applied to individuals and species. Managing ecosystems for their own sake can lead to arguments supporting the complete removal of humans for the good of ecosystems. This is a problem (such action would be in

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violation of Management Tenet 1). Identification of a problem is progress, but conventional approaches have failed to find solutions.

4.3.3 Making tradeoffs among goals Whether the focal level of management is individuals, species, ecosystems, or the biosphere, achieving the objectives of management usually requires tradeoffs (different from mitigation) among conflicting goals as well as maintaining system integrity and minimizing risk. Management of individual organisms may be aimed at obtaining or maintaining products such as labor or services. Health is also an objective. A racehorse or office worker exemplify individuals. Individuals can be sources of pleasure, work, blood, tissues, and organs for various purposes. Health is achieved when body weight or temperature are within normal limits. The pursuit of goods and services from individuals must be weighed against the consequences of demanding too much; for example, working one’s employees or racehorses too long or strenuously can result in injury or illness. Death is avoided for humans and sought for resources. We decide to kill some organisms for food—the extreme in sacrificing the health of the individuals that are killed. Current management of species often aims at controlling a species’ interference with production of goods and services, for example pests such as disease organisms, parasites, and weeds. Another common goal is breeding for genetic lineages useful to humans (e.g., domesticated animals and plants for their fur, food, fiber, and chemicals). The populations of some species are managed for the benefit of tourists and the tourist-based economy, as in African game parks. However, we cannot control or contain the responses species exhibit in reaction to our actions to achieve these ends, much less control the reactions among other species associated with a species we are attempting to control. Maintaining the health and integrity of a species is often at odds with the goods or services to be achieved; balance is often sought between the risks of extinction and the goods derived. The smallpox virus may be driven to the brink of extinction, the ultimate of poor species-level integrity,

in exchange for eliminating the associated risks of human disease. Management aimed at ecosystems or the biosphere also has various tangible and intangible goals that may conflict, just as for individual organisms and species. Ecosystems or the biosphere may be “managed” to enhance production of food, oxygen, shelter, decomposition of waste, prevention of soil erosion, detoxification of harmful chemicals, and the fulfillment of aesthetic needs such as those related to outdoor recreation, ecotourism, religion, and psychological well-being. Individual organisms depend on ecosystems and the biosphere to meet the necessities of life whether human or nonhuman. All species, whether human or nonhuman, depend on ecosystems. Whether or not any particular species goes extinct depends, in part, on dynamics within such systems, including the loss of other species. Thus, one of the services of ecosystems and the biosphere is sustaining constituent species—human and nonhuman. Management at the ecosystem level must find balance between human demands and nonhuman demands for ecosystem services. Management at the ecosystem level must find a balance between meeting human needs and the associated risks to ecosystem integrity, including the risk of human extinction. This is a balance we fail to find in the analysis of simulation models as abstractions of the system(s).

4.3.4 Monitoring and evaluating through comparison with norms As emphasized above, the state, condition, integrity, or health of all levels of biological organization are important to management focused at any level. Attributes at each level must be monitored. For individual organisms these are exemplified by blood pressure, photosynthetic rate, body size, temperature, emotional state, pulse, metabolic rate, behavioral characteristics, blood chemistry, respiration, and nutrient intake. For species, characteristics such as population size, age at first reproduction, mean body size, geographic range, and others exemplified in Chapter 2 are (or can be) monitored. A number of ecosystem traits have been identified17 and many are monitored, but with much greater logistical difficulty and with much

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less success than for individuals. The biosphere is also monitored, and the fossil record provides little to go on in regard to the limits of its natural variation for characteristics such as carbon fixation, and rates of turnover for energy and materials (see, however, Fig. 1.2 regarding the atmosphere). We are just now beginning to appreciate extinction rates as characteristic of the biosphere with variability within limits. Measures of characteristics like these serve as indices for evaluating changes from the normal ranges of natural variation. Monitoring is intensified if there are signs of problems that require action. Ideally, monitoring can pertain to specific parts of an individual (feathers, eyes, leaves, hearts, or other organs), species (individual organisms and sub-populations), ecosystems (individual species and communities), and the biosphere (ecosystems and species). However, parts exhibiting normal attributes are not equivalent to good health for the whole. For species, ecosystems, and the biosphere, the ill health of parts does not necessarily indicate ill health of the whole because of normal variation among the parts. For example, a species that begins experiencing lower than normal growth rate or smaller than normal body size might be in the normal process of being constrained to its carrying capacity. However, if its population size begins to fall outside the normal range of variation, there may be an associated ecosystem-level problem, assuming that population variation within normal limits is critical to maintaining the health of an ecosystem. The abnormality or normality of individuals, species, ecosystems, and the biosphere involves genetic code behind homeostatic properties. Individuals have long been recognized as having heritable characteristics that translate to heritable traits for species. The same holds for species and ecosystems as described in Chapter 3. Thus, for individuals, species, ecosystems, and, by extension, the biosphere, there are phenotypic (observed) expressions of their genetically coded traits along with the influence of various intrinsic and extrinsic factors. The results are what are monitored in measurements taken of the observed. At all four levels, of course, environmental influences also contribute significantly to variation.

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The sets of species represented in ecosystems, for example, vary through the processes of ecological mechanics in reactions to their environments, just as an individual reacts to environmental stimuli. Management focusing on individuals or species often includes consideration of the physical environment and its effects. If we focus on the set of species represented in an ecosystem or the biosphere, we have a situation parallel to that for individuals and species. In each case the environment is considered important to the history, dynamics, and characteristics of the focal unit. The focal unit, in all cases, is a living system and often its environment is seen as subject to management to deal with problems or objectives for the focal system when such problems appear to be attributable to the physical environment. The following section describes the typical methods for obtaining normative information, the challenge of determining the impact of human influence, and value of species-level patterns (especially frequency distributions) as sources of normative information. 4.3.4.1 Obtaining normative information For management purposes, evaluating the condition of individuals, species, or the sets of species represented in ecosystems requires phenotypical information about both the current state and the “normal” state. The normal state is measured by values commonly observed or accepted as standards of reference. The difference between the observed and the normal state is evaluated and used in making decisions. Normative information (i.e., about the normal, as opposed to abnormal state) comes from both a history of observations as well as comparative studies. Both encompass the effects of typical inherent variability and variability in response to extenuating circumstances that vary themselves. At the individual level, for example, the body temperature or heart rate of animals is influenced by factors such as age, species, time of day, season, and activity. Metabolic rates are temperature dependent; body weights vary by the time of day, food intake, age, sex, species, and a host of other factors. Ingestion and respiratory rates are the same, as are interactions between individuals and other

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elements of its environment. These characteristics are the basis for establishing normative information, which can come from a history of measurements for the individual or from comparison with others of the same species.18 The health of an individual is diagnosed and evaluated based on the direction and extent of departure from normal states. Illness represents a departure from the form, or order, characteristic of the homeostatic properties of individuals under prevailing circumstances. Species characteristics also have expected or typical values, which are subject to the influence of various environmental factors. The historic values for individual species and the central tendencies of the patterns presented in Chapter 2 are manifestations of these characteristics. These characteristics not only distinguish species but also serve as standards of reference. Comparison among species for purposes of evaluation or assessment is less common than comparison among individuals. Those that are made usually involve physical characteristics such as mean body size, rather than mode of participation within ecosystems, such as rate of prey consumption or population density (although this is changing through advances in the field of macroecology). Most normative information has been derived from a history of data about particular species, rather than through comparative information from/among a variety of species.19 Normative information for ecosystems and the biosphere is rare compared to information for many species, and even rarer compared to what we know of human individuals. Hence there is relatively little experience to draw upon for characterizing typical ecosystem states and their variability in time, space, and relation to environmental factors. Nevertheless, characteristic properties such as species numbers are known to vary with conditions of the physical environment (e.g., Brown 1995, Rosenzweig 1995). Ecosystem dynamics are known to be related to numbers of species in patterns useful for comparing ecosystems (e.g., variability in plant biomass and CO2 flux: Steffen et al. 2004). The homeostatic recovery of ecosystems in the healing process of succession after severe local disturbance is one of return to form and function typical for the habitat in which they are found.

4.3.4.2 Impact of human influence The impact of humans is a major challenge in obtaining normative information for ecosystems. Like other kinds of influences, our influence results in change, which sometimes, but not often, can be documented (Appendix 4.2, Chapter 6). As true for all species, humans have always exerted influence; dramatic repercussions predate recorded history (Redman 1999). Changes, including those resulting from human influence, occur as part of natural variation among species (human influences count among the ei of Fig. 1.4). However, when changes result in abnormal ecosystem properties, such ecosystems are subject to atypical stress and can be evaluated as unhealthy. Such abnormal changes include increased rates of nutrient transfer, changes in production, decreased size distribution among species, reduced trophic level, and increased population variability (Appendix 4.2). For example, reduced species numbers is recognized as an important measure of stressed ecosystems; however, in some ecosystems under stress, species numbers may initially increase (Woodwell and Houghton 1990). Owing to paucity of data and anthropogenic effects, existing data on ecosystems may not represent normal conditions;20 means and variance are usually missing for measures of ecosystems. We are faced with a relative paucity of patterns in the variation among ecosystems. Thus, for ecosystems, even more than for species, reliable normative information is missing from the evaluation process. Measurements of ecosystems can only be compared with minimal previous information to serve as standards of reference. There is little information regarding the degree to which the characteristics of ecosystems have departed from normal, especially in relation to natural variability. 4.3.4.3 Value of frequency distributions based on patterns One valuable aspect of frequency distributions is the normative information they represent (their cybernetic quality, Fowler 2008). The growing number of such distributions in species-level patterns helps identify the traits that can be monitored both for making comparisons among individual species and among sets of species such as those represented in ecosystems. Empirical observations

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show the nature of the sets of species contributing to individual ecosystems and others comprising the biota of the biosphere. Such information reveals spatial and temporal variability in relation to environmental factors. Frequency distributions among sets of species (e.g., the distribution of the means from various sets of species, or “ecosystem frequency distributions”) can be constructed regarding traits such as numbers of species, mean trophic level, total metabolic rate, or mean body size. This allows for the same type of comparison among sets of species (e.g., those specific to individual ecosystems as combined in ecosystem frequency distributions) as those made in comparisons among individuals and species.21 Measures of an individual ecosystem over time allow for specific assessment particular to natural changes in that system. Comparison of ecosystem traits in relation to environmental factors and monitoring their change in time helps account for effects of climate and other abiotic environmental conditions. Measuring ecosystems over space in correlation to abiotic factors such as temperature and solar radiation can achieve similar objectives. Progress may thus be possible in finding the abnormal among ecosystem, even if controlling ecosystems to restore them is largely beyond our grasp as an important aim in management (Mangel et al. 1996). Normative information is inherent to specieslevel patterns as applied to finding the abnormal for individual species (Fowler 2008, Fowler and Hobbs 2002). Individual species outside the normal ranges of natural variation can be evaluated as potential ecosystem-level problems or sources (causes) of such problems. However, health among species is no better indicator of the health of ecosystems or the biosphere than the health of individuals in assessing the health of a species.

4.3.5 Evaluating for relative risks and benefits Every change, action, or influence contemplated in the management of individuals, species, ecosystems, or the biosphere involves a variety of risks and benefits. Our inability to identify all such risks and benefits is one of the main problems

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with conventional approaches to management. Another is that we have no way of assigning realistic importance to the role any particular factor should play in decision making. Because of complexity, which increases with the scale of the system, we can never identify all the tradeoffs among the risks and benefits of any given action. Thus, in conventional management, irresolvable conflict is inevitable in attempting to adequately evaluate tradeoffs among risks and benefits. At the level of an individual organism, motivation to take action when departure from typical states is observed is based on prior knowledge of the consequences of such departures. For horses, cats, bears, or other individuals, a fever of 2 degrees is rarely fatal and may indicate only that the body is undertaking a normal healing process. However, humans with body temperatures of 108°F (42°C) are known to suffer brain death within minutes. Thus, within the reciprocal relation between manager and individual, a change in the individual’s status may mean the loss or gain of goods, services, and performance. For example, an employer assumes that a worker with a high fever or elevated blood pressure will produce less than usual or even die. Thus, fundamental among the risks recognized for individuals are the risks of death and the loss of all further options for producing goods or services, companionship, contributions toward species-level sustainability, love, and contributions to family and social life. Furthermore, intrinsic value is placed on individuals within many human cultures. This value is, in part, manifested in the importance placed on the risk of death. It is occasionally extended to pets and plants that are often enjoyed in hobbies and even individuals of wild species. However, other factors count among the risks and benefits of taking action to bring an individual back within the normal ranges of natural variation. If medical action is taken to reduce a fever, resources are used and there are other repercussions involving risks beyond the individual in question. For example, the individual with the fever may be genetically predisposed to succumbing to the cause of the fever, a condition that could be passed on to potential offspring. Benefits include the possibility that the individual is one with the intellectual capacity to find solutions to the conflicts inherent in these

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complexities. We cannot identify all risks and benefits, nor can we assign realistic relative importance to them. Objective evaluation of the alternatives is impossible in conventional decision-making. Likewise for species, management action to alter a species characteristic (such as population size) to achieve either normal or abnormal states should be based on consideration of the consequences. Such consideration may be partially informed by previous action or experience, as would be the case in adaptive management (Grumbine 1997, Mangel et al. 1996, Moote et al. 1994, Walters 1986, Walters 1992). Limited information exists regarding many of the risks and benefits associated with altered states of populations, including the total population of a species. Extremely reduced populations are in greater danger of extinction. Yet normal fluctuations occur in populations, and some reduction in population level stimulates productivity through short-term density-dependent responses (e.g., Rosenberg et al. 1993). Extremely large populations (as we would judge six billion gorillas to be) represent a problem to be solved, but with consequences attendant to whatever we chose to do. A population with extremely skewed sex ratios may experience population decline owing to an inability to reproduce. An extremely dense population may suffer starvation with the risk of decline that could result in extinction. Or, an extremely dense population may offer an opportunity for diseases and parasites whose infestations could bring about the same result. The relationship between species and the people involved in their conventional management is perceived differently than the relationship between individuals and their managers. For example, there are legal rights attributed to individuals (primarily humans, of course) who can act to achieve those rights in contrast to the few rights granted to other species that have no legal recourse other than those taken by people acting on their behalf. Among the potential results of management is alteration of not only a species’ phenotype but also their genotype (e.g., Conover and Munch 2002, Law et al. 1993). Thus, genetic effects of harvesting natural populations may change their potential to produce goods and services, but risk of induced genetic alteration is usually less important in the

minds of managers than the short-term benefit, or goal, of making an economic profit or obtaining goods for human use. In contrast, selective breeding of individuals (almost exclusively individuals of nonhuman species) is done to achieve genetic alterations that will increase production of goods, services, or qualities valued by humans. New dilemmas arise as we learn more about the risks and benefits of any given action. At the species level we continue to encounter our inability to understand all risks and benefits, and continue to fail in attempts to determine their relative importance. Extinction is a recognized risk for species, analogous to death for individuals. Intrinsic value is placed on species, as embodied by the thinking behind the US Endangered Species Act. However, the potential loss of products and services (to humans) behind the protection of an endangered species is sometimes considered more important than any intrinsic, or long-term, value the species may have in its relationships with other species and ecosystems. Often, in conventional management, more value is placed on entire species than on individuals of nonhuman species; for example, a salmon species is valued more than an individual sea lion. What is the relative importance of saving an endangered population of salmon or trout in comparison to individual seals or sea lions that consume them? Management of a species may entail killing individuals as a source of products for human use, yet preservation of species requires limits on mortality. How do we find an objective balance in such opposing values? In extending our management of a species and individuals22 to include ecosystems and the biosphere, accounting for the risks of altered system states is even more important and more challenging. With the complexity of ecosystems and the biosphere, we need to recognize with even greater clarity the impossibility of ever being able to list all the risks and benefits, let alone measure them or assign relative importance to them. Our current forms of management fail to recognize the full suite of consequences to ecosystems and the biosphere stemming from our actions. We also fail to recognize all the effects that altered ecosystems (or the biosphere) may have on species and individuals (including humans). Such reactions

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include extinction as one of many homeostatic processes. Both ecosystems and the biosphere, when subjected to stress, are expected to depart from their normal states. For instance, a reduction in mean body size is a common sign of ecosystem stress. This change extends beyond intraspecific processes to include the interspecific of selective extinction and speciation. The larger bodied species are often among the first to experience extinction and more smaller bodied species may emerge (Appendix 4.2). Thus, one set of risks to humans consists of whatever comes with a shift toward a higher proportion of smaller bodied species where our influence results in such a shift, whether for an ecosystem or the biosphere. Among small-bodied species are viruses, bacteria, insects, parasites, and weeds. The evolution of more virulent species and increased prevalence of disease among humans and livestock is a potential associated risk. Encouraging species desirable to humans, such as food crops, is profitable in the short-term, but there are high long-term costs in dealing with diseases and pests resulting from ecosystem alteration (Pimentel et al. 1992, 1993). Approached transitively, acting to counteract (or prevent) or to purposely cause departure from characteristic states by species or individuals would be based on our knowledge of the consequences or results of such departures in previous situations. There is limited information regarding the risks and benefits of changes in ecosystems (Ehrlich 1985, 1991, Odum 1985, Pimentel 1986, Rapport et al. 1985). In agricultural settings, some benefits are well known. However, the risks associated with ecosystems or the biosphere maintained in altered states are relatively undocumented, especially risks to ecosystems themselves. Those risks that are recognized, such as diseases and pests in monocultures (Pimentel, Acquay et al. 1992, Pimentel, Stachow et al. 1992, Pimentel et al. 1993, 1995), are much less appreciated than for individuals and species. Negative effects, from the human perspective, are exemplified by increases in pest population levels and especially pesticide-resistant species. Other impacts include the extinction of species and the fate of the survivors.

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While some factors have been recognized, the consequences of altering ecosystems or the biosphere, whether through normal or transitively engineered influence, is and will remain largely unknown. This gap in knowledge can be blamed, in part, on our lack of experience with “ecosystem” or “biosphere management” and the lack of a welldeveloped scientific understanding and approach to the study of ecosystems and the biosphere over appropriate time scales. Existing experience with managing ecosystems (e.g., agriculture) is not enough to account for many changes in evolutionary time scales. Evolutionary changes are only recently emerging among the problems we face in such management. As a result, risks to ecosystems and the biosphere have rarely been acknowledged in management to the same extent as risks to individuals and species. In the end, the gap in knowledge is one that cannot be filled using conventional approaches. 4.3.5.1 Time scales When comparing management at the level of individuals, species and ecosystems, managers must consider larger temporal and spatial scales. Longterm goals and long-run risks become increasingly obvious in the higher levels of biological organization. One important focus of current management is to minimize risks to which humans (the managers themselves) are exposed. Naturally, the scope of risks that must be considered grows with the level of management. Managers must consider risks to all subsequent generations. Ultimately, leaving a species-level descendent of the human species becomes an issue when evaluating risks at the ecosystem level. Human extinction is virtually guaranteed in the long run, but we humans may find value in prolonging human existence over shorter time scales. Doing things that increase our chances of extinction seems counter-productive (and hardly an example of sustainability) even though, in the end, we may continue doing them.23 Long time scales in the reactions of ecosystem and biosphere impede our recognizing associated changes in management decisions. Attempting to consider both short-term and long-term time scales results in conflict. For example, scientists may identify problems that occur over decades or millennia;

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while managers discount these problems because they are focusing on short-term risks and benefits. Greater awareness of historical examples of destroyed ecosystems and the collapse of human populations might help the public and managers consider longer-term impacts. (For historical accounts of precipitous declines in human populations, exemplified by near extinction on Easter Island, see Catton 1980, Costanza 1995, Costanza et al. 2007, Crosby 1986, Diamond 1986, 2004, Ehrenfeld 1993, Ponting 1991, Redman 1999, Tainter 1988, Yoffee and Cowgill 1988.) However, knowing that human societies have failed in the past only emphasizes the need to account for dynamics over longer time scales; it does not resolve, find solutions, strike a balance, or suggest specific goals; knowing the characteristics of the societies that went extinct compared to those that survive is a different matter. 4.3.5.2 Risk of extinction In the growing list of benefits and risks to be weighed against each other are the risks to individuals and the risks to species. In managing our interaction with other species, the death of individuals among other species is often promoted. By extension, in transitive “ecosystem management,” the extinction of species may or may not be considered desirable. The death of an individual is of crucial concern in management at the level of individuals, human or nonhuman. The near-eradication of smallpox, an intentional (transitive) extinction, however, meant the mortality of nearly all individual smallpox viruses. While this is consistent with the management objective of reduced mortality of individual humans, at the level of the viral species it is an extinction. For the ecosystem it changes the internal dynamics among species with serious consequences.24 Following conventional philosophy, other forms of “ecosystem management” might aim at preservation of species, even encouraging multiplication of certain species by genetic engineering of ecosystems as in agriculture. The purposeful introduction of new species has had mixed results with many extreme consequences. From the human perspective, management (whatever the focal level in transitive management) cannot be confined to consideration of the

probability of extinction of species we may value or need; it must include the risk of our own species’ extinction. Cascades of extinction and ripple or domino effects25 are typical of species dynamics at the ecosystem level. The interconnectedness within and among such systems includes dynamics involving evolution and extinction. We humans, as a species comprising an integral part of ecosystems, are subject to these and all other risks to species in ecosystems and the biosphere. The challenge for humans as managers is to participate sustainably in ecosystems and the biosphere so they can continue to sustain all species. We cannot know everything. The repeated recurrence of this observation forces the conclusion that it is humanly impossible to identify all risks and benefits that we need to know about for good management. Nor can we know their relative importance, especially at the level of the ecosystem or biosphere. The risks and benefits may be infinite in number and involve all levels of biological organization simultaneously; change at any level involves risks and benefits at all others. Transitive management focused on species often creates problems for individuals or ecosystems. Transitive management focused on ecosystems often creates problems for both species and individuals. Faced with such complexity, accounting for risks and benefits simultaneously requires even more emphasis on identifying and resolving conflicts and achieving balance. If this is humanly impossible, what are the alternatives? This question emphasizes the need to consider the replacement of the failing parts of conventional approaches with alternatives less vulnerable to human limits.

4.3.6 Identifying the cause(s) of problems To find that an individual organism, a species, an ecosystem, or the biosphere is abnormal (outside the normal ranges of natural variation) is to discover a pathology. This is important whether management is transitive (and we assume we can control the cause if it is nonhuman) or intransitive (and we take action to deal with the parts that are of human origin especially where humans are shown to be abnormal).

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Managing in reference to individual organisms includes determining the cause of unexpected or undesired changes, especially those resulting in abnormal or pathological states (Haskell et al. 1992). This process often involves specialists such as medical doctors, veterinarians, employers, and animal trainers. It also entails complex sets of prior knowledge of challenges to the individual system. Such understanding allows distinction. For example, the symptoms of infestation by a disease organism may differ from those of overexertion; malnutrition may express itself differently from psychological stress. Finding specific causes of illness requires knowledge of cause-specific effects, including the variability of such effects and their interactions with extenuating factors. If there is no prior history or knowledge to bring to bear, guesswork and trial-and-error processes come into play. The roots of a problem may lie at other levels of biological organization. Difficulties for individuals often stem from other levels in the hierarchy of biological organization. Species-level problems can affect individuals, as occurs when a competing species is overpopulated and there is insufficient food. Microbial species present a species-level problem to individuals who become infected by them. Ecosystems that have variable populations of resource species present a problem to individuals in the form of unpredictable food supplies. Damaged ecosystems may reduce the variety of services available to an individual, fail to meet aesthetic needs or result in psychological challenges. The inevitable conflict encountered in transitive management arises from the essential reality that what is necessary to maintain an ecosystem within normal ranges of natural variation often means death among individual organisms (Table 3.1). In human values, what is good for the ecosystem may be bad for the individual. Solving this dilemma transitively, in favor of human individuals, has contributed to the problems before us. There is little acceptance of such opposing forces as natural—making natural systems what they are. What we experience as conflict is less a problem to be solved and more an experience of the reality of ecosystem dynamics. Abnormality in such dynamics is another matter. Management at the species level also entails determining the causes of altered states and related

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risks. Specialists such as population biologists infer the causes of altered species-level characteristics based upon symptoms noted during monitoring. Different causes, such as harvests, restricted migration, supplemental feeding, diseases and selective breeding can elicit different reactions or symptoms. Documented past experience helps identify causal factors contributing to change. Species react to influence variably and a given symptom can result from various causes. Conventionally, managers use guesswork more than for individuals, as a constraint experienced from a relative lack of interspecific comparative information. The conflicts arising from opposing forces are encountered again and plague conventional approaches when we try to meet human needs and preserve or design the conditions of other species. Further examples are seen when the altered states of species may be caused by a problem at a different level of biological organization. For example, a single hunter overharvesting a rare indigenous island bird may be an individual-level problem for a species. A species-level problem for species might be a large population size that encourages evolution of predators or pathogens, which then increases the risk of extinction. An ecosystem with variable populations of prey species may be a problem for a predatory species. At the ecosystem level, and in spite of relative lack of comparative information, determining why ecosystems are altered is an integral part of conventional management. Relatively little is known about the details of ecosystem changes and their causes, compared to what is known about individual humans as biological systems or even a particular species. Information concerning ecosystem change gives some indication of the causes (Appendix 4.2). For example, eutrophication from pollution causes changes in freshwater systems in recognizable patterns; habitat fragmentation increases extinction. Such causes are often characterized generically as human impact. However, the specific mechanics of cause-and-effect relationships are not as well understood as they are for species and individuals. Furthermore, information has been gathered on only a small portion of ecosystems within the biosphere. Agricultural and aquacultural management

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have provided a great deal of experience in (and relevant data for) intended change, and clearly humans are the cause behind such changes. Specific experimental work entails clearly identifiable causes of change by design (e.g., Woodwell and Houghton 1990), but well-documented causes are rare for subsequent (especially long-term) or indirect changes (Appendix 4.2), and for evolutionary changes in particular. A chief exception is the evolutionary response of pests such as fungi, paraisites, and weeds to control measures exemplified by using pesticides. We increasingly understand genetic changes caused by harvesting practices. The dilemma of opposing forces arising from interactions between hierarchies is also seen at this level. Changes in ecosystems may be required to support, or be caused by, other levels of biological organization. For example, a species-level cause for ecosystem change could occur when any particular species becomes overpopulated, uses too much energy, or shows enough population variability that extinction rates rise and reduce species numbers within the ecosystem. An individual-level cause for ecosystem change might be exemplified by the ordering of defoliation of a forest during a war.26 An ecosystem-level cause of ecosystem change would be exemplified by the emergence of excessive species numbers leading to subsequent excessive diversity-dependent losses that require millions of years to replace. Given the difficulty of monitoring ecosystems, our ignorance of their normative states, as well as the limits of science, the specific causes of ecosystem change are difficult to determine unambiguously. In conventional approaches, subjective opinions are used much more at the ecosystem level than for either individuals, species, or populations. For example, is the Hawaiian monk seal’s plight a problem for the ecosystem at large? Is the species in decline due to human impact or is it just a natural extinction about to happen normally? With further experience, the changes resulting from different ecosystem-level management actions will likely be more widely recognized and understood. This is borne out in medical, agricultural, and aquacultural settings. Monocultures almost always attract pest species. Control efforts such as use of chemical or biological pesticides and herbicides almost always

result in evolutionary responses that change the nature of interspecies interactions—not to mention the resulting pollution and effects on other parts of the system (e.g., human health). As with costs and benefits, there is little hope that we can know enough about the cause-andeffect relationships to clearly define sources of problems for every element of potential management interest. Not only can we not identify what individual organisms, species, or ecosystems we might wish to become responsible for maintaining (in transitive approaches), but we cannot identify all the causes of any given problem (Fig. 1.4). Our best hope is to identify as many as possible of the problems we experience, and especially those we cause, and deal with them insofar as we can. Part of this challenge is finding ways of being a sustainable species within ecosystems and the biosphere so as to be sustainable. This includes doing what it takes to avoid two kinds of change: 1) altering systems so they can no longer sustain us and 2) modifying systems so that they reject us (e.g., through emergent diseases). These objectives extend to other species as we extend the limits of our management to be more holistic.

4.3.7 Assessing the options for management action Human needs for the basics such as food, water, and shelter are clear, and the benefits of action to meet them are at least partially known. In management these benefits need to be weighed against the costs—at all levels, all temporal scales, and all spatial scales—to other species as well as our own (Management Tenets 3, 4, and 6 combined). Respecting the needs of other individuals, species, and ecosystems while meeting human needs is among the major challenges we face in management. Salmon need water in streams while we need the same resources for irrigation, power generation, waste disposal, and manufacturing. Science discovers that individuals, species, or ecosystems exhibit abnormality. What, if anything, should we do? What are our options? How do we weigh the risks and benefits of each option, including the option of no action? For example, how can we assess the ecological, economic, and cultural benefits of

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protecting endangered salmon vs. the risk of economic losses from limiting the supply of water for irrigation and hydroelectric power? The complexity of what must be taken into account dictates that there is no means of human design to do so. It is humanly impossible. Science may be helpful in identifying some (but not all) of the options, but provides little or no help in weighing costs and benefits that transcend hierarchical boundaries, especially costs and benefits that are unknown. At the individual level, when abnormality is identified, we consider appropriate action, based on the risks associated with an individual’s abnormal states (symptoms). We weigh the chances of success in taking any particular action against the costs. Symptomatic relief is a common option, especially if the root causes are beyond diagnosis, or too expensive or complex to treat directly. Thus, cirrhosis of the liver may be treated symptomatically to prolong life without addressing the alcoholism that causes it. Symptomatic treatment does not deal with the deeper systemic psychological/emotional, genetic, or social conditions that may have led to alcoholism. To broaden the range of options it is also important to distinguish among causes related to other individuals, species (including that of the individual), or the individual’s ecosystem. Such distinction opens the door to more systemic solutions and also helps to identify at least some of the relative costs and benefits, assuming that the solutions are within our power to bring about. Solving problems or meeting needs for individuals, however, may create problems for other levels, again revealing conflict in opposing forces across scales of time and levels of biological organization. For example, saving a life by veterinary/medical means may increase the incidence of deleterious genes within the species’ population or lead to overpopulation (abnormal population size). The conflict in such situations is real and natural. Influence on ecosystems (which we may evaluate as negative) is necessary to meet the food requirements of individuals (usually seen as a good). In conventional management there is no means to simultaneously find, weigh, and assess all the options with the complex of risks and benefits associated with each.

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In management applied to species, appropriate choices among options must also be made, whether meeting their needs or solving their problems. Action to solve problems may be taken after species have exhibited abnormal characteristics, and the causes have been identified. Incentive for action depends on the risks associated with a species’ abnormal characteristics. The chances of success are weighed against the costs of action or inaction. Of critical importance is the difference between what we can control and what we cannot. Problems of human origin are most amenable to solution. In such cases, symptomatic relief, rather than removal of cause, is often seen as less costly when assessed in human terms, such as an economic impact. This can occur when the root causes are beyond diagnosis, or too expensive or complex to treat directly, for example establishing fish hatcheries rather than removing dams. Values that apply to species often weigh against those of individuals; if required to prevent human extinction, individual sacrifice could be seen as justifiable. Meeting species level needs includes action at the individual, species, and ecosystem levels, but again exposes the conflict of opposing forces among the various levels of biological organization. Distinguishing the levels at which action can be taken is essential in weighing the options for action and begins to eliminate all but intransitive options. It is common practice to favor human needs over the needs of other species, but this exacerbates other problems—some to the long-term detriment of humans. Modifying ecosystems to promote resource species for human consumption is also common, often in conflict with the objective of maintaining ecosystems within their normal ranges of natural variability for things like biodiversity. The failure of conventional approaches to management is largely due to our inability, because of the complexity of reality combined with our finite nature, to weigh all costs and all benefits of all options. At the ecosystem level, options regarding appropriate intervention or nonintervention are assessed with very little knowledge of the degree to which ecosystem conditions are abnormal or the risk of altering them. There is little to go on when departures from normal conditions are not measured or

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measurable. Even when such information is available, there is little leverage for action when the risks are not known. In assessing ecosystem-level options, the chances of success and the costs and benefits of various options should be weighed, at other levels. Unfortunately, realistic assessment at the ecosystem level is again impossible because we cannot account for all factors, costs, and benefits and are unable to compare risk across different time frames and across different levels of biological organization.27 What would be the risks to human health of modifying ecosystems so that they comprise predominantly productive species with short life cycles to meet our needs for food? In the absence of an approach that addresses the complete collection of such questions, options are typically assessed by weighing a few short-term costs and benefits against a few long-term (and very uncertain or unknown) gains and risks. Decisions are generally made in favor of meeting short-term human needs, often measured in economic terms, thus economic factors count among the factors contributing to our risk of extinction. At the ecosystem level, symptomatic relief, rather than removal of cause, is almost always the option chosen.28 When conflicts arise, ecosystem-level objectives are almost always given less weight than individual or species objectives. No effective means of resolving such conflicts has been established in conventional management, especially in its transitive forms. This is a dangerous situation since the ecosystem level of complexity has more influence on its parts than parts do on the whole (Allen and Starr 1982, Bateson 1972, 1979, Campbell 1974, O’Neill et al. 1986, Salthe 1985, Wilber 1996).

4.3.8 Taking management action As managers, we know there are needs to be met for humans (both individuals and as a species), as well as other species and their individual organisms, and the ecosystems of which we are all parts. We know there are problems at various levels when individuals, species, and ecosystems show abnormal properties. We also know that there are risks and benefits to solving these problems, some of which we do not know. We know that there are

limits to what we can do. We know we are dealing with complexity—experienced in the numbers of things we know we should account for (individuals, species, ecosystems, the biosphere, and the interactions and relationships among them. We know reality is complex (Appendix 1.1). We know there are things that we do not know and others that we cannot know. The impossibility of dealing with this complexity in conventional approaches is emphasized by the failure of science to account for complexity—to amalgamate, integrate, and consider it. We know it is important to weigh costs and benefits and include those we have not identified, all reflecting the conflicts we experience and know to be within the dynamics of complex systems. Faced with all this uncertainty, decisions must be made and action taken. How does information about the limits to variation among species serve to solve many of these problems? Before delving into the answer to that question, consider a few final constraints and options. When individuals are the focal system, action must be possible and desirable and is often taken with the objective of changing the individual. An office manager may decide to reduce demands on an individual employee, a form of self-restraint and intransitive management. A coach may judge the required exercise regime to be too strenuous for a particular athlete and temper it accordingly. In symptomatic treatment, a coach faced with an athlete in pain may choose to administer painkilling drugs rather than allow rest for recovery. If cause is outside the focal system, the manager may attempt to remove the cause of the problem by modifying the larger system, for example by reducing social or economic pressures to perform that result in ill health or injury. Such options essentially eliminate the possibility of control, but may be more beneficial in the long run. When consequences are unknown, the manager of individuals may compensate with a safety factor. For example, a farmer managing a new farmhand may initially limit work requirements to avoid overexertion when thresholds for exhaustion are unknown. This is an intransitive action in which the farmer controls his demands. Similarly, a veterinarian may administer test levels of drugs to a horse if its allergic reactions are unknown, then

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limit dosage based on the information; to avoid toxic effects, a horticulturist may apply minimal amounts of fertilizer to a valuable ornamental tree until safe levels are known. For species, whether or not action is taken may be based on the degree to which species-level characteristics are observed to be abnormal. In transitive approaches, reducing the population of a species to stimulate production is actively intended to cause such a departure (as exemplified in fisheries management and forestry). More intransitively, managers can restrict the taking of products by consumers; for example, hunters may be restricted to taking fewer deer from a declining herd than from a stable or increasing herd; that is, humans can manage humans, not the deer species—the focal unit of transitive approaches. If a disease is causing a population decline, transitive action may or may not be taken in an attempt to modify the system to remove the cause of the problem through population management of a different species. The ramifications of such actions are unpredictable in the larger ecosystem or biosphere. Options for symptomatic relief are often chosen. Thus, salmon hatcheries are built instead of removing dams; elk are fed hay instead of protecting their habitat from human settlement; chemicals are used to control pests rather than letting the ecosystem proceed through successional stages away from monocultures to more normal diversity. Symptomatic relief may deal with immediate needs, but postpones dealing with the causes to a later time, if ever. For example, feeding malnourished children saves lives but leads to increased overpopulation and intensified starvation. This can be especially dangerous if the cause of starvation is a growing population that is already overpopulated. Symptomatic relief, however, almost always exchanges one problem for another by “moving” the problem to a different part of the overall system in time, space, or process. For example, antibiotics are used to offset the effects of exposure and risks of diseases caused by the monoculture nature of the human population, geographic range size, and mobility but lead to genetic changes in populationlevel immunity. Uncertainty at the species level is at least as important as at the individual level. Variability

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among species and variety in their reactions to environmental factors makes prediction elusive. Our experience in attempting to build models of populations reveals more uncertainty at the species level rather than more predictability. Therefore, even more speculation is used in managing species than individuals. As a result, prudence suggests an even higher margin for safety in view of the degree to which decisions are made in ignorance, if we follow conventional approaches. It must be kept in mind that the exercise of prudence, and finding a margin for safety, are part of the nonobjective process of step 4 in the top row of Figure 1.1. In management applied to ecosystems, the decision to act or not to act, or to postpone action, is taken once options have been considered. As currently practiced, “ecosystem management” is generally aimed at relieving symptoms of a problem, or meeting current human needs rather than making needs sustainable. Thus, management is often only a set of transitive, extractive population-level measures, focused on nonhuman populations, rather than self-control within the human population. These are independent of changes (desired or otherwise) at the ecosystem level or within the biosphere. Typically, transitive management does not rise to the focal level of the ecosystem. As with managing individuals and species or populations, ecosystem managers can restrict the taking of products, an intransitive approach with greater chances of success. Reducing human impact on ecosystems often is achieved by imposing limits on individual users or segments of the human population; these are examples of human self-control, but only at the individual level. Species-level management to reduce the human population is avoided socially and politically—the denial involved again contributing to the problem of overpopulation. The users and the managers are very rarely one and the same, and a great deal of human conflict often results. For example, the increases in small-bodied, rapidly reproducing species in the Gulf of Bothnia ecosystem (Rapport 1989c) might be judged excessive—partly as a result of pollution. If such an evaluation were reached, action might be to limit the amount of pollution allowed to enter the water. Discharge would be restricted by regulating

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behavior by individuals, businesses, and industry, but the production of the pollutants (at the human species level) would not be addressed and, at the ecosystem level, the restrictions on industry would result in only symptomatic relief.29 Such symptomatic treatment places increasing constraint on ever-expanding human activities at the individual and group levels, while the human population increases. Thus, tourists have to have guides to visit the Galapagos Islands and game parks in Africa, commercial fishermen have to abide by restrictive quotas, parts of the Great Barrier Reef are closed to human visitation (Kenchington 1990), and camping is by reservation only in many parts of the world. Such management alternatives are an option, but to act in accordance with the focal level of the ecosystem (a collection of species), the human species (not just the individuals affected by regulations) must also be subject to management. Not dealing with the root problems, the prime example being species-level management of the human population, accentuates and prolongs undesired symptoms. In transitive management, population (nonhuman) and individual management are practiced to symptomatically solve ecosystem problems. For example, in agriculture, as pests develop resistance to chemical treatment, resources are devoted to alternative chemicals. As new disease organisms, weeds, and pests invade and evolve in ecosystems made vulnerable by their altered states, more effort is expended in disease and pest control. These are transitive actions at the population level and, by conventional standards, are options for “ecosystem management”. They maintain altered ecosystem states to meet human needs, but work against the conflicting goal of allowing ecosystems to achieve normal states.30 Such management actions may postpone some effects of human influence while exacerbating others. They do not address root causes of altered ecosystem states, so the eventual need for even more restrictions on individual humans is inevitable; in the absence of preventative action, consequences such as disease and starvation may be imposed on the human species by the larger system (something we are already seeing, Pimentel et al. 2007). Because of our ignorance concerning ecosystems and the biosphere and their complexity, a

precautionary approach is even more important than for species and individuals. This emphasizes the need to look at all levels of biological organization for solutions to observed problems and for guidance in management that applies to ecosystems.

4.4 Fatal flaws in conventional management by focal level Although systemic management (intransitive management) was mentioned briefly, the processes outlined above are largely those of conventional management. They are essentially a combination of mostly transitive with a few intransitive approaches, but usually without comprehensive, consistent guidance. They apply management to focal levels or systems as units, corresponding to individual organisms, species, ecosystems, and the biosphere. The discussion illustrates the gravity of problems introduced earlier in the chapter; it highlights several “fatal flaws” in conventional management which conspire to result in doing things that often cause rather than solve environmental problems: Inconsistent adherence to management tenets Attempted control ignores most repercussions Conflicts are irresolvable Vulnerability to the fallible, finite nature of humans Belief systems deny the gravity of these problems

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As is beginning to become apparent, these problems are not encountered in systemic management as introduced in Chapter 1 (Fowler 2003).

4.4.1 Inconsistent adherence to management tenets Instructive patterns are seen in the problems that emerge in comparing approaches at different focal levels. As reviewed at the beginning of this chapter, these problems include failure to meet the nine management tenets: Humans are not objectively included and considered in management, usually given priority in special ways and ignored in others.

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We manipulate systems to proceed as though we have control where we do not. We are far from achieving consideration of complexity, especially in inadequate considerations of ecosystems and the biosphere. There is little if any consistency in application at the various levels of biological organization. Abnormal situations abound. Risks are among the factors of complexity not adequately woven into management. Management decisions are not fully informed by anything like a complete consideration of complexity. Science is not used for its strengths, and we fall prey to its weaknesses. Many goals and objectives involve subjective guesswork.

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in the face of environmental and anthropogenic change (Appendix 4.2). This involves the balance between human needs and the capacity of systems to meet them such that both human, other species, and resource systems exhibit normal properties.

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All such problems are failures to adequately adhere to management principles developed in the Airlie House31 meetings (Holt and Talbot 1978, Mangel et al. 1996) and those developed in considerations of “ecosystem management” in recent scientific literature (Appendix 4.1, Fowler et al. 1999, Fowler and Hobbs 2002, McCormick 1999). Efforts such as the Airlie House meetings, the work sponsored by the American Ecological Society (Christensen et al. 1996), and many other efforts (Arkema, et al. 2006, Ecosystem Principles Advisory Panel 1998, Fowler 2003, Francis et al. 2007, Lackey 1998, McCormick 1999) have had the expressed purpose of bringing together scientists and experts in “natural resource management” to identify the fundamental aspects of the management process. The common patterns are embodied in the nine tenets of management introduced in Chapter 1 (noting again the exception of the nature and timing of stakeholder involvement), fleshed out in the literature mentioned above, and left standing after exposure of the flaws of the transitive in conventional management. These failures were described at the beginning of this chapter and illustrated in more detail in consideration of focal level management above. Primary among goals consistently identified for management in regard to resource use has been the maintenance of resource systems in states that can provide sustained benefits and exhibit resilience

4.4.2 Attempted control ignores repercussions Management Tenet 2 emphasizes that we may influence but not control other systems. Interconnectedness is much more prevalent than we can deal with in conventional management; we cannot avoid secondary or indirect effects of our attempts to control (Fowler 2002, Fowler and Hobbs 2002). Attempts to control individuals has taught us that certain changes, behaviors, or levels of production are not possible; we cannot make an Olympic athlete out of everyone. Undesired changes often result from attempts to control, manifested as side effects, secondary effects, or cumulative effects. Employees resign, pets die, and stresses express themselves in various pathologies. The lack of control is even more obvious in managing species, as exemplified by evolutionary reactions, such as changes in gene frequency, and life history characteristics. Influence brings about changes within the systems in which managed species are represented, changes that are unpredictable and often of negative consequence to humans, including the managers. At the level of ecosystems and the biosphere, belief that control is possible is probably at the heart of impasses in carrying out successful management. Changes in reaction to our influence are beyond our control, especially evolutionary/ coevolutionary changes, which are further complicated by ecological mechanics. Because of the complexity involved in interactions such as consumer/resource relationships and consumption of common resources, it is impossible to predict outcomes in other parts of the system and even more impossible for changes at other levels of organization. The resulting changes include feedback as consequences of direct importance to us humans. Underlying the concept of transitive management is the fundamental assumption that we have control over our environment, an assumption that is refuted by the problems we face. Attempts to control ecosystems result in changes. Some may be to

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the advantage of individuals and cause problems for species. Some may benefit species but present risk to individuals. The complex, unpredictable, changing nature of living systems dictates that we do not have the control over other elements of life necessary to practice transitive management over long time frames. Our ability to control is probably impossible for ecosystems (Mangel et al. 1996) and the biosphere, highly unlikely at the species level, and difficult at the individual level, even when the individual and the manager are the same human individual.32 In the final analysis, we have maximum control over ourselves.2 Now we must face the distinction between what is possible and what is not. Identifying when, where, and how control (Plate 4.2) is possible is important at all focal levels of management. Fundamental to this is recognizing that managers almost always have more control over their impacts and influence on a transitively managed system than over the nature of, or process within, the managed system. Systemic management uses self-control, which emerges as the common element of success in management regardless of the level of biological organization.33 Lack of control is clearly exemplified in the genetic effects of human influence on other species.34 This leads to the question of how to exhibit control at the human level so as to achieve objectives at all levels of biological organization simultaneously.

4.4.3 Conflicts are irresolvable So far, resolution of the conflict inherent in the interplay of managing different levels of biological organization simultaneously has not emerged in conventional transitive approaches, in violation of Management Tenet 4. Conventional approaches fail to prescribe an appropriate balance among individuals, species, ecosystems, and the biosphere in management.35 We cannot expect otherwise because of: (1) the opposing forces between different hierarchic levels,36 (2) our ignorance of complexity, and (3) the related dynamics of emergence. Long- and short-term, broad scale and local, and ecosystem and individual objectives are often in conflict. Recognition of such conflict helps identify another major stumbling block in conventional management: there is no means of finding balance

or compromise. Intransitive management can fill this gap. It adheres to all 9 tenets of management as fleshed out in Chapter 6; it solves most of the problems identified above.

4.4.4 Vulnerability to the fallible, finite nature of humans It hardly bears repeating, but the importance of the matter is at the core of the problems of conventional management: we humans are finite, fallible, and biased, whether as scientists, politicians, religious leaders, or environmentalists. In principle, this is well understood but not fully taken into account in our choice of decision-making processes; we continue to choose the top row over the bottom row of Figure 1.1. Humans are thought to be of central importance in decision-making as defined by the role of stakeholders in conventional management. In this role, we choose, interpret, and convert partial information to management objectives. As embodied in conventional management this process epitomizes the problems described by Dante in his epic “Comedy” (later renamed Divine Comedy, Meeker 1997)—using fragments of the whole counterproductively. It involves misusing piecemeal knowledge combined with human values—eating of fruit from “the tree of knowledge of good and evil” as suggested to be fatal in the Bible (Bateson 1972). It is not holistic in the root sense of the word holy; it does not involve “thinking like a mountain” (Leopold 1949). The arrogance and hubris of this problem has been the subject of many scholarly works, yet we continue with this as our way of deriving goals, policy, and objectives for management. We even see it to be so important as to train specialists to undertake the conversion process (Brosnan and Groom 2006) rather than understanding that it is an example of intellectual alchemy (converting kinds of information; Belgrano and Fowler 2008). Finding consonance between management question and empirical pattern (bottom row of Fig. 1.1) is not an option in conventional management. As will be seen, finding consonance is not a matter of rejecting the reductionistic quality of science and human thought as much as it is realistically picking the reductionistic information so as to avoid

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misuse—getting a match between management question and scientific information (Belgrano and Fowler 2008, Hobbs and Fowler 2008).

4.4.5 Belief systems deny the gravity of these problems People involved in conventional management typically share beliefs (or assumptions) that: Using current approaches to resolve conflicts is the best we can do; inconsistency is a problem to be endured, not solved. Existing ways of considering complexity suffice; there is really no compelling need to consider alternatives, especially if they require difficult change and sacrifice. Existing ways of weighing the relative importance of various factors are adequate, i.e., through human constructs such as models, lists, and meetings (e.g., the NEPA process, Cantor 1996), especially as they bring human values into the process. We are doing things that meet the tenets of management as best possible. Human intellect is sufficient to continue following conventional approaches rather than be used to find replacements for failing parts.

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These beliefs may be a primary factor in preventing progress toward an approach that will replace many flawed aspects of conventional management and begin to solve the problems before us. The fact that human constructs involve varying accuracy in their depiction of a particular reality is not accounted for in conventional management. Partial information only indirectly related to management questions is often brought to bear in decision making. A major contribution made by systemic management is to accept the human limits behind these errors to move to defining appropriate choices and uses of information. Science is used in a way that observes exemplars that work (consonant realities are the models, perceived and represented directly in human constructs). This is done in a way that largely relieves us from having to shoulder the load of integrating complexity and dealing with the problems of conflict, control, and repercussions. This integration is accomplished a priori as shown in Figure 1.4 (and as will be treated

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again in Chapter 6, see also Belgrano and Fowler 2008, Fowler and Hobbs 2002). We have seen hints regarding the most direct representations of reality possible (i.e., measure of real phenomena, empirical observations of real-world models) and how they can be used instead of models constructed through incomplete combinations of limited indirectly related human concepts. The information from real-world models has been illustrated in the preceding chapters (especially in the example for the eastern Bering Sea in previous chapters). These issues will be revisited in Chapter 6. With this in mind, further but still superficial development of the concept of systemic management is seen in the following section where it is extended to ecosystems and the biosphere. It explores solutions to the dilemma of conflicting objectives, finding balance among risks and benefits, and reconciling the opposing forces in nature.

4.5 Including ecosystems and the biosphere in management In conventional forms of management, we often isolate or separate the focal level of biological organization from the others (reductionism and dualism). As shown above, this approach results in irresolvable conflict and is a major inadequacy of conventional/transitive approaches: we do not know and cannot find ways to balance the objectives for individuals with those for species. The common dilemma is our inability to find ways to simultaneously meet objectives among the various levels of biological organization (from individuals to the biosphere). We have insight to the kinds of dynamics involved in hierarchical conflict through the work of people like John Nash (1950a,b). This insight does less to provide tools to figure out what to do than it does to better understand that the options for what works (and will work if we try them) appear in a natural system. To deal with the problem we confront in finding consistency in our management at the various levels of biological organization, we must fully understand that humans—as individuals, communities, and as a species—participate in ecosystems and the biosphere. A key step forward is realizing that the same is true, of course, for all other

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species. All are made up of subatomic particles, atoms, molecules, elements, compounds, cells, and organs, and all occur in a larger context of ecosystems, the biosphere, and the rest of the universe. All are involved in dynamics, processes, interrelationships and complexity. Therefore, management that includes ecosystems and the biosphere should provide for the needs of individuals of all species, including humans. It should also meet specieslevel needs, exemplified by defining or prescribing the conduct of human affairs to account for the risks of our own extinction. It means management to include all other species so that sustainability is extended to them and their ecosystems. This means regulating human activities in relationship to ecosystems while simultaneously regulating human activities at other levels.37 To be comprehensive (holistic), such activities would be carried out to achieve the variety of goals listed above, including opening the option for ecosystems and the biosphere to return to normal status, even if we do not know about the limits within which such qualities are expected to vary. Ecosystems and the biosphere should not be subjected to abnormal influences (or observed to exhibit abnormality) any more than should individuals or species. Chapter 5 continues developing systemic management as it applies at various levels, including human interaction with the biosphere (Fuentes 1993, Huntley et al. 1991, Lubchenco et al. 1991, Myers 1989, Vallentyne 1993). Common among the principles and the approaches discussed at the Airlie House meetings and similar venues is the necessity of including ecosystems and the biosphere. This is a step toward considering complexity, particularly including higher levels of biological organization in the hierarchy of life. Occasionally ecosystems or the biosphere are proposed as a focal level of management. Both are levels of biological organization for which the management tenets should apply. However, in all cases of transitive management, achieving focal level management objectives transitively often creates problems at a different level. Without the intransitive (human self-control), there is no recognized process in current management philosophy for solving the resulting dilemmas and paradoxes. As seen in the previous sections, conflict is accentuated rather than resolved if we approach

“ecosystem management” or “biosphere management” transitively, with ecosystems or the biosphere as focal units. This section considers how we can carry out intransitive management in a way that resolves conflict and considers communities, ecosystems, and the biosphere with all their inherent and intrinsic interactions, processes, and other aspects of complexity.

4.5.1 Accounting for complexity Expanding the definition of management to include the conduct of human affairs at the species-level is a step toward including ecosystems because we are one of the participating species, and managing our species’ interactions with ecosystems is a singlespecies application. As a species (and as individuals, families, or communities), we influence ecosystems and the biosphere just as do all species (and their components). Our roles, our influence, and the position we take within ecosystems are elements over which we have some level of control. Defining management as regulation of these factors and influences can include (but not be restricted to) the goal of avoiding abnormal risks of human extinction along with the host of other risks all taken collectively (keeping in mind that risk cannot be eliminated entirely). In progressing toward a definition of management this way, we are left with need for guidance as to how to proceed, whether as individuals or as a species, especially in finding solutions to natural conflict. For our species, this guidance is provided in information exemplified by patterns showing human abnormality (Fowler 2008). Finding a position for humans within the normal range of natural variation would account for complexity in that complexity is integrated in such information (Fig. 1.4), through what is observed naturally rather than through human constructs (i.e., moving stakeholders from the top row to the bottom row of Fig. 1.1).

4.5.2 Resolving conflict How do we account for all of complexity, and at the same time find the necessary balance among the opposing forces that emerge as conflicts that are irresolvable in conventional (primarily transitive) approaches? We do so by managing systemically

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wherein reality-based patterns provide consistent guidance (Hobbs and Fowler 2008). The resolution of conflict is one of the main accomplishments achieved by following observable empirically successful examples of sustainability. The patterns we see are products (Fig. 1.4) of the very opposing forces that give rise to what we experience as conflict ending in debate (Fig. 1.1). Conflict is resolved when interpreted as the forces integrated by observed phenomena (we understand this based on Nash’s insights without using the insights but rather use the empirically observed results of natural processes). As we will see in Chapter 6, empirical solutions exist in the examples provided by other species, as tenuous and vulnerable as they are. As such, the sustainability, options, and guidance exemplified by other species are made available to us in information about the limits to the natural variation among them, as seen in species-level patterns (e.g., frequency distributions, Fowler and Hobbs 2002). These species have emerged, through processes including the trial and error dynamics of selective extinction and speciation, as a trial and error (or Bayesian-like integration, Appendix 4.4) process, as exemplars—some more so than others—of the very limited sustainability that is achievable. They have so far escaped extinction by solving the problems of conflict experienced in the opposing forces among the various processes of nature. Humans potentially have an advantage over other species due to our capacity for creative imagination, intellectual reasoning, introspective epistemology, and behavioral adaptability and resilience. Guided by the experience of other species, we may be able to optimize these advantages. The alternative is to succumb to the forces of nature, and fall prey (for example) to Combinations 5 and 6, of Table 3.1— especially Combination 6—through forces that lead to the risk of our extinction. The selective forces of nature, whether at the level of the gene, individual, or species, present all species with the same dilemmas, manifested in the conflicts and management issues we must address. Contributing to conflict are the opposing forces in natural selection generically described in Chapter 3 and modeled in Appendix 3.2. Empirically observed species frequency distributions exemplified in Chapter 2 represent the balance struck in natural systems—today of course, including all

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human abnormality. These balances include those among the various forces of the nonevolutionary processes and between these forces and those of natural selection at all levels. The categories represented by the most numerous species represent the best examples of a full accounting of risks as manifestation of the balance struck in nature among the same naturally occurring, opposing forces that pose dilemmas for humans. Among the risks accounted for are those that simultaneously fully account for the benefits and the interplay among them. For example, a normal risk of extinction equals a normal chance of continued existence as a species (i.e., sustainability; Management Tenet 6). Thus, under normal circumstances, existing individuals, species, and ecosystems represent transient evolutionary solutions to the problem of long-term sustainable existence within living systems. Existing life forms are the result of a successfully achieved balance (as dynamic balances or trajectories toward elusive balance) among opposing forces, even if it is temporary in geological time scales. By mimicking the solutions existing individuals, species, and ecosystems offer by example, we find a solution to the fundamental dilemmas of management outlined above. This approach is as value-neutral38 and objective as possible; these life forms represent examples of options that account for risk to exemplify sustainability—they are not yet extinct. The world’s ecosystems and biosphere have endured human abnormalities for only a geological instant compared to the time we and most other species have been around. How is it that we solve the problems of conventional management by adopting systemic management? What is it about Figure 1.4 that means we have dealt with complexity (and have dodged the dilemma of conflict if we use empirical information to guide management)? The following section explains how the information in Chapters 2 (and, later, 6) can guide us to achieving sustainability in a way that addresses these questions by managing our relationships with other species, ecosystems and the biosphere.

4.5.3 Nature’s Monte Carlo experiments in sustainability What do the dynamics of selective extinction and speciation have to do with patterns being integrative

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accounts of the infinite of reality? What does Chapter 3 have to do with Figure 1.4 in accounting for complexity? As described in Appendix 4.4, an analogy can be drawn between selective extinction and speciation on one hand, and the Monte Carlo (randomized experimental) aspect of Bayesian statistics (Howson and Urbach 1991) on the other.39 Species are nature’s trial-and-error models of success, as tenuous as it may be, reflective of all the factors involved in their emergence. These factors take the place of data used in Bayesian statistics wherein probabilities are represented in quantitative models.40 The probability distributions represented by species frequency distributions carry information, part of it in genetic code (DNA), parallel to the computer code used in Bayesian models (Fowler 2008). Thus, species frequency distributions, as probability distributions, reflect the constraints known to operate in natural systems (Fowler and Hobbs 2002). They are cybernetic in nature. Existing species represent an integration of all factors in their environment, history, explanation, and nature (Belgrano and Fowler 2008; Fig. 1.4). They are, in part, products of natural selection, including those expressed through selective extinction and speciation—the risk of extinction is taken into account. They include an integration of all other factors that come into play, such as measurement error and the complete suite of ecological mechanics, including anthropogenic influence and the associated belief systems. The opposing forces that give rise to irresolvable human conflict in conventional approaches to management are among the factors involved. The resolution of dilemmas is inherent to natural patterns. As such, natural patterns represent practical information for use in systemic management, characterized by intransitive coping, succeeding, surviving, or getting by without abnormal influence on larger systems such as ecosystems and the biosphere—largely prevented by the constraints of the more inclusive systems. 4.5.3.1 Including other hierarchical levels Natural selection, combined with the other forces of nature, provides a valuable source of information for guidance in conducting human affairs, precisely because of the way the resulting patterns integrate, and account for complexity (Fig. 1.4). The

frequency distributions of individuals, species, and ecosystems serve not only as normative information for evaluating systems at the respective levels, but also as a basis for identifying the need for action. Action without control is ill-advised, but control can be exhibited in human action guided by examples of success. In conventional transitive management, the complete set of risks and benefits of action are rarely (if ever) identifiable, owing to complexity and lack of experience. However, solutions to such problems have evolved as exemplified by other individuals, species, and ecosystems, emerging within the context of this complexity and attendant risks (i.e., nature’s multilevel Nash equilibria, Nash 1950a,b). The risks are part of the forces within the evolutionary processes contributing to the observed species frequency distributions. At the individual level, we already use such an approach. For example, comparisons among individuals within a species establish the most riskaversive body temperature, body mass, blood pressure, respiration, and ingestion rates, even our interactions with other individuals and species: values close to the average (or those that maximize biodiversity, Fowler 2008). We learn from role models; as individuals we are partly who we are based on our interactions with other people. Successful forms of interpersonal relationships emerge, just as body temperature evolved as a sustainable solution to a complex set of factors, including chemical, physiological, and environmental elements. Even if one knew all the factors involved and how they operate mechanically, it would be impossible to predict an optimal body temperature a priori. Likewise, when faced with managing multiple levels of biological organization we can be guided as a species by existing examples of successes represented by other species. We can understand ourselves as a species defined, in part, by our interactions with other species. We determine safe body temperatures, heart rates, and behavioral traits by the empirical examples of other individuals. In parallel (including another hierarchical level, or logical type, Bateson 1979), other species represent empirical examples of sustainability that are informative for our species. For example, when faced with the need to estimate a sustainable harvest of a resource species, guidance

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is found in the frequency distribution of consumption rates by other species that feed on the resource in question (e.g., Fig. 2.6). Restricting our consumption rates to avoid the abnormal within the limits observed within empirical data on consumption rates is an example of intransitive management applied in a single-species approach (control of our species in interaction with another). At the ecosystem level, this approach would mean confining overall human consumption to the limits of consumption rates observed for other species from a particular ecosystem (e.g., Fig. 2.7, Fowler and Hobbs 2003). At the biosphere level, it would be achieved by confining consumption by humans to within the limits of natural variation in consumption among nonhuman species for the earth as illustrated in Figure 2.10. Other ecosystem level applications involve, for example, the advisable numbers of species to harvest.41 At the biosphere level we are also faced with the larger question of how many humans can be sustained. Here distributions regarding population density (Figs 2.16, 2.23, and 2.31), geographic range size (Fig. 2.27), and population size (Fig. 2.17) provide guidance. 4.5.3.2 Effort and sacrifice (management) rather than conflict Existing species represent the current best examples of adaptive management as carried out through natural processes.42 Characteristics shared by the species most numerous in their frequency distributions are likely to represent evidence of optimal forms of successful risk aversion and benefit maximization—mutually workable relationships between them and their environment. Risks and benefits include those associated with ecological mechanics and with the reciprocal interaction between hierarchical levels—between systems and their components. This includes both the supportive (e.g., provision of materials and services) and the limiting (materials and services are finite and shared among components) functions of ecosystems for humans as one of many components. No risk or benefit is excluded from the information of frequency distributions, and all are included in proportion to their actual relative importance. Examples of sustainability include the goods and

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services provided/used within the limits established by forces operating within ecosystems (or the biosphere). Characteristics of common and enduring species represent minimum risk solutions while characteristics of rare species or kinds of species with high turnover represent maximum or higher risk solutions. Characteristics of highly abnormal species (completely outside the normal ranges of natural variation) represent extremely pathological and high risk situations (as often the case for humans; Fowler and Hobbs 2002). For the human species to achieve sustainable species-level characteristics that emerge in the constraints on variation among species will, in many cases, require individual-level sacrifice. What were unresolved conflicts now become identified sacrifices in the form of actions necessary for achieving sustainability. These are sacrifices individual humans can make to contribute to sustainability in achieving balances inherent in the forces of nature or complexity in general. Costs are inherent to achieving sustainability by accounting for all risks, including risks such as our own extinction. These are short-term human costs for long-term human good (including long-term good for all associated systems—for example, other species, ecosystems, and the bioshpere). Individual sacrifices (or, more positively, contributions) toward achieving sustainability go well beyond contributing to an environmental organization, recycling, and driving eco-friendly vehicles. The extent of changes necessary to achieve sustainability will be more clearly seen in Chapter 6 (e.g., see Fowler 2008). As is becoming increasingly clear to many scientists, many of the problems we face are tied to overpopulation, keeping in mind the very important fact that each problem must be treated directly on its own merits. In a typically density-dependent fashion, the need for such sacrifice will be much less after (and if) the human population can be reduced or is reduced by systemic forces (e.g., global pandemic). The issue is to decide that accounting for risks collectively (including that of human extinction) is a “good” worth working for. Sustainability can be understood as a value comparable to that of the normal for individuals (e.g., body temperature, blood pressure, etc.) but extended to embrace

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all forms of life simultaneously. Sustainability is a “good” worth sacrificing for, while experiencing the disadvantages of the individual-level changes required to achieve desired species-level change. Such individual level contributions are a matter of making up for lost ground from a history in which individual level needs and short-term issues have been considered of greater importance than protecting ecosystems, the biosphere, and future generations of humans from current human excesses. Management cannot be restricted transitively to the focal level of ecosystems as described above in order to achieve “ecosystem management”. Lack of control, unpredictable reactions, and intensified conflict lead to feedback and consequences we want to avoid. Problems at the ecosystem level drive our consideration to even larger systems, such as the biosphere. How far can we extend this process to include larger and larger systems (or more and more inclusive systems) before realizing we are not in control? We are left with only the alternative of controlling ourselves to limit our influence on all systems. This is the core of management because individual, species, ecosystem, and biosphere cannot be separated in terms of application and complexity. There may be a transcendent quality to the emergent nature of the more inclusive systems but it always includes its components through the interconnected quality of nature. Mimicking nature’s examples of sustainability at the species level (Fowler 2008) places proper importance on ecosystems, partly because ecosystems are made up of individuals and populations of unique sets of species. Species are exposed to the constraints of larger systems such as ecosystems and the biosphere. Greater weight is given to the species level than the individual level because species are made up of individuals. Even greater weight is given to ecosystems and the biosphere for the same reason. This pattern in weighting stems from the fact that constraints of more inclusive systems confine the constituent elements. It is a natural weighting that negates the option of human design. The reverse effects, of a similar nature, emphasize that parts of systems can never be ignored (e.g., if all individuals of a particular species die the species goes extinct). Other than the balance struck in the evolution of existing species

and sets of species, there is no means of weighting things for which we have no measures. A transitive approach cannot be used to sustainably “manage” ecosystems, and even less so the biosphere.

4.6 The case for systemic management We are left with the option of guiding human affairs, by the example of other species’ successes, toward sustainable species-level participation in (influence on) ecosystems and the biosphere. This approach meets Management Tenets 4 and 5 and accounts for the various hierarchical levels of biological organization with empirical examples. It is an intransitive approach, which applies Management Tenet 3. It is based on the acceptance of individuals, species, ecosystems, and the biosphere as inseparable; this acceptance of inseparability is needed to meet all of the management tenets, especially Management Tenets 2, 3, 4, 6, and 7. It is not called “ecosystem management” partly because that term implies potential for focal level success in transitive management, an approach that has been rejected as impossible. It is also not called “ecosystem management” because it is not restricted to our interactions and influence on ecosystems (it includes other species, groups of species, and the biosphere). Systemic management considers individuals, species, ecosystems, and the biosphere as parts of an amalgamation that makes them inseparable differentiated elements of reality. Our interactions with all levels make up the parts of systemic management. In the game of evolutionary dynamics, the objective is to stay in the game. Although the presence of any species is tenuous, the genetic code of species that have succeeded in staying in the game is information about how the game might best be played.43 This information is illustrated by the species-level patterns shown in previous chapters (with more to be seen in Chapter 6) and especially those yet to be discovered and better portrayed in their interrelationships and relationships with the environment. Each distribution includes insight from information found in genetic codes. Through systemic management, humans are the managers and also the ones who are managed.44 We have insufficient control over other elements of life,

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and the laws of nature, to proceed otherwise and we court disaster by pretending that we can control. Although far from complete (we cannot control that our influence has consequences), our greatest control is over ourselves to find a sustainable level of influence. As argued above, we are best served in this regard by conducting human species-level affairs in accord with examples of sustainability demonstrated through the limits observed in variation among nonhuman species. One of the many reasons for doing so is to account for the risk of human extinction. Extinction is one of the processes that contribute to rarity of species within the tails of frequency distributions (Chapter 3). In following the guidance of information from species frequency distributions, the elements that have been identified as important to account for in “ecosystem management” are integrated into the process (see Appendices 4.1 and 4.3 and Management Tenets 1–9, Chapter 1 and above). In practice, this means finding the species-level patterns consonant with management questions that we face (Belgrano and Fowler 2008). Decisions and policy can then be based on mimicking natural examples of sustainability by constraining human activity so as to avoid abnormalities. This is an implementation of Management Tenet 5 (Fowler and Hobbs 2002, Mangel et al. 1996). Proceeding this way is precautionary. We will see numerous examples of guiding information in Chapter 6.45 A primary scientific constraint and difficulty is finding information that represents the normal range of natural variation in a world so heavily influenced by humans. The primary challenge, overall, is management action based on that information.

4.7 The eastern Bering Sea example The typical transitive approach to managing an ecosystem such as the eastern Bering Sea is to manipulate the abundance of nonhuman species and thus the composition of the ecosystem. Typical goals are to stimulate greater production of resources for harvest/utilization by humans. Typical methods are to apply incentives or disincentives to limit or promote fishing of certain species, or to close certain areas or seasons to fishing. Often, in ecosystems more generally, these methods are based

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on knowledge of predator/prey relationships, with the thought that we can replace competing predators, or the belief that if we control predators there will be more of their prey available for human use. For example, a species such as the arrowtooth flounder in the eastern Bering Sea might be seen as over-abundant (population size beyond the normal range of natural variation for such species), as reflected in its position in the frequency distribution of consumption of walleye pollock (extreme right of the top left panel of Fig. 2.6). One option, in conventional management, would be incentives to increase harvest of the flounder to reduce its population. Such an approach always has consequences that are unanticipated and contrary to our desires. Some experts or stakeholders involved in the decision-making process might foresee some of the consequences and argue against actions being contemplated—creating conflict without resolution. This is typical of conventional management, stakeholders have varying opinions and interpretations regarding specific parts of the information used in management (top row Fig. 1.1). It does not result in objectives consonant with management questions in a way that accounts for complexity. It does not account for secondary or other higher-order effects the actions will have, or the fact that many of these effects are both unknown and unpredictable. It does not consider evolutionary reactions to human influence that have already been set in motion (also unknown, unpredictable, and especially unprovable with current forms of science and logistic constraints), nor those that would be initiated by such action. The repercussions for us as managers are unknown. Species-level patterns that fail the test of consonance (match with the management question, Belgrano and Fowler 2008, Hobbs and Fowler 2008) are not the basis for managing other species transitively. In the eastern Bering Sea, the process of conventional management is perhaps most clearly exemplified in the certification of the walleye pollock fishery as sustainable by the Marine Stewardship Council in 2004. This typifies action by a group of scientists, managers, and other stakeholders in rejection/violation of Management Tenet 5. The decision to certify the fishery as sustainable was

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an exercise of decision-making depicted in the top row of Figure 1.1 and described above. The decision rejected the abnormality of the commercial catch of this species (Fig. 1.7 and top panel of Fig. 4.1, Fowler 1999b, Fowler and Hobbs 2002, 2003, Fowler and Perez 1999) as a problem to be solved. Instead, the certification was based on artificial combination, interpretation, translation, and evaluation of less relevant piecemeal information. Also involved, of course, were the values, belief systems, thinking, and habits behind conventional management. Economic factors played a role. In this example we see a clear connection between conventional management and abnormality; certification embraced the abnormality as acceptable. In contrast, fully embracing Management Tenet 5 (especially in combination with Management Tenets 1 and 2) would lead to the conclusion that the fishery is certifiably unsustainable. Overall, this example makes clear the vulnerability of current management to the subjectivity and fallibility of human nature. As in all of conventional management, the action taken by the Marine Stewardship Council was not malicious, or ill intended. It was a matter of doing the best possible using the paradigm behind conventional thinking in attempting to achieve an optimal outcome—again defined through conventional approaches and the values involved. The same holds true for all aspects of management in the eastern Bering Sea. The harvest of resources is carried out without objectively addressing questions such as: “What is sustainable age or size selectivity within the commercial harvest of walleye pollock (or any other species)?” For the most part, such questions are not posed as clear management questions (top row, Fig. 1.1). What portion of the eastern Bering Sea should be set aside in marine protected areas? What portion of the geographic range of any particular species (e.g., Steller sea lion, fur seal, walleye pollock, thick-billed murre) in the eastern Bering Sea should be set aside in marine protected areas? Decisions to harvest at rates, in locations, at times, and with selectivity that are abnormal compared to other species clearly contribute to the abnormal influence we have in, not only the eastern Bering Sea, but all ecosystems. Again, such decisions are not made maliciously. In fact, those making

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Figure 4.1 Species frequency distributions showing the rates at which marine mammals consume at various levels of biological organization within the eastern Bering Sea, the marine environment, and the biosphere showing consumption by humans in comparison to provide initial indications of the kinds of changes we will have to undertake to fall within the normal range of natural variation (from Fowler 2002, Fowler and Perez 1999).

the decisions are responsible caring people. They are people doing the best job possible within the confines of conventional management, knowing that we humans need food, the fishing industry depends on catches that are economically viable, and that the productivity of resource populations is stimulated by being harvested.

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By contrast, systemic management has the goal of providing for human needs while simultaneously and consistently ensuring that such needs are sustainable and can be met sustainably. All species depend upon the sustainability of the services that ecosystems provide. The methodology involves the use of species-level patterns (species frequency distributions) as guidance for sustainably harvesting fish biomass, by mimicking other species’ proven (though imperfect) examples of sustainability. For example, increased harvests of arrowtooth flounder might be part of the strategy, but harvest rates would be restricted to avoid abnormal levels (i.e., a pattern such as shown in the top panel of Figure 4.1 for walleye pollock, using such a pattern that would be developed for the arrowtooth flounder). Such an intransitive approach is not restricted to providing guidance for the harvesting of an individual species from a particular ecosystem such as the eastern Bering Sea. It also provides guidance for harvesting from species groups and other ecosystems as well as extensions to include the entire marine environment and the biosphere (Fig. 4.1). We are left with several issues that have yet to be adequately addressed. In particular, there is the matter of human influence as a factor in contributing to what species-level patterns are today (Fig. 1.4). The sustainable take of walleye pollock as exemplified by nonhuman consumer species might be a greater fraction of standing stock biomass if their populations were larger. However, if the predator populations were larger, the walleye pollock population likely would be smaller. These and other factors cannot be ignored in dealing with complexity (as required by Management Tenets 3 and 4). They are part of the difficulty we face as a result of a history of practicing conventional management without accounting for complexity. However, harvesting one species is only one isolated example of management that needs adequate guidance. There are directly related questions: What should management establish as an appropriate age composition for the harvests? What should the harvest rate be if we take only adult fish? What are the optimal levels of harvest in terms of numbers of individual fish rather than biomass?

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Management expands to similar questions for other species, groups of species, other ecosystems and the biosphere: What is the optimal number of species to harvest? What amount of biomass and numbers are to be taken from entire ecosystems? At what level should we be producing pesticides or carbon dioxide that find their way into various ecosystems around the world, including the eastern Bering Sea.

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As we are beginning to see, these are also issues that can be addressed through systemic management. By ignoring complexity, conventional singlespecies management may create what we would conventionally evaluate as problems at the ecosystem level (including extinctions). Systemic management might do the same. There are two points: (1) there are consequences to our management no matter how we proceed and we will often judge some negatively; and (2) we cannot confine our management to one issue because complexity demands that we address as many issues as can be identified. If extinction rates are abnormal, we are responsible for doing an inventory of any ways we might be contributing to see if there are human abnormalities to be corrected. Pollution may be a factor in the eastern Bering Sea and the problems we see in that system. As we will see in Chapter 6, application of guidance from species-level patterns would lead to restricted pest control (no other species produces as many toxins released in quantities that humans are using). We may be able to influence (rather than control) other species using guidance from species frequency distributions, but the extent of such influence will likely be quite limited compared to what we have historically attempted in the mission of control. There are benefits to an intransitive single-species management approach (e.g., we achieve sustainability, find balance in meeting our needs with the capacity for systems to meet them, and reduce the prevalence of pathologies

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in various systems). However, the consequences continue to involve what we will evaluate as problems and many of those will occur at other levels of biological organization. Reducing the harvest of walleye pollock causes individual fishers to lose income or jobs. Harvesting walleye pollock at any level results in competition with other predators. The harvest affects the ecosystem in which pollock occur, and some of these effects may be undesirable to us, but in all likelihood would be within the normal range of natural variation. In management, control rests with humans as self-control, difficult as that may be. The numbers of species we choose to harvest, the amount of biomass we choose to remove from an ecosystem, and the amount of biomass we choose to harvest from any particular species can be based on limits apparent in species-level patterns. The quality of the data we have in hand for producing useful species frequency distributions is a factor yet to be addressed.

4.8 Summary and preview This chapter has presented examples of the failure of conventional management and some of the reasons for these failures, and has pointed toward systemic management as an alternative to solve these problems. Conventional management fails to meet the nine tenets of management. Human interests (especially factors such as economic factors) are over-emphasized in comparison to an objective accounting for our roles in ecosystems and the biosphere. That is, we fail to meet Management Tenets 1 and 2, believing that our emphasis on humans assures adequate consideration. Conventional management fails to meet Management Tenet 4 in lacking consistency in consideration of other elements of reality, and Management Tenet 3 in not accounting

for reality in general. In principle we know we are finite in being human and cannot account for all unintended consequences of our actions; yet these principles are largely ignored in conventional management. All too often we believe that we have control and lead ourselves into excessive influence, frequently to find ourselves involved in abnormal interactions with the nonhuman—in violation of Management Tenets 2, 5, and 6. We believe we can generate management advice (e.g., through meetings, congresses, panels, expert advice, political agendas, special interest groups) rather than simply looking to nature for guiding information through direct observation. We proceed this way, largely ignoring Management Tenet 3 and also 2, 4, 5, 6, 7, and 9 in spite of practical experience that tells us it is humanly impossible to account for complexity and reality otherwise. Among the factors we have failed to take into account are the many risks and their interactions—all parts of reality—and have focused on sustainable development rather than achieving sustainability. The choice and use of information in today’s management involves serious logical errors. Science is used to emphasize isolated factors rather than observe realities directly related to (consonant with) management questions in a way that meets all nine management tenets. As a result we are left with few clear directions, have few objective standards, lack adequate criteria for management action, and face seemingly insurmountable problems for which we are largely responsible. These failures leave us with systemic management as an alternative defined by the tenets of management and, therefore, a way forward so as to adhere to them all. Chapter 5 describes how well systemic management is defined by, and adheres to, the tenets of management in a way that solves many of the problems so clearly plaguing conventional approaches.

CHAPTER 5

Why systemic management works

Water is H2O, hydrogen two parts, oxygen one, but there is also a third thing that makes it water and nobody knows what that is. —D. H. Lawrence

The preceding chapters provide the background needed to understand the foundations of systemic management—a significant part being that of avoiding the abnormal. This book focuses primarily on the species-level aspect1 of such management— directed at avoiding human abnormality at the species level. Comparing our species to others (Fowler 2008, Fowler and Hobbs 2003, Figs 1.7, 4.1) provides a basis for evaluating our species. Understanding and avoiding the abnormal allows for setting goals, and evaluating our progress in achieving them. If there is hope of achieving sustainability, the process must involve our own self-control; one of the first steps is to understand ourselves as a species.2 Regulating and limiting ourselves as a species must ultimately be done by individuals, within a context of incentives, disincentives, social norms, and education created by collective human institutions and effort. However, thinking or behaving as if the human species is separable from (not a part of, not subject to the laws of) reality will not work—a lesson learned from experience in “resource management”. Systemic management addresses the state of all other systems, but not as systems under our control. Rather they are understood as systems that do, and will, respond to our actions. As systems that include self-regulating and homeostatic dynamics (Brown 1995, Camazine et al. 2001, Heylighen 2003, Solé and Bascompte 2006), ecosystems and the biosphere are capable of recovery from abnormal human influence and will respond to management to relieve them of

abnormal human influence. Our objectives for other systems will be attained indirectly through their reactions to such management, predominantly in the direction of sustainability—a state of health or normalcy. Otherwise, the combined responses of these systems to human influence will pose risks we clearly wish to avoid for all concerned. These are the risks that prevent the accumulation of species with characteristics beyond the normal ranges of natural variation as displayed graphically for patterns shown in Chapters 2 and 6; the risks that result from the homeostatic dynamics of systems such as ecosystems and the biosphere. These include risks presented by ecosystems with abnormal qualities. Systemic management is guided by a combination of factors familiar to the recent scientific treatment of complexity,3 but practical application of this guidance requires replacing at least some elements of current management.4 Previous chapters described how systemic management considers ecosystems and the biosphere as biotic and abiotic environments in which species, including humans, evolve as both parts and products, and as systems in which such components exert a variety of influences. This chapter describes how systemic management does more than embrace all of the nine tenets of management laid out in Chapter 1. It does so, fully, consistently, and simultaneously so as to largely solve other problems with conventional management as described in the previous chapter. Systemic management is consonant reality-based management. Where applied, it fully accounts for the inclusive aspects of complexity,5 121

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and includes as one of its parts the management of our interactions and relationships with ecosystems to meet the need for more effective “ecosystem management” or “ecosystem-based management” (ecosystems are part of reality). Thus, systemic management is consistent at the individual, species, community, ecosystem, and biosphere levels,6 and provides guidance for setting goals and making decisions. The limitations of systemic management are discussed, as is the importance of extinction as a management issue. The chapter concludes with a basic protocol for applying systemic management with examples from another visit to the eastern Bering Sea.

5.1 Systemic management adheres to the tenets of management Haeuber and Franklin (1996) said that “ . . . we may not be able to define [ecosystem management], but we know it when we see it”. They make the case that we have made a great deal of progress in developing criteria that should be met by “ecosystem management”, but we do not have a widely accepted form of management that meets these criteria. Accounting for complexity drives the need beyond ecosystems; management must apply to all levels of biological organization, including the biosphere (Fowler 2002, Fuentes 1993, Huntley et al. 1991, Lubchenco et al. 1991, Myers 1989, Vallentyne 1993) and the Earth (Clark 1989, Myers 1993). It must account for ecological mechanics, natural selection, and processes involving the flow of nutrients, predation, and competition—all parts of complexity. This section evaluates how well systemic management meets the nine management tenets first laid out in Chapter 1 and revisited in Chapter 4 (where the failures of conventional management were documented). These tenets distill the desirable attributes of management emerging from years of effort to define it (e.g., Christensen et al. 1996, Fowler 2002, Holt and Talbot 1978, Mangel et al. 1996, McCormick 1999, and the references in Appendices 4.1 and 4.3). Systemic management is defined as the management of human interactions with other systems so as to avoid the abnormal—a definition that emerges from this history.

5.1.1 Management Tenet 1: Including humans—management must be based on an understanding of humans as part of complex biological systems Christensen et al. (1996) stress, as do many others (Appendices 4.1, 4.3), that management must recognize that there is a place for humans in the grand scheme of things. It cannot be emphasized enough that the sustainability of humans as natural elements of ecosystems and the biosphere is an integral part of systemic management. Just as a person with a seven-degree fever is a natural phenomenon, the abnormality of the human species is natural and subject to the natural processes resulting from such pathologies. As parts of systems, we are constrained by the natural laws that govern all species. Our abnormality in comparison to other species subjects us to the same forces that operate homeostatically to bound such variation. Finding a normal, sustainable existence for the human species in its many dimensions, a realistic niche for humans, is a fundamental goal of systemic management. It is a means of finding a realistic way to fit into reality. Systemic management attempts to adjust human impact so we can sustainably coexist with other species, within ecosystems and the biosphere. It aims to increase the likelihood that humans will not be excluded from the Earth’s biota through the homeostatic processes of ecosystems and the biosphere. Systemic management ensures that the needs of humans are accounted for in management, in part, through the identification of needs that are unsustainable. Such management rejects the option of doing things that lead to an abnormal risk of our own extinction, but includes extinction as one of the risks we face as a species. In systemic management, humans, like other species, are considered to directly experience the laws of nature over all scales of time, space, and hierarchical complexity. Human experience is extended to direct empirical observation through which we see all species subject to, and conforming to, these laws. We can use the constructs of models to help understand, see, and measure the manifestations of these laws, but not to fully recreate reality (Pilkey and Pilkey-Jarvis 2007). No species, including humans, is capable of sufficient control to change or eliminate these laws.

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We are not making up the rules; we are learning to follow them, or ignoring them to our peril. Humans are faced with a decision: Are we going to change or are we going to let nature make changes for us? If we are going to change, what will it be? Humans create change whether or not decisions are based on systemic guidance. With goals established using information about limits to natural variation, humans shoulder the responsibility both for taking action to achieve such goals and for the consequences of failure to do so. No lawyers represent future generations of humans or other species, and few laws overtly protect them, but if there were such laws, the responsibility of today’s society would be perceived differently. Opposition to change in a sustainable direction and the acceleration of change in the opposite direction are evidence that changes of the magnitude needed are unlikely and nature will take its course. Fortunately humans have the potential for collectively awakening to the imperative for change. This potential is basis for hope without which no change will occur (Fullan 1997). As mentioned in earlier chapters, one aspect of including humans in our concept of complexity has to do with past anthropogenic influence. Not only are we abnormal in many ways now (Fowler and Hobbs 2002, 2003), but we have been for long enough to have set in motion many changes in nonhuman systems, at least some of which are important to account for in what we do now. The ripple effects7 of our influence have long-term consequences, including coevolutionary interactions set in motion by our past and present influence on the systems of which we are a part. Many of these effects are largely irreversible8 and systemic management offers the option of taking all precautionary actions we can to avoid the potential of these changes resulting in abnormal burdens on succeeding generations of humans and other species—including the risk of extinction.9 Although there may be adaptability left at the ecosystem level (i.e., we are not likely to cause the extinction of all species), the opportunity systemic management presents to our species is that of following the lead of other species: adapting to the changes we have set in motion, on top of changes that are independent of our influence. Interconnectedness and complexity (Management

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Tenets 3 and 4) demand this consideration of humans and systemic management makes it happen. Thus, systemic management makes it possible to account for the effects of current and past human influence. Comparing the population of our species with that of the dozens of other species for which we have approximate estimates of population size can be misleading if we are trying to find what is sustainable in the absence of abnormal human influence (Fig. 5.1, Fowler 2008). Because the sets of species with which we can compare our species currently are parts of ecosystems that we have heavily influenced, they themselves are not necessarily normal. We are accountable for the abnormality we have created and current species-level patterns better account for that abnormality than we find possible in conventional practices.

5.1.2 Management Tenet 2: Limited control— management must recognize that control over other species and ecosystems is impossible Systemic management is explicitly limited to control in the human sphere—human action, influence, and characteristics. It is assumed a priori that, like any form of management, systemic management will have consequences, intended and unintended, over which we have little or no control and which we certainly cannot prevent. Based on the principles of complexity and connectedness (Management Tenet 3), systemic management uses empirical information to guide human action such that the goals and objectives we hold for both human and nonhuman systems may be realized among such consequences. The consequences, feedback, ripple effects and other impacts of our management are accounted for, owing to the fact that such effects are part of what always happens whether the management action is what we do as a species or as individuals.10 Systemic management solves the problem of our tendency to think we can decide what is best for us or other systems based on human concepts, emotions, science, special interests, political agendas, and partial information or knowledge about things not consonant with any specific management question. Our attempts to piece together the

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(A) Past (ten thousand years ago)

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Figure 5.1 Species frequency distributions of population size for 64 species of mammals of approximately human body size, including humans, compared for a (hypothetical) past (A), a (measured) present (B) and a (potential or hypothetical) future (C) (Fowler 2005, 2008). The curves of A and C represent an approximation of the shape of the range of normal variation expected if humans were within the distribution (e.g., to maximize biodiversity, Fowler 2008), and simultaneously avoid abnormality for other measures (e.g., energy use, resource consumption, etc.). The data and curves of B show a general idea of the shape of the distribution as seen today if more of the endangered species were included. The shift in position and change in shape from (A) to (B) represents a result of the influence of humans, in part owing to the position of humans in this distribution. The potential for change between B and C is only under our control to the extent that we have control over ourselves.

parts of reality remain a human construct rather than reality. Within reality are limits to the natural variation of such things as predation rates, CO2 production, body size, water consumption,

metabolic rate, or range size. In systemic management, we observe and measure these things as directly as possible through scientific studies to account for complexity rather than yield to the temptation to turn simply to ourselves for the guiding information. This self-control is one way systemic management accounts for human limits. When nonhuman systems are abnormal, systemic management makes action to repair the other systems through mitigation, engineering, or manipulation a very low priority—in many cases precludes it entirely as being beyond our control. Instead, the first order of business is to eliminate all human abnormality with any potential for counting among the factors contributing to observed nonhuman abnormality. Thus, we look for, and eliminate, human abnormality when problems are identified for ecosystems in the form of endangered species, shortened food chains, abnormal productivity, or elevated extinction rates.11 Abnormality in nonhuman systems is treated as symptomatic of human abnormality until proven otherwise; abnormalities within the human realm are treated as symptoms of other kinds of human abnormalities until proven otherwise. Symptomatic relief is avoided in favor of dealing with root causes of human origin. If there is no human abnormality in all ways we can identify (particularly as a species), the occurrence of other problems would then be viewed as largely beyond our capacity to prevent. This reverses the burden of proof so that we are assured that we have done everything possible to avoid being the causative agent behind observed abnormality (human or nonhuman). Making the structure and function of nonhuman systems dependent on support and transitive action by our species is a recipe for instability not sustainability. Thus, noting any abnormality is a stimulus to look for human abnormality of any kind. However, in the spirit of Management Tenet 3, the independent discovery of any human abnormality is cause for restorative action owing to the systemic repercussions involved (especially because so many nonhuman elements of reality are involved— some beyond knowing about and whether or not cause-and-effect links have established).

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5.1.3 Management Tenet 3: Complexity and interconnectedness—management approaches must account for reality in its complexity over the various scales of time, space, and biological organization Because reality is complex (Appendix 1.1) and heavily, if not completely, interconnected, our actions have many consequences (Christensen et al. 1996).12 Because of this interconnectedness, complexity is accounted for when guidance for management is based on empirical patterns and the information they provide regarding the limits to natural variation (Fig. 1.4). Such guidance accounts for various systems and their influences and feedback—whether those influences and repercussions are normal or abnormal. As outlined in previous chapters, the species depicted in species frequency distributions exemplify both exposure to, and influence upon, reality and its interconnectedness (Belgrano and Fowler 2008). Patterns reflect the complexity of the reality of which they are a part in its entirety. For species-level patterns, these include the extrinsic/ exogenous and intrinsic/endogenous factors for each species,13 the reciprocity among all species (direct or indirect) along with all the other elements of reality, and the consequences of that reciprocity. Scientists cannot model this complexity, but if we could, the model would involve equations representing everything, including the reciprocity of all interactions.14 We would evaluate any model through conventional approaches to ecosystem simulation, for example, by judging its output (behavior) against observed patterns such as the distributions shown in Chapter 2 to determine how realistic the models are; variation in the components of the model would need to be confined to the limits observed in reality. In systemic management we are simply using empirical observations of reality itself—reality as the model—rather than models we construct or concepts we develop. The real-world models chosen (and those models, as abstractions, we make to represent them) provide information consonant with various management questions. Thus, when the management question is about predation (by humans) we examine empirical data about predation, direct observation of predation, rather than simulating it. All forms of predation, by all species, are taken into account directly (along with every-

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thing else) in frequency distributions for predation rates. Therefore, we find guidance for systemic management that fully accounts for complexity when we use information on the limits to natural variation (“fully accounts for” is the infinite of Fig. 1.4). Accounting for complexity in guiding information is one of the biggest steps in implementing systemic management (compared to conventional approaches), but this is only one step in meeting this challenge accomplished in systemic management. Such information accounts for complexity only in providing guidance for individual questions (Belgrano and Fowler 2008). However, there are many management questions. Each management question can be both refined and expanded. Each specific management question (e.g., sustainable consumption of biomass from a specific resource species, or allocation of consumption over alternative species) is a focused component to the carrying out of systemic management.15 In a bit more detail, there are four components to dealing with complexity as accomplished in systemic management (Belgrano and Fowler 2008, Fowler and Hobbs 2002, Fowler et al. 1999). Systemic management is based on guiding information that integrates all factors (Fig. 1.4), addresses the diversity of questions we can ask, takes advantage of known correlative relationships, and accounts for the complexity of interconnectedness.

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5.1.3.1 Systemic management uses guiding information that integrates all factors The use of empirical patterns incorporates a complete accounting (Plate 5.1) for complexity, owing to the fact that the patterns we see are a product of every contributing factor whether science has been able to show the relationship or not. As emergent from complexity, the patterns observed in frequency distributions provide us with an objective weighting of the importance of each contributing factor—all of the interactions and synergistic effects of the processes and elements involved. Thus, the infinite of the right side of the “equation” in Figure 1.4 is all inclusive. It includes everything from subatomic particles to galaxy clusters and all levels of biological organization

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and biotic systems. It includes all physical and chemical forces (magnetism, adhesion, surface tensions, gravity), and all biological phenomena and processes (e.g., evolution, extinction, mortality, predation, competition, camouflage, behavior, reproduction, embryological development, photosynthesis, coevolution, mimicry, vision, and functional responses). It includes everything in its corresponding temporal and spatial scale with all the effects of history. Nothing is excluded; everything is taken into account in direct proportion to its relative and actual/true importance. This is in stark contrast to the limited lists, models, meetings, and stakeholder conversion of nonconsonant information used in conventional management. The difference is depicted in the comparison of the top and bottom rows of Figure 1.1. 5.1.3.2 Systemic management addresses the diversity of questions Fully implemented systemic management accounts for complexity by being applied to all management questions.16 However, there are finite limits to our ability to think of all management questions (Appendix 5.1). Human limitations are converted from being a source of mistakes (top row of Fig. 1.1) to limits realized in being unable to identify all dimensions over which patterns can be observed. Our reductionistic limitations cannot be avoided. Precaution (NRC 1999) is necessary to account for this human limitation, and humility (Ehrenfeld 1981) is necessary to accept that we can never overcome it. Precaution in systemic management is achieved in taking all human abnormality seriously. However there is a difference between being limited in our ability to ask all relevant management questions and the limits in our ability to account for complexity in dealing with any one now recognized. Systemic management circumvents the latter. Listing questions is a problem for any form of management, but the scope of options is completely open in systemic management. By contrast, conventional management is stymied by questions such as “What is an appropriate level for the human population, consistent with sustainable resource consumption, CO2 production, water consumption, and range size?” Thus, in spite of the advantages of systemic management, we remain

limited in our ability to bring to light all relevant questions or define all management issues. It can address many known management questions, fully accounting for complexity in each individual case. Systemic management is not simply a matter of achieving sustainable resource consumption; and yet, without sustainable resource consumption, we fail to completely apply systemic management. Thus, reducing our population (Fig. 5.1, Fowler 2005) must be included with reducing the amount of energy and resources we consume, along with the CO2 we produce, and space we occupy—all must be part of management and are in systemic management. Systemic management can also be applied to finding sustainability in the number of species that we consume, and the allocation of our consumption from alternative resource species, and over space and season (Fowler 1999a, Fowler and Crawford 2004). There is resolution of the numerous conflicts identified in Chapter 4 in the sense of achieving Nash equilibria3 in the conflicting forces among the various levels of biological organization. Further consistency (Hobbs and Fowler 2008) results from the connectedness involved in using empirical examples of sustainability as a source of guiding information. For example, a reduction in our population (whether willfully or as a product of systemic homeostatic reorganization), will result in a reduction in things like CO2 production, energy, and resource consumption. Likewise, reducing our CO2 production, consumption of resources, geographic range, and consumption of energy would result in reducing our population. There is reciprocity. All would be guided by information on the limits to natural variation as exemplified in Chapter 2 (with more to be seen in Chapter 6). One way systemic management accounts for complexity is through achieving sustainability in our interactions with the various levels of biological organization. For example, the hierarchical nature of life is involved in regulating our harvest of resources from other species, sets of species (e.g., communities), ecosystems, and the biosphere.6 Accounting for complexity requires that we actually reduce such harvests, and then regulate them at sustainable levels. Although we will never know how to ask all management questions, we can see the ways to ask many more. Concern about our use of

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water (Fowler 2008, Vörörsmarty et al. 2000) and nitrogen (Vitousek et al. 1981) can be extended to any other element or compound. Systemic management involves posing the related management questions so that matching (consonant) measures of both human and nonhuman species can be made. This allows for the comparison of human with nonhuman species to identify abnormality. Management would aim to relieve the nonhuman of any abnormal human influence. 5.1.3.3 Systemic management takes advantage of known correlative relationships Management is called for, as part of systemic management, if the human species is abnormal in relation to the limits in variation for any particular species-level metric when comparisons involve all nonhuman species (e.g., exemplified by the primary production humans appropriate, Fowler 2008). If our abnormality involves excesses, reductions are required; if it involves deficiency, increases are required. Steps to alleviate such abnormality get us started in the right direction to initiate the change needed to achieve sustainability. The extent of abnormality also gives us some initial idea of the magnitude of change ultimately required. However, precision regarding the goal or endpoint (Management Tenet 9) is missing. Humans may fail to maximize sustainability at certain points within the normal range of natural variation when such variation is that observed among all species. Not all species are like humans in other ways. Systemic management allows us to refine goals to better achieve sustainability through overt or direct consideration of human species-level characteristics (e.g., body size as one among many). This is achieved with correlative information (e.g., Fowler 2005). Accounting directly for correlative pattern is an essential contribution of systemic management, and demanded in cases where there are correlative relationships in macroecological patterns (such as the case of population density in relation to body size, Fig. 2.31). Human population densities comparable to those of bacteria would be intolerable (a million billion per km2, Peters 198317), even to people who are most convinced our species has no overpopulation problem. In systemic thinking this problem is solved by carefully selecting the

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species in the pattern we use for comparison/guidance. When there are correlative elements to the species-level pattern, we need information for species that are similar to humans in regard to things we are incapable of changing (at least in the short term), such as our body size or metabolic rate, or in regard to things we decide we do not want to change, such as choosing to take adult fish, rather than juveniles, in commercial fishing. In this latter case, the guiding information for fishing would be based on information from predator species of body size similar to that of humans, and that make adult fish at least part of their diet. The information consonant with the management question (“What is a sustainable harvest of adult fish”—to be refined for particular species, season, and other relevant factors) is measures of their consumption rates of adult fish. A further refinement, using mammals of our body size, would likely improve the quality of guiding information. Figure 5.2 shows body size, mammalian taxonomy, and trophic level as they are directly accounted for in comparisons involving population density. Correlative information involving mammalian taxonomy and body size was used in Figure 4.1 in regard to consumption from the biosphere.18 Again, we do not know about all correlative relationships and must accept our human limitations in this regard as basis for precaution as well as encouragement to conduct research that will add to the information useful to the guidance of management (thus, there is basis for emphasizing further development of the field of macroecology beyond the academic motivation expressed by Lawton 1999). Systemic management, therefore, includes applied macroecology with informative patterns at the species level of biological organization. Systemic management also directly accounts for environmental factors. Thus, another form of correlative information used to account for complexity comes from consideration of contextual factors in addition to human features such as body size. In this case, management questions are refined by addressing variable environmental factors through correlated changes in observed limits to natural variation. Predation rates serve as another example. We know that predation rates change with the density of prey (Holling 1959). Through systemic management any question regarding harvest rates

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log10 (density, nos. km–2) Figure 5.2 Frequency distribution for the population density of 368 species of herbivorous mammals other than humans (Damuth 1987). The top panel shows all 368 species, while the bottom panel shows the population density of humans (assuming 270 per k2) compared to that of 29 species of similar body size to that of humans (68 kg ± 50%), using the same scales to emphasize the size and relative location of the sub-sample that pertains to the management question relevant to humans. Note that, in comparison to mammals in general, humans appear more sustainable than is the case when the question is refined to information that more directly applies. Further refinement would include trophic level by using species of a trophic level comparable to that of humans (i.e., species with both body size and trophic level similar to humans, Fowler 2005).

accounts for the density of the resource species through such correlation.19 Here we are dealing with biotic contextual factors.20 Systemic management also accounts for changes in the abiotic environment over both time and space. Management questions are refined to account for such factors the same way as for biotic factors. Here, we use correlative relationships in empirical data to account for climate, weather, season, insolation, soil types, or water quality. Thus, systemic

management takes into account both temporal changes and spatial heterogeneity. Temporal factors include such things as decadal cycles (Francis and Hare 1994), regime shifts, global warming, and glaciation. Spatial heterogeneity would include factors such as altitude, rainfall, relative humidity, and temperature. As with biotic factors, both interpolation and extrapolation would be used in taking advantage of correlations among species frequency distributions and abiotic factors over both space and time to guide systemic management. In all cases, the factors we see as driving forces in correlative information are automatically taken into account in the complexity behind natural variation across all species, over broad time frames, and widely dispersed locations. We use correlative information in the refinement of management questions to more accurately find measurements of the set of species that best serve as role models in their response to current environmental conditions. This refinement leads to better defining the region of observed natural variation that is appropriate for our species under current conditions and in the locale under consideration. As laid out in Chapter 2, correlative relationships can be multidimensional, such that more than a few species-level characteristics can be considered simultaneously. 5.1.3.4 Systemic management accounts for complexity through interconnectedness Because of the reciprocal interrelationships among things, avoiding the abnormal in one thing (dimension or species-level metric) automatically relieves other systems of the effects of that abnormality. Thus, reducing our CO2 output to mimic that of other species (Fowler 2008) would address the identified problems of global warming (insofar as we contribute), oceanic acidification (insofar as we contribute), and all associated ramifications (e.g., changes in the distributions of species, disruption of embryological and morphological development, altered weather patterns, etc.) whether directly or indirectly related. We know of a few examples to list; science has yet to provide us with a full list (ultimately impossible, given the complexity involved). All such relationships and elements of complexity are involved—in the ways they are actually

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involved (not how we might imagine them to be involved). The reciprocity of interactions that pose a risk of extinction for our species is involved. Even more direct interconnectedness is involved. Reducing our species’ production of CO2, for example, would have a direct impact on our consumption of carbon-based energy. Reducing our species’ consumption of carbon-based energy would have a direct impact on our production of CO2. The two go hand in hand. A reduction in our population would result in a reduction of energy consumption and CO2 production. A reduction in any of the three would result in the reduction of our consumption of biomass (biotic resources). A reduction in our geographic range size and population density would result in a reduction in our population. These relationships may seem obvious (and to some, trivial) but they are among the realities of interconnectedness that are part of systemic management (Hobbs and Fowler 2008). These intraspecific relationships, and those involving interspecific interconnections, count among the interconnectedness accounted for in systemic management. They involve all of the dimensions over which we can measure species and involve the diversity of patterns displayed in Chapter 2 (plus those we have yet to discover). Thus, not only does systemic management deal with the diversity/complexity of management options before us, as covered above, it also accounts for the systemic effects of our abnormalities by relieving nonhuman as well as human systems of such effects. This involves, then, both the breadth of dimensions over which we can compare ourselves to other species and the multitude of interactions and relationships that characterize complex systems.

5.1.4 Management Tenet 4: Simultaneous consistency—management must be applied consistently among its applications and must apply simultaneously at the various levels of biological organization The search for “ecosystem management” is doomed to failure if the error-prone aspects of current forms of single-species management (identified in Chapter 4) are carried forward to higher

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levels of biological organization. In other words, if we attempt to conduct ecosystem management while excluding individuals and species as well as more inclusive systems (e.g., the biosphere), we will eventually learn that ecosystem management is not enough either. Similarly, management will fail if it is not based on information that accounts for finer scales of resolution, the components of individuals, cells, and molecules. Systemic management applies to, and accounts for, all levels, limited only by the human capacity for identifying management questions and capacity to conduct the research to characterize consonant patterns that provide answers. Information to guide interactions between our species and other species, between our species and other groups of species, between our species and ecosystems, and between our species and the biosphere was exemplified in Chapter 4, where Figure 4.1 shows information for application in regard to resource consumption (exposes human abnormality and indications of the extent of change needed to alleviate systems of that abnormality). Such information exists for dealing with CO2 production, population size, population density, other forms of resource consumption, energy consumption (Fowler and Hobbs 2002, 2003), and size selectivity in resource consumption (Etnier and Fowler 2005). In Chapter 6, we will see both these and other examples of such information. All are interconnected in various ways and management regarding one will always be consistent with management for the others (Hobbs and Fowler 2008). Observed limits to natural variation amalgamate time, spatial, and organizational scales (Fig. 1.4) to account for opposing forces among the various levels of biological organization. Observed patterns follow temporal and spatial variation; internal variation involves finer scale change. Being a sustainable species is achievable through guidance provided by species-level patterns because extant species occur within, are products of, are exposed to, have influences on, and are emergent from, complexity in the milieu of all such scales. Such patterns account for all of complexity, dynamics, and interdisciplinary issues, without our knowing the details, facts, or other explanatory information. Scale and context were identified as important to management by Christensen et al. (1996), not only

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in space and time, but also across the various levels of biological organization. Systemic management accounts for the hierarchical nature of reality in two ways. First, guidance is based on the elements of reality that involve an integration of complexity that includes hierarchical structure. Second, systemic management proceeds in regulation of our interactions with those elements, particularly each level in the hierarchy of biological organization.21 Consistency is achieved in the process because the guiding information is from systems governed by the laws of nature—laws that, by their nature, cannot be broken. There is consistency within and among these systems even though there are innumerable opposing forces involved in ways that seem to present unresolvable conflict. Thus, guidance for the harvest of an individual resource species will be consistent with guidance for the take from an ecosystem or the biosphere. Guidance for biomass consumption from the biosphere will limit the number of species and the geographic space over which harvests can be taken (as will guidance directly related to questions regarding the number of species taken, and geographic range), and this will be consistent with harvest from any one individual species. The same applies for other issues such as CO2 production, size selectivity, or energy consumption, whether in an ecosystem or the biosphere. Consistency is achieved, in part, because of the interconnectedness of things as they occur in reality (Management Tenet 3). For example, some scientists might think our CO2 production is less important than our appropriation of net primary production (Fowler 2008). If action were taken to reduce our production of CO2 by five orders of magnitude, it could not be done without a consistent effect on our use of energy (and visa versa). We might not complete the change needed to achieve optimal energy use, but the change would be consistent. Likewise, if the human population were three orders of magnitude smaller than it is now, both our production of CO2 and use of energy would also be smaller. Reducing our range size might seem unimportant. If action were taken to reduce our range size, it would result in a consistent reduction in numbers of species that we use as resources and the rate at which we introduce species to ecosystems they do not normally inhabit. Such consistency cannot be

escaped, and by solving one problem others are solved simultaneously.

5.1.5 Management Tenet 5: Avoiding the abnormal—management must undertake to ensure that processes, relationships, individuals, species, and ecosystems are within (or will return to) their normal range of natural variation This management criterion specifies that both the components of systems, and systems, should not exhibit abnormal qualities, characteristics, or interrelationships (Christensen et al. 1996, Ecosystem Principles Advisory Panel 1998, Fowler and Hobbs 2002, Mangel et al. 1996, McCormick 1999). The principle behind this tenet is that everything is limited by virtue of its finite nature. Thus we see limits to the variation of body temperature, body weight, population size, global primary production, resource consumption, and even variation in population numbers (i.e., the variability of variance has it own limits). The results of future studies will add to this list—a list barely initiated in Chapter 2. One of the basic goals of systemic management is to do what is necessary and possible 22 to ensure that processes, relationships, individuals, species, and ecosystems, are within (or will return to) their respective normal ranges of natural variation. However, systemic management does not attempt to control the nonhuman as a way of guaranteeing that abnormal situations are avoided. Systemic management adheres simultaneously to Management Tenets 5 and 2 to consistently fulfil the requirements of Management Tenet 4. This is done through avoiding the abnormal, ensuring that it is the human that is controlled and within the normal range of natural variation so that normal conditions can be achieved by the nonhuman, primarily through their own processes. As seen above, avoiding the abnormal is inherently a matter of accounting for complexity (Management Tenet 3). One way of understanding this involves understanding the role models of nature as the results of natural adaptive management experiments (Fowler 2008). Adaptive management is one way of dealing with uncertainty,

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(Christensen et al. 1996, Grumbine 1997, Mangel et al. 1996, Moote et al. 1994, Walters 1986, 1992). Recognizing that we do not know what we need to know, adaptive management includes a trial-anderror component to learning. In conventional adaptive approaches, management must be viewed as based on hypotheses to be tested by research and monitoring (Christensen et al. 1996). By contrast, systemic management avoids conducting the experiments ourselves. Other species are the results of experiments in adaptive management through the trial-and-error processes of natural selection, including the process of selective extinction and speciation (Chapter 3). Thus, we take advantage of adaptive management that has been accomplished in nature when we avoid the abnormal. Different forms of management at the species level have been tried billions of times over geological time scales. The rates of consumption from resource species (Figs 2.6 and 4.1) that result from these processes are the basis for distinguishing between sustainable and nonsustainable options. Varying degrees of sustainability are shown within patterns such as those included in Chapter 2—sustainability as it varies within the limits to variation seen in species-level characteristics (Fowler and Hobbs 2002). This is parallel to sustainability observed in patterns for individuals as they involve blood pressure, body size (Calle et al., 1999), and body temperature. One element of management that is often considered as a tenet in it own right is the responsibility for proving that undesirable damage will not occur as a result of proposed management action (Dayton, 1998, Mangel et al. 1996). Systemic management accepts on principle that everything we do has consequences. Some are negative and others positive; some are intended, many are unintended. The balance, and the most optimal ways forward, are exposed through information within empirical patterns; the intent of systemic management is to avoid abnormality. What we see comes as close as we can get to proof of what works. In this sense, for systemic management, the burden of proof is the responsibility of proving that being abnormal is acceptable/sustainable. Examples of, and guiding information for, meeting the requirements of Management Tenet 5 have

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been published elsewhere (Fowler 2003, 2008, Fowler and Hobbs 2002, 2003). This chapter adds to an understanding of how adhering to this tenet is also consistent with, and fulfils, the other tenets of management.

5.1.6 Management Tenet 6: Sustainability and risk—management must be risk averse and exercise precaution in achieving sustainability Sustainability may be the most common principle mentioned in the literature on extending management to include ecosystems (e.g., Christensen et al. 1996). Sustainability is at the heart of systemic management as outlined in this book, which includes accounting for risks in general (the collective consideration of risk embodied in Fig. 1.4). One of these risks is the risk of extinction faced by all species, including humans. Human extinction would likely be considered the ultimate low point in sustainability from our point of view even though, under current circumstances, our extinction might be a step toward greater sustainability for ecosystems and the biosphere. Our species is clearly not sustainable as it now exists, as demonstrated in the numerous ways we exhibit pathological interactions with other species, ecosystems, and the biosphere (e.g., Fig. 4.1, Fowler and Hobbs 2003). Implementing systemic management would lead in the direction of sustainability because it accounts for the risk of human extinction and other risks simultaneously and considers each risk in proportion to its relative importance.23 Ecosystems capable of sustainably supporting their component species (including the human component) are among the goals. We want to avoid consequences of our influence that make risk abnormal. Ecosystems forced to sustain us at the current levels of demand on them will continue to react in ways that are beyond our control and present us with increasing costs, risks, and challenges. At the core of systemic management is the objective of finding a sustainable niche for humans that is in dynamic (i.e., limited stochastic) balance with larger systems and their other components. Under such circumstances, human presence and influence would not be so pervasive as to degrade these systems’ capacity to support the species that depend on them (including our species)

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and, at the same time, not so limited as to court an abnormal risk of human extinction from being rare or overprotective of the environment. Such an approach would account for the costs to humans, but not remove them. Sustainability means facing normal risks while simultaneously deriving the goods and services necessary to keep our species among the participants within ecosystems and the biosphere. Achieving this balance is crucial. In the dynamic balances ultimately to be achieved through systemic management, both consumptive and nonconsumptive elements are taken into account such that long-term options are ensured (Holt and Talbot 1978). Sacrifice is necessary in the short term in order to bring about the changes required to achieve sustainability.24 Once sustainability is achieved (even maximized, Fowler 2008), the extent of sacrifice required would be reduced. Irreversible and long-term changes have already accumulated as a result of human influence. The adverse effects of these changes are yet to be measured; some will not be felt for generations. Adaptability is and will be necessary to adjust to these factors, but there is no rationale for making things worse (unless, of course, we accept the conventional restriction of our rationale to the short-term, to the individual, or simply to our species or species we select based on human values). 5.1.6.1 Diversity and variability Part of what we want for ecosystem sustainability is biological diversity; we want to avoid abnormal risk of extinction for other species. Diversity is exemplified in the variation among species, but also among individuals and ecosystems. However, we recognize that change is an inherent property of living systems. As Christensen et al. (1996) note: “Recognizing that change and evolution are inherent in ecosystem sustainability, Ecosystem Management avoids attempts to “freeze” ecosystems in a particular state or configuration”. Doing so would be an attempt to keep systems outside the normal range of natural variation for variation itself (Figs 2.18–2.20); zero variation is abnormal. Such attempts would also be in violation of Tenet 2 by trying to do the impossible. However, all variation involves the finite and therefore has limits, giving rise to Management Tenet 5: ” . . . neither the

resource nor other components of the ecosystem should be perturbed beyond natural boundaries of variation” (Mangel et al. 1996). This prescription includes humans as required by Management Tenet 1. Variability is clearly a natural characteristic of living systems, most evident among populations and within ecosystems (Botkin 1990). Variability in population level is an inherent characteristic of species but the variability in the variance of populations is limited (Figs 2.18–2.20) as it is for all species-level characteristics when compared across ecosystems. More generally, variability, or diversity, is the very essence of what we see for species across the various dimensions over which they can be measured (Chapter 2). Although variability is unpredictable, and chaotic, it is not unbounded (Fowler and Hobbs 2002). The variability of variation is limited; that is, variability itself shows patterns. Species frequency distributions, exemplified by the tiny sample in this book, exhibit observable bounds. Limits are set and variation caused by combinations of ecological mechanics (including such things as strange attractors, Gleick 1987), microevolution, selective extinction and speciation, the nature of carbon atoms, DNA, and other categories of factors—including those yet to be discovered, as discussed above in regard to complexity (Fowler and Hobbs 2002; Fig. 1.4). Thus, accepting variability as a natural characteristic of systems is not a basis for rejecting the concept of limits or balance, nor the related effects of opposing forces. Species outside the ranges of normal natural variability are out of balance, aberrant, or pathological; they are subject to the risks inherent in the homeostatic processes that preserve the bounds of natural variability and give rise to pattern. Such risks prevent the accumulation of species in the tails of species frequency distributions such as those shown in this book. Based on Management Tenet 1, humans are subject to the forces of nature when we are the abnormal— abnormal risk. Achieving sustainability requires that we regain a position within the normal ranges of natural variation among species. When we manage ourselves to achieve normal participation by our species within various systems, we account for both the variability and the balances of nature—both risks and sustainability.

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Doing so results in protecting other systems from abnormal human influence. The reactions of such systems include risks to humans and abnormality among other species, ecosystems and the biosphere. The homeostatic processes of living systems are part of what leads to their formation, patterns achieved in dynamic balance—balances that differ according to the influences of the abiotic environment. Achieving participation that results in more normal influence by humans helps open options for the future. Some options often considered in conventional management will have to be sacrificed to achieve the objective of sustainability. The corrective aspect of solving environmental problems requires measures which account for dynamics of differing time scales. Short-term costs in management action are the price to be paid both for correcting problems that have accumulated from lack of action in the past and for achieving long-term gains. 5.1.6.2 Avoiding risks Avoiding the risks of abnormality tends toward sustainability; progress on one front is simultaneously progress on the other. Risks are innumerable and unknowable at all levels, some arising from our role as part of larger systems, some from our interactions and relationships with other systems, and some from the systems of which we are comprised. The existence of species at various positions within their normal range of natural variation is evidence of the potential for sustainability in the face of these collective risks and constraints (Fowler and Hobbs 2002). Simultaneously, these species and the patterns they form exist in part because death and extinction are natural contributing factors (Chapter 3). Thus, various risks are taken into account in systemic management at the species level, just one of which is the irreversible change that would be represented by our own extinction. Finding a place for humans within the normal ranges of natural variability does not avoid risk; it avoids the abnormal of risks that are normal parts of the complexity we face. Risk will not vanish; it is an essential factor in natural selection that influences the form and function observed in living systems. Falling closer to the central tendencies of variation among species can be expected to lead to

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fewer risks, or lower overall risk, than is the case of being pathologically abnormal. 5.1.6.3 Evolutionary time scales Risk reduction is embodied in regaining a position within species frequency distributions to account for several temporal, spatial, and physical scales and any of their relevant hierarchies. The risk of species extinction and the risk of selective individual mortality involve evolutionary time scales. Because other mechanical processes are also involved in the formation of species frequency distributions, risks faced over shorter time scales are accounted for in using these distributions for guidance. For example, radical declines in human population could result from events such as epidemics, war, bioterrorism, starvation, or sudden climate change. Such changes have been known to occur in the past (e.g., Redman 1999). These risks over varying time scales are taken into account collectively by using information on the limits to natural variation to guide human endeavor, and they are taken into account in proportion to their relative importance.25 Time scales are of utmost importance as it is necessary to account for the fact that processes such as extinction and evolution can involve much longer time frames than the several thousand years that we have been using tools, cultivating crops, harvesting fish, managing water (land, crime, timber, air quality, energy), and “controlling” predators, disease, and weeds. Such time scales are far beyond those of modern empirical science. Our population has grown exponentially over the last 5000 years (with significant dips in response to the homeostatic effects of epidemics such as the black plague and influenza). Most change, in all respects, has occurred in the last several hundred years with the acceleration of industrial technology—a very tiny fraction of human existence on this planet. In the grand scheme of things we still represent an experiment of nature (Ehrlich and Ehrlich 1996). Our “accomplishments” or “successes” are not in any way proof that what we have become can be sustained (recall Combination 6 in Table 3.1) although they are often judged as positive based on human value systems. The empirical examples represented by other species are themselves tenuous proof of sustainability

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in view of the high turnover among species over evolutionary time scales. Only by collectively considering emergent patterns involving examples over space, time, and numerous species can we most effectively tap into information most revealing of sustainability. Life presumably began with one simple species and developed through multicellular forms to the diversity we are threatening today. The shapes and forms of macroecological patterns represent higher-level evolution throughout that process and are likely to continue to change. All we have to go on is the hope that we can remain part of the picture (for at least a while longer) by mimicking26 other species that have survived, in part, by being malleable and adaptive.

5.1.7 Management Tenet 7: Knowledge and information—management must be based on information Mangel et al. (1996) indicate that management should be based on understanding. Understanding extends to larger systems, like ecosystems or the biosphere, and should include understanding of human systems. Understanding should include the emergent at all levels. Christensen et al. (1996) maintain that “current knowledge and paradigms of ecosystem function are provisional, incomplete, and subject to change”. Holt and Talbot (1978) say that management decisions should include a safety factor to allow for the fact that knowledge is limited and institutions are imperfect. How big should such safety factors be to meet the requirement of Management Tenet 9? The principles behind other management tenets reflect what we currently understand. We know that we are part of nature and that nature is complex; we know things are interconnected; we know we cannot control everything; we know there are limits; we know we do not and cannot know everything; and we know we need guidance. Systemic management finds and uses guidance that accounts for the things we cannot know (Fig. 1.4). Thus, systemic management takes what we know (including what we know about existing knowledge, information, and the limits to human endeavor) and uses information on limits to guide the process of establishing goals and objectives, knowing that they will

change with circumstances that themselves change in ways that are beyond our control. Information is deeply ingrained in the process of using the limits to natural variation to guide human endeavor (Belgrano and Fowler 2008, Fowler 2008); it is cybernetic and accounts for both what we know and what we do not know, all combined in proportion to the relative importance of each component, in accord with the other management tenets. The complexity behind species frequency distributions includes factors such as the properties of chemical compounds and the processes in their formation. It includes the centrifugal force of the Earth’s spin, physiological dynamics, pheromones, territoriality, and seasonal cycles in the weather. The genetic code (genome) of all species is information included in the information inherent to such patterns (Fowler 2008, Wilson 1985). Figure 5.3 illustrates the cybernetic information content (equivalent to the measure of diversity) of species frequency distributions and the progress to be accomplished through systemic management. The top panel shows the frequency distribution for population size among species of similar body size to that of humans. The information content of this graph reflects the combination of factors that were involved in the emergent pattern we see (Fig. 1.4). The various contributing factors are taken into account in proportion to their actual relative importance. Thus, the extent to which economic forces are behind the position of humans is reflected in this information, as are the decisions to ignore historic warnings about the problems of overpopulation. Also included are the evolutionary factors behind the forces of reproductive urges, efforts to save lives, fear of death, programs to alleviate starvation, the development of agricultural and medical practices, and the politics of individual rights, each represented in proportion to its relative importance. The bottom panel of Figure 5.3 represents a measure of the information content of the upper panel as a function of the position of humans (based on the Shannon information or biodiversity index, Nielsen 2000, Pielou 1969). Thus, the current information content of the full set of data is shown by the second lowest point of the ogive (the curve of solid points), directly below, and corresponding to, the

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Figure 5.3 The frequency distribution of population sizes among 64 species of mammals of approximately human body size showing an index of the information content and biodiversity of this sample as a function of the position of humans (bottom panel, Fowler 2008). Assuming no change in the distribution of nonhuman populations, the historic increase in human population has reduced the biodiversity (and information) from levels shown at the maximum of the curve represented by the points to the current state represented by the second last point at the right (bottom).

current (2007) population of humans. The remaining part of the ogive shows how the information content of the sample would change if the human population were to be reduced and shows a maximum at a population level of about 9.6 million. This would simultaneously maximize the information and biodiversity of the sample of species (including humans, Management Tenet 1). This human population is larger than both the geometric mean (0.16 million) and the arithmetic mean (2.35 million) of the nonhuman species. Similar changes in biodiversity and information content would be seen as a result of systemic management to change the position of humans in other frequency distributions (e.g., energy use, water

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use, biomass consumption, Fowler 2008). Thus, there are two points to be made about information as measured in diversity. First, there is information in the frequency distributions among the nonhuman species. Second, there would be an increase in the information content of measures of species as a consequence of systemic management that includes humans in the sample (Management Tenet 1). The species frequency distributions that include humans under current circumstances are not as informative as they would be if humans succeeded in avoiding the many ways in which we are currently abnormal (this represents a direct reflection of the uninformative nature of current measures of humans, Fowler and Hobbs 2003). Through systemic management to reposition humans, the world’s biodiversity would show directly measurable changes, even if other species did not respond. However, following human change, the information content of species frequency distributions would change through responses by the nonhuman species to human action to become even more informative. In conventional approaches, we seek and use information about those things we think are most important (usually involving the nonhuman), deal with those (usually transitively), and assume the unimportant factors (and things we have not thought of, or cannot measure) will add precision in minor ways when we get around to dealing with them. This creates several grave risks, all involving assumptions that: We can judge the relative importance of various factors. The downstream effects of what we judge to be unimportant factors now will be unimportant in the future. The collective effects of what we see as unimportant are not more important than any one of the factors we now think of as important. What we do not know is likely to be unimportant.

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The net effect is to make guiding information a human construct, directly implanting our own limitations in the basis for our decisions (top row of Fig. 1.1). By contrast, in systemic management (bottom row of Fig. 1.1), the guiding information accounts

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for everything in proportion to its relative importance and is subject only to the limits of the human capacity to observe—a major limitation but not brought directly into the management process in regard to any question we decide to address and for which there is consonant information. We might think the effects of a black hole, a billion light years away, are unimportant in our decisions regarding appropriate predation rates for humans. That in fact might be the case. If so, such effects are also unimportant in empirical information on the limits to predation rates (or CO2 production, or geographic range size) for other species. If, on the other hand, they are important, it is the actual level of importance that is taken into account, in the same way. This holds true for every element of complexity (i.e., every part of reality). A significant step forward in this regard is the fact that the unknowable (an extreme of uncertainty exemplified by the inability to predict the properties of salt based on knowledge of chlorine and sodium) is represented in the complexity taken into account in proportion to the relative importance of each factor. Such factors include the effects of coevolutionary dynamics, selective extinction, and speciation (Chapter 3), sexual selection, all ecological mechanics, and all physical and chemical factors—all things we sometimes think we will ultimately understand through scientific investigation (e.g., Wilson 1998). The unknown of the future is, of course, left out (Fig. 1.4), but patterns from the past allow for an appreciable level of insight into the future (patterns as statistical models, Pilkey and Pilkey-Jarvis 2007, like the model anyone can use to predict the difference between the weather in July compared to December). Included in such models are those things we do not know about now but which will become the focus of an academic discipline when we discover them, if we discover them at all.

5.1.8 Management Tenet 8: Including science—management must include scientific methods and principles in research, monitoring and assessment Limited knowledge is an important issue, because it is associated with risk. This is especially true when integrated as a factor in the decision making

of conventional management, compared to the relative immunity of systemic management to such limits. Systemic management brings to bear the knowledge, or the fact, that we know that we cannot know or find the information necessary to fully understand the mechanics of biological systems such as ecosystems or the biosphere.27 This is one of the most serious inadequacies of conventional science as a basis for management. This is not to criticize science as much as it is to acknowledge the limitation of science and the finite nature of the human mind. However, problems result when we ignore this limitation in the way we bring science to bear in management. Alone, each discipline is insufficient and pairs, trios, or other combinations are little better (maybe worse).28 We need to involve science in a way that takes into account all of complexity, including the reality of the limitations of science. The need to account for breadth of information is a need expressed in calls for interdisciplinary approaches to management, of which there are many. “The full range of knowledge and skills from the natural and social sciences must be brought to bear in dealing with conservation problems” (Mangel et al. 1996). To be fully interdisciplinary requires including epistemology and philosophy, complex systems science, chaos theory, emergence, and knowledge of our limitations. How do we circumvent the limitations of science to achieve the needed information, and still involve science? The answer brings us back to empirical information regarding each management question. Figure 5.4 shows some of the natural sciences involved in understanding the dynamics, processes, and mechanics behind the formation of species frequency distributions. Even the amalgamation of these fields and the knowledge in whole systems thinking29 is only a step toward understanding species frequency distributions as emergent and natural phenomena. For a more complete list of sciences that relate to intrinsic/endogenous and extrinsic/exogenous factors, we would have to include subatomic physics, chemistry, atomic physics, and classic physics as well as climatology, geophysics, astrophysics, and astronomy. Again, species-level patterns are something that science can measure, observe, and study as the products of complexity, but we cannot

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Paleontology Evolutionary biology Genetics (esp. pop. gen.) Evol. stable strat.

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Figure 5.4 A graphic representation of the processes of evolution and selective extinction and speciation (similar to graphs in Chapter 3, esp. Appendix 3.2) showing some of the biological disciplines that contribute to knowledge of the species-level characteristics over which these processes operate and some of the processes involved. To varying extents, these disciplines also contribute limited information concerning the specific nature of selectivity and biases in the processes. Ecological mechanics are not included in the graph per se, but are represented by biology and all of its sub-disciplines. The lines within the graph exemplify selective extinction and speciation and evolutionary processes. The arrows indicate the limited contributions of some of the existing disciplines to our incomplete knowledge of the various processes.

exhaustively study this complexity in scientific studies. The consilience sought by Wilson (1998) is an impossibility as a human endeavor (Berry 2000) but is achieved in nature (i.e., reality is made up of all parts, including those not subject to scientific observation, Fig. 1.4). Thus, all the academic fields combined do not cover the complexity of reality in its entirety. Even if they did, we, as stakeholders in our current position in Figure 1.1, would not be able to combine (recombine) this information in a way that accounts for everything in proportion to its relative importance. To deal with this Humpty Dumpty syndrome (Fowler 2003, Fowler and Hobbs 2002, Nixon and Kremer 1977) we take advantage of the factors brought into account by species-level patterns as natural phenomena that we observe (with the consilience found in reality, Fig. 1.4). Then science

becomes critical for what it can do very well: objectively, and reductionistically study a phenomenon to obtain information about its natural limits. Each part we subject to study, however, is always within its full context (Fig. 1.4). The best science for management then becomes that aspect of science that studies the pattern that is consonant with the management question being asked (bottom row of Fig. 1.1; Belgrano and Fowler 2008, Hobbs and Fowler 2008). As outlined above, mimicry based on patterns automatically includes immeasurably more than what we now know of the factors that are involved; objective, carefully conducted science is an essential tool for discovering, characterizing, and analyzing such patterns. Thus, as introduced in Chapter 4, accounting for complexity is accomplished for us, not so much by the science involved as through the natural processes that

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work in ways analogous to the process of Bayesian integration, 30 including the combination of processes involved in selective extinction, speciation, and evolution as it applies to change within species. Our own understanding of these factors will grow through the future development of new and existing fields of science. However, species-level patterns—past, present, and future—account for things about which we know nothing (Fig. 1.4), and fields of science we do not know to include in diagrams such as Figure 5.4 which is itself confined to our embryonic understanding of selective extinction and speciation (Chapter 3). In the past, the field of evolutionary biology has not had as much influence on management as has our understanding of ecological mechanics; evolutionarily enlightenment is needed in our management process (Brown and Parman 1993, Conover and Munch 2002, Fenberg and Roy 2008, Law et al. 1993, Stokes and Law 2000, Swain et al. 2007, Thompson 2005). Most of the maximum sustainable yield approaches and their derivatives are based on population dynamics. The evolutionary effect of harvesting is very rarely, if ever, taken in to account; it is often assumed to be unimportant and left to be proven important through future study. 31 Conservation biology now pays attention to the risk of extinction, but management to reduce the anthropogenic causes of extinction and to account for the risk of our own extinction has not yet emerged. The things studied in the fields of paleontology and evolutionary biology are brought into systemic management, which identifies a place for humans within the limits to natural variation (Fowler 2008). Part of what species are exposed to in the diffusion-like processes of natural selection is the coevolutionary dynamics neglected in most current approaches to management. 5.1.8.1 Empiricism vs. amalgamation of science As outlined earlier, it is not the disciplines of science per se, as much as the objects of their study that are important in systemic management, especially as seen in their combination.32 What we have been doing in conventional attempts to convert the products of science to action has not prevented (and probably contributed to) the problems we face (e.g., MEA 2005a,b, Appendix 4.2). Some of this has to

do with science (Appendix 5.1), but a great deal has to do with our belief that we can effectively combine the products of science rather than make an effective choice of those products without having to convert, combine, or interpret. Complex systems science and whole system thinking are behind the concept that we can treat patterns, including species-level patterns, as emergent natural phenomena.33 They emerge through a much broader integration of the factors, processes, mechanisms, and physical manifestations of things which science can investigate but cannot adequately represent, especially in their combinations. Rather than rely on any particular science for advice, therefore, systemic management depends on simple empirical observations—observations consonant with management questions. The advice is based on the observed not the science. Each observation is a piece of information generated by a specific kind of science, specialized in making such observations, often of things we cannot observe with our ordinary senses. Species-level patterns containing such observations integrate all such information (Fig. 1.4, with the “equation” integrating the infinite being specific to each observation). The information content of the genetic code that contributes to what species are (and thus their locations in species frequency distributions) is made available to us through these distributions. This is a major step forward because, even though we cannot have the detailed information in hand, such information is taken into account.34 Thus, most of the unknowable35 of such information is not ignored and although we cannot reveal the details, the unknowable is accounted for automatically. All species, and the sustainability they represent in species-level patterns, exist within, emerge from, and reflect all of the complex systems we need to take into account in meeting Management Tenets 3, 7, and 8. 5.1.8.2 Monitoring The role of science goes beyond the process of gathering data regarding measures of natural variation and its limits—observing patterns. Monitoring, analysis, and assessment should always precede and accompany our use of resources (Holt and Talbot 1978, Mangel et al. 1996), our production

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of waste(s), or changes in the distribution of our population (or any other measure subject to management). We need to be clear about correlative relationships within species-level patterns and monitor factors known to be involved. These include the variety of influential biotic and abiotic factors in order to make direct use of such information. In the management of our use of resources, these would include the population levels of resource species and changes in the abiotic environment in which they occur. Thus, in systemic management, the population levels of resource species would be monitored to enable use of correlative information regarding predation rates that change in functional response to resource population levels. Regulation of harvests can then be based on patterns in functional response relationships. Environmental conditions are also important to monitor so that we can use species-level patterns in their relationship to circumstances in the abiotic environment. Correlative information is crucial to systemic management to account for climate and geographic variation. As many species as possible would be monitored, both for assessing related changes in species-level patterns in response to management and for determining our relative position in the ranges of variability observed. If humans undertake change to mimic other species, the other species will react, leading to less abnormality among systems relieved of abnormal human influence—sustainably. Monitoring the human is also an important component of information needed to evaluate progress in systemic management. The results of continuous monitoring would be made available and reviewed as with any form of management to ensure, for example, that the established sustainable harvest rates are not exceeded and that desired results are being achieved in the responses among nonhuman systems. This would apply to any aspect of systemic management. We would monitor the size of the human population, water consumption, production of CO2, energy use, numbers of species we consume, and as many other species-level measurements as possible. An annual “state of the species” assessment for humans would serve to help assess and guide progress—always carried out through comparisons with other species. Public awareness

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through such a program would be of immeasurable value in motivating action. 5.1.8.3 Models as tools for research—carefully chosen for guidance Models are useful tools and need to be included in our tool kit (Christensen et al. 1996), especially for research in which measurement is a primary element. They are invaluable in helping us generate hypotheses to be examined empirically. However, simulation models such as ecosystem models are an incomplete representation of ecological mechanics; they include very little regarding other factors. Left out, either entirely or in large part, are the elements of evolution and the driving factors behind evolution and ecological mechanics—factors both intrinsic and extrinsic to the system. They include little or nothing involving selective extinction and speciation. Other models such as those found in the Appendices of Chapter 3 are also ecosystem models, and are as equally valid as any other for the species sets represented by populations in ecosystems. Both the models based on ecological mechanics and those based on the mechanics of evolution, speciation, and extinction focus largely on the respective mechanics to the relative neglect of many other elements of complexity. In the end, what we observe in nature is the result of the combined set of processes, only a few of which can be partially captured in models. Models help us think about, but only superficially represent, reality (Pilkey and Pilkey-Jarvis 2007). They carry the limitation of reductionism. As with the case of natural patterns themselves, such reductionism has its value when models are carefully selected. For systemic management, their power to represent patterns as subsets of reality makes them valuable when, as models, they are consonant with specific management questions. Thus, one form of modeling is to be encouraged. Models that mimic the “shapes” of species sets such as that shown in Chapter 2 (see, esp., Fig. 2.34) are very useful especially if variance about central tendencies is included. Such empirical models demonstrate the relationships among species-level measures; those already published provide a good start (e.g., Brown 1995, Charnov 1993, Gaston and Blackburn 2000, Peters 1983,

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Rosenzweig 1995). 36 Through these relationships we better understand the end results of the dynamics we explore with the more process-or mechanics-oriented simulation models (i.e., simulation of dynamics over time). Where humans fall in the multidimensional space of such relationships then becomes more clear and the degree to which we exhibit abnormal tendencies can be assessed and used to set goals for change (management). These lead to taking advantage of correlative information. They fall in the category of what is known as statistical models (Pilkey and Pilkey-Jarvis 2007).

to perception, but perception does not guarantee change. The laws of nature do not change just because we perceive them, and our perception of needed change does not ensure we will make the change. Until we have a sense that we have any degree of control over ourselves, it is nonsense to think we have control over the more inclusive systems with all of the other species, their comparable complexities, and many interactions.

5.1.8.4 Importance of social sciences Radical changes in individual thinking and human behavior will be needed to achieve human sustainability. The social sciences are valuable in recognizing that the complexity of human affairs is not to be ignored any more than that of ecosystems, other species, or individuals of any species. Predicting, monitoring, and assessing the sociological effects of management actions (Mangel et al. 1996) requires considering the opposing forces involved and the related conflicts we experience (Chapter 4) in management. More important, however, are the changes required in human systems to achieve sustainability. This will involve such disciplines as economics, religion, sociology, ethics, psychology, politics, international affairs, race relations, and epistemology.37 Each field of study involves the investigation, documentation, and understanding of human realities. These human realities include their dynamics, processes, mechanics, and interrelationships. We have to confront our own complexity, and realize that these sciences are also limited, and the objects of their study are subject to the laws of nature along with everything else. Within the products of these fields of study is information about how change can be achieved systemically. Systemic management involves systemic human change. It remains to be seen whether or not we humans (both individually and collectively) have sufficient self-awareness and self-control to rid ourselves of abnormality within the various species-level patterns, before nature accomplishes the task for us. The various forms of science contribute

Management of any kind is ineffective without a sense of which way, and how far, to go. For management to succeed, measurable goals and objectives are necessary, with special emphasis on sustainability (Christensen et al. 1996). This book helps construct the foundations for successful management by outlining measures (i.e., metrics) of importance to our species and its sustainability. It also points the way toward discovery of many more such measures. As pointed out by Fowler (2003, 2008) and Fowler and Hobbs (2002, 2003) (and as will be seen in Chapter 6), we have some initial benchmarks for factors important to sustainability—each of which provides measures of problems, including overpopulation, excessive energy use, abnormal CO2 production, the consumption of too many species, and pathological resource consumption. Inherent in these species-level assessments of problems are the goals or objectives to be achieved; a measure of overpopulation automatically suggests a goal. While ultimate goals are contained in measures made in comparisons with other species in the absence of abnormal human influence, initial goals for a variety of species-level measures can be generated.38 Interim objectives are determined by such factors as the momentum of change in the opposite direction, political will, and extent of species self-awareness among the population. Although the broader goal of sustainability may never be precisely measurable, it includes reducing the abnormal in risks of our own extinction. The magnitude of risk requires action despite uncertainty about details; we have enough information

5.1.9 Management tenet 9: Goals and objectives—management must have clearly defined, measurable goals and objectives

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to know the direction we should be headed and initial estimates of the magnitude of change that is necessary. Within the mix of appropriately chosen species, we are then faced with the challenge of further change in response to environmental variability. Thus, continued refinement of goals includes accounting for past abnormal anthropogenic influence and waiting to see how other species, ecosystems, and the biosphere respond, not only to change by humans but to that of the Earth (e.g., glaciation, global warming). We need to find correlative information to help focus on the portion of the range of natural variation among nonhuman species that best applies to us humans. Also, we need to account for the nature of the biotic systems with which we are interacting, and the present and future conditions under which we are contemplating management. Further information of this kind will result in greater clarity by focusing on smaller portions of the overall range of variation, and narrowing the range of options, so that goals and objectives can become more specific. We cannot expect the optimal ranges to remain fixed. Variation is itself one of the measures of things that cannot be zero (Figs 2.18–2.20). Circumstances change and part of systemic management is to account for such variation by using correlative information regarding environmental circumstances.39

5.2 Limitations of systemic management Systemic management needs to be adopted with a full understanding of what it is. This means understanding both the problems it solves as well as its own limitations. The limitations are largely human limitations as addressed in this section, and include: Priorities are not clearly specified. Species frequency distributions are inadequately developed. Implementation requires addressing component questions. Acquiring information is logistically difficult. Statistical and biodiversity measures are reference points—not magical numbers.

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5.2.1 Priorities are not clearly specified Systemic management has limited effectiveness in setting priorities, answering such questions as: If we are overharvesting two species of fish, as two problems to solve, which should be solved first? Is it more important to produce less CO2 or reduce our appropriation of net primary production (Fowler 2008)? Is it more important to reduce our population or reduce our geographic range size (Fowler 2005)? The answers to such questions cannot be known with a great deal of certainty, especially if objectivity remains a goal. For an individual management question, the guiding information within the consonant pattern accounts for the relative importance of the various factors we want to take into account in each case. Furthermore, we can now address a wide variety of questions but cannot ask all questions. However, priorities have not been specified for those we can ask. Several guidelines seem appropriate and are presented below. It must be recognized that these guidelines may be challenged on several grounds but seem reasonable while experience leads to the development of better options. First, priority may be given to management that contributes to the solution of a number of known problems simultaneously. Interconnectedness guarantees that this will happen in any case; even unknown problems are included. However, we know problems such as excessive CO2 production, water consumption, energy consumption, geographic range size, biomass consumption, and overpopulation are all interrelated. Thus, placing a high priority on reducing human abnormality for any one is justified because it will contribute to reducing the abnormality in the others (as well as problems indirectly associated with any one).40 But this does not justify avoiding attention to other problems in their own right. It acknowledges the fact that addressing the related suite of problems, without solving the population problem, will result in forced population reduction without regard to consequences for individuals (e.g., mass starvation). Second, priority may be proportional to the hierarchical level of organization of the system we

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exemplifies a very real ecosystem-based management question—serious questions rarely posed in today’s management. For example, Figure 5.5 addresses this question in regard to walleye pollock production in the eastern Bering Sea as an area within the geographic range of the Steller sea lion, an endangered species as of the late 1990s. We cannot control what portion of walleye pollock production that is left unconsumed by fisheries will be consumed by sea lions, or whether it will result in a suitable mix of species for an adequate sea lion diet. We can, however, address the question before us in a way that guides management action. With empirical information

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are influencing (higher for higher levels of biological organization or the more inclusive systems of which we, as a species, are a part). This seems rational on the grounds that inclusive systems control and limit component systems—their subsystems—more than the reverse (Ahl and Allen 1996, Campbell 1974, O’Neill et al. 1986, Wilber 1995). For example, this would mean that systemically managing our total harvest of biomass from an ecosystem would take priority over managing our harvest from any one species, and managing our consumption of biomass from the biosphere would take priority over managing either the harvest of an individual species or that from an ecosystem. The pathologies in Figure 4.1 would be treated with highest priority given to the bottom panel and lowest to that of the top panel. Certainly, full systemic management requires undertaking management at all levels, starting with issues where we have information (and promoting scientific efforts to produce more, especially where it is lacking). In any case, we would strive to avoid the abnormal in total harvests from ecosystems, even if we do not have information on the limits to natural variation in the rates of predation on a particular resource species. Given a choice, it is probably better to place a higher priority on avoiding abnormal interaction with ecosystems than with individual species. Part of the argument for considering ecosystem applications of higher priority than single species applications has to do with the fact that, at the ecosystem level, we are automatically (but only partially) considering the effects of our influence on all the species involved rather than a particular species. In the case of an endangered species, for example, we can (in conventional thinking) ask: “How much of the production of a particular resource species should be left so that it can be consumed by the endangered species?” First, we recognize that we have no control over which species get the production left after our harvests. Then we recognize that we can address the question of how much would be left for all other species that consume the resource species in question. The management question here, as a systemic question, is: “How much of the production of a particular resource species should be left for all other species in the ecosystem of the endangered species?” This

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consonant with the question we can confine ourselves to unharvested production that is within the limits to natural variation in what other species leave for the rest of the ecosystem. Consumption and nonconsumption are dealt with consistently (Hobbs and Fowler 2008). A third way to set priorities is to follow the lead of the medical application of systemic management. If a person’s temperature and body weight are both abnormally high, priority for initial action will probably be given to the fever, even if body weight is equally abnormal. This decision would be based on knowledge of the known risks of imminent death associated with fevers. The problem of being overweight would not be left unattended, but the choice of which problem to address first would be made based on known risks. Alternatively, abnormal cholesterol levels and body weight might be treated on the basis of which one shows the greatest abnormality. That is, risk in some cases may be proportional to the degree that the situation is pathological. Similarly, reducing the amount of energy we are appropriating within ecosystems and the biosphere (Fowler 2008), would be given higher priority than becoming strict vegetarians to change our trophic level (Fig. 2.3), because humans are near the center of all frequency distributions for trophic level. Becoming vegetarians would contribute to solving the problem of energy appropriation but energy appropriation requires dealing with overpopulation to solve the majority of the problem and would be given priority both because of the magnitude of the problem and the degree to which it would solve other related problems. A final means of setting priority may be found in information linking human abnormality to abnormality in the risk of extinction—not just human extinction, but extinction in general. This is an area where simulation modeling can conceivably play a very important role.

5.2.2 Species-level patterns (frequency distributions) are inadequately developed When applying frequency distributions to human individuals, we distinguish the normal range of natural variability from the abnormal (aberrant, anomalous, pathological) range. Body temperature,

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blood pressure, or body weight (Calle et al., 1999) sufficiently outside the normal ranges of natural variation present abnormal risk. However, their occurrence cannot be characterized as unnatural, nor are the risks of being outside the normal ranges unnatural nor unreal. Frequency distributions for individual-level metrics (individuallevel patterns) are emergent natural phenomena, just as are species-level patterns (or species frequency distributions). What we do not have for species frequency distributions, that we do have for individual frequency distributions, is the indepth knowledge of their regularity, how they vary with other characteristics (as weight varies with age and gender), and what the magnitude, predictability, and nature of some of the risks are. Death is known to be associated with fever and the increased risk with increased fever is an accepted phenomenon. The precautionary principle dictates that, when we do not know, we act conservatively until we find out.41 Future research will improve our understanding of what is normal and what is abnormal at the species level. The risks of extinction that are associated with varying degrees of abnormality will become better understood through research involving both empirical information and modeling. The entire range of variability is natural and humans are part of natural variability. We are subject to the laws of nature that present risks that define normal ranges of natural variation. These are also natural. Being within the normal ranges of natural variability accounts for the complexity of factors that present the risks. Dealing with complexity, however, is not a simple issue, even with insight that shows how to account for it. If we look at systemic management as benchmarking,42 it is not a matter of doing what any other individual species does, nor does it involve seeing an individual species (especially ourselves) as optimal in their sustainability. In nature’s trialand-error approach to finding sustainability, many trials are failures; over 99.9% of all species have ultimately failed. Optimality only hazily emerges in measures such as statistical central tendency or maximized biodiversity (Fowler 2008) for specieslevel patterns appropriate to humans and prevailing conditions.

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Even collectively, groups of species as currently observed are not a perfect basis for defining ultimate sustainability for humans. Abnormal human influence is inherent to these distributions as we see them today. They reflect what is sustainable in the face of human influence. In view of the current extinction crisis, such sustainablity is quite tenuous. Our contribution to the environment experienced by these species has not been within the normal ranges of natural variability for some time. This is clearly shown in Figures 5.1–5.3, for example. If current population sizes of other mammals of our body size were not responding to the atypical levels (and effects) of the human population (and all other human abnormalities), they would serve much better as frames of reference, as approximated with the hypothetical distribution for such conditions in the top and bottom panels of Figure 5.1. Part of being adaptable involves changing the sustainability targets for our species as systems respond to changes we can muster (assuming we can make such changes in time to avoid human extinction as a systemic reaction to past and present abnormal human influence). Many existing species frequency distributions do not adequately account for space. The consumption rates for walleye pollock in Figures 2.6 and 5.5 are from major portions of its geographic range—pollock populations. They do not necessarily represent consumption rates for the species as a whole. Estimates of portions consumed from the entire species would be necessary to produce a more useful species-level pattern if we are asking questions about our harvest from the species as a whole. Some species that consume walleye pollock may take very few (or very little biomass) because of very limited overlap in geographic ranges. This would be one of the many factors contributing to what we see (an ei in Fig. 1.4 contributing to patterns such as that of Fig. 2.6). Alternatively, consumption by one consuming species may appear abnormal if measured only in an area of overlap and not applied to the resource species as a whole. For example, such problems could be more extreme for the pattern in consumption rates of fish species of the Northwest Atlantic (Appendix Fig. 2.1.5) than for walleye pollock if they represent a smaller fraction of the overall geographic range of the prey species.

Another reason we want to be cautious in the use of existing species frequency distributions is the nearly total lack of temporal variability (at all time scales) of these distributions. Assessing sets of species, as well as individual species (especially humans) depends on such information. Consistency in the shapes and positions of such distributions for other systems, across space and time, will help evaluate the utility and correlative analysis of any individual distribution. In fisheries management, for example, the walleye pollock fishery would be managed by constraining our take to less than 15% of current harvest levels if we applied existing information directly (Fowler 2008). While beginning by making large reductions in the take is undoubtedly advisable, a full 85% reduction may either be too much or too little.43 We would be much more confident in suggesting such radical management if we had 30 other sets of fishery data like that for walleye pollock covering the entire geographic range of the resource species, all indicating very similar optimal take rates. We would be even more confident in such a recommendation if 10 separate sets of data, spread over decades of time, for walleye pollock in its complete geographic range, showed the same distribution (even if data for the consumer species individually showed them to reposition themselves within the histogram representing the full set of walleye pollock consuming species). Similarly, data for predators feeding on species otherwise similar to walleye pollock would be informative.44 Consistency in cross-system comparisons and consistency in time would improve the foundation for decision making. How do such distributions vary over evolutionary time? In all cases, covariants such as body size, life history strategy, trophic level, and geographic range are helpful in directly accounting for such factors as was done for body size in looking at population density (Fig. 2.31, Fowler 2005). The need for data and research is clear. This is particularly obvious for the multidimensional relationships mentioned in Chapter 2 and above. Interspecific multidimensional patterns are increasingly recognized (e.g., Brown 1995, Charnov 1993, Gaston and Blackburn 2000, Peters 1983, Rosenzweig 1995). Metabolic rates in relation to body size are

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clearly quite linear in log-log space. Clarity is developing in regard to the density/body size relationship (Damuth 1987, 2007, Schmid et al. 2001) and population variation/body size relationship (Sinclair 1996). Not all relationships can be expected to be so clear. Any relationship between total (global) population size and rate of increase has yet to emerge through direct observation, even though we expect one based on body size/density and body size/rate of increase relationships (see Appendix Fig. 2.1.22). Local density and range size appear to be related (Hanski and Gyllenberg 1997), but what about geographic range and rate of resource consumption? What about all the many multidimensional interrelationships yet to be examined (Table 2.1)? The need to be adaptable is crucial to any approach to management, emphasized by the prevalence of adaptable species (the abundant smaller bodied species can evolve more rapidly, on the whole, than their larger bodied counterparts). Behavioral adaptations are within our options if we can use the information content of species frequency distributions. However, being adaptable includes being wary of the limitations of even these rich information sources. The changes we have set in motion, at all levels of organization and time scales, are changes we want to be prepared to accommodate. These are in addition to the kinds of changes nature will present to us that are relatively independent of our influence (e.g., periods of glaciation, shifts in the magnetic pole, cycles in solar radiation, or continental drift).

5.2.3 Implementation requires addressing component questions Using empirical information in systemic management establishes goals and objectives. However, knowing an ultimate objective does not specify the means of achieving it. While this is a limiting quality of systemic management, it is certainly not a problem devoid of solution—using systemic management. The more detailed aspects of change involve the need to address more specific questions. Many such issues can be addressed with measures and information that science can produce. However, complexity is not always a matter

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of objective scientific insight. There are also issues requiring systemic management that effect such things as religious beliefs, designing political systems, formulating educational programs, or altering existing law in order to achieve sustainability—most of which do not lend themselves to precise scientific measure. They are subjective but systemic, nevertheless. Thus, evidence of a fever does not contain advice as to how (or whether) to achieve a reduction in body temperature, any more than evidence that the human population is too large provides guidance on how (or whether) to reduce it. Refined components of each issue (refined questions, Appendix 5.2), however, lend themselves to the application of systemic information in setting goals to be achieved (Management Tenet 9). For example, we can systemically rule out ways that will not work (e.g., birth rates of zero would be below the normal range of natural variation for birth rates, if we were to decide to take action to reduce the human population through controlling birth). Systemic management would prohibit such extremes. In all cases, we have the benefit of the more significant contributions of systemic management: identification of goals to be achieved, situations to avoid, measures of the problems before us, and addressing subsidiary questions of implementation. Thus, identifying problems, having a basis for assessment of status and needs, and setting goals do not solve the problems. These are important steps, but knowing what to do is different from knowing how to do it. Both are different from actually doing it (praxis). Navigation requires three things: current location, destination, and course to follow. With specific species-level patterns we have information about the first two. The latter requires refined component questions, and relevant patterns—including those for individuals. Experience and role models of what to do (praxis) are important at all levels. How do we reduce the population of our species? If agencies that regulate our interaction with resource species (usually known as resource management agencies) could restrict our take of resources to normal levels in comparison to other species, the human population would decline because of reduced food supplies. Such a program could not be implemented

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without unprecedented coercion (or certainly unprecedented cooperation) on a global scale. No institutional or social structure exists wherein regulating our use of resources can be carried out to reduce the human population so that demand does not exceed supply (the issue is usually avoided as a matter of policy). How can management address the religious, economic, cultural, psychological, and social complexity of such change? How can community-based management (Western and Wright 1994), elected officials, businesses, family planning, and other religious, social, and economic entities be enlisted in reducing population? A species frequency distribution for population level can be used to set goals, measure problems, and assess our species and any progress toward achieving goals, but does not help devise implementation strategies. These are left to other aspects of systemic management, particularly refined forms of related questions, and include different levels of biological organization. Doing so, it is all too easy to reach the conclusion that we cannot purposefully reduce our population through human action. It may be inadvisable; few if any other species purposefully regulate their populations. The alternative is that of accepting the forces of nature that regulate the populations of other species. These include disease, starvation, predation, and intraspecific strife. In nature this is the pattern. In nature, such forces involve natural selection. Carried out through human action, it is clearly impossible to avoid abnormal selectivity in such management. Joseph Meeker (1997) said: “The origins of environmental crises lie deep in human cultural traditions at levels of human mentality that have remained unchanged for several thousand years. . . . Given such depth, it is possible that “solutions” are more than can be hoped for. Humanity may have to settle for the distinction of being the first species ever to understand the causes of its own extinction. That would be no small accomplishment”. We may experience a population reduction caused by a massive pandemic, or combinations of other major forces (that we would judge to be a catastrophe), rather than extinction. However, the systemic forces of nature ultimately prevail. The species-level patterns we have to work with indicate that a huge change is both likely and necessary for the restoration of sustainability.

5.2.4 Acquiring information is logistically difficult Existing information on species-level patterns is the result of decades of expensive, difficult research. A species frequency distribution such as that for the consumption rates of walleye pollock (Figs 2.6 and 4.1) requires millions of dollars to produce. The sampling of stomach contents or feces from predatory species to study their diets involves hours of labor by many technicians, and years of study. Because of logistic difficulties, more precise estimations of population numbers do not exist for the species in Figure 5.1 and Appendix Figure 2.1.22 and are completely missing for many species. Collecting such data is not easy and all data are subject to measurement error. Luckily, libraries and databases maintained in numerous laboratories contain a great deal of useful information that has yet to be synthesized into species frequency distributions. Most, however, were collected for other reasons and suffer the inadequacies of many sets of data such as those shown in this book. Despite the difficulties, the potential benefits for management justify the importance of effort to acquire the best information we can bring to the tasks before us. The production of such data sets constitutes the science best suited for management (Belgrano and Fowler 2008).

5.2.5 Statistical and biodiversity measures are reference points—not magical numbers One of the lures of maximum sustainable yield approaches to managing our fish, wildlife, and forestry resources has been its simplicity, especially a single number that emerged as a solution to an equation. Statistical measures of central tendencies, and biodiversity maxima (Fowler 2008) among other species in species frequency distributions can be misused in the same way. They are often likely to be close to optimal circumstances. They are certainly much better than anything outside the limits shown by species-level patterns free of abnormal human influence. However, we should avoid seeing these reference points as simple end points for our management for a number of reasons. First is the bias in our depiction of most existing species-level patterns (e.g., frequency distributions)

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because of atypical human influence, mentioned numerous times earlier in this book. Second is a lack of clarity stemming from the preliminary nature of existing samples. Temporal variability, cross-system variability (among ecosystems, taxonomic categories, or any other species sets), and variability in multidimensional space are important to work out in greater detail, over both time and space. Third is the difference in measures of central tendency themselves for attributes that show varying kinds of statistical properties. Means and modes are very rarely the same in variations that are often, but not always, log-normally distributed and often dependent on how the variable being measured is defined (e.g., tons of biomass harvested vs. portion of standing stock of a prey resource consumed). Central tendencies or statistical limits may differ from positions for humans that would maximize information (Fig. 5.3, Fowler 2008). Fourth is the difference in degree to which various contributions to the formation of species frequency distributions come from extinction, speciation, evolution, and nonevolutionary factors such as ecological mechanics. For example, the concentration of species in small geographic ranges, producing large “refuges” from the direct effects of each species (Fowler and Hobbs 2002, 2003), could result from evolution of habitat specialization, or it could come from the extinction of all species with large ranges. Producing information to distinguish among each of the contributions will be an endless task. In the meantime, knowledge that factors such as connectivity lead to instability in ecosystems reminds us that being an extreme outlier is not advisable. Fifth is a matter of “time-in-place”—how long a species has existed. A species at the edge of a species frequency distribution that has been there for two million years may be a better example of sustainability than a species in the middle (e.g., at the mean) for only a thousand years. Turnover will be impossible to account for in the foreseeable future. The weight we would assign to a species according to the time it has shown a particular characteristic would be expected to vary across the spectrum of values measured in producing species frequency distributions. Ultimately, sustainability does not seem to be an option outside the normal ranges of

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natural variation. It is impossible to find evidence of sustainability, even where positive weightings might exist, in regions of species frequency distributions not occupied by species. Sixth involves the effects of the physical environment. Part of the variability we expect to see among species frequency distributions for a particular species characteristic is related to the mechanical and evolutionary effects of the physical environment through selection at all levels. This means that we can expect to see differences in species frequency distributions depending on such factors as location, habitat, temporal variation, and chemical composition of their physical environment (e.g., soil type and water quality). These factors must be considered as covariates in the use of species frequency distributions, similar to establishing harvest rates of fish by using mean water temperature, insofar as temperature might influence the position of central tendencies. Finally, measures such as information indices (Fig. 5.3, Fowler 2008) may better reflect sustainability than do simple measures of central tendencies. Being within the normal range of natural variation is a clear objective when we are so far removed, and maximizing the biodiversity of systems may be a way forward. For many situations it will require less change than would matching ourselves to means or other central tendencies of species frequency distributions (Fowler 2008). Again, however, fixed points are not what we are after as there is the matter of normal variance in all cases. Most, or all, of the concerns related to the factors listed above can be dealt with empirically, through correlation analysis and modeling in multidimensional space, based on empirical interrelationships (i.e., not models restricted to ecological mechanics or simulation models). However, the databases to carry out such research are limited and very difficult to produce. The identity of these data bases depends on asking both the correct management question and the corresponding (consonant, isomorphic) scientific question (Appendix 5.2).

5.3 Why extinction should be a management issue In the history of the Earth, billions of species— over 99.9% of all those that have ever existed—have

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gone extinct. Statistically, no species’ chances for avoiding extinction are high and the human species is no exception. Another ice age may occur, or asteroids may strike the Earth, drastically reducing or eliminating the human (and other species’) population within days or decades. Chances are high that we eventually will go the course of most other species. Ultimately, when our sun undergoes the changes astronomers project, life on Earth will likely be impossible. Aside from such risks, which are beyond our control, the abnormal, extreme position of humans in comparison to other species in so many species-level patterns (Fowler 2008, Fowler and Hobbs 2003, and as will be seen in Chapter 6) justifies action—management by humans (and of humans, Management Tenet 2) to prevent abnormal risks of human extinction owing to anthropogenic causes. Simultaneously, of course, we would be striving to minimize any abnormality in the risk of extinction among other species owing to our influence. In Chapter 3, it was briefly argued that extinction is a more significant force than evolution or the nonevolutionary factors of ecological mechanics in contributing to the formation of patterns among species. This will be the subject of intense scientific debate for some time. The validity of such an argument may never be proven. However, the burden of proving (or disproving) it shifts in systemic management. Those who maintain that extinction is not a risk worth being concerned about now have to prove their case. We are concerned about it for other species, why not for ourselves? Systemic management levels the playing field. If it is not important, the matter of proving so is shouldered by those who support that position in their decision making while scientists debate the issue from both sides (Mangel et al. 1996). In the mean time, its actual importance is taken into account (Fig. 1.4) in systemic management and the debate becomes a moot point. Nevertheless, some relevant issues are worth considering. The argument that extinction is an important factor to consider in management may be summarized as follows. The processes of extinction are hierarchically inclusive of natural selection at the individual level such that what is good for the part may be lethal for the whole (Table 3.1, Appendix

3.2). Both forms of selection are inclusive of ecological mechanics.45 Human concerns should not be limited to the risks of ecologically mechanical reactions of ecosystems to our influence (e.g., diseases and starvation). It is likely that extinction supersedes these as a controlling or constraining factor, even though disease and starvation can be involved in the complex of factors behind extinction. It follows, therefore, that extinction is probably more influential in forming and maintaining species frequency distributions than are other forces. If this is the case (consistent with the behavior of models like those of Chapter 3) we would be advised to make extinction a priority management issue. Our concern would not be confined to the risk of extinction for other species, but would include all species (i.e., would not ignore humans, a requirement of Management Tenet 1). Precautionary approaches are to be taken in the face of uncertainty. We are uncertain about the degree to which extinction is involved, but we know it is involved. The pattern in hierarchical constraint seen in complex systems (Ahl and Allen 1996, Campbell 1974, O’Neill et al. 1986, Wilber 1995) is basis for precaution. Extinction (as with all forms of selective systems failure) is involved in emergence at each stage in the history of hierarchical development (Morowitz 2002). Extinction is incorporated in the application of intransitive management guided by species-level patterns. Regardless of its conceptual importance, it is considered in proportion to its effects and actual importance in guiding information based on empirical information—automatically, inherently, and systemically. Systemic management is precautionary in this regard, and not just in regard to the risk of extinction. No precaution based on guesswork, opinion, or political stance is justified because all risks are accounted for collectively, especially in avoiding the abnormal in all cases wherein we find ourselves to be atypical.

5.4 A protocol for systemic management The guidelines below are intended to provide a very brief overview of the steps in implementing systemic management. They are based on the

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management tenets described earlier, primarily in regard to setting goals for species-level issues, including our species’ interactions with other levels of biological organization. Implementation forces consideration of issues at other levels—issues that can also be approached systemically. One overarching objective of systemic management is to alleviate our species, individual humans, and all other biotic systems of the effects of abnormal human species-level attributes (Christensen et al. 1996, Fowler and Hobbs 2002, Mangel et al. 1996). Optimally, this involves finding the ways to participate in larger systems that minimize any abnormality of risks. This avoids the inherent risks of extremes, including extinction as one of the risks all species face (Chapter 3). Observed limits to natural variation among species, seen through research revealing species-level patterns, serve as preliminary standards of reference (Fowler 2008). They embody consideration of complexity, including limits, through their Bayesian-like integration of the complete suite of factors species encounter in their participation within ecosystems and other larger systems (Plate 5.2). In very abbreviated and oversimplified form, the protocol for management to achieve sustainability (with focus on involving the human species) is: 1. Define a management question (and define others through subdividing, resolving, refining, and expanding). 2. Identify relevant levels of biological organization (species in most of the examples of this book) and associated characteristic(s) to be measured. 3. Find the limits to the normal range of natural variation through research on the identified characteristic(s). 4. Take action to avoid human abnormality wherever abnormality is discovered (change our species when it is a species-level question). There is an extremely important shift in the involvement of stakeholders to be noted at this point. This was depicted in Figure 1.1; stakeholders no longer convert information, biases, political positions, emotions, partially insightful understanding, economic interests, or specialized agenda to an objective. That role is terminated. Instead they are involved in asking questions (both manage-

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ment and research questions). This is perhaps the most significant difference between systemic management and conventional management, especially in the progress it makes in achieving objectivity. This objectivity is achieved at all levels: consonant reality-based management applies to its components—ecosystem-based, biosphere-based, or single species-based management. The key element involved in the process outlined above is that of asking questions: the management question and the science question must be consonant (isomorphic, with identical units, logical types and circumstances). A matter of consonance important to this book is the matter of species-level consonance—comparing species wherein we compare ours with others. When we ask management questions about our species, information about other species is consonant information insofar as it involves species-to-species comparisons. This is fleshed out in Appendix 5.2. The process outlined above is repeated for every management question that can be imagined in accounting for complexity. Implementation requires addressing questions at all levels; stakeholders are directly involved in implementation (praxis). Below is an example of how this protocol can be applied to a fisheries management issue.

5.4.1 Defining the question Questions that do not have to do with the human are rejected as management questions, at the outset. They do not involve action we can take and present situations impossible to control (Management Tenet 2); they are not directly applicable. Thus, a question such as “What is the sustainable level of a population of Steller sea lions?” is not directly applicable as we cannot control sea lion populations, although sea lions that show abnormality prompts asking questions about what we might be doing to influence them or their systems. This leads to directly applicable questions. Such questions include “What is a sustainable harvest of Steller sea lions?” or “What is a sustainable harvest of walleye pollock—one of the sea lion’s prey species?” When dealing with an endangered species, we would ask as many relevant questions as we can. For example we would ask:

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What is a sustainable harvest of biomass (by humans) from the endangered species’ ecosystem (e.g., the Steller sea lion as an endangered species in the eastern Bering Sea and North Pacific)? What is a sustainable harvest of biomass and numbers (by humans) from all prey species for the endangered species as a group (two questions, involving a species group as shown in the second panel of Fig. 4.1)? What is a sustainable harvest of biomass and numbers (by humans) from each individual prey species for the endangered species (top panel of Fig. 4.1)? What is the optimal allocation of catches (by humans) over time, space, and the species that serves as resources for the endangered species (three questions combined)? What is the sustainable rate of production (by humans) for toxic substances that find their way into ecosystems occupied at any time by the endangered species? What is the sustainable rate of production of CO2 (by humans) that affects ecosystems occupied at any time by the endangered species? ●

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The list of questions is endless and involves any concern held by any stakeholder. In defining a management question, we begin to account for correlative information and complexity. Consider the question: “What is the most sustainable harvest of walleye pollock?” We can refine this question (see Appendix 5.2) to “What is the most sustainable consumption rate of walleye pollock biomass, in the eastern Bering Sea, during the summer, by warm-blooded species with a body size similar to that of humans, when the walleye pollock biomass is estimated to be seven million metric tons?” The latter version initiates consideration of correlative information. Such information includes species-level characteristics, both for humans and the other predators, and accounts for environmental circumstances. More can be added as further research elucidates more such correlative relationships. For this example such relationships could include sun spot activity if consumption rates of pollock by nonhuman species are found to show a correlation with sun spot activity. The refinement process involves reductionism; it makes

use of reductionism as one of the strengths of our thinking and its expression in science. Component questions must also be asked— beginning the process of guiding the implementation of a goal involving total catch. What is the appropriate allocation of catch across age or size (Etnier and Fowler 2005)? When and where should walleye pollock be caught (two questions)? How should we allocate catch across sex? What is the optimal catch when measured in numbers? What is the most sustainable catch of walleye pollock at the age and size we wish to take (two questions)? These are questions involving objectives. Who enforces the resulting regulations? What fishing techniques, technology, and equipment are to be used? These latter two questions involve implementation. All add to the consideration of complexity through the involvement of component systems and respective management for consistent consideration of hierarchical organization (Management Tenet 4). We must also expand the question (Appendix 5.2). The taking of walleye pollock adds to the harvest within the ecosystem, it adds to the take from various groups of species, it adds to our harvest from the biosphere, and it adds to the number of species that we are harvesting. Do these still fit within the normal range of natural variation when they include our predation on walleye pollock? In other words, other questions deal with whether or not it is appropriate to be harvesting walleye pollock at all. Thus, the meta-level questions are not ignored and are added to systemic management in further consideration of hierarchy though consideration of inclusive systems.

5.4.2 Identifying characteristic(s) to be measured Ensuring the measurements are directly related to a specific management question is fundamentally important. Mere relevance does not suffice; the pattern produced in research must be consonant with the management question (Fowler 2008, Hobbs and Fowler 2008).46 The concept of maximum sustainable yield became a problem in conventional management in failing to ensure such consonance (Fowler and Smith 2004). One set of information typically used in conventional management is the rate

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of population increase as a function of population size/density (directly leading to estimates of what has erroneously been called Maximum Sustainable Yield—MSY). This pattern is consonant with a different management question. The consonant management question is: “At what rate should the human population be increasing if it is at half of its normal levels?” The question in systemic management that people are trying to address with MSY is: “What is the rate at which we humans can most sustainably harvest a resource species?” The latter is addressed directly with information about rates at which resource species are harvested (consumed) by other species. Part of the consonance achieved with such information involves the species-level aspect of the management question, science question, and the pattern revealed by science. This goes on to include the action taken. Thus, the management question defines the measurements to be made. In systemic management, the asking of management questions is one of two roles of all interested parties (bottom row of Fig. 1.1). The initial question (in the example above) regarding the harvest of walleye pollock leads to measuring consumption rates among all species of consumers that feed on this species—thus defining the best science for this management issue. Various units of measure are involved; for example, harvest rates should be measured in both biomass and numbers per unit time for the relevant area. Furthermore, measurements should be expressed in identical mathematical transformations. If comparisons (revealing abnormality) are based on log transformation, these need to be converted back to raw units for management. This may involve tons or numbers per unit time, Figure 1.7. Another aspect of management involves portion of the standing stock taken in harvests as the unit of management; log transformed values used for comparison must be converted back to the measures defined by the management question (e.g., fraction of the population of walleye pollock harvested, Fig. 2.6). In each case, if comparable measures of human consumption indicate that our species is outside the normal range of natural variation, we have a problem, and management action is needed. If we start with a management question regarding the sustainable harvest of walleye pollock

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as the resource species, a more refined question requires measures of biomass consumption (distinct from numbers of individuals, which would be addressed as a separate question). Further refinement involves such things as biomass consumption in a more specific geographic region, during a specific season, under specified environmental circumstances, at a particular level of the resource species population, by specified kinds of species, but still involving walleye pollock as the resource species. We get such information by measures that involve consumption rates among mammalian predators of human body size, collected during the summer, in the eastern Bering Sea, during conditions of high sun spot activity, and when walleye pollock occur at a biomass of seven million metric tons, where predation is measured in units of biomass consumed per unit time. If we want to harvest adult walleye pollock we would use measures of the consumption of adults by walleye pollock predators. This example illustrates the impossibility of accounting for everything directly in formulating management questions because we cannot list all of the factors that may be involved. However, it also illustrates the value of doing what we can and the progress represented in this process made by systemic management in comparison to conventional approaches.

5.4.3 Finding the normal range of natural variation Field research and analysis of historical data provide information to define limits to variation consonant with the management question. The resulting information is the scientific information that best meets the need of management (Belgrano and Fowler 2008). The research that produces it constitutes the science best suited for management, thus addressing a long standing need in management (NRC 2004) in view of the limitations of science (Appendix 5.1). Logistical constraints prevent being explicit about many (most) issues involved. Where information is lacking completely, correlative information often offers an alternative to help inform by way of extrapolation and interpolation based on information from other systems, locations, and conditions. Comparisons across morphological,

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temporal, and spatial scales inform us about correlative relationships to directly account for such things as body size (see Peters 1983), season, and latitude. Thus, in addressing a question regarding sustainable catches in fisheries, body size of both the predator (humans) and prey can be taken into account directly in correlative sub-patterns involving consumption—consumption being the most important element of consonance. To the extent that useful data are already available, the relevant frequency distribution can be used to define limits to natural variation. Further field research (with research questions defined by scientists and other stakeholders; bottom row of Fig. 1.1) adds to the collection of needed data. Any time existing data result in the conclusion that humans are outside the normal range of natural variation, management action is indicated (as in the case of harvests from walleye pollock, finfish, and the eastern Bering Sea in regard to the questions we are addressing, e.g., Fig. 4.1). If we are inside the range of variation exhibited by other species, we may be in a position of sustainability. However, we cannot know we are being sustainable under such circumstances because we cannot prove that we are. We cannot know exactly where, within the normal range of natural variation we should be to maximize sustainability, but can make gains in using correlative information in refining questions (see Appendix 1.3 and Fowler and Perez 1999, for guidance on producing histograms representative of species-level patterns as frequency distributions useful to management).

5.4.4 Taking management action Management is most important in the action taken (praxis); prior to that everything involves preparation devoid of effect without action (i.e., merely words, data, discussion, research, plans, concepts). Management itself is action that is taken to change or confine human behavior, influence or characteristics to avoid abnormality. This is where the social, legal, institutional, ethical, religious, economic, racial, and behavioral aspects of human endeavor at all levels—from individual to international—are involved in achieving specified objectives. Part of the reason systemic management was given

the name involves the systemic quality of change required of humans. Note that human endeavor that is involved in specifying the objectives is restricted to obtaining empirical information and using experience from past trial-and-error processes; the setting of goals is taken out of the hands of stakeholders (in the top row of Fig. 1.1) and replaced with empirical information. Establishing goals, as an activity by stakeholders, is replaced with the role of asking questions; defining clear, consonant management and research questions. Even scientists are limited to gathering empirical data, displaying, and then interpreting the limits to natural variation rather than giving advice based on their necessarily human concepts, thinking, models, or consensus. In systemic management, human endeavor is less a matter of guiding and much more a matter of management itself—achieving the objectives specified by observed patterns.47 Guidance is provided by what we observe and have experienced. The abstractness of words, ideas, models, concepts, and nonconsonant information make them important only insofar as they help lead to more management questions, result in correlative information (seeing correlative subpatterns), produce representations of relevant natural patterns, and undertake subsequent management action. The simplifications made in considering the example of fishing for walleye pollock, dealing with endangered sea lions, and the eastern Bering Sea ecosystem above cannot be overlooked. Ideally, systemic management requires addressing all management questions, refined and expanded to the extent possible. The same protocol can be applied in the other realms and dimensions. In the Bering Sea, there may be concern about global warming; this gives rise to management questions about CO2 production. Other concerns may relate to pollution which give rise to questions about the sustainability of production of the tens of thousands of manufactured chemicals. Interested parties might question the focus of harvests on particular species. This gives rise to questions regarding the sustainability of the numbers of species harvested, and the allocation of harvests across those species (selectivity among them). Some may pursue the concept of

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marine protected areas which leads to conducting research on the portion of the ecosystem occupied by consuming species (Fig. 2.15). On a more global scale, questions concerning the sustainability of our population size, energy consumption, water consumption, and geographic range size come to mind as we consider the state of the planet in general. All such issues can be considered separately in local regions, ecosystems and the biosphere. This list is only the beginning of another list that also proves impossible to complete. Managing systemically proceeds on all fronts for which we have the capacity to ask management questions and obtain informative, integrative, guiding information. An analog with individual health would be to manage by maintaining pulse, respiration, body temperature, and metabolic rate while attempting also to, for example, bring body weight into the normal range of variability. We would attempt the same for our species. Each level involves refining and expanding management questions, identifying measures, determining the normal range or variation, and taking corrective action where abnormality is found. In all cases, the implementation of systemic management must include individuals in solving management problems for our species. However, solving the problem of overconsumption of energy or biomass is not dealt with systemically by requiring people to subsist on a stalk of celery per day. It is important that we maintain ingestion rates, mortality rates, and birth rates that are within the normal range of natural variation. We cannot place the solving of species-level problems on the shoulders of individual people through short-term solutions. Only through longterm normal sacrifices at the individual level can we accomplish our objectives transitively. This is where systemic management becomes a matter of addressing individual-level questions to define the process of implementation. On all fronts, we must remain open to the option that the best and most complete solution will be found through systemic effects of ecosystems and the biosphere— solutions that will temporarily be abnormal for us (and judged in our value systems as horrific), but completely normal for the more inclusive systems involved.

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5.5 The eastern Bering Sea example As is now clear, systemic management applies at all levels (individuals through to the biosphere); systemic management is reality-based management and all are hierarchical parts of reality. In regard to ecosystems, it must be based on answers to specific management questions about specific aspects of human interactions with ecosystems (as described above; see also Belgrano and Fowler 2008, Fowler 2003, 2008, and Fowler and Hobbs 2002, 2003). These questions form part of the stepby-step process of systemic management based on our roles within an ecosystem, particularly as a species (but including individuals so as to avoid being confined to species-level management). Clearly specified management questions then require consonant data for guidance in avoiding the abnormal—data from scientific studies to represent the best scientific information available. In order to guide harvest practices under different environmental conditions such studies need to include data collected under various climatic regimes. Similar studies for other marine systems, as well as terrestrial, freshwater, desert, and alpine systems, will be helpful for inter-ecosystem comparative studies (and the correlative patterns involved). How much biomass can sustainably be removed from the eastern Bering Sea by humans? The answer to this question lies within information such as that shown in Figure 4.1. In its current form, such information is limited compared to what we would have if there had been no abnormal human influence historically. It is limited to conditions insofar as they have responded to human influence. The ways to produce better information, and actions to take while waiting for its accumulation, are now clear. Scientists can now direct their attention to research that produces data consonant with the many questions facing managers—one set of data per question. These include questions such as: How many species can sustainably be harvested as resources? How do we allocate our harvests across space and alternative species? What portion of an ecosystem should be left as a reserve (or in multiple reserves)? These and other questions lead to needs for information (research, field studies, synthesis

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of existing information) about the limits to natural variation as illustrated in species-level patterns. Within an ecosystem, our species interacts with other species; we influence each individual species. One of our more direct influences involves our harvest of resources. Measuring consumption by other species provides consonant information for influence that involves consumption. More consonance is achieved by more specificity—directed and intentional reductionism. How does predation vary according to prey density, trophic level, body size, age, and life history of the resource species? How does it vary according to body size, geographic range size, trophic level, and population density of the predators? The sustainable harvest of adult walleye pollock may be even less than indicated by Figure 4.1 when such factors are considered directly. More research is needed. The reductionism involved is a careful choice of the products of science—avoiding the misdirected reductionism of conventional management (top row of Fig. 1.1; Belgrano and Fowler 2008). Our conscious purpose becomes one of holistic design. To address questions regarding sustainable harvest rates, scientists studying the eastern Bering Sea can emphasize studies exemplified by those of Livingston (1993) and Sobolevsky and Mathisen (1996). Similar studies by Overholtz et al. (1991) apply to the Northwest Atlantic. Studies such as that of Melin et al. (2008) apply to the Eastern Pacific and include information to directly account for correlative environmental conditions. The results of such studies achieve consonance in regard to management questions about harvest rates. Harvest rates involve consumption and measures of consumption do not have to be translated or converted to find management advice; there is consonance in units. Confined to simple measures of consumption, however, such information is somewhat superficial compared to what can be achieved, even with the limited data we have in hand today when we proceed to taking advantage of the correlative structure of patterns. Figure 5.6, for example, shows the relationship between the harvest rates in commercial fisheries in relation to total mortality rate (M, from Mertz and Myers 1998) for 44 species of fish. In conventional management, comparisons between fishing mortal-

ity and total natural mortality are occasionally used to assess fisheries. As can be seen, many fisheries today are overharvested using this kind of conventional comparison (i.e., most of the solid points are above the 1:1 line). There is a significant lack of consonance here, however, in that commercial harvests represent consumption by one species; total mortality is total mortality—it includes the mortality caused by all consuming species. They are not the same thing; they are of different logical types. One is consumption per species (commercial harvests by humans), the other is the sum over all sources of mortality (which includes a variable number of predators each of which contributes to part of that mortality along with mortality caused by such things as diseases, accident, or cannibalism). Correlative information regarding M (and thereby body size, intrinsic rate of increase, and other interrelated factors—Chapter 2) can result in refined consonant information found by plotting the mortality per consuming species (predation) against M. Doing so allows us to address management questions such as “What is the sustainable harvest of fish species with a natural mortality rate (M) of 0.4?” In Figure 5.6, this correlative information is based on data from consuming species (e.g., those represented in Fig. 2.6, and Appendix Fig. 2.1.5, also including data for the predators on three species of ungulates from Kunkel and Pletscher 1999). As can be seen, this results in a much different evaluation of fisheries; all fishing rates are above the line representing the relationship between predator-specific consumption rates and M. In other words, all fishing represented by these data are unsustainable by systemic standards. A biosphere-based approach to management (systemic management that would include all ecosystems and species) would result in harvests that would be much closer to the line representing nonhuman predators than is represented by the 1:1 line of Figure 5.6. Management might best be served by objectives based on a regression line of harvest (consumption per species) rates against M wherein the statistical fit involves consumption rates that maximize biodiversity for each prey species (Fowler 2008), rather than the simple rates of consumption for individual species as shown in Figure 5.6.

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The pattern in Figure 5.6 raises the (scientific) question of whether or not there is an increase in the number of predators with increasing M. If there is such an increase, this would be a factor contributing to the nature of the relationship between mean predation rate and M, possibly making it a nonlinear relationship. The number of predators is obviously one of the factors that contributes to observed patterns, so is taken into account a priori in managerial use of the pattern. However, the number of predators would become a very important correlative variable, in any case, as refined research addresses such questions. We humans are adding ourselves to the count of predators in every case where we initiate harvests (such as done in commercial fishing). Increasing the number of predators involved must be part of what is taken into account with information regarding predator count as it accounts for variation in observed predation rates. Science and management involve reciprocity in question generation.

Figure 5.6 Two means of assessing commercial fisheries, one based on comparing mortality caused by fishing with total natural mortality (solid circles and solid line; Mertz and Myers 1998), and the other based on comparing fishing mortality with predator-specific mortality (solid circles and dashed line). The line for the latter involves a least squares fit of the data plotted in Figure 2.6 and Appendix Figure 2.1.5 (combined with data for ungulates from Kunkel and Pletscher 1999) to the corresponding total mortality rates for the respective species.

Still other questions relate to whether or not we can more directly take into account other processes, clearly relevant factors, and issues. For example: How do we account for the evolutionary impacts of our harvesting on each individual resource species? With consonant information, species-level patterns as natural phenomena provide a form of Bayesian integration (Chapters 3, 4, 5, and 6) that accounts for such issues. Thus, empirical information consonant with questions related to sustainable harvest rates already accounts for genetic effects (Fig. 1.4) as factors that contribute to patterns in consumption (harvest) rates. However, more directly accounting for genetic effects can involve both correlative analysis and the posing of new management questions. Thus, asking management questions about the sustainable size selectivity of fisheries harvests can lead to the use of consonant information on the size selectivity of marine mammals (e.g., Etnier and Fowler 2005)—directly addressing evolutionary impact while simultaneously accounting for

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other aspects of complexity. Furthermore, selectivity may involve correlative patterns that allow us to account for other factors directly (e.g., body size, Etnier and Fowler 2005). Such correlative patterns could involve other forms of selectivity (e.g., patterns in selectivity by sex might involve correlations in which selectivity by size, location, or season are involved). Systemic management also brings resolution to any differences in management advice stemming from different sciences by recognizing that the sciences are no more than (but fundamentally and critically important) elements of human perception of realities—realities that themselves are integrated (and given respective weights) in the combination of respective processes in their contribution to the various species-level patterns (frequency distributions, Fig. 1.4). These apply to any ecosystem, the eastern Bering Sea or the Serengeti. With growing clarity, we understand correlative relationships within patterns as integral parts of emergent ecosystem characteristics. Ecosystems have real form and pattern to their function (e.g., Fig. 1.34). Correlative relationships provide the basis for taking various factors into account overtly or directly; when scientists/stakeholders are asked to consider a factor in management, it can be accomplished with empirical information. For example, we account for human body size and trophic level (attributes over which we have little short-term control) by finding our position within consonant species-level patterns as correlated with these features. Thus, we can establish a sustainable take of walleye pollock by comparing our harvest rates with those of other species of our body size and trophic level. We can account for climate directly if species-level patterns are correlated with seasonal temperature, climate regimes (as in the data provided by Melin et al. 2008), or annual precipitation. Extinction is an imminent risk for endangered species. The Steller sea lion is designated as an endangered species in parts of its geographic range, including parts of the eastern Bering Sea. How would systemic management attempt to reduce the risk of extinction of the Steller sea lion? Because of the principle of complexity and interconnectedness, whatever we do affects other species in

innumerable and unprovable ways (some positive and some negative when judged in human value systems). In systemic management, we would ask management questions about all of our direct and indirect interactions with the Steller sea lion and its environment. We would frame each interaction as a management question. We would ask about our harvest of sea lions, and any species the Steller sea lion uses as a forage species. We would ask about our harvests and impacts on all species in the ecosystems in which it occurs. We would ask about our production of chemicals that enter its environment, and CO2 that may be changing the climate and pH of the waters inhabited by the sea lions and all other species with which sea lions interact. For each and every such question, we would follow the protocol presented above, beginning by correcting all the known problems, such as reducing our harvest of biomass from the eastern Bering Sea to levels within the limits to natural variation illustrated by data shown in Figure 4.1. For each and every component of our influence the full suite of consequences plays out in the complexity we account for automatically in systemic management. Only if we are within the normal range of natural variation of everything we can think of can we rest with any assurance that we have done everything within our power. Then, if the sea lions go extinct, it may still be a matter of past human influence or questions we do not know to ask, but we are powerless to change such things now. It is always possible that the sea lions are another species succumbing to the laws of nature and its dynamic process, regardless of any pathological anthropogenic influence. It may be, itself, experiencing the results of evolutionary suicide (Parvinen 2005, Rankin and López-Sepulcre 2005)—as an evolutionary deadend (Potter 1990). However remote or important this possibility may be, it is not a reason for avoiding our responsibilities in avoiding the abnormal— one of which is the anthropogenic contribution to the current extinction crisis to be treated in more detail in Chapter 6 as it involves the complex or full suite of abnormalities characteristic of humans in today’s world. We cannot prove that what we are doing is causing reduced population levels of many, or even most, endangered species. To deal with

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complexity, systemic management amalgamates all such concerns to see the endangered species of the world as probable (not proven) consequences of abnormal biodiversity, resource consumption, chemical production, and energy monopoly (Fowler 2008). These, of course, are impossible to uncouple from the human population explosion (Fig. 5.1) expressed through the complete set of all related interactive processes and influences. These include our geographic range size, habitat appropriation, introduced species, monopolization of primary productivity, and those things we do not know to list, but for which we have the responsibility of correcting when the pathology comes to our attention. Having established objectives for fishery harvests, systemic management then proceeds to questions of implementation where a great deal of past experience is brought to bear in addressing questions regarding gear type, technology, laws, regulations, enforcement, monitoring, economics, and other factors that involve individual fishers, regulatory bodies, and all relevant officials. Individuals participate in the management process by acting to help achieve established objectives and draw on personal experience to be involved with successful change (but avoid abnormal activities in the process). Experience shows how to successfully achieve established objectives and the exercise of the lessons learned through that experience is systemic management. If change toward established objectives is not being achieved, management is not being implemented. The details of implementation depend on past management experience; the objectives are established as described in this book.48

5.6 Summary and preview This chapter has shown that systemic management meets all nine tenets of management. This is not surprising because it is defined by them. The requirements of each tenet are met while simultaneously and consistently adhering to all nine. It goes beyond these tenets to embrace other principles and laws such as the law of unintended consequences, and the principle of human limitations (being finite). It fully embodies reversal of the burden of proof.

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Simultaneous consistency is a hallmark of systemic management (Hobbs and Fowler 2008). Through systemic management, human factors, including economic factors, are accounted for objectively in defining sustainable roles for humans in ecosystems and the biosphere to adhere to Management Tenet 1. Control is restricted to situations where it is a most likely option—control of the human to meet Management Tenet 2 by using guiding information (Management Tenets 5 and 9) that meets the requirements of all nine tenets. Empirical information accounts for complexity consistently when humans are held within the normal range of natural variation to adhere to all tenets. This rejects the concept of conventional management in which it is assumed that humans can adequately generate guidance in other ways (top row, Fig. 1.1). Systemic management meets Management Tenet 3 (accounting for complexity/reality) through the integration and consistency of factors achieved in nature used as the source of information. Complexity is addressed in a protocol that breaks down the process and requires that component questions be addressed, that other questions be addressed, that expanded questions be addressed, and that correlative information be used. Consistency is achieved, not only across the various levels of biological organization (Management Tenet 4) but also among the questions being addressed—all by confining humans to within the normal range of natural variation (Management Tenets 4 and 5). Risks (Management Tenet 6) are part of the complexity addressed in systemic management by using information (Management Tenet 7) on the normal range of natural variation (Management Tenet 5) as an accounting of complexity (Management Tenets 3 and 4). Science (Management Tenet 8) is fundamentally important to observing the normal ranges of natural variation relevant to each management question, monitoring progress in achieving success, and identifying problems. Through the data collected, goals and standards (Management Tenet 9) emerge in observed limits to natural variation (Management Tenet 5) to guide human action (Management Tenets 1 and 2). This chapter shows that the failures of conventional management identified in Chapter 4 are largely overcome by systemic management. The

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finite of the human prevails, however; the finite of human nature is one of the elements of complexity dealt with in systemic management (Management Tenet 3). Because of this reality, we remain limited in our capacity to ask all the relevant management questions. This is one of the main challenges for the future: asking the right questions—taking our role in the bottom row of Figure 1.1 seriously. Also crucial is obtaining the consonant information (matching natural pattern and data with the management question and ending in consonant action). This is essential for finding guiding information regarding specific activities necessary to achieve goals—consonant implementation. What may be the main contribution of systemic management is the capacity to address the right questions whether they involve individuals, families, society, our species, or the interactions between these systems and other species, ecosystems, or the biosphere. For every question represented by measurable counterparts in nature, using empirical information for a complete accounting of complexity (Fig. 1.4) provides complete compliance with all nine management tenets. The more questions asked, guiding information found, and action taken, the more complexity has been taken into account. Solving one problem contributes to solving related problems, including problems unknown to us at this time. This chapter has also shown that human limitations prevent exact precision for what is sustainable within the normal range of natural variation; the variance of variability requires that we do not try. We can never identify nor obtain information for all necessary correlative information. This need not be a particularly troublesome limitation because variation itself is one of the features of natural systems. Nevertheless, by using nature’s examples of sustainability, with as much correlative information as we can obtain, we refine the objectives for achieving sustainability, not only for ourselves but for the systems upon which we depend (Management Tenets 1, 4, 5, and 9). The goals are embedded in the measures of pathology

we currently exhibit. The daunting challenges we face can now be measured objectively. An academic treatment of problems such as we will see in Chapter 6 contributes to an objective view of our precarious situation and measures of needed change. Guiding information is to be found in directly related role models when the appropriate questions are asked; when full consonance is achieved between management question and empirical information. These realms represent the fertile ground for asking more questions, seeking the component questions, and proceeding with implementation based on ever more detailed component questions. However, if the expanded, higher-level questions are not asked, or if the guiding information is ignored, systemic management is not being conducted and cannot be effective. This has been one purpose of this book—to show how to pose questions above the individual- and species-levels of biological organization, in a way that can be addressed objectively. Full systemic management, however, cannot ignore individuallevel applications; this book provides focus on the species-level to exemplify the process. However, objectivity is not enough. Where do we find hope after seeing that we are in the deep trouble we will see in Chapter 6 (Fowler 2008)? Social, psychological, emotional, and religious issues are at stake as well as multiple belief systems. Economic, political, educational, cultural, and philosophical issues are involved. The complexity of human civilization, and the forces of our history, habits, and hormones cannot be ignored. In each and every such realm there are innumerable detailed concerns. Such complexity is systemic. After adopting systemic management in principle, we find a natural expansion from the objective to the subjective—fully embracing sustainability in spirit. We carry within our genes the seeds of our own destruction (Table 3.1, even the capacity to obliterate life on Earth). We also carry the seeds of promise—issues, treated by, but beyond the scope of this book. These provide fertile ground for extensive development.

CHAPTER 6

Humans: a species beyond limits

We have today to learn to get back into accord with the wisdom of nature. . . . —Joseph Campbell, The Power of Myth

This chapter examines what is necessary to fulfil the requirements of Management Tenets 1, 2, and 5: avoiding abnormal human participation within ecosystems and the biosphere. This chapter also provides examples of management questions, guiding information, and goals or policy that are all consonant (match, are consistent and isomorphic). These examples show how consonance between science and management dissolves the barriers between them and makes conversion of information unnecessary. Some of the questions posed in previous chapters are answered; new questions are posed and answered. This chapter illustrates the staggering immensity of the problems we face and the goals to be sought in solving them. The first section illustrates patterns that demonstrate the abnormality of humans and preliminary approximations of goals for related systemic management. As a species, we are extremely aberrant in our use of water, energy, and resources, in our production of CO2, in the size of our geographic range, and in the size and density of our population. Other patterns add to this list, and this list will grow in future research to show more extremes of our anomalous nature. In systemic management, these empirical patterns can be used to guide action toward relieving various systems of abnormal human influence (Management Tenet 5). In essence, this would increase our species-level fitness and the sustainability of other species, ecosystems, and the biosphere. In later sections, the effective use of empirical information is described by way of example, with more detail regarding systemic management applied to problems at all levels of biological organization— individual, species, ecosystems, and the biosphere.

These examples demonstrate how systemic management accounts for complexity (Management Tenets 3 and 4), in part because ecosystems and other more inclusive systems (including their components, their processes and interrelationships, and their aggregate complexity) are all among the factors that both show their own limited variation and contribute to the limited variation we see in all systems. This chapter has the objective of implementing our understanding that the emergence of patterns involves all contributing factors; empirical patterns account for all contributing factors— reality, and all systems and dynamics that are part of reality. The focus is on shifting toward sustainability by regulating human interactions with other biotic systems. All such systems will respond to our actions. Finally, more detail is provided for using systemic management to find sustainable levels of human influence in the eastern Bering Sea.

6.1 Limits to natural variation as limits to sustainability In considering the needs of the human species, questions of sustainability immediately arise. What level of biomass can we sustainably remove from an ecosystem (e.g., for food or fiber; see Fig. 4.1)? How much should be left for other species? We need space but how much space can we sustainably occupy as a species. All species need space; how much space should be protected from direct human influence? How many species can be sustainably used to meet our needs; how much biomass can be sustainably harvested from any group of species? How should 159

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we allocate harvests of resources across alternative species? How much energy can we sustainably use and how much primary production should be left for other species and their ecosystems? How much CO2 can we sustainably produce? This list will grow longer as we face the complexity of the reality with which we are dealing. Questions such as those above include both ecosystem- and biosphere-level issues. As recognized historically, they also include our interactions with nonhuman species, individually and collectively. As for any species, meeting our needs requires other species, ecosystems, and a biosphere that can supply what is needed by all species— sustainably. What portion of the energy captured, and biomass produced, in ecosystems and the biosphere, should we leave for those systems and the species involved? We depend on ecosystem services. How do we ensure that every species is sustainably provided with the variety of ecosystem services upon which all species depend? We must adjust our needs to correspond to what other species, ecosystems, and the biosphere can support so that they retain sustainable qualities, services, and interrelationships. In doing so, we have the responsibility of addressing the disruption to other species, ecosystems, and the biosphere that have been attributed, or are attributable, to human influence (Appendix 6.1). For example, pollution is recognized as a global problem. This observations leads us to questions such as: “At what rate can estrogenic compounds be sustainably produced?” It is important to find balance between benefits and risks both for meeting our needs and solving problems among other species, ecosystems, and the biosphere. Management Tenets 3, 4, and 7 require that policy and action be based on information that accounts for the complexity of reality to balance advantages with costs. When systems such as other species, ecosystems, and the biosphere exhibit abnormal characteristics, it is crucial that we identify any potential anthropogenic causes, direct or indirect—human abnormalities. Atypical human factors are open to being changed—action that will be to the mutual and ultimate benefit of all systems. Hence, we begin by noting that many of the problems other species experience, most known ecosystem-level problems (Appendix 4.2)

and biosphere-level problems (such as global warming, extinction, pollution, oceanic acidification) involve elements that are clearly of human origin (Appendices 6.1 and 6.2). Reversing the burden of proof leads directly from the observation of these problems to management questions regarding the sustainability of human activities— any that may be contributing, directly or indirectly. We are lucky, in this regard, because we stand a chance of solving problems measurable as human abnormality. Fixing/repairing other species, ecosystems, or the biosphere is something that would be highly unlikely (probably impossible) otherwise (Management Tenet 2). The following sections offer examples of sets of information produced by the biological sciences that have practical application. They involve comparisons between humans and other species in regard to: resource use, CO2 production, energy use, geographic range, and other issues—all of which are closely tied to the rapidly growing size of the human population, belief systems in conventional management, and resulting environmental degradation. This section illustrates the use of information on the limits to natural variation in addressing a variety of questions aimed at matching our needs to the limits of various systems to sustain us without eroding their capacity to both sustain us and to sustain other species. The picture that emerges is sobering. However, with understanding and an appreciation for the actual magnitude (Management Tenet 9, Chapter 1) of our problems comes hope for change that will solve problems—problems that include the current extinction crisis and any abnormal risk of our own extinction.

6.1.1 Use of biological resources Humans are consuming biological resources at rates that are exceedingly aberrant in comparison to the rates at which other species consume the same resources (Fowler 2003, 2008, Fowler and Hobbs 2002, 2003). This section develops in some detail how patterns can be used to assess current consumption rates and identify goals for achieving sustainability. Commercial harvests of fish are used as one example because the methodology of

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systemic management has been most thoroughly developed for regulating our use of these resources (e.g., Etnier and Fowler 2005, Fowler 1999a, 2002, 2008, Fowler et al. 1999). Another example involves the sport harvest of ungulates. Evaluations have yet to be completed for consumption of other groups of species, for example, tree species used for lumber, species of mammals treated as game, and species of waterfowl taken for food and sport—not to mention agricultural species. It is important to emphasize that the impact of human resource use is magnified because, through global trade, we move harvested resource materials to locations well outside the natural geographic range of the resource species rather than recycling them in situ. Our impacts are further magnified by other factors related to resource consumption, such as the number of species consumed, impact on population size of resource species, and evolutionary impacts through genetic engineering, selective harvesting, and breeding. Each issue gives rise to a distinct management question. Scientific research provides systemic management with information about a pattern that matches (is consonant with) each question (Belgrano and Fowler 2008, Fowler 2003, Fowler and Smith 2004). Food is primary among resources needed by consumer species, including humans. Management of harvests for human food supplies include those taken from individual species, taxonomic groups, communities, ecosystems, and the biosphere. Managers are responsible for achieving sustainable consumption rates regardless of the level of biological organization being addressed—rates that are consistent across the various levels of hierarchical organization. Managers regulating harvests from a single resource species, for example, must ask, “What levels of harvest are sustainable?” This question is insufficiently addressed if it is not also applied to ecosystems and the biosphere. The recognized failure1 of quantitative population models, or models of productivity, in managing single species as sources of consumable products (such as food) has stimulated intense pressure to advance to ecosystems and the biosphere in broader approaches to management. To be more realistic, the entire suite of interactions must be taken into account in making management decisions. These

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interactions include genetic effects (Conover and Munch 2002, Ehrlich 1989, Etnier and Fowler 2005, Law et al. 1993, Mangel et al. 1993–2005). With systemic management, managers are able to account for complexity so as to include all the elements important to “ecosystem-based management”. Currently, this complexity is not incorporated in the deliberations that managers or any other stakeholders undertake (the conceptual alchemy of the top row of Fig. 1.1). In systemic management, complexity is addressed through using information on the limits to natural variation (Fowler 2003, Fowler and Hobbs 2003, Management Tenet 5) in integrative patterns (Belgrano and Fowler 2008). Each individual species in species-level patterns is a provisional example of success in facing all factors involved in the spatial, temporal, and hierarchical complexities of reality with the interactions among all components (Fig. 1.4). By definition, these factors are the environment within which evolution2 occurs. In the sense of adaptive management (Grumbine 1997, Mangel et al. 1996, Moote et al. 1994, Walters 1986, 1992), other species have passed the filter of risks and collectively reveal advisable forms of sustainability in their Monte Carlo (trialand-error; see Chapter 3) processes. Evolutionary processes (including selective extinction and speciation) have contributed to the origin of empirically observed sustainable levels of harvest that can be taken from their resources.3 What we observe are the results of all contributing processes (Fig. 1.4)— results that account for the full spectrum of ecosystem interactions we would consider important if we were capable of perfect knowledge. 6.1.1.1 Single species approaches In accounting for complexity, management must include regulating our interactions with other species on a species-by-species basis—one part of the reality-based nature of systemic management. Our use of other species as resources serves to introduce the concept. Other interactions with species (e.g., genetic effects, population suppression, or redistribution in geographic space) cannot be neglected and should be treated in the same way with their own distinct management questions. The first example involves the management question: “What is a sustainable harvest rate for fisheries

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in the take of walleye pollock (Theragra chalcogramma)?” This involves resource use as human predation on a species of fish. Figure 6.1 shows the pattern for predation rates on this species by a variety of consumer species in the 1980s. This figure is a modification of Figure 2.2 to compare the take of walleye pollock by U.S. commercial fisheries to predation rates by other species. The amount of walleye pollock biomass recently harvested by humans (top panel) is 39 times larger than the arithmetic mean of that by other species (bottom panel), over 200 times larger than the mode of the consumption rates by other species and over 1.5 times as much as the total consumed by the other species represented in this sample. One option for achieving sustainability would be to take the data behind

Figure 6.1 at face value and apply systemic management by reducing harvests of walleye pollock to 2% of recent levels (close to the arithmetic mean for the other species). There are other less extreme options (Fowler 2008), but none would have allowed for harvests larger than 20% of what has been taken on the basis of the policy-setting process of conventional management (top row Fig. 1.1). Figure 6.2 shows such a reduction (from recent rates represented by the dashed line on the right to the solid line at the peak of the curve in the 0.4

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Figure 6.1 Frequency distribution of vertebrate species (N = 21), including humans, that consume walleye pollock ( Theragra chalcogramma) in terms of the fraction of standing stock biomass of that species that were consumed in the eastern Bering Sea in the 1980s. Data are shown in linear scale in the top panel and in log10 scale in the bottom panel for walleye pollock of age three and older (Livingston 1993 and personal communication). The total take by humans represented here is about one million metric tons.

Figure 6.2 Illustration of the change that would be needed through systemic management of the harvest of walleye pollock (age three or older) in the eastern Bering Sea as a preliminarily maximization of sustainability. It uses the mean of the distribution shown in Figure 6.1 to establish a simplistic management goal (the mean is the peak of the curve in both panels even though such smooth curves rarely capture the complexity of such patterns; e.g., a symmetric curve may be entirely inappropriate). Management would be actions taken to reduce harvest rates (from levels represented by the dashed line) to correspond to the mean among the nonhuman species (represented by the solid line), as shown in the bottom panel—a change of about two orders of magnitude—as a first approximation of change needed in management with the objective of sustainability.

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bottom panel) to achieve levels corresponding to the statistical central tendencies observed in predation rates. For the application of systemic management, this figure conveys both an initial impression of the concept and, at the same time, a rough indication of the dramatic challenges before us. Further examples in the sections below will provide more detail regarding both the concept and the challenges. In Chapter 5, standards of reference other than statistical metrics were introduced (e.g., maximizing biodiversity) and will be revisited below. Current management of the harvest of walleye pollock is based on conventional single-species approaches. Estimates of biomass, recruitment, and production are converted to advice for management of harvest rates (lacking, avoiding, or rejecting empirical information consonant with the management questions; top row Fig. 1.1). To involve ecosystem considerations, harvests are reduced, in conventional management, from what would otherwise be considered reasonable, by an ad hoc and incomplete consideration (by teams of advisors, scientists, stakeholders) of only a few other factors, such as the needs of other predators for walleye pollock. For example, such reductions would include smaller allowable takes in consideration of an endangered species for which pollock serves as a prey species. Takes would be reduced from conventionally established harvest levels based on maximum sustainable yield (MSY) approaches, or spawner-recruit relationships, and models of a limited number of factors only partially related to the management question being addressed. The evolutionary pressures introduced by such harvest rates and their direct genetic effects are unknown and are not taken into account. Also unknown and unaccounted for are the effects of the harvest in all other interactions among other associated species in the ecosystem, including indirect evolutionary effects (i.e., higher order coevolutionary interactions). Most of the unintended consequences of the resulting harvest rates are not taken into account. Among the questions left unasked is: “How much pollock should be left for all other species, the ecosystem, and the biosphere?” Socioeconomic and other short-term human values count heavily among factors used to decide what harvest rates

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should be. In an era of management in which the burden of proof is increasingly placed on the users (Holling and Meffe 1996, Mangel et al. 1996, Wood 1994), taking more than the extremes (e.g., above the 0.95 statistical confidence limits) within the normal ranges of natural variation among other species cannot be justified scientifically and is in violation of Management Tenet 5. Official certification of such an approach as sustainable is also in violation of Management Tenet 5. Another initial application of systemic management would be to use the mode4 in a pattern such as that shown in Figure 6.1 as a first approximation of an estimate of the most risk free and sustainable harvest level achievable. So far, no single statistical measure has been identified as best, but a harvest rate near to central tendencies of distributions like those illustrated above would be better than what is practiced in current management. Finding a harvest level that maximizes biodiversity/information is probably a preferred option (Fig. 5.3, Fowler 2008). As will be seen later, finding ourselves within the confidence limits of such variation is no guarantee that we are sustainable, but would present less justification for change than in cases where we are clearly abnormal. Systemic management is not a matter of avoiding abnormality in only a few ways. For example, the consumption (harvest) of biomass is only one aspect of resource use. We harvest both biomass and individual fish when we harvest walleye pollock. What is the most sustainable harvest of walleye pollock measured as numbers of fish? A totally different appraisal emerges if the information brought to bear is the numbers of individual fish consumed rather than biomass (Fig. 6.3, especially the bottom panel). Recent takes by humans, measured in numbers, is below the mean for predation rates among nonhuman predators, but well above the mode (linear scale). The difference between Figures 6.1 and 6.3 is explained by the fact that current management focuses more on harvesting adult fish than younger fish. Such selectivity will be dealt with below. The important point to be made here, is that what appears on the surface to be sustainable based on numbers alone can be erroneously interpreted to indicate that current management is sustainable. However, such is clearly not the case

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in terms of biomass (Fig. 6.1). Numbers and biomass are two different dimensions and therefore two different aspects of the complexity of managing our harvest of a resource species; they involve two different management questions. Were we to offer management advice by using the mean of the data for numbers in the data for Figure 6.3 alone we would run the risk of violating sustainability in our harvest of biomass.5 This begins to provide insight into the complexity of systemic management, a point yet to be fully developed. Thus, even in the case of numbers alone, things are more complicated than they seem at first glance. In using Figure 6.3 we have not explicitly accounted for allocation of harvests across age. Taking as many adult walleye pollock as are taken in commercial fisheries may not be sustainable owing to both abnormal harvest rates and size selectivity. The size (and, therefore, genetic) selectivity of commercial fisheries does not match that of the nonhuman predators (Etnier and Fowler 2005). Below, we will treat this issue directly. Figure 6.4 shows the take from several other individual species of fish by commercial fisheries in comparison to that of other predatory species (from Overholtz et al. 1991)6 exactly as was done for walleye pollock above. In each case, the consumption by humans of each of the three species is above the central tendencies, and it is outside (or clearly at the edge of) the normal range of natural variability for two (hake and herring). Spiny dogfish are the only species with consumption rates larger than that for humans in the consumption of mackerel. This species is one of the elasmobranchs that often predominate in parts of the northwest Atlantic as a result of human influence (Sherman 1994). This points out the need to avoid the dangers of assessing and guiding human involvement in ecosystems through comparison with any single species and emphasizes the need to account for abnormal human influence (which is accomplished systemically in these data). The limitations that would be imposed on human consumption through systemic management of the fisheries depicted in Figure 6.4 would have implications for the fishing industry similar to those for the walleye pollock fishery (Fig. 6.2). For herring, take by humans should have been about 2% (98% less)

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Figure 6.3 Frequency distribution of 21 vertebrate species, including humans, that consume walleye pollock ( Theragra chalcogramma) in terms of fraction of numbers of individual walleye pollock consumed (data are shown in linear scale in the top panel and in log10 scale in the bottom panel; from Livingston 1993 and personal communication). Humans are represented by the respective cross hatched portions of the bar corresponding to the rate of harvest taken by commercial fisheries.

of that reported for 1988–1992 in order to correspond to the mean consumption rates for other predators. Hake harvests should have been about 10%, and mackerel harvests should have been less than 20% of the harvest rates of the same period in order to match the mean consumption rates of other predators on the same species. If these data were from systems undisturbed by abnormal human influence, they would reflect rough approximations of ultimate sustainability for these systems. As they are, they clearly represent systems subject to what was abnormal human influence. Based on these comparisons alone, we are still several steps away from understanding the full application of systemic management. For example, are the means of such distributions the best measures of sustainability or

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are the predatory species represented by these data the best examples of sustainability for our species? There are more such questions—all related to the application of systemic management. How do we account for the effects of functional responses of predators to prey abundance? What are the ways in which abnormal human influence can be taken into account more directly, that is, other than knowing that these patterns reflect human influence? The harvest of resources is not confined to fish, or marine ecosystems. The sport harvest of

ungulates provides contrast, both in being within terrestrial ecosystems, and in being an example wherein humans are not abnormal compared to other species. Figure 6.5 shows mortality rates by nonhuman species in their predation on three species of ungulates (females of Odocoileus virginianus, Cervus elaphus, and Alces alces) in a specific region of the United States and Canada (Kunkel and Pletscher 1999). On the surface, there is no reason to be concerned about the harvest rates used in this system, measured in simple terms of

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the pattern in mortality rates caused. As with the fishery examples above, however, management also involves decisions about selectivity by age, size, and other genotypic characteristics. There are other management questions; the sustainability of harvests of males needs to be addressed with patterns in selectivity by sex. The refinement of management questions, in this case, goes beyond the single question “What

is a sustainable harvest of deer?” Other questions need to be addressed in attempts to tackle the complexity of nature. These include: “What is a sustainable harvest for species with the body size and reproductive rate of deer when taken by species with the body size of humans?” In this example there is little concern about consonance in regard to body size as the predators (Canus lupus, Puma concolor, Ursus arctos, Ursus americanus, Canus latrans) are close to human body size, and are mammals. Concerns that are left to be addressed involve issues such as genetic selectivity (which generates new questions). The research by Kunkel and Pletscher (1999) exemplifies studies that succeed in producing information involving patterns that are consonant with management questions regarding sustainable harvest rates—in this case, questions about sustainable harvest rates of ungulates. The examples for fish and ungulates provide an introduction to the concept of systemic management applied in our interaction and influence on individual species. They offer an assessment of our species-level roles in ecosystems by comparing humans with other species in species-to-species interactions. They provide crude indications of the changes needed in fisheries management to satisfy the tenets of management (Chapters 1 and 5). In management action (praxis), humans are made subject to control (i.e., through action wherein control is more of an option by limiting harvests, Management Tenet 2). Furthermore, these examples involve our interactions, as a species, with other individual species. It is thus a matter of single species application of systemic management regardless of how it is viewed. Through such examples, we begin to account for complexity because ecosystems, other more inclusive systems, and history are all reflected in the patterns among species used as sources of guiding information (Fig. 1.4; Belgrano and Fowler 2008, Fowler 2002, Fowler and Perez 1999, Fowler et al. 1999, Fowler and Hobbs 2002, Fowler and Crawford 2004; Appendix 4.4). This complexity contributes to the factors that prevent the accumulation of species that might have otherwise fallen outside the normal range of natural variation. Thus, complexity is accounted for in the guiding information (Fig. 1.4) as an indirect process. However, it

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6.1.1.1.1 Dealing with abnormal anthropogenic effects, past and present Figure 6.6 illustrates changes that occurred in the biomass of cetacean populations in the eastern Bering Sea, at least partially due to commercial whaling. Thus, estimates of the biomass consumed by cetacean species also differ between these two periods. The total biomass of cetaceans in recent years has been about one third of what it was in the 1940s. This is very close to the change in the consumption rates observed between these two periods of time as shown in Figure 6.6. Likewise, estimates of biomass consumed by all nonhuman mammalian species are less now than before recent anthropogenic effects. Populations of fur seals (Callorhinus ursinus), northern sea lions (Eumetopias jubatus), and other species are below historically observed levels (NRC 1996). Therefore, we likely would find that advisable sustainable harvest levels to be taken from walleye pollock would be higher if based on data for previous periods (i.e., from ecosystems free of abnormal disturbance by humans) than is indicated in Figures 6.1 and 6.2 (e.g., three times as much if the data in Fig. 6.6 are at all representative). The data behind Figures 6.1 and 6.2 are based on an ecosystem already modified by the various abnormal effects of humans— effects that are outside the normal range of natural variation (including harvesting, CO2 production, and pollution). Although current data indicate what is currently sustainable (i.e., account for human impacts insofar as they involve reactions to human influence), at some point in the future, we will need to have some idea of what is ultimately sustainable (with humans fitting in normally in all ways conceivable). Such estimates can be achieved in at least three ways, a combination of which is advisable. First, we can use

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must be clear that deciding to take our harvests so that they fall within the normal ranges of natural variation for consumption levels observed among nonhuman predators alone, is just a beginning and, with the data we have in hand, we only begin to see evidence of precision in guidance. We have yet to achieve a complete understanding of how to overtly or directly account for a variety of factors integral to the patterns.

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Figure 6.6 Annual biomass consumption from the Bering Sea by cetacean populations as estimated by Sobolevsky and Mathiesen (1996) for the 1940s and 1990s.

data from historical monitoring (or estimates of historical conditions) such as would be the case in estimating the consumption rates for the cetaceans in the top panel of Figure 6.6. These were presumably subject to less abnormal human influence than are the systems observed today. Second, we can monitor the consumption by other species as the system recovers from human disturbance in response to management that places humans within the normal range of natural variation for all of the ways we are now abnormal (e.g., confining human consumption to levels within the normal range of natural variation, reducing CO2 production, finding normal genetic selectivity in our harvests—all the ways normality can be achieved). Third, we can use information from comparable systems at other times and places in correlative relationships. 6.1.1.1.2 Dealing with correlated species-level characteristics Medical doctors do not give the same advice to men and women, or to adults and children, for

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managing diets and exercise to obtain advisable body weight. Similarly, we treat neither the heart rate nor the ingestion of food the same for shrews and elephants; they are different species with different characteristics. This is true for individual shrews and elephants and for each individual species. Body size, trophic level, and metabolism are among the factors to take into account. We are a homeothermic mammalian species with an average body size of approximately 60 to 70 kg. Thus, small heterothermic fish species are not as appropriate as examples of sustainability for humans as other mammalian species would be. Figure 6.7 illustrates the frequency distributions that would be used in systemic management of our harvests in the single-species approaches introduced above if such management were based on data from other

mammals. Our take of walleye pollock would be reduced to 3.6% of recent takes if we assume that using the mean among mammalian examples suffices to provide adequate guiding information. For hake, herring, and mackerel, the reductions would be to 7.0%, 3.3%, and 5.9% of recent harvests, respectively. As can be seen, the more complexity we deal with, and, in particular, when various factors are dealt with directly or overtly, the more we appreciate the degree to which we have failed to account for complexity, and failed to achieve sustainability in current management. Although using data from other mammals is critically necessary, by itself, it is insufficient for dealing with complexity completely. For example, Figures 6.6 and 6.7 do not overtly account for body size appropriate for humans.

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Figure 6.7 The consumption (harvest) of fish by commercial fisheries compared to that by marine mammals for four species of fish prey. Comparison is illustrated in frequency distributions for rates measured as log10 of the portion of the standing stock biomass of prey taken annually. Commercial takes of walleye pollock are compared to consumption by six other mammalian species (e.g., northern fur seals, Callorhinus ursinus, the bar to the left of the fisheries take, and harbor seals, Phoca vitulina, one of the three species represented by the tallest bar). Takes of herring by fisheries are compared to consumption by seven species of mammals (e.g., fin whales, Balaenoptera physalus in the uppermost bar below humans). The consumption of hake involves a comparison of commercial fishing with seven species of marine mammals, including harbor porpoise (Phocoena phocoena, represented by the shortest bar among the marine mammals). The consumption of mackerel is represented by ten species of marine mammals (e.g., minke whales, Balaenoptera acutorostrata, one of two species in the bar at the far left). The data presented here represent the same systems shown in Figures 6.1 and 6.4.

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6.1.1.1.3 Dealing with environmental factors The evolutionary history of species, including environmental factors, and all human influence, are part of the explanation of natural patterns such that they represent an integration of such factors (Fig. 1.4). Because species in different environments respond to different seasonal and decadal changes, the distributions for the same species may be different under different circumstances. Thus, to further account for complexity directly, these dynamics must be taken into account in the same way species-level characteristics were taken into account above. The dynamics of species frequency distributions over time, differences over space, and reactions to environmental circumstances can be used as information. Patterns change with circumstances that can be treated explicitly. Thus, we could use distributions like those presented in Figure 6.7 for the harvest of individual species during years of climate corresponding to that during which the data were collected. We would need different sets of data for alternate climate regimes and different population levels of the resource species (while, of course, simultaneously accounting for other species-level characteristics as discussed above). 6.1.1.2 Multispecies approaches What is a sustainable harvest from a particular group of fish species? Here we are expanding the application of systemic management from the single-species approaches introduced above. We are now considering human interactions with groups of species in the progression toward interactions with ecosystems and the biosphere. Communities, guilds, taxonomic groups, or trophic assemblies of species are also subject to human effects that need regulation. How much of the production by any species, or group of species should be left unharvested to sustain such systems? Each needs resources and energy to sustain its normal structure and function. Groups can be defined in numerous ways, including spatially determined groups, groups of specific body size, groups of specified life-history traits, groups with specific reproductive strategies, groups preyed upon by a specific predator, or groups defined by numbers of predator species that prey upon them. A group of particular interest to

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management involves predator/prey pairs, always two in the simplest case, but which also could include a predator and all of its prey. Management is required to consider such groups, all as parts of the spectrum from individual species to the biosphere, and all in accounting for complexity at various levels of biological organization. Finding a way to regulate our interactions with such groups is thus a piece of systemic management; the parts of systemic management include all such interactions. Resource use serves as a continuing theme in demonstrating how to take advantage of information on species-level patterns, but again is not the only element of human influence to be involved in the full treatment of complexity. The limits to natural variation in resource consumption from several groups of species are demonstrated in Figure 6.8. This figure shows consumption rates by humans in comparison to the natural variation in the consumption rates among consumers feeding on three different groups of fish. First, the finfish of the eastern Bering Sea are a taxonomic group (panel A, Fig. 6.8) fed upon by 20 species of marine mammals. Consumption by humans would have to be reduced by about 97% to correspond to the mean of consumption rates by nonhuman species based on these data. Second, mackerel, herring, and hake are commercially valuable fish in the northwest Atlantic (panel B, Fig. 6.8). The total harvest of these species would have to be reduced from recent harvest levels by about 98% to correspond to the mean of the consumption rates estimated for the marine mammals (about 3000 metric tons per year). Third, the fish off the southwest coast of Africa include four species, some of which are of commercial value. If all were harvested commercially, the data represented in panel C of Figure 6.8 would imply a need to reduce the overall take of biomass from this collection by more than 99% of recent harvests (primarily catches of anchovy at the rate of about 480,000 metric tons per year), continuing to assume that we have adequately matched (achieved consonance7 between) empirical pattern and management question (a failure we obviously risk in comparing ourselves to birds). What is a sustainable harvest from the combination of deer, elk, and moose in the system depicted

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Figure 6.9 The pattern of variation in total number of female animals killed by the predators and human hunters in the cervid species-group shown as individual species in Fig. 6.5. Whereas the rates shown in Fig. 6.5 apply to the respective populations, the numbers shown here are pertinent to the population of the species group defined by the animals tagged in the study.

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Figure 6.8 Variation in consumption rates (log10 metric tons of biomass consumed annually), comparing commercial harvest by humans with consumption by other predators: (A) human and marine mammal predation on finfish in the eastern Bering Sea (Fowler and Perez 1999); (B) human and marine mammal predation on hake, herring, and mackerel in the northwest Atlantic (same species as represented in Figs. 6.4 and 7.7; Overholtz et al. 1991, and personal communication); (C) human and seabird predation on lanternfish (Lampanyctodes hectoris), lightfish (Mourolicus muelleri), and anchovy (Engraulis capensis) off the southwest coast of Africa (Crawford et al. 1991). Numbers in parentheses are samples sizes (numbers of species) including humans. See original sources for lists of predators involved.

in Figure 6.5? Figure 6.9 shows the pattern consonant with this management question when measured in units of numbers of individual animals. Not only does this demonstrate a multispecies application in a terrestrial system, it again shows

humans to be well within the limits of variation— again, not necessarily fully sustainable. What is an advisable allocation of take over the three resource species in the ungulate example? Deer made up about 36% of the cervids harvest by humans compared to 0–100% for nonhuman predators (mean of about 48%). Elk made up about 43% of the total cervid harvest by humans compared to 0–46% for the nonhuman species (mean of 28%). Moose made up about 24% of the cervid harvest by humans compared to 0–42% (mean of 24%) for nonhuman predators. As with the harvest of individual species, the allocation of harvests by humans across the individual species show no abnormality compared to the observed variation for the nonhuman consumer species (although the harvest of elk might have been close to an excessively large portion of the overall harvest). What portion of the production by deer, elk, and moose should be left for the other species within the ecosystem (and for normal recruitment to the respective populations)? These and similar questions can be addressed consistently for both fisheries and ungulate systems (Fowler and Hobbs 2008). 6.1.1.3 Ecosystem approaches We would be guilty of repeating past errors if we believed that managing our interactions with specific nonhuman species, species groups, or the

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combination of individual species and species groups is sufficient, even by avoiding the abnormal in every case. As we understand from the principles of management, complexity requires considering all components of reality (Management Tenets 3 and 4, Chapter 1). Other questions remain: How many species can be harvested if we harvest them individually as introduced above? What is the total harvest that can be taken sustainably from any particular ecosystem? What portions of the production and standing stock of species groups, ecosystems, and the biosphere should not be harvested? How do we allocate our harvests among alternative species? How many trophic levels, and what range of trophic levels, should be considered in our selection of resource species? How should we allocate our harvest among trophic levels? The patterns of natural systems provide answers to these questions. For example, Figure 6.6 illustrates the concept of resource use within ecosystems by showing the annual take of biomass from the Bering Sea by cetaceans. Management of our use of ecosystems can use such information as preliminary guidance based on the observed limits to natural variation. There are two ways to account for some of the influence of humans that has been historically (especially recently) outside the normal range of natural variation. First, the top panel of Figure 6.6 is a better indicator than the bottom panel of what ultimately can be sustainably taken from the Bering Sea after it has been allowed to recover from abnormal past and current anthropogenic influences. Second, the bottom panel is more of an indication of what is sustainable under current circumstances to account for the plethora of current and past human influences. Many cetaceans, however, are larger than humans and we need to account for factors such as our body size as directly as possible. Figure 6.10 shows preliminary patterns in sustainable harvest rates in consumption of biomass from the eastern Bering Sea, the Georges Bank, and the Benguela ecosystems. Figure 6.11 shows the change that would have to be made in harvests from the eastern Bering Sea if we were to use the mean of the data from Figure 6.10 (top panel) as a management goal. The example in Figure 6.11

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Figure 6.10 Variation in consumption rates (log10 metric tons of biomass consumed annually), comparing commercial harvest by humans with consumption by other predators for three ecosystems: (A) humans and marine mammals in the eastern Bering Sea ecosystem north of the Aleutian Islands (Fowler and Perez 1999), (B) humans and marine mammals in the Georges Bank ecosystem of the northwest Atlantic (Backus and Bourne 1986), and (C) humans and seabirds in the Benguela ecosystem off the southwest coast of Africa (Crawford et al. 1991). Sample sizes shown in parentheses (number of species) include humans.

again emphasizes the importance of assumptions being made. Smoothed symmetric curves cannot adequately represent the underlying data, the geometric mean is probably not the best standard, the pattern in hand may not apply in today’s world, and there are undoubtedly other correlative factors that can be brought to bear in further analysis.

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Figure 6.11 Illustration of the change needed for systemic management to bring human consumption into the normal range of natural variation (in this case, equivalent to the mean) of the total harvest of biomass from the ecosystem of the eastern Bering Sea, based on data for consumption rates by 20 species of marine mammals.

However, as a preliminary indication of needed change based on these data, the total harvest of biomass from the eastern Bering Sea would have to be reduced by about 97% (from about 2 million metric tons per year, late in the 20th century, to about 60,000) to correspond to the mean of biomass consumption by mammalian species. We need to further deal with complexity through the use of correlative information in the management of our use of ecosystems, just as we do for our use of individual species or groups of species. Again, this means accounting for anthropogenic influence, correlated species-level characteristics, and environmental circumstances. Data based on consumption among marine mammals prior to abnormal human influence by humans, would be

better than what we now have in hand, for estimating what might be sustainable after ecosystems have recovered from abnormal human influence. Data for the consumption rates among species of a body size similar to that of humans serves better than data for species of other body size or taxa. Thus, data for marine mammals would be preferable to that for marine birds in the Benguela ecosystem (marine mammals are not included in the bottom panel of Fig. 6.10). Data on consumption collected during climatic conditions similar to the present would add to the quality of the resulting guidance. Further accuracy would be achieved with systems free of abnormal human influence long enough to have recovered. 6.1.1.4 Biosphere approaches, marine and terrestrial environments Hierarchical complexity extends beyond species, species groups, or ecosystems to the biosphere. In each case we currently have very limited data for explicitly considering human characteristics and environmental factors in correlative relationships. On the conceptual path from species to the biosphere, however, we pass through the marine environment. The fractal-like pattern of systemic management becomes more apparent as we proceed through the various levels of biological organization. We continue with the theme of resource use in our introduction of information to guide systemic management, being mindful that many other management questions need to be addressed. Figure 6.12 compares consumption of biomass by humans to the pattern among other species. It shows limits to natural variation seen in the consumption of biomass from both marine and terrestrial environments, as well as that for the biosphere, based on information for consumption by various mammalian consumers. Managing to make the removal of biomass from the marine environment in fisheries (i.e., consumption by humans) correspond to the mean among other species of mammals would require a reduction of about 99%. To make our ingestion of biomass in the biosphere correspond to the mean of that among other mammalian species would require a reduction of over 99.9%. We now see, with growing clarity, the magnitude of our problems, which, without solution, will

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our use of the overall marine or terrestrial environment or the entire biosphere. Such interactions are more complex than our use of individual species and ecosystems. Managing at this scale again brings the need to account for anthropogenic influence, species-level characteristics (so the characteristics of species we choose as role models are similar to those of humans), and environmental circumstances. In other words, with systemic management at this scale, the approach follows the pattern established above for individual species, species groups, and ecosystems—now using information exemplified in Figure 6.12.

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log10 (biomass consumed, t/yr) Figure 6.12 Variation in consumption rates (log10 metric tons of biomass consumed annually), comparing food consumption by humans and other mammals: (A) total catch of worldwide commercial fisheries in the marine environment (NRC 1999) compared with consumption (ingestion) by marine mammals; (B) total human food ingestion compared with ingestion by terrestrial mammals; and (C) total worldwide human ingestion of food compared to other terrestrial and marine mammals (nonhuman species of A and B combined). Ingestion rates are calculated using estimates of population and body size from Nowak (1991) and Ridgway and Harrison (1981–1999) combined with relationships between ingestion rates and body size from Peters (1983).

continue to contribute to unsustainable conditions for systems at all levels As we expand our horizons to enquire about sustainable interactions with systems containing ecosystems, we face even greater complexity than for ecosystems themselves. This happens in managing

The consumption of water effects, directly or indirectly, other biota—with ultimate feedback effecting humans. It is a biosphere-level issue that illustrates the importance of controlling our interaction with everything in the biosphere. A variety of situations compel the asking of management questions regarding the sustainable use of water. We have observed the depletion of subterranean aquifers, rivers that no longer flow to their original endpoints, and lakes that have experienced drops in their water levels or dried up entirely. These involve serious ecosystem effects (some involve the complete loss of ecosystems) and raise questions such as: How much water can we sustainably use? How much water should be left for other species, ecosystems, and the biosphere? Figure 6.13 illustrates the level of aberrant water usage by humans. This comparison uses estimated water consumption for 42 species of terrestrial mammals based on body size and population estimates from Nowak (1991) and Ridgway and Harrison (1981–1999), and water consumption rates from Chew (1965). Humans are shown with an estimated 4700 km3 use of water projected for the year 2025 (Vörösmarty et al. 2000). This represents 0.0576% of the estimated supply of freshwater assuming that there are 8,152,960 km3 of freshwater on our planet (Encyclopedia Britannica 1977). Human use of water may seem small but it exceeds that of other species by over five orders of magnitude, primarily because of irrigation along with industrial and personal uses other

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Figure 6.13 A frequency distribution representing the use of fresh water for 43 species of terrestrial mammals including humans. See text for details.

than drinking (e.g., waste disposal, cooking, washing). Ancillary aspects of our use of water include its pollution by fertilizers, pesticides, and other chemicals on its way to other functions within ecosystems and the biosphere. These involve other specific management questions (one of which will be dealt with below). Here, the direct measure of water consumption is one of many simple metrics, or measures, of humans that can be matched with consonant information in informative patterns. One use of water is considered in systemic management when we ask the management question: “What portion of the world’s freshwater supply can sustainably be consumed by our species each year?” Figure 6.13 represents the pattern consonant with this question; systemic management would be the reduction of water usage to relieve other species, ecosystems, and the biosphere of human abnormality. Thus, we are faced with asking the management question (“What portion of the world’s water supply should be left for other species?”—complimentary to the question above). This addresses the need to ensure that other species, ecosystems, and the biosphere have a sustainable water supply. The concept of sustainability in systemic management is not confined to humans. Rivers that no longer flow to lakes or continental coastlines, and the disappearance of wetlands, small lakes, and ponds remind us to ask such questions on

several spatial scales in systemic management. The answer to each question is found in patterns like that represented by Figure 6.13. For the Earth as a whole, the pattern would involve the portion of the Earth’s freshwater supply not consumed by each species. The pattern in Figure 6.13 is the log of the portion consumed; the pattern to address the question just posed would be the log of the portion not consumed (i.e., 1.0 minus the portion consumed). The two questions would thus be addressed with data and action that would be consistent (Hobbs and Fowler 2008). All other species would be given consideration on an equal basis to that of humans.

6.1.3 Carbon dioxide production Resource use is only one category of a species’ interactions with other species, groups of species, ecosystems, terrestrial biota, the physical environment, and the biosphere. The production of CO2 effects climate as well as the acidity of fresh and marine waters, and, directly or indirectly, other species and ecosystems. Dynamics involving various pathways result in reactions that involve us humans—consequences, feedback, repercussions. Such reactions involve both humans and nonhumans and include effects to be experienced in the future—evolutionary time scales are not to be ignored. It is another biosphere-level issue that emphasizes the importance of regulating our interaction with everything. Figure 6.14 shows CO2 production in the biosphere by humans in comparison to that of 63 species of mammals of body size similar to that of humans (e.g., American antelope, Antilocapra americana; and Dall’s porpoise, Phocoenoides dalli; see Fowler and Perez 1999). To reduce our CO2 production to correspond to that of the mean of other species would require a reduction of nearly 100%, very similar to the situation with water above. There is a difference of about six orders of magnitude between the production of CO2 by humans and the mean of CO2 production by other species of mammals of our body size. Our CO2 production is almost four orders of magnitude larger than what would be required to match the level that would maximize diversity (Fowler 2008). Achieving that level would require a reduction of over 99.98%, or

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Figure 6.14 Comparison of CO2 production in the biosphere by humans and other mammals with similar body sizes (N = 64 as shown in Fig. 2.12, here including humans, Fowler and Perez 1999).

cutting it in half almost 13 times (an 80% reduction would need to be repeated over 5.5 times). Many species of mammals today are endangered as a result of human influence and we would expect the mean of CO2 production among other species to increase if human influences of all kinds were normal. Climatic and other environmental circumstances undoubtedly make patterns such as that shown in Figure 6.14 different for different areas. For management, a distribution such as this may not be significantly different for other heterotrophic species of any body size (i.e., body size may not be correlated with species-level CO2 production) but it would be important to know before identifying the ultimate goal, even though the direction to be heading, and a rough idea of the levels to head for are only too alarmingly apparent.

6.1.4 Energy use The effects any species has on the rest of its environment (including ecosystems,8 other species, and the biosphere) varies according to type of interaction, such as its occupation of space, food consumption, or use of energy—each one having its own effect with synergistic effects involving every element and its interactions intrinsic and extrinsic to the system. Every species needs energy. Systemic management is an attempt to avoid abnormal influence on the chances that any other species will have its needs met sustainably—reducing human

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needs if they are not normal/sustainable. Energy consumption is one index of the magnifying effect of human impact (as reflected in rates of extinction, Ehrlich 1995). While the role of energy in amplifying human effect on the Earth’s ecosystems is impossible to measure precisely, the magnitude of human abnormality when compared to other species can be obtained directly to account for all such effects. Other species above the primary producer level are restricted to energy ingested; humans, of course, ingest energy but also use energy from outside living ecosystems (e.g., hydroelectric energy, fossil fuel, nuclear energy, and solar power). Considering only the energy ingested by humans (part of the total for humans in Fig. 6.15), human consumption of energy is about 18 times greater than the mean for herbivorous mammals and 180 times that of the mode (arithmetic scale). Energy ingested per unit area by humans is about 640 times that of the geometric mean among the nonhuman species represented in Figure 6.15. In comparison with carnivores, the metabolic energy usage per square kilometer for humans would be approximately an additional order of magnitude larger.9 The ultimate effects of energy use by humans are magnified by energy used from sources outside the living ecosystem. The total worldwide (noningested) consumption from these sources amounts

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to about 3.76 × 1020 joules (357 quadrillion BTU) per year.10 This is a per capita consumption of 1.85 × 108 joules per person per day, or about 18.5 times as much as ingested (10 × 106 joules per day per person, Wright 1990). In other words, the average human in today’s world has roughly the equivalent of 18 slaves at his or her service (93 in the United States). These represent magnification factors of roughly one or two orders of magnitude beyond those of energy required to meet metabolic needs.11 Thus, in terms of energy, the human footprint for the average individual is between 18 and 100 times that for individuals of other species. At the species level, as shown in Figure 6.15, the human use of ingested and noningested energy may be combined for assessment with information on the limits to natural variation in energy used per unit area. Total energy consumption per km 2 for humans is over 205 times greater than the mean (arithmetic) for herbivorous mammals (2050 times greater than the mode). Ingested energy accounts for about 5% of the total energy consumption by the human species (about 1% for the population in the United States). The assessment in Figure 6.15 depends on assumptions about human geographic range size (see following section). We free ourselves of these assumptions by treating energy use directly as shown in Figure 6.16 where information on energy consumption is presented as a biosphere issue. In this figure, total energy consumed per species is shown in preliminary patterns including humans in two ways. Figure 6.16 compares humans and other species of similar size in regard to both ingestion and total energy consumption. The mean of energy consumption for other species is about 512 trillion joules per day. The energy ingested by humans is between 1000 and 2000 times greater than the mean for other species—a measure of our footprint for energy consumption (through ingestion) as a species. The total energy consumed by humans is approximately 20,000 times greater than the mean for the other species. As shown in Figure 6.17, comparing total human energy consumption (ingested and noningested) to energy use by 96 species of mammals regardless of body size, total human consumption is over 21,000 times greater than the mean for other species (based on the geometric mean).

0.3 0.2

Humans (Ingestion) (Total)

0.1 0.0

4

2

6 8 10 log10 (million joules/day)

12

14

Figure 6.16 Approximate frequency distribution comparing energy consumed by humans and that of 63 mammalian species of similar body size (Fowler and Perez 1999). Ingestion was calculated for all species from equations in Peters (1983). The total for humans includes other forms of energy consumption.

0.20 Portion of species

176

0.15 0.10

Humans (Total)

0.05 0.00

0

2

4 6 8 log10 (billion joules/year)

10

12

Figure 6.17 Approximate frequency distribution comparing total energy consumption by humans and 96 species of mammals regardless of body size (an expanded sample including the species of Fig. 6.16, based on data from Nowak 1991, and Ridgway and Harrison 1981–1999 and the estimation procedures of Fowler and Perez 1999).

How much noningested energy do nonhuman species use? Very little in comparison to humans. Primary producers distinguish themselves in relying totally on sunlight. Heat provided by insolation and motion assisted by winds (Plate 6.1) and water currents are examples for higher trophic levels. Consonant patterns have yet to be developed to address the sustainability of such factors. Energy consumption by humans, at the scale shown above, clearly abnormal among species, is

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0.25 Portion of species

a major factor in creating problems identified for other species, ecosystems, and the biosphere as identified in earlier chapters (e.g., Appendix 4.2). Determining all of the effects on ecosystems of both obtaining and using energy is an impossible task but involves numerous detailed questions for which we can find consonant information (e.g., amount of nonbiodegradable materials produced, patterns in surface area covered by structures, amount of soil moved, energy used in food storage/refrigeration, and average distance traveled in a lifetime). Various aspects of this influence include harvesting food as an energy source for metabolic needs, and wood for fuel; each represents extraction from the system with corresponding ecosystemlevel effects. The energy used in these activities also has effects, including those activities that do not involve the extraction of energy for ingestion (e.g., construction, manufacturing, production, distribution, waste disposal, etc.). See later sections of this chapter for consideration of factors involved in extinction. Another approach to evaluating energy usage is to employ an index represented by the overall fraction of ecosystem-level energy budgets that come under human influence. Such an index of effects would be a measure that can be compared among species in a way that applies to the entire biosphere. For example, Vitousek et al. (1986) and Wright (1990) have indicated that humans now use, influence, or control between 20% and 40% of the Earth’s primary productivity. This is almost six orders of magnitude greater than the mean of the same influence by other species (Fig. 6.18, Fowler 2008).12 Management to remove the abnormality of human energy use shown in Figures 6.15 and 6.18 would dramatically reduce human influence through energy consumption whether it be on any species (including our own), full scale ecosystems, or the biosphere. The caveats noted for earlier examples also apply here. Systemic management would be based on considering anthropogenic influence on the position of the species in such distributions, other species-level attributes, and environmental circumstances, just as for resource use and CO2 production (always implicitly, based on the emergence of such patterns, and explicitly, through correlative analysis, as often as possible).

177

0.20 0.15

Humans

0.10

(Total)

0.05 0.00

–10 –8 –6 –4 –2 0 –12 log10 (portion of primary production controlled)

Figure 6.18 Approximate frequency distribution comparing total energy garnered within the biosphere by humans with energy consumed by 96 species of mammals, expressed as a fraction of the energy (2.4 × 1021 joules per year) estimated to be available for higher trophic levels through primary production (Wright 1990).

6.1.5 Geographic range and mobility Another element of complexity, or another way species can be measured, involves their geographic range, or the geographic space occupied. What portion of an ecosystem, a continent, or the biosphere should humans occupy in the process of using resources, producing CO2, or consuming energy? The complimentary question must also be asked: What portion should be put into reserves for protecting other species from direct human influence? Empirical patterns find consistent answers (Hobbs and Fowler 2008). For purposes of appraisal, the current geographic range of our species can be assumed to be 70% of the Earth’s terrestrial surface, excluding Antarctica, although some ascribe our presence to as much as 95% (Pimentel et al. 1992). Figure 6.19 compares this 70% with the geographic ranges of 523 species of North American mammals of all body sizes. A more realistic comparison would be with mammals of similar body size, a species-level attribute correlated with geographic range size (see Fig. 2.28). If we were to attempt to reduce our geographic range to the mean of that for other species of our body size, we would need to confine ourselves to about 760,000 km2 (a reduction of over 99%). As exemplified above, and in other examples of systemic management, the interrelationship

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Portion of species

1.0 0.8 0.6 Humans

0.4 0.2 0.0

0

10 20 30 40 50 60 70 80 90 100 110 Geographic range (106 km2)

other species—certainly in terms of the maximum as measured for individuals of our species who represent the extremes of distance they travel each year. We are undoubtedly exhibiting abnormal mobility in comparison to species of our body size and trophic level. Our mobility affects the mobility of other species (e.g., parasites, intentionally and unintentionally introduced species, and disease organisms) with the related risks.

Portion of species

0.20

6.1.6 Population

0.15 Humans

0.10 0.05 0.00

–1

0 1 2 3 4 5 6 7 log10 (geographic range, 103 km2)

8

Figure 6.19 Species frequency distributions comparing human geographic range size with 523 mammals of all body sizes found in North America (Pagel et al. 1991a and personal communication, 4/30/91). A very small fraction of this sample have geographic ranges that extend beyond North America that are not accounted for in this illustration. The top panel shows actual area, with the geographic range size for humans assumed to be about 70% of the Earth’s terrestrial surface, excluding Antarctica. The bottom panel shows the same information in log10 scale.

of species characteristics must be considered. Geographic range cannot be considered to the exclusion of other related factors. Among things accounted for automatically are the facts that as geographic range size increases, more and more ecosystems are influenced. Such increases lead to competing for resources with more and more other species. Increased human involvement in more ecosystems results, among other things, in exposure to more diseases (Garrett 1994). Mobility is pertinent to the number of ecosystems in which we are involved. Aided by bicycles, cars, trains, and airplanes, many humans travel millions of kilometers per year. It is highly likely that information on the limits to natural variation in distances traveled per unit time would prove humans to be well above the maximum for

Few would argue that the changes described in the previous sections can be achieved without resulting in a reduction in the human population. Reducing resource consumption would reduce the human population just as reducing the human population would reduce resource consumption. The aspect of complexity we confront here is that while things are interconnected, solving one problem does not guarantee that we solve all problems. Systemic management takes advantage of connections to identify other management questions—each to be addressed systemically. Here the connection between population and other problems (e.g., CO2 production, resource consumption, and geographic range size) highlights the need to be sure that they are not ignored. Reversing the connection—noting the reciprocal connection between resource consumption and population size—emphasizes that the population issue must be confronted as directly as possible on its own terms. Every issue must be evaluated directly (i.e., with empirical patterns directly matching, or consonant with, the question regarding what is sustainable). Also, it is important to know that dealing with only a select set of problems can run serious risks. In this section we deal directly with human overpopulation as an issue raised by consideration of the challenges discussed above—abnormal consumption of biomass and energy, abnormal CO2 production, and geographic range size far outside the normal range of natural variation. As was seen in Chapter 5, the consideration of one issue leads to the discovery of others. All will require changes by each of us individually, as well as socially, if we are to succeed willfully as opposed to being victims of systemic change.

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What, then, is a sustainable number of human beings? The following sections illustrate how patterns can be used to estimate a sustainable human population size. The results are compared to estimates in the literature compiled through conventional approaches and the current application of science. We begin with the latter. 6.1.6.1 Conventional considerations In the scientific and popular literature alike, most references to the problem13 of overpopulation are qualitative. The problem is identified as excessive numbers of humans (Appendix 6.2), but how excessive is rarely evaluated (Catton 1980). Existing evaluations are usually made on the basis of conventional methods (see Cohen 1995a,b) and almost always focus on one specific relevant factor such as water, energy, space, or resources rather than the combination of all relevant factors. Factors deemed relevant are usually considered individually but occasionally are treated in small subsets. The full set of factors is never dealt with completely; the complexity of reality is never fully considered. For example, no full accounting for the impact of our water use has been carried out in a way that simultaneously accounts for all other factors such as habitat, energy use, and coevolutionary processes (to meet the requirements of Management Tenets 3, 4, and 7). The impossibility of doing so in conventional approaches would seem to bring us to an impasse. However, the problem is overcome by using existing empirical normative information (Management Tenet 5) directly addressing population size. By doing so, everything is taken into account (Fig. 1.4, Belgrano and Fowler 2008). Cohen (1997), Fowler and Perez (1999), and Fowler (2005) take initial steps in this more systemic form of evaluation. Conventional approaches to assessing the carrying capacity of the Earth for humans are summarized in Table 6.1 and Figure 6.20. Table 6.1 presents a variety of numerical estimates of sustainable levels of the human population. Figure 6.20 shows a frequency distribution of other estimates compiled by Cohen (1995b, and as explained in Appendix 6.3). The collection of scientific opinion represented in Table 6.1 leads to the conclusion that an optimal human population would be

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between 5% and 35% of late 20th century levels, a range of about 0.29 to 1.99 billion. This is different from the mean of estimates in Figure 6.20 (55.02 billion; Cohen 1995b, Appendix 6.3) by approximately an order of magnitude. According to the estimates from Cohen (1995b), the human population could increase almost tenfold to reach this mean and still be sustainable. The difference between those two sets of assessments is explained by the fact that estimates in Table 6.1 are based on combinations of a few selected ecological factors and relatively complex systems approaches, while estimates in Figure 6.20 are based primarily on consideration of single factors or single species population models for humans. The human population can also be assessed based on comparisons with historic levels. Today’s human population of over 6 billion is as much as 400 times denser and 1000 times larger than 10,000 years ago, before the beginning of organized agriculture, when human population is estimated to have been about 5 to 10 million (see Appendix 6.3). These numbers can and will be debated, a point to be emphasized, but not to the detriment of the quality of difference since there is no doubt that the current population is many times denser and more numerous than in prehistoric periods. 6.1.6.2 Human population density evaluated with information on empirical limits to natural variation Current human population density is greater than any other herbivorous species of our body size (Fig. 6.21). Assuming a human population density of about 11.3 per square kilometer (Appendix 6.3), the population as we enter the 21st century is about 4.9 times the mean expected for herbivores of similar size. This assumes the entire Earth is sustainably inhabitable by humans (Table 6.2). By comparison, the mean density of humans for all terrestrial habitat except Antarctica, is 55.9 per square kilometer, 24.2 times the mean for similar-sized species of herbivorous mammals. The mean density of humans for what we conventionally consider the habitable portion of the Earth (assumed to be 20% of non-Antarctic terrestrial area) is 275 per square km, 120-fold greater. Note that this assumed habitable portion of the Earth is 16 times larger than the arithmetic mean geographic range size for other

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Table 6.1 Rough estimates of sustainable levels of the human population (billions) for the entire earth (WP = world population) and the overpopulation factor (OP—number of times by which the current world population has exceeded that value) as extracted from the scientiﬁc literature and presented in chronological order* WP

OP

Source

1 2 3 4 5 6

2.78 6.00 0.56 2.28 1.03 1.99

2.1 1.0 10.4 2.5 5.6 2.9

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.50 0.40 0.57 0.02 0.52 2.00 0.10 10.40 1.38 6.00 1.89 0.94 1.50 1.52 1.11 2.78 0.67 1.47

11.5 14.4 10.2 288.5 11.1 2.9 57.0 0.6 4.2 1.0 3.1 6.1 3.8 3.8 5.2 2.1 8.7 3.9

25

2.00

2.9

L. Brown (1971) (<250 million for U.S.) Commoner (1971) Hardin (1971) Paddock (1971) (<205 million for U.S.) Odum (1972) (5 acres per person) Rosenzweig (1974) (based on acreage available and required for population in 1970s—210 million) Rosenzweig (1974) (based on water supply) Catton (1980) Catton (1980) Walker (1984) (carnivorous humans in particular habitat type) Fearnside (1985, 1990) (estimate of indigenous densities) Ehrlich as quoted by Tudge (1989) Soulé as quoted by Tudge (1989) FAO (see Fearnside 1990) Ehrlich and Ehrlich (1990) (extrapolated from Chinese estimate for China) Meadows (1991) Costanza (1992) (current European standards) Costanza (1992) (current U.S. standards) Ehrlich and Ehrlich (1992) Grant (1992) (using mean of 125–150 for U.S.) Pimentel and Pimentel (1992) (should be <100 million in U.S.) Werbos (1992) Werbos (1992) Whelpton (1939) (less than the 1939 population—assumed to be 132 million for the U.S.) David Pimentel (Feb. 21, 1994, AAAS meeting, San Francisco, CA).

*Some of these estimates are based on country-specific estimates extrapolated to the world by assuming the density estimated for the country applies to 20.4 million km of land habitable by humans (about 6% of the terrestrial surface of the Earth).

mammalian species, and 120 times larger than the geometric mean (Fig. 6.19). Indices of human overpopulation shown in Table 6.2 are based on comparison to other species from relationships between population density and body size (Damuth 1987, Peters 1983). The numbers in parentheses are the sizes of the human population (in millions) if they were consistent with the population size at the mean for mammals with body size similar to that of humans (assuming a human adult body mass of 68 kg). The numbers without parentheses are indices representing

the factor by which the current human population density exceeds the mean density of other mammals of similar body size. Indices are calculated by dividing the measure of human population density by the mean density of mammalian herbivores or carnivores using statistical procedures identified. Density for humans is the total human population divided by the area indicated. Figure 6.21 shows the derivation of the information in the second column of the bottom row of Table 6.2. A human population, equivalent to the mean for other herbivores (i.e., assuming we

HUM A NS: A SPECIES BEYO ND L IMI T S

Portion of estimates

0.10 0.08 0.06 31% between 20 and 1000 billion

0.04 0.02 0.00

0

2 4 6 8 10 12 14 16 18 Carrying capacity estimates (billions)

20

Figure 6.20 The frequency distribution of estimates of the carrying capacity of the Earth for humans based on conventional thinking, primarily single-factor considerations and population models, from the compilation by Cohen (1995b). One estimate of a billion billion is ignored as are those with only an upper or a lower bound.

5 Humans

log10 (density, nos. per k2)

4

d

3 2 1

lan

t ep xc ) e ( c 2 d cti an r ll l nta ce A e A rfa th l su ta th To Ear of 0%

of

0 –1 –2 –3

–2

–1

0 1 2 log10 (weight, kg)

3

4

5

Figure 6.21 The relationship between population density and body size (log transformed) for 368 species of herbivorous mammals from Damuth (1987). The regression line (solid sloped line) is based on geometric mean regression analysis. The dashed line is parallel to the regression line based on (and located above) all data points except that for humans. The vertical line (for human body size) compares human density with that for other mammals of the same size as the human population if it were spread uniformly over varying parts of the Earth (i.e., arrows point to human population density as it depends on the geographic range assumed for humans as identified for each arrow).

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are strict vegetarians) of similar body size, would be about 48 million people occupying 20% of the Earth’s land surface—a population lower than the Earth has seen for over 6000 years (Catton 1980). (It is to be reiterated that 20% of the Earth’s land surface is well above the mean of geographic range sizes seen in Figure 6.19, and about 40 times larger than the mean geographic range size for other species of mammals of our body size.) With a current population about 120 times larger than 48 million, humans are well outside the 95% confidence limits for density among the nonhuman mammalian herbivore species (Fig. 6.22). Measures of overpopulation increase when we take trophic level into account explicitly as one of the correlative variables behind observed variation. We are not strict herbivores and we need to account for correlative relationships involving trophic level as outlined above in regard to resource use (part of accounting for correlative factors in general). When we do so, we find that the current human population is between 578 and 2470 times more densely populated than the mean of other species of our body size at higher trophic levels (Table 6.2, Appendix 6.4, see also Appendix 6.5 for densities associated with the various nations of the world). Optimal levels for humans may occur between the estimated mean densities for herbivores and those for carnivores in view of our omnivorous habits— direct omnivore information would be of optimal use in this regard. It is entirely possible, however, that mean density for omnivores is higher than either more strict herbivores or carnivores owing to a broader spectrum of species in the diet. Direct (consonant) measures are needed. Figure 6.23 (A) shows conventional estimates of sustainable human population, many of which use procedures which make direct use of density (Appendix 6.4). In these projections, sustainable density is calculated on the basis of numerous assumptions (e.g., the availability of resources or energy). Density is often combined with assumptions about geographic range (and thus involve the model: P = A × D; where P = population, A = Area occupied, and D = density). Figure 6.23 (B, C) uses empirical information about density (and the correlative pattern involving geographic range size) rather than a select few assumptions

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Table 6.2 The human population (millions) evaluated with information for other mammalian species of similar body size showing both expected human population size (in parentheses) and indices of overpopulation (no parentheses) for the indicated areas, trophic levels, and density–body size relationships Human density determined for

Damuth1 Herbivores

Damuth2 Herbivores

Peters3 Herbivores

Total Earth’s surface Land surface only (excluding Antarctica) Twenty percent of land surface inhabitable

2.8 (2046) 13.8 (417)

4.9 (1180) 24.0 (241)

5.6 (1038) 27.3 (212)

69.1 (83)

119.9 (48)

136.3 (42)

Peters 4 General 23.6 (245) 115.6 (50) 577.9 (10)

Peters5 Carnivores 100.7 (57) 494.0 (12) 2470.2 (2)

For D = density in individuals per square kilometer and W = body size in kilograms, the relationships used are 1 D = 95.5 W –0.75 from Damuth (1981) using mammalian herbivores with ordinary least squares regression. 2 D = 91.05 W –0.87 using data from Damuth (1987) for mammalian herbivores with geometric mean regression. 3 D = 103 W –0.93 from Peters (1983) for mammalian herbivores using ordinary least squares regression (see also Damuth 1987). 4 D = 30 W –0.98 from Peters (1983) for animals in general using ordinary least squares regression (see also Damuth 1987). 5 D = 15 W –1.16 from Peters (1983) for mammalian carnivores using ordinary least squares regression.

about factors that determine density. Just as harvesting a resource species involves both biomass and numbers, however, populations involve more than density and more than density as related to geographic range size. Dealing with complexity means considering total population size measured directly—consonance with the question being addressed: “What is the most sustainable total population size for humans?” This involves using patterns for total population size to account for complexity, then using those factors selected for conventional consideration in correlative analysis of such patterns. 6.1.6.3 Total population evaluated with patterns for total population The previous sections are useful for direct evaluation of density, but pertinent to evaluation of total population size only as correlative information. Consistent with the developing pattern for application of systemic management, the set of data needed to evaluate population size involves estimates of population size. When the management question involves sustainable total species-level population size, the consonant informative pattern involves measures of global total population size. This section evaluates human population size (not density, growth, location, or variation) using

information on population size: simple numbers of individuals. As usual, correlative information is involved, and it is probable that population size is related to body size as is density (Fig. 6.21; see also Appendix Fig. 2.1.22). Figure 6.24 shows the total human population (in numbers) to be much larger than the largest of all populations for other species of mammals with body sizes similar to that of humans (Appendix 6.4). The mean total population size for the nonhuman species shown in Figure 6.24 is 2.34 million, less than 0.04% of the current human population size. The current human population (about 6 billion) is about 2500 times more numerous than the mean of the nonhuman species of Figure 6.24. If the populations of these species are 10% of what they would be without the collective effects of abnormal human influence (i.e., including our population size, habitat destruction, global warming, pollution, energy use, and resource consumption), our species would still be 250 times more numerous than the mean in the absence of abnormal human influences. Based on data in log10 scales, the geometric mean population size for the other species is about 157,000. The human population is about 37,000 times this large (over four orders of magnitude larger).

HUM A NS: A SPECIES BEYO ND L IMI T S

Portion of estimates

(A)

0.08 0.06 0.04 0.02 0.00

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Multiple of expected density

Portion of species

(B) 0.9

0.6

0.3

(B) Portion of estimates

Portion of species

(A) 0.10

Portion of species

(C) 0.16 0.12 0.08

Humans

0.04 0.00

–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 2.5 log10 (multiple of expected density)

Figure 6.22 The frequency distribution of 368 mammalian herbivore species for density expressed as a multiple of that predicted from the regression line in Figure 6.21. (A) The subset that falls in the range of 0–2 for this multiple. (B) The position of humans in comparison to the frequency distribution for the entire sample, on a linear scale. (C) The position of humans in comparison to the frequency distribution for the entire sample on a log10 scale assuming humans were redistributed over 20% of the Earth’s nonAntarctic land surface.

Figure 6.25 illustrates information similar to that of Figure 6.24 but based on biomass instead of numbers. In this case the mammalian species used for comparison are not restricted to those of human body size. The estimated mass for each species is

Portion of estimates

5 15 25 35 45 55 65 75 85 95 105 115 125 Multiple of expected density

Derived estimates

0.3 0.2 0.1 0.0

0.4 0.3

–4.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 log10 (carrying capacity estimates, billions) Empirical estimates (range size ignored)

0.2

Human population at end of 1990s

0.1 0.0

Humans (C)

0.0

0.4

183

0.4 0.3 0.2 0.1 0.0

–4.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 log10 (carrying capacity estimates, billions) Empirical estimates (including range size) Human population at end of 1990s –4.0 –3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 log10 (carrying capacity estimates, billions)

Figure 6.23 Three frequency distributions of estimates of the Earth’s carrying capacity for humans (log billions). (A) “Derived” estimates from the combined data of Figure 6.20 (Cohen 1995b) and Table 6.1 (N = 86, mean of 6.75 billion) as estimates based on conventional approaches. (B) “Empirical” estimates, with a mean of 49 million, derived from 368 species of mammalian herbivores without considering human trophic level or geographic range size, but accounting for body size using the regression line of Figure 6.21 (Damuth 1987), and all suitable habitat available to humans. (C) The estimates in (B) shown shifted to the left to account for geographic range size (but not trophic level) and the 1996 estimate of the human population.

biased because total biomass was determined by multiplying the population size by the mean adult body mass rather than the mean size based on prevailing age structure. Mean human body size was assumed to be 68 kg. Here, the mass of the human species is about 900 times larger than the mean mass among the nonhuman species in linear scale and 15,000 times larger in log10 scale.

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0.20

0.1

0.0

Portion of species

Portion of species

(A) 0.2

Humans

–4

–3 –2 –1 0 1 2 3 log10 (population size, millions)

0.15 0.10

Human population

0.05 0.00 –1.0

4

Mean for biosphere

1.0

3.0 5.0 7.0 log10 (mass, metric tons)

9.0

11.0

Portion of species

(B) 0.8 Figure 6.25 The mass of the human population compared to the mass of 96 mammal species represented by approximations for population size and body size in the published literature (Fowler and Perez 1999; body mass and population approximations are from Nowak 1991 and Ridgway and Harrison 1981–1999), and an overall mean for the biosphere.

0.6 0.4 0.2 0.0

0

5

10 15 20 Population size (millions)

25

30

see Appendix 6.5 for an introduction to the variety of national-level challenges). Whether or not we should manage our population directly or indirectly will be treated briefly below.

Portion of species

(C) 1.0 0.8 0.6 0.4

6.1.7 Other species-level characteristics

Humans

0.2 0.0

0

1000

2000

3000

4000

5000

6000

Population size (millions) Figure 6.24 The human population compared to populations of 63 mammal species of similar body size (Fowler and Perez 1999; body mass and population approximations are from Nowak 1991 and Ridgway and Harrison 1981–1999). The data are shown in both log10 scales (A) and in linear scales (B, C). The range of population size spanned by nonhuman species (B) is concentrated into one bar when scales are expanded to include humans (C) with a bin size of 100 million (i.e., the single bar in C spans population size from 0 to 100 million).

Several more examples of relevant species level characteristics are presented below to further illustrate how information on the limits to natural variation can be used for evaluating the human species, setting normative standards and establishing management goals for human action. Such examples help emphasize the: Number of dimensions over which comparisons between humans and nonhuman species can be made Synergistic/correlative effects of interrelated characteristics Complexity of implications for management Importance of human overpopulation Degree of risk our species faces, and poses for other biotic systems

●

●

● ● ●

Also shown in Figure 6.25 is the mean biomass per species if there are 45 million species and a total biomass of 1.1 × 1013 metric tons in the Earth’s biosphere. The human population is about 1600 times larger than this mean. The challenge of dealing with overpopulation is emphasized by the national and international components of the problem (e.g.,

6.1.7.1 Density dependence and rate of increase Having temporarily broken from the natural constraints of population limitation normally imposed by ecosystems (Catton 1980) and the biosphere (and

HUM A NS: A SPECIES BEYO ND L IMI T S

having rendered other elements of the systems around us unsustainable/endangered), humans are the one species at the extreme in measures of density dependence (a measure of the constraining effects on a population under normal circumstances, Fig. 6.26). Almost all other species exhibit homeostatic density dependence to maintain normal population density, numbers and biomass as shown in Figures 6.21–25. Thus, not only is the human population too large, it has also been growing consistently for an abnormally long time. This brings in a related interspecific comparison (and normative information) wherein the average species has a mean rate of realized increase (different from the maximum) no greater (or less) than 0.0. Greater values lead to infinite (impossible) population size and smaller values lead to extinction (as seen if we extrapolate projections in models without modification in the rate of change). Thus, with data for measures of average rate of increase for a period of ten generation times or more, most species would cluster around zero and humans would very likely be an outlier with an abnormally large positive value.

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6.1.7.2 Body mass Figure 6.27 shows a comparison of human body mass to that of other species. Obviously, a few other species have an adult body size that is larger than that of humans but the overwhelming majority are smaller (Chapter 2). The extreme we represent emphasizes the importance of using body size as a factor involved in correlative patterns when comparing humans with other species in regard to other species-level characteristics (e.g., density as covered above). The combination of being extremely large, and abnormally numerous, abnormally widespread, along with consuming abnormal quantities of resources, and producing abnormal quantities of waste and toxic substances to support these extremes results in vulnerability, not only for our species, but also for the systems of which we are a part. We are subject to the risks that prevent the accumulation of species in the extremes (e.g., extinction). 6.1.7.3 Number of resource species consumed As demonstrated in Chapter 2 (Appendix Figs 2.1.2 and 2.1.3), most species so far examined consume less than 20 resource species. The modes among frequency distributions regarding numbers of

0.7

0.5

0.7

0.4

0.6

0.1 0.0

5–10

Figure 6.26 Humans in the species frequency distribution of density dependence shown in Appendix Figure 2.1.10. The frequency distribution is for a collection of 64 species of invertebrates, fish, birds, and mammals by level of density dependence. Density dependence is measured by the slope of the correlation between (Xt +1 – Xt)/ Xt and Xt according to five categories: A, positive and statistically significant; B, positive but not significant; C, negative but not significant; D, negative and significant at the 0.10 significance level; E, negative and significant at the 0.05 significance level.

Humans

0.2

1–5

E

0.5–1

B C D Category of density dependence

0.1–0.5

A

0.3

0.05–0.1

0.0

0.4

0.01–0.05

0.1

0.5

0.005–0.01

Humans

0.001–0.005

0.2

0.0005–0.001

0.3

Portion of species

Portion of species

0.6

Body size (length in m)

Figure 6.27 Species frequency distribution for body size approximated for animals, showing the position of humans (from May 1978, 1986).

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species consumed are much smaller. Humans consume 7000 species of plants (Wilson 1985), 100–1000 times more than other consumer species sampled so far (Appendix Figs 2.1.2 and 2.1.3). In addition, humans consume animal species (for present purposes, assumed to be about 1000 species). Animals are taken both purposely and incidentally, as in commercial fishing that results in the mortality of invertebrates, nontarget fish, mammals, and bird species (Alverson et al. 1994). Adding to the mortality caused by humans are roadkills and deaths caused by pesticide applications, sport hunting, and various other human activities. Here we are dealing with species actually consumed; total mortality caused for each species would be a matter of consideration to be dealt with on a case-by-case basis in parallel with the mortality rates considered for fish and ungulates above. The assumed total number of species consumed by humans, 8000, is shown in Figure 6.28 compared with consumption by the species from Appendix Figures 2.1.2 and 2.1.3, each of which consume less than 50 resource species. This comparison, however, falls very short of achieving consonance with any related management question owing to the taxonomic differences between humans and insects (included here to show how such comparisons can be made, and

Portion of species

1.0 0.8 0.6 0.4

Humans

0.2 0.0

1 3 5 7 Number of species consumed (thousands)

Figure 6.28 Humans shown in the frequency distribution of invertebrate (e.g., grasshopper) species according to the number of species they consume, combining information from Appendix Figures 2.1.2 and 2.1.3 (from Schoenly et al. 1991, Thompson 1982, and Ueckert and Hansen 1971). Humans are represented as consuming 8000 species—7000 plants (Wilson 1985) and an estimated 1000 animals.

9

to emphasize the need for research to obtain such information for mammals of our body size). 6.1.7.4 Population effects on nonhuman species Human influence, such as overharvesting and ecosystem damage, has reduced, or is reducing, the populations of other species, especially resource species. Some reductions are to the point of extinction, thus complicating comparisons with similar effects by other species (e.g., Fig. 6.6). In commercial fishing, resource populations are depressed as a matter of policy, often to 20 – 40% of “virgin” population levels. This issue will be treated in detail through systemic comparisons in consideration of what are sometimes called global control rules below in the final reconsideration of management in the eastern Bering Sea. The consequences of our actions for other species and ecosystems are largely beyond our control (Management Tenet 8), but always part of our influence because of the interconnected complexity of real-world systems (Management Tenet 3). In other cases, human influence has maintained resource species at artificially high levels. For example, the 1.3 billion cattle (Wright 1990) under human domestication are almost three orders of magnitude more numerous than any of the nonhuman/nondomestic species of similar body size (e.g., in comparison to the 2.34 million mean for species in Fig. 6.24). Cattle are allowed to range on Forest Service lands in the U.S. as a management policy. Assuming one animal for every five acres (49 per km 2), and a body weight of 200 kg, such stocking rates are almost two orders of magnitude beyond the mean density of nondomesticated herbivores of similar size (Figs 6.21, 6.22). Other domesticated species show similar abnormality in comparison to consonant patterns. The 1.1 billion sheep, 0.8 billion pigs, and 0.1 billion domestic buffalo (Wright 1990) are all orders of magnitude above the central tendencies for population numbers among nonagricultural species of similar size and trophic level (e.g., those in Figs 6.21 and 6.22). The millions of pets, such as dogs and cats, and their predation on other species, have yet to be assessed through the use of patterns exhibiting consonant limits to natural variation, but similar results can be expected.

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6.1.7.5 Number of consuming species (humans as host species) Data have yet to be assembled to illustrate the limits to natural variation for comparison, but the 1100 plus species for which humans serve as a resource species (Table 6.3) may be another departure from the norm among nonhuman species. For example, only 50–60 species of diseases, parasites, and predators have been identified for northern fur seals (Fowler 1998). Compilation of other such counts is necessary to characterize the consonant pattern and establish the normal range of variation to be used for comparison. Our exposure to so many of the world’s ecosystems through our large geographic range and mobility contributes to any likely abnormality in this regard. The diseases we experience present a risk over which we have limited control. The “control” we do have (influence) depends to a large extent on our use of toxic chemicals, introduction of nonnative species, and the associated resources—all themselves at abnormal rates. Risks from disease can be expected to decline if we can manage to reduce our geographic range size and population density. Of course, these reductions will happen as a result of the effects of disease in the case of a severe pandemic—a natural and normal dynamic within ecosystems. Without such changes, we can expect exposure to an increasing

variety of viral and bacterial diseases at least some of which will develop increasing control resistance owing to their rapid evolutionary rate. If we find no realistic means of reducing our population, there is little to do but acknowledge and live with the variety of risks involved—many completely unknown. Human health (Fowler 2005) obviously counts among them as does the ultimate risk of our own extinction. 6.1.7.6 Selective breeding and genetic effects of harvests As Jordano (1987) points out, we have made little progress in modeling webs of genetic interactions, compared with other ways of modeling ecosystems (e.g., analysis of food webs). We know little about “keystone effects” (system-dominating extremes by an individual species) in genetic or coevolutionary interactions in spite of the ubiquity of such relationships (Lederberg 1993, Thompson 2005). The numbers of domestic species humans breed, and the effects of pesticides and antibiotics, must mean that the (unmeasured) selective pressures exerted by humans are well beyond any such measure for other species, both in terms of numbers of species impacted and levels of influence in many individual cases. We are undoubtedly an evolutionary “keystone” species.

Table 6.3 Approximate numbers of species that prey upon or parasitize humans, exclusive of the vertebrates Taxa

Number of species

References*

Rickettsiae Viruses Bacteria Protozoa Fungi Worms Crustaceans (insects)

9 >300 >50 73 >100 >280 >350 (>240)

2, 5, 10, 16 4, 5, 9, 10, 11 3, 5, 17 1, 5, 7, 19 5, 6, 15 1, 2, 3, 8, 13, 14, 16, 18 2–5, 16

Total

187

>1100

* (1) Swellengrebel and Sterman (1961); (2) Faust et al. (1985); (3) Evans and Feldman (1989); (4) Meheus and Spier (1989), Meheus and DeSchryver (1989); (5) Katz et al. (1989); (6) Wilson and Plunlatt (1965); (7) Honigberg (1990); (8) Coombs and Crompton (1991); (9) Zuckerman et al. (1990); (10) Horsefall and Tamm (1965); (11) WHO (1985); (12) WHO (1987); (13) Donaldson (1979); (14) Wilson (1991); (15) Emmons et al. (1977); (16) Strickland (1984); (17) WHO (1982); (18) Malek (1980); Ashford and Crewe (1998).

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There is limited information characterizing patterns in the selective pressures among co-occurring species to enable an objective quantitative picture of what is normal compared to abnormal. In an attempt to remedy this situation, Etnier and Fowler (2005) present a preliminary example of studies which can be conducted to provide information for systemic management that addresses management questions such as “What size class selectivity is appropriate for the harvest of walleye pollock?” This kind of question can be asked for any resource species. For example: “What should the mean length be in a sustainable harvest of Atlantic cod?” Figure 6.29 compares the size selectivity of commercial harvests of this species with that among nonhuman predators. Based on information that is consonant with the question, this comparison shows how humans are an outlier as we are in so many other ways. In the systemic management of humans in this system (as part of the cod’s ecosystem), the mean size would be reduced from what was about 50 cm to less than 40 cm. The mortality rate (for which the individuals taken would have this mean length), or the intensity of the harvest and any related selectivity, would be regulated by information for cod like the pattern shown in Figure 6.7 for walleye pollock. The overall size distribution of cod in commercial catches (with

Portion of cases

0.3

0.2 Humans 0.1

0.0

10.0

20.0

30.0 40.0 Length (cm)

50.0

Figure 6.29 The frequency distribution of the mean size of Atlantic cod (Gadus morhua) taken by 5 species of marine mammals (in 19 studies,1983–1996) compared to that in the take of cod by commercial fisheries prior to their collapse (from Etnier and Fowler 2005).

means such as shown in Fig. 6.29) would be regulated so as to match the size distribution in the diets of marine mammals as examples of what works in natural systems (Etnier and Fowler 2005). These kinds of selectivity are not only what works, of course, but are fundamental in determining the structure and function of ecosystems—their very nature. Introducing abnormal selectivity presents risks we have yet to appreciate in their influence on the very nature of ecosystems. 6.1.7.7 Genetic engineering Direct genetic impact on other species is known among “organisms” such as plasmids. Any pattern in the numbers of species influenced has yet to be constructed. Genetic influence other than coevolutionary impacts may not be measurable. The numbers and kinds of species subject to genetic change through genetic engineering by humans is undoubtedly highly abnormal compared to what occurs in natural systems. It is safe to assert that no species of our body size practices genetic engineering making our species’ “success” in being able to do so a potential lethal species-level mutation. The long-term consequences of these practices are almost completely unknown and the short-term consequences are only superficially being recognized (largely in realizing the short-term conscious purpose of supplying food for the human population). 6.1.7.8 Extinction rates caused Perhaps the most dramatic of differences between humans and other species is the rate of extinction for which we are responsible (Appendix 6.6). The human contribution to extinction is about 1000 times greater than that of background rates. This comparison, however, involves an error in logical typing when it comes to assessment of abnormality—it lacks consonance. Background rates include the cumulative effect of all other species and abiotic causes (May et al. 1995). For consonance, we need estimates of extinction caused by individual species so that we can evaluate the extinction caused by our species by comparison. Nevertheless, to begin to appreciate the magnitude of the problem, it is of note that today’s extinction crisis is equivalent in magnitude to the major

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6.1.7.9 Estrogenic compounds produced Pollution is one of the many factors involved in environmental problems we face today. This raises questions such as “At what rate can our species produce estrogen sustainably?” This same question, of course, can be asked for any one of the tens of thousands of compounds humans have learned to fabricate (using resources and energy to do so). Many of these make their way, in one form or another, into the ecosystems about which we are concerned in management. The cumulative aspect of these problems as they affect all ecosystems, involves the biosphere as a whole. Figure 6.30 shows the pattern consonant with the management question at hand. This pattern is shown more as a demonstration of the kind of information needed for management than as basis for a precise measure of the problem before us.

0.2 Portion of species

extinction events recognized in geological history (Raven and Cracraft 1999). Based on a more consonant approach to evaluation, humans can be compared with other species. Our species is causing extinction rates that are probably more than nine orders of magnitude greater than that of the average among other species! If all species were behaving like humans, the expected duration of all species now on Earth would be measured in hours to months. This means that, on the average, each of us, as individual humans, has an extremely large ecological effect. On average, each one of us (as individual humans) may exert an effect equivalent to that of as many as ten entire nonhuman species—species and individuals participating in ecosystems in more sustainable ways. This emphasizes how interconnected the parts of nature are. Our effects multiply through the web of ecological interactions and relationships to result in synergistic effects that are beyond our comprehension. If they could be measured for all species, the frequency distribution for extinction rates caused would have to be presented in log scales (as with so many examples in this book) in order to see any of the scatter among most species and still include humans; it would probably be similar to Figure 6.17, the top panel of Figure 6.24, or Figure 6.25 (partly because these figures represent factors involved in the causes of extinction).

189

Humans 0.1

0.0 –4.0

Cattle

–2.0

0.0 2.0 4.0 6.0 8.0 log10 (E2 production, kg/yr)

10.0

Figure 6.30 Log10 17E-estradiol (E2) production by humans and cattle (Bos taurus) in comparison to that for 63 nondomestic nonhuman mammalian species, assuming nonhuman species exhibit a per capita production equivalent to that of pigs (Sus domestica).

Estrogen production as presented in Figure 6.30 is based on assumptions that emphasize the need for quality research to produce better information. Reliable data for the mean per capita production of 17E-estradiol for other species is needed, taking into account factors such as gender, body size, and age (correlative factors). In this example, we have information for estimated 17E-estradiol production for humans that is more reliable than that for nondomestic nonhuman species. The rate of production estimated for other species in Figure 6.30 relies on assuming per-capita production roughly equivalent to that for several domestic species for which there are at least partially reliable data (cattle, sheep, and pigs; Fowler and Lee, in preparation). As is no surprise, owing primarily to the population effect, the production of 17E-estradiol by humans is abnormal compared to that of other species, a difference that will probably not change dramatically with refined research. Humans produce estrogen both as a product of physiological processes as well as synthetically. If we address the question of sustainability in the production of estrogenic compounds, as a class of chemicals, the abnormality becomes more exaggerated, owing to the estrogenic nature of the tons of pesticides and pharmaceuticals produced and used by humans each year. Again, Figure 6.30 is intended more to get a first glimpse of the extent of the problem before us, and to demonstrate the direction to be

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taken in consonant research, than it is to provide a precise measure of what is actually sustainable. As with many other issues (e.g., energy monopolized, biodiversity, and artificial selectivity), the problem of estrogen production is compounded in magnitude by the estrogen production by domestic species which serve as sources of food for our species (shown for cattle in Fig. 6.30). When scientists find the resources to conduct refined research consonant with the management question(s) it will be important to know how variation in estrogen production relates to correlative factors (across species) such as body size, rate of increase per generation time, and trophic level.

6.2 Consideration of hierarchy Systemic management can be applied at all levels of biological organization to deal with the problems identified earlier in this chapter. Systemic management is reality-based management and both ecosystems and the biosphere are parts of reality. Thus, both ecosystem-based and biospherebased management are integral parts of systemic management. The use of integral patterns to establish objectives ensures that hierarchical complexity is taken into account implicitly. Teasing out correlative patterns leads to explicit consideration. Complexity is also involved in the extent to which management is applied. Just as we can regulate our individual body weight, and interactions with each other and our environment as individuals, so we can manage our species’ resource use, geographic range, and interactions with other species, various ecosystems, and the biosphere. If population size and other characteristics of our species are abnormal, interconnectedness and complexity dictate that achieving normalcy will involve management action at the individual and species levels in our interactions with all other hierarchical levels of biological organization. It is tempting to use this information in familiar, conventional ways. For example, we might try to examine the costs and benefits of reducing our energy consumption. If we yield to this temptation, we have to remind ourselves that we cannot identify or evaluate all costs and benefits. The complexity of nature, or the complexity of reality, prohibits

doing so. Most importantly, we cannot weigh the relative importance of those few costs and benefits we can identify. Since we cannot complete such a process, we have to avoid believing that beginning it has any merit beyond the very valuable progress made in exposing more complexity—and very importantly, leading to, and refining, more management questions. Using incomplete and incorrect (nonconsonant) information can easily lead us to mistakes like those we have made in the past. As inherent to the examples of this chapter, the way around such human limitations is to take advantage of information in which complexity is already taken into account—inherent to guiding information (Fig. 1.4, Belgrano and Fowler 2008). Thus, systemic management accounts for not only the complexity within each level of biological organization, but across the spectrum of biological hierarchies as well. Management actions to achieve normalcy in human interactions with nonhuman systems aims to achieve states, or restore properties, at the level of biological organization corresponding to those at which the problem(s) were discovered (the focal level for assessment— Chapter 4). Achieving normalcy involves human interaction with all levels of biological organization. In all cases, action taken on one level will involve benefits and costs at other levels—costs and benefits, according to human value systems, that prevent objectivity if used to make decisions on their own. Within hierarchical organization we find individuals within species. There are tradeoffs between levels including these two. In systemic management, it is possible to take advantage of very limited latitude for mitigation as tradeoff between individual-level and species-level factors—all within the human realm and bounded by limits to natural variation, as in all cases. These are exemplified by energy consumption shown in Figure 6.31—tradeoffs confined to our species. Other species will obviously always be involved but, in the example shown in Figure 6.31, we avoid forcing other species, ecosystems, or the biosphere to absorb the effects of human abnormality. Other human abnormalities, such as the quantity of radioactive materials produced, or distances traveled in a lifetime, are to be dealt with on their own—as distinct management questions.

Per capita energy consumption log10 (joules per year)

HUM A NS: A SPECIES BEYO ND L IMI T S

US consumption rates

18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2

Global mean consumption rates c

a

b e

f

d

Change necessary to achieve sustainability

18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2

Vector components:

e

Portion of species

0.0

0.1

–3

–2

–1

0

1

2

3

4

f h

g

Population (log10 millions)

Population (log10 millions) –4

191

5

Humans

0.2

6

0.0

0.1

–4

–3

–2

–1

0

1

2

3

4

5

6

Humans

0.2

Figure 6.31 The limits for tradeoff between two aspects of energy consumption by humans: per capita consumption and consumption by the total population. Current combinations (US and global consumption rates) are unsustainable. The two arrows in the inset box represent the individual (down arrow) and population (left arrow) components of the change required to achieve sustainability. See text for details.

What are the tradeoffs? In Figure 6.31 we see the relationship between individual (per capita) consumption of energy and total population size that maintains a constant total (species-level) energy consumption. The latter is shown for several options in the diagonal lines, each diagonal line representing a constant species-level total. For each level of total energy consumption (i.e., each diagonal line), the two variables involved are population size and per capita energy consumption. The bottom panels are the same as Figure 6.24A but inverted so that the population scale is the same as that for the panels above. Limits to options are illustrated on the left and central tendencies are depicted on the right, as explained in the following.

Two vertical lines in Figure 6.31 (a and b), represent the lower and upper 0.95 confidence limits to population size based on the pattern in the panel below. The diagonal lines c and d represent the 0.95 confidence limits for total consumption of energy by the nonhuman species in Figure 6.16 (Fowler 2008) plotted to bound the options for our species in regard to this variable. For any of the diagonal lines (e.g., c or d), total energy consumption for humans is constant as a function of population size and per capita energy consumption, one in tradeoff with the other. In place of the bounds of lines c and d, we can focus more on peak sustainability within these bounds by using measures of central tendency (mean, mode, median) or the measure that maximizes the diversity of energy

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consumption (Fowler 2008). The latter is represented by the diagonal line (g) passing through point f for the total species-level consumption of energy. Also plotted in Figure 6.31 is a horizontal line (e). This represents the per capita energy consumption equivalent to that required to meet metabolic needs of individuals (assumed to be 10 million joules per day for humans; 9.56 = log [365 × 107]). The combination of lines (a, b, c, and e) represents crude bounds to the sustainable options for total energy consumption for our species. The top panels of Figure 6.31 also contain a pair of individual points representing the current human population (directly above the human population as represented in the lower panels) with two alternative values for per capita energy consumption. The lower of these two points represents the mean per capita energy consumption world wide, and the upper is what worldwide total energy consumption would be with a per capita level of energy consumption equivalent to that for people in the United States. We see that these points fall outside the 95% confidence limits for both population size and energy consumption and are, therefore, not sustainable options. One way to manage is by changing only our per capita consumption. This assumes that we can compensate for excessive population through action confined to reduced energy consumption by individuals. The results of such management would move the points representing humans downward (and not to the left) in Figure 6.31. For example, per capita consumption could be reduced so that total energy consumption would fall on the line (g) representing maximized diversity in total energy consumption for our species (a reduction in the total for our species of 99.98% or cutting both individual and species-level consumption in half over 12 times; Fowler 2008). Another option would be to make consumption by the human population correspond to the mean of consumption among other species (from Fig. 6.16). By comparison, this would involve a reduction of 99.95%, or cutting energy consumption in half a little over 11 times. In terms we can relate to, this would require that every person on Earth (or the average person) reduce their consumption to eating only about five raisins or

a quarter of a carrot per day and simultaneously giving up all other uses of energy! This is not an option; either case represents abnormality well below the edges of the space bounded by lines a, b, c, and e. It is an abnormal level of ingestion. Here, opinion and objective evaluation match. The other simplistic option is that of confining change to a reduction in our population. To achieve a position on line g without changing per capita consumption would involve attaining a population of less than 1.2 million. This is well within the 95% confidence limits of population size and therefore more realistic than the consumption of celery needed to solve the problem through individual action above, but a population few would find acceptable. In contrast, line h in Figure 6.31 represents the population level that would maximize biodiversity with regard to the species-level measure of total global population (almost 10 million, Fowler 2008). Achieving such a population size would require a reduction of about 99.85% or cutting the human population in half over nine times. This exercise shows that current abnormality (the position of the two points in Fig. 6.31) clearly involves both population- and individual-level contributions. Which would have to change most to achieve sustainability? Figure 6.31 shows that the per capita portion of the contribution to being abnormal is smaller than the population component (the box inset to the right). That is, lowering either of the two labeled points in the top panel to correspond to a level closer to line e would require much less change than the shift required to move them to the left to be closer to either line b or that representing the population level that maximizes biodiversity. This is not an artifact of the scale of these figures. The population component of the vector toward sustainability is more than 2.7 times larger than the per capita component. The former would require being cut in half about 9.5 times while the latter would only require that this be done about 3.5 times. Achieving sustainability in both population and per capita consumption involves huge changes. Options considered in most conventional thinking are minuscule by comparison. As can be seen, some of the most sustainable options for our species are near point f in Figure 6.31.

HUM A NS: A SPECIES BEYO ND L IMI T S

If we insist on an abnormal consumption of energy at the individual level (to maintain what we think of as acceptable standards of living—an opinion on our part that plays into decision-making and what we see; Figs 1.1 and 1.4), it can best be attained with a reduction in the population to follow the diagonal line through point f to the left and upward (smaller population, increased per capita consumption). This would be effective systemic mitigation rather than placing the burden of human abnormality on other species, ecosystems, or the biosphere— taking advantage of tradeoffs within our species. Note the challenge we encounter here, however: increasing our standard of living (e.g., plentiful food, material wealth, and accessible/quality medical services with the energy they require) has a strong tendency to result in the mechanical reaction of increased (rather than decreased) population level. This exemplifies the dilemma (lack of consistency) faced by philanthropic organizations that simultaneously provide funding for dealing with population issues on one hand and qualityof-life issues on the other. In general, bounded spaces such as that shown in Figure 6.31 are merely crude representations of those observed in nature (recall Fig. 2.34) and occur within systems where such bounds involve many dimensions. Research to define such a space is an extremely important exercise in finding the limits to a set of sustainable options and the abnormality in extremes where we can identify realistic action (management of the human). The difficulty experienced in contemplating such action can be translated to empathy for other species in their being forced to change in reaction to our abnormality— understanding not often brought to conventional management realistically. In the case represented by Figure 6.31, it is clear that populations can be abnormally small— something that might result from systemic reactions to current human abnormality (e.g., an extreme pandemic), but not something we would deliberately seek any more than we would restrict per capita consumption to unrealistic levels. Clearly, a human population of less than 4000 would never be thought of as sustainable (we would be on our own endangered species lists) and a standard of living confined to simply meeting metabolic

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needs is likely to be rejected by most (an opinion and real force behind our abnormalities). As in all cases, sustainability is not to be confined to a single value for either population or per capita energy consumption. Data on the variance of per capita energy consumption for other species would be useful to establish the limits to normal variation in this measure of species. A human population of 9.62 million (which maximizes biodiversity for population size under current circumstances, Fowler 2008) is intermediate to the extremes represented on one end by today’s population and on the other end by the lower 95% confidence limit among nonhuman populations. Maximum sustainability is likely to be found close to where lines g, h, and e cross in close proximity. This is where diversity is maximized for both total energy consumption and population size. It involves a normal per capita energy consumption. Other measures of diversity might result in even better consistency— something actually achieved in nature, of course. A population reduced to the mode among nonhuman species otherwise similar to humans (Fig. 6.24 and bottom panels of Fig. 6.31) would allow for a bit of freedom to maintain a standard of living (involving per capita energy consumption) close to that of the current global average but much less than enjoyed, and taken for granted, in countries such as the United States. Simply because we enjoy something does not make it sustainable. The following sections and Appendix 6.1 give further examples of the factors involved in the complexity of interconnections, including management with applications at the individual, species, and ecosystem levels. They provide examples of information we often attempt to use in conventional management involving connections such as those made explicit in Figure 6.31. Knowing about individual connections or relationships involves understanding as steps in the right direction but on an endless and impossible path.14 Although they provide motivation for action, they are not the basis for sound goals. Although they relate directly to human population issues, we have yet to decide what specifically to do: reduce the human population to solve other problems identified in this chapter (Plate 6.2), solve those problems knowing that the human population would respond, or let

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nature take its course. Before addressing this issue, the following sections are intended to help point out a very few of the kinds of things that might be involved. Decisions must be made by society knowing that there is a composite of risks and benefits involved in the complexity (the infinite of Fig. 1.4) behind empirical information in species-level patterns. Known connections with overpopulation include the risk of catastrophic changes such as starvation or disease spreading to be even more extensive and intensive than it is today (malnutrition involving over 50% of our population, Pimentel et al. 2007). Alternatively, or in conjunction with such events, we may find ourselves in the population-reducing consequences of war over everincreasing scarcity of renewable resources such as food and water, especially following further concentrations of humans fleeing the effects of global warming. The connections often involve forces or factors that contribute to population reduction and concomitantly themselves respond to population reduction—a reciprocity exemplified by the connection between CO2 production and population. Of particular concern is the current anthropogenic extinction event (Appendix 6.6), reminding us of the risk of human extinction as an unintended consequence of what we are doing now and as a result of what we have evolved to be including our belief systems and all the other aspects of being human.

6.2.1 Individual level We are only beginning to understand the problems caused by overpopulation, and the corresponding benefits of a smaller population, measured either in terms of abnormality or conventional value systems. Millions of people have direct personal experience of poverty, starvation, disease, and conflict over dwindling resources (including water, food, fuel, and land). Millions experience other indirect results—increased competition for jobs and housing due to immigration, increased crowding, conflicts over territory and resources. To the extent that these problems would decline with reduced population, each individual would experience fewer (and, at the population level, a smaller portion of individuals would experience them).

Any action(s) that contribute to reducing human population to sustainable levels could take centuries,15 or could occur suddenly due to one or more catastrophic events, which are all too easy to imagine and with increasing chances of happening. In either case (barring things like widespread radioactive impacts), a smaller population would relieve surviving individuals from a variety of stresses caused by daily life on an overcrowded planet. Food, water, shelter, and space would be abundant, or adequately available; but only on average (there will always be issues regarding social inequity—variance in such things is also impossible to avoid, but abnormality in such matters is a systemic problem, well beyond the focus of this book). Vulnerability to pollution-related diseases such as cancer, heart disease, and diseases related to compromised immune systems would decrease owing to reduced or eliminated use of chemicals. There would be a reduction in the transmission of communicable diseases through human contact. With a reduction in the widespread monoculture of human overpopulation, we might expect a reduced rate of emergence of new diseases through evolving virulent strains. Individuals would, on average, benefit from more spiritual, emotional, and aesthetic experiences of a more normal natural world and more contact hours per day with that world. The psychological benefits are unfathomable. A controlled approach to reducing population to sustainable levels would require individual sacrifices for the sake of future generations of all forms of life. A question now before us is: Are those sacrifices to be made in direct control of our individual survival and mortality (things we consider at the roots of basic human rights) or are they to be made in reducing or eliminating the abnormal uses and consumption of energy, chemicals, and resources in a way that systemic change results in reduced human population? A third alternative is an uncontrolled population reduction brought about systemically (starvation, disease, wars, or various combinations of such factors) in reaction to conditions brought about by current and past management. This is where different patterns enter as insight, if not for guidance. Very few if any other species willfully sacrifice their survival or reproduction so altruistically as to be for the benefit of

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the system of which they are a part. The pattern here is one in which limits are established systemically—the whole (ecosystems, biosphere) brings controlling forces to bear on the parts (species, individuals). These patterns are basis for arguing that changes in our survival and reproduction need to be the indirect result of our matching the patterns of other species in energy consumption, chemical use, geographic range size, and resource consumption (all the ways we are abnormal in our interactions with other species and ecosystems) to allow systemic reduction of our survival and reproductive rates. We are confronted with the depth/magnitude of our challenges and the realization that it is highly unlikely that we will willfully achieve sustainability. Although the game of sustainability may be an infinite game (Carse 1986), and one we could play (i.e., there is hope), the strength of socioeconomic forces, our genetic nature (Table 3.1), our ways of thinking, and other aspects of what we are as a species dictate that science will document the unfolding of our reality (even our own extinction) more than provide information that gets used for achieving real sustainability. Birth control (a sacrifice of having fewer children) is not a “fit” option, not only because it is inconsistent with patterns among other species, but because of the (at least potential) genetic effects of exerting “intelligence” in making the decision to limit family size to reduce the population (individuals making decisions about fitness is all too reminiscent of Hitler’s actions). The same holds for mortality—decreased longevity is not an option among individuals for self control at the species level.16 Other sacrifices individuals are likely to experience with reduced population levels (still involving human values) might include reduced use or availability of technology (either as a result of reduced population or as a contribution to its reduction). Culture would change; some forms of cultural expression would be sacrificed and others enhanced. In achieving change, choices by individuals will be paramount. There are so many political, religious, social, racial, educational, economic, and psychological issues to deal with that the task of human-managed social change (as opposed to change systemically imposed by nature) seems

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nearly impossible. It may be impossible (as, in so many cases, bringing us back to combinations of Table 3.1 wherein evolved characteristics lead to extinction). Some of the complexity of reality is impressed upon us when we consider the meetings, brainstorming, consideration of options, and decisions for action that will have to ensue worldwide when and if we remember collectively that despite our apparent supremacy, our species is only a tiny part of the universe within which we are trying to find a sustainable existence. The likely benefits to the individuals of other species in a reduced human population are also real. These realities are impossible to overemphasize (although very possible to overvalue as is done in biocentric views of what we need to do). However, it is beyond the scope of consideration here. Nevertheless, these benefits (primarily reduced abnormality from the perspective of systemic management) must be acknowledged as counting among changes that would be achieved in restoring humans to normalcy. Taking systemic action is always a matter of extending our compassion, and concepts of justice and fairness, to all individuals of all species. Equity in the form of neutral fitness may prevail at the core of natural patterns.

6.2.2 Species level Risks to the human species (i.e., as a species, not as individuals) in reducing population density are minimal compared to the benefits—both inherent to reduced abnormality. Some risks of overpopulation to humans at the population or species level have been temporarily averted or delayed in conventional management—but at the expense of other systems (e.g., degraded ecosystems and ultimate associated human risks). The elevated human population has been made possible, in part, by reliance on fossil fuels as a source of energy outside the living ecosystem (Catton 1980). To the extent the depletion of fossil fuels can be postponed, they can be used to help support more than a minimal standard of living while the population is reduced, a standard that could hopefully be maintained afterwards,17 but for which there is no guarantee. Long-term benefits of population reduction at the species-level include diminished

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risks of extinction from diseases, loss of food, or loss of other goods and services provided by ecosystems but lost in their destruction or disturbance. Exacerbating the risk of extinction, in this regard, would be factors intrinsic to our species such as wars or other conflict stimulated by reactions to ecosystem degradation. The rates of evolutionary reactions among other species to human presence, such as pesticide resistance and emergent diseases, would likely decline, but details are impossible to predict. While there is some risk of extinction among species that currently require human support (e.g., domestic species and pets), the majority of effects experienced by other species would be a reduction in human-related factors that currently contribute to elevated risk of their extinction. Human overpopulation is one of the contributing factors behind the numbers and extinction rate among endangered species (Appendix 6.6). Overpopulation clearly contributes to the overutilization of resources with its connections and contributions to risks (abnormality) for other species.

6.2.3 Ecosystem level At the ecosystem level, the experience of human abnormality is probably not a terminal problem. Following any reduction of the human population or any complete removal of humans, ecosystems will experience the opportunity to rebuild from the remaining species as they have from numerous mass extinctions of the past (Rosenzweig 1995). For ecosystems, a reduction or elimination of the human population is equivalent to curing an ecosystem-level disease (Hern 1993).18 Assuming that ecosystems will survive this event, they will likely be more immune to similar events in the future— part of the development of an ecosystem-level phenotype with a species composition more resistant to the overpopulation of any component species than is now the case. Over time, similar (albeit, less extreme) disruptions19 have been survived by ecosystems and, in the process they may have become better at dealing with such problems (e.g., through the selective prevalence of diseases and parasites, as an ecosystem-level parallel to leucocytes or immunity).20 In comparison to natural rates of

change, the rates of loss of diversity, and other ecosystem-level changes (MEA 2005a,b), are parts of the ecosystem experience of human overpopulation and all of the abnormality it brings with it in terms of our interactions with ecosystems. Each would be alleviated by a smaller human population and progress would be made toward the objective of ecosystems with normal attributes.

6.3 The eastern Bering Sea example The commercial fisheries of the eastern Bering Sea produce food to help meet human needs. Such benefits are recognized as part of what are referred to as ecosystem services. As with all ecosystems, the eastern Bering Sea provides ecosystem services to all of its species. Of the overall production within this ecosystem, each species consumes a part. Very importantly, there is a part of this production that each species does not consume. The part that is not consumed is available for the other species. These dynamics involve a web of trophic interactions; trophic interactions characterize ecosystems with every species of that ecosystem involved. Similarly, each species is involved in coevolutionary interactions with other species so that there is also a web of genetic interactions. Both the trophic and evolutionary webs involve higher-order interactions and feedback—coevolution occurs in indirect interactions as well as in direct interactions. These webs cross hierarchical scale. In Chapter 1 and above, patterns were introduced representing the amount of biomass consumed within the eastern Bering Sea ecosystem by the consuming species. Frequency distributions for this measure of species have been presented for consumption from individual species (Figs 6.1–3), groups of species (Fig. 6.8A), and the entire eastern Bering Sea ecosystem (top panel of Fig. 6.10). This information can guide decisions about what level of biomass to harvest in commercial fisheries as well as our allocation of harvest among alternative species (Fig. 2.8, Fowler 1999a). Similar data can be used for allocating harvests over space (Fig. 2.13) and setting aside marine reserves (Fig. 2.15), or making seasonal allocations (see Fowler and Crawford 2004 for a partially consonant example, based on data for consumption by seabirds as

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presented by Crawford et al. 1991 illustrating the process if data for mammals were available). Using data that show the limits to natural variation in patterns consonant to questions regarding sustainable consumption from the ecosystem (e.g., Figs 6.8, 6.10, and 6.11 for biomass consumption) would be an example of applying systemic management directly at the ecosystem level, i.e., addressing the ecosystem-level question of what levels of biomass extraction are sustainable. Regulating our consumption is only one aspect of management; not consuming is another part —what is not consumed is left for the remaining species of the ecosystem, for the ecosystem as a whole, and for the biosphere. At the individual species level, part of the biomass produced by a resource species is not consumed by each consumer (e.g., production by walleye pollock that is not consumed; Hobbs and Fowler 2008). Future research can determine the portions of production that are not consumed by each consumer species when the production is that of multispecies groups or full ecosystems. Such data would be consonant with management questions regarding production that should not be consumed whether it is production from any individual species, species group, or entire ecosystem. Such information serves to guide us in insuring the sustainability of the system for all species—full scale sustainability. As usual, management based on such information is consistent with the complementary information regarding what can sustainably be consumed (Hobbs and Fowler 2008).

6.3.1 Anthropogenic influence As with all patterns, the patterns we observe in the eastern Bering Sea today reflect both current and historical abnormal human influence. These patterns, and the trends within them, are, in part, the product of all anthropogenic activities. Systemic management has the objective of making those circumstances free of human abnormality. In the meantime, existing patterns provide one means of accounting for anthropogenic influence: it is inherent to the information they embody (Fig. 1.4). Anthropogenic factors also influence the quality of information represented by patterns in another way. Human limitations not only prevent accurate

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projection of future circumstances, they also influence our observation of patterns. All information is subject to errors of estimation and other statistical imprecision—the anthropogenic factors of imperfect science. The information in hand is a start, but it is not perfect.21 Part of attaining useful scientific information is the matter of science conducted with as much quality as is possible. In the eastern Bering Sea, the patterns useful in managing the walleye pollock fishery above (Figs 6.1–3; see also Fowler 2008, Hobbs and Fowler 2008) represent results of the kind of science needed in management. These patterns reflect abnormal human influence realized through past fishing, whaling, toxic wastes, global warming, marine acidification, and sealing. The matter of anthropogenic influence is involved in these patterns, whether it involves patterns regarding predation rates on individual species, finfish as a group of species, or for total biomass consumed in the ecosystem as a whole. Integrative patterns and quality science are two aspects of accounting for anthropogenic influence. A third component of accounting for anthropogenic influence involves correlative relationships. Comparing patterns across space, time, and systems makes it possible for scientists to directly account for a variety of factors. These include the variety of anthropogenic factors. Thus, as patterns vary in correlation with harvest rates, pollution levels and habitat modification can be used to directly incorporate anthropogenic factors—always in regard to patterns consonant with the management question so that the correlative relationships are pertinent to refined questions. As in other ecosystems, a great deal of research will be required in the eastern Bering Sea to find and use such patterns. A fourth part of accounting for human influence directly involves the element of adaptive management introduced previously. We could, for example, be precautionary and reduce our harvest of fish biomass to levels even less than indicated by existing species-level patterns for consumption while obtaining more accurate and complete data. Better determination of the statistical central tendencies of species-level patterns, or measures of maximized biodiversity, might result in a decision to harvest more or less than indicated initially. There

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are other factors that influence observed patterns, however, and, to be systemic in approach, we would also reduce our use of polluting chemicals, our production of CO2, and consumption of energy— involving all influential factors directly or indirectly involved. In other words, we would endeavor to fit into our world normally in every conceivable way not simply for the benefit of the eastern Bering Sea but for all ecosystems. During such management (and following, if we have survived systemic forces that work toward the same ends), we would monitor to observe changes in the frequency distribution for biomass consumption among nonhuman species to see if the patterns shift to show higher or lower advisable harvest rates, recognizing that this can involve many decades. This monitoring would also help document interannual (short-term) variation and related relocation of species within patterns. Similarly, over longer time scales, patterns in relation to regime shifts in the climate, and across seasons will add specificity (and allow for addressing more refined management questions).

6.3.2 Other correlative factors The complexity of ecosystems and environmental conditions requires considering other elements of importance. Species-level features are often correlated (Chapter 2) and, in our management oriented research, we need to choose species otherwise similar to humans as a part of taking ourselves into account (Management Tenet 1). Body size is one element to account for, as illustrated above in relationship to consumption rates, geographic range, CO2 production, and population numbers. Trophic level (both human and resource) is to be taken into account as was done in consideration of population numbers and geographic range. Environmental conditions such as climate are to be taken into account (i.e., we know that empirical patterns in harvest rates and allocation across resource species changes with such conditions; Melin et al. 2008). Other questions relate to different dimensions: How many resource species should be harvested? What should be the seasonal and spatial allocation of harvests?

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What portion of the ecosystem should be set aside in marine protected areas?

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Answers to these questions all can be represented in patterns among species, most of which have yet to be described for the eastern Bering Sea ecosystem, and many of which will involve correlative sub-patterns. As previous examples have shown, there are two ways forward and it is advisable to proceed on both fronts simultaneously. One, of course, involves research to develop, better describe, document, and understand the relevant species-level patterns for any ecosystem such as the eastern Bering Sea. This involves conducting the kind of biological/ecological studies that produce information to reveal patterns such as those seen above and in Chapter 2, specifically comparing humans to other species as done in this chapter. The depiction of species-level patterns (see methodology in Appendix 1.3, Fowler and Perez 1999) can be developed for metrics such as the number of species consumed as they are related to other factors such as body size, trophic level, or metabolic rate. Adaptive approaches would most likely be necessary to account for past human influence on all fronts, but reconstructive modeling and estimates of populations of marine mammals prior to human intervention would be helpful (useful for gaining insight, but, in management, important only if used for producing information consonant with management questions). The second approach complements the first and involves inter-ecosystem comparisons and ecosystem comparison over time. The same kinds of information on the limits to natural variation as listed above would be found for other systems. For example, the numbers of species consumed is a feature of ecosystems studied intensely in food web research and many relevant data and patterns are already published. One advantage of this approach is that, for a few ecosystems, data have been archived in existing publications and data bases. Disadvantages of both approaches include the logistical difficulty of collecting new data; monitoring is critical to any form of management (Management Tenet 8). Comparisons across ecosystems are exemplified in Figure 6.32. This figure shows a pattern in which

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Figure 6.32 The mean size of individual prey items taken in 85 cases of commercial harvesting and marine mammal food habits as related to the mean size of prey/resource populations (from surveys of commercially valuable fish populations). Solid line is linear regression for food habits means, heavy dashed line is linear regression for commercial means, and light dashed line represents y = x. The slope of the regression line relating dietary data to size is statistically significantly less than 1.0 (p < 0.001, Etnier and Fowler 2005).

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the normal aspects of size selectivity are correlated with body size. Not only is this a biosphere-level pattern to engender and support ecosystem-based management (ecosystems being part of the biosphere), it is also an example of information that (like that of Fig. 6.29) also opens the door to direct application of evolutionarily enlightened management. Patterns regarding things like harvest rates account for evolutionary dynamics (being integral in nature). The pattern depicted in Figure 6.29 exemplifies information that allows for directly addressing questions of selectivity. It again points to the need to directly involve as much as possible in correlative information (Fig. 6.32 involves body size again). An ecosystem-based approach would result in a regression line for commercial fisheries in harvests of all species (heavy dashed line) that corresponds to that for marine mammals (solid line). A single-species approach for walleye pollock would make use of data like that in Figure 6.33 (similar to that for cod in Fig. 6.29, part of the overall collection of data for a variety of species presented in Etnier and Fowler 2005). Both approaches (characterizing specific ecosystems, and making ecosystem comparisons) would be possible while waiting for systems to respond to relief from abnormal human influence (e.g., reduced commercial harvests, reduced CO2 production, reduced pesticide production), and while conducting research to provide more accurate characterization of consonant patterns. As in all cases, it is important that much larger samples be made available than we currently have in hand.

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6.3.3 Control rules in fisheries Conventional management of commercial fisheries involves decisions and policies produced by committees, panels, managers, and stakeholders—often in a political context (top row of Fig. 1.1). Several of these are embodied in what are sometimes called global control rules (e.g., McBeath 2004, Restrepo et al. 1998). The essence of these rules as conventionally applied are depicted in the top panel of Figure 6.34. Involved are the rate of harvest at “virgin” population levels of the resource species (the maximum), the degree to which a resource population is reduced (along the abscissa), and the reduction in

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Figure 6.33 The mean size of walleye pollock taken in commercial fisheries (n = 21) compared to the mean size of walleye pollock taken by 7 species of marine mammals (from Etnier and Fowler 2005). These data represent a subset of the data shown in Figure 6.32.

harvest rate mandated to protect the resource from overharvesting (the ordinate). Population reduction is carried out, in conventional approaches, taking into account our knowledge that doing so

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Consumption/harvest rate index

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usually stimulates production. Reductions to 40% of unharvested levels is a common “rule of thumb” based on patterns in density-dependent relationships in the population dynamics of species like those being harvested. Population reduction is not accompanied by a reduction in fishing rate until a threshold is reached. In other words, in fisheries management, current control rules permit, or establish by policy, a value of 1.0 for the length of line A in the top panel. Initial harvest rates (fishing mortality on “virgin” populations) are established on understanding of population dynamics

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Figure 6.34 A comparison between functional response curves and control rules in fishery management (Fowler et al. in preparation). The top panel compares the shape (the maxima are equivalent), and the bottom panel compares both the shape and the magnitude of allowable harvests (hypothetically chosen to be 10% of those in conventional management, more than all of the values most sustainable for hake, herring, and mackerel presented in the discussion of Fig. 6.7). See text for details regarding the shape of the response curve (length of line A).

(particularly the concept of MSY; Restrepo et al. 1998) which, as seen in the discussion of Figure 5.6, often involves total mortality rates (M) estimated for the resource species. Target population levels are also established on the basis of our understanding of population dynamics, specifically the population level that would result in the maximum rate of change in recovery from population reductions. The protective reduction in harvest at low resource population levels is rather arbitrary but attempts are made to reduce harvests to zero for populations at levels between 0.0 and 20.0% of “virgin” levels (K; the solid lines in Fig. 6.34 would drop to 0.0 at 0.2 on the abscissa). Systemic management would deal with each of the components of control rules individually with consonant empirical patterns (Fowler et. al., in preparation). We have already seen the kinds of information that would be used to establish the upper limit to harvest rates (e.g., Fig. 5.6). Such information leads to guidance that reduces the upper limit to small fractions of harvest rates used in conventional management (often to less than 10% of conventionally established rates—7.0%, 3.3%, and 5.9% of recent harvests for hake, herring, and mackerel, respectively, as seen above; 16.8% if we use the regression line of Fig. 5.6). Systemically established rates are exemplified by the upper asymptote of the curve in the lower panel of Figure 6.34 assuming 10% of conventional rates applies (actual values would be based on information such as that in Fig. 5.6—5.4% if we used the mean of values for the species just mentioned). In systemic management, the degree to which a population is reduced is not guided by the intention of stimulating production (line A in Fig. 6.34), even though such a reaction is probably often one of the results (this is a pattern observed and characterized in past research). Instead, observed population reductions would serve as the consonant guidance; that is, the intent would be to mimic natural patterns in population reduction rather than to stimulate production. As Pimm (1991) has shown, nonhuman predators often have effects in which prey populations actually increase (instead of decline); there is a great deal of variation in population changes as a result of the effects of predation (Fig. 6.35). Thus, in the full suite of applications of

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population size would be based on information such as that shown in Figure 6.36 (Fowler et al., in preparation). Here the average rate at which nonhuman consumers consume resources when resource populations are at 40% of their normal or “carrying capacity” levels is about 56% of the corresponding maximum. The general shape of the resulting “generic” curve is shown in the bottom panel of Figure 6.34. As is the case for population reduction, the pattern in Figure 6.36 is informative regarding what we would strive for on a global (biosphere or even ecosystem) level, but open to a great deal of research regarding correlative factors to enable application in local situations, and for specific species. As in all cases, information we have in hand is information collected from systems subjected to abnormal species-level influence by humans (all the ways we are demonstrated to be abnormal, as shown in this chapter, plus those ways for which we have not made measurements and comparisons). Better information will come from studies of systems relieved of human abnormality

Portion of cases

systemic management, there would be an average reduction of about 10% (i.e, the populations would be reduced on average to about 90% of their “virgin” levels, rather than more consistently to 40.0% as practiced in conventional management: Fowler et al., in preparation). Here, we are back to the patterns in variance and limits to variation in variation (the standard deviations of a variable are not fixed quantities and vary within limits themselves). Thus, instead of an intentional reduction to within narrow limits, management would reduce populations to varying extents depending on circumstances (i.e., a single value of 60% reduction to 40% of “virgin” levels would be avoided, even as a “rule of thumb,” owing to the fact that such a policy would result in variance which would be much less than that shown in Fig. 6.35). Future research would examine the correlative components of the variation shown in Figure 6.35 to better identify advisable reductions for species with specific characteristics (e.g., features of their life-history strategy, body size, and trophic level—a wealth of opportunity for research). Such research would also involve environmental factors such as climate, latitude, and depth. It would include explicit study of anthropogenic influence. Finally, the shape of the curve used to enforce restrictions on harvest rates as a function of

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log10 (portion of predation-free population) Figure 6.35 The pattern in predatory effects on prey populations shown as log10 of the ratio of the altered population density to the original population density (e.g., 0.5 corresponds to a population reduced to half its original density, log100.5 = –0.301). This sample involves 61 cases of predator/prey pairs across a wide variety of taxa, trophic levels, and locations (from Fowler et al. in prep.).

Figure 6.36 The pattern observed empirically in the length of line A in Figure 6.34 (N = 170). This measure is the ratio of predation rates measured when prey populations are at 40% of maximum levels to that observed at virgin populations’ levels (population index of 1.0 in Fig. 6.34). The value used for line A in Figure 6.34 is the mean for consumption rates among 13 cases in populations reduced to 0.4 of the population levels observed under more normal circumstances (population levels for resource species at which fisheries are often managed to have a fishing mortality rate equivalent to that implemented for “virgin” populations, or A = 1.0—the latter often close to the total natural mortality rate, M, for resource species as described for Fig. 5.6).

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whether that happens as a result of management or through systemic reorganization (which would include the self-organizing dynamics of complex systems [Ahl and Allen 1996, Brown 1995, Burns et al. 1991, Callicott 1992, 2006, Kauffman 1993, Levin 1999, Lewin 1992, Maturana and Varela 1980, Norton 1991, Rapport 1992, Rosen 1987, Waldrop 1992], but would include the contextual factors of inclusive hierarchical systems) to force humans into more normal roles within ecosystems and the biosphere. As in other cases, however, current data are indicative of sustainability that accounts for the anthropogenic influence expressed.

6.3.4 Marine protected areas What portion of the eastern Bering Sea should be set aside in marine protected area (MPA) status? Figure 2.15 shows the pattern consonant with this question. As pointed out by Fowler and Johnson (in prep.), the matter of excluding humans from any particular region of this ecosystem is probably not as important as reducing harvest rates, pollution, or CO2 production. This conclusion is based on the fact that almost any portion we might choose for MPAs falls within the range of variation exhibited by other species in the portions of the ecosystem outside their geographic ranges. We could choose to use the mean of areas not occupied by other species (36%) and decide to set aside 533,000 km2, but other options are not unacceptable. The northern fur seal population is declining and classified as depleted under terms of the Marine Mammal Protections Act (Towell et al. 2006). What portion of this species’ geographic range (within the eastern Bering Sea) should be set aside in MPAs? The northern fur seal occupies the full ecosystem to one extent or another; this leads to the same conclusion as reached above for the full ecosystem. What portion of the geographic range of bearded seal (Erignathus barbatus) should be set aside in MPAs? The map shown in Plate 6.3 shows the geographic range of the bearded seal in its overlap with the eastern Bering Sea. Also shown are subsections of the bearded seal’s range within the eastern Bering Sea as defined by varying numbers of other marine mammal species (N = 20) with geographic ranges overlapping that of the bearded seal. Thus, on a species-by-species basis, the portion

of the bearded seal’s range that is occupied (where there are overlapping geographic ranges) can be calculated. Similarly, the portion of the bearded seal’s range within the eastern Bering Sea that is not occupied can be determined (i.e., if 40% is occupied or occurs in the overlap of the geographic range of a particular species with that of the bearded seal, then 60% is not occupied). Figure 6.37 shows the frequency distribution for the portion of the geographic range of the bearded seal within the eastern Bering Sea not unoccupied by the other marine mammals in this ecosystem (mean = 0.47 or about 346,000 sq km). Again, there is a broad span of options without being abnormal (it is essentially impossible to be abnormal). Nevertheless, we could choose to set aside marine protected areas with a total equivalent to the mean of unoccupied area among the nonhuman mammalian species. Or, in this case, picking the mode (50%) might be an advisable option. This exercise can be repeated for every species in this ecosystem (and, of course, for every ecosystem). Overall, there is no basis for concluding that there is abnormality in the area we have designated for protection within the eastern Bering Sea. Harvesting spread across the entire ecosystem cannot be seen as abnormal (Fowler and Johnson in prep.). In contrast, the distribution of biomass consumption and the level of biomass consumption are distinct issues for which abnormality may be a problem (e.g., Figs 6.1, 6.8). Thus, the real problems (i.e., human abnormality) are found in the rates at which we are harvesting, in the selectivity of our harvests, in the production of polluting chemical compounds, in producing CO2, and in other ways that have yet to be identified, and measured and illustrated, as above in this chapter and elsewhere (e.g., Fowler 2003, 2005, 2008, Etnier and Fowler 2005, Fowler and Hobbs 2002, 2003, Hobbs and Fowler 2008). Part of what managers are trying to deal with when addressing questions of protected areas is the distribution (allocation) of harvests over space. Fowler and Crawford (2004) demonstrate how this would be done, using data for birds, when data for mammals would be more appropriate (the data for birds would be very helpful in looking at correlative relationships across various species-level characteristics). For management of the distribution of fishery

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Figure 6.37 The pattern in the portion of the geographic range of the bearded seal in the eastern Bering Sea that is not occupied by each of the other marine mammal species (N = 20) based on information shown in Plate 6.3.

catches within the eastern Bering Sea, the density of individuals for species represented in the map of Plate 6.4 would be used to calculate their consumption as spatially distributed within this ecosystem and used to allocate fishery catches. Changes in response to (correlated with) changes in climate would be used to account for such factors directly.

6.3.5 Asking new questions While we await information from systems free of abnormal human influence, systemic management would extend management questions to other realms and ask new management questions: Should we transport harvested food from the eastern Bering Sea to other ecosystems? How many ecosystems should we be using for resources (living space, or solid waste disposal)? How much nitrogen (or any other element) should we be using (or releasing to the environment)? What is a sustainable geographic range for our species? Should it include the eastern Bering Sea?

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Complexity involves many such questions; finding consonance between these questions and the patterns that inform decision making is crucial.

6.4 Summary and preview This and the preceding chapters have 1) laid out the basis for, 2) illustrated the choices of scientific

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information to be used in, and 3) provided examples of, systemic management. Systemic management was shown to be an approach that can be applied not only to ecosystems, but also individual species, groups of species, communities, and the biosphere. We could call the ecosystem application “ecosystem management” but the unrealistic connotation of control over ecosystems argues against such terminology (Management Tenet 2), as does the breadth of application across the various levels of biological organization (Management Tenets 3, 4). We could call it “ecosystem-based management” but this implies loss of “individual-“ and “species-“ based management—all of which have to be part of management to be realistic. Calling it “systemic management” or “reality-based management” implies transcending hierarchical boundaries between the various levels of biological organization, including individuals, and applying to them all simultaneously. Systemic management significantly reduces conflicts prevalent in conventional forms of management. This is because the elements of conflict are dealt with inherently in the kinds of information used. Such information is carefully chosen from the spectrum of research results produced by the biological sciences—information that is consonant with management questions. It requires no conversion, thus obviating conflict. What was basis for conflict (top row of Fig. 1.1) becomes basis for asking clear management questions (bottom row of Fig. 1.1). Chapter 4 presented a critique of conventional management to clarify its inadequacies for comparison with the advantages of systemic management. Chapter 5 put forward an evaluation of how well systemic management meets the needs for management as identified in the literature and suggests how it can be implemented when guided by information on the limits to natural variation when the information is consonant with the management question being addressed. This chapter used specific examples to add clarity to the issue of consonance between management question and guiding patterns to more clearly define the kinds of biological/ecological research needed to provide guiding information. In the epilogue, we visit the issues of management a bit more philosophically than in the previous chapters.

CHAPTER 7

Epilogue

Without hope, all we can do is eat and drink the last of our resources as we watch our planet slowly die. Let us have faith in ourselves, in our intellect, in our staunch spirit. —Jane Goodall

Confined to science, this book might have done little more than reveal some of the extreme abnormalities of our species in cold statistical terms (e.g., the graphs of Chapter 6). It might have stopped with attempts to explain patterns among species—especially the pattern of human abnormality. The underlying message might have been a prediction about how things are going to be after the homeostatic forces of nature return sustainability to ecosystems and the biosphere—with humans either extinct or in more normal positions within interspecific variation. Confined to science, this book would have been part of the growing awareness of ourselves as a species, comparing ourselves with other species as part of our unfolding story—maybe to be marked by a species-level rite of passage. As easy as it is to predict that we will suffer for our hubris and arrogance, however, it also seems that we hold the potential of taking self awareness as a species to the level we occasionally do as individuals. We are likely the first species with any self awareness regarding the systemic consequences of our actions—impacts that make us a malignancy in the biosphere. Self-restraint may be an option for us as a species; on many occasions we achieve self-restraint as individuals, even in the face of impulses, urges, and emotions that often get us into trouble. Blocking progress, however, is an artificial wall between reality and management that is unnecessary, and counterproductive; current passage through this wall results in a needless gap between science and action at great cost to all forms of life. This impediment is a product of our thinking, beliefs, habits, and their evolutionary origins. These things are obviously 204

not alone, but they count among the factors contributing to our abnormality as a species and the consequences for others. However, thinking, beliefs, and habits are also things wherein we have hope of making change—taking responsibility as a species. I would have found it unfulfilling to stop with nothing more than a presentation of the science, even irresponsible. Certainly, confining myself to science would not have met my official responsibilities! Realistic management is something we aspire to achieve whether as individuals or as a species. Knowing that the two can be diametrically opposed when conceived in simple terms is a major step forward. Knowing that the paradox is not insurmountable is an even more encompassing step. Our species’ predicament in today’s world can be characterized as an addiction. We exhibit dependencies on energy and resources to the detriment of other species and ecosystems that also occupy this planet. Not only have species gone extinct at the hand of man, but full ecosystems have fallen victim to our dependencies. It is well known that addicts, as individuals, have a limited chance of recovery, even with the support of fellow addicts and professional counsel. Can our species, at the species level, undertake a successful self-intervention? This book is my attempt to provide information that looks to other species for help, as mute and unconditional as they are in their interactions with us. A wide variety of experts and organizations have worked very hard at defining management so as to expand beyond what we do as individuals, and to progress beyond what we do in our interactions with other species. Experience has shown that the

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results of these efforts are essential; in many cases, the resulting advice has been around for centuries as part of ancient wisdom traditions (Hobbs and Fowler 2008). Across time, a variety of tenets have emerged. When we accept these tenets collectively, the words of Wordsworth apply: “Come forth into the light of things, let nature be your teacher”. Progress toward objectivity cannot be achieved, however, if we do not reject one tenet repeatedly found in the history of our thinking: putting stakeholders in the position of attempting the impossible—converting one kind of information to another, and, in the process, making values other than that of sustainability the basis for policy. We have to face, accept, and work with reality; the laws of nature cannot be violated—the patterns of nature, as products of reality, provide guidance. There is no suitable alternative that I know of—nothing that offers comparable objectivity. Can we achieve sustainability or are we going to suffer as victims of being, doing, and thinking that are incompatible with reality? Like anyone who seeks to make a difference and to make the world a better place for all forms of life, I encounter the fact that change in the human realm depends, in part, upon what individuals can accomplish. Change, as a species, is critical and always involves individuals. People change only insofar as they see, know, and are aware of reality. This book is an attempt to reflect reality, as science reveals it, so that people can learn from it. My hope is that we can be considerate of the quality of life for the future generations of all species, for the future for all forms of life. However, my advocacy puts me at risk, appearing as though I know the details about what is next, how to implement such management, or what to do now. These are things I cannot know any more than anyone else, although I hope that I have something to contribute. I have tried my best in this book to provide a way to get guidance for our goals as a species consistent with goals as individuals, communities, and societies in all our interactions with both each other and the nonhuman—sustainability at all levels and in all realms. Although there is management advice found in the information I have displayed, this book is much more a prescription for finding that kind of advice—learning from

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and mimicking nature. As in our personal lives, it seems that the best way forward for our species is to proceed based on what can be learned from collective experience, now including the experience of other species. Management itself, however, involves many details, determined in part through the resolution of objectives for our species in a way that reverts to us as individuals. Systemic management leads to its own implementation and involves many things we already know how to do quite well,1 amplified by learning to do things we have never done before, discontinuing doing some things we are doing now, and doing many things differently. Systemic management is a reality-based selfguiding process. When we manage our personal lives, we rely heavily on experience—largely in a systemic way (Zimberoff and Hartman 2001; or as a trial-and-error processes in the comedy of survival, Meeker 1997). Our lessons as individuals mean a great deal to us and we find it difficult to avoid making personal needs pre-eminent when it comes to deciding what to do as a species; at that level, species-level experience is necessary and may be the opposite of what individual level experience would have us believe. What is good for us as a species, may not be good for ecosystems or the biosphere—what is good for a part can be lethal for the whole. Fatal flaws can evolve (Potter 1990). Thus, to define large-scale objectives, I had to acknowledge my own limitations, especially my ignorance; there is something to be said for the wisdom of insecurity (Watts 1951). I am not a species and cannot have that experience. At all levels, guiding information is found in nature and experience. However, when species-level goals and objectives are established, there are subsidiary issues that ultimately break down to action by individuals. The Achilles heel of conventional management seems to be the thought processes, belief systems, and values involved in establishing policy—they tend to be the basis for management. In contrast, clear thinking is fundamental to asking the right questions (Chapter 5) in systemic management and empirical information becomes the basis for decision making to replace the human constructs that make conventional management so prone to error. Thinking is crucial in directing the science needed to observe guiding

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information, but the guiding information is ultimately reality-based (systemic). Guidance based on the alchemy of converting nonconsonant information to goals and policy (rather than using such information to reveal correlative patterns) brings on more problems than are solved. Thus, seeking guiding information that eliminates gaps between science and management is a key element in systemic management, and depends upon asking the right questions (both for defining goals and guidance in achieving them—fractal, involving all levels of biological organization). However, large-scale questions and management action do not find resolution if individual-level elements are ignored. The personal, interpersonal, and psychological2 dimensions that are involved are beyond the realm of this book partly because of their complexity. This is not quite the problem it might seem on the surface, however, as our thinking, habits, and actions (and their effects from the past) are accounted for a priori in integrative patterns—we are part of reality. Trial-and-error processes are universal phenomena, including those through which we have learned a great deal regarding personal or individual matters. They are basis for change and changes in our thinking and beliefs are a key part of systemic management. There is an extremely superficial quality to the few objectives for systemic management found in this book. I have been able to present only a tiny bit of information regarding goals for our species’ relationships with other natural systems in their hierarchical organization (e.g., our interactions with, and influence on, other species, ecosystems, and the biosphere). My treatment of implementation is even more superficial. Even though systemic management will provide its own guidance, it cannot happen without the openness, and resolve, to actually make the changes that are revealed as necessary by empirical patterns. The examples I have presented are meant to serve as models of the process, to initiate the adoption of systemic management, and to help point to the observing (and changes in thinking) that can circumvent current impasses and be the basis for hope. The questions initiated for the eastern Bering Sea represent only the most minute of steps toward implementation for only one example. The majority of the

preceding chapters dealt with the rationale behind systemic management, a few examples of goals to be achieved, the methodology for refining goals, and opening the door to more detailed information regarding implementation. However, any realistic grasp of the potential brings the realization that we are faced with a daunting, possibly impossible, challenge. Possibly impossible? I do not want to underestimate the difficulty we face. Nor do I want to appear idealistic in the face of the reality of that difficulty. The problems before us are immense and current efforts to solve them are extremely superficial. The misapprehension of the magnitude of our problems is exemplified by current goals for reducing CO2 production—reductions of 50% or 80% fall woefully short of the mark and would have to be repeated several times to achieve true sustainability. Nevertheless, there is progress: many people are aware of global warming and our contribution to it. Of more importance, is the realization that debate over such issues is a matter of opinion that can lead to very useful management questions. However, the vast majority of problems like global warming are not given similar attention by worldwide team efforts to educate people. Thus, as I am often reminded, it is highly likely that the adoption and implementation of systemic management is beyond our capacity to accomplish. I am not sure which is the most difficult: accepting the systemic approach or making the changes the approach requires. To succeed, both will have to happen, but both present immense challenges. In fact, if I were to wager on the odds of our achieving sustainability, I would bet against it. Why? What is the basis for considering the chances of success so slim? Acceptance and implementation both involve individuals directly. This simultaneously exposes both the hope and the extreme difficulty. Implementation will clearly be a monumental undertaking. In view of the difficulty, reduction of the human population, for example, may occur only as a result of the nonhuman forces of nature rather than the result of any direct form of management. Cutting our harvests of fish by 90–99% seems draconian in conventional thinking as does the reduction of our energy consumption by 99.9%. The problems we face may be characterized as a

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more generic form of nature deficit disorder than envisioned initially—natural selection and natural limits have not been allowed to have their normal effects on our species for centuries and we experience being extinction prone as a serious risk yet to be acknowledged. However, acceptance of systemic management may be even more demanding than implementation. A crucial step is a systemic change in thinking. If there is anything to what I have presented in the foregoing chapters, systemic thinking largely removes us from the process of decision making when it comes to establishing specific goals for our species—a humiliating thought (humility has long been recognized as essential; Bateson 1972, Jacobson 1990, McIntyre 1998, Meffe et al. 2006, Rasmussen 1996, H. Smith 1994, White 1967). Many scientists will find it nearly impossible to back out of their role as advisors and consultants to focus, instead, on asking good management questions—questions that then define the science they can conduct with results they can convey to managers as self-evident advice. The role of scientists as experts becomes confined largely to being specialists regarding patterns consonant with specific management questions. This takes advantage of realistic reductionism to avoid the misdirected reductionism (Belgrano and Fowler 2008) of conventional management. The complexity and reality of the human system presents what may be insurmountable obstacles nearly impossible to overcome. Maybe it is impossible; the strong possibility of our being on the path to an evolutionary dead-end is very real. We are subject to the pruning rules among species; we are, after all, a species. Nature will take its course whether or not we change our thinking. Although we are witness to an anthropogenic systemic shock wave comparable to those of historic meteor strikes, Mother Nature recovered from those and is likely to do the same after our demise (or population reduction). As such, we have the option of doing no more than watching ourselves as victims of what we are, and what we do—results of the forces of nature around and within us. As Meeker (1997) says: “Humanity may have to settle for the distinction of being the first species ever to understand the causes of its own extinction”.

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It may be our only option. Much of what we are and do is beyond our control. It is entirely possible that we are beyond achieving true sustainability. We experience our genetic programming for procreation, our evolved dependencies on other species (e.g., clothing, drugs, food, fiber), our emotions concerning death, the pangs of hunger, and empathy for other humans as they experience the aftermath of our past. Recreational activities that are destructive to nature seem to us to be essential; they are taken for granted as realistic options. We think we understand human rights and hold them sacrosanct. A similar hubris is behind our economic systems. Economic values trump ecological realities in the majority of conventional decision making. These things serve us fairly well as individuals or even as human communities. However, as a species, many such ways add to the risks we face; the extinction of humans involves the end of all our economic systems, business, and industry. The way out may be impossible owing to what we have become. Many of the reactions to this book can be expected to add to the list of ways it can be seen as impossible—including the many reactions starting with the words: “Yes, but . . . ”. The phrase “Yes, but . . . ” is usually the introduction of an argument to stay on course,3 a course called into serious question when we open our eyes. It is a course of misdirected reductionism (Belgrano and Fowler 2008) for which the fatal errors have been recognized for thousands of years. Why should we expect things to change now? As a species becoming self-aware, we may be much like the adolescent faced with two choices: (1) taking advice from others based on their experience or (2) learning from personal experience (occasionally fatal). Although we may be quite adolescent as a species (Plotkin 2008), this choice, of course, is not confined to adolescence. In the case of being a species, we face this choice in learning from other species and history—natural history. The experiences of historical societies constitute a history from which we can learn pertinent lessons (Costanza 1995, Costanza et al. 2005, Diamond 2004, Ehrlich and Ehrlich 1996, Ponting 1991, Redman 1999). We fail to learn from history so frequently that it is easy to argue that it will not happen now—the fatality here being our extinction.

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As much as I expect that it will not happen, and as difficult as it will be, I remain convinced that systemic management is a workable option;4 otherwise I would not have persisted in publishing this book. There are examples where we do learn from others and from history; we know we can. Although world war, starvation, and disease of monumentally abnormal proportions, may lead to our own extinction, as a result of our past management practices, I believe we can take meaningful actions that deal with the complexity of being sustainable, including these risks. At least four factors provide hope: immense change can be accomplished with time, humans are adaptable and resilient, goals and objectives are helpful, there is power in the human mind.

●

● ● ●

This list assumes that those cases where we have learned from the experience of others, and from history, are experiences about the learning process from which we cannot only benefit from learning but also learn about learning. In the latter, the learning I refer to is finding that we can, in fact, learn this way. The lesson is a meta-level lesson; we are not simply learning this way but learning that it is a way to learn. It is thus categorical learning, fractally inclusive, hierarchically transcendent learning, or Bateson’s (1972) deuterolearning—an experiential learning about learning. In the process, we know that it is the pattern that is important. The advice of an elder or mentor, is not the same as the common ground of the advice of many. What we can learn from other species depends on patterns—information from numerous species. Change will take time: The changes required of us are not to be made overnight. While some changes (like changing our minds) can conceivably take only years or decades, others will take a long time, even hundreds to thousands of years. Abnormally extreme measures are not part of systemic management unless they are precipitated by systemic reactions (either as reactions to our achieving the normal in ways where we can, or in reaction to our lack of action). Purposeful individual sacrifice that is abnormal would be contrary to the spirit

of systemic thinking but is arguably better than “staying the course”. However, this is not a rationale for procrastination—the sooner we get started, the better. Nature may “beat us to the draw” with a set of what we will consider catastrophic, and even abnormal (infrequent, but entirely natural, 5 and, in the long-term, normal) adjustments in the organizational dynamics of the systems of which we are a part. A reaction by ecosystems or the biosphere to result in a pandemic or our extinction, under current circumstances, would be perfectly natural as well as normal. Humans are adaptable and resilient: Further basis for hope is found in our adaptability. Our history of adaptation brings along with it the recognition that the changes we need to make are within reach. Change is often spurred by the depths of hopelessness—there can be hope in hopelessness! We are a very resilient species, owing, in part, to the resilience of individuals. Some individuals are more resilient than others. Although we may have brought ourselves to a situation where a great deal of suffering and death may result (and be necessary), our resilience is basis for hoping that we can change to avoid more extinction, especially our own. Even more optimistically, our resilience is basis for hoping that we can do more than avoid past errors: it can be used to achieve sustainability. Goals and objectives are helpful: Hope can also be found in accepting goals and objectives. Hopelessness would seem to be a natural byproduct of aimless activity in the face of problems with no obvious solution. With the goals and objectives that emerge through empirical information, a sense of direction is basis for hope. Hope can be engendered by the communal spirit of common direction. Social facilitation of progress is embodied in prestige given to those who take steps in the direction of achieving systemic goals. Any actual progress toward those goals is quite meaningful and serves as the basis for hope, as tiny as the steps might seem—problems get created incrementally and they can be solved incrementally. To the extent that we become aware of the magnitude of problems, progress is made toward the goal of an informed public and such progress engenders hope. There is hope that an informed public will result in the emergence of unpredictable,

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but creative, approaches to achieving empirically defined objectives. There is power in the human mind: Finally, the nature of our minds also offers hope. Our intellect or intelligence may make us a novelty in at least one way that can work to our advantage. Novelty is a part of the trial-and-error dynamics of natural selection and emergence; some things work— intelligence may be one of them. Intellect is a double-edged sword, however, and often serves to draw us into the problems we now can measure for our species (e.g., see Rees 2002). Our intelligence can be, and is, used to deal with the short-term matters so familiar to everyone—misdirected reductionism (Belgrano and Fowler 2008). As Redman (1999) points out repeatedly, decisions by previous societies seemed logical at the time, but ultimately led to their demise (see also: Costanza 1995, Costanza et al. 2007, Diamond 2004, Ehrlich and Ehrlich 1996, Ponting 1991). Likewise, our intelligence could become another of the species-level characteristics that leads us to extinction6 along with most other species. Human nature has resulted in the variety of wonders we have accomplished, including those extremes exemplified in Chapter 6. Our species may merit being listed in the Guinness Book of World Records simply for the number of times it could be listed—a meta-record. Nevertheless, the human mind also provides the means to recognize patterns, problems that occur in patterns, lessons from the past, and lessons to be learned from observing nature to face and deal with larger scale problems. Recognizing problems caused by our own limitations, particularly our thinking, is a step forward. We can draw upon our capacity to wisely sort through our options, distinguishing between those that will work and those that will not. A major message of this book is that, following Nash’s philosophy, things have to work at all levels; as Carse maintains, they have to be part of an infinite game. Less tragedy and more comedy (Meeker 1997) are options at the species level. In the spirit of adaptive management, nature has supplied us with answers to our questions; biomimicry is an option at all levels. Collaborative learning (e.g., Clinchy et al. 1996) can extend to the level of the species; other species may not be able to learn from each other’s experience, but we can—we can

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be mentored as a species by other species. We can see and use the information in nature’s Bayesian integrations of reality. We can use our intellect to see and mimic examples of things that work, and know what constitutes reliable guidance in full consideration of complexity. I have tried to present the content and arguments of this book in an objective manner. I join others in my failure in this regard. My principal bias stems from care, concern, and consideration for the lifesystems of our world, including our species and its long-term chances of survival—future generations and the well-being of individuals in the process of change. While humans are of concern, the wellbeing of all other species, the individuals of those species, and the living systems of which they (and we) are a part are all at stake. These include ecosystems and the biosphere. In order to be sustainable, we have to be in mutually sustainable relationships with each other—in part, the lessons of ecology. What happens will be influenced by what we do, regardless of what we do. In view of the ways Mother Nature works, it seems that we have to ask ourselves if there is justification for change, if we want to change, and then if we have what it takes to do so. This leads to two clearly subjective issues that seem important: 1. Elements of change—what are some of the underlying changes that might lead to adopting systemic management? 2. Human factors—what are some of the human institutions and their roles in management and change?

7.1 Elements of change It has been my experience that our current belief systems are, themselves, major obstacles to accepting reality as the basis for management. One of the most effective elements in our path around these obstacles would seem to be education. In the spirit of systemic management, I think that we need to be reminded that education may be more a matter of experience than attempts to learn in structured classroom settings. As Eleanore Roosevelt said: “Learn from the mistakes of others. You can’t live

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long enough to make them all yourself” (Roosevelt 2002). This applies at the species level. Once we have arrived at beliefs and understanding consonant with reality, we will be able to move beyond words to management in action. This section considers each of these in turn: ● ● ●

Belief systems that stand in our road Education as an agent of change Management in action.

7.1.1 Belief systems that stand in our road Belief systems are part of human complexity and their influence from the past contribute to what we see around us—are accounted for in natural patterns, especially those that expose our abnormality. One of the beliefs that has contributed to our failures is our belief that so many of our models (concepts, information, or knowledge) of one or more thing(s) can be used without correlative conversion in the management of something else. This involves serious “disconnects” and results in misguidance in the translation of information to artificially make the connection. Our choice of information rarely fully informs management. We fail to recognize the importance of achieving full consonance between management question and guiding information. Models involving/about related factors are only that; they are insightful and help understand. They are not the emergent patterns showing us what actually works. Models that are most useful are those that represent patterns consonant with management questions even if the representation is imperfect. The patterns are reductionistic in their specificity, but reflect the reality involved in their emergence; the reductionistic nature of science becomes its strength—at least we can take advantage of this limitation of science to achieve the holism required of management. During the 1970s, I taught courses on how to achieve maximum sustainable yield (MSY) in fisheries (and wildlife) management at Utah State University. Since then, I have experienced a growing clarity about both the utility and the danger of such approaches—models are indispensable analytical tools for better understanding, and measuring, for discovering and representing patterns,

and processes, but are not the reality they attempt to represent. Like words and concepts, they are not what they represent (Watts 1951); they are abstractions (Pilkey and Pilkey-Jarvis 2007). More importantly, they are rarely in the units, or of the logical type, framed by our management questions when we take the time and effort to be clear about what those questions are. We humans show a remarkable tenacity to believe that we have the skill and capacity to reconstruct realities in models, concepts, or theories. This runs contrary to all experience of reality wherein every perception is only a representation of a part of reality. Each is a reductionistic depiction, not complete ultimate reality. This is true, whether in the various disciplines of science, politics, or in metaphorical stories of the problem itself (e.g., the blind person examining an elephant). The Humpty Dumpty phenomenon mentioned several times earlier in this book always prevails, whether it involves an individual or a group of people. The belief that we can piece things together to work out solutions, rather than observe the solutions in nature, always prevents a full accounting of complexity. To me, it seems all too likely that our tendency to think otherwise is one of the results of our abnormal connections with nature (Appendix 1.2, a symptom of nature-deficit disorder, Louv 2005) such that our thinking is another victim. In the words of Thomas Berry (Berry 1997): “If we devastate or distort the natural world, to that extent our inner world is distorted because our inner world is determined by our outer experience”. Through connections we have in today’s world (including through scientific observation) we are victims of systems abnormally altered by our effects on them—a serious “catch 22” (Appendix 1.2). Wilson (1998) claims that one day we will have produced enough knowledge in scientific endeavor to achieve a full accounting of complexity, or at least an accounting adequate for decision making. Wilson’s belief is typical and is admirable and good as far as it goes. However, complexity involves the infinite—ultimate reality (Appendix 1.1)—and no man-made combination of the finite can ever completely recreate this reality (nor fully represent it, Bateson 1972, Watts 1951). Models are always based on subsets of reality (Caughley 1981, Pilkey and

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Pilkey-Jarvis 2007) and the subset for which we need management information cannot be recreated from incomplete subsets that are merely related or relevant—with one fortunate exception: the consonant pattern in which they may form correlative sub-patterns. We know our knowledge and understanding is limited, but not that these limitations can never be overcome by extending or improving upon current approaches. The finite can never fully represent the infinite but is always a product of it; emergent patterns account for the infinite, each in its own way. Because of this emergence (Morowitz 2002), patterns observed in nature (i.e., carefully chosen, useful, subsets of nature, each consonant with a specific management question) are the closest thing we can get to the guiding information we need. A primary challenge is finding the patterns consonant with our management questions, then using our skills to find representations (models, diagrams, pictures) that minimize the influence of our limitations—the science that best serves management. Thus, among the challenges I see ahead of us, two seem outstanding: 1. Achieving widespread recognition of human limits (finiteness); 2. Understanding that we can take advantage of specific kinds of finite patterns in accepting them as emergent from the infinite of ultimate reality to account for it, but not fully represent it. If these challenges can be met, it will be a huge step toward achieving the successes that are possible through systemic management. Figure 1.4 (see also the diagrams in Belgrano and Fowler 2008) was introduced as what I think is a major step in providing the basis for setting goals and accounting for complexity in systemic management. Emergence is now understood and accepted in principle in complex systems science.7 Many religious and spiritual leaders, thinkers, and philosophers would consider its acceptance beyond the halls of academia as a monumental step forward. Consistent with Management Tenet 2, I do not have the control to make people believe what has been presented in this book. Such understanding often comes only with personal experience just as seeing the patterns in stereograms is more a matter

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of personal experience than of following instructions. My hope is that this book will provide a window to insight among readers that will lead to the changes that nature tells us are needed. The debate that ensues will be part of such experience for everyone. But this step is only a step and does not count unless changes like those indicated in Chapter 6 are realized. When, and if, they are realized, there is the possibility that there will be historians in the future who will be able to look back on our current translation (Brosnan and Groom 2006) of scientific information to guidance for management as a form of intellectual alchemy—trying the impossible of converting one thing (or things) to another.

7.1.2 Education as an agent of change Individual change and action often depend upon being informed. Education is critical to the adoption of consonant reality-based management. It is key to understanding how systemic management goes beyond existing tenets and circumvents the obstacles of current thinking. The challenges are daunting. Systemic management entails being sustainable, and changing what our species is and does depends on education. I agree with others (Christensen et al. 1996, Mangel et al. 1996) who see a critical need for people in all walks of life to understand both what needs to be done and why. It is important to understand the need to expand our reliance on thinking to place more emphasis on observing.8 It is important that the connection between overpopulation and other abnormalities noted for our species (Chapter 6) be clear. It is important that we understand that there are related problems, some which will be discovered and documented, many of which will never be known. It is also important that as much of the public as possible, and especially leaders, understand both management and the basic principles and foundation of science—not only its utility but also its limitations. There are balances among the benefits and risks of change, and we cannot know more than a very few. Education requires presenting the limits of science, the critical importance of information it produces, and the critical skill for selecting the most useful information. All must be presented in an

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easily understood form. There are many elements to this process. One I see as a key at all levels is learning that maps, books, thoughts, perceptions, statues, money, models, photos, and other representations are not what they represent—all in the spirit of Korzybski’s (1933) observation that “maps are not the territory” (Bateson 1972, Watts 1951). Abstractions are never the reality to which they refer but finding consonance between our abstractions and the reality defined by our management questions is crucial. Knowing this would help instill a recognition of the limits and strengths of science (e.g., reductionism to effectively focus on consonant guiding patterns), along with all other human institutions. It is critical that our educational systems help people understand that science is crucial to knowing that things are interconnected, have explanations, and are complex. Science brings problems to our attention—when they are human abnormalities we can take action to solve them. Educators would emphasize the critical nature of science for objectively and carefully choosing, observing, documenting, and analyzing empirical information that can guide us when consonant with specific management questions.9 Education would help distinguish between information that validates our understanding of reality as complex/interconnected and information that is consonant with management questions. It would emphasize the need and ability to ask clear management questions. Crucial is the training of people to find consonance between management questions and empirical patterns. The capacity to ask science questions that lead to finding consonant information is equally important. The importance of first-hand trial-and-error personal experience would be placed in contrast to conventional education (especially training that maintains the status quo). The trial-and-error aspect of natural selection can be appreciated as universal, and part of learning in play behavior, experience, and adaptive management. Education can instill the importance of the shift from the top row of Figure 1.1 to the bottom row so that the combination of the rational and the intuitive will result in realistic management. The importance of the combination of wisdom (knowledge, information, reason, logic, education,

rational thinking—the head and the objective) and spirit (emotion, concerns, insight, intuition, love, religion, care—the heart and the subjective) as a basis for asking the right questions is crucial to the transformation behind accepting systemic management. Either one by itself is extremely error prone; in their consistent combination, we find a realistic way forward. Rather than teach people to uses these factors in debate, polarization, or politics, education can train people to use them to pose good management questions. Clearly, education should lead to people trained to ask good management questions adhering to all Management Tenets, but specifically 2 and 5. Scientists should be trained to conduct research that reveals patterns consonant with each management question. Thus, rather than educating specialists to convert information themselves (Brosnan and Groom 2006), both scientists and managers need to be trained to recognize the impossibility of the task of thinking of all relevant information and work with each other to use only consonant empirical information to guide management. This shift is perhaps the single most important change we need to make in solving the problems before us. In this regard, education must proceed toward training people to focus on asking clear management questions, posing consonant research questions, and producing empirical patterns consonant with the management questions. The extremely superficial nature of current “green” activities must be understood and, in some cases, recognized as counterproductive. Hope comes from progress made in such activities when they represent steps as tiny as they may be toward reducing abnormality seen by comparing humans to other species. Characterizing such efforts as superficial (for those cases that do reduce abnormality) is not meant to demonize such efforts but to demonstrate the minute portion of the immense journey before us that such steps represent. Those efforts that result in increased abnormality (e.g., suggesting the use of alternate sources of energy rather than reducing energy consumption) are examples of conventional thinking rather than real problem solving. Education can help clarify that what we want (enjoy, find attractive, or think is right), or take for granted, for ourselves, is often far from sustainable; what is good

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for the parts may not be good for the whole. What is good for us as individuals may not be good for us as a species—it may lead to our extinction. What is good for our species may not be good for ecosystems or the biosphere—it is leading to extinction (in all likelihood, including our own). Education can help instill the whole/parts aspects of systemic management. Curricula, especially at the university level, can present systemic management as biomimicry at various hierarchical levels to make clear that biomimicry at each level is a part of systemic management. Special emphasis can be placed on helping students understand that biomimicry at one level (e.g., species) makes it possible to address sustainability at component levels (i.e., biomimicry among individuals). Seminars on adaptive management can be structured to treat natural selection at all levels as nature’s process of adaptive management. The genomes of species can be presented as the blueprints of species that have survived the process of natural selection that weeds out options that do not work. Courses on the concept of footprint (Rees and Wackernagel 1996) can take the footprint approach to new, and more numerous examples. This will result in greater specificity and more realistic measurable goals. The creativity of brainstorming in classroom situations can lead to comparing our footprints with those of other species to measure our abnormality—footprints involving energy use, CO2 production (Plate 7.1), geographic range size, and the many other ways species can be measured. Instruction can instill the recognition of standards of reference and goals as embedded in the information found in measures of other species. Hierarchically, education involving the footprint concept can clearly distinguish its application to individuals and its application to species. Education can take advantage of analogy and show, for example, how systemic management extends the concept of democracy to other species so that information in its most general form includes the voice of all beings. Public forums, workshops, and specialized training can lead to learning how systemic thinking involves the extension of systems thinking so that “deep ecology” is made a part of more inclusive holism (the infinite of reality with all its parts)—at the roots

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of the term “holy” in religious philosophy. Any educational effort can emphasize how ecosystembased management is not the only part of systemic management; biosphere-based management is also included. Courses on management can develop the reality-based aspect of systemic management. Instructors can use further analogy, and wholepart relationships, to show that “best practices” and benchmarking in business become part of the overall process of systemic management. Students planning to get involved in business can be taught to address higher-level questions. Lesson plans can include emphasis on asking questions regarding whether or not any particular business, industry, or occupation should even exist. Further analogy can be brought to bear in education by emphasizing that systemic management extends the matter of health to species, ecosystems, and the biosphere. Students can be taught critical thinking—extended so as to address questions regarding the sustainability of past medical systems (e.g., acknowledgment of their contributions to overpopulation,10 inferior immune systems, abnormal dependencies, and evolved characteristics detrimental to the species). The same holds for agriculture where courses need to be developed so as to treat the abnormality of monocultures and uniform aged populations. Schools with programs involving ecopsychology can emphasize that our dependencies, or specieslevel addictions (as manifest in the abnormalities we see), are not simply a species-level problem—we are involved as individuals and our feelings, emotions, and beliefs are part of the problem. Educational programs and materials can be developed to treat the issue of control across hierarchies: teams, families, and social organizations depend on their members controlling themselves more than trying to control the systems of which they are a part. By analogy (and extension in whole-part relationships), students can be taught that the same holds for our species as we more clearly see our species as a part of ecosystems and the biosphere. Courses in restoration ecology can emphasize the importance of restoring humans to sustainable roles at all levels and that ecosystems will undergo self-organizing change to achieve restoration to more normal states. Courses that now emphasize the importance of ecosystem services to humans

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can be expanded to make clear that the same services that we cherish are important to all species— sustainability involves all species simultaneously. Philosophy and religious education can include the message that we humans are part of reality, with all of our religions, sciences, educational institutions, political parties, and environmental organizations. Courses in management can make clear that we are part of ecosystems and the biosphere in a way where consonant reality-based management brings objectivity and consistency in goals. Similar educational programs can help students understand that comanagement is part of systemic management in having all stakeholders involved in asking the right questions to become part of the solution rather than part of the problem. Courses in evolutionary biology can instill an understanding that natural selection is part of reality and that reality-based management is evolutionarily enlightened as demanded of management (e.g., Brown and Parman 1993)—natural selection at all levels is part of reality. In an interdisciplinary fashion, students can be taught that the evolutionary impacts of medicine and agriculture are part of what we must address. One of my fantasies is to have most of the human population aware of not only human limits, but limits in general, limits to natural variation and the extent to which we are abnormal, especially as a species. Education is critical in showing how information on the limits to natural variation accounts for selective extinction and speciation to contribute to the evolutionarily enlightened nature of systemic management. Again, courses can dwell on the fractal and hierarchical nature of reality as fundamental not only in regard to evolutionary realms but also structural and relational realms. I know that effective communication of these ideas is no small challenge. Creative, alternative modes of presentation are required. We have to be open to the advantages of learning from the direct experience of nature as happens in aboriginal societies where such experience is prominent compared to synthetic education. In today’s world, information is produced faster than current educational systems can transfer it to society.11 Requiring more emphasis on learning about the process of systemic management (and overpopulation as one of many

challenges to be dealt with) will further tax our overburdened educational systems unless priorities are reorganized completely. The importance of healthy ecosystems to humans, both as individuals and as a species, is a message of such import that such difficulties have to be overcome. To the extent that education can promote an understanding of this for humans is simply one step toward adopting systemic thinking in place of conventional thinking. The importance of ecosystem services to the nonhuman beings with which we share this planet is something we can only appreciate if we learn to appreciate it for ourselves. Courses in law, ethics, and philosophy can lead to understanding that, in the global sense, there is no justice in denying these benefits to the nonhuman. Along with everyone else, I share the responsibility for change. Our species cannot change without participation by individuals in their various roles in families, communities, organizations, and governments. Managers, business leaders, spiritual and religious leaders, educators, leaders of civil society, and leaders of nongovernmental organizations are all individuals. Within each group are individuals asking to be led, choosing leaders to lead, allowing themselves to be led, or sharing in leadership. Guiding information is important to what is done in every case. Understanding the limitations, as well as the power, of human endeavor is critical—emphasizing education that can lead to that understanding. Classes, and training materials are needed to show how systemic management provides a foundation for unity exemplified by its consistency (Hobbs and Fowler 2008). People will learn that the effects of politics largely disappear other than as starting points for asking the right questions. Managers will have to be retrained. Implementation of systemic management will require the understanding and participation of people worldwide in an effort sustained over generations. Is it possible, can everyone be trained to participate? If so, how can we begin? As introduced in Chapter 5, the recipe for change is found, in part, in the details of questions yet to be asked—refined questions dealing with specific parts of larger questions. Training in how to ask questions is critical. When research exposes problems (e.g., those

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revealed by the Millennium Ecosystem Assessment project, MEA 2005a,b) we need to know how to use such information to ask management questions. We need training that leads to people skilled in dealing with anthropogenic causes rather than finding ways to avoid human responsibility through mitigation that deals primarily with symptoms. People interested in environmental law, legislation, and leadership must be trained to understand the need for consistency between man-made law and natural law. Experts need to learn how to change laws that do not extend justice and provide rights to the nonhuman. Developing legislation to make abnormality unlawful will depend on highly trained and experienced individuals. With training in both areas, we stand a chance of getting to management that addresses the question: “How much primary production should we leave for the sustainability of nonhuman species, as well as the ecosystems and the biosphere of which we are a part?” This would represent progress of fundamental importance in our quest for management at the ecosystem level—human interaction with ecosystems. To be systemic, prospective legislators and leaders would be trained to carry this on to the biosphere as a whole.

7.1.3 Management in action With conviction engendered by belief comes action—management itself (praxis). How do people at all levels—as individuals, families, and multifaceted societies and cultures—actually change? Much of my excitement about systemic management comes from realizing that part of the answer to this question involves its consistency. To the extent this consistency is realized, human conflict is reduced in the matter of accepting the objectives provided by natural systems and finding guidance for implementation. In this sense, systemic management opens the door for everyone to join in achieving common objectives defined for us, not by us. Unity of purpose would be a big step.12 Understanding, at least superficially, the nature of reality is a huge challenge, a fact well known to philosophers, and thinkers in the world’s wisdom traditions. I will never forget the excitement experienced when I realized that systemic management

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involves the common ground of science and religion—reality, something both have at their roots but from which both have strayed.13 Knowing that we can never fully understand reality is a step forward, but accepting that reality exists is key; knowing that we cannot understand reality does not mean it does not exist. Knowing that there are laws, patterns, and features to reality is fundamental to getting around our inability to ever know all of the details behind them. We know from science and our own personal experience that there are patterns, mechanics, and processes; there are interrelationships, time scales, and hierarchies. Reality is differentiated in innumerable ways—it is infinitely complex (Appendix 1.1). Thus, implementing systemic management is not a simple issue. It may prove impossible or it may simply be rejected as an option; regardless, there will be a universe with or without humans— that universe will be affected by our decision. Implementation depends on the complex question of acceptance. Management itself is the actual solving of the problems revealed. Reducing the human population appears to be much less a matter of direct action and more the indirect (but consistent) result of solving other problems. Governmental coercion is the type of transitive management that is prone to failure; success depends on the involvement of individuals seeking sustainability. Success may prove impossible. With or without management of ourselves, we continue to face the risk of systemic constraints (e.g., starvation, diseases, war, extinction)—Mother Nature’s solution to the problem. It is of consequence to ecosystems, the biosphere, and our future measured in terms of the degree to which their abnormality is relieved of causes found in abnormal human impact. Expanding knowledge is helpful in convincing ourselves of complexity. The continued practice of science as we have seen in the bulk of its history will further verify our knowledge that things are complicated, interconnected, and changing. However, knowledge, understanding, and words are just that. They do not constitute management any more than do plans, good intentions, meetings, goals, or voiced acceptance of what we should be doing. Management, especially systemic management, is actual change and action—the doing itself

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(praxis, clearly helped by motivation and commitment, but only helped). It is the implementation of goals, laws, and understanding. This book is just a book. The proof of its value will be in consonant pattern-based action.

7.2 Human factors Management Tenet 1 dictates overt inclusion of humans in management to avoid excluding ourselves and to put responsibility where it can be effective. Human elements of special note are: ● ● ● ● ●

Organizations Science Politics Religion Individuals

7.2.1 Organizations Separate books could be (and are being) written about religious, economic, sociological, legal, psychological, literary, and political aspects of management—especially in terms of environmental concerns. Uniting such efforts with common identifiable goals will be a major change towards working with nature—fitting in—rather than molding nature to our designs (systemic Frankenstein’s monsters that usually work to our ultimate peril). It would be a balanced combination of stewardship, unity, and cooperation with nature (the attitudes toward nature listed by Redman 1999). Within the human species, a variety of shortterm economic, cultural, political, religious, legal, ethnic, psychological, racial, and social interests are at stake in management—largely driven by human nature. Mangel et al. (1996) indicated that: “Effective conservation requires understanding and taking account of the motives, interests, and values of all users and stakeholders, but not by simply averaging their positions”. The conflicts mentioned in Chapters 4 and 5 often play out in different objectives among and within such groups. They are all important and involve thinking, values, and beliefs that can lead to meaningful management questions. Current approaches fail to establish and account for objective importance;

they fail to find balance. However, in the framework of Johnston (1991, 1994), there are overarching issues that unite them to overcome the polarization of conflict to find the balance in nature. At least two factors argue for stakeholders being united in understanding the goals and how to proceed in making the change (management) itself, rather than being polarized in picking objectives different from what can be observed in nature or discovered through experience. First, is the fact that all special interest groups experience human limitations in the ability to account for the complexity of reality. We are united in our common finite (limited) capacity to list and evaluate things and in our being subject to human values that are often misleading and diminish our objectivity. There are limits and bias in thought which prevent its use as an objective basis for management. This is a principle that precludes current ways stakeholders attempt to resolve conflict based on partially relevant fragmented information, interests, or values. Second, there are objectives we hold in common, and are held in common more holistically when we include the nonhuman. If our species is presently unsustainable, everyone has a common interest in reducing risks, including the extreme risk of our extinction. Common interests underline the need to unite in achieving species-level goals derived from natural role models, to unite in the observation of reality, to unite in the compilation of observations, and to unite in agreement that other anthropogenic processes are inadequate for generating guidance (i.e., that models, thoughts, and theories are finite and limited in ways that can lead to errors when used in management if they are not representations of natural patterns consonant with management questions). We can be united in our knowledge that if the human species goes extinct, all special interest groups, nations, religions, politics, and monetary economics go with it, whether or not we can know that they contributed. The changes necessary within each organization are themselves very systemic and involve a major shift in habits, beliefs, structure, priorities, and activities. In regard to natural resources, for example, committees would cease their reliance on expert opinion (even collective expert opinion) and experts would become those best suited to present

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empirical information consonant with management questions. Such committees would be tasked with asking clear management questions and ensuring that consonant patterns are produced by science carried out with as much objectivity and quality as possible. Thus, the setting of goals and objectives for our species can be guided no more by special interests (including nations, cultures, political groups, or races) than by a discipline or combination of disciplines of science. Both involve human constructs that place human-derived importance on things. Both suffer from the inability to exhaustively list or consider the factors that need to be considered. To continue to base management on such constructs would continue to make human limitations the basis for management rather than using our limited capacity to observe and, by observing, find guidance. Continuing to use nonconsonant abstractions leads to management with the flaws identified in Chapter 4 to perpetuate tragedy (Meeker 1997). Organizational missions would place economic values in their proper perspective: forces and factors within the human realm that contribute to the patterns we observe. Legislative mandates to avoid the abnormal would be based on this as a fact and such values would be prevented from being the basis for decisions and policy. It would be recognized that economic forces are notoriously at odds with reality; they involve making an abstraction into a powerful (but artificial) reality in the interconnectedness of human and nonhuman systems. It would be recognized that economic forces have been significant in systemic changes that have resulted in the current set of global problems. By ignoring what we know about the complexity of reality, and continuing to use economic values to influence management, we risk further acceleration of these trends. The same holds true for the interests of any group of humans determined to make their interests anything more than a completely objective part of the decision-making process. The issues raised are real (i.e., they count among the ei of Fig. 1.4) but impossible to evaluate objectively and exhaustively. Organizations are extended in systemic management. Other species are among the

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stakeholders whose interests are taken into account when we use information for the patterns in variation among species to establish goals and policies. They are part of the organizations of which we are all parts—ecosystems and the biosphere as largescale organizations. Considering other species as parts of these organizations is a matter of human self-interest because abnormal extinction of species erodes the sustainability of ecosystems; as unsustainable ecosystems reorganize to regain dynamic equilibrium, the parts that are sacrificed in the adjustments can easily include humans. Over the years, such risks have been noted repeatedly (e.g., Ehrlich and Ehrlich 1981, Union of Concerned Scientists 1992); will we heed them? It is my opinion, as well as that of others (Aplet et al. 1993, Gilbert 1988, Knight and Bates 1995), that the institutional approach to managing our use of ecosystems and the biosphere, and action to result in the reduction of our population in particular, must transcend special interests and politics, and receive widespread support. Management of our use of natural resources through conventional approaches is done in a bureaucratic structure that is very human centered and resource specific. Current attempts to manage ecosystems combine conventional frameworks to result in a complexity of administration and structure that competes with the complexity of the system itself (e.g., the Greater Yellowstone Ecosystem, Clark et al. 1991, Clark and Zaunbrecher 1987) but ultimately falls far short and misses entirely the elegant coherence of nature. As such, most current approaches remain ad hoc. They rarely deal effectively with the ultimate causes of observed problems nor the impossibility of transitive nonconsonant patternbased approaches—a common example being to force systems to provide short-term support for humans rather than make ourselves supportable for the long haul. Current international organizational structures for dealing with management seem even less effective. The complex issues and dynamics of nationalism, politics, religion, economics, and social traditions make reaching consensus on how to proceed an impossible task in conventional thinking. Without clearly defined form and function to the process, the course nature will be

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driven by human nature more than we want (e.g., escalating warfare possibly culminating in global nuclear war, reliance on drugs rather than natural immunity for resisting disease, increased starvation, or ultimately our extinction). If human action is insufficient, other aspects of systemic limits will prevail. In considering all the abnormalities we exhibit, and with lag effects that remain negative long enough, it is easy to think of the human species as risking extinction because of what we have done and are doing. Change in institutional structure is needed. Currently there is no effective feedback mechanism for agencies regulating the human consumption of resources to indicate that the demand (by both an excessive human population and excessive per capita consumption) is too high to be sustained by resources. Thus, human overpopulation is not dealt with through current forms of management. In fisheries or forestry, for example, the issue is usually avoided as a matter of policy, as it often is in the work of various environmental organizations—the antithesis of systemic management. Connectivity and interrelatedness are denied. There are no agencies, bureaux, or departments to receive the kind of information found in Chapter 6 and make it the basis for action; that is, there is little if any institutional acceptance of Management Tenet 5. There is inadequate legislation prohibiting the abnormal; existing examples include resistance to the abnormal killing of one another (itself inconsistently applied). While scientists and various agencies and organizations dealing with ecosystem degradation may convey the message that human population size is behind the deterioration, there is no coherent socially supported means for translating the message into action. The changes required of humans involve all the ways we think of ourselves as a special species—our religions,14 social systems,15 politics,16 ethics and morals,17 laws,18 economics,19 psyches,20 governments,21 and nations.22 Change must be systemic within our species as well as in our interactions with the nonhuman. These and other related human issues reveal the complexity of species-level systems, which are true for all species. All can be involved in bringing systemic experience to understanding and change—systemic change.

7.2.2 Science Science must continue documenting, explaining, and characterizing phenomena involved in the reality of which we are a part; science must continue pointing to problems in need of managerial solution. In particular, science needs to focus on comparing humans to other species (e.g., our mobility, home-range size, per capita consumption, per capita production of waste and chemicals, transport of other species, and the myriad forms of selectivity on other species) as a means of producing the kind of information most suitable for addressing questions about sustainability for our species. Science, however, must go on to promote emergent messages. Discovering new realities proves what we already know: realities exist to be documented. Explaining, and characterizing these realities adds to what we already know: phenomena have explanations and characteristics. Discovering new processes proves what we already know: interrelationships and interactions exist and can be characterized. Human activities have consequences through these interconnections. The burden of proof must shift; today (in conventional approaches), those who claim human impacts are excessive are required to prove their case. The burden of proof must now shift; it must rest on the shoulders of those who maintain otherwise. It is important that scientists rely on the emergence of the concept of emergence; emergent patterns reflect (account for) the complexity behind them. Complexity is inherent to what we see. Scientists have the responsibility of conveying this message to managers to ensure that consonant information is used to relieve the management process of as much human error as possible. Managers have the responsibility of asking what management question scientists are trying to develop when concerns are expressed by scientists (e.g., concerns about global warming, oceanic acidification, extinction, loss of top soil, nutrient cycling, energy consumption, . . . .. any one of the complex of issues with which we are faced). In other words, both scientists and managers need to remind each other to confine their own and each other’s use of concerns to the posing of clear management questions (second step in bottom row of Fig. 1.1).

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Those of us who are scientists have a clear role in providing information that can help understand the nature, shape, variation, and constraints of patterns within natural variation consonant with specific management questions (Appendices 1.3, 5.2, Fowler and Hobbs 2002, Fowler and Perez 1999). Such patterns would include those seen over time and space at all levels of biological organization, and in response to environmental circumstances. Comparisons must include measurements at the individual level (e.g., amount of energy used, distances traveled, time spent in buildings, diversity of species within 200 m, and biomass ingested). More comparisons between humans and other species will contribute to our becoming self-aware as a species, but will also lead to the growing list of species-level dimensions over which we can make comparisons (e.g., the portion of water diverted from streams and rivers, use of energy from noningested sources, production of chemical compounds). Such efforts have been initiated at the species level in work referenced in many parts of this book (e.g., Chapters 2 and 6)—macroecology is to be emphasized as a very important field of science! Bringing humans into the realms of such science is a crucial extension of studies of the nonhuman (e.g., see particularly, Brown 1995, Charnov 1993, Gaston and Blackburn 2000, Peters 1983, Rosenzweig 1995). Other related efforts are underway at the ecosystem level (e.g., the Millennium Ecosystem Assessment project sponsored by a variety of international organizations) where there is a great need for information on patterns among ecosystems (Rapport and Moll 2000). It will be helpful for scientists to be clear about limitations in communicating with managers and the public in general. In the extreme, scientists who clearly understand human limitations can refuse to serve on panels or advisory teams when asked to do anything other than look at empirical information in patterns congruent (consonant or fully in accord) with the management question(s) being addressed. It is important to communicate the limitations of conventional approaches and the ways that they fail to account for complexity, account for the unknown, and provide a full objective weighing of the relative importance of the various factors involved. Scientists need to be clear about this

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in communicating with managers and the general public. It is important to ask conventional managers how they account for things such as evolutionary dynamics, chemistry, extinction, behavior, and historical change—or any other of the parts of the infinite list involved in Figure 1.4. Their experience in taking such questions seriously can help bring about change through their realization that it is impossible using conventional means. Humility is an important element in the lives of scientists and the way we serve society. This goes beyond acknowledging nature as a superior source of guiding information that largely confines us to being expert observers with little other role in goal setting. Accepting the limits of science includes accepting the human limitations we bring to our research and the application of the results. Discovering a wheel, or lever, is a source of excitement but raises questions regarding limits that should be placed on their production and use. We understand that we can move rocks with levers, but such understanding does nothing to tell us how many rocks to move or whether to use levers for other purposes. Similar questions concern the discovery and use of atomic energy, dynamite, gunpowder, fire, and electricity. We can discover (notice or observe) that reduced populations of resource species exert homeostatic tendencies to regain normal population levels. We can note that a pint of blood withdrawn from a human is replaced in a week. Neither serves as basis for concluding that we can harvest the increased production at the rates it is observed to be produced. The “conscious purpose” (Bateson 1972), or transitive use, of the nonconsonant information we produce can be dangerous because such action also involves all the things of which we are unaware (unconscious); we have the responsibility of alerting those who would use our information, to make them aware of this reality. The goal of fitting in or finding sustainability, not only for humans but also for the nonhuman, is primary. If this involves a value, it supersedes other values (Belgrano and Fowler 2008)—avoiding the abnormal applies at all levels and involves the risk of our extinction. We have the crucial responsibility for conducting the best science possible to observe and document instructive patterns in nature.23

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Although not the focus of this book, research comparing humans as individuals, with individuals of other species (still as species-level comparisons) has barely been initiated. Mean per capita energy consumption and variance in per capita energy consumption initiates the concept. Such comparisons can be extended to mobility (e.g., home range size), CO2 production, time spent in various activities (e.g., finding food, in constructed enclosures, getting exercise, interacting with other species), and a host of other ways comparisons can be made. This would add specificity to the various dimensions of footprints (Rees and Wackernagel 1996) we have both as a species and as individuals, now combined with information that can supply goals and objectives. More research comparing humans and other species is a pressing need. Dealing with complexity includes dealing with the variety of questions to be asked. Luckily, some research on species-level metrics over which comparisons can be made can involve data that are already available; however the need for further research is unlimited. An important aspect of science is that of identifying more clearly the kinds of species-level characteristics and interactions that lend to the risk of extinction. We are beginning to get some clarity, superficial as it might be, regarding the qualities of species that make them extinction prone. Modeling exercised as a way to explore and understand such relationships is to be encouraged as, for example, the case for the extinction risks associated with the varying kinds of density dependence (e.g., Henle et al. 2004). Similar work is needed for a better understanding of extinction risk for species that are extremely overpopulated, acknowledging a priori that the matter is beyond complete representation in models (Pilkey and Pilkey-Jarvis 2007). Empirical information would serve our purposes much better than the result of simulation models, as it would for all of the ways we will find ourselves to be abnormal. In addition to the extreme of extinction, the alternative of precipitous and extreme population reduction should be examined with more empirical information in the form of statistical models (the kind of human construct most useful to us when consonant with management questions). What is the probability of decline

(and extent of decline) in populations among nonhuman species overpopulated to the extreme we humans now experience? Keep in mind that such science does not detract from the need to deal with human overpopulation directly and on its own, owing to its effects on the extinction rates of other species, oceanic acidification, obliteration of complete ecosystems, health problems for individuals (of all species). . . . the infinite of Figure 1.4 to deal with complexity.

7.2.3 Politics In a systemic view of the future, politics would be confined to arguments about accuracy in process (e.g., whether or not the right question is being asked correctly, or whether the correct information is being used to address the question), not about objectives and endpoints. These are supplied by empirical information. Governments would find consistency in their objectives, both among and within nations (Hobbs and Fowler 2008). Accepting the need for a reduced human population, reduced resource consumption, or reduced CO2 production, would be given the species-level urgency experienced in reducing a fever at the level of the individual. The magnitude of reductions needed on all fronts would be seen as basis for action knowing that current efforts (such as efforts to reduce green-house gas production) are good steps in the right direction, but only very small steps—usually minuscule. Such consistency would be supported by various groups within society in exercising the humility that comes from recognizing human limitations. Governments adopting systemic management would refrain from asking for advice generated by special panels or steering committees using (and translating) only partially related and incomplete information and would instead ask such groups to consider empirical information directly consonant with their management questions with the objective of making use of observed patterns. Relevant information would be used in analysis of variance of such patterns with refined management questions in mind. Government agencies involved in management would support science that focuses on identifying the normal range of natural variation

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within patterns consonant with their management questions, whether such variation is among individuals, species, or ecosystems. For species this would include CO2 production, size of parks and another protected areas, energy consumption, resource consumption, and population size (including density and distribution). Research to examine the limits to variation in relation to temporal and spacial heterogeneity, would be funded and encouraged as a form of science preferred to research that makes obvious the fact that reality is complicated. Academic science cannot be dropped as it helps lead to more and critical management questions; it helps identify correlative patterns to make direct use of the information scientists produce (there is a huge difference between correlative conversion of information and the alchemy of conventional conversion). Governments, along with the rest of society, would support educational programs to make people aware of cases wherein humans are abnormal and what needs to be done. Stress would be placed on the unity of purpose that would emerge among governments, businesses, the sciences, religion, social and environmental organizations, and other elements of society as forced by the consistency of nature (Hobbs and Fowler 2008). The reality of such consistency is emphasized experientially in the consistency of problems we see in human abnormality. Environmental organizations, for example, would not give up on their missions. However, goals and objectives would be consistent among organizations, whether they are concerned about human population size, resource harvesting, pollution, immigration, habitat, biodiversity, extinction, CO2 production, or other elements of reality. The production of more energy (using wind, tides, sunlight, garbage) is inconsistent with a pattern showing that energy usage is already abnormal. The goals and objectives for such organizations would be consistent with those of government. There would be no more reason for difference of opinion than there would be among physiologists, geneticists, morphologists, and neurologists regarding the appropriate body temperature for individual humans, hummingbirds, or elephants. To the extent that achieving systemic sustainability

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is viewed as utopian (or proves in fact to be impossible for us to achieve) we have grounds for being skeptical about success.

7.2.4 Religions At their core (as opposed to many or most institutional and personal manifestations), the various wisdom traditions of the world place primary importance on the concept of veracity (truth or truthfulness, H. Smith 1994). Facing reality/truth is at the core of systemic management and is a common element of both religion and science; systemic management is reality-based to include any God or gods and beliefs about them. Religions can debate about who are the most believable and historically recognized messengers regarding reality, much as science does with its prominent contributors. However, both are dealing with the underlying assumption, or belief, that there is a reality to be understood, observed, and accounted for in decision making. Both face the fact that the whole of reality is made up of its constituent parts—the realities we face in day-to-day life. The holism of religion and the reductionism of science meet and find common ground in systemic thinking and the consonant reality-based aspect of systemic management. Science and institutional religion may share major imperfections owing to human limitations—so many as to make comparison of religion and science as practiced today a ludicrous comparison. However, we can see that we are “throwing the baby out with the bath water” if the common elements of reality behind them are ignored— especially in terms of changes in both that can reclaim consistency. Movements are afoot in spiritual and religious circles to deal with the problems that we face in the more inclusive living systems of the world of which we are a part (Rasmussen 1996, Swimme and Berry 1994). These are exemplified by organizations such as Earth Ministry, National Religious Partnership for the Environment, North American Coalition on Religion and Ecology, Interfaith Network for Earth Concerns, Interfaith Center for Environmental Stewardship, Coalition on the Environment in Jewish Life, and the Evangelical Environmental Network. Systemic management

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points the way for such efforts to recognize unity of purpose through the common ground of reality (i.e., the infinite of Fig. 1.4, Appendix 1.1)—not only among such organizations but also between such organizations and other institutions, governments and organizations. Religion and science may find relief from polarized perceptions if open-mindedness in both realms finds common ground, not only in the immanence of God as natural law (Morowitz 2002) as well as in other parts of reality, but also in the transcendence involved in the emergent nature of everything. Humans are not the only species to emerge from reality. The larger (more inclusive) wholes seen in ecosystems and the biosphere are subject to pruning rules (including natural selection among species; Chapters 3) acting on their parts to give rise to emergent qualities also. If the pattern of emergent qualities applies to everything with parts, the universe must have its own emergent qualities. The composite of such qualities at all levels is a form of transcendence. Reality has both an immanent and a transcendent nature simultaneously. As many have noted, science and religion may have much more in common than one would be led to believe in watching and listening to the superficial of popular discourse. Religion, more than science, assumes the role of defining morals, ethics, and the right or wrong of what we do. Evil, as defined by Reinhold Niebuhr (Brown 1986), involves action without regard to other parts of the universe around us. It is this perspective that is behind the concept of systemic management as thinking and action that account not only for the nonhuman but the human on time scales heretofore largely ignored. Consideration of the nonhuman is inherent to asking questions such as “How much primary production should we leave for other species?” Patterns in what is left for other species, by other species, provide integrative holistic bases for addressing such questions. Action based on such information involves compassion and justice well beyond anything achieved in the largely anthropocentric approaches of today, while avoiding the opposite extreme of biocentrism. Consideration of the nonhuman is inherent to asking all management questions so as to seek and use consonant emergent information.

7.2.5 Individuals Much of what we do as a species is done through the collective action of individuals.24 This places the responsibility for success where it brings out the greatest difficulty as well as both the greatest control and potential. Sacrifice, effort, and cost25 are involved in the actions individual humans must take to bring about the changes needed for our species to achieve sustainability. To the degree that we experience the conflict of seeking personal good when it runs counter to what is necessary for the sustainability of all species (and ecosystems and the biosphere), we are experiencing responsibility not recognized by previous generations. Reducing birth rates (for those who value large families), consumption, energy use, CO2 production, and all the influence we have on the ecosystems and the biosphere will reduce our quality of life as defined by current (especially western European/North American) standards. This predicament can only get worse for future generations if we do not make sacrifices in taking action now. Sufficient changes now (or in the near future) will increase the quality of life for future generations (of both humans and nonhumans); indeed it can be expected to enhance the likelihood that humans can continue into the long-term future. In the long-term, a reduced human population can be expected to result in less rather than more international conflict over resources, starvation, malnutrition, and the risks of disease (including evolution of new or more virulent diseases)—if ecosystems and the biosphere have time to recover from changes we have caused, and are causing, so as to support us. Huge challenges are ahead in reducing the risks we face. These challenges include the scope of change required of individual humans—including changes in our consciousness, thinking, habits, and values. The momentum of change in the opposite direction has been building for centuries. We need to remember that natural selection at the individual level often results in factors that are not adaptive at the species level (evolutionary suicide, Combinations 5 and 6, Table 3.1)—cases where what is good for the part is lethal for the whole. Thus, emotions and values (especially those based on monetary economies) can easily be in conflict

EPILOGUE

with natural forces involving greater complexity. Unless we can overcome our individual genetic programming (along with the socially and culturally learned programming) and consciously act for the benefit of species-level survival, the consequences will include increased risks of starvation, plagues, war, and ultimately our extinction. We can act in our choice of leaders, in the organizations that we contribute to, in our selection of mates, in our use of resources, and in the number of children we have. Whatever we do, however, it will not ultimately work if it is not to the benefit of both individuals and our species, in the spirit of Nash equilibria (Combination 1, Table 3.1) or infinite games wherein the objective is to continue the game (Carse 1986). Individuals can ask questions. For example, managers can be asked if the evolutionary impact of any particular management strategy has been taken into consideration. Has the decision to permit the development of land (e.g., for recreation or shelter) accounted for evolutionary impacts on other species? Is a sustainable level of energy, space, or biomass being left for other species—not just one, but all other species? Have managers taken into account the degree to which ecosystems will be altered and how such alteration results in feedback to our species? If so, how were such considerations weighed against economic, social, or other human interests without those interests superseding sustainability? Were decisions made so as to account for future generations of both humans and other species? Has the establishment of harvest quotas, limits, or zones for harvesting resource species (e.g., fish, trees, or commercial crops), taken into account the evolutionary impact such takes have on both target species and other species? Have you taken into account the monoculture effects of modern agriculture? Have the butterfly effects of any particular action been taken into account? The impossibility of such considerations in conventional thinking will hopefully be acknowledged so as to open minds to adopting systemic management and then be able to answer in the affirmative. Individuals can write, publish, talk, and form action/advocacy organizations.26 Books (including novels), plays, art, educational materials, and advocacy organizations aimed at each of the

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species-level dimensions for which we find ourselves to be abnormal would be good steps toward making the human population aware of the magnitude of our problem(s). The mass media may be one form of technology that serves us well if used to educate people around the world of the plight before us. Individuals are involved in politics, religions, education, and other social institutions. I agree with Brian Czech (2000) who sees major social change as a key element in achieving sustainability. It cannot be done with a change in economic thinking alone. It will have to involve our legal system. It will have to involve the arts, the media, and much more. It is not clear that it can happen. It is clear that it will be impossible without motivated individuals armed with an understanding and appreciation well beyond that involved in the decision making and management we witness today. The changes must be systemic.

7.3 Looking back and looking forward The preceding chapters bring us to the realization that the problems before us are immense— much larger than acknowledged in conventional thinking,27 in fact, larger by orders of magnitude. Solving these problems is systemic management. Changing is action (praxis) that is reality-based. It is a holistic form of management with parts exemplified by ecosystem-based components, biospherebased components, and single-species-based components. Science provides us with measures of the human-based problems, as problems open to solution through human action. Science provides us with explanation of these problems, including an understanding of the concept of emergence and the complexity of factors involved (Plate 7.2). Science helps us understand the integrative nature of guiding patterns so that their use in establishing goals for management account for the infinite nature of reality. However, there remains the matter of action to achieve such goals. What can or should we do to achieve the objectives of sustainability? The means are different from the ends. We are at the point of realizing that the importance of achieving the goals is challenged only by the importance of how

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we achieve them. In the spirit of systemic thinking, are there “patterns of praxis” that inform us in this regard? Can we find information in the nonhuman that tells us about what to do to achieve the goals we find from the same source? If not, can we use our own experience? We can carefully use examples of success in current management action—now with realistic goals. We have legislation in place for, and organizations with experience in, setting quotas for the harvest of marine fish resources—quotas can be set to avoid the abnormal. We regulate the harvest of timber, and the occupation of land—those processes can now be used to avoid the abnormal. Successful means of regulating ourselves can be carried forward by replacing existing goals with the more realistic goals established by integrative patterns consonant with the management question involved. Caution, however, is imperative. It is tempting, for example, to take information such as that provided by Pimentel et al. (2007) and move toward producing more food, exercise more disease prevention, distribute food more equitably and seek/ develop technology to help. We know how to do such things and we know that there are problems associated with technology (Pratarelli and Chiarelli 2007). We now know that the use of technology exacerbates problems such as energy and resource consumption by our species at rates that are already pathological. Such action would not solve the problem of excessive human numbers, the abnormal production of CO2, or production of chemicals (some of which are behind the problems outlined by Pimentel et al. 2007). Research exemplified by Pimentel et al. (2007) is exemplary of the basis for asking clear management questions. Again, the patterns produced by science in response to good management questions might lead us to think that the matter of change is simple: we know how to achieve goals when they are clear. As with all things, its not quite so simple and we begin to realize the magnitude of the challenge before us. By direct action to achieve the goals made clear in the preceding chapters, we immediately encounter the problems that we have not been able to deal with in the past—problems such as our overpopulation. Now we can use systemic

management to cut back on our consumption of energy, our production of CO2, our occupation of space, our practice of medicine, agricultural production, use of chemicals, and the multitude of other ways that involve human excess. In most, if not all cases, there would always be the consistency between such actions and associated results involving a decrease in our population through starvation, disease, and other factors that are often judged as draconian (and unacceptable in current thinking) when we face their reality. Systemically, this would be an approach that would reduce most if not all of the pathological effects we have on the world around us and solve the problem of our overpopulation, along with all associated abnormalities. By doing so, we directly encounter the emotions and human values that prevent objectivity in conventional management. Such an approach solves more problems than are being solved conventionally. It can be seen as an approach in which human intellect finds a means to avoid an abnormal risk of human extinction—an evolutionary feature sufficient to transcend itself and realize its own dangers. If this novelty (like the repeated evolution of the eye) has species-level advantages in species-level fitness, maybe it is enough to work with. It would be a matter of an infinite game (Carse 1986) in which we humans took the initiative, acted proactively, took responsibility, and faced reality. In terms of an infinite game it would involve one of the currencies of ecology—life and death. Systemic management is not a matter of simply sending a check to one’s favorite environmental organization. Certainly, human values, when we let them be our guide, lead to the quick conclusion that death is an unacceptable means of achieving sustainability. Human values and the risk they present in the form of a fatal flaw leading to evolutionary suicide, however, are to be reckoned with as parts of the reality we face. They put us in the position of remaining where we find ourselves now—using human values as the basis for management (top row of Fig. 1.1), rather than the transcendent value of sustainability as it applies to all systems, human and nonhuman. If we follow the pattern of using our values to direct us to the task of asking management

EPILOGUE

questions (rather than set policy), we see another option. Are there patterns in praxis? What do other species do to find their place in natural patterns? What we find, when we look to other species, is that very few, if any, species exercise self-restraint as a species. There are biosocial factors that contribute to population regulation, for example, but intrinsic (endogenous) factors, for the most part, are not willful other than that they serve for the fitness of individuals and incidentally work to regulate population (Fowler 1995). If gorillas had evolved the capacity for tool use, medicine, and agriculture, instead of humans, it would probably be they, rather than us, faced with the dilemmas we now confront. Comparisons of historical human cultures brings us to the same conclusion regarding subpopulations of our species (Costanza 1995, Costanza et al. 2007, Diamond 2004, Ehrlich and Ehrlich 1996, Ponting 1991, Redman 1999). What we find is that it is the organizational dynamics of the systems of which we are a part that are primary in giving rise to patterns by placing limits on their components (Ahl and Allen 1996). We, along with other species, are limited by the systems of which we are a part. There is only a finite amount of space, a finite amount of energy, and a finite amount of each elemental resource (e.g., phosphorus, sulphur, chlorine) to be shared among the species that occupy our planet. The effects of the collective set of species on any one are more powerful than the effect of any one on the others (noting the current effects of humans as a dangerous potential, but temporary, counterexample). Where are we led with this information? Our best option may be to accept the forces of nature and let them do the job of restoring humans to a normal fit within the universe. In this regard, management action would be a matter of letting nature’s forces give rise to the mortality and reduced birth rates necessary for the infinite game to be played realistically. This would promote a natural form of selectivity, rather than unilateral action by a world leader (a la Hitler or Pol Pot), or even a democratically adopted regime for population reduction (all involving politics). Such a regime would, itself, violate the principles of systemic management in addition to being unethical by current standards

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(here we see an example of human values that are consistent with systemic action—not always the case). As difficult as it might be to accept, and as horrendous as we might evaluate it to be, the acceptance of nature’s forces, may be our best option. We can hope that they are not so extreme as to result in our extinction—perhaps sparing a few indigenous cultures that are capable of living sustainably. We can realize that the unintended consequences of medicine and agriculture, technology, and conventionally applied science, as products of an evolving mind, are among the reasons we find ourselves in the current predicament. They serve well in the interest of individuals, but not our species.28 Our beliefs and modes of thinking are involved and have their consequences. As much as we fear diseases, death, and starvation, acting on such fears is an example of cerebral alchemy: translating an emotion into action rather than using it to turn to the process of asking good management questions. We seem to be faced with two options: continuing on the course we are now taking (basically making no change, in which case the forces of nature will do the job for us), or taking action ourselves in accordance with the laws of nature (in which case the forces of nature also work with us). The former involves more risk, with increasing risks as we move further into the territory of the abnormal or pathological. The latter makes use of intellect in a way that undoubtedly has no precedent in evolutionary history; we have a species-level selfawareness that reflects back on our selfawareness as individuals. The latter, more than the former, involves an encounter with reality akin to a mystical experience. There is a choice.

7.4 Conclusion Researching and writing this book have involved personal lessons beyond my wildest dreams or expectations. Informed individual action to change our species seems to be a critical element in our achieving effective management. We need leadership and guidance to help achieve sustainability, and individual motivation and action is fundamental. We need realistic guidance—based on reality. The inability of humans to control the natural

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world in all its complexity—individuals, species, ecosystems, and the entire biosphere—is apparent. Giving up our tendency to attempt control of the uncontrollable requires a change in paradigm regarding our perception of reality—the same change that leads us to guiding information. This change goes beyond the rational to include the emotional, aesthetic, and spiritual and depends on experience. I sense that we are just beginning to perceive ourselves as a species facing a situation much like individuals do in their personal lives. While, as an individual, I cannot orchestrate my

entire world, I seek to adapt myself to live as well as I can in circumstances largely beyond my control. The main thing under my control is myself, and I would be fooling myself to believe I have complete control there. In accord with elements of eastern philosophy, trying to control ecosystems or the biosphere is self-defeating.29 Based on an understanding of life30 augmented by the concept of natural selection at multiple levels, the best we can do as a species is to make change with, and be guided by, empirical patterns in nature—the infinite guiding the finite.

Notes

Chapter 1 1. Sustainability is defined for the purpose of this book as “what works”. It is the normal, as opposed to the abnormal (which does not work). It is the healthy in contrast to the pathological; it is the genuine compared to the artificial, or abstract. See Fowler and Hobbs (2002) regarding the importance previous efforts have placed on recognizing limits in defining what is normal for management. 2. Emergence, as used in this book, means that there is an explanation to the origin of everything (Belgrano and Fowler 2008, Morowitz 2002). We cannot know the complete story of this origin but it involves all contributing factors (see Bateson 1979, Clayton 2004, Emlen et al. 1998, Jørgensen et al. 1999, Lewin 1992, Nagel 1979, Nielsen 2000, Prigogine 1978, Scheiner et al. 1993, Solé and Bascompte 2006), and these factors (reality, Appendix 1.1) are accounted for in what we see. 3. Consideration, mention, or concern about human extinction as the result of human-caused changes (feedback from our influence) is frequently found in the writings of biologists (e.g., Boulter 2002) and environmental scientists. But it is also found in the writings of a variety of other philosophers and scientists. Examples of each include Bateson (1979), Brown (1995), Burns et al. (1991), Cairns (1991), Capra (1982), Catton (1980), Cawte (1978), Chaloner and Hallam (1989), Chiras (1992), Darwin (1953), Ehrlich (1986, 1989), Eldredge (1991), Garrett (1994), Greenway (1995), Hassan (1981), Hern (1993), Jarvis (1978), Jenkins (1985), Jφrgensen (1992), Laughlin and Brady (1978), Lederberg (1973, 1988, 1993), Macy (1995), McNeill (1989), Mines (1971), Noss and Cooperrider (1994), Ovington (1975), B. Patten (1991), Pennycuick (1992), Ponting (1991), Potter (1990), Reed (1989), Rosenzweig (1974), Roszak (1992), Salzman (1994), Tiger and Fox (1989), Trotter and McCulloch (1984), Tudge (1989), and Whitmore (1980). 4. The value of ecosystems, in human terms, has received extensive treatment in recent years. This work is exemplified by Ehrlich (1991), Ehrlich and Mooney (1983), Ehrlich and Wilson (1991), Farnworth and Golley (1974), Myers

(1985, 1989), Nash (1982), Nash (1991), Norse (1985), Norton (1987, 1989), Rapport (1992), Sagoff (1992), and Seal (1985). 5. A discipline of psychology called ecopsychology focuses on such issues at the personal level (Roszak et al. 1995). Other aspects of separation from nature are likely more emergent at the species level. A number of authors have treated these issues (e.g., Bateson 1972, 1979, Bateson and Bateson 1987, Gore 1992). The issue of control and our attempts to control may be one facet of potential pathology in these matters (Holling and Meffe 1996). 6. Such experience may lead to comprehension of nature that is occasionally seen as an alternative (or compliment) to understanding gained through western science, which is often subject to criticisms by scientists themselves. These criticisms often center on the mechanistic views that have failed to produce the understanding and explanations once thought to be so promising of Newtonian–Cartesian based science (see Mayr 1982, Peters 1991, Rosenberg 1985, Simberloff 1980). Comprehension involves perception and fundamental to perception is experience (Wilber 1996). Simple communication involves personal experience of this principle. Communication with a person who has no personal experience with the subject matter is nearly impossible (certainly more difficult) when compared to the mutual understanding between two people both of whom have common personal experience. People who live in proximity to nature (within ecosystems, in direct contact with the sources of their food, sources of materials, and the limitations of diseases, predators, and competition) have perceptions and comprehensions not possible for those that learn secondhand from classes, reading, explanations on television, or even research. 7. For an introduction to problems and limitations identified in the use of models in ecosystem studies see Gross (1989), Hagen (1992), Hedgpeth (1977), Holling (1993), Kareiva (1989), Karplus (1977), Kingsland (1985), Moran (1984), Orians (1974, 1975), Pimentel (1966), Pimm (1991), Schlesinger (1989), Schnute and Richards (2001), and Ulanowicz (1989). For a treatment of the more general limitations of models see Pilkey and Pilkey-Jarvis (2007).

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8. There are many references documenting, listing, or describing specific laws or agreements that require management that includes ecosystems (e.g., Angermeier and Karr 1994, Belsky 1995, Christensen et al. 1995, Clark and Zaunbrecher 1987, Davis and Simon 1994a, Francis 1993, Jarvis 1978, Keiter 1988, Mannion 1991, Munn 1993, Noss 1990, Rapport et al. 1985, Salwasser et al. 1993, Sample et al. 1993, Thorne-Miller and Catena 1991, Wallace 1994, Westman 1990a, and Wood 1994). 9. The concept of ecosystem health has been the subject of much consideration in recent decades (Callicott 1992, 1995, Christensen et al. 1996, Costanza 1992, Costanza et al. 1992, Ehrenfeld 1992, 1993, Hannon 1992, Hargrove 1992, Haskell et al. 1992, Karr 1990, 1991, 1992, Keddy et al. 1993, Norton 1991, Rapport 1989a,b, 1992, Ulanowicz 1992, Woodley et al. 1993), including the publication of a journal (EcoHealth). 10. CCAMLR, Canberra (1980) as reprinted in Wallace (1994). 11. See, for example, the U.S. Marine Mammal Commission’s compendium of such agreements (Wallace 1994). 12. An essential part of the message of this book is the matter of learning from nature (Hobbs and Fowler 2008). This has been an approach to management advocated for centuries. Smith (1958) indicates that Taoism is basically ecological and “ . . . seeks to be in tune with nature”. Zeno (335–264 BC) maintained: “The purpose of life is to live in agreement with nature”. Cicero (106–43 BC) added: “Those things are better which are perfected by nature than those which are finished by art”. In the Bible, we are advised to “ . . . ask now the beasts, and they shall teach thee; and the fowls of the air, and they shall teach thee; or speak to the earth and it shall teach thee; and the fishes of the sea shall declare unto thee” (Job 12:7–8). Later (about 75 AD), Plutarch advised that “The soul of man . . . is a portion or a copy of the soul of the Universe and is joined together on principles and in proportions corresponding to those which govern the Universe”. Roger Bacon, in the thirteenth century, while advocating unrealistic control, nevertheless saw that we needed to conform to the laws of nature, suggesting: “Nature can only be mastered by obeying its laws”. Various writers, philosophers, and thinkers have added their voices to the matter of learning from nature. Henry Miller is one: “The world is not to be put in order, the world is order. It is for us to put ourselves in unison with this order”. Using nature as a teacher is carried forward in the writings of Henry Thoreau and John Muir who saw “ . . . sensitive observation of nature as the source of wisdom” (Norton 1994). Aldo Leopold said: “A thing is right when it tends to preserve the integrity, stability and beauty of the biotic community. It is wrong

when it tends to do otherwise”. Christensen et al. (1996) characterized Leopold’s perspective as that in which wilderness provides a “base-datum of normality”. Bateson (1979) indicated that we should follow the examples of nature. The use of nature as a source of guidance has been revisited numerous times (e.g., Chiras, 1992, Willis, 1995) and is embodied in the process of biomimicry (Benyus, 2002). The writings of people like Wendell Berry carry forward the philosophy of earlier luminaries (e.g., Berry, 2001, Berry and Wirzba, 2002). 13. A good match (consonance) between management question and informative pattern (and thus the science that reveals the pattern) involves common units, common logical typing, and common circumstances (Belgrano and Fowler 2008)—a one-to-one mapping or isomorphism. 14. Some aboriginal societies may be sustainable now. A book similar to this might be written by making comparisons with and among such societies (e.g., see Diamond 2004). To maintain a clear distinction and draw upon a much broader (and hopefully less contentious) field of knowledge, the comparative approach adopted for this book is restricted to interspecific comparisons. The challenges of achieving sustainability will be addressed again in Chapter 6 and in the Epilogue—challenges that may be beyond our capacity to meet. 15. This point will be amplified in later chapters. The basic idea is exemplified by the question: “What is an appropriate body temperature?” The answer is found in observations of body temperature, not a simulation model based on information or observations regarding physiological processes, genetics, environmental temperature, behavior, or chemistry (all related to body temperature) from studies conducted independently of body temperature.

Chapter 2 1. As introduced in Chapter 1, consonance between pattern and management question involves an isomorphism, or one-to-one mapping between the two. When consonant, they each have the same units, have the same logical type, fall in the same category, and involve the same circumstances (context). 2. A blue whale, weighing 136,000 kg, can be assigned the upper end of such a size range (recognizing that a few plants exceed this size). Thus, species in the lower 0.1% of the size range comprise organisms 136 kg or smaller. Based on the distribution of terrestrial mammals from May (1978, 1986), over 99% of terrestrial mammals are less than this size. Since insects (about 2/3 of all species; World Conservation Monitoring Centre 1992)

NOTES

and other invertebrates, as well as most other species (most other vertebrates, microbial species, most plants, etc.), are smaller than 136 kg, 99% is a conservative estimate of the portion of species within the first 0.1% of the size range. 3. Many examples of information regarding observed patterns in body size, with discussion of contributing factors and interpretations are found in the literature; some include consideration of life history characteristics such as generation time (e.g., Aarssen et al. 2006, Anderson 1977, Basset and Kitching 1991, Bonner 1968, Brown 1995, Brown et al. 1993, Damuth 1992, Dial and Marzluff 1988, 1989, Fleming 1973, Gaston and Blackburn 2000, Gaston and Lawton 1988a,b, Hutchinson and MacArthur 1959, Maurer et al. 1992, May 1988, Rosenzweig 1995, Sinclair 1996, Stanley 1973, Van Valen 1973a). 4. Such literature is abundant (e.g., Anderson 1977, Patterson 1984, Pimm 1982, Rosenzweig 1995, Strong et al. 1984, Sugihara et al. 1989, Warburton 1989, Yodzis 1984). 5. We most be mindful of the fact that virtually all materials consumed are recycled within the ecosystem where consumed. This is different from human harvest wherein much is removed from the ecosystem. Also involved here is selectivity—a topic involving a different pattern related to evolutionary impact of consumption, human or nonhuman. 6. Population density is the focus of numerous papers (e.g., Basset and Kitching 1991, Bock 1984a,b, 1987, Bock and Ricklefs 1983, Brown 1995, Collins and Glenn 1990, Damuth 1991, Gaston and Blackburn 2000, Gaston and Lawton 1988a, 1990a,b, Glazier 1986, Gotelli and Simberloff 1987, Hanski 1982, Hengeveld and Haeck 1981, Lawton 1990, Preston 1949, 1962, and the many papers that cite these in extending analysis and observations). 7. Data used to construct this figure are crude population estimates (from Nowak 1991, and Ridgway and Harrison 1981–1999) for species from approximately 20 kg to roughly 140 kg. The numbers are for species of mammals regardless of trophic level (includes both herbivores and carnivores) and habitats (e.g., marine and terrestrial). Midpoints were used where ranges were reported for both population and body size, mean masses were used to account for differences in size between the sexes. See Fowler and Perez (1999) for further detail. 8. White (1978) indicates that, among animals, true asexual reproduction accounts for only about 0.1% of the species. Other references to the preponderance of sexual reproduction are less quantitative (e.g., Mayr 1982, says “But sexual reproduction is by far the most predominant form”) and Eldredge (1985), for example, says: “Indeed, strict asexuality (as opposed to some form of alteration of generations) is truly rare and, apparently,

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always represents a secondary loss of sexuality”. See also Buss (1988) and Maynard Smith (1989) and the review of Blackwelder and Garoian (1986). For very small species, it is possible that asexual species outnumber the sexual. 9. Blackwelder and Garoian (1986) review reproductive modes in respect to definition as well as incidence. 10. The distribution of species over metabolic rates per unit area could be presented similarly, using equations relating body size and metabolic rates but the distribution would be very similar to that of Figure 2.19 or 2.20 (offset by factors relating metabolism and density to body size). 11. A variety of ways to look at a stereogram to see the three dimensional image are presented in Horibuchi and Inoue (1994). 12. This is analogous to the “morphology” of a species as it would be seen in the frequency distributions among the individuals of that species. For example, if the weight, blood pressure, and pulse of a large representative sample of individual humans were plotted as in Figure 2.34, a three dimensional shape would emerge. With increasing weight (and concomitant age), blood pressure would rise and pulse would drop. A similar form would be observed for size, body temperature, and pulse. These relationships would give rise to form with variability about the underlying correlations. Within the scatter of points, as a pattern, the bulk of points would be concentrated among the smaller size ranges owing primarily to the age structure of our population. 13. These four domains are (1) continued work in conventional ecology (ecological mechanics), (2) evolutionary processes of natural selection at the individual level, (3) natural selection at the species level (selective extinction and speciation), and (4) the ways environmental factors are in involved in the first three. Listing such domains is not intended to imply that there are not interactions and synergistic effects/factors involved in any combination or subset. 14. The concept of emergence (Bateson 1979, Clayton 2004, Cowan et al. 1994, Emlen et al. 1998, Jørgensen et al. 1999, Lewin 1992, Morowitz 2002, Nagel 1979, Nielsen 2000, Prigogine 1978, Scheiner et al. 1993) is at the core of the integrative nature of patterns and will be treated repeatedly throughout this book. The infinite of Figure 1.2 is the complexity (or reality of Appendix 1.1) accounted for in its emergent patterns and emergence is one of the processes involved in reality. 15. Consider the example of the rate of predation by one species on another. This seemingly mechanical process is not unrelated to, or not without influence from, evolutionary changes that may lead to greater capture efficiency. The capacity for avoiding predation is also a

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matter of natural selection on the part of the prey species. Both, and the balance between them, influence the rate at which predation is realized and thus the location of a species on the relevant species frequency distribution.

Chapter 3 1. It might be argued that implementation of the U.S. Endangered Species Act, and research on population viability represent a huge effort and thus provide a counter example to this statement. These are largely mitigating efforts dealing with symptoms of deeper problems and do not manage to prevent the anthropogenic causes of reduced populations or endangered species in the spirit of Management Tenet 2 (Chapter 1). 2. These dynamics are parallel to some of the elements of population dynamics (specifically with the death and birth of individual organisms as contributing components). As with populations of individuals, “populations” of species in systems like ecosystems also experience movement (immigration and emigration). 3. Species frequency distributions are emergent from complexity (Fig. 1.4). The process of emergence (itself complex and involving reality: Appendix 1.1) is integrative. The integrative aspect of emergence is analogous to Bayesian statistical integration brought out later in this book and embodied in selective extinction and speciation described in this chapter (see also, Fowler 1999a,b, Fowler et al. 1999). This integrative process is also analogous to the formation of natural patterns as Nash equilibria (Fowler 2008, Nash 1950a,b), as pointed out at several points in this book. 4. At this point it needs to be emphasized that complexity includes hierarchical organization such that diversity among the components of any biological organization (e.g., species within an ecosystem) is only part of the picture. Species comprise individuals. But individuals are composed of organs, which involve cells, consisting of organic compounds, that are made up of elements. The matter of complexity in hierarchical organization goes the other way as well. The biosphere is made up of communities within ecosystems. These scales of structure are accompanied by scales of space and time. 5. Mentioning ecosystems without mention of other groups of species (such as those found in the biosphere) is not meant to exclude such groups—all groups of species evolve through selective extinction and speciation involving their genomes just as all groups of individuals do so through natural selection among individuals and the genes they carry. The effect of extinction on ecosystems depends on the kinds of species that suffer extinction (Hubbell 2001, Petchey et al. 2004, Solan et al. 2004).

6. This simple example is restricted to selective removal (death and extinction) so does not involve birth or speciation that are also part of the process of natural selection as a whole. A similar graph could be constructed wherein only selective speciation resulted in change. In reality, of course, both, in various mixes, are involved. Part of the oversimplification of this example involves the fact that for both processes of reproduction there can be a “backfilling” that will result in less change than depicted (recombination on the part of genetic dynamics among individuals, and possibly diversity dependent cladogenesis among species, for example). The point of the illustration is only to show tendencies that can result from both forms of selection. 7. Or “more making” (Eldredge 1985) through cladogenesis. 8. These exceptions can be either spatial or temporal. That is, some species within a particular set of species may evolve in a direction that is opposite the overall tendency just as some isolated segments within a lineage may show reversal of the overall trend over time. 9. See, for example, Charlesworth et al. (1982), Gould and Eldredge (1977), Maynard Smith, (1983), Slatkin (1983), Stanley (1975a,b, 1979, 1989), and Vrba (1980). 10. Often referred to as a rule, this is simply a statement saying that body size tends to increase more often than decrease over evolutionary time, both within and among lineages. For discussion of this phenomenon see Futuyma (1986a), and Gillman (2007) Newell (1949). 11. For further consideration of directional evolution, especially regarding specialization see Berenbaum (1996), Brasier (1988), Ferry-Graham et al. (2002), Futuyma and Moreno (1988), Gill (1989), Gould (1982a,b), McNamara (1990), Newell (1949), Stanley (1989), Travis and Mueller (1989), Valkenburgh et al. (2004), Vrba (1980), and Williams (1992). 12. Sometimes referred to as “Wright’s rule” (Maynard Smith, 1983) there is ample consideration of what might be called randomness in evolutionary direction as exemplified by Charlesworth et al. (1982), Gould and Eldredge (1977), Maynard Smith, (1983), Slatkin (1983), Stanley (1975a, 1979, 1989), and Vrba (1980). 13. See also the references in endnote 11. 14. As will be apparent in many of the references of this chapter (especially Appendix 3.1), many people have contributed to thinking about the concept of selective extinction and speciation to provide descriptions of its fundamental elements. Listing specific references does injustice to those omitted. However, in the interest of providing a guide to the concept the following references should also be helpful: Bateson (1979), Brandon (1988), Damuth (1985), Eldredge (1985), Fowler and MacMahon

NOTES

(1982), Ghiselin (1969), Jablonski (2007), Maynard Smith (1983), Okasha (2006), Reed (1981), Slatkin (1981), Vrba (1989), and Williams (1992). 15. This simply means that the interaction a species experiences with its environment in regard to selective extinction and speciation will depend not only on the environment but on the characteristics of the species. Property (attribute, characteristic) dependence is being emphasized more in this book than the effects of the environment. The latter would be very important in a book looking at the evolution of ecosystems from the point of view of comparative ecosystem studies that integrate selective extinction and speciation as influenced by environments of different kinds. See the sections later in this chapter dealing with the effects of the environment in selective processes at the species level. 16. This is obvious when we note the similarities of coral reef ecosystems regardless of their location, and similarity of desert ecosystems whereever we find them, compared to the differences between any coral reef and any desert ecosystem. 17. Another element in species-level dynamics, of course, is movement. Species move in and out of spatially defined species assemblages just as individuals move in and out of a population. This is an important element within the factors contributing to the formation of species-level patterns, but not one given its due consideration in this book. It is mentioned in this endnote to ensure that it is included in considering the complexity of factors involved. It is part of the category of ecological dynamics, and can be measured as a species-level attribute (mobility). At larger scales, of course, evolutionary changes that allow expansion or relocation of the geographic ranges of species also occur. 18. See endnote 3, Chapter 1. 19. This process (Fortey 1989, Raup 1984) has been described as “the evolution of one species into another” (Ehrlich and Wilson 1991). Another term “extinction by transformation” is used (but not accepted in principle) by Eldredge (1985). The definition of species one adopts may determine how many species will be dealt with in a species frequency distribution. But this is of little consequence to whether or not the dynamics of selective extinction and speciation apply as described and explored in this and the following chapters (Fowler and MacMahon 1982). 20. Any species-level characteristic can be involved in place of body size as used in the example here. Other characteristics were dealt with in Chapter 2. The dynamics of changing category are treated in the appendices of this chapter insofar as they involve extinction and speciation, but of course, they always include simple

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ecological mechanics as well (e.g., changes in population size or geographic range). 21. Note that the word “similar” is used here because species at two points in a temporal lineage cannot be identical in all their traits but do resemble each other. Part of the difference between pseudo-extinction and terminal extinction is that the latter involves a more discrete end point marked by the death of the last individual, whereas taxonomists will argue over the definition of the point in time at which one species has evolved to become another through anagenesis. 22. The recognition of mass extinctions exemplifies knowledge about variability in extinction rates. Discussion and debate regarding mass extinctions abound in the literature; the topic of variability in extinction is central to many papers (e.g., Elliot 1986, Signor 1990). Recently variation in extinction rates has been associated with climate change (e.g., Benton and Twitchett 2003, Kiehl and Shields 2005, Ward 2007, Wignall 2001). 23. See Hull (1976, 1978, 1980) and others (Eldredge 1985, Salthe 1985, Williams 1992) for consideration of species as individual units (holons, see Wilber 1996) in this regard. 24. References treating the effects of the physical environment in speciation include Cracraft (1985a), Hallam (1989), Knoll (1989), Raup and Boyajian (1988), Signor (1990), and Stanley (1984). 25. The biotic components of extinction are embodied, in part, in the coevolutionary interactions (Futuyma and Slatkin 1983a) among species wherein one species experiences altered selective forces caused by a change in another. Literature on biotic sources of extinction is exemplified by Brooker et al. (2007), Futumya (1989), Hoffman (1989b), Maynard Smith (1976a,b, 1988, 1989), Mayr (1982), Miller (1956), Mitter and Brooks (1983), Rankin and López-Sepulcre (2005), Roughgarden (1983), Signor (1990), Simberloff (1983), Simberloff and Boecklen (1991), Stanley (1989), Stanley et al. (1983), Stenseth (1985, 1986, 1989), Stenseth, and Maynard Smith (1984), Vermeij (1983), Williams (1992), and Wilson (1980). 26. As indicated by Okasha (2006), the concept of “punctuated equilibria” has been a focal point of considerable palaeontological attention (e.g., Eldredge 1985, Eldredge and Gould 1972, Gould 1982a, Gould and Eldredge 1977, Hoffman 1982, 1983, Stanley 1989, Stanley et al. 1983). 27. We must keep in mind the hierarchical nature of this statement. There is variety within each distribution and there is variety among the sets of distributions. Each has its own mean, a mean that varies from set to set to contribute to higher level patterns of variability which may be correlated with environmental factors (e.g., as ecosystem characteristics that have their own patterns over space and time).

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28. Wholes are always more than the sum of their parts— one aspect of emergence. 29. The confined variation within frequency distributions results in patterns that can be characterized as Nash equilibria (Nash 1950a,b, involving both individuals and species), but always one in which all factors are considered as part of the “game” being played in nature (Fig. 1.4). Thus, all ecological mechanics also play their role in producing nature’s Nash equilibria (Fowler 2008). 30. The expense of sex at the individual level has been recognized for decades. The attraction of predation for many animals is only one of the disadvantages of sexual reproduction to individuals. Among other elements of the “widely discussed selective disadvantage of sex” (at the individual level of course, May and Anderson 1983, page 192) are the costs of parental investment in egg materials, dilution of genetic materials genotype mismatch with environment, and high mortality rate. There are advantages, to be sure, but as stated by Williams (1975) “The impossibility of sex being an immediate reproductive adaptation in higher organisms would seem to be as firmly established a conclusion as can be found in current evolutionary thought”. “Ultimately, one would expect sexual reproduction to disappear from any species in which asexuality is an option” (Mooney 1993). In fact, a certain portion of species are known to lose their sexual reproductive mode through evolution over relevant time scales (Stebbins 1960). Sexual selection is recognized as a likely contributor to “evolutionary suicide” (Morrow and Fricke 2004, Morrow and Pitcher 2003). These difficulties give rise to an enigma when it comes to trying to explain the preponderance of sexual reproduction among all species. The dilemma is reflected in much of the volume of literature devoted to sexual reproduction and its evolution (e.g., Emerson 1960, Ghiselin 1974, Lewis 1987, Maynard Smith 1971, 1978a,b, 1988, Michod and Levin 1988, Nicholson 1960, Policansky 1987, 1987, Roe 1983, Schultz 1977, Shapiro 1987, Stebbins 1960, Werren, 1987, Williams 1975, Wilson 1975). It is within these considerations that the advantage at the species level emerges to be seen as a situation that falls into Combination 7 (or possibly, but not probably, 8) of Table 3.1. 31. It should be kept in mind that almost all species have gone extinct and that they possessed characteristics that arose through their evolution, primarily through natural selection among individuals. Today, we are seeing increasing awareness of the details of how this occurs as more attention is being paid in research to the concept of “evolutionary suicide” (see previous endnote). 32. There is confusion in the literature about the distinction between selective extinction and speciation

and group selection but the predominant focus of group selection is that described here (e.g., Arnold and Fristrup, 1982). For the variety of treatments, and consideration of the history of the concept of group selection see Darlington (1971), Eldredge (1985), Futuyma (1986a), Hagen (1992), Rosenzweig (1974), Roughgarden (1983, 1991), Salthe (1985), Travis and Mueller (1989), Van Valen (1971), Williams (1992), and Wilson (1983). 33. A number of references can be consulted regarding this point (e.g., Briggs 1988, Cambell and Clark 1981, Futuyma 1973, Howe 1977, Karr 1982a, Lovejoy et al. 1984, van der Maarel 1975, Martin and Klein 1984, Maynard Smith 1989, Owen-Smith 1988, Pimm 1980, Pimm and Gilpin 1989, Stanley 1984, Wilcox and Murphy 1985). 34. This literature also contains the variety of terms used to describe the dynamic of one or more extinctions resulting from earlier extinctions. They have been called ripple effects, cascading extinction (or cascading effects), cascade of extinctions (Owen-Smith 1988, Schindler 1989, Terborgh and Winter 1980), linked extinction (Gilbert 1980), and secondary extinctions (or secondary effects, Pimm and Gilpin 1989). 35. This is a common theme in palaeontological and ecological work (e.g., see Cracraft 1985a, Eldredge 1991, Hallam 1989, Knoll 1989, Parsons 1991a,b, Signor 1990). 36. Van Valen (1973b) described the process of species evolving in response to each other’s evolution by quoting from L. Carroll’s “Through the Looking Glass” (“Now here, you see, it takes all the running you can do, to keep in the same place.”). Such processes are thus termed the Red Queen model of evolution (Maynard Smith 1988). 37. The development of the study of coevolutionary interactions and their role in speciation is far beyond comprehensive review in this book. The kinds of interactions thought to play roles in speciation as listed here are found in a number of papers (e.g., see Bakker 1983, Barrett 1983, Ehrlich and Mooney 1983, Futuyma 1983, 1989, Futuyma and Slatkin 1983a,b,c, Gilbert 1983, Janzen 1983, Janzen and Martin 1982, Maynard Smith 1976a, 1989, Miller 1956, Mitter and Brooks 1983, Signor 1990, Stenseth 1985, 1986, 1989, Stenseth and Maynard Smith 1984, Van Valen 1973b, Vermeij 1983). 38. This represents a form of ecosystem organization responding to the stressful (including extinction causing) effects of the physical environment (self- organization within context). It is an example of the kinds of patterns expected in complex systems resulting from the effects of stress as identified by Prigogine (Prigogine and Stengers 1984). 39. Grant presents useful references regarding the early history of elements of the idea of selective extinction and speciation. In addition to Darwin, Grant includes Gause

NOTES

(1934), Grant (1963, 1977, 1985), Lewontin (1970), Mayr (1942, 1954), Park (1948), and Wright (1956). 40. Steven Stanley, a principal contributor to selective extinction and speciation as applied to genealogical hierarchies, also presents useful review and reference material regarding the history of the concept. Significant early contributions to development of the concept, according to Stanley (1975a) include Eldredge and Gould (1972), and Wright (1967). Stanley’s book (Stanley, 1979) is a rich contribution as well as source of historical information. 41. Further references regarding the history of selective extinction and speciation are found in the literature relating to various terms used to refer to either selective extinction and speciation in its more restricted applications or for terms used to refer to the various components or applications of the process (see the next section and Appendix 3.6). 42. See the following references and their bibliographic leads regarding examples of, and history regarding selective speciation in, the more general matter of natural selection among species: Arnold and Fristrup (1982), Eldredge (1985), Fowler and MacMahon (1982), Maynard Smith (1983), Salthe (1985), Slatkin (1981), Vrba (1980), Williams (1992). Perhaps the most extensive presentation of this history is found in Okasha (2006). 43. These include (as examples) the work of Eldredge and Cracraft (1980), Gould (1982b, 1985), Stanley (1975b, 1979), Vermeij (1987), Vrba (1983, 1984), Vrba and Eldredge (1984), and Vrba and Gould (1986). See also Appendix 3.1. 44. Links between conventional and evolutionary/ genetic views of living systems were pointed out by Arnold and Fristrup (1982), and more complete treatments are found in Eldredge (1985), and Salthe (1985). We see increasing interest in the interface of ecosystem ecology and selectivity at the species level, exemplified by Henle et al. (2004), Hubbell (2001), Petchey et al. (2004), and Solan et al. (2004). 45. The history of such assumptions has been noted by Tim Smith (1994) who notes that considering primary factors has been assumed adequate since the mid 1950s in fisheries science. This assumption maintains that when we get around to considering the secondary and higher order factors we will be further refining (polishing) a relatively accurate characterization (or measure) of reality rather than changing it dramatically. 46. Application of the paradigm in ecological studies occurs with increasing frequency in the scientific literature. Examples include Brown and Maurer (1987), Cristoffer (1990), Dickerson and Robinson (1986), Gaston (1990), Gaston and Lawton (1988a,b), Glazier (1986, 1987a,b), Hoffman (1984), Holmes and Price (1986), Kitchell (1985),

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Lawton (1989a,b, 1990), Lawton and MacGarvin (1986), and McGowan (1990). 47. Although the point is made, there still remains to be completed any real synthesis of patterns in ecosystem structure and function in relation to abiotic factors. Empirical information to do so is quite limited relative to similar information at the species and individual level of biological organization. 48. This “thought process” involves debate, meetings, panels, experts, opinion, misuse of fragmented information, stakeholder bias, politics, special interests, and law suits that render decision making directly subject to human limits. All are carried out at great cost, not only in financial terms, but, more importantly, in terms of problems that result from the process—the growing number of problems we now see exemplified by global warming, extinction, pollution, habitat destruction, and all of the ramifications of such problems. This “thought process” is to be contrasted to the “seeing” involved in the use of empirically observed patterns. 49. Keep in mind that very few of the examples in Chapter 2 were actually relevant to ecosystems as such (i.e., did not involve the full set of species in any ecosystem). Most were for similar, related or relevant sub-sets of species. All pertained to dimensions of relevance to ecosystems for which measurements can be made (or have been made but that are not yet published as specieslevel patterns). 50. Family units being families such as the Heneman family or the Kiyota family within the human species but applicable to all species.

Chapter 4 1. This involves noting a pattern. Successes found to be common to all levels represent useful information. The comparison is of management applied to individuals, species, guilds, communities, ecosystems, and the biosphere considered as holons (holons defined as units, things, elements, parts, wholes, each with parts that are also holons; Allen and Starr 1982, Burns et al. 1991, Koestler 1967, 1978, Salthe 1985, Wilber 1996) thereby showing commonality in progressing fractally through the various levels of biological organization (a pattern that connects, Bateson 1979). The holons we are dealing with are systems—systems within systems to give reality its systemic quality. 2. The issue of control, and where it resides, is recognized in many publications that treat complex systems, especially living systems. For humans, species-level control (over ourselves) becomes an issue in what ecosystems do and how they respond. Considering the published

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thinking collectively, it is clear that we are operating with an unrealistic impression of how much control we have over our environment (not that we do not have significant or even self-defeating impact, power, and influence). This is especially true in consideration of the concept of “ecosystem management” in that parts very rarely (maybe never) have the control over the whole that the whole has over the part (e.g., Allen and Starr 1982, Bateson 1972, 1979, Brussard 1993, Buss 1988, Darwin 1953, Ehrenfeld 1993, Ehrlich 1980, Fowler and Hobbs 2002, Glendinning 1995, Gnomes and Kanner 1995, Greenway 1995, Holmes 1983, Koestler 1978, Mangel et al. 1996, Mannion 1991, McNeill 1989, 1993, Norton 1987, O’Conner 1995, O’Neill et al. 1986, Ponting 1991, Sagoff 1992, Salthe 1985, Schaef 1992, Stanley 1995, Wilber 1995, 1996). Even as individuals it is clear that we do not have as much control as we might like (e.g., Beattie 1987, Whitfield 1987). 3. A number of works make this point (e.g., Agee and Johnson 1988, Allen and Hoekstra 1992, Bateson 1979, Christensen et al. 1996, van Dobben H. and LoweMcConnell 1975, Mangel et al. 1996, Moote et al. 1994, Munro and Holdgate 1991, Norton 1987, Pimm 1991, Peters 1991, Stanley 1995, Walters 1992; but see Hilborn and Ludwig 1993). Models are useful for understanding, but as indicated by Christiansen et al. (1996) and Lee (1993), models can never include all factors, relationships, and dynamics (Pilkey and Pilkey-Jarvis 2007) and are never the reality that they represent. 4. The impossibility is exemplified by the inability of physicists and chemists to take all of the existing knowledge of hydrogen and oxygen, combine it, and predict the qualities of water: “Water is H2O, hydrogen two parts, oxygen one, but there is also a third thing, that makes it water and nobody knows what that is.” —D. H. Lawrence Sodium and chorine are lethal as elements but essential to our survival in the compound we call salt. Wholes are more than the sum of their parts in ways beyond our comprehension, at least at this stage of human understanding. 5. Consideration often consists of thoughts or opinions as human constructs subject to their bias and inadequacies but without a complete accounting of our limited ability to perceive reality in all of its complexity. Stakeholders are asked to do the considering/accounting (step 4 in the top row of Fig. 1.1). 6. It may be possible to manage all species within an ecosystem by harvesting them based on the concept of MSY, but this would be far from an ecosystem approach to management. Smith (p. 331 in T. Smith 1994) presents

an historical example of decisions (in fisheries research) to focus on those parts of complex systems thought to be most important with the overt assumption that the rest was unimportant enough to offer only minor changes in our perception and management when, and if, the other factors could ever be taken into account. 7. These statements are meant to categorize the general state of “ecosystem management” as currently practiced as compared to other management. They are not meant to ignore or minimize the importance of changes in management advocated and exemplified by Karr (1990, 1992), Rapport et al. (1981) and others (see Christensen et al. 1996, Mangel et al. 1996, Moote et al. 1994). Ideas with merit, however, are not always implemented, and the inadequacies, difficulties and imperfections of many such approaches have to be recognized. 8. The evolution and spread of pest species (e.g., weeds, insects, fungi) is no more than the proverbial tip of the iceberg of ecosystem responses to the dramatic influence of such management (exemplified by agricultural systems). This would include the development of pesticide-resistant forms of such species, to say nothing of human pathologies resulting from the use of pesticides. Other consequences of such practices include habitat destruction and food supplies that contribute to human overpopulation—all, of course, contributing to the extinction crisis. The list goes on in the complexity and interconnectedness of reality. 9. Most work includes humans as a natural (but not necessarily normal) part of ecosystems (e.g., Agee and Johnson 1988, Allen and Hoekstra 1992, Angermeier and Karr 1994, Bratton 1992, Cairns 1991, Christensen et al. 1996, Keiter 1988, Mangel et al. 1996, McNamee 1986, Norton 1987, D. Patten 1991, Pyle 1980, Reed 1989, Regier 1993). Our focus on the human part of nature is behind the anthropocentric aspects of bias in conventional management. 10. This topic has become a subject of a number of papers and books (see endnote 9 of Chapter 1, and Law et al. 1993, Pimentel, Stachow et al. 1992, Sagoff 1992, and Schaeffer et al. 1988). 11. Taking holistic approaches is a commonly mentioned theme or requirement for “ecosystem management” (Belsky 1995, Christensen et al. 1996, Clark et al. 1991, Franklin 1993a,b, Mangel et al. 1996, Moote et al. 1994). 12. Clearly defined management should also identify specific human activities needed to achieve sustainability. It is important to know what humans do in carrying out “ecosystem management”. This aspect of management includes the realms of economics, social sciences, psychology, politics, and religion and not within the scope of this book to deal with in detail (although treated superficially in Chapter 6). This is lamentable in the sense that

NOTES

these are the very things that are key in the implementation of management. It is the exercise of human will as it involves these aspects of our species and its behavior that are fundamental to achieving the objectives clarified in this book. 13. See endnote 3 of this chapter and endnote 7 of Chapter 1. 14. This includes management that deals with the emergent (Morowitz 2002) attributes of ecosystems (e.g., when ecosystem attributes are assessed and found outside the normal ranges of natural variation for ecosystems). Ecosystems contribute to the biotic environment for individuals and species. 15. This may include the value of sound health independent of other goods, services, or performance, especially from the perspective of human individuals. From an evolutionary point of view this amounts to maximizing fitness. 16. This includes protecting individuals from radiation or chemicals that produce genetic mutations as well as changes in genetic engineering to produce desirable qualities (e.g., bacteria that make insulin). For species it includes protection from altered gene frequencies and for ecosystems it includes avoiding excessive extinctions, especially selective (e.g., according to trophic level, body size, etc.). 17. Link et al. (2002), Odum (1985), Rapport et al. (1985), and others list ecosystem attributes. Others are represented by species frequency distributions (especially their means) as those shown in Chapter 2. It is unclear when there are indications of problems or which observed changes are most problematic (e.g., when the mean of a species frequency distribution is outside the normal range of natural variation for such means) or should be given priority in monitoring. Ecosystem characteristics or attributes that have been identified include mean trophic level, mean body size, incidence of symbiotic dependence, mean level of mobility, incidence of specialization, proportion of sexually reproducing species, mean population variability, mean maximum potential rate of increase per unit time, mean number of consuming species per resource species, mean number of resource species per consuming species, mean level of resource consumption, mean geographic range size, mean population density, mean extent of Allee effects, total metabolic rate, mean turnover of energy or materials, mean level of resource suppression (keystone roles), and total number of species. In view of the multidimensional combinations (Table 2.1), there will be more ecosystem-level attributes than conceivably possible to measure, independent of the logistic difficulties involved.

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Some ecosystem attributes are demonstrated in three dimensional graphs such as Figure 2.34. Within the boundaries or surfaces of such representations (as ill defined or fuzzy as they are) there are shapes that are characteristic of the sets of species that contribute to the makeup of ecosystems. Within such graphic representations, there may be texture, striations, or lumps that are also characteristic of certain sets of species (e.g., see Holling 1992). 18. When the information comes from a history of the individual it takes into account the nature of the individual, recognizing that it varies from other individuals genotypically (as well as phenotypically, of course) and the norm is specific to that individual. When information comes from comparison to other individuals, the comparison involves information about how the individual fits within phenotypic variation (with genotypic components) for other individuals of the same kind. Both provide normative information, one more specific than the other. Individuals can also be compared among species to see normative patterns as they relate to a variety of correlative factors both intrinsic and extrinsic. 19. As with individuals, when the information comes from a history of a particular species, it takes into account the nature of the species, recognizing that it varies from other species genetically (as well as phenotypically) and resulting norms are specific to that species. When information comes from comparison to other species, the comparison involves information about how the species fits within phenotypic variation (with genotypic components) among species as units at a particular level of biological organization. Again, both provide normative information, one more specific than the other. Interspecies comparisons are part of what emerges in using macroecological patterns. 20. See Angermeier and Karr (1994), Christensen et al. (1996), Ehrlich (1980), Foster (1980), Gilbert (1983), Janzen (1983), Meyer and Turner (1994), Mlot (1990), Noss (1990), Pagel et al. (1991a), Simpson and Beck (1965), Spellerberg (1991), Terborgh and Van Schaik (1987), and Vitousek (1994). 21. Thus, a history of measures of the sets of species in individual ecosystems takes into account the nature of the specific ecosystem, recognizing that it varies from the sets of species in other ecosystems genotypically (as well as phenotypically) and norms are specific to that ecosystem. When information comes from comparison to other ecosystems the comparison involves information about how the ecosystem fits within phenotypic variation (with genotypic components) among other ecosystems. As with species and individuals, both provide normative information, one more specific than the other.

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22. We would expect a bit of consistency among the various levels partly because they are genetically based (all have components with heritable traits) as a form of common ground. Also, all are holons (see endnote 1) and the common nature of holons dictates common themes here. In spite of the fractal qualities of nature, conventional management does not find the desired consistency. 23. Continuing to do things that increase our risk of extinction would be an example of Combinations 5 and 6 (Table 3.1) wherein what we do, to the extent that the things we do are natural results of our own evolutionary development, leads us into greater risk of extinction. 24. This includes the strong possibility that smallpox was a species serving as part (other parts include food limitation and predation) of the ecosystem-level dynamic keeping humans from exerting abnormal destructive influence by placing limits on the human population. 25. See Chapter 3 (specifically endnote 34). 26. The action in this example would be a group action and the contrived nature of the example serves to make the point that components very rarely get away with significant impacts on aggregate hierarchical levels. In contrast, aggregate levels are ordinarily much more effective in influencing their components (Bateson 1979, Salthe 1985, Wilber 1996). 27. In other words, comparisons are made across different “logical types” in Bateson’s (1979) terminology. Note that the risks at other levels must be considered, but a priori weighting of risks at the individual level in conventional management inhibits consideration of ecosystem level risks and risks important to humans on longer time scales. 28. Thus, fish hatcheries are built as partial compensation for the effects of dams and irrigation on rivers rather than reducing human need for dams and irrigation. Pathogens are eradicated rather than precluding human settlement in areas occupied by such species. 29. This example demonstrates symptomatic relief as a matter of seeking solutions to a problem through action taken at other than the source of the problem. The magnitude of technological processes and the excessive human populations it supports are not seen as the problem and these problems are not solved. Instead, the situation created by these causes is dealt with at a different location within the system and is thus symptomatic. These approaches are often taken by looking to different levels of biological organization than involved in the source of the problem. Here, industry and the individuals involved suffer from a species-level problem. This is also a classic case of density dependence in the sense that individuals are asked to sacrifice to control effects by the group. In Ehrlich and Holdren’s (1974,

see also Ehrlich and Ehrlich 1990) equation, I = PAT (I = impact on the environment, P = population level, A = affluence, T = technology), if AT is taken as a standard of living, to control I, as population increases, AT has to drop. The per capita freedoms are limited. 30. These are ecosystem states that are within the normal range of natural variation for existing abiotic environmental circumstances and that can both support humans at sustainable population levels and, at the same time, pose normal risks of extinction to humans as well as other species. 31. A series of workshops were held to address natural resource conservation at Airlie House, Virginia in 1974 and 1975. This resulted in publication of a report (Holt and Talbot 1978) that lists five national and international organizations that sponsored the meetings. This exercise was repeated March 6–9, 1994 in a workshop organized and sponsored by the U.S. Marine Mammal Commission that resulted in another report on the same topic, taking into account progress in the previous two decades (Mangel et al. 1996). 32. The genetic nature of our being contributes to our values, decisions, and actions. These are closely tied to our individual fitness and not to inter-species altruism, or even strong intra-species temporal altruism (i.e., thinking of the good of individual humans hundreds or thousands of years from now). These forces oppose attempts to control ourselves as individuals to act in the best interest of future generations, other species, and ecosystems. They are involved in the conflict we experience. Importantly, they can contribute to risks of extinction (Table 3.1). 33. It is worth noting that this (control) as well as the other principles we are dealing with is not restricted to issues regarding our use of natural resources. The instructive patterns that emerge in the considerations reviewed above are parallel to management within other realms (Fowler 2003). These include the principles, tenets, and rules for management applicable in the fields of psychology, religion, and business. 34. A possible reaction to this claim is to call for proof that there are genetic responses. For management, the burden of proof that genetic responses do not occur lays on the shoulders of those who would take action assuming it does not (Mangel et al. 1996). Although Type I errors (concluding something is true when it is not) are part of what scientists try to avoid, Type II errors (concluding something is false when it is true) are to be avoided in management (Peterman 1990). 35. None can be forgotten. We should, however, keep in mind here the potential need for placing some extra emphasis on ecosystems or the biosphere as the aggregate

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level in biological hierarchies. This is because the strength of effects of more inclusive systems is greater on species and individuals than the effects of individuals on species, ecosystems, or the biosphere (Allen and Starr 1982, Campbell 1974, O’Neill et al. 1986, Salthe 1985, Wilber 1996). The effects of many species on one are greater than those of one on many; one holon has less influence on its many counterparts than vise versa. 36. Recall that six of the eight combinations of forces from Table 3.1 involved opposing dynamic forces depicted graphically and quantitatively in Appendix 3.2. The implication here is that opposing selective forces contribute to the origin of conflict. It is natural. It occurs in the dynamics of ecological mechanics as well. The dynamic balance of carrying capacity, for example is a balance of a complex of opposing forces (e.g., species that supply food, pollinating services, dispersal etc. on the supportive side, and limits to food supply, pathogens, parasites, and predators on the negative side). And, it occurs between ecological mechanics and selective forces. 37. Many definitions of management are restricted to the control of human behavior since it is really the only aspect of management wherein control is real. The implementation of this aspect of management is beyond the scope of this book (but will be treated briefly in Chapter 6). It cannot be left to sociologists, economists, ethicists, legal experts, behaviorists, or any other science or scientist alone. It involves society, the people within it, and the changes they are willing to make as individuals in implementing species-level change. The changes individuals make, how they make them, why they make them, and when they make them are the subjects of study of fields such as those listed. But these fields of study are less the source of change than they are means of understanding it, documenting it, or characterizing it. Change of the type necessary to make a difference will depend on (but not be limited to) realized change within the individual human. 38. It assumes that, in nature, the inherent value of individuals, species, or ecosystems may easily be different from what we humans might decide. This means that one, more than another, may be more significant in ecosystem dynamics, for example. Extinction guarantees the loss of individual organisms and, vise versa, the death of all individuals guarantees the extinction of a species. The relative importance of each process, factor, and relationship is accounted for automatically, as is that of each component part of every system. It avoids human values that lead to problems because of inadequate consideration of complexity. It incorporates any values involved in sustainability. 39. In statistical work, Monte Carlo and Bayesian approaches to parameter estimation are similar to what

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happens in selective extinction and speciation. The abstract models of these analytical techniques cannot account for the total complexity of factors but the resulting natural patterns account for everything to which they are exposed—their full history and explanation. Species frequency distributions along any of their dimensions (attributes) are observations relevant to probability distributions. The most risk-averse traits are interpreted to be represented by the most abundant kinds of species. Cumulative risks contribute to preventing the accumulation of species in the tails of species frequency distributions. They are the results of nature’s Monte Carlo experiments (part of which are the diffusion processes of the models of Chapter 3 that rely on elements of complexity as they are involved at all levels). 40. Here the probability of the data (one of the factors used in equations to derive Bayesian statistics) is not dependent on human sampling error. The trial-and-error processes are exposed to samples of actual reality (we assume that reality has a probability of 1.0) and not measures of it. 41. Care is necessary to be sure that fair comparisons are made. In this case, for example, we would want to avoid using sets of species that consume only body parts (e.g., suck blood, eat fins, or consume leaves). If our management question involves mortality of individuals in harvesting, the species set for comparison must also kill individuals from the resource species. The selective forces must be made comparable by measures of the same dynamics. We would want to make comparisons based on species of our body size just as we would want to compare pulse for individuals of the same body size. 42. One of the principles of desirable management often identified (see Appendix 4.3) is adaptability. In the face of uncertainty and ignorance, a trial-and-error approach helps learn what works and how systems respond to various influences. Adaptive management (Holling 1978, Walters 1986, 1992) is given prime importance as a way of dealing with many of the challenges faced in resource management (Christensen et al. 1996, Hilborn et al. 1995, Mangel et al. 1996). Although humans will have to be adaptive to take on this form of management, the point here is that other species adapt through their evolution. The adaptive aspect of selective extinction and speciation results in the persistence of species that not only solve problems faced in natural selection at the individual level, but also have properties that are adaptive at the species level (a solution to the games found in Nash equilibria, Nash 1950a,b). Existing species are the surviving examples of adaptive management already conducted in nature with results and information awaiting our use (Fowler 2008).

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43. The information content of species frequency distributions is a bit like having information (Fowler 2008) about the hands held by other players in card games; we then play where the objective is to continue playing (Meeker 1997). The kinds of hands that consistently win form a pattern. By recognizing the pattern we can “stack the deck” in our favor. Being a stochastic process, however, we can never allow ourselves to believe that we have control. The game being played is a form of “infinite” game (Carse 1986) compared to the finite games that are exemplified more by the transitive approach. (See also Nash, 1950a,b, regarding the game theoretical aspect of nature’s solutions to complex games as Nash equilibria wherein things that work do so because they work at all levels of biological complexity.) 44. The element of control again emerges because it is important to recognize the limitation of laws, regulations, and mandates established by governments as means of achieving control (especially over change). Laws and regulations are a form of control that is transitive if generated by government independent of public support, and more intransitive if it emerges through popular support. This emphasizes the necessity of including individual humans as critical elements in change because much of the hope that exists rests on the capacity for individual change. 45. In Chapter 5 (see Appendix 5.2 for the process of breaking down management questions into their components as well as the process for expanding questions), the protocol for management of the kind being defined here is presented.

Chapter 5 1. Here, “species-level” is used to refer to the most direct application of systemic management emphasized in this book—dealing with the human species. This is not meant to detract from the fact that it includes dealing with our species’ interactions with other species, groups of species, ecosystems, and the biosphere. Nor is it meant to detract from the fact that systemic management also applies to individuals and their interactions with other individuals, species, and ecosystems. Current medical applications involve the importance of normal blood pressure, body temperature, and nutrient intake. Systemic management includes, however, personal matters as emphasized by the importance of control issues in the psychological realm (e.g., Beattie 1987, Whitfield 1987) as a matter of implementation of Management Tenet 2. 2. As infants we begin the process of self-definition, differentiation, and integration as we become aware of others of like kind (e.g., Wilber 1996). In the species-level

analogy, self-awareness is clearly a growing part of what our species is experiencing (e.g., Darwin 1953, Tiger and Fox 1989). Comparisons of humans with other species (Fowler and Hobbs 2002, 2003, Chapter 6) show humans to be “out of bounds”. These are simply measures and information of use in decision-making regarding our sustainability. Systemic management includes the objective of species-level applications parallel to individual-level applications, including cases where individuals within our species must change to achieve consistency. 3. This would be the combination ultimately achieved in extending “whole systems thinking” to fully include both the context of systems and the content of systems and their interactions. All extrinsic, and all intrinsic, factors, elements, processes, dynamics, interactions, and relationships would be represented as the factors that contribute to the formation of species-level patterns (Fig. 1.4). They represent both nature’s Bayesian integration of complexity and nature’s Nash equilibria (where things must simultaneously be for the good of both wholes and parts). They are the results of nature’s adaptive management. 4. Adopting systemic management represents a clear replacement of the transitive aspects of current approaches. This is not the only major change, however, and probably will not be seen as the most significant of changes involved. As depicted in Figure 1.1, one current role of stakeholders is replaced by observed patterns. 5. There are no limits to full scale holism (systems are always within other systems) in seeing species-level patterns as emergent natural phenomena. The categories of ecological mechanics, and the evolutionary processes of natural selection at the levels of both individuals and species were listed in earlier chapters. If there are other factors, categories of processes, scales of time, space or organization, or other aspects of complexity to be brought to bear, they are not ignored. They are not ignored in understanding species frequency distributions as natural phenomena emerging from all such factors (Fig. 1.4). 6. See endnote 1, Chapter 4, regarding the fractal nature of this pattern from one level to the next—a pattern that connects (Bateson 1979). Consistency involves simultaneous resolution for the conflicts identified in Chapter 4 (i.e., nature’s Nash equilibria are successes observed to account for all levels of biological organization and the associated risks, interconnections, and complexity). 7. Ripple effects (including all things variously referred to as unintended consequences, side effects, domino effects, or downstream effects) involve all relationships and interactions. For evolutionary interactions (including coevolutionary variation) ripple effects include change set in motion, that results in evolutionary change by

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other species that itself stimulates evolutionary change in still other species throughout any biotic system to include reciprocity so that there are consequences (feedback) to the species initiating the influence. 8. Our influence on ecosystems over time has proceeded from that of being largely mechanical, to include larger selective forces in coevolutionary dynamics, to now include the evolution of species sets through the extinction of species within them (e.g., those represented in ecosystems). Thus our influence has expanded, especially in its magnitude, in the various scales of time, space, and level of biological organization. Virtually all of the evolutionary change we are responsible for is irreversible. 9. Further precaution may be advisable in the form of overcompensation at first. For example, initial reductions in our population that are more than indicated by existing consonant species-level patterns (Fowler 2005, 2008) might have advantages over smaller reductions. This would better insure the recovery of ecosystems and their recovery might occur more rapidly so that any abnormal risks to future generations of humans (and ecosystems) are minimized earlier. 10. Here we are thinking of individuals as would be involved in the management embodied in medicine, participation in social systems, and family dynamics. Fully systemic management also addresses the matter of resources used to maintain human health, the advisability of purposeful extinction of other species to control disease, and the genetic impact on our species of saving human lives. The list of expanded questions is virtually boundless. 11. This is in parallel with management at the individual level wherein it is clear that the first order of business in repairing dysfunctional systems is mostly a matter of changing yourself rather than trying to change others— an alternative emphasized in most therapeutic work. 12. Things are interconnected, or at least much more interconnected than can ever be represented in any artificial model. This is a principle underlying all realms of management and is embodied in many of the sciences. It is a basic principle in ecology and is recognized in the field of physics (or science in general) in Bell’s Theorem (Bell 1964). Species in ecosystems are interconnected in webs of coevolutionary interactions (Lederberg 1993, Thompson 2005). 13. Complexity that is intrinsic involves the “interiority” (Wilber 1996) of species and individuals. We are made up of organs, cells, and molecules (and all of the things that make up organs, cells, and molecules, including all interactions and processes). Species are made up of individuals and their components. These are taken into account as part of the ecological mechanics of processes

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and interactions among units (holons) of such systems insofar as they contribute to the formation of species frequency distributions. 14. Recall the model for ex of the last chapter. The complexity of reality (compared to a single number) involves all constants, variables, dimensions, relationships, hierarchies, scales, factors, and elements that cannot be represented in a model but which make up reality. Reality involves dynamics not captured in the static nature of the value of ex, to increase the scope of the infinite in complexity. Reality includes the context within in which ex occurs. Reality is the right hand side of the “equation” in Figure 1.4—the infinite of the universe of factors involved. 15. Thus, the reductionistic guiding information is not reductionistic in what is taken into account and is free of the human elements of decision-making (bias, politics, emotions, limitations). The management question addressed and pattern used to answer it are reductionistic—thus meaning that there is an infinite number of questions to be addressed in fully accounting for complexity. There is no end to the research that is needed. 16. At the individual level, the analogy is that of maintaining normal body temperature, and blood pressure, and body weight, and pulse, and blood sugar levels, and respiration rates, and variation in them—not just one or a few of them and not at fixed levels. Normal variation is also important. Furthermore, the addressing of such questions has to be expanded to ask whether management actions we take to maintain our health are sustainable—whether such measures (e.g., devoting resources, and the creation of waste, plus the long-term effects of medical measures) should be taken at all. Management at the species level must include CO2 production, energy consumption, water consumption, range size, population density—the list goes on. 17. This “estimate” is based on an extrapolation of the relationship between population density and body size found in Peters (1983; his Fig. 10.3, page 169, where D = 30W –0.98, D is measured in nos. km–2 and body mass is measured in kg). See also Appendix Figure 2.1.22 regarding total population size in relationship to generation time and, therefore, body size. 18. Note that we may see clear evidence of current problems in regard to factors such as total population biomass, biomass consumption, or energy consumption (as will be treated in Chapter 6) without relying on information regarding body size (e.g., Damuth 1987, Schmid et al. 2001). Precision regarding the extent of such problems will likely require information regarding any relationships between these species-level measures and body size.

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19. The advice we would get in addressing the predation question this way would therefore be consistent with the guidance we would get when addressing another management question directly. The management question would be “How much should we reduce our harvest of [species x] if its population declines by [y] percent?” Direct measures of the reductions in predation rates observed in nature would provide the guiding information. Such reductions would have averages and variation, and would show empirically observable limits to such variation. 20. This is parallel to accepting as natural the increased respiration rates associated with physical exercise for individual humans. But, it opens the matter of management to other questions, in this case: “What portion of each day is optimally devoted to exercise with a respiration rate that is 20% higher than average? What is a sustainable total daily respiration rate? How many heart beats per day is most sustainable? In ecosystems, what is the most sustainable harvest during el Niño years? Is the advised species composition of catches different in el Niño years compared to other years?” 21. An important distinction here is that each application is a focused application while the guiding information holistically accounts for complexity with each of the infinite individual factors involved in an objective way. 22. Possible, here, is used in the sense that systemic management does not mean that we accept individuals with abnormal qualities any more than we accept abnormal qualities for our species. Thus, the transitive action of imposed starvation to solve the problem of overconsumption by our species is not an option while accepting starvation as a consequence of overpopulation is. Systemic forces will result in the reduction of the human population in what we will evaluate as abnormal events if we find no way to reduce it. Such a solution to the problem of overpopulation may be the only realistic option—it is what works for other species. 23. We can return here to the equation for ex and point out that the contribution of each element of complexity in the model is added in proportion to its importance. Thus, in reality, the alignment of the planets is considered in proportion to the strength of its actual influence in determining the position and variation of species within species frequency distributions. The same holds for other elements of reality, exemplified by sun spots, the energy from the sun, tidal cycles, the strength of the carbon/ oxygen bond in chemical compounds, pheromones, gravity, behavior, social facilitation, evolution, DNA, body size, strange attractors—again, the list goes on. 24. In other words, individual humans will have to contribute by falling in different regions of the spectra of

natural variation for individuals until the needed change is accomplished. Time is a clearly important factor here as hurried change can place individuals completely outside such distributions to be abnormal—a situation to be avoided in systemic management (but accepted if imposed systemically). 25. What is being conveyed here is that, if the alignment of the planets has a relatively small influence compared to that of the population size of the predator in determining a predation rate observed in Figure 4.1, that difference is taken into account. If the alignment of the planets has a butterfly effect (Gleick 1987), that is taken into account in relation to its appropriate scale of time. Also taken into account are those cases wherein the collective influence of all butterfly effects are larger than any one obviously important factor (the latter being a case in which a factor is determined to be important by science, a process often restricted to showing [proving] effects are significant at the 0.95 confidence level, before they are accepted as real). 26. Systemic management is a matter of mimicry wherein the concept of biomimicry (e.g., Benyus 2002) is both refined and expanded to address questions of limits (Fowler 2003). Thus we can approach the issue of feeding humans and the issues of how many to feed, how much food should be extracted from ecosystems, and how much should be extracted from the biosphere. It therefore becomes self-limiting so that any one form of biomimicry does not cause problems at other levels of biological organization. There is consistency. 27. That is, we cannot know all of the detail involved in the complexity of such systems, and any advice generated without consonant information produced by a particular field of science, or set of such fields of sciences, represents a reductionistic error in ignoring what would be considered by all forms of the sciences that would be focused on what we do not know now, if such fields of sciences existed. Advice stemming from what the various fields of sciences can measure as consonant information is different in that the measurements are of things that themselves reflect complexity, account for complexity, and result from complexity. This same complexity is reflected in nonconsonant information, but has to be converted—an alchemical process. 28. This limitation of science has been referred to as the Humpty Dumpty phenomenon (Fowler 2003, Fowler and Hobbs 2002, Nixon and Kremer 1977); scientists cannot combine their concepts and information to fully replicate reality (Bateson 1979). Each represents the study of parts of reality and each is finite as is true of any human construct (e.g., mathematics is also finite in this regard: Gödel’s Theorem, Gödel 1931, Makous 2000).

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29. Whole systems thinking (e.g., Wilber 1996) goes beyond the approaches of Brown (1995) and Rosenzweig (1995). These latter examples serve as notable steps toward “macroecology” as a synthesis of biological thought. But whole systems thinking is not restricted to biology, even if much of complex systems science can be traced to roots in biological (especially ecosystem-level) thought. The complex systems we often think of are themselves within larger contexts, larger systems. Inclusiveness is elusive; temporal scales are not to be ignored. Whole systems thinking is not particularly familiar to science as practiced in the last century or so. It is not so foreign, however, to religions and philosophies that grapple with “the Great One (or Oneness)”, the “unspeakable infinite” (note the infinite of Fig. 1.4), Supreme Being, etc. It is not clear how holistic it is possible, or necessary, to think. To the degree that there is (or we can find) a common denominator in wholeness or synthesis (infinite) among the sciences, religion, and philosophy, there is hope of understanding empirical patterns as emergent sources of guidance (and maybe common ground between science and religion). 30. See Chapter 3, endnote 2 of Chapter 1, and Appendix 4.4 (also involving Nash equilibria, Nash 1950a,b; and adaptive management, Holling 1978, Walters 1986, 1992). 31. We are reminded of experiences exemplified by proving that smoking tobacco and using DDT are harmful after a history of resisting the possibility. The law of unintended consequences dictates that we accept that the reality of effects of things that we do that have not yet been proven scientifically to be real. This is integral to accepting the combination of complexity and interconnectedness. 32. This emphasizes the importance of the combination in what is studied by science (reality), rather than the combination of the products of science, when it comes to management. 33. A variety of authors have considered the matter of emergence, its relevance to various levels of biological organization, and its role in natural selection—also at various levels. Distinctions are sometimes drawn between aggregate and emergent properties. In the sense of Figure 1.4, this book remains open to the option that everything is emergent and that all components of complexity can be involved (Morowitz 2002). For various views and considerations consult Allen and Starr (1982), Bateson (1972, 1979), Brown (1995), El-Hani and Emmeche (2000), Eldredge (1985, 1989), Emlen et al. (1998), Fralish et al. (1993), Jørgensen et al. (1999), Kauffman (1993), Levinton (1988), Lewin (1992), Mayr (1982), Morse (1993), Nielsen and Müller (2000), Nixon and Kremer (1977), Pagel et al. (1991a,b), Salt (1979), Salthe (1985), Scheiner et al. (1993),

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Smith (1977), Stanley (1989), Vrba (1984), Whittaker (1975), Wilber (1996) and Williams (1992). 34. This information can apply at the various levels of biological organization. We already know about such work in medicine through research on individuals. But, information is also embodied in measures of species in species-level patterns to include an integration of the genetic information emergent from the Bayesian form of trial-and-error processes of selective extinction and speciation (as described in Chapter 3, Appendix 4.4). Or it can be integrated information that emerges as ecosystem-level descriptions. This is information about ecosystems. Measures of ecosystems are the result, and the dynamics of ecosystems as whole units (i.e., changes in the measures of central tendency for species-level patterns over time) may be monitored and described so that we can determine the normal range of natural variation for such systems. 35. The unknowable of the future is included only insofar as the past can be used to predict the future—as exemplified by seasonal cycles in both abiotic and biotic systems. This emphasizes the need to use correlative information regarding such things as weather to characterize frequency distributions under various conditions of the physical environment. 36. These include the work showing relationships between body size and other traits such as age at first reproduction, rates of increase, generation time, density, population variation, and geographic range (Chapter 2, Peters 1983, Schmidt-Nielsen 1984, Sinclair 1996). Food web analysis has resulted in demonstrating other patterns regarding trophic level, species numbers, connectivity. Continued ecosystem ecology can be expected to see more relationships associated with abiotic factors in geographic heterogeneity (e.g., latitudinal gradients in species diversity, Rosenzweig 1995). 37. It also, of course, involves cellular biology, medicine, physiology, chemistry, physics, molecular biology, atomic physics, nuclear physics, and all of the other sciences that deal with the facts regarding what we are made of, our origins, and nature. 38. Clear indications of the direction we need to take are presented in this book. Included are initial indications of the magnitude of needed change. However, specific goals or exact/fixed endpoints are not presented. It is clear that reducing CO2 production, energy/resource consumption, and population size/density are among the elements of primary importance in starting. The immensity of needed change in each case is approximated. Where we want to end up more exactly should be (and can be no other than) a decision based on further consideration of complexity through both refinement and expansion of

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various related management questions. Also included, would be factors such as the standard of living wanted by society (our species) as brought to the asking of management questions—but only as a factor to consider as a matter of fine-tuning where we might fall within the normal ranges of natural variation (e.g., having even a smaller population than might otherwise be optimal in order to keep energy consumption, CO2 production, biomass harvesting, and other related factors within their normal ranges of natural variation). 39. In addition to these factors, is the observation that the maximization of probabilities within a probability distribution or maximization of biodiversity within a biodiversity curve (e.g., Fig. 5.3, Fowler 2008) does not involve a sharp peak. We may be lucky enough to have a great deal of relevant data for a highly refined management question. However, the peak of a probability distribution or biodiversity curve is a single number surrounded by a range of other numbers with nearly equivalent probabilities or biodiversities. Measured in log scales, there is a great deal of latitude for options (although quite small in comparison to the extent of human abnormality). This is exactly as it is for body size, blood pressure, and body temperature in regard to individuals. We would be hard pressed to maintain our body temperature at exactly one value for what we consider equivalent circumstances. 40. We are reminded here that another contribution will be made in the changes individuals can make. In North America our per capita consumption of energy is about 93 times larger than that of other comparable species (18-fold, worldwide; Chapter 6). However, as will be made more clear in Chapter 6, reducing our per capita consumption to 1% or 2% of current levels is only a tiny fraction of the change required for the human species to consume energy at a normal rate. In spite of what appears as a major per capita component, however, our per capita consumption accounts for very little of the discrepancy between the total consumption by humans and the mean of that by other species (Fowler 2008; some of which is attributable to their underpopulated condition owing to abnormal human influence). This emphasizes the importance of the population factor (over 99% of the difference between humans and other species). The per capita consumption would need to be cut in half 4.16 times while the total would need to be cut in half 10.9 times, or 6.73 halvings through population reduction to match the mean among other species. 41. At the species level, we might choose to act on population size, initially, with more emphasis than on geographic range. But such guesswork is dangerous because the numbers of diseases we are exposed to in our broad geographic distribution, the degree to which we transport

species (increasing their mobility), the numbers of ecosystems we connect via our broad distribution, and other negative effects could very well be more important than taken into account by such a decision. Precaution dictates that we do all we can to achieve a normal position within species-level patterns for every way we find ourselves to be abnormal in all the ways we can find to measure ourselves as a species. For individuals some characteristics, such as hair length, may not be as important as others such as body temperature. This has to be the case for species as well. Until we can sort out which species-level characteristics are of lesser consequence it is precautionary to adhere to as many as possible. 42. Benchmarking is a process used in business management wherein other businesses are mimicked where the other businesses are interpreted to be successes (Bogan and English 1994, Boxwell 1994, Camp 1995, Spendolini 1992). 43. Part of the precautionary measures necessary here involves being open to the possibility that other predators on walleye pollock will increase in abundance to cause walleye pollock to decline in abundance, resulting in a decline in predation by other predators. Under such circumstances, the total take of walleye pollock based on the species frequency distribution we have today might be an overestimate of sustainable harvest levels. 44. The mean consumption rates from walleye pollock, herring, hake, and mackerel by marine mammals (see Fig. 2.6 and Appendix Fig. 2.1.5) are 0.45%, 0.18%, 2.5%, and 0.18%, respectively. With enough comparable sets of data, any patterns in relation to life history strategy, body size, and environmental factors would be very helpful in guiding management. Although we are dealing with only a tiny sample in this case, it is of note that the harvest rate of 13% for walleye pollock in recent years is well beyond the range of variation for the mean consumption rates taken from these species by marine mammals. 45. Thus, selective extinction will often act to remove species with characteristics emergent at other levels. Extinction risks would be higher among species with chaotic population dynamics than species with less variable populations, for example. Those that go extinct would do so even though such dynamics may be the result of circumstances involving ecological mechanics and evolutionary processes through natural selection at levels hierarchically below the species. Extinction places constraint on the potentials that are exploited (explored) in the diffusion-like processes of natural selection at other levels. In this sense, selective extinction and speciation supersedes the mechanical and lower level evolutionary processes, even depending on them for a supply of

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species over which to exert constraint. Evolutionary (e.g., through natural selection among individuals) processes that produce extinction-prone species cannot overpower the risk. Ecological mechanics that result in extinction cannot override extinction. On the other hand, extinction can weed out such species so that the species left behind may be less prone to such risk, thus molding the set of species in a way that is more powerful than the other processes when they oppose each other. In other words, tendencies toward producing extinction proneness (Categories 5 and 6 in Table 3.1) may be reduced, but more through the extinction process than through processes at lower levels. 46. There must be a pair-wise correspondence between management question and the matching research question. There must be a similar correspondence between pattern revealed through research and the objective revealed. This correspondence is consonance in which there is an isomorphism that precludes stakeholder translation conversion typical of conventional management (top row, Fig. 1.1). Consonance involves identical units, the same logical types (Bateson 1972), and the same circumstances. There must be a one-to one mapping between both questions, and both the pattern and management objective. The refinement of management questions must be reflected in parallel refinement of the science question. Refinement makes the question(s) open to correlative information—uses correlative information when it is known to occur. 47. The issue here is parallel to the matter of not asking scientists (or medical doctors) to use information about metabolic chemistry, cellular structure, and anatomy to decide what we should use as the proper body temperature for humans. Instead we ask them to collect and interpret data dealing directly with the mean, variance, and normal limits to natural variation in body temperature for humans. The general question might be refined in parallel to that for the harvest of walleye pollock by asking “What is the appropriate body temperature for a 5 year-old boy, playing basketball, at an ambient temperature of 33°C?” Gender, age, weather, and activity now come into play, but in all cases where we find a body temperature of 42°C, action is taken to solve what is recognized as a problem. 48. Individuals practice systemic management in learning from other individuals, groups learn from other groups (e.g., married couples learn from other married couples, teacher/class combinations learn from other teacher/class combinations, communities learn from other communities, cultures learn from other cultures, nations learn from other nations), businesses from other businesses (in the benchmarking process), and our

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species is in the position of learning from other species. This is all a matter of seeing the collaborative learning process (Goldberger et al. 1996) as one that crosses hierarchical/holarchical boundaries to apply at all levels of organization.

Chapter 6 1. The word “failure” is used advisedly. It is important to distinguish between 1) the value of models as tools in research and 2) what we now see as the erroneous belief (what might be called fatally erroneous belief) that it is safe to use models with output that is not consonant with the management questions. The empirical failure of such approaches is becoming widely documented and recognized (Frank and Leggett 1994, Larkin 1977, Ludwig et al. 1993, Magnuson 1986, Malone 1995, McIntosh 1985, Moss 1989, Nelson and Soulé 1986, Peters 1991, Pilkey and Pilkey-Jarvis 2007, Rosenberg et al. 1993, T. Smith 1994, Walters 1986). What look like successes may simply be cases wherein problems have yet to be manifested or discovered. Type II errors in detecting problems (Mangel et al. 1996, Peterman 1990, Peterman and M’Gonigle 1992, Peters 1991) prevent a full appreciation of their prevalence. This emphasizes the importance of precautionary measures in practical application of limited knowledge (Holt and Talbot 1978, Mangel et al. 1996). A major point of systemic management is to use empirical information, rather than simulation models, to be as precautionary as possible. As will be developed in later sections, a major problem introduced earlier in this book is that of category confusion in which models and management question are not consonant (they involve different units and are not isomorphic). 2. The environment also elicits phenotypic expression. In other words, ecological mechanics (various ecological and physical processes, including such things as sunspot cycles, regime shifts in the climate, decadal oscillations in the weather/climate, predator/prey dynamics, and top-down and bottom-up trophic effects) are also involved in the processes that result in species-level patterns as information on the limits to natural variation. They are part of the complete suite of factors that make patterns what they are (Fig. 1.4). 3. Further evolution is likely to alter the position of individual species within species frequency distributions. However, constraints, such as those of selective extinction and speciation, are likely to prevent the shapes of such distributions from changing markedly. These changes (shapes of distributions such as Fig. 2.34), involve ecosystem evolution, one of the factors that cannot be discounted (Chapter 3). They are automatically

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accounted for in systemic management to the extent that evolutionary changes in ecosystems are involved in the full suite of factors at play in the origins of species-level patterns (Fig. 1.4). 4. This is in contrast to using the mean of the log transformed version of these data (geometric mean). Both the distributions themselves, and the central tendencies of such distributions (and their relative utility) are among the details of the application of systemic management that will be subject to fruitful debate and further research. For example, the point at which humans could be located within frequency distributions to maximize biodiversity or the information content of such distributions would be a consideration (see Fig. 5.3, Fowler 2008). 5. The larger take in biomass, of course, is taken because of the monetary value of reproductive aged fish which are taken in large numbers—a conventional management decision. Is it a sustainable mode of harvest? We encounter new questions (dimensions) that need addressing. What is the most sustainable age (or reproductive) composition of a harvest? If we remain fixed in taking only adults, what is the most sustainable harvest when confined to taking adult aged individuals? We would need empirical distributions among predatory species showing the mean age (or reproductive value) of consumed fish to address the first question. Observed patterns for predation rates on adults (fish of the age/size taken in commercial harvests) would be needed for the second question. Care must be exercised here, as in all cases. Determining what is ultimately sustainable will require information on the limits to natural variation that is representative or typical of systems free of abnormal human influence. Also, we will be best served by data for species otherwise similar to humans in regard to things we cannot change easily (e.g., our body size). 6. W. Overholtz supplied information (personal communication, 1/24/97) regarding the approximate mean biomass levels for each of the four species and information on takes by commercial fisheries used to determine the fraction of the total catches that applied to each species consistent with the total from Overholtz et al. (1991) 7. The concept of consonance was developed in some detail in Chapter 5. Basically it means finding informative patterns that are expressed in the same units and have the same logical type (have the same category), as defined by the question. Finding consonance between question and pattern results in an isomorphic match. See Fowler (2003) and Fowler and Smith (2004) for further details. 8. Note here, that for one species, an ecosystem could be defined as the geographic area (plus all biota and environment) made up of the union of all the geographic

ranges for the set of species with geographic ranges overlapping that species’ geographic range. There are many other alternatives for defining an area to be called an ecosystem, even the arbitrary nature of political boundaries for nations. In systemic management, each would be treated consistently with all others. 9. The departure of humans from the mean is based on comparisons with other mammals without regard to body size. Including species differing from humans in body size is an option here, in part, because of the lack of a relationship between body size and energy consumption per unit area (see Damuth 1987, 2007) thus making energy consumption something that can be compared to a large variety of other species. 10. For the energy used by humans in addition to metabolic energy, the 1992 values reported for the world consumption of energy (World Almanac 1993) was divided by the total human population (5.7 billion) to achieve a per capita use of energy. This includes energy as fuel provided by ecosystems (e.g., wood, beasts of burden) thus having two kinds of impacts: the acquiring of fuel products and the impact of its later use. 11. In other words (as will be seen in later sections of this chapter), if the standard of reference for population density were measured in terms of its impact on biotic environments (rather than simple population density), the density that is most risk averse and maintains a standard of living comparable to today’s human society might be one to two orders of magnitude less than would be indicated based on the patterns in density related to body mass (e.g., Fig. 2.31; or sustainable density as will be seen in Table 6.2). The actual reduction needed could be much more owing to nonlinearity. This enters the arena of tradeoffs between per capita (individual) and population (species) issues that involve hierarchical considerations to be amplified later in the chapter. 12. Sixty percent of the primary production distributed among 20 million species is to be compared to 40% for one, humans. This represents a difference of seven orders of magnitude [(0.4/1)/(0.6/2 × 107) = 1.33 × 107] between the mean for other species and the use of primary production by humans (and human dominated domestic species). With repeated cycling through ecosystems and degradation to heat, some of this difference will be lost. Even if the difference were an order of magnitude (it is a difference of less than a factor of 1.2 if 10% of the energy is passed between trophic levels) the remaining difference of six orders of magnitude means that humans are still monopolizing energy at a level where our energy use is a million times greater than that of nonhuman species. 13. From this point on, the issue of overpopulation is referred to as a problem in that our population is

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abnormally large (Fowler 2005 and the results of this chapter)—a pathological situation. Usage of this term can be seen as a matter of prejudice. This prejudice is justified on the basis of the comparisons between humans and other species in this chapter rather than on opinion. Calling overpopulation a problem is not meant to indicate or imply that there are not advantages (in human value systems) to our current population level in the complexity of such issues. The argument is that systemic consideration would indicate that, all risks and all benefits considered (Fig. 1.4), the risks outweigh all the benefits so as to include risks (and benefits) to other species, ecosystems, and the biosphere. 14. That is, it is no more possible to make a complete list of all factors involved in the complexity of reality, than it is to make models of it. Even with partial lists of the factors we know about, there is no means of assigning relative importance to each one. There is no methodology of human design that can replace empirical observation to account for reality in the way depicted in Figure 1.4. 15. A decline to 0.001 (0.1% or 6 million) of the current population would require approximately ten halvings, or about 300 years if it were reduced by half every 30 years (about the rate at which it was doubling in the last decades of the 20th century). 16. Furthermore, the alternative for reducing the population by increasing mortality through direct action will always be rejected when people consider how to reduce the human population. Any species without a genetically based opposition to mortality is unlikely to have survived. Natural selection at the individual level can be expected, more often than not, to produce opposition to death especially among age classes prior to achieving maximal reproductive value. 17. A precipitous depletion of fossil fuels would lead to identification of many of the species level risks currently and temporarily avoided, an experiment that may be worth trying to avoid but with a risk that may be out of our control. 18. Recently a great deal of effort and thought has gone to the issue of ecosystem health. During its short history, the International Society for Ecosystem Health specifically focused on this issue as do current journals such as EcoHealth and the consortium it represents. From the perspective of ecosystems as evaluated here, many or most ecosystems are ill and the disease is human overpopulation (magnified by, and, in part, caused by energetic/technological means, per capita consumption/ production, and the other departures from normal variation for other species-level and individual-level dimensions). The problem of overpopulation is often referred to as an ecosystem-level cancer (see the references in

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Hern 1993). However, the analogy fails in the sense that for individuals, if a cancer is not removed (or otherwise treated), death is a common outcome, whereas for an ecosystem to die, all populations of all relevant species would have to drop to zero (this can include humans, Boulter 2002). The ecosystem disease represented by humans is more likely only to result in losses of certain species and gains in others until humans count among those that are removed or reduced, much like individuals deal with some diseases through their immune systems. Following such ecosystem changes, the remaining species are left for evolutionary change to re-establish a more sustainable form of ecosystem adapted to the respective physical habitat and perhaps more resistant to the invasion and effects of species as disruptive as humans have been recently. In any case, it is clear that ecosystems are ill and we are the disease. 19. The disruptive effect of the human population has also been considered analogous to an explosion or “population bomb” in attempts to communicate the problem to an audience beyond scientists (Ehrlich 1968, and Ehrlich and Ehrlich 1990). The analogy is apt in that the volume of gases produced in an explosion is three to four orders of magnitude greater than the explosive material in the initial devise (Van Nostrands Scientific Encyclopedia 1958). Part of the explosive force is created by the heat of the resulting gases as an amplifying factor, much as energy, technology, and affluence amplify the effects of our population. The effect of an explosion can be measured in terms of rock moved or broken, for example, much as can the impact of humans be measured in terms of rates of extinction caused (8–12 orders of magnitude beyond the effects of the mean for other species, Appendix 6.6). 20. This is one of the examples that may apply as group selection among species. Those groups of species that contain species capable of removing disruptive species may be groups (e.g., the set represented in an ecosystem) that contribute more species to later “generations” of ecosystems. In this sense, the matter of overpopulation is probably beneficial to ecosystems in the long run as it will help produce ecosystems that reduce the risk of any similar species emerging to threaten the destruction of all of life. Part of what contributes to evolutionary change (now at the level of groups of species, including those defined by ecosystems) is the stress of factors involved in selective forces. Just as the immune systems of individuals can be stimulated toward greater efficacy by exposure to pathogens, so ecosystems are probably effected by exposure to overpopulation in their history. 21. In order to do our best at dealing with complexity, we must be very careful here to resist the temptation to take a transitive approach in “improving” the reality (change

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species-level patterns by altering nonhuman species and ecosystems) upon which they are based. The intransitive of systemic management is to manage ourselves so as to relieve systems of human abnormality so that they can organically respond, and, after such response, to reveal more accurately what is sustainable. The science involved always involves monitoring to reveal what is normal, follow change, and account for correlative factors underlying species-level patterns consonant with management questions. 22. Energy consumption per unit area was estimated by multiplying ingestion rates found using the equation from Peters (1983; I = 10.7W 0.70 where I is the ingestion rate per individual in Watts, and W is body mass, kg) by the densities reported in Damuth (1987) for the herbivore mammals of his sample, using the corresponding body mass (?). The estimated energy ingested per unit area by humans was based on assuming a density of 250 per square kilometer by assuming that the human population occupies a geographic range within the normal range of natural variation among species otherwise similar to humans (Appendix 6.4).

Chapter 7 1. This happens through breaking down every management question to its components (Chapter 5). Ultimately, these deal with the specific aspects of implementation— fractal application. Guidance is found in the detail where action can be taken, empirical information, but always using the processes outlined in this book for the overarching issues. Laws, regulations, and decisions about daily life would all be based on past experience and knowledge of what works. The importance of role models and mentoring has long been recognized in business, leadership, family, and social dynamics. Decisions and actions are based on knowing the difference between failed and successful approaches, again a trial-anderror approach, but used only insofar as they work to achieve goals established in consideration of higher-level questions. 2. The psychological aspect of change involves realizing that we are basing our management decisions on what are basically illusions (advice for management and policy based on the magical thinking involved in conversion of partial, nonconsonant/dissonant, information—top row Fig. 1.1). The illusion that such an approach is acceptable may be the key illusion to overcome and involves very personal kinds of change. Until we experience this as real cognitive dissonance, this critical step is unlikely to be taken—especially at the species level. But there is more. The psychological aspect of change also involves

knowing that emotions are to be used as motivation for asking the right questions; they are not to be, themselves, among the things converted directly to action. 3. The “course” has been identified in numerous analogies. The trajectory of our species has been likened to the Titanic, headed toward an iceberg, where our actions in conventional management amount to no more than the matter of rearranging the deck chairs (e.g., Parenteau 1998). We are seen as fiddling while Rome is burning, or shoveling fuel to a runaway train (Czech 2000, 2006). We seem to be the proverbial “bull in a china shop”—a problem we attempt to solve by moving a few bits of the china rather than putting the bull where it belongs (with science that studies the breaking of the china as science that is provided with major funding to help decide which china should be moved first). 4. The choice we face as a species is much like the choice faced by individuals who are told they are overweight, or that smoking is bad for their health. Many individuals in such situations make no change in their lives—choosing their life as they live it over what it could be otherwise. Likewise, we can continue, as a species, ignoring signs that there are risks we might wish to avoid. Management is a matter of choice—individual, social, political, national, and international. As a species, we face the message that we are a malignancy—a message to be accepted by us as individuals and taken to the species level. 5. It would not be unnatural, for example, to experience global warming as a self-exaggerating process in CO2 contributed to the atmosphere by volcanoes. We know that the redistribution of mass on the Earth’s surface results in isostatic readjustments (e.g., Whitehouse et al. 2007). If the trillions of tons of water from melting glaciers press tectonic plates into accelerated subduction, we should not be surprised to see increased earthquake and volcanic activity, the latter of which is known to add CO2 to the atmosphere. As always, however, things are never simple: volcanoes also produce other gases that counteract the greenhouse effect of CO2. 6. Recall Table 3.1 and the cases where evolution leads species to extinction—evolutionary suicide or fatal flaws that emerge through natural selection among genes and individuals (Potter 1990). The hope here lies in our being able to exercise existing intellect to our advantage. However, it is also possible that evolution has not yet completed the process of preparing us for the integrative thinking necessary to avoid causing our own demise. It is possible, for example, that integrative thinking is dependent on coordinated function of the two hemispheres of the human brain and that evolution has not yet brought us to the point of having the necessary intellect to do what is good for all living systems so as

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to include those of which we are a part and upon which we depend. 7. There is even a specialized journal devoted to consideration of the concept and its importance (Emergence is a publication of the Institute for the Study of Coherence and Emergence published by Lawrence Erlbaum Associates. Inc., Publishers, Mahwah, New Jersey; ISSN 1579–3250). 8. This is important to not abandon thinking. Thinking about thinking, addressing the epistemological aspects of what we do, is critical. Thinking is crucial to the conduct of good science, and the choice of science to provide information consonant with management questions. Thinking is a serious problem, however, as a way to combine and convert nonconsonant information to consonant information. We continue to rush into trouble by training more people to practice such alchemy (often seen as an important function of our educational system in conventional terms). The alternative is to train specialists to observe (see rather than think) so that guiding information is produced by good science—information consonant with good management questions. 9. This would follow the protocol developed in Chapter 5, exemplified by science to collect data on patterns for population size when the question involves establishing a sustainable population for humans. Succeeding analyses would involve accounting for patterns involving body size and other correlative patterns of relevance to humans and environmental factors. However (see endnote 1), the data needed are those directly defined by (consonant with) the management question (i.e., not component, tangential, merely relevant, or connected questions). The factors that are not consonant with one question would be consonant with another—there is a reciprocity. 10. The contributions of medicine to our dependency on other species, artificial chemicals, and changes in our genetic makeup (the evolutionary effects of medicine on our species) are also part of what must be covered in course work. 11. Between 1993 and 1994, books were listed in Science magazine (after having been sent to them by publishers) at the rate of about one every 45 minutes of the 40 hour work week. Scientific papers were published at the rate of one every 18 seconds in the early 1980s (Bartholomew 1986). Books with topics related to ecology were logged into the University of Washington library system at the rate of over 400 per year between 1994 and 1997. This material, gleaned from measurements and observations, is now to be complemented by the encyclopedic volume of information contained in each species’ genome (e.g., house mouse with equivalent of 15 editions of Encyclopedia Britannica, Wilson 1985) and the

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unfathomable combination of such information in the form of species-level patterns (Fig. 5.4). 12. Consistency in objectives is achieved in systemic management (Management Tenet 4). One of the consequences would be markedly reducing conflict among special interest groups, political factions, religions, and other groups founded on various anthropogenic, and very often anthropocentric, belief systems (Hobbs and Fowler 2008). Adopting systemic management would not only put environmental organizations on a common foundation, with common goals, but would also greatly alleviate conflicts between environmental organizations and governments, between government agencies, between environmental organizations and business/industry, and between government and economically driven institutions. There might be divergence of opinion regarding what questions to ask in finding information relevant to implementation, but the objectives would be held as common objectives, even among nations, religions, and races. 13. Religion has strayed by adhering to beliefs (realities) that have little or no counterpart in reality and by losing a great deal of reverence for the nonhuman—even the holistic which distinguishes religion from science. Science is reductionistic and seems to be regaining lost commonality with religion in understanding that everything is emergent in a way that accounts for complexity— including the nonhuman elements of reality and reciprocity of relationships (Fig. 1.4). 14. Does the infinite of ultimate reality (Fig. 1.4) have anything in common with religious belief systems (e.g., ultimate reality being God)? Can scientists and members of religious organizations realize common ground in this regard? What role can religion play in social change? 15. What balance between standard of living and population size do we want for ourselves? How do we insure an acceptable standard of living for both other species and future generations of our species? Can our cultures and society find common ground in reality to move forward? Can we see a biotic democracy in the information presented to us by other species? What social systems have endured long enough to provide evidence that they work (are sustainable)? 16. Can there be unanimity among individuals and social factions to move stakeholders from decisionmaking to question-asking? How, besides taking a systemic approach, are conflicts between differing views to be resolved in ways that provide an objective basis for decision-making? What are the political systems that lead to change and adaptive strategies that will work? 17. Is it right to fight for the freedom of giving birth and providing food for everyone if it contributes to the risk of

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extinction, particularly human extinction? What are the rights of other species and the individuals within each? 18. Under systemic management, laws such as the Endangered Species Act in the United States would not be necessary as we would be dealing with all of the anthropogenic contributions to endangerment we can possibly imagine. Efforts to save endangered species are well intentioned and may even be a way to help get some species through the period of time during which we are solving anthropogenic problems that contribute to their endangerment. However, attempts to save other species count as mitigating actions that cause other problems and have their own unintended consequences. If we were a species free of pathological attributes for everything we can measure now (e.g., a population of two to ten million people, appropriating one ten millionth of the Earth’s primary production, and occupying a geographic range of ten million square kilometers) there would very likely be little concern about endangered species. Can we make our laws consistent with the laws of nature? If we were managing systemically we would be playing the game as an infinite game (of which there is only one, Carse 1986) and there would be no need for laws to protect wilderness because people would understand that the game is to be played so as to continue the game, not to contribute to risks in which the game can no longer be played (Clark 1989, Eldredge 1991). We already know how to make laws that help achieve established objectives; can we use our experience with legal issues to help guide us to sustainability? 19. To what extent are economic systems resulting in ecological risks, including that of our own extinction? How do we deal with those that are posing such problems? Will it be possible for economic forces, views, and paradigms to be seen as human constructs subject to the constraints of reality? When will we realize that sustainable economic growth is an oxymoron? When the human species goes extinct, all monetary economics goes with it to leave the systemic economy intact; reality may be different but it will still be there. The effects of economic influence are among the ei in Figure 1.4 to result in seeing what we see in human abnormality in Chapter 6. We know the influence exists and have the emotional manifestation of it when we think of the consequences of actions such as reducing the harvest of fish. 20. How pathological, unrealistic, or misleading are our world views as they develop in what is increasingly an environment that is of human design; how much of nature-deficit disorder is a matter of a degraded form of nature? Would it be better if we were personally experiencing more of natural systems as superficially sampled

in therapeutic work in natural settings (Outward Bound, vision quests, ecopsychology), or with animals or horticulture (pet assisted therapy, horticultural therapy)? What are the habits we need so as to result in being a species that is sustainable? How extensive, interrelated, and pathological is control in our thinking, behavior, and habits as manifested in personal, family, business, economic, and ecological realms? How do we deal with our emotional reaction to knowing that saving the lives of children (and the practice of medicine in a more general sense) helps exaggerate the problem of overpopulation? How do we deal with the emotional reaction of knowing that our practice of agriculture, medicine, and most humanitarian aid exaggerates the problem of overpopulation? Will hope prevail in avoiding our extinction? The overwhelming odds are basis for real pessimism, yet humans are behaviorally extremely adaptable. Can the changes be made? What are the relationships between hope and controlling behaviors? Is psychological change of primary importance, or is it just another critical element that will not work alone? 21. What kind of governments are required to deal with issues so pervasive, extensive, and extreme as overpopulation, use of water, and appropriation of net primary productivity that are measured in orders of magnitude (overpopulation might be measured on a “Malthus scale” as we do earthquakes on a “Richter scale”)? How can government be, not just reinvented, but redesigned entirely so as to meet needs that are identified as important for governmental action in bringing about change, whether by government leaders themselves, the people within society, or a combination of both? 22. Different geographic regions and the peoples in them overlap or contain different biotic regions, ecosystems, and physical habitat. Which ones should be designated as human habitat? How do the religious, ethnic, political, and economic issues of such questions get addressed? The capacity to sustain human populations differs from habitat to habitat, country to country (recall Appendix Fig. 6.5.1). How do we resolve conflicts between nations with different resources, different populations, and different levels of sustainability for human populations? 23. Scientists would be advised to present themselves as experts only in the sense of their knowledge of patterns consonant with specific management questions. Questions posed by managers would be answered: “I don’t know, but the consonant pattern is . . . ”. More specifically, when asked what the appropriate body temperature for humans is, experts would reply “I can’t know independent of direct observation, but for a three year old girl, at 9:00 am, in a 40°C environment, immediately after

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running 100 meters it usually runs about 38°C”. When asked what the sustainable harvest of walleye pollock of the eastern Bering Sea might be, experts might indicate: “We can’t derive the answer other than through our best estimates of consumption realized for other species. The empirical information indicates that for species with the life history strategy of pollock, nonhuman predators otherwise similar to humans generally consume less than 2% of the standing stock biomass. We need more data for such consumption rates under existing climatological circumstances.” Empirical observations must involve measurements of what is addressed in the question. We must ensure that such observations are of the same logical types that are involved in the processes and levels of biological complexity being addressed and specified by the question. 24. This is paralleled at the ecosystem level. Changes by individuals within our species results in species-level changes. Thus, part of what happens is a result of collective effort through the control exhibited by each individual. If we as a species make the changes indicated as necessary by species-level patterns, the rest of the system will respond in ways over which we have no control. But there is a collective response and it requires a collective effort insofar as we have control. 25. Temporal scales enter here in that we cannot make the changes overnight. Individual level (mechanical or short-term) change sufficient to make up for the population-level contribution to the problem of overconsumption (whether it be biomass or energy) would require that everyone cut their consumption to levels that would result in death. This being out of the question as a single element for solving the problem requires change at the population level with a different change for individuals—reduced birth rates or increased mortality. This involves a longer time frame—that associated with species or population level processes. We are forced to deal with time scale, in part, because it would not be realistic to require individuals to do abnormal (evaluated by comparison with other species—not just in regard to recent history within our species) things to achieve management (even though systemic feedback may involve what we might call abnormal mortality, for example). Thus, to achieve a realistic rate of energy consumption by our species cannot be accomplished by asking each individual human to consume only a stick of celery as their total daily allotment of energy. 26. Such efforts would involve bringing to the attention of the world the fact than excessive CO2 production is a much larger problem than currently recognized (e.g., reducing CO2 by 2% per year would take almost 450 years to maximize the information index for CO2

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production—assuming other species won’t respond to being relieved of this and other abnormal human influence; Fowler 2008). The other ways we find ourselves to be having pathological influences deserve similar attention and could use the formation of corresponding advocacy groups—all with consistent goals. In the end, everyone has to go beyond advocacy and be part of the change. 27. Keep in mind that abnormal is here defined not simply with reference to current or past human standards, but by comparison to other species as well. Reducing CO2 production by 80% (as is often expressed as a desirable goal in current efforts to face and deal with the problem) would have to be repeated over five times to bring human production in line with CO2 production by other species (to maximize diversity, Fowler 2008). Noningested energy consumption by nonhuman species at our trophic level is essentially zero; biomimicry of this example of sustainability will probably seem ludicrous to many. With such examples, however, we begin to appreciate what we have become as a species. 28. The conditions contributing to a raised risk of major pandemic (see “The coming plague” by Garrett 1994), involve much more than exposure to larger numbers of disease organisms as we occupy more and more ecosystems. Counting among the contributing factors is the human population density, our compromised immune systems (e.g., from the effects of toxins), our mobility, genetically inherited immune deficiency in lives saved by medicine, and the reduced resistance brought on by sedentary lives. The genetic effects of our current attempts to “control” disease produces strains or species even more likely to break through what natural immunity, and artificial chemical defenses, we have left. In terms of catastrophe theory, we have developed the conditions for a “perfect storm”. Much if this situation is attributable to action carried out in the interest of what are considered good for individuals, societies, or even the short-term interests of our species. Extending consideration to the interests of the nonhuman and long-term interests of our species is part of systemic management. 29. An analogy that may be helpful here is that of the philosophy of Asian martial arts. Central to much of this philosophy is the concept of working with forces rather than struggling against them (Watts 1951). There are clearly limiting and controlling forces by which ecosystems affect species, including humans. It would seem advisable to move from a view that focuses on a perceived adversarial component to these forces, to a view wherein there is useful and helpful content to information we gather from these forces and their manifestations. As indicated by H. Smith (1994), “. . . the concept of

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wu wei . . . . translates literally as inaction but in Taoism means pure effectiveness. Action in the mode of wu wei is action in which friction—in interpersonal relationships, in interpsychic conflict, and in relation to nature—is reduced to a minimum.” 30. Use of the term “life” is not restricted to the human experience often called life, especially as experienced

simply as individuals. It refers to this and comparable aspects of all living systems at all levels of organization, including those that may be intermediate to and extend beyond the spectrum from cells and individuals through species to ecosystems and beyond. It includes all beings with which we share this planet and all biotic systems in which we find ourselves to be a part.

List of Appendices

The following is a list of 22 appendices for the first six chapters of this book (there are none associated with the Epilogue). All are available online (listed according to Appendix number) at: http://www.afsc.noaa. gov/Publications/misc_pdf/Fowler_appendices.pdf. Pages Chapter 1: Appendix 1.1 – Reality/complexity Appendix 1.2 – Species-level failure to thrive Appendix 1.3 – Sample patterns for the eastern Bering Sea (construction of Figs 1.6 through 1.8) Chapter 2: Appendix 2.1 – Variety in patterns among species Chapter 3: Appendix 3.1 – Species characteristics and selectivity in extinction and speciation Appendix 3.2 – Evolutionary contributions to the formation of species-level patterns (frequency distributions) Appendix 3.3 – Selective evolution between two categories Appendix 3.4 – Selective extinction, speciation, and evolution for two categories Appendix 3.5 – Selective extinction and speciation in numerous categories Appendix 3.6 – Alternative terms for the processes of selective extinction and speciation Chapter 4: Appendix 4.1 – What should management applied to ecosystems include? Appendix 4.2 – Ecosystem changes Appendix 4.3 – Principles of “ecosystem management” Appendix 4.4 – The Bayesian interpretation of selective extinction and speciation

1–3 4–7 8–10

11–27

28–40 41–62 63 64–65 66–68 69–72

73–82 83–88 89–91 92–95

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LIST OF APPENDICES

Chapter 5: Appendix 5.1 – Science is inadequate for management Appendix 5.2 – Asking management and research questions Chapter 6: Appendix 6.1 – Identifying human elements in needed management Appendix 6.2 – Overpopulation as contributing cause to environmental degradation Appendix 6.3 – Conventional assessment of human population size Appendix 6.4 – The human population evaluated by interspecific comparisons Appendix 6.5 – The challenge of population at the national level Appendix 6.6 – Human contribution to extinction

96–98 99–104

105–112 113–117 118–124 125–129 130–131 132–135

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Author Index

Figures and tables are indexed in bold. Aarssen, L. W. 23, 24, 51 Aber, J. D. 78, 82, 88 Abramson, S. C. 2, 4, 85, 109 Acquay, H. 101 Agee, J. K. 81, 85, 87 Agusti, S. 44 Ahl, V. 142, 148, 202, 225 Alexander, R. D. 67 Allee, W. C. 71 Allen, A. P. 44 Allen, T. F. H. 73, 74, 79, 80, 81, 86, 87, 106, 110, 138, 142, 148, 202, 225 Allmon, W. D. 69 Alverson, D. L. 186 Amato, E. D. 87, 217 Anderson, D. J. 25 Anderson, J. E. 87, 217 Anderson, R. M. 66 Anderson, S. 25, 42 Angermeier, P. L. 9, 87, 98 Angliss, R. P. 32f2.15 Aplet, G. H. 9, 217 Apollonio, S. 35 Appleman, P. 70 Arkema, K. K. 2, 4, 85, 109 Arnold, A. J. 51, 56, 59, 63, 67, 71, 72, 73, 74, 88 Asford, R. W. 187t6.3 Baker, J. D. 27, 55, 109, 161, 166 Bakker, R. T. 68, 70 Barker, D. 40, 45 Barrett, J. A. 70 Bartuska, A. M. ix, 2, 3, 4, 8, 9, 10, 11, 12, 78, 81, 82, 85, 87, 92, 98, 109, 115, 122, 125, 129, 130, 131, 134, 139, 140, 149, 211 Bascompte, J. 3, 12, 20, 26, 26f2.5, 41, 121 Basset, Y. 23, 33, 44 Bates, S. F. 217 Bateson, G. 3, 6, 7, 10, 12, 20, 50, 59, 70, 71, 79, 80, 81, 85, 90, 91, 104, 106, 110, 114, 122, 136, 138, 150, 207, 210, 212, 219 Bateson, M. C. 7

Beattie, M. 80, 121 Beaver, R. A. 25, 25f2.3, 25f2.4 Beck, W. S. 71, 72, 98 Beddington, J. R. 6, 89, 100, 161 Begon, M. 74 Belensky, M. E. 157 Belgrano, A. 1, 2, 3, 3f1.1 , 4, 5, 10, 12, 13, 16, 17, 18, 19, 20, 22, 24, 30, 44, 46, 50, 51, 75, 79, 83, 84, 91, 110, 111, 114, 117, 125, 134, 137, 146, 151, 153, 154, 161, 166, 179, 190, 207, 209, 211, 219 Bell, J. S. 24 Belsky, M. H. 9, 87 Benton, M. J. 61 Benyus, J. M. 12, 134 Berenbaum, M. R. 58 Berger, J. J. 88 Berry, T. 1, 210, 221 Berry, W. 12 Bierregaard, R. O. 69 Biltonen, M. 101 Binford, C. H. 187t6.3 Blackburn, T. M. 23, 32, 33, 40, 42, 43, 44, 50, 51, 52, 73, 74, 88, 139, 144, 219 Blackwelder, R. 36, 37, 58 Blueweiss, L. 37, 40 Bock, C. E. 32, 33 Bogan, C. E. 92, 143 Bolnick, D. I. 58 Bonabeau, E. 121 Bonner, J. T. 23 Borgia, G. 67 Boecklen, W. 62 Botkin, D. B. 132 Boulter, M. 6, 24, 196 Bourne, D. W. 28f2.7 Boxwell, R. J. 92, 143 Boyajian, G. E. 62 Boyce, M. S. 43 Boykin, D. B. 12 Brady, I. A. 6 Brandon, R. N. 59 Brasier, M. D. 58 Bratton, S. P. 87, 88 Briand, F. 25

Briggs, D. E. G. 69 Brodziak, J. K. T. 87, 88, 96 Brooker, R. W. 62 Brooks, D. R. 62, 70 Brooks, J. L. 70, 71 Brosnan, D. M. 2, 4, 91, 110, 211, 212 Brown, J. H. ix, 2, 3, 4, 6, 8, 9, 10, 11, 12, 23, 23f2.1, 24, 25, 31, 32, 33, 40, 42, 42f2.27, 43f2.28, 44, 52, 60, 73, 74, 78, 81, 82, 85, 87, 92, 98, 109, 115, 121, 122, 125, 129, 130, 131, 134, 136, 138, 139, 140, 144, 149, 202, 211 Brown, J. S. 55, 67, 69, 138, 214, 219 Brown, R. M. 222 Brown, V. K. 44 Brubaker, H. W. 87, 101, 177 Brubaker, L. A. 9, 112, 122 Brussard, P. F. 80 Bulmer, M. G. 36 Burke, S. 3, 4, 81, 85, 87, 92, 100, 130, 161 Burns, T. P. 6, 79, 202 Buss, L. W. 36, 58, 80 Cairns, J. 6, 8, 87 Calder, W. A. 40 Calle, E. E. 131, 143 Callicott, J. B. 9, 202 Camazine, M. 121 Cameron, A. 61 Camp, R. C. 92, 143 Campbell, D. T. 106, 110, 142, 148 Campbell, T. M. 69 Canada 165 Canter, L. W. 12, 81, 111 Capra, F. 6 Cardinale, B. J. 56, 61, 73, 74 Carlin, B. F. 6f1.3 Carpenter, S. R. 17 Carroll, C. R. 6 Carroll, D. R. 207 Carse, J. P. 116, 195, 223, 224 Castilla, J. C. 44 Catena, J. 9 Catton, W. R. 6, 102, 179, 181, 184, 195

279

280

AUTHOR INDEX

Caughley, G. 91, 210 Cawte, J. 6 Ceballos, G. 60 Chaloner, W. G. 6 Charlesworth, B. 58, 59 Charnov, E. L. 39, 39f2.26, 40, 46, 139, 144, 219 Chew, R. M. 173 Chiarelli, B. 224 Chiras, D. D. 6, 12 Christensen, N. L. ix, 1, 3, 4, 8, 9, 10, 11, 12, 78, 81, 82, 85, 87, 92, 98, 109, 115, 122, 125, 129, 130, 131, 134, 139, 140, 149, 211 Clark, E. J. 62 Clark, T. W. 9, 69, 87, 217 Clark, W. C. 78, 82 Clarke, M. E. 2, 4, 6, 10, 11, 85, 109, 122 Clayton, P. 3, 12, 20, 50 Clinchy, B. M. 157 Cohen, J. E. 25, 26, 179, 181f6.20, 183f6.23 Colborn, T. 78, 82 Collie, J. S. 35f2.18, 43, 44, 138 Collins, S. L. 33 Connell, J. H. 36f2.20, 43 Conover, D. O. 8, 83, 89, 93, 100, 138, 161 Coombs, I. 187t6.3 Cooperrider, A. Y. 6 Cooperstein, S. 6, 108, 194, 224 Corkett, C. J. 91 Cortner, H. J. 3, 4, 81, 85, 87, 92, 100, 130, 161 Corzilius, D. B. 87, 101, 177 Costanza, R. 3, 6, 9, 92, 93, 102, 207, 209, 225 Cowan, G. A. 50 Cowgill, G. L. 102 Cox, R. K. 9, 12 Cracraft, J. 6, 61, 62, 68, 69, 72, 73, 189 Crawford, R. J. M. 12, 19, 24, 31f2.13, 38, 50, 126, 166, 170f6.8, 196, 197, 202 Crewe, W. 187t6.3 Cristoffer, C. 44, 74 Crompton, D. W. T. 187t6.3 Crosby, A. W. 102 Dalsgaard, J. 78, 82 Damuth, J. D. 23, 24f2.2, 30f2.11, 33, 33f2.16, 37, 37f2.23, 40, 44, 45f2.31, 47f2.35, 48f2.36, 49t2.2, 51, 58, 59, 66, 72, 73, 74, 127, 128f5.2, 145, 175, 175f6.15, 180, 181f6.21, 182t6.2, 183f6.23 Darlington, P. J. 67 Darwin, C. G. 6, 80

Darwin, C. R. 56, 58, 62, 66, 67, 70, 71, 73, 77, 121 Davies, R. G. 32, 51, 52, 60, 61 Davis, W. S. 9, 94 Dawkins, R. 66 Day, D. 60 Dayton, P. K. 131 De Vries, H. 71 DeAngelis, D. L. 80, 106, 110, 142, 148 DeFries, R. S. 78, 82 DeLong, R. L. 154, 156, 198 DeMaster, D. P. 27, 55, 109, 161, 166 Deneubourg, J.-L. 121 Denham, M. C. 40, 45 DeSchryver, A. 187t6.3 Despommier, D. D. 187t6.3 Dewsbury, B. M. 2, 4, 85, 109 Dial, K. P. 23, 37 Diamond, J. M. 19, 92, 102, 207, 209, 225 Dickerson, J. E. 74 Dietz, T. 2, 85 Dobzhansky, T. 66 Dodson, M. M. 44 Donaldson, R. J. 187t6.3 Doube, B. M. 44 Downing, A. L. 56, 61, 73, 74 Duarte, C. M. 44 Dumanoski, D. 78, 82 Dumas, A. R. 87, 101, 177 Dunstan, J. C. 81 Dytham, C. 62 Edwards, S. F. 87, 88, 96 Ehrenfeld, D. J. 9, 80, 102, 126 Ehrlich, A. H. 61, 108, 133, 177, 207, 209, 217, 225 Ehrlich, P. R. 6, 8, 61, 70, 80, 98, 101, 108, 133, 161, 175, 177, 207, 209, 217, 225 Eisenberg, J. F. 44 Eldredge, N. 6, 36, 58, 59, 60, 61, 62, 67, 68, 69, 71, 72, 73, 74, 138 El-Hani, C. N. 138 Elliot, D. K. 61 Emerson, A. E. 66, 71 Emlen, J. M. 3, 12, 20, 50, 138 Emmeche, C. 138 Emmons, C. W. 187t6.3 Emmons, L. A. 69 Enger, E. 74 Englehardt, J. L. 56, 61, 73, 74 English, M. J. 92, 143 Enquist, B. J. 44 Enright, J. T. 28 Erwin, T. L. 25 Estes, J. A. 26

Etnier, M. A. 38, 38f2.24, 129, 155, 156, 161, 164, 188, 188f6.29, 199, 199f6.33, 202 Evans, A.S. 187t6.3 Ezcurra, E. 9, 112, 122 Farnworth, E. G. 6 Faust, E. C. 187t6.3 Feldman, H. A. 187t6.3 Fenberg, P. B. 8, 83, 138 Fenton, W. N. vii Ferry-Graham, L. A. 58 Fleming, T. H. 23 Fogarty, M. J. 6, 89, 100, 161 Fortey, R. A. 60 Foster, K. L. 29f2.8, 154, 164, 170f6.8 Foster, R. B. 98 Fowler, C. W. ix, 1, 2, 3, 3f1.1, 4, 5, 8, 10, 12, 13, 15f1.6, 15, 16, 16f1.7, 17, 18, 19, 20, 22, 24, 24f2.2, 25, 26, 27, 29, 29f2.9, 30, 30f2.11, 30f2.12, 33, 34f2.17, 35f2.18, 38, 38f2.24, 40, 44, 45, 46, 46f2.33, 50, 51, 55, 56, 57f3.1, 59, 60, 61, 63, 64, 65, 68, 69, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 84, 85, 88, 89, 91, 98, 99, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 121, 122, 123, 124f5.1, 125, 126, 127, 128, 128f5.2, 129, 130, 131, 132, 133, 134 135, 135f5.3, 136, 137, 138, 140, 141, 143, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 160, 161, 162 163, 164, 166, 169, 170, 170f6.8, 171f6.10, 174, 175f6.14, 175f6.15, 176f6.16, 176f6.17, 177, 179, 184, 184f6.24, 187, 188, 188f6.29, 190, 191, 192, 196, 197, 198, 199, 199f6.33, 202, 205, 207, 208, 211, 214, 219, 220, 221, 225 Fox, H. 37, 40 Fox, R. 6, 121 Fralish, J. S. 138 Francis, G. R. 9 Francis, R. C. 2, 4, 6, 9, 10, 11, 85, 109, 128 Frank, K. T. 161 Franklin, J. 11, 85, 122 Franklin, J. F. 4, 87 Franks, N. R. 121 Freeberg, M. H. 186 Freeman, C. C. 3, 12, 20, 50, 138 Fricke, C. 66 Fristrup, K. 51, 56, 59, 63, 67, 71, 72, 73, 74, 88 Froese, R. 78, 82 Froude, V. A. 6f1.3 Fuentes, E. R. 9, 112, 122

AUTHOR INDEX

Fullan, M. 123 Futuyma, D. J. 58, 62, 67, 68, 69, 70, 72, 74 Gabriel, W. L. 199, 200 Garoian, G. S. 36, 37, 58 Garrett, L. 6, 178 Gaston, K. J. 23, 32, 33, 40, 42, 43, 44, 50, 51, 52, 73, 74, 88, 139, 144, 219 Ghiselin, M. T. 59, 66 Gibson, A. 6f1.3 Gilbert, L. E. 69, 70, 98 Gilbert, V. 217 Gilinsky, N. L. 72 Gill, D. E. 58 Gillman, M. R. 58 Gillooly, J. F. 44 Gilpin, M. E. 43, 69, 88 Glazier, D. S. 33, 74 Gleick, J. 10, 83, 92, 132, 133 Glendinning, C. 80 Glenn, S. M. 33 Gnomes, M. E. 80 Gödel, K. 91, 136 Godfray, H. C. J. 32f2.14, 43, 44, 98, 138, 178f6.19 Goldberger, N. R. 157 Golley, F. B. 6, 72, 73, 74, 95 Gomes, M. E. 7, 8 Goolishian, H. A. 8 Gore, A. 7 Gotelli, N. J. 33 Gould, S. J. 58, 59, 62, 71, 72, 73 Graham, J. H. 3, 12, 20, 50, 138 Grantham, T. A. 65, 73 Graumlich, L. J. 92, 102, 207, 209, 225 Gray, J. S. 35 Greedwood, J. J. D. 44 Green, J. 126, 127, 173 Green, R. E. 61 Greenway, R. 6, 80 Greenwood, P. H. 72 Grier, C. C. 127 Grifo, F. T. 60, 61 Groom, M. J. 2, 4, 6, 91, 110, 207, 211, 212 Gross, L. J. 8 Grosz, J. R. 127 Grumbine, R. E. 4, 92, 100, 130, 161 Guerry, A. D. 11, 32, 53 Gwadz, R. W. 187t6.3 Gyllenberg, M. 145 Haddad, B. 91 Haeck, J. 33 Haeuber, R. 85, 122 Hagen, J. B. 8, 67, 72, 73, 74 Hallam, A. 6, 62, 68, 69

Halley, J. 35 Hannon, B. 9 Hansen, R. M. 186f6.28 Hanski, I. 33, 35f2.19, 36, 36f2.21, 43, 145 Hanson, J. M. 8, 83, 138 Hare, R. C. 128 Hargrove, E. 9 Harper, J. L. 74 Harrison, R. 33, 34f2.17, 173, 173f6.12, 176f6.17, 184f6.24 Hartman, D. 205 Hartman, W. L. 35 Harvey, A. H. 87, 217 Harvey, C. 28, 101 Harvey, P. H. 32f2.14, 43, 44, 98, 138, 178f6.19 Haskell, B. D. 3, 6, 9, 93 Hassan, F. A. 6 Haeuber, R. 11 Hawkins, B. A. 32 Heath, C. W. 131, 143 Hedgpeth, J. W. 8 Hengeveld, R. 33 Henle, K. 73, 74, 220 Hern, W. M. 6, 196 Herricks, E. E. 8, 87 Heumier, T. A. 25, 25f2.3, 25f2.4, 186f6.28 Heylighen, F. 121 Higashi, M. 6, 79, 202 Hilborn, R. 8, 81, 115, 161 Hixon, M. E. 2, 4, 6, 10, 11, 85, 109 Hobbs, L. ix, 1, 2, 3, 4, 8, 10, 12, 13, 19, 22, 33, 40, 46, 50, 76, 80, 81, 82, 84, 91, 99, 109, 111, 113, 114, 115, 117, 118, 121, 122, 125, 126, 129, 130, 131, 132, 133, 135, 136, 137, 140, 143, 147, 148, 149, 150, 153, 157, 160, 161, 166, 170, 174, 177, 197, 202, 205, 214, 219, 220, 221 Hobbs, R. C. 27, 55, 109, 161, 166, 197 Hodgson, H. vii Hoekstra, T. W. 73, 74, 81, 86, 87 Hoffman, A. 62, 71, 74 Holdgate, M. W. 81 Holdren, J. P. 108 Holling, C. S. 2, 7, 8, 89, 96, 115, 127, 138, 163 Holmes, J. C. 74, 80 Holt, S. J. 4. 10, 109, 122, 132, 134, 138, 161 Hone, J. 40, 45 Honigberg, B. M. 187t6.3 Horgan, J. 81 Horibuchi, S. 58 Horsefall, F. K. 187t6.3 Houghton, R. A. 98, 104 Howarth, R. W. 78, 82

Howe, H. F. 69 Howson, C. 114 Hubbell, S. P. 50, 56, 61, 73, 74 Hudson, A. J. 3, 12, 20, 50, 138 Hugueny, B. 42 Hull, D. L. 4, 61, 71, 72 Huntly, B. J. 9, 112, 122 Hutchinson, G. E. 23 Hutchinson, T. C. 9, 35, 96 Inchausti, P. 35 Inoue, Y. 58 Jablonski, D. 56, 59, 62, 67, 70, 71, 74, 77 Jacobson, S. K. 207 Janzen, D. H. 68, 70, 98 Jarvis, S. 6, 9 Jenkins, R. E. 6 Jørgensen, S. E. 3, 12, 20, 50, 80, 138 Johnson, D. R. 81, 85, 87 Johnson, N. 9, 217 Johnson, R. M. 91 Jope, K. L. 81 Jordan, W. R. 88 Jordano, P. 187 Juanes, F. 44 Jung, R. C. 187t6.3 Kadzma, V. 37, 40 Kangas, P. 80, 86 Kanner, A. D. 7, 8, 80 Karieva, P. 8 Karplus, W. J. 8 Karr, J. R. 6, 9, 69, 85, 87, 98 Kates, R. W. 78, 82 Katz, M. 187t6.3 Kauffman, S. A. 138, 202 Kaufman, D. M. 10 Kay, J. J. 9 Keddy, P. A. 9 Keiter, R. B. 9, 87 Kelt, D. A. 25 Kenchington, R. A. 108 Kerfoot, W. C. 8 Kerster, H. W. 8, 87 Kiehl, J. T. 61 Kikkawa, J. 25 Kingsland, S. E. 8 Kitchell, J. A. 74 Kitching, R. L. 23, 26, 33, 44 Klein, R. G. 69 Knight, R. L. 217 Knoll, A. H. 62, 68, 69 Koehl, M. A. R. 10 Koestler, A. 79, 80 Kremer, J. N. 81, 136, 137, 138 Kuhn, T. S. 62

281

282

AUTHOR INDEX

Kunkel, K. 154, 155f5.6, 165, 166f6.5, 166 Kwon-Chung, K. J. 187t6.3 LaBarbera, M. 58, 59 Lackey, R. T. 2, 85, 109 Lammers, R. B. 126, 127, 173 Lande, R. 58, 59 Larkin, P. A. 161 Latham, R. E. 69 Laughlin, C. D. 6 Lavigne, D. 9, 12, 85 Law, R. 8, 83, 87, 89, 93, 100, 138, 161 Lawton, J. H. 12, 23, 25, 33, 42, 43, 44, 61, 74, 188, 217 Lederberg, J. 6, 124, 187 Lee, H. T. 9 Lee, K. N. 81 Leggett, W. C. 161 Lehman, C. L. 68 Leopold, A. 110 Letcher, A. J. 44 Levin, B. R. 66, 202 Levinton, J. S. 72, 88, 138 Lewin, R. 3, 12, 20, 50, 60, 138, 202 Lewis, W. M. 66 Lewontin, R. C. 56, 59, 63, 71, 72 Likens, G. E. 78, 82 Link, J. S. 87, 88, 96 Livingston, P. A. 16f1.7, 27, 27f2.6, 154, 162f6.1 Lodge, K. L. 32f2.15 López-Sepulcre, A. 62, 66, 156 Loucks, O. L. 138 Louv, R. 7, 210 Lovejoy, T. E. 69 Low, L. L. 199, 200 Lowe-McConnell, R. H. 81 Lubchenco, J. 9, 112, 122 Ludwig, D. 8, 81, 91, 115, 161 Lyell, C. 71 McAlister, W. B. 13, 14, 14f1.5, 15f1.6, 16f1.7 MacArthur, R. H. 23, 72 McBeath, J. 199 MacCall, A. D. 199, 200 MacCleery, D. W. 4, 9 McCormick, F. J. 2, 109, 122, 130 McCulloch, B. 6 Mace, P. M. 199, 200 MacGarvin, M. 74 McGlade, J. M. 8, 82, 87, 89, 100, 38, 161 McGowan, J. A. 12, 74 McIntosh, R. P. 72, 138, 161 McIntyre, M. E. 207 MacMahon, J. A. 19, 25, 51, 56, 57f3.1, 59, 60, 61, 63, 68, 70, 71, 72, 73, 74, 88

McNamara, K. J. 58 McNamee, T. 87 McNaughton, S. J. 26 McNeely, J. A. 91 McNeill, W. H. 6, 80 McSweeney, G. D. 6f1.3 Macy, J. 6 Magnuson, J. J. 8, 161 Makous, W. 91, 136 Malek, E. A. 187t6.3 Malone, C. R. 4, 85, 161 Mangel, M. ix, 2, 4, 11, 80, 81, 85, 87, 91, 92, 99, 100, 109, 110, 115, 117, 122, 130, 131, 132, 134, 136, 138, 148, 149, 161, 163, 211 Mannion, A. M. 9, 80 Mark, A. F. 6f1.3 Marquet, P. A. 23, 44, 48 Martin, P. S. 68, 69, 70 Marzluff, J. M. 25, 37 Mathews, J. T. 78, 82 Mathisen, O. A. 15, 17f1.8, 154, 167 Matson, P. A. 78, 82, 177 Maturana, H. R. 202 Maurer, B. A. 23, 44, 74 May, R. M. 23, 25, 26, 42, 43, 44, 61, 66, 68, 138, 188 Maynard Smith, J. 36, 58, 59, 62, 66, 68, 69, 70, 72, 74 Mayr, E. 7, 36, 62, 69, 71, 72, 138 Meaney, J. J. 87, 101, 177 Meeker, J. W. 110, 116, 146, 205, 209, 217 Meffe, G. K. ix, 2, 4, 6, 7, 11, 80, 81, 85, 87, 89, 91, 92, 99, 100, 109, 110, 115, 117, 122, 130, 131, 132, 136, 138, 148, 149, 161, 163, 207, 211 Meheus, A. 187t6.3 Melián, C. J. 26, 26f2.5, 41 Melillo, J. M. 127 Melin, S. R. 154, 156, 198 Melzer, D. 50 Menon, V. 9, 12 Merton, R. K. 10 Mertz, G. 154, 155f5.6 Methot, R. D. 199, 200 Meyer, W. B. 78, 82, 98 M’Gonigle, M. 161 Michod, R. E. 66 Miller, A. H. 62, 69, 70 Mills, A. 3, 12, 20, 50, 138 Mines, S. 6 Mitchell, R. B. 9 Mitsch, W. J. 80, 138 Mittelbach, G. G. 56, 61, 73, 74 Mitter, C. 62, 70 Mlot, C. 98 Moll, R. 219 Mooney, H. A. 6, 70

Mooney, S. M. 66 Moote, M. A. 3, 4, 81, 85, 87, 92, 100, 130, 161 Moran, E. F. 5, 8, 10, 78, 82 Moreno, G. 58 Morowitz, H. J. 3, 12, 20, 50, 55, 89, 138, 148, 211, 222 Morrow, E. H. 66 Morse, D. R. 44 Morse, S. S. 138 Moses, M. 39, 39f2.26 Moss, B. 161 Mu’’ller, F. F. 138 Mueller, L. D. 58, 67 Munch, S. B. 8, 83, 89, 93, 100, 138, 161 Munn, R. E. 9 Munro, D. A. 81 Murphy, D. D. 69 Murawski, S. A. 2, 4, 6, 10, 11, 29f2.8, 85, 109, 154, 164, 170f6.8, 186 Myers, J. P. 78, 82 Myers, N. 6, 9, 61, 112, 122 Myers, R. A. 6, 154, 155f5.6 Nagel, E. 3, 12, 20, 50 Nakashima, D. 37, 40 Nash, J. F. 55, 64, 111, 113, 114, 115, 116, 138 Nash, R. 6, 7 Nash, S. 6 National Research Council (NRC) 2, 4, 5, 6, 8, 9, 10, 13, 82, 84, 151, 167 Navarrete, S. A. 44 Nee, S. 44 Neisenbaum, R. A. 69 Nelson, K. 161 Newell, N. D. 58 Newman, C. M. 25, 26 Nicholson, A. J. 66 Nicoletto, P. F. 23f2.1, 24, 31, 42, 42f2.27 Nielsen, S. N. 2, 12, 20, 50, 134, 138 Niklas, K. J. 44 Nixon, S. W. 81, 136, 137, 138 Norse, E. A. 6 Norton, B. G. 3, 4, 6, 9, 12, 80, 81, 87, 93, 202 Noss, R. F. 6, 9, 98 Nowak, R. M. 33, 34f2.17, 68, 173, 173f6.12, 176f6.17, 184f6.24 O’Connor, T. 80 Odum, E. P. 101 Ohsumi, S. 29f2.10 Okasha, S. xii, 56, 59, 62, 70, 71, 72, 73, 77 Olson, A. M. 9, 112, 122 Olson, G. 61

AUTHOR INDEX

Olson, J. T. 9, 217 Olson, V. 61 O’Neill, J. A. S. 87, 101, 177 O’Neill, R. V. 80, 106, 110, 142, 148 Onsi, D. E. 87, 101, 177 Orians, G. H. 8, 12 Orme, C. D. L. 32, 51, 52, 60, 61 Ormerod, P. 55 Overholtz, W. J. 29f2.8, 154, 164, 170f6.8 Ovington, J. D. 6 Owen-Smith, R. N. 69 Page, T. 11 Pagel, M. D. 32f2.14, 40, 43, 44, 45, 98, 138, 178f6.19 Paine, R. T. 12, 26 Park, O. 71 Park, T. 71 Parker, A. C. vii Parman, A. O. 55, 67, 138, 214 Parsons, P. A. 69 Parvinen, K. 66, 156 Patten, B. C. 3, 6, 12, 20, 50, 79, 138, 202 Patten, D. 87 Patterson, B. D. 25, 43 Pattison, J. R. 187t6.3 Pauly, D. 78, 82 Pearl, M. C. 61 Pennycuick, C. J. 6, 10 Perez, M. A. 12, 13, 14f1.5, 15f1.6, 16f1.7, 24f2.2, 29f2.9, 30f2.11, 30f2.12, 33, 34f2.17, 35f2.18, 46, 118, 152, 166, 170f6.8, 171f6.10. 174, 175f6.14, 175f6.15, 176f6.16, 176f6.17, 179, 184f6.24, 198, 219 Persson, L. 56, 61, 73, 74 Petchey, O. L. 56, 61, 73, 74 Peterman, R. M. 110, 161 Peters, H. 44 Peters, R. 37, 40 Peters, R. H. 7, 8, 33, 37, 42, 44, 81, 127, 139, 144, 152, 161, 173f6.12, 175, 175f6.15, 180, 182t6.2, 219 Petraits, P. 69 Petrelli, J. M. 131, 143 Phillips, C. G. 4 Pielou, E. C. 134 Pilkey, O. H. 8, 74, 79, 81, 91, 122, 136, 139, 140, 161, 210, 211, 220 Pilkey-Jarvis, L. 8, 74, 79, 81, 91, 122, 136, 139, 140, 161, 210, 211, 220 Pimentel, D. 6, 8, 28, 87, 101, 177, 194, 224 Pimm, S. L. 8, 25, 26, 43, 69, 81, 200 Pines, D. 50 Pitcher, T. E. 66

Pither, J. 23, 24, 51 Pletscher, D. H. 154, 155f5.6, 165, 166f6.5, 166 Plotkin, B. 7, 207 Plunlatt, O. A. 187t6.3 Policansky, D. 66 Ponting, C. 6, 80, 102, 207, 209, 225 Pope, J. G. 186 Potter, V. R. 6, 66, 156, 205 Power, M. J. 26 Powers, J. E. 199, 200 Pratarelli, M. E. 224 Preston, F. W. 33 Price, P. W. 74 Prigogine, I. 3, 12, 20, 50, 70 Punt, A. E. 89 Pyle, R. M. 87 Ralston, S. 2, 4, 6, 10, 11, 85, 109 Randell, H. 6, 108, 194, 224 Randolph, J. 4 Rankin, D. J. 62, 66, 156 Rankin, J. M. 69 Rapport, D. J. 6, 9, 35, 53, 85, 101, 107, 202, 219 Rasmussen, L. L. 207, 221 Raup, D. M. 60, 61, 62 Raven, P. H. 6, 189 Ream, R. R. 202 Redfearn, A. J. 43 Redford, K. H. 44 Redman, C. L. 80, 92, 98, 102, 133, 207, 209, 216, 225 Reed, C. G. 6, 87 Reed, E. S. 59 Rees, W. E. 209, 213, 220 Regal, P. J. 81 Regier, H. A. 9, 35, 85, 87, 96, 101 Reiners, W. A. 127 Rensch, B. 71 Resosudarmo, P. 28, 101 Restrepo, V. R. 199, 200 Richards, J. F. 78, 82 Richards, L. J. 8 Ricklefs, R. E. 11, 32, 33 Ridgway, S. H. 33, 34f2.17, 173, 173f6.12, 176f6.17, 184f6.24 Robinson, J. G. 44 Robinson, J. V. 74 Rockford, L. L. 2, 85 Rodriguez, C. 131, 143 Roe, S. A. 66 Roosevelt, D. 210 Rosen, R. 202 Rosenberg, A. A. 6, 7, 89, 100, 161 Rosenzweig, M. L. 6, 23, 25, 32, 35, 40, 42, 61, 67, 73, 74, 98, 136, 140, 140, 144, 161, 196, 219

283

Ross, R. M. 69 Roszak, T. 6, 7, 8 Roughgarden, J. 12, 62, 67, 68 Roy, K. 8, 83, 138 Ruesink, J. L. 56, 61, 73, 74 Ruggiero, A. 32 Rusler, R. D. 23 Ryan, P.G. 31f2.13, 197 Safina, C. 8 Sagoff, M. 4, 6, 7, 80, 87 Sala, E. 26, 26f2.5, 40 Salisbury, J. 126, 127, 173 Salt, F. W. 138 Salthe, S. N. 61, 67, 71, 72, 73, 74, 79, 80, 104, 106, 110, 138 Salwasser, H. 4, 9 Salzman, L. 6 Sample, V. A. 9, 217 Sams, S. 37, 40 Sanderson, A. 5, 10, 78, 82, 98 Sarre, S. 73, 74, 220 Schaef, A. W. 80 Schaefer, M. B. 92 Schaeffer, D. J. 8, 87 Schamp, B. S. 23, 24, 51 Scheiner, S. M. 3, 12, 20, 50, 138 Schindler, D. W. 69, 78, 82 Schlesinger, W. H. 8, 78, 82 Schmid, P. E. 33, 40, 44, 127, 145 Schmid-Araya, J. M. 33, 40, 44, 127, 145 Schmidt, K. 71 Schmidt-Nielsen, K. 40, 140 Schnute, J. T. 8 Schoener, T. W. 43 Schoenly, K. 25, 25f2.3, 25f2.4, 26, 186f6.28 Schultz, R. J. 66 Scott, D. C. vii Shapiro, D. Y. 66 Shelden, K. E. W. 27, 55, 109, 161, 166 Shephard, J. G. 6, 89, 100, 161 Sherman, K. 164 Shields, C. A. 61 Sibly, R. M. 40, 45 Signor, P. W. 61, 62, 68, 70 Sih, A. 8 Silver, C. S. 78, 82 Simberloff, D. 7, 33, 61, 62 Simon, T. P. 9, 94 Simpson, G. G. 71, 72, 98 Sinclair, A. F. 8, 83, 138 Sinclair, A. R. E. 23, 37, 37f2.22, 40, 43, 44, 44f2.30, 45, 46f2.32, 49t2.2 Siniff, D. B. 154, 156, 198 Sissenwine, M. P. 6, 89, 100, 161

284

AUTHOR INDEX

Slatkin, M. 51, 58, 59, 62, 68, 70, 71, 72, 74 Smith, A. D. M. 89 Smith, B. F. 74 Smith, F. E. 138 Smith, H. 12, 207, 221 Smith, T. D. 8, 12, 19, 46, 74, 83, 84, 85, 92, 150, 161, 169 Snellgrove, T. A. 4, 9 Sneyd, J. 121 Sobolevsky, Y. I. 15, 17f1.8, 154, 167f6.6 Solan, M. 56, 61, 73, 74 Solé, R. V. 3, 12, 20, 121 Soulé, M. E. 61, 161 Sousa, W. P. 36f2.20, 43 Southwood, T. R. E. 25, 40 Spellerberg, I. E. 98 Spencer, P. D. 35f2.18 Spendolini, M. J. 92, 143 Spier, R. E. 187t6.3 Srivastava, D. S. 56, 61, 73, 74 Stachow, U. 87, 101, 177 Stanley, F. M. 23 Stanley, S. M. 58, 59, 62, 68, 69, 71, 72, 73, 80, 81, 85, 86, 138, 140 Stanley, T. R. 4 Starr, T. B. 73, 74, 79, 80, 106, 110, 138 Stebbins, G. L. 66 Steffen, W. 5, 10, 78, 82, 92, 98, 102, 207, 209, 225 Steiner, C. F. 56, 61, 73, 74 Steneck, R. S. 62, 68 Stengers, I. 70 Stenseth, N. C. 62, 70 Sterman, M. M. 187t6.3 Stevens, G. C. 10 Stewart, R. E. 2, 85 Stokes, T. K. 8, 83, 87, 89, 100, 138, 161 Stork, N. E. 44, 61, 188 Straskraba, M. 3, 12, 20, 50 Strickland, G. T. 187t6.3 Strong, D. R. 25 Sugihara, G. 25, 26 Swain, D. P. 8, 83, 138 Swellengrebel, N. H. 187t6.3 Swimme, B. 221

Taylor, L. R. 43 Temple, S. A. 69 Terborgh, J. 69, 98 Theraulaz, G. 121 Thomas, C. D. 61 Thomas, W. L. 6 Thompson, G. G. 199, 200 Thompson, J. N. 8, 67, 69, 83, 124, 138, 186f6.28, 187 Thorne-Miller, B. 9 Thorpe, C. 85, 101 Thun, M. J. 131, 143 Tiger, L. 6, 121 Tilman, D. 26, 68 Tilman, D. G. 78, 82 Tokeshi, M. 33, 40, 44, 127, 145 Torres, F. 78, 82 Towell, R. G. 202 Travis, J. 58, 67 Travis, J. M. J. 62 Trombla, R. 25, 26 Trotter, M. M. 6 Tudge, C. 6 Turner, B. L. 78, 82, 98 Twitchett, R. J. 61 Tyson, P. D. 5, 10, 78, 82, 98

Tainter, J. A. 102 Takacs, D. A. 87, 101, 177 Talbot, L. M. ix, 2, 4, 10, 11, 80, 81, 85, 87, 91, 92, 99, 100, 109, 110, 115, 117, 122, 130, 131, 132, 134, 136, 138, 148, 149, 161, 163, 211 Tamm, I. 187t6.3 Tamura, T. 29f2.10 Taper, M. L. 23 Tarule, J. M. 157 Taylor, B. L. 199, 200

Wackernagel, M. 213, 220 Wade, P. R. 27, 55, 109, 161, 166, 199, 200 Wagner, F. H. 8 Waide, J. B. 80, 106, 110, 142, 148 Wainwright, P. C. 58 Waldrop, M. M. 202 Wallace, A. R. 62, 70, 71, 73 Wallace, M. B. 3, 4, 81, 85, 87, 92, 100, 131, 161 Wallace, P. A. W. vii Wallace, R. L. 9

Ueckert, D. N. 186f6.28 Ulanowicz, R. E. 8, 9 Urbach, P. 114 Utz, U. P. 187t6.3 Vallentyne, J. R. 9, 112, 122 van der Maarel, E. 69 van Dobben, W. H. 81 Van Schaik, C. P. 98 Van Valen, L. 23, 67, 68, 69, 70 Van Valkenburgh, B. 58, 62, 66, 68 Van Vuren, D. H. 25, 40, 49t2.2 VanderMeulen, M. A. 3, 12, 20, 50, 138 VanderVoort, M. E. 69 Varela, F. J. 202 Vermeij, G. J. 62, 70, 73 Vitousek, P. M. 78, 82, 98, 127, 177 Vörörsmarty, C. J. 126, 127, 173 Vrba, E. S. 58, 59, 64, 72, 73, 138

Walters, C. 8, 81, 92, 100, 115, 131, 138, 161 Wamithi, M. 9, 12 Wang, X. 58, 66 Warburton, K. 25 Ward, P. D. 61 Warne, R. 39, 39f2.26 Warren, P. H. 56, 61, 73, 74 Wassenberg, K. 44 Watts, A. W. 205, 210, 212 Weigand, K. 73, 74, 220 Welbourn, P. M. 35 Werren, J. H. 66 Western, D. 61, 146 Westman, W. E. 9 White, L. 207 White, M. J. D. 36, 60, 61 Whitfield, C. L. 80, 121 Whitmore, T. C. 6, 24 Whittaker, R. H. 138 Whittemore, D. G. 87, 217 Wignall, P. B. 61 Wilber, K. 7, 52, 61, 79, 80, 104, 106, 110, 121, 125, 136, 138, 142, 148 Wilcox, B. A. 61, 69 Williams, A. J. 31f2.13, 197 Williams, D. S. 62 Williams, G. C. 10, 56, 58, 59, 61, 62, 66, 67, 71, 72, 73, 138 Williamson, M. H. 44 Willis, D. 12 Wilson, E. O. 6, 12, 60, 66, 67, 72, 134, 136, 137, 186, 186f6.28, 210 Wilson, J. W. 187t6.3 Wilson, M. E. 187t6.3 Wirzba, N. 12 Witzig, J. F. 199, 200 Wood, C. A. 9, 163 Woodley, S. 9 Woodward, G. 56, 61, 73, 74 Woodwell, G. M. 35, 78, 82, 98, 104 Worm, B. 6, 61 Wright, D. H. 176f6.15, 177, 177f6.18, 186 Wright, R. M. 146 Wynne-Edwards, V. C. 71 Yan, N. D. 35 Yodzis, P. 25, 26 Yoffee, N. 102 York, A. E. 202 Zahavi, A. 66 Zaunbrecher, D. 9, 217 Zeveloff, S. I. 43 Zimberoff, D. 205 Zuckerman, A. J. 187t6.3

Subject Index

Figures and tables are indexed in bold. abiotic environments 52, 59, 68, 70, 74, 99, 121, 128, 134, 139, 188 accounting for 99, 128, 139 see also biotic environments; climate; physical environments; weather abnormal 13, 36, 40, 87, 97, 103, 104, 109, 128, 132, 143 avoiding 3–4, 20, 22–3, 50–1, 54, 55, 60, 76, 82, 94, 117–18, 121, 122, 130, 131, 132, 133, 148, 153, 156, 157, 159 identification of 22, 23 individual species 97, 99, 105 influence 112, 114, 174 see also human influence, abnormal nonhuman species 40, 124, 127, 129 patterns 4, 6, 8, 10–11, 46 population, see also overpopulation species 22, 39, 54, 78, 115, 122, 133, 139, 143, 176–7, 201 variation 35 abnormal human influence see also human influence, abnormal adaptive management 92, 100, 115, 130–1, 134, 161, 197, 198, 209, 212, 213, 222 behavior 20–1, 144, 145, 197, 198 see also benchmarking; biomimicry; evolution Alaska (eastern Bering Sea) 13 see also eastern Bering Sea alchemy 84, 110, 206, 221, 225 fallacious information conversion 8, 10, 12, 18, 90, 91, 110, 126, 138, 163, 205, 211, 212 see also correlative information conversion allocation, see also harvests, allocation of

altered states 87, 103, 108 American Ecological Society 109 anagenesis 58, 59, 61, 63, 64t3.1, 65, 67, 69, 77 change of species lineage 64 and extinction 60, 66 see also cladogenesis animals 37, 70, 97, 103, 170, 170f6.9, 186 body size 24, 185f6.27 consumption of 186, 186f6.28 density dependence 33, 186 Antarctic 9, 93, 177, 179 Anthropocene Era 5 antropocentric factors, see also values, accounting for anthropogenics 18, 61, 68, 78, 98, 109, 138, 148, 156, 160, 167, 194, 197, 207, 215, 216 human limitations 3–5, 8, 10, 18, 19, 24, 30, 32, 81, 85, 91, 126, 141, 157, 190, 197, 216–7, 219–21 influence 5, 15, 88, 114, 123, 156, 171, 172, 173, 177, 197, 201, 202 abnormal 141, 167 see also abnormal; human influence, abnormal values 18, 52 see also values antibiotics 89, 107, 187 aphids 36 frequency 36f2.21 arthropods 34 frequency distributions 35f2.19 asking questions, see also questions, management Atlantic Ocean 28, 29 Northwest 28f2.7, 29f2.8, 144, 154, 164, 169, 170f6.8, 171f6.10 attributes, species-level, see also characteristics, species-level Australia 38 averse, see also risk, aversion avians, see also birds

avoiding, see also abnormal, avoiding bacteria 26, 33, 64, 101, 127, 187 behavior 17, 66, 75, 77, 90, 96, 107–8, 109, 145, 148, 219 characteristics 96, 114 human 140, 152 interactions 30, 50 belief/thinking systems 1, 8, 11, 12, 78, 79, 85, 91, 108, 109, 111, 117–18, 138, 158, 160, 163, 194, 195, 198, 204, 205, 206, 209, 210, 213, 215, 216, 221, 225 part of reality 92–3, 121, 221 accounting for 11, 12–13f1.4, 18, 15, 91, 160, 194, see also complexity, accounting for; human limitations, accounting for; economics, in systemic management; informative integrative patterns; politics, accounting for systemically; unknown/unknowable, in systemic management; values, accounting for benchmarking 11, 92, 140, 143, 213 see also adaptive management; biomimicry Benguela 31, 171, 171f6.10 Bering Sea, see also eastern Bering Sea best science for management: definition 1, 137, 146, 151 examples 151, 160, 218 see also human influence, abnormal responsibility for 219 see also questions, consonance bimodality 37, 39 biocentric factors, see also values, accounting for

285

286

SUBJECT INDEX

biodiversity 4, 86, 90, 105, 114, 135, 141, 143, 154, 157, 163, 192, 193, 197, 221 index 134–5 maximizing 4, 147, 163 measures 141, 146–7 biological organization 149, 158, 159, 161, 203 complexity 190 hierarchies of 116, 129–30, 141–2 levels 3, 9, 20, 55, 56, 71, 78, 81–2, 88, 92, 93–6, 101, 102, 103–6, 108, 109–10, 111–12, 114, 118f4.1, 120, 122, 125, 126, 127, 129–30, 146, 149, 157, 158, 159, 161, 169, 172, 190, 203, 219 scales in 124 and species 63 biomass 6, 16, 16f1.7, 27, 29, 37, 119, 130, 163, 183, 184 consumption rates 14, 135, 141, 151, 167f6.6, 170f6.8, 171f6.10, 197 from Earth’s biosphere 29f2.8, 29f2.9, 29f2.10 harvests of 142, 159 sustainable 150, 153, 164 production 160 species frequency distributions 37, 37f2.23 biomimicry 134, 137, 209, 213 biosphere 1, 2, 9, 55, 72, 77, 78, 79–80, 82, 87, 97, 98–9, 107, 108, 109, 116, 153, 159, 184–5 consumption rates 24, 29, 29f2.9, 29f2.10, 31, 89–90, 115, 131, 142, 153 control, lack of 109, 110 deterioration 5 and human influence on 3, 54, 80, 85, 111, 112, 174 energy consumption 175, 177f6.18 water usage 173 in management 81, 85, 89–90, 93, 94, 95–6, 100, 101, 111–16, 118f4.1, 120, 122, 134, 154, 157, 190, 213, 214 patterns 31, 40, 43, 199 problems 160 species 20, 22, 34, 61, 112 removal of 57f3.1; risks to 77, 102 standards 10 structure 22, 23 biotic environments 62, 68, 69, 70, 81, 82, 88, 128, 139, 141, 149, 184

accounting for 99, 128 see also complexity, accounting for and human interactions 159 see also abiotic environments birds 28f2.7, 32, 33, 38–9, 42–3, 53, 103, 186, 202 geographic range size 32, 51 see also seabirds birth rates 57, 61, 63, 64, 71, 145, 153, 195 body size 23, 24, 39, 40, 44, 45, 45f2.31, 47, 47f2.34, 47f2.35, 48, 51, 53, 58, 60, 63, 66, 67, 73, 75, 88, 96, 97, 98, 101, 123, 131, 144–5, 152, 154, 156, 166, 169, 172, 173f6.12, 178 characteristics 41, 42 and consumption rates 198 as correlative variable 29, 34f2.17, 37, 40, 41–44f2.27–2.30, 45f1.31, 47f2.34, 48f2.36, 124f5.1, 127, 128f5.2, 135f5.3, 145, 152, 154, 156, 166, 172–175f6.14, 176f6.16, 177, 179, 181f6.21, 182t6.2, 183f6.23, 184f6.24, 185, 189f6.30, 198–199f6.32, 201 and geographic range size 42, 42f2.27, 43 and home range size 42 human 17, 29, 29f2.10, 30, 31, 34f2.17, 124f5.1, 127, 128f5.2, 134, 135f5.3, 150, 151, 166, 168, 171, 174, 176f6.16, 178f6.19, 180, 181, 185f6.27 near 29, 34f2.17, 47–8, 127, 193, 198 mammals 23f2.1, 29–30, 37f2.23, 42, 42f2.27, 47, 47f2.35, 48f2.36, 124f5.1, 127, 174, 176f6.16, 177, 178f6.19, 180, 182t6.2, 185f6.27 patterns 39, 40, 42, 131, 134 population density 41, 44, 45f2.31, 127, 180, 181f6.21 trophic level 47–8 population variation 43–4, 145 reduced 6 species 6, 12, 22, 23, 23f2.1, 24, 39, 40, 60, 73, 75 and other characteristics 41–5; terrestrial 37, 173 body temperature 15, 53, 82, 97, 114, 115, 126, 130, 131, 143, 145, 153, 221 body weight 76, 82, 96, 97, 130, 143, 153, 168, 186, 190

see also body size burden of proof 85, 90–1, 124, 131, 148, 157, 160, 163, 218 butterfly effects 10, 83, 92 carbon dioxide (CO2) 5, 5f1.2, 10 production 30f2.12, 31, 81, 84, 119, 126, 128–9, 130, 139, 141, 150, 152, 156, 159, 160, 174, 175f6.14, 177, 178, 194, 198, 202 Caribbean 26f2.5 carnivores: body mass 48 density of 181 trophic levels 47–8, 48f2.36, 75 carrying capacity, see also Earth, carrying capacity; overpopulation cephalopods 38 cetaceans (whales) 15 biomass of 17f1.8 biomass consumption by 29f2.10, 167f6.6 changes 1, 5, 8, 104, 140 ecosystems 6, 11, 13, 101, 104 marine 9 environmental 10, 56 human 11, 17, 20, 104, 122–3, 135, 152, 160, 171, 178, 194–5, 205–9, 211, 212, 214, 218, 222, 225 belief systems 1, 138, 206, 209, 210–1, 216 paradigm 9, 118, 134, 226 see also stakeholders, changing roles species 56, 58, 59, 60, 61, 63, 64, 67,69, 88, 103, 138 and sustainability 127 characteristics, species-level 23, 40, 47, 49t2.2, see also biomass; birth rate; body size; body temperature; body weight; carbon dioxide production; distributions; evolutionary plasticity; fitness; geographic range; home ranges; metabolic rate; patterns; population; predation; selectivity; species characteristics;

SUBJECT INDEX

species frequency species-level patterns; trophic levels; variation chemicals 50, 51, 96, 104, 107, 108, 114, 125, 134, 136, 147, 152, 156, 174, 187, 189, 194–5, 197–8, 202, 218, 219, 224 cladogenesis 61, 64, 64t3.1, 66–7 and extinction 61, 67 selective 62, 64–5, 66 see also anagenesis; speciation climate 7, 11, 52, 57, 75, 99, 128, 139, 156, 169, 174, 198, 201, 203 change 68, 69, 133, 175 see also global warming cod 188, 188f6.29 coevolution 8, 18, 19, 30, 55, 67, 68, 69, 83, 88, 109, 123, 136, 138, 187, 196 commercial fisheries 19, 32, 53, 84, 127, 142f5.3, 155f5.6, 160–1, 164, 164f6.3, 168f6.7, 169, 186, 196, 199, 199f6.32 see also fish; fisheries; eastern Bering Sea complexity 9, 17, 22, 110, 121, 139 accounting for 3, 12, 44, 75, 112, 114, 119, 122, 125, 126, 130, 136, 137, 143, 149, 156, 157, 158, 159, 161, 168, 169, 171–2, 210, 211 through emergence 2, 3, 4, 12, 13f1.4, 18, 22, 23, 32, 50, 54, 55, 77, 114, 136, 148, 159, 210, 211, 218 see also belief/thinking systems, accounting for; consequences of management; control, lack of; correlative information conversion; economics, in systemic management; human limitations, accounting for; infinite, the, accounting for; informative integrative patterns; natural selection, species level, accounting for; politics, accounting for systemically; values, accounting for considerations of 109, 111, 149

see also complexity, accounting for conceptual alchemy, see alchemy conscious purpose 86, 154, 188, 219, 223 conservation 60, 88, 136, 138 consequences of management 2, 6, 40, 51, 80, 100–2, 116–17, 119, 125, 131, 156, 186, 204, 218 direct 3 indirect 3, 38, 79, 86, 223 intended 123 see also conscious purpose unintended 53, 82, 120, 123, 157, 199, 223, 225 see also complexity, accounting for; control, lack of; domino effects; ripple effects consistency: in systemic management 2, 3, 4, 26, 126, 129–30, 157, 214, 215, 220–1, 224 lack of (conventional management) 12, 81, 95, 109, 111, 120, 193, consonance, see also questions, consonance consumer species 25, 26, 38, 144, 161–2, 186, 197 nonhuman 119, 170 consumption rates 15, 28, 31, 47, 90, 143 humans vs. other species 115, 164f6.3 see also abnormal species frequency distributions 31f2.13 biomass 16f1.7, 27f2.6, 28f2.7, 29f2.9, f2.10, 31f2.13, 118f4.1, 165f6.4, 167f6.6, 168f6.7, 170f6.8, 171f6.10, 172f6.11, 173f6.12, energy 30f2.11, 175f6.15, 176f6.16, f6.17, number of species 186f6.28, prey numbers 164f6.3, water 174f6.13, see also biomass; biosphere; eastern Bering Sea; human population; plants; predation; walleye pollock control, lack of 2—3, 36, 40, 53, 79, 80, 82, 94, 96, 105, 106, 109–10, 116–17, 120, 122, 123–4, 234,

287

140, 142, 149, 156, 186, 187, 203, 207, 213, 225–6 control rules 186, 199, 200, 200f6.34, 201–2 conventional management 2, 8, 12, 17–18, 20, 30, 55, 70, 75, 77, 79, 80, 81–2, 83, 84, 85, 86, 87, 89, 90–1, 93, 94, 103, 105, 106, 110, 113, 121, 126, 136, 149, 150, 151, 179, 195, 203, 205, 207, 224 and abnormality 117–18, 193 complexity 119 errors 1 failures of 5, 9, 10, 11, 19, 78, 84, 88, 92, 95, 108, 111, 117, 120, 122, 157–8, 155, 218 and species 100, 105 transitive 79, 80–1, 82, 106, 109, 110, 114 see also human limitations; stakeholders; symptomatic relief; transitive management conventional thinking 79, 80 see also belief/thinking systems, accounting for correlative information conversion 38, 40–1, 51–2, 75, 125, 127–8, 139–41, 150–6, 172, 177, 181, 184–5, 190, 197–9, 201, 206, 210–1, 221 see also body size, as correlative variable; geographic ranges, as correlative variable dead-ends, evolutionary 67, 156, 207 see also evolutionary suicide deaths 39, 56, 61, 67–8, 82, 83, 96, 99, 100, 102, 103, 133, 134, 143, 186, 207, 208, 224, 225 debate 17–18, 50, 52, 59, 67, 72, 80, 83, 148, 179, 206, 211, 212 decision-making 2, 3, 4, 18, 43, 80, 81, 83, 84, 85, 91, 92, 106, 110, 111, 117, 122–3, 136, 148, 193, 194 conventional 8, 10, 12, 100 systemic, see also abnormal, avoiding; stakeholders, changing roles; empirical guidance 1, 2, 12, 19, 75, 113, 115, 122–3, 125–6, 131, 236, 138, 145, 148, 152, 159, 177, 194, 205, 208, 212, 216–7, 220, 226 see also empirical patterns see also patterns diets 28, 29, 58, 146

288

SUBJECT INDEX

directionality 58, 59, 67, 74 disease 6, 27, 34, 68, 87, 100, 101, 103, 107, 108, 148, 178, 187, 194, 196, 208, 218, 224, 225 diversity 61–2, 63, 76, 134 dependence 61, 104 loss of 196 of species 60, 219 and variability 132–3 DNA 51, 114, 132 domestic species (livestock) 33, 38, 86, 96, 186, 187, 189, 190 domino effects 69, 102 see also consequences of management, ripple effects Earth 122, 141, 147–8 consumption rates from biosphere 29f2.9, 29f2.10 carrying capacity 34, 97, 179, 181f6.20, 183f6.23, 201 forests 5–6 New Zealand 6f1.3 eastern Bering Sea 2, 12, 13, 14f1.5, 15f1.6, 17f1.8, 32, 78, 118f4.1, 122, 150, 151, 152, 153, 154, 156, 162f6.2, 169, 171, 171f6.10, 172, 172f6.11, 198, 206 commercial fisheries 203 consumption rates 27, 27f2.6, 53, 117, 196, 202 human influence 159 marine ecosystems 12, 13, 14–15, 16, 16f1.7, 17, 18, 19, 29, 31–2, 32f2.15, 53, 56, 75–7, 78, 111, 117–1 20, 196, 203, 203f6.37 marine mammals: geographic range 203f6.37, 203 and marine protection areas (MPA) 202–3 patterns 18, 22, 52–4, 197, 198 systemic management 122, 196–203 ecological mechanics 50–1, 52, 73, 74, 75, 86, 97, 109, 114, 115, 122, 132, 136, 138, 139, 147, 148 economics 7, 17, 18, 157, 158 in conventional management 80, 85, 89, 91, 100, 106, 118, 120, 207 in systemic management 134, 149, 152, 157, 217, 223 see also informative integrative patterns ecopsychology 7

ecosystem-based management 85–6, 87, 95–6, 100, 101, 102, 105–6, 108, 109, 110, 112, 116, 117, 122, 129, 131, 134, 161, 172, 189, 190, 203 ecosystems 2, 5, 6, 12, 12, 19, 28, 53, 55, 60, 73, 82, 93, 98, 101, 107, 108, 109, 116, 142, 153, 159, 160, 184–5, 187 changes in 101, 109–10 degradation of 196 and humans 111, 115, 132 North Pacific 76, 150 Serengeti (Tanzania) 156 southwest Africa 169 structure and function and species-level selection, see natural selection, species level sustainability 131 see also sustainability, ecosystems see also Atlantic Ocean; Benguela; eastern Bering Sea; Georges Bank education 145, 219, 211–5, 221, 223 emergence 3, 12, 18, 19–20, 22, 50, 54 dynamics 110 of observed patterns 55 of species 62 see also complexity, accounting for, through emergence empirical information 12, 42, 45, 113, 123, 136, 142, 143, 145, 148, 152, 155, 157, 158, 179, 181, 194 empirical patterns 159, 177, 200 see also informative integrative patterns empiricism 72, 76, 90, 111, 113, 114, 123, 125, 128, 133–4, 136, 143 observations 138 endangered species 9, 34, 48, 78, 85, 87, 88, 90, 100, 104–5, 123, 142, 149–50, 156, 175, 202 reduction of 156–7 Endangered Species Act (ESA) 100 energy 50, 62, 87, 157, 160 budgets 177 consumption/use 75, 81, 129, 130, 141, 143, 153, 159, 160, 191–3, 195, 198, by humans 175, 175f6.15, 176, 176f6.16, 176f6.17, 177, 177f6.18, 190, 191f6.31, 192;

by mammals 176f6.16, 176f6.17, 177, 177f6.18; reduction of 192; by other species 30, 38, 51, 175 environment 2, 5, 8, 9, 29, 50, 51, 58, 69 changes in 10–12, 34–5, 56, 68, 88 adapting to 109 conditions 50, 56, 58, 69, 128, 139, 153, 154, 198 factors 20, 24, 52, 57, 68, 69–70, 80, 88–9, 98, 99, 107, 116, 127, 169, 172, 201 patterns 23 physical 69, 70, 74, 88 problems 9, 108, 133, 189 variation 50, 51, 59, 66, 141 estrogen production 189, 189f6.30, 190 Europe 9 European Union 9 evaluation 18, 94 of fisheries 154 of humans 8, 11, 20, 77, 189 of systems 114 see also abnormal; human influence, abnormal evolution 24, 25, 66, 83, 92, 109, 139, 147, 148, 196 accounting for in management see also complexity, accounting for; emergence; natural selection, species level, accounting for change 58, 59, 67, 138 conversion to another species 60 and emergence 161 processes 63–4, 76, 114, 137f5.4 of species 66, 77 humans and 189 see also natural selection evolutionary biology 51, 138 evolutionary dynamics 51, 66 evolutionary enlightenment 8, 55, 62, 66, 67, 77, 83, 138, 199, 214 see also emergence; selectivity evolutionary plasticity 62, 66 see also generation time evolutionary suicide 66–57, 95, 156, 195, 207, 222, 224 human 77, 113, 133, 148, 158, 195, 209, 213, 222 see also extinction, risks of, human

SUBJECT INDEX

extinction 1, 4, 6, 17, 19, 20, 24, 33, 50, 51–2, 57, 59, 63, 67, 100–1, 139, 147, 177, 186 processes 59 rates 61, 62, 66, 74, 97, 175, 188–9 abnormal 119, 123 result of human activities 62, 160, 175, 188–9, 194 risks of 114, 123, 131–2, 133, 138, 143, 196 human 6, 20, 36, 42, 54, 66, 77, 82, 90, 101, 102, 103, 105, 112, 113, 115, 117, 129, 140–1, 144, 146, 148, 187, 207, 209, 218, 223, 224 accounting for, see also complexity, accounting for nonhuman accounting for, see also complexity, accounting for see also evolutionary suicide selective 21, 55, 56, 57, 58, 59, 60, 61–2, 63, 64, 64t3.1, 65–7, 70–4, 76, 77, 88, 94, 95, 96, 113–14, 131–2, 136, 137f5.4, 138, 139, 161, 214 see also species characteristics species 60, 67, 69, 75, 101, 102, 147–8 fish: consumption rates 144, 197 species 28, 46 decline in 13–15; population variation 34 fisheries/fishing 6, 17, 35, 38, 76, 82, 83, 89, 141 harvests 141, 147, 154, 157 reduction of 197 limits to 117–18 management 144, 146, 149, 166, 200f6.34 mortality rates 186 sustainable catches in 152, 169 see also commercial fisheries fitness (sustainability): neutral 195 species-level 57–8, 59, 64, 66, 67 fixed points 147 flounder 117, 119 food 161 chains 6 consumption 89–90, 173f6.12, 175 harvests 177 supplies 103 webs 25–6, 187 see also diets;

nutrients forests 5–6, 6f1.3 fossil fuels 175, 195 frequency distributions, see also species frequency distributions fresh water: mammals 44, 174f6.13 supply 173, 174, 174f6.13 systems 86, 103 fundamental assumptions, control of environment 109–10 generation time 39f2.25, 48, 63, 66 and characteristics 41 future 6, 59 rate of increase 39, 40, 44, 45, 46, 46f2.32, 46f2.33, 53, 185, 190 see also evolutionary plasticity genetics 8, 51, 56–7, 58, 76, 82, 86, 89, 93, 95, 97, 100, 102, 104, 107, 110, 116, 138, 161, 163, 195 engineering 188 modeling webs of 187 genomes 57, 63, 83, 134 geographic ranges 202, as correlative variable 42f2.27, 43f2.28, 44, 52, 76, 181, 183f6.23, 198 human 176, 177f6.19, 187 mammals 178f6.19 patterns in 70, 195 size 23, 40, 42, 43, 51, 72–3, 92, 96, 107, 115, 118, 129, 141, 144, 145, 147, 147, 153–4, 157, 159, 160, 176, 177, 178, 178f6.19, 179–80, 181, 183f6.23, 195, 198, 213 species 31, 32, 33, 42, 53, 69, 177, 181, 182, 202 see also body size, and geographic range size Georges Bank 28f2.7, 171 global control rules, see also control rules global problems 77, 78, 82, 90, 141, 158, 159, 160, 202 deforestation 1 see also biodiversity; extinction; carbon dioxide; endangered species; energy; global warming; ocean acidification; overpopulation; pollution; water

289

global warming 5, 30, 53, 78, 90, 128, 194, 197 goals, see also objectives/goals goods 95, 99, 115 and humans 7, 100 loss of 6 grasslands 6 group selection 67, 71, 72, 169 habitats 34, 42, 59, 68, 70, 78, 87, 98, 118, 147, 179, 197 hake 164, 165f6.4, 169 harvests 16, 24, 31, 43, 83, 88–9, 103, 127–8, 139, 171 allocation of 119, 152, 153, 159–60, 164, 171 fish 150, 196; seasonal 198; spatial 198 from biosphere 173f6.12 of cervids 170 commercial 171f6.10 from ecosystems 15–16f1.7, 118f4.1, 171f6.10, 172f6.11 evolutionary effects 138, 163 fish 93, 119, 162–3, 168f6.7 reduction of 197 limiting 166, 200 from multiple species 15–16f1.7, 118f4.1, 155f5.6, 169–170f6.8, f6.9, 199f6.32 production 117 rates 15, 24, 40, 46, 119, 127–8, 151, 163, 165–6, 197, 198, 202 patterns 199; sustainable 46, 47, 139, 154, 155, 156, 161; of virgin population levels 199, 200–1, 201f6.36 of species 87, 100, 115, 118, 119, 120, 130, 142, 152, 198 see also cod; hake; herring; mackerel; walleye pollock health 121, 153 biological organization 96 biosphere 99 ecosystems 7, 9, 11, 87, 95, 99 human 187 typical 95 see also abnormal, avoiding; human influence, abnormal; sustainability

290

SUBJECT INDEX

herbivores: body mass 44 frequency distributions 128f5.2, 183f6.22 population density/body size 45, 45f2.31, 47–8, 179, 180–1, 181f6.21 trophic levels 33f2.16, 44, 48f2.36 herring 164, 165f6.4, 169 hierarchies: complexity 122, 161, 172, 190 conflicts 111 constraints 148 fractal 77, 122 and inclusive systems 150 interactions between 104, 110 levels 62, 68, 73, 104, 110, 114, 115, 116, 141–2, 213 and reality 130 and species 133 structure 28, 29, 77, 81, 130 see also wholes/parts systems holistics 54, 86, 216, 222 accounting for complexity 3 design 154 management 104, 223 guidance 8 of reality 2 home ranges : and body size 42–3, 43f2.29 size 41, 218 mammals 42–3 species characteristics 48–9 and variation 48–9 homeostatic dynamics 121 homeostatic processes 122, 132, 133 homeostatic properties 65, 97, 98, 100–1 human abnormality, see also human influence, abnormal human footprints 176, 213, 220 as individuals 176 as a species 176, 213 human influence 1, 19, 30, 39, 40, 50, 52, 86, 87, 97, 98, 108, 110, 117, 119, 123, 131, 132, 139, 147, 153, 164 abnormal 11, 15, 16, 18, 40, 61, 82, 102, 112, 113, 117, 121, 123, 126, 129, 133, 135, 139, 141, 143, 146, 149, 153, 156, 159, 160, 193, 196, 197, 203, 204 comparison with other species 77, 12, 118, 127, 140 16, 18, 53–4, 98, 99, 159, 193 carbon dioxide production 175 energy consumption 175–7

functional response 200 geographic range 178 harvest rates 15, 18, 118, 162–5, 168, 170–3, 201 size selectivity 187–8, 199 toxin production 189 water usage 174 and biosphere 160 controlling, see also intransitive management and ecosystems 11, 15, 12, 35, 85, 88, 160 individual species 154 on nonhuman species 3, 186 on other species 21, 30, 39, 40, 88, 123, 164, 165, 169, 172, 175, 190, 197, 199, 201, 203 and sustainability 171 human limitations 3, 4–5, 8, 10, 18, 19, 20, 24, 30, 32, 78, 81, 85, 91–2, 102, 158, 197, 211, 214, 216, 217, 219, 220, 221 accounting for 111, 124, 126, 127, 157, 190 human population 6, 20, 24, 26, 30, 87, 102, 107, 108, 123, 126, 127, 133, 135, 139, 144, 145–6, 178, 179, 180t6.1, 181, 182, 184, 184f6.25, 185, 214, 218, 223 body mass 53, 75, 184f6.25, 185 comparisons with other species 182–4, 184f6.25, 198 consumption rates 75, 76, 105, 115, 151, 164, 167, 172f6.11, 173f6.12, 175, 176, 186f6.28, 218 density 179–182f6.21, t6.2 dependence 180, 183f6.22, 183f6.23, 184, 185, 185f6.26 increase of 108, 135f5.3, 151, 157, 160, 179, 185 interactions: with ecosystems 122, 153; nonhuman species 29, 160; other species 80, 113, 127, 154, 160, 170–1 limitations 3, 4, 8, 10, 18, 19, 24, 30, 32, 46, 50, 78, 79, 81, 85, 86, 91, 126, 127, 141, 157, 190, 197, 209, 219, 221 needs 80, 85, 103, 104, 105, 107 and ecosystems 93, 96 and nonhuman species 1, 8, 11, 29, 40, 76, 79, 80, 96, 123, 160, 193, 217

reduction of 90, 102, 115, 130, 135, 178, 187, 192–4, 195, 196, 206, 215, 222 size 179, 180, 181, 182, 182f6.2, 184f6.24, 185, 192, 193, 218, 220, 221 sustainable levels 180t6.1, 181, 181f6.20, 182, 193, 194 values 78, 111, 133, 156, 163, 190 see also overpopulation humanitarian aid 82 Humpty Dumpty syndrome 10, 18, 81, 91, 136, 137 individual species 23, 26, 32, 59, 63, 70, 76, 78, 81, 82, 85, 93, 98, 99, 105, 108, 114, 116, 119, 129, 131, 143, 144, 153, 158, 159, 161, 170, 173, 197 charcteristics 42, 57, 62 consumption rates 14, 16, 28, 29 harvests of 130, 142, 170 management, see also conventional management; transitive management patterns 40, 161 systemic management, see also self-control, species level variation 36 infinite, the 3, 12, 13f1.4, 210, 215, 226 accounting for 12, 18, 20, 24, 75, 114, 125, 138, 211, 219, 222, 223 information 1, 2, 12, 17, 18, 19, 20, 21, 81, 100, 125, 129, 141, 153–4, 160 choice/selection 83, 84, 91, 103–4, 111 complexity 125, 160, 190 conversion, see also alchemy consonance 125, 127, 141, 149, 154, 155, 158, 161, 174, 177, 188, 198, 203, 212, 218 correlative 127, 128, 139, 140, 141, 150, 151, 152, 154, 157, 158, 172, 182, 199 guiding 125, 127, 131, 135–6, 141, 148, 150, 153, 157, 158, 159, 166–7, 168, 190, 196, 197, 200, 203, 205–6, 210, 219 in management 9, 10, 17, 18, 22, 110, 117, 120, 134, 139, 189, 215 systemic 114, 172, 188 normative 5, 97–8, 99, 179 and patterns 23, 37, 38, 46, 54, 78, 112, 114, 161, 166, 169, 174, 188, 197, 198, 208, 217, 219

SUBJECT INDEX

and species 135, 135 see also best science for management; empirical information; scientific information informative integrative patterns 4–5, 30, 44, 53, 172 see also abiotic environments, accounting for; biotic environments, accounting for; complexity, accounting for; economics, in systemic management; human limitations, accounting for; politics, accounting for systemically; values, accounting for ingestion rates 41, 97, 114, 153, 168, 173f6.12, 176, 177, 192 energy 175, 175f6.15, 176f6.16, 192 insects 23, 25, 25f2.3, 47, 60, 64, 101, 186 interactions: human and nonhuman species 205 strengths 26 interconnections 6, 81, 129, 193, 218 Interagency Ecosystem Management Task Force 4 International Whaling Commission 17 intransitive management 1, 3, 12, 18, 54, 87, 93, 107, 110, 112, 115, 116, 119, 120–1 see also self-control, species level irrigation 104–5, 173–4 isomorphism 17, 147, 149, 159 see questions, consonance keystone effects 187 laws of nature 2, 17, 80, 116–17, 122, 140, 143, 156 legislation 5, 18 U.S.A. 9–10 lifetime reproductive effort (LRE) 39, 39f2.26, 40 limits, see human limitations; natural variation, limits to logical alchemy 84 see also alchemy LRE, see also lifetime reproductive effort mackerel 29, 29f2.8, 164, 165f6.4, 169 macroecology 11, 13, 22, 23, 52, 54, 55, 60, 62, 63, 127, 134, 219

macroevolution 19 making change, see also change mammals 28, 48, 48f2.36, 49, 166, 202 body mass 24f2.2, 43, 44f2.30 and home range size 43f2.29 consumption rates 37f2.23, 173f6.12, 186 generation time 46f2.32 energy use 175f6.15 of human size 33–4, 34f2.17, 184f6.24 patterns 30 population: and body size 135f5.3; density 47; increase 39f2.25; variation 47f2.35 species frequency distributions, see species frequency distributions see also body size, mammals; marine mammals; North America, mammals; terrestrial mammals management: adaptive, see also adaptive management actions 4, 48, 54, 78, 83–4, 87, 88, 90, 94, 95, 100, 104, 106–8, 117, 120, 123, 131,133, 140, 142, 145, 151, 152–3, 159, 160, 166, 190, 193 definition 112 implementation 93, 106, 111, 157, 158, 159 see also conventional management; systemic management management questions, see also questions, management managers: complexity 5 conventional 219 objectives 4 patterns 54, 155 and protected areas 202 questions 223 roles 10, 18, 87, 90, 94, 199, 202, 207, 212, 214, 218, 219, 223 and systemic management 161 marine ecosystems 13, 78 marine environments 173 marine fish 35f2.18 Marine Mammal Protection Act (MMPA) 9, 202 marine mammals 14, 27, 28f2.7, 31, 32f2.15, 198, 199f6.32 body mass 24f2.2

291

consumption rates 29f2.9, 76, 169, 172f6.11, 173f6.12 diets 29f2.8, 38, 188 geographic range 203f6.37, harvests of 168f6.7 population size 34f2.17 and prey 38f2.24 species frequency distributions 118f4.1 sustainability 142 trophic levels 11ap2.1, 12f2.1.1 variability 16f1.7 see also eastern Bering Sea, marine mammals marine protected areas (MPA) 19, 118, 153, 198 Marine Stewardship Council 18, 117, 118 maximum sustainable yield (MSY) 46, 80, 83, 84, 89, 138, 146, 150, 151, 163 measures: of biodiversity 197–8 characteristics 150 consumption 154 counterparts 158 errors 114 of humans 135, 175 see also abnormal of species 129, 132, 139, 154, 177 metabolic rate 21, 96, 97, 123, 127, 144–5, 153, 198 of humans 17 total 88, 99 microevolution 59, 66, 70, 75, 132 mimicry, see also biomimicry mitigation 85, 86, 89–90, 124, 190 MMPA, see also Marine Mammal Protection Act mobility 52, 70, 107, 177–8, 187, 190, 218, 220 models 82–3, 89, 126, 136, 139–40, 143 complexity 125 empirical (patterns) 125 information-based 74, 84 of nature 130 populations 107 quantitive 161 and reality 91, 111 reconstructive 198 simulation 161 statistical 74, 92–3, 96 monitoring 33, 83, 88, 94–5, 96, 99, 103, 104, 131, 136, 138, 139, 140, 157, 167, 198 morphology 49, 62, 84, 151–2

292

SUBJECT INDEX

mortality rates 56, 60, 63, 77, 86, 102, 133, 153, 155f5.6, 165, 166f6.5, 195 total 154, 186, 200, 201f6.36 moths 36, 36f2.21 MPA, see also marine protected areas; eastern Bering Sea, and marine protected areas MSY, see also maximum sustainable yield multispecies 12, 15, 19, 85, 87, 169–70, 197 mutation 58, 59, 64, 77, 188 Namibia 31f2.13 Nash equilibria 55, 64, 114, 115, 121, 122, 126, 138 natural resources 8, 30, 109 natural selection 20, 50, 51–2, 55, 56, 57f3.1, 59, 61, 64, 70, 74, 88, 114, 122, 131, 146, 148, 207 individual level 50, 51–2, 54, 55, 59, 64, 65, 68, 71, 72, 77, 148, 222 levels of 24, 56, 113, 214, 226 conflict among 65–6, 67 see also evolutionary suicide risk 133 species level 23, 51–2, 55, 60, 71, 73, 88, 222 accounting for 74, natural variation 88, 94, 98, 125, 126, 127, 129, 133, 134, 138, 142–3, 149, 152, 219 and ecosystems 76, 87 and human population 112, 118f4.1, 123, 138 and individual species 99–100 limits to 13, 23, 43, 53, 88, 89, 97, 123, 125, 126, 129, 133, 134, 138, 149, 153–4, 157, 160, 161, 169, 176, 178, 184, 186–7, 190, 197, 198, 214 normal range of 11, 15, 53, 76, 82, 87, 88, 89, 97, 99, 102, 117, 120, 121, 130, 132, 133, 141, 143, 144, 147, 149, 150, 151–2, 153, 157, 163, 164, 166–7, 172f6.11, 178, 220–1 and species 49, 76, 98, 113, 128, 132, 164, 169 sustainability 159–60 nature 24, 84, 157 complexity of 166, 190 dependence on 7–8 learning from 19 for leisure pursuits 7 observation of 19

patterns 24 nature-deficit disorder 7, 207, 210 New Zealand, forests 6f1.3 niche theory 122, 131 nitrogen 126–7, 203 nonevolutionary factors 19, 20, 50, 54, 86, 113, 147, 148 normal, see also sustainability, definition, normal normal range of natural variation see also natural variation, normal range of; patterns; species frequency distributions nonhuman species: avoiding control of 130 changes in systems123 consumption by 115, 198, 201 see also predation; predation, consumption rates density dependence 36 and ecosystems 79 human influence on 2, 3, 26, 50, 52, 76, 101, 110, 144, 154, 164, 172, 175, 182, 186, 187, 197–8, 206 see also abnormal, avoiding control of 1, 3, 79, 94, 159 predators 64, 163, 164, 167, 170, 188, 200 species 15, 27, 27f2.6, 31, 33, 35, 36, 40, 76, 135, 141, 150, 169, 170, 176, 182, 183, 187, 189, 193, 198, 214, 215, 216 manipulation of 117; population effects 186 sustainability 205 variation 117 see also human influence North America 7 birds 32 mammals 31, 177 body size 23f2.1; geographic range 42f2.27, 43f2.28, 178f6.19 terrestrial mammals 25, 32f2.14 North Pacific Fishery Management Council 17 numbers, see also population, numbers nutrients 50, 98, 122 objectives/goals: in conventional management 8, 84, 85, 91, 95, 96, 100, 108, 109, 110, 117 in systemic management 1, 4, 121, 54, 75, 119, 121, 122, 130, 131, 134, 140–1, 145, 152

see also abnormal, avoiding; biodiversity, maximizing; sustainability objectivity 4, 5, 123, 134, 197 in conventional management 8, 20, 91, 111, 216, 224 in systemic management 1, 2, 75, 78, 149, 205, 214, 217 see also economics, in systemic management; complexity, accounting for ocean acidification 5, 75, 78, 90, 174, 141, 145, 149, 152, 153, 154, 157, 158 omnivores 181 organization of environment 59, 62, 63, 68, 69, 70 see also biological organization; stakeholders Origin of Species (Darwin) 71 ourselves, control over, see selfawareness overharvesting 202 overpopulation 78, 82, 83, 103, 105, 107, 127, 134, 140, 141, 143, 178–84, 195 human 180, 181, 184f6.24, 194, 196 see also human population paleontology 62, 70, 71, 72, 73, 74, 77, 138 paradigms: belief systems 62, 67, 118 human change 9, 19, 134 parasites 25–6 , 27, 36f2.20, 68, 69, 70, 100, 187 pathological, see also abnormal; human influence, abnormal pattern-based management 12, 17, 18–19, 20, 32, 50, 55, 109 consonance 129, 166 see also systemic management patterns: analysis of 98, 137, 138 and complexity 12–13, 13f1.4, 14, 17, 54, 114, 125, 182 and conflict 113 choice of (for management), see also best science; questions, consonance correlative 38, 40, 43, 51, 75, 156, 185, 190, 197, 199, 221 emergence of 4, 24, 34, 53, 134, 138, 143, 159, 210, 211, 218 integrative nature 12, 22, 30, 46, 111, 113–14, 156, 161, 169, 190, 197

SUBJECT INDEX

management 22, 23, 33, 40, 117, 139, 211, 212, 220, 224 multidimensional 144 natural variation 1, 3, 4, 19, 46, 195, 201, 217, 210, 225 observed (in nature) 24, 25, 35, 49, 55, 63, 78, 125, 126, 129, 138, 197–8, 211, 217 risks 121 see also species frequency distributions; species-level patterns pesticides 101, 104, 119, 174, 186, 187, 189, 196, 199 pests 6, 18, 79, 80, 81, 86, 93, 96, 101, 104, 107, 108 phenotypes 51, 100, 196 philosophy 10, 19, 102, 158, 203 physical environments 62, 69, 70, 74, 88, 147 see also abiotic environments physical scales 133 phytoplankton 28 piecemeal, see also reductionism plants 24, 44 consumption rates (human) 186, 186f6.28 politics 84, 140, 145, 158 accounting for systemically 2, 15–16, 18, 140, 145, 148, 149, 158, 195, 212, 214, 218, 220–1 in conventional management 8, 10, 12, 17, 79, 90, 107, 110, 120, 134, 199, 210, 217, of individual rights 134 pollution 1, 9, 15, 26, 52, 53, 68, 75, 78, 90, 104, 107–8, 119, 152, 160, 174, 189, 194, 197, 198, 202 population 16, 34, 35, 35f2.19, 36, 36f2.20, 43–5, 61–3, 64, 65, 88–9 body mass 47 density 33, 37, 44, 127, 144, 145 growth/increase 37, 37f2.22, 39, 40, 46f2.33, 150–1 numbers 25, 33–4, 42, 62, 71, 98, 164, 198 reduction of 88, 89, 100, 104, 107, 126 reduction of 141, 145, 156–7, 195–6, 199–200, 220, 225 target levels 200 total 33–4, 100, 182, 191, 191f6.31 variation 34–5, 35f2.18, 36, 40, 42–3, 62–3, 65, 98, 132, 145 see also body size; human population;

overpopulation praxis, see also management, action; management, implementation predation 56, 68, 122, 125, 127–8, 136, 139, 142, 146, 154, 164, 169, 200 consumption rates 27, 27f2.6, 28, 28f2.7, 29, 29f2.9, 30, 34, 162–3, 197, predator-prey interactions 8, 38, 50, 52, 69, 87, 117, 165, 169 predators 26, 28, 37, 43, 46, 53, 68, 103, 119, 152, 154, 155, 187 consumption rates 170f6.8 prey species 25, 28, 31, 40, 52, 68, 103, 144, 150, 152, 163, 199f6.32 body size 53 density 154, 201 humans as 187t6.3 predation rates 201f6.35, 201f6.36 sustainable harvest 150 primary consumers 25, 28, 75 primary producers 24, 28, 43, 177 Principles of Geology (Lyell) 71 problems, see also global problems proof, burden of 91, 148, 157, 163 pseudo-extinction 60, 65, 66 questions: consonance 30, 125, 127, 137, 147, 151, 158, 159, 170, 182, 186, 189, 197, 203–5 on harvest rates 154 management 1, 2–3, 4, 6, 7–8, 15, 16–18, 19, 20, 75, 84, 111, 117, 126, 130, 141, 149, 150, 151, 152, 153, 155, 156, 157, 160, 161, 163, 164, 166, 174, 188, 190, 198 and patterns 22, 23, 24, 26, 29, 40, 41 policy setters 101–2 research 152, 212 on water 173 see also decision-making randomness 59, 64, 67 range, normal range of natural variation, see also natural variation, normal range of reality 52, 67, 77, 82, 84, 89, 90, 91, 92, 103, 120, 122, 159, 161 accounting for, see also complexity, accounting for complexity of 24, 50, 55, 93, 105, 106, 124–5, 137, 160, 171, 190, 195 elements of 116, 125, 130

293

and humans 79, 80, 85, 111, 123 infinite of 113–14 and models 139 reality-based management, see also systemic management reductionism 10, 81, 84, 110–11, 126, 127, 137, 139, 150, 154, 207, 209, 212 misuse of 81, 84, 111, 207 use of 110, 137, 139, 150, 154, 207, 210, 212 see also human limitations, accounting for relationships: correlative 75, 125, 127, 128, 139, 156, 167, 172, 181 ecological 85, 87 and ecosystems 12, 121 with humans and nonhuman species 1, 6–7, 8, 76 and other species 54 with humans and other systems 75, 133, 206 with species 52, 69, 75, 100 religion 96, 140, 212, 214, 215, 216, 217, 218, 221–2, 223 accounting for, see also belief/ thinking systems, accounting for; infinite, the, accounting for; unknown/unknowable, in systemic management replication 63, 64, 66, 137 reproduction, see sexual reproduction research 4, 8, 24, 26, 34, 41, 41t4.1, 43, 44, 48, 49, 50, 51, 52, 53, 54, 72–3, 83, 88, 90, 127, 129, 131, 136,139, 143, 144, 146, 147, 149, 150, 151, 152, 153, 154, 155, 161, 186, 189–90, 193, 197, 199, 201, 203 see also best science for management reserves 153, 177 marine 196, 202–3 resource: consumption 145, 157, 159, 160, 185–6, 195 restriction of 145–6; sustainable 24, 25, 26, 31, 32, 35, 37, 40, 42, 43, 61, 75, 126 ecosystems, use of 203

294

SUBJECT INDEX

resource: (Cont.) individual 8, 28, 29, 38 management of 109 see also transitive management managing human use of 139, 161 see also intransitive management renewable 194 species 2, 13, 16, 17, 18, 27, 28, 31, 38, 105, 114–15, 130, 131, 142, 151, 154, 164, 169, 182, 186, 197 humans as 187 use 160, 169, 171, 174, 177 see also food ripple effects 80, 88, 123 risk 78, 99–100, 101, 105–6, 109, 133 abnormal 132 aversion 4, 115 and limited knowledge 136 in patterns 54 reduction of 133 sand eels 38 science 23, 24, 26, 49, 50, 105, 109, 120, 137–8 revelation of patterns 195 products of 78, 84 see also best science for management scientific information 4, 5, 10, 12, 17–18, 22, 54, 82, 83–4, 90, 91, 95, 111, 136, 145, 151, 197 seabirds 31f.13, 38, 196–7 see also birds selective extinction, see also extinction, selective selective speciation, see speciation, selective selective systems failure 55, 76, 148 see also adaptive management; extinction, selective; trial-and-error selectivity 36, 38, 40, 55–6, 57, 69, 74 fish harvests 163, 188 individual level 57, 58 opposing forces 65 see also natural selection, levels of, conflict among size 189–9 see also extinction, selective; speciation, selective self-awareness 110, 117, 121, 140 self-control, species level 1, 3, 12, 93, 110, 120, 121 see also intransitive management services 6, 7, 95, 99, 100, 115, 193

sexual reproduction 37, 56, 57, 58, 66, 67, 77, 134, 195 Shetland Islands 38, 16ap2.1, 16apf2.1.11 single-species 8, 15, 19, 22, 40, 63, 69, 82, 85, 88–9, 119, 129, 161, 163, 166, 179, 199 social sciences 140 socioeconomics 163, 195 South Africa 31f2.13 spatial scales 11, 17, 101, 126, 129–30, 133, 152 specialists 79, 85, 103, 110 specialization 70 speciation 17, 19, 20, 50, 51–2, 59, 61–2, 63, 67, 69–70, 84, 139, 147, 214 nonselective 57, 58 patterns 71, 113 selective 55, 56, 59, 63, 64, 66, 67, 70–4, 75, 76, 77, 88, 94, 101, 113–14, 131, 132, 136, 137f5.4, 138, 139 species 11, 12, 20, 41, 49t2.2, 51, 112, 116 abnormal characteristics 105 body mass 24, 114, 183, 184f6.24, 184f6.25, 185 change 116 comparisons among 57, 98–9, 114 conflicts 113 density dependence 185f6.26 human consumption of 164 interactions with 97, 114, 161 strength 26, 26f2.5 management of 87, 93, 96, 97, 100, 103, 105, 107, 108, 153 patterns 19, 20, 22, 23, 60 reduction 146 survival of 194–5 turnover 134, 147 see also body size, species species characteristics 22, 23, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72–3, 94, 96, 97–8, 100, 103, 105, 107, 115, 127, 128, 131, 137f5.4, 149, 169, 172, 173, 184, 185, 190 abnormal 130, 160, 190 ecosystems 61, 75, 98 interrelationships 61, 177 three dimensions 41, 46–7, 47f2.34, 48–50 two dimensions 26, 33, 40, 41, 42–3, 44, 49, 191 species frequency distributions 12–13, 13f1.4, 16f1.7, 20, 24, 33, 36f2.20, 37,

41t2.1, 50, 51, 52, 53, 56, 57, 60, 61–2, 63, 65, 67, 70, 74, 76, 77, 98, 120, 124f5.1, 125, 128, 132, 134, 135, 136, 141, 143, 144, 145, 148, 169 human population 143 individual species 114 information 145 patterns 16f1.7 predators 25f2.4 species 114, 115, 132, 133, 134, 142f5.5, 196 predation 125, 162f6.1; rarity of 117 trophic levels 25f2.3 species-level selectivity 20, 58, 70, 74, 75 species-level patterns 22, 68, 71, 72, 73, 74–6, 77, 95, 97, 98–9, 116, 117, 119, 123, 129, 133, 136–8, 143, 144, 145, 146–7, 148, 152, 154, 155, 161, 169, 194, 198 consumption rates 165f6.4 correlative relationships 138–9 stakeholders 2, 3f1.1, 4, 7, 8, 10, 12, 16, 18, 20, 80, 81, 83, 85, 87, 89, 90, 91, 110, 117, 126, 137,149, 150, 152, 161 changing roles (in systemic management) 2, 3f1.1, 4, 18, 20, 149, 152, 214, 216 standards, for ecosystems 10–12, 87, 94 starvation 6, 100, 107, 108, 133, 134, 146, 148, 194 statistics 91 analysis 93 Bayesian 114, 149 central tendencies 83, 143, 147, 162–3, 164, 186, 191–2, 197 models 81, 140 Steller sea lion 15, 156 subpatterns 42, 100 correlative 46, 51, 75, 152,198, 211 sustainability 2, 16, 18–19, 20–1, 30, 76, 83, 89, 113, 115, 120, 121, 131–2, 140, 146, 149, 158, 167, 197 changes required 11, 20, 191f6.31, 192, 194–5 definition: normal 82, 85, 109, 121, 122, 130, 132, 133, 208, 225 what works 21, 132, 188, 111 ecosystems 16, 83, 112, 131, 132, 204, 217 harvest rates 24, 26, 40, 81, 83, 114–15, 155, 164, 166

SUBJECT INDEX

and humans 1, 19, 79–80, 95, 115, 117, 122, 132, 140, 144, 159, 168, 197 see also abnormal, avoiding nonhuman species 1, 205 objectives 160 population 33–4, 192 total 34 of species 1, 4, 15, 20, 40, 77, 138, 143, 153, 218, 222 systemic 4, 9–10, 174, 221 see also extinction symbiotic interactions 68, 94 symptomatic relief 85, 105, 106, 107, 108, 124 systemic management as reality-based management 20, 113, 121, 149, 153, 161, 190, 203, 205–6, 221, 223 definition 20, 121, 149, 153, 161, 190, 203, 205, 206, 207, 211, 213, 214, 221, 223 patterns 113 species-level, see also self-control, species level see also abnormal, avoiding; evolutionary enlightenment; human influence, controlling; intransitive management; sustainability targets, see also objectives/goals taxonomy 62, 64, 71, 72, 77, 127, 161, 169, 186 temporal scales 17, 101, 126, 129–30, 133, 145, 152 temporal variability 144 temperature (weather) 52, 156 changes 68, 99 regime 50, 52 water 56, 147 terrestrial mammals: body mass 24f2.2 energy consumption 30f2.11 frequency distributions 174f6.13 North America 25, 32f2.14 population size 34f2.17 U.S.A. 25 see also body size, terrestrial species

tradeoffs 78, 80, 94, 96, 190–1, 191f6.31, 193 traits, see also characteristics transitive management 3, 9, 18, 78, 81, 85, 86, 87–8, 89, 93, 94, 95, 102, 103, 107, 108, 109, 110, 112, 116, 148, 153, 215 trial-and-error: processes 76, 92, 103, 114, 131, 152, 161, 205, 206, 209, 212 natural selection 60 see also selective systems failure trophic levels 6, 11, 12–13, 16, 17, 23, 24–5, 47, 52, 67, 73, 75, 76, 127, 143, 144, 154, 156, 171, 176, 178, 198 see also body size, population density, trophic levels; marine mammals, trophic levels trophic webs 196 as correlative variable 76 ungulates 154, 155f5.6, 161, 165, 166, 170 mortality rates 166f6.5, 186 unknown/unknowable: in conventional management 10, 82, 89, 94, 101, 105, 106, 117, 163, 219 in systemic management 32, 50, 141, 158 see also complexity, accounting for U.S.A 165 legislation 9–10 values 4–9, 12, 17–19, 78, 79, 81–2, 87, 91, 94–5, 97, 103, 105, 110, 111, 118, 132, 163, 195, 205, 207, 216–7, 219, 222, 224–5 accounting for 11, 15–16, 17, 18, 216 see also anthropogenics; belief/thinking systems variability 11, 13, 14, 15f1.6, 16f1.7, 17f1.8, 19, 26, 33, 58, 59, 81, 97, 132 variation 204

295

central tendencies of 76, 133 correlative 23, 41, 42–3 nonhuman species 35, 117 normal 4, 10–11, 13, 14, 30, 53, 97 among species 26 patterns of 4, 5f1.2, 19, 22, 23, 40, 43, 46, 50, 51, 58, 63, 154, 219 population34–5, 35f2.18, 36, 39, 43, 44, 47, 97 size 15f1.6, 34–5, 35f2.18 among species 15, 22, 47, 51, 59, 60, 64, 76, 78, 98, 106, 127, 132, 147, 152, 202, 217 see also population, variation; natural variation vertebrates 27, 28, 28f2.7, 34 species frequency distributions 35f2.19 walleye pollock (Theragra chalcogramma) 28, 76, 118, 119, 142f5.5, 164, 197, 199 commercial fisheries 199f6.33 consumption rates 27f2.6, 117, 144, 146, 162f6.1, 164f6.3 geographic range size 27 sustainable harvest 53, 117–18, 149, 150, 151, 152, 154, 156, 161–2, 163, 167, 188 reduction of 120, 162f6.2 water 126–7, 141, 156 human consumption 153, 159, 173–4, 174f6.13 weather 52, 75, 128, 134 whales, see also cetaceans whaling 53, 197 whole/parts systems 18, 28, 79, 106, 110, 121, 125, 136, 138, 148, 195, 213, 222 see also biological organization, hierarchies of; complexity, accounting for; hierarchies World Conservation Monitoring Centre 7, 78, 82 zooplankton 28

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Appendix 1.1

The following material is Appendix 1.1 for Chapter 1 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Reality/complexity Systemic management is reality-based management and the term “reality” is used throughout this book; the term deserves definition. We need at least some common understanding of what is meant. In simple terms, the way reality is defined and used in this book, nothing is excluded—absolutely nothing. Defining reality is an ultimately impossible challenge because we can’t know or conceptualize reality completely; we are dealing with the ineffable. Trying to capture the concept in words is futile owing to limitations of the human mind, imperfection, and limitations of words as human constructs, and the infinite of reality. Nevertheless, the following is an attempt to put words to this task so that there is at least a superficial sense of how the term reality is being used. Complexity is also a frequently used term. In this book, complexity refers to the complex nature of reality. Reality, as used in this book, refers to everything—not just collectively but to directly include all of the parts of reality (i.e., holarchically, or a nonexclusive combination of monism and dualism that also allows for reductionism). Reality is a system made up of systems. If it exists, happens, or occurs, it is part of the reality to which the term refers; reality embodies the holistic. This includes a “supernatural” God, if such a God is real. The whole of reality includes its emergent characteristics thus transcending its parts. Being the singular whole-with-its-parts, the only thing of which reality

can be a part is itself—the ultimate of holism, and something that some religions might refer to as God. It includes Gregory Bateson’s (1972) pleroma and creatura and their combination and interactions. Each of us is a part; our minds are parts; our societies, religions, emotions, perceptions, beliefs, and values are parts. All real things can be referred to as realities so there is potential for confusion. This confusion is avoided in this book by using the term “a reality” (or “the reality”) to refer to any part of the ultimate reality referred to by the use of the more general term “reality”. Thus, every species is a reality, every process is a reality, every belief is a reality, every quark is a reality, every force is a reality, and every black hole is a reality; energy is a reality, our economic system is a reality, but each is always merely one of the parts of reality with all of its interactions and connections with, and effects on, the other parts. All parts, connections, and effects are components of reality making it a system with all of its sub-systems. In set theoretical terms, the word reality is used in this book to refer to the union of all infinite sets. This assumes that everything finite is itself infinitely complex (e.g., there is an infinite set of points between any two points separated by a finite distance, in time or space). The risk in this definition is that of confining the definition to the aggregate (a kind of monism). Reality involves an explicit (holarchic) existence for all of the real parts— including its subsystems. Thus, categories of parts, such as physical, material, or substantive elements of reality are subdivisions of reality as are categories that include relationships and interactions that are not tangible—to include the mind, the spiritual, cybernetic, and informative. Therefore, included in the reality being defined here are all processes and forces (e.g., evolution, 1

2

SYSTEMIC MANAGEMENT

predation, emergence, gravity, photosynthesis, magnetism, decomposition, oxidation, feedback, politics, extinction, consciousness, chemical reactions, conversion from energy to matter and matter to energy, dispersal, experience) even though our senses tell us that they do not have the same kind of substantive or physical existence of more material things. They may be of different realms but every realm is part of reality. Qualities are considered parts of reality so that mass, color, information content, size, and shape are real, as are short-term, or long-term phenomena. Time and space are real parts of reality. History and historical events are real1 as are our concepts of them, even if those concepts are incorrect. To the extent that things are interconnected, this interconnectedness is part of reality. Duality is part of reality to the extent that things are not so interconnected as to be the same thing, but only if they are not interconnected in reality; separation in space and time are parts of reality as are the connections. Thus, the way reality is experienced by any element/component/part of reality is different from that in the experience of any other component; in this sense everything has its own universe and the union of these universes is reality. The primacy (relative importance or value—both human assigned and actual) of any one thing is included as it is in reality; thus, the importance or reality of ideas, concepts, matter, forces, dynamics, or interactions are what they are (not necessarily what we believe them to be even though our beliefs are real and are parts of reality; every belief is a reality). To repeat, nothing is excluded from reality as the term is used in this book. If it does not exist or occur, then, by definition, it is not part of the reality defined here (even though a belief in its existence is real but only as a belief); the truth about the past is a reality. The truth about reality is a reality and every belief about reality is a reality. Every point in the universe and every relationship between it and every other point is included. Every subatomic particle and every kind of energy is included. Every process and interaction between all points is included. Every effect and influence is included. Every subset of points is included, as are the relationships, interactions, and the effects of every subset on every other subset (each set involving its

own infinite complexity). Every pattern, category, level of organization, aggregate, piece, and type is included. The reciprocal relationships and influences among all things and all categories (aggregates of things) are included. Reality is. What is is reality, including the truth and our beliefs about time. Dividing reality into categories is fair—reality consists of parts that fall into categories. Because systemic management is reality-based, and ecosystems are part of reality, systemic management includes ecosystem-based management (along with biosphere-based, and other-component-based management). However, each subdivision is a real part (a reality) and not the ultimate reality meant by the term as used here—which includes all parts. Each part is always a crucial part, for without it within the definition there is denial or ignorance and associated (real) risks. Such denial is real—a real part of reality. The tangibles include things from chemicals and elements to black holes and galaxy clusters and beyond. The mechanics of nature are considered part of reality but only parts, not full reality, nor given more (or less) importance than is realized in reality. The dictionaries of all languages of the world contain various terms used to refer to the parts of reality—falling in categories such as verbs, nouns, and adjectives. One possible exception is the term “God” in cases where this term is defined as ultimate reality so that nothing is excluded. In such cases it would be the reality being defined here and would not be restricted to any part— such as the “mind” in Bateson’s terminology. If any terms are uniquely human constructs (i.e., do not have a counterpart in the remainder of reality), then they are real only as human constructs— but still real as human constructs and have their influence like every other part of reality. The other terms are assumed to have a counterpart (or referent) such that there is something (a reality) to which appearances and perceptions correspond. If humans go extinct, most such parts of realty are assumed to continue to exist, and ultimate reality continues in its altered form. Change is part of reality. Reality is ultimate complexity (see Appendix 3 of Fowler 2003). Finally, our perceptions of reality (as realities and real parts of reality, including our perceptions

A P P E N D I X 1 .1

of our perceptions) are all we have to work with. Our minds and the things in them are real. The finite and limited nature of perceptions mean that we can be mistaken—and we are wrong quite often. In the extreme, we encounter situations in which there is no referent in reality for what we think we perceive. This book is about trying to choose, organize, and use our perceptions so as to better define ways that work (develop a form of management that will work). One perception is that of the processes we use in learning and the trial-and-error process of defining what works— things that survive as parts of reality. As individuals, we do this through our own experience, by the experience of others, occasionally expanded to include the experience of various human organizations, and the experience of other species. Various human holons can do the same thing; as individuals, we learn not only from our own experience, but also from that of others. The benchmarking carried out by businesses is an example of learning from experience, and it is possible in comparative studies of communities, cultures, and nations. Carrying this pattern to higher (more inclusive) levels, as done in this book, results in the capacity to define sustainability for our species in a way that accounts not only for ecosystems but complexity in general. The trial-and-error process of defining what works crosses holarchical boundaries to contribute to the emergent patterns that can be appreciated metaphorically as nature’s Nash equilibria (Fowler 2008, Fowler and Hobbs 2002). Our models, maps, concepts, and perceptions of these patterns are as close as we can get to informative guidance when the pattern corresponds directly to

3

(is consonant with) the management question in hand (Belgrano and Fowler 2008).

Note 1. History is obviously not the present—it involves a difference in time. We know, however, that there is a truth about that history, whether or not we accept it, or understand it. We often react to lies regarding the past with comments such as “The reality is, however, that the Soviets caught more whales than they reported”. In this sense, it is the truth that is real; the lie is only real as a lie (there is no other reality with which it bears consonance). The present is a more convincing reality to us (than the past) in that it is all that our senses can respond to; our minds, however, can respond to memories, thoughts, interpretations, emotions, and many other realities within our minds, whether or not there is a matching truth or reality that continues to exist upon our death.

References Bateson, G. 1972. Conscious purpose versus nature. In G. Bateson (ed.). Steps to an ecology of mind, pp. 426–439. Chandler Publishing Co., San Francisco, CA. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In S.I. Munoz (ed.). Ecology research progress, pp. 5–31. Nova Science Publishers, Hauppauge, NY. Fowler, C.W. 2003. Tenets, principles, and criteria for management: the basis for systemic management. Marine Fisheries Review 65: 1–55. Fowler, C.W. 2008. Maximizing biodiversity, information and sustainability. Biodiversity and Conservation 17: 41–55. Fowler, C.W. and L. Hobbs 2002. Limits to natural variation: implications for systemic management. Animal Biodiversity and Conservation 25: 7–45.

Appendix 1.2

The following material is Appendix 1.2 for Chapter 1 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Species-level failure to thrive Homo sapiens, like other species, is but one of many that temporarily occupy a place in the Earth’s ecosystems as one of Nature’s experiments. Accepting this almost trivial concept is critical; facing the risk of our own extinction is part of the reality to be included in accounting for complexity. Accepting death is important in the psychological health of individuals (Yalom 1980). Accepting the reality of extinction, especially human extinction, logically follows the perception of our nature as a species (Tiger and Fox 1989). Critical to the way of thinking behind systemic management is overcoming the dualism of thinking that our species is so different from other species that we are immune to natural processes that impact all species (in violation of Management Tenets 1 and 2). Accepting ourselves as a species in this regard is necessary but not simple; no species has evolved solutions to all problems,1 and imperfections have to be recognized (Williams 1992). However, collectively, the information nonhuman species represent is wisdom beyond that produced by the intelligence we like to ascribe to ourselves.2 We have experienced temporarily what seems like freedom from the constraints more inclusive systems place on constituent species, especially in terms of our population size (Catton 1980)—and face the long-term consequences of attempts at breaking Nature’s laws. This temporary situation, however, leads to the issue of altered relationships with Nature in a more general context,3 which can 4

be viewed in two ways. First are the effects on humans of experiencing environments that are themselves reacting to abnormal levels of human influence. On top of this is our attempt to minimize the effects of Nature (distance ourselves from Nature), whatever its condition, followed by the effects these efforts have had on what we are as a species. It is important to know to what extent the human species is abnormal and to identify the individual- and species-level experiences of this abnormality. At the individual level, we know that children who grow up without human (especially parental) contact develop a condition called reactive attachment disorder of infancy or hospitalism (American Psychiatric Association 1980). Products of an abnormal situation, they fail to thrive without the benefits of that association.4 There is the risk that we humans, as a species, are experiencing a parallel phenomenon resulting from increasingly abnormal relationships with the nonhuman (part of which would be the Nature-deficit disorder; Louv 2005). In addition to the ecological factors covered in this book, we must consider other more individual-level effects on our species stemming from abnormal relationships with nonhuman systems and its numerous effects (whether we judge them to be positive or negative). Does our species need a less sheltered association with Nature to thrive? We must consider the complexity of this issue in examining the variety of ways we might be experiencing in the limited time most of modern society spends in direct contact with other species. The interdependencies among species, including humans, are undoubtedly beyond those currently known and are too complex to be addressed by conventional science. Humans are dependent on ecosystems for much more than material products.5 When individuals of other species are withdrawn

A P P E N D I X 1. 2

from natural settings to live (and especially to be born) in captivity, their reintroduction to natural habitats is often fatal without extensive retraining and habituation. We have succeeded in separating ourselves from Nature in temporarily solving the “problems” of natural constraints. In the process we may have placed ourselves in a predicament similar to that of captive reared wild species. The brain development of children occurs in the first five years of life. For most of modern society this occurs in exposure to what is primarily a manmade environment of machines, buildings, televised “reality”, and packaged food from grocery stores. The entire ecopsychological endeavor (e.g., Roszak 1995) is directed toward the emotional, psychological, and spiritual aspects of human relationships with Nature, and the implications of our separation from (abnormal interactions with) Nature.6 The variety of symptoms that arise when such interactions are disrupted are important in exploring the benefits of more open, continuous, and direct exposure to Nature in a more natural state.7 Gore (1992) suggests that current human society is dysfunctional; the Nature-deficit disorder (Louv 2005) may be more pervasive than we are aware. It is important to know how disrupted relationships with Nature (ecosystems) contribute to problems we are experiencing, especially if some exacerbate the contributing causes of problems such as overpopulation (American Society of Mammalogists 1970, Calhoun 1962, Galle et al. 1972, McMichael 1993, Metzner 1995, Tainter 1988). Such aspects of the species-level failure we may be experiencing are linked to our own risk of extinction. Degraded ecosystems, lack of normal exposure to natural ecosystems (even Nature, or reality in general), and being abnormal among species in many ways, may be manifested in many of the problems humans currently face but that are often attributed to other factors. During most of our evolutionary history (but much less today), humans had continuous and intimate direct contact with Nature and living organisms during both the formative years, and in establishing social identity.8 It is possible, then, that there is a society/specieslevel counterpart of reactive attachment disorder of infancy that results from the separation of humans from a natural biotic environment. As a syndrome,

5

it may have a variety of symptoms, including: Ecological indications that our species is at risk of extinction. Loss of benefits through homeostatic processes of constraint, including natural selection.9 Altered (especially unrealistic) perceptual views of the world. Individual experiences that collectively contribute to a variety of social problems.

●

●

●

●

If an inability to recognize such a disorder is one of its symptoms, examining it through research and debate may be difficult, but is all the more important.

Notes 1. See Potter (1990) for consideration of the collective problems humans face as the product of such evolution as a “fatal flaw”. This is related to the matter of “selfish genes” and the realization that selection at the individual level cannot be relied upon to produce adaptive specieslevel properties (Dawkins 1976—also expressed in “evolutionary suicide” as developed in Chapter 3). 2. Bateson (1979) clearly argues that the wisdom, intelligence, or mind embodied in the information-based aspects of the living natural world would lead to this conclusion. Schull (1990; see also the reactions to Schull’s paper in the pages following his paper in the same journal) actually attributes the quality of intelligence to the information-based nature of species. 3. The concept of separation from Nature, as manifested in a variety of ways, is mentioned in a number of works (e.g., Ehrenfeld 1993, Louv 2005, Mander 1991, McMichael 1993, Orr 1994, Ponting 1991, Potter 1990, Roszak 1992, Roszak et al. 1995, Tiger and Fox 1989). 4. One of the benefits of this association may be learning the limits of functional participation as an individual. These would include learning about boundaries, risks, and harmful behaviors that, over the long term, are detrimental to individuals through reactions from larger systems (families, societies, etc. ) within which such behaviors are not tolerated. 5. The Outward Bound program (and growing numbers of similar programs, including animal-or horticulturally-assisted approaches) of therapeutic treatment for emotional, personal, and psychological problems embodies the concept of Nature as an environmental context for learning, change, and healing. The draw of the outdoors for recreational purposes indicates a primal need for a break from conditions so foreign to

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those under which our species evolved. These experiences of reconnecting with Nature are superficial and inconsequential in comparison to living in and directly experiencing the natural environment (especially its constraints and risks) characteristic of aboriginal cultures and the circumstances under which we evolved. Are there limits, boundaries and risks that a species integrates by way of having its individuals in more intimate contact with natural forces that are constant reminders of their existence? 6. Included in this must be the emotional issues of realizing what we have become as a species. If a psychologically healthy person were to suddenly realize that something they are doing results in the death of other people, they would predictably have strong emotional reactions (causing the death of one person per year is about 100 times the average rate people cause death, assuming that all humans die because of what other humans do, that is, killing one person per year is probably actually several orders of magnitude more than average). Such reactions are adaptive in that they prevent the loss of other individuals, some of whom may carry related genes. There may be a parallel emotional reaction to knowing that, as a species, we may be doing things that result in the extinction of other species that is nine or more orders of magnitude higher than for other species (see Chapter 6). If there is emotion in such reactions, and the reaction contributes to society’s assumption of responsibility for change, we may be “preadapted” to survive as a species. If not, or if there are feelings that such effects are justified for individual goods, the chances of leading ourselves to our own extinction are much greater. 7. This would include loss of language to communicate many of the relationships between humans and Nature understood through experiential learning in connection with Nature (Armstrong 1995). Other losses would include mythological and religious or social customs that have the effect of preventing problems like those we see today, even if there is not a detailed, or mechanistic, understanding, or even intention, of their function (see, e.g., Metzner 1995, Stevens 1994, Tudge 1989, World Wildlife Fund 1986). If the ability to evaluate our environment is inherent (at the core of the biophilia hypothesis, Kellert and Wilson 1993), what happens if its phenotypic development is precluded by lack of exposure to the complexity of life in the richness of natural (especially normal) ecosystems? If there is an innate intelligence that relates to the perception and understanding of natural patterns (Gardner 1995), what happens if its development depends on direct exposure to Nature? What might be the collective effect experienced by our species in the loss of these and other processes yet to be identified?

8. The subtitle to Bateson’s (1979) book, Mind and Nature: a Necessary Unity, is relevant to the point of this section. This and others of Bateson’s books lay out the process of understanding as it is manifested in human thought and analogous processes in Nature. To the extent that these processes are learned and necessary (even taken for granted) among aboriginal peoples who live in close association with the natural world, there is hope for a more refined definition of the syndrome referred to here as species-level failure to thrive. To the extent Bateson’s “unity” has been disrupted in modern society, we are experiencing the syndrome. 9. We have escaped many of the forces of natural selection that suppress genetic code for a variety of conditions, traits, and genetic diseases that are undoubtedly gaining prevalence within our species (Carney 1980). These add to genetic variability and, at the species level might be a benefit in providing future options, but at what cost in future suffering and medical expense to society? Our medical system is one of interfering with the process of natural selection, simultaneously making us more dependent on resources and more vulnerable to ecosystem forces (e. g., the effects of disease).

References American Psychiatric Association. 1980. Diagnostic and statistical manual of mental disorders.American Psychiatric Association, Washington, DC. American Society of Mammologists. 1970. Resolution on population growth. Journal of Mammalogy 51: 856. Armstrong, J. 1995. Keepers of the earth. In T. Roszak, M.E. Gomes, and A.D. Kanner (eds). Ecopsychology: restoring the earth, healing the mind, pp. 316–324. Sierra Club Books, San Francisco, CA. Bateson, G. 1979. Mind and nature: a necessary unity. Dutton, New York, NY. Calhoun, J.B. 1962. Population density and social pathology. Scientific American 206(2): 129–148. Carney, T.P. 1980. Instant evolution: we’d better get good at it. University of Notre Dame Press, London. Catton, W.R., Jr. 1980. Overshoot: The ecological basis of revolutionary change. University of Illinois Press, Chicago, IL. Dawkins, R. 1976. The selfish gene. Oxford University Press, New York, NY. Ehrenfeld, D.J. 1993. Beginning again: people and nature in the new millennium. Oxford University Press, New York, NY. Galle, O.R., W.R. Gove, and J.M. McPherson. 1972. Population density and pathology: what are the relations for man? Science 176: 23–29.

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Gardner, H. 1995. Reflections on multiple intelligences. Phi Delta Kappan 77: 200–209 Gore, A. 1992. Earth in the balance. Houghton Mifflin Co., New York, NY. Kellert, S.R. and E.O. Wilson (eds). 1993. The biophilia hypothesis. Island Press, Washington, DC. Louv, R. 2005. Last child in the woods: saving our children from nature-deficit disorder. Algonquin Books, Chapel Hill, NC. Mander, J. 1991. In the absence of the sacred. Sierra Club Books, San Francisco, CA. McMichael, A.J. 1993. Planetary overload; global environmental change and the health of the human species. Cambridge University Press, New York, NY. Metzner, R. 1995. The psychology of the humannature relationship. In: T. Roszak, M.E. Gomes, and A.D. Kanner (eds). Ecopsychology: restoring the earth, healing the mind, pp. 55–67. Sierra Club Books, San Francisco, CA. Orr, D.W. 1994. Earth in mind; on education environment and the human prospect. Island Press, Washington, DC. Ponting, C. 1991. A green history of the world: the environment and the collapse of great civilizations. SinclairStevenson, London. Potter, V.R. 1990. Getting to the year 3000: can global bioethics overcome evolution’s fatal flaw? Perspectives in Biology and Medicine 34: 89–98.

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Roszak, T. 1992. The voice of the earth. Simon and Schuster, New York, NY. Roszak, T. 1995. Where psyche meets gaia. In T. Roszak, M.E. Gomes, and A.D. Kanner (eds). Ecopsychology: restoring the earth, healing the mind, pp. 1–17. Sierra Club Books, San Francisco, CA. Roszak, T., M.E. Gomes, and A.D. Kanner (eds). 1995. Ecopsychology: restoring the earth, healing the mind. Sierra Club Books, San Francisco, CA. Schull, J. 1990. Are species intelligent? Behavioral and Brain Science 13: 63–75. Stevens, J.E. 1994. Science and religion at work. Bioscience 44: 60–64. Tainter, J.A. 1988. The collapse of complex societies. Cambridge University Press, Cambridge. Tiger, L. and R. Fox. 1989. The imperial animal. Holt, Rinehart and Winston, New York, NY. Tudge, C. 1989. The rise and fall of Homo sapiens sapiens. Philosophical Transactions of the Royal Society of London, Series B 325: 479–488. Williams, G.C. 1992. Natural selection: domains, levels, and challenges. Oxford University Press, New York, NY. World Wildlife Fund. 1986. Assisi declarations; messages on man and nature from Buddhism, Christianity, Hinduism, Islam and Judaism. World Wildlife Fund, London. Yalom, I.D. 1980. Existential psychotherapy. Basic Books, Inc., New York, NY.

Appendix 1.3

The following material is Appendix 1.3 for Chapter 1 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Sample patterns for the eastern Bering Sea (construction of Figs 1.6 through 1.8) This appendix provides details and references involved in producing Figures 1.6–1.8. It also exemplifies the general process of constructing species frequency distributions (described in detail in Fowler and Perez 1999, with further examples in Chapter 2). The data used in constructing Figure 1.6 and the bottom row of Figure 1.7 are shown in Appendix Table 1.3.1. The original data sources, specific research, and procedures used to produce these data are contained in the references provided by Perez and McAlister (1993) and Fowler and Perez (1999). Data for the top row of Figure 1.7 are from Livingston 1993 and data for the middle row are from Fowler and Perez (1999). An important point: there are an immense amount of work and analyses behind the compilation of the data used in constructing these figures. Estimating population is a very difficult task and subject to measurement error, one source of the variation demonstrated in these figures. The populations themselves fluctuate over time, another source of variation. The first step in constructing patterns like those shown in Figure 1.7 is to accumulate the estimates for population size (averaged over seasons to account for times when there are more or fewer individuals within the ecosystem owing to migrations). To calculate estimates of consumption rates, information on body size and size-specific consumption rates must be taken into account. 8

Population numbers are multiplied by mean body size to find biomass; consumption rates are then estimated by multiplying biomass estimates by specific consumption rates, which are estimated from previously established relationships between body size and consumption rates. These quantitative relationships are the end product of even more complicated studies. Information on the composition of the diet allows for breaking down the total consumption of the bottom row of Figure 1.7 to its components as exemplified in the top two rows. The details of such computations are presented in Perez and McAlister (1993). At all steps, additional sources of variation enter into the picture to contribute to variance in the resulting estimates shown as data in Table 1.3.1. With data on population size (or consumption rates, or any other measure of a species) figures like Figures 1.6–1.8 can be constructed. For Figure 1.6 and the bottom row of Figure 1.7, the species of Table 1.3.1 (N = 20) were subdivided into categories. Each category was chosen as an equal subdivision of population size (Fig. 1.6), biomass consumed (first panel of bottom row of Fig. 1.7), or the log of biomass consumed (second panel of bottom row of Fig. 1.7). The number of species in each category was then divided by 20 to obtain the corresponding portion of species. Thus, the five species with estimated population sizes between 50,000 and 100,000 represented 25% (0.25) of the total as shown in one of the 10 categories for the top panel of Figure 1.6. Each category spans a segment in the range of population size, here in increments of 50,000 animals. Similarly, each category of biomass consumption for the consumption rates within the eastern Bering Sea (lower panel of the left column of Fig. 1.7) represents a 100,000-ton increment across the range shown. In this case, four of the 20 species fell in the second category (100,000–200,000

A P P E N D I X 1. 3

9

Appendix Table 1.3.1 List of 20 species of marine mammals found in the eastern Bering Sea, with estimates of population size (averaged over seasons as found in this region) and resource biomass consumed annually (103 metric tons (t)) and log10 (103 t) for the late 20th century from the same region (from Fowler and Perez 1999, and Perez and McAlister 1993). Species

Balaena mysticetus Balaenoptera acutorostrata Balaenoptera physalus Berardius bairdii Callorhinus ursinus Delphinapterus leucas Enhydra lutris Erignathus barbatus Eschrichtius robustus Eumetopias jubatus Megaptera novaeangliae Mesoplodon stejnegeri Orcinus orca Phoca fasciata Phoca hispida Phoca largha Phoca vitulina Phocoena phocoena Phocoenoides dalli Physeter macrocephalus

Body mass (kg)

46,000 6,000 49,000 8,000 43 800 20 241 18,000 212 30,000 2,000 4,000 46 34 62 49 50 95 36,000

Population size

148 1,900 500 209 219,750 10,750 79,000 77,500 2,500 32,000 63 200 500 66,000 300,500 77,000 45,000 750 64,100 3,791

tons of biomass consumed annually which was represented by 20%, or 0.2 of species). Sobolevsky and Mathisen (1996) present information similar to that of Figure 1.6, for the entire Bering Sea, but as confined to the cetaceans. In this case, there is also information for the late 1940s to be compared to the more recent conditions of the late 1980s or early 1990s. Figure 1.8 presents these data as a frequency distribution (here placing the bars over the midpoints of each category instead of over the upper end of the range represented). The procedures used to construct Figures 1.6–1.8 are straightforward manipulations using common spreadsheet software. In essence, the production of species frequency distributions is a simple matter of “binning” species into categories defined by equivalent subdivisions of the range of any species-level measure. It is parallel to the process used in producing any frequency distribution (e.g., for

Daily energy requirements (103 kcal)

Diet energy value (kcal/g)

603.1 130.9 632.3 268.1 7.1 47.7 4.9 12.2 298.4 20.7 437.7 94.8 159.4 3.5 2.8 4.4 3.7 6.0 9.6 828.5

1.80 1.72 2.00 1.20 1.31 1.30 0.90 1.30 1.00 1.30 1.80 1.20 1.80 1.20 1.20 1.39 1.40 1.63 1.33 1.20

Consumption (103 t)

log10(103 t)

18.1 52.6 57.5 17.0 432.4 143.5 157.1 265.1 271.5 185.2 5.5 5.8 16.1 70.7 256.7 89.1 43.3 1.0 169.0 952.8

1.257 1.721 1.760 1.230 2.636 2.157 2.196 2.423 2.434 2.268 0.744 0.760 1.207 1.850 2.409 1.950 1.637 −0.001 2.228 2.979

individual organisms as measured by body size, body temperature, or rate of heart beat) as described in many general statistical texts (Alder and Roessler 1964, Dixon and Massey 1957, Huntsberger 1961, Schmid 1983).

References Alder, H.L. and E.B. Roessler. 1964. Introduction to probability and statistics. W.H. Freeman and Co., San Francisco, CA. Dixon, W.J. and F.J. Massey, Jr 1957. Introduction to statistical analysis. McGraw-Hill, New York, NY. Fowler, C.W. and M.A. Perez. 1999. Constructing Species Frequency Distributions—a step toward systemic management. NOAA Techinical Memorandum NMFS-AFSC-109. US Department of Commerce, Seattle, WA. Huntsberger, D.V. 1961. Elements of statistical inference. Allyn and Bacon, Boston, MA.

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Perez, M.A. and W.B. McAlister. 1993. Estimates of food consumption by marine mammals in the eastern Bering Sea. NOAA Techinical Memorandum NMFSAFSC-14. US Deptartment of Commerce, Seattle, WA. Schmid, C.F. 1983. Statistical graphics: design principles and practices. Wiley, New York, NY.

Sobolevsky, Y.I. and O.A. Mathisen. 1996. Distribution, abundance, and trophic relationships of Bering Sea cetaceans. In O.A. Mathisen and K.O. Coyle (eds). Ecology of the Bering Sea: a review of Russian literature, pp. 265–275. Alaska Sea Grant College Program Report No. 96–01. University of Alaska, Fairbanks.

Appendix 2.1

The following material is Appendix 2.1 for Chapter 2 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Variety in patterns among species 1.1 Single species-level characteristics There are many examples of published patterns among species; some are patterns not included in Figures 2.1–2.36 of Chapter 2, and some are further examples of these patterns. This appendix presents brief descriptions and graphic presentations of a selection of patterns. The objective of this appendix is not to be exhaustive. Its goal is much more than that of emphasizing the potential for further research on patterns with the view that, even in looking for undiscovered patterns, being exhaustive is not a realistic possibility. Such research is important, however, and will lead to better understanding of the complexity in ways we can measure species and approach management. For those cases where new measurements are found, the progress would accomplish two things of practical importance: (1) lead to new management questions regarding the sustainable fit of our species within ecosystems and the biosphere, and (2) add to correlative information that can be used to refine these management questions as introduced in Chapter 2. 1.1.1 Trophic level Specific taxonomic groups are often confined to subsets of the overall range of trophic levels observed among species. Appendix Figure 2.1.1 demonstrates this for the trophic levels of 97 species of marine mammals (from Pauley et al. 1998).

Here we see a bimodal distribution in which the lower mode represents the large baleen whales (11 species) that are filter feeders. Marine mammal species at the higher trophic levels are fish or mammal predators. It is not rare to observe more than one mode in species-level patterns exemplified by this case. 1.1.2 Symbiotic interactions It is uncommon to see species that are completely interdependent (obligate dependence), wherein the extinction of either species results in the extinction of the other. The great majority of completely dependent symbiotic interactions that have been described involve no more than two species. One factor involved in producing this situation is the fact that the extinction rate (a risk) of any dependent species is always higher than that of the species upon which it depends. When the dependence is mutual, and the risk equivalent, it is always higher than would be the case without the dependence. With more than two species the risk is even more extreme. Obviously, interdependence could conceivably entail three or even more species if it were simply a random phenomenon. The structure of the webs of such interdependence would have a variety of potential forms, the simplest being linear. Rings or cycles of dependence (e.g., species A depends on species B, B depends on C, and C depends on A) are known to occur (e.g., Reagan and Waide 1996). However, obligate interdependence (A depends on B, etc. so that the extinction of one guarantees that of all others) in rings is not common and may not exist in natural systems. Even rings that are not obligate are not common (Berendse 1993, Cohen et al. 1990, Hall and Raffaelli 1993, Lawton 1989b, May 1973, Pimm 1982), but they do exist (see Reagan and Waide 1996, Thompson 1982 and references therein). 11

SYSTEMIC MANAGEMENT

0.20

0.5

0.16

0.4

Portion of species

Portion of species

12

0.12 0.08 0.04 0.00 2.8

3.2

3.6 4.0 Trophic level

4.4

4.8

Appendix Figure 2.1.1 The distribution of species across trophic level for 97 species of marine mammals, from Pauley et al. (1998).

0.3 0.2 0.1 0.0

0–10 11–20 21–30 31–40 Number of species consumed

Appendix Figure 2.1.3 Frequency distribution of 14 species of grasshoppers in Colorado according to the number of species of plants they consume (from Thompson 1982 and Ueckert and Hansen 1971).

0.7 Portion of species

0.6 0.5 0.4 0.3 0.2 0.1 0.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Number of species consumed

Appendix Figure 2.1.2 Frequency distribution of species according to the number of prey they consume based on species from 95 insect-dominated food webs (from Schoenly et al., 1991).

1.1.3 Specialization—number of prey species Science includes observations that have led to the conclusion that most species tend to be consumers of only a small portion of the resource species available to them. Complete specialization may be common in some samples of species, but not all, and consumption of all available species is never observed. This tendency is demonstrated in the pattern in Appendix Figure 2.1.2 (Schoenly et al. 1991). In this sample of insect-dominated food webs, there are few extreme generalists compared to the numbers of more specialized species. Care must be exercised in conclusions regarding general species-level patterns based on local

samples. Over the entire geographic range of most species, more prey species are available, and consumed, than in more confined regions (see, Fox and Morrow 1981). In other words, with increasing area the diversity of diet (number of species consumed for the area in which dietary diversity is being measured) also increases. In spite of what might be the results of some forms of natural selection, complete specialization may not be the most prevalent form of interaction (Bleiweiss 1990, Futuyma 1983, Orians and Kunin 1990). For example, of seven species of grasshoppers (Thompson 1982, from Ueckert and Hansen 1971) none feed on only one species of plant and five of the seven feed on more than ten species. The complete set of data shows a decline in consumer species numbers with greater diversity in diet (Appendix Fig. 2.1.3), consistent with the tendency shown in Appendix Figure 2.1.2. Both natural selection at the individual level and risk of extinction are likely involved as explanatory factors in the complexity behind these patterns and, therefore, reflected by the patterns. There is less chance of extinction when risk is spread over a number of resource species than is the case for specialization in spite of the fact that specialization can result from natural selection among individuals. This may be a case, as will be seen in Chapter 3, of natural selection among individuals and natural selection among species acting in opposition.

A P P E N D I X 2 .1

0.6 Portion of species

0.5 0.4 0.3 0.2 0.1 0.0

Strong negative Weak negative Strong positive

Appendix Figure 2.1.4 The proportion of eight populations of seven species of marine intertidal invertebrate species categorized according to the strength of their effects on the population-level recruitment of algal species they consume (from Paine 1992).

The balance is part of what contributes to the emergence of pattern. However, we must remain mindful of the fact that the origin of patterns involves many factors in addition to two forms of natural selection working against each other. 1.1.4 Interaction strength Appendix Figure 2.1.4 shows the results of early work on interaction strength. In this work the majority of intertidal marine consumers had either very little or only intermediate influence on resource species. Some had small positive effects (see also Lawton 1992). It is highly likely that the pattern of interaction strength shown in Appendix Figure 2.1.4 is a general pattern in being characterized by an intermediate maximum surrounded by fewer species toward the extremes—representing natural variability (Power et al., 1996) within bounds. Singletailed patterns could occur for interactions that are confined to measures of either positive or negative effects. 1.1.5 Predation/consumption rates Appendix Figure 2.1.5 illustrates patterns in consumption rates among nonhuman vertebrate predatory species in the northwestern Atlantic Ocean in their take of four prey species. Data in this graph are presented in terms of total biomass consumed rather than portion of the standing stock (as was used in Fig. 2.6). As with previous examples in

13

Chapter 2, these do not include the microconsumers such as disease organisms and parasites and do not reflect the pattern representative of the entire geographic range for these species. They pertain to a specified ecosystem at a specific time (and under specific conditions) as with the data for walleye pollock in Figure 2.6. Another ecosystem for which there are data regarding consumption rates is the Benguela ecosystem off the southwest coast of Africa. Appendix Figure 2.1.6 shows the consumption rates by avian predators on a single resource species (anchovy), from a group of resource species (lantern fish, Lampanyctodes hectoris; lightfish, Maurolicus muelleri; anchovy, and hake, Merluccius sp.), and from the entire ecosystem (from Crawford et al. 1991). 1.1.6 Population variability Rothschild (1986) examined a pattern in population variation for at least 12 species of fish. Rothschild’s data demonstrate, as do those of Appendix Figure 2.1.7, that the bulk of species sampled occur at the low end of the range of population variability. The ecosystems in which the species of Rothschild’s study occur are subject to several identifiable abnormal human impacts. Fishing is known to contribute to variation in fish populations (Anderson et al. 2008). Observed patterns, therefore, are subject to both these and the indirect effects of all human influence whether abnormal or normal. Thus, human influence is behind the levels of observed variability as in the case of many agricultural systems and disturbed systems in general (Apollonio 1994). Also involved is the nonrandom nature of the sample of fish species in Rothchild’s work. Commercial fisheries concentrate on productive fish species with life histories associated with high variability. In spite of these caveats, Rothschild’s (1986) work shows that there are few species that show high population variation compared to the numbers of species that exhibit intermediate population variation—the pattern we would expect. Also consistent with what we would expect, there is rarity among species showing extremely low population variation. This is shown in Appendix Figure 2.1.7B. This particular pattern is based on measurements of the ratio of largest to smallest observed population levels (Nmax/Nmin)

14

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0.4 Herring

Portion of species

Portion of species

0.3

0.2

0.1

0.0 1.5

1.9

2.3

2.7

3.1

3.5

3.9

4.3

Hake 0.3 0.2 0.1 0.0 1.3 1.7 2.1 2.5 2.9 3.3 3.7 4.1 4.5 4.9 5.3 5.7

4.7

log10 (tonnes biomass consumed)

log10 (tonnes biomass consumed)

0.3

0.3 Mackerel

Portion of species

Portion of species

0.4

0.2 0.1 0.0 1.0

1.8 2.6 3.4 4.2 5.0 5.8 6.6 log10 (tonnes biomass consumed)

7.4

Sandeel 0.2

0.1

0.0 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 log10 (tonnes biomass consumed)

Appendix Figure 2.1.5 Frequency distributions among nonhuman vertebrate species that consume hake (Merluccius bilinearis), herring (Clupea harengus), mackerel (Scomber scombrus), and sand eel (Ammodytes americanus) in the northwestern part of the Atlantic Ocean according to the biomass they consume (log10 metric tons per year, Overholtz et al. 1991).

for a variety of species, from Hassell et al. (1976). The fact that some insect species may have originally been specifically chosen for study is a potential source of bias, as would be varying length of time periods over which observations were made. Species we humans consider to be pests are more likely to experience outbreaks in agricultural monocultures and may account for much of the long right-hand tail of the distribution. Seventeen percent (13) of the species are not included in the top panel (A) of this graph because they exhibited variability beyond the range shown and obscure the nature of the remaining part of the distribution when included. Eleven of these excluded species are insects. A population above or below its carrying capacity can behave in a variety of ways. It may return to typical levels through a smooth monotonic trajectory, or it may change so rapidly as to result in “overshoot” and experience diminishing cyclic

fluctuation (damped oscillations) in approaching stability. Alternatively, the population might undergo continued oscillations (stable limit cycles), or, in the extreme of variation, exhibit chaotic fluctuations. Hassell et al. (1976) examined data for a number of populations and categorized species according to their dynamic behavior (Appendix Fig. 2.1.8). The pattern is consistent with that shown by other data. Species with monotonic damping are the least variable and most numerous. At the other extreme, stable limit cycles and chaotic behavior represent more variability and the fewest species fall into these categories. Another measure of population variation is displayed in Appendix Figure 2.1.9 based on measures of the standard deviation of observed rate of change for various mammal populations (thus different from, but related to, the extent of population change). This distribution shows another example of bimodal character.

A P P E N D I X 2 .1

0.20

0.4

Portion of species

(A) 0.5

Portion of species

(A) 0.25

0.15 0.10 0.05

0.3 0.2 0.1 0.0

0.00 0.0

1.0

3.0 5.0 2.0 4.0 log10 (tons, annually)

6.0

40–50 60–70 Nmax/Nmin

80–90

Portion of species

0.15 0.10 0.05 0.00

1.0

2.0 3.0 4.0 5.0 log10 (tons, annually)

6.0

7.0

1.0

2.0 3.0 4.0 5.0 log10 (tons, annually)

6.0

7.0

0.20

0.10

Appendix Figure 2.1.6 Patterns in the consumption rates among avian predators in the Benguela ecosystem: (A) the consumption rates on anchovy (Engraulis capensis), (B) the consumption rates on four species of fish, and (C) the consumption rates from the entire ecosystem (compare to Fig. 1.7; from Crawford et al. 1991, Fowler and Perez 1999).

1.1.7 Density dependence Other measures of density dependence might result in distributions different from that shown in Figure 2.21. However, they would not remove the characteristic intermediate mode if they cover

0

1

2 3 log (Nmax/Nmin)

4

5

Appendix Figure 2.1.7 The frequency distribution of species of terrestrial insects, aquatic and marine invertebrates, birds, fishes, and small (nonmarine) mammals distributed over an index of variability calculated as the largest population level (Nmax) divided by the smallest population level (Nmin) observed over time (Hassell et al. 1976). Thirteen species of insects (17% of the total) showed Nmax /Nmin values beyond 90 and are not included in panel A (raw numbers) but are shown in panel B wherein the measure of fluctuation in population numbers is expressed as the log10 transformations of Nmax /Nmin from panel A.

1.0 Portion of species

Portion of species

0.10

(C) 0.30 Portion of species

20–30

(B) 0.20

0.20

0.00 0.0

0–10

7.0

(B) 0.30

0.00 0.0

15

0.8 0.6 0.4 0.2 0.0 Monotonic damping Stable limit cycles Damped oscillations Chaos

Appendix Figure 2.1.8 The frequency distribution of 24 insect species by population variability. Categories of increasing variability in population change are monotonic damping, damped oscillations, stable limit cycles, or chaotic fluctuations (from Hassell et al. 1976).

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0.7 0.6 Portion of species

0.16 0.12 0.08 0.04 0.00 –1.8 –1.4 –1.0 –0.6 –0.2 0.2 0.6 log10 (SDrobs)

0.5 0.4 0.3 0.2 0.1

1.0

0.0

1.4

Appendix Figure 2.1.9 Population variation observed for 55 species of mammals worldwide, expressed as the log10 transformation of the standard deviation of the observed instantaneous rate of change (robs, based on data from Sinclair (1996).

the full spectrum of density dependence. A mode at one extreme is the only option for the pattern shown in Appendix Figure 2.1.10 because information on the magnitude (rather than statistical significance) of negative slope in the relationship between population density and rate of change is missing for the species as displayed in this sample. This pattern is based on data from a combined variety of metazoan taxa (Pimm 1982, Tanner 1966). This figure does demonstrate a lack of species with little density dependence. Very uncommon are species with positive slopes—the antithesis of densitydependent population regulation. Most species show intermediate levels of density dependence in which recovery from reduced population levels actually happens but without leading to extreme population fluctuation (Appendix Fig. 2.1.8). 1.1.8 Other factors 1.1.8.1 Age/size composition of consumed resources Allocation over different phenotypic categories among the individuals of a resource species is also a matter for management. Patterns with units defined by relevant management questions are exemplified by Appendix Figure 2.1.11 which shows how 12 species of sea birds allocate their consumption of sand eel near the Shetland Isles, across the various size classes of this resource species.

A

B C D Category of density dependence

E

Appendix Figure 2.1.10 The frequency distribution for a collection of 64 species of invertebrates, fish, birds, and mammals by level of density dependence from Tanner (1966, see also Pimm 1982). Density dependence is measured by the slope of the correlation between (Xt +1 – Xt)/ Xt and Xt according to five categories: A—positive and statistically significant; B—positive but not significant; C—negative but not significant; D—negative and significant at the 0.10 significance level; E—negative and significant at the 0.05 significance level.

0.4 Portion of species

Portion of species

0.20

0.3 0.2 0.1 0.0 20

40

60

80 100 120 140 160 180 200 220 Modal length (mm)

Appendix Figure 2.1.11 The frequency distribution of modal size (mm) for sand eel (Ammodytes marinus) consumed by 12 species of sea birds that forage near Shetland Island (Furness 1990).

1.1.8.2 Generation time Sinclair (1996) presented estimated generation times (in years) for 73 species of mammals; these are displayed in Appendix Figure 2.1.12. As with intrinsic rate of increase (Fig. 2.22), generation time shows a bimodal pattern when expressed in log10 values, keeping in mind that these are for a group of species in a specific taxonomic category. A similar

A P P E N D I X 2 .1

0.35

17

0.15 Portion of species

Portion of species

0.30 0.25 0.20 0.15 0.10

0.10

0.05

0.05 0.00 –1.2

0.00 –0.6

0.0 0.6 1.2 1.8 log10 (generation time)

2.4

3.0

–3.0 –2.0 –1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 log10 (home range, km2)

Appendix Figure 2.1.12 A species frequency distribution showing the generation times (years) for 73 species of mammals in log10 scale (from Sinclair 1996).

Appendix Figure 2.1.13 The frequency distribution for home range size (log10 km2) for 280 species of terrestrial mammals (from Kelt and Van Vuren, with data provided by D. A. Kelt).

pattern could, at least in theory, be determined for the full set of species from an ecosystem. The characteristics of such a pattern would represent attributes of the ecosystem, just as would the characteristics of other patterns presented in Chapter 2 if they represented all (or a large random sample) of the species of any particular ecosystem. As shown by Makarieva and Gorskov (2004), the generation times of larger systems span four orders of magnitude or more.

plants found in their leaves, stems, and roots when compared across a wide variety of species. Similar patterns are found in animals as exemplified by the correlations among various body parts and body size (e.g., Peters 1983). All such patterns are of relevance to the nutrient and energy dynamics of ecosystems and the patterns in such dynamics as ecosystem-level attributes.

1.1.8.3 Home range size Distinct from the geographic range of an entire species is the home range or the area used by individuals in their foraging and reproductive activities. The frequency distribution for the home ranges of a sample of mammalian species is shown in Appendix Figure 2.1.13. The mammalian species within any ecosystem would have their own distinctive pattern as would the full collection of animal species for any such system. 1.1.8.4 Biomass partitioning/body structure Species frequency distributions regarding the allocation of biomass within individuals are another example of species-level patterns often seen as ecosystem structure. Examples are found in the distribution of above-ground compared to below-ground components of plants (Enquist and Niklas 2002). Clear patterns of biomass allocation are seen in the correlations between biomass of

1.1.8.5 Chromosome count Another example of a single-feature pattern among species involves the quantity of DNA material. Polyploidy and chromosome counts (Appendix Fig. 2.1.14, shown as chromosome number) vary among species and may relate to evolutionary plasticity. As treated and reviewed by Masterson (1994), Osmond et al. (1980), and Rosenzweig (1974, 1995), many plant species show evidence of more than one set of chromosomes and their frequency distributions in this regard are similar to that of Appendix Figure 2.1.14. This, again, is a taxonomically restricted sample and patterns will undoubtedly differ among taxa. In particular, animals show much less polyploidy than do plants (Rosenzweig 1995).

1.2 Two species-level characteristics 1.2.1 Body size and geographic range Based on the patterns of Figures 2.27 and 2.28, a fitted continuous surface representing species

18

SYSTEMIC MANAGEMENT

(A) 0.10

0.35

Portion of species

Portion of species

0.30 0.25 0.20 0.15 0.10 0.05 0.00

0.05

0.00 –3.0 1–5

–1.0

1.0 3.0 5.0 log10 (home range size)

7.0

–1.0

1.0 3.0 5.0 log10 (home range size)

7.0

–1.0

1.0 3.0 5.0 log10 (home range size)

7.0

11–15 21–25 31–35 41–45 51–55 61–65 71–75

Chromosome numbers

Portion of species

(B) 0.10 Portion of species

Appendix Figure 2.1.14 The pattern in haploid chromosome numbers among 19,680 species of angiosperm plants (based on guard cell size in fossil plants and expressed as a portion of the total of 19,838 species including those with over 75 chromosomes) from Masterson (1994, and personal comm., 1996).

0.05

0.00 –3.0

log

(bo

dy

size

rap

og

ge g(

)

)

ge

an

r hic

lo

Appendix Figure 2.1.15 The general shape of the pattern expected for geographic range as related to body size (both in log scale) for a hypothetical and large samples of species, based on patterns consistent with those shown in Figures 2.27 and 2.28.

numbers over body size and geographic range might appear as shown in Appendix Figure 2.1.15 for the complete set of species within an ecosystem. The shape, but not necessarily the position, of such distributions may be expected to remain consistent from habitat to habitat (e.g., marine to terrestrial). 1.2.2 Body size and home range size An alternative means of viewing the bivariate relationship of Figure 2.29 is shown in Appendix Figure 2.1.16. Any of the body size-specific patterns would be relevant to management questions framed

Portion of species

(C) 0.10

0.05

0.00 –3.0

Appendix Figure 2.1.16 Three component patterns from that shown in Figure 2.1.29. These frequency distributions display the species in body size categories corresponding to ranges of log10 mass (g) from 0 to 2.5 (A), from 2.5 to 4.5 (B) and from 4.5 to 7 (C).

to deal with body size directly. This would include home range. Management questions dealing with home range as the focal issue, of course, must make use of data such at those in Appendix Figure 2.1.16 and can, therefore, simultaneously account directly for body size.

A P P E N D I X 2 .1

1.0 Portion of species

1.2.3 Body size and population variability Appendix Figure 2.1.17 shows a pattern among species illustrating the correlative relationships between body size and population variation. A larger portion of the invertebrates (small bodied species) show high population variability in comparison to vertebrates (larger species). This is consistent with Figure 2.30 and the difference between Figures 2.19A and 2.19B.

Invertebrates

0.8

Vertebrates

0.6 0.4 0.2 0.0

1.2.5 Body size and generation time There is a correlation between time to maturation and body mass (Peters 1983) making it no surprise that there is a relation between generation time and body mass. Generation times, such as those shown in Appendix Figure 2.1.12, show a correlation with the body mass of the corresponding species. This is a relationship exemplified by the sample of species displayed in Appendix Figure 2.1.19. The distribution of species across generation time falls within a fairly narrow range for any particular body mass compared to that for the entire sample. What portion of the geographic range of a species with a generation time of 20 years should be set aside as areas from which humans are excluded? This management question would direct research toward patterns involving overlaps in geographic ranges for an answer (e.g., Fig. 2.15

Ten or less

More than ten

Index of variability Appendix Figure 2.1.17 The frequency distribution of vertebrates (large species) and invertebrates (small species) according to population variability, from Hassell et al. (1976).

1

log10 (rmax)

1.2.4 Body size and intrinsic rate of increase One of the patterns commonly cited in studies of relationships between body size and other species-level characteristics involves the maximum rate of increase (r max, e.g., Blueweiss et al. 1978, Peters 1983). An example of this relationship is shown in Appendix Figure 2.1.18 based on data for 61 populations of 43 species of mammals from Sinclair (1996). The bimodality of Figures 2.22, and Appendix Figure 2.1.12 are now explained on the basis of what appears to be a real break, or gap, in the species representing this relationship (at about 1 kg; see Holling 1992 for consideration of “lumps” and gaps within taxonomic groups, and Caughley 1987 in regard to ecological type). Do such gaps occur more among taxonomic groups than among sets of species representing an entire ecosysem? Is there an explanation for such gaps; are they related to human influence?

19

0

–1

–2 –3

–2

–1

0 1 2 3 log10 (body mass, kg)

4

5

6

Appendix Figure 2.1.18 The correlation between the intrinsic rate of increase, log10(rmax), and body mass (log10 kg) based on data for 61 populations of 43 species of mammals, from Sinclair (1996).

as it would apply for species such as the northern fur seal that occupy all of the eastern Bering Sea). However, the pattern chosen for guidance would also have to involve generation time (to be consonant with the question). Because of the relationship shown in Appendix Figure 2.1.19, body size would be accounted for automatically, even though the question might be refined a bit more by asking about specific species that have a body size within the variation observed to a generation time of 20 years. The same would hold true for home range sizes, rates of increase, and other features correlated with body size—consistency (Management Tenet 4, Chapter 1).

20

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0.10

2 Portion of species

1

0

–1 –3

–2

–1 0 1 2 log10 (body mass, kg)

3

Appendix Figure 2.1.19 The correlation between generation time (log10 years) and body mass (log10 kg) for 79 populations of 55 species of mammals from Sinclair (1996).

1.2.6 Body size and population size Based on the decline of density with body size, we might expect total population size to also show a decline in correlation with body size. This pattern was observed by Greenwood et al. (1996) for nonvolant wild mammals in Britain (see also, Gaston and Blackburn 2000). Freedman (1989) lists species with body size similar to that of humans and corresponding approximations of population sizes. According to this information, crabeater seals may number between 15 and 30 million, kangaroos (at least two species) 19 million, ringed seals 6–7 million, caribou or reindeer 3 million, harp seals 2–3 million, dolphins more than 2 million, northern fur seal 2 million, and wildebeest 1.4 million. Other than humans, these species seem to represent the upper extremes of population size for this range of body size. The mean of population size for Appendix Figure 2.1.8 (about 300,000) is clearly less than most total populations for species of smaller organisms, such as bacteria, that often occur in numbers that are many (e.g., 20, Makarieva and Gorshkov 2004) orders of magnitude larger. The general shape of the two-dimensional species frequency distribution of body size and total population size has yet to be clearly described with empirical information for a large set of species for populations measured for their entire geographic ranges (e.g., all species within an ecosystem but with geographic ranges including areas outside the ecosystem).

0.05

0.00

4

–2.0 –1.0 0.0 1.0 2.0 3.0 log10 (body mass, kg)

4.0

0.10 Carnivores Portion of species

log10 (T)

Herbivores

0.05

0.00

–2.0 –1.0 0.0 1.0 2.0 3.0 log10 (body mass, kg)

4.0

Appendix Figure 2.1.20 Data for two sets of mammals divided according to their trophic level to show a comparison of the frequency distribution for body size for 72 species of carnivores and 163 species of herbivores (from Kelt and Van Vuren 2001).

1.2.7 Body size and trophic level The patterns for body size (Figs 2.1 and 2.2) and trophic level (Fig. 2.3) lead to the prediction that we would see few large-bodied species at high trophic levels. Appendix Figure 2.1.20 illustrates a set of empirical data showing that the variance of body size is larger among herbivores than carnivores with a higher upper limit to the body size of herbivores than for carnivores. Most species at high trophic levels are small and exemplified by hyperparasites (Fowler and MacMahon 1982). These patterns are consistent with expectations based on consideration of risks of extinction coupled with energy flow within food chains. Consideration of species frequency distributions in the twodimensional space of body size and trophic level, as well as factors contributing to the patterns are found in Anderson (1977), J. Brown (1971, 1981),

A P P E N D I X 2 .1

Brown and Maurer (1987), Glazier (1987a), Grayson (1977), Lawton (1995), and Terborgh (1974).

1.2.9 Body size and other species-level characteristics Within the pattern for reproductive mode, few species are asexual reproducers (0.1% for animals8). But there is also a pattern related to body size. Most asexually reproducing animal species are small bodied while nearly all large-bodied species are sexual reproducers. Most of the examples of specialization are found among smaller-bodied species such as insects (Gaston 1988, Hanski 1990, Niemalä et al. 1981, Woiwood and Hanski 1992) and lower trophic levels (especially plants). Most large mammals are generalists rather than specialists, although there are rare exceptions, such as the giant panda (notably endangered) and koala. Indeed, as indicated by Lawton and MacGarvin (1986) large-bodied species such as mammals are generally more polyphagous than insects. In regard to mobility, birds, as a relatively mobile group of species, have a significantly greater proportion of large genera than do mammals of the same body size (“large” here, meaning numbers of species per genus, Glazier 1987a). 1.2.10 Other patterns involving two dimensions 1.2.10.1 Home range size and trophic level Appendix Figure 2.1.21 shows patterns involving trophic level and home range size (Kelt and Van Vuren 2001). Holling (1992) considers home range

0.10 Portion of species

Herbivores

0.05

0.00 –3.0

–1.0

1.0 3.0 5.0 log10 (home range size)

7.0

0.10 Carnivores Portion of species

1.2.8 Body size and metabolic rate Metabolic rate, one of the earliest of recognized species-level properties, is correlated with body size (generally body mass to the 3/4 power, Peters 1983). Based on the patterns of Figures 2.1 and 2.2, we would expect a concentration of species among those with lower metabolic rates (a pattern similar in shape to the pattern in Fig. 2.1). The bivariate pattern, therefore would resemble Figure 2.31 with a positive rather than negative slope. The opposite (but equal) slopes lead to a lack of correlation between energy consumption per unit area and body size (Damuth 1987, 2007). Such relationships are basic to patterns in energy flow within ecosystems.

21

0.05

0.00 –3.0

–1.0

1.0

3.0

5.0

7.0

log10 (home range size) Appendix Figure 2.1.21 A comparison of the frequency distribution for the mean home range size of herbivores and carnivores among 163 species of mammals from Kelt and Van Vuren (2001).

size in relation to trophic level and shows similar patterns with references regarding the history of related studies. 1.2.10.2 Intrinsic rate of increase and trophic level Most consumer species have higher intrinsic rates of increase (r) than their resources (Hassell and Anderson 1989, May 1982). Among large species (i.e., low r), most of the species that serve as consumers are pathogens or parasites (higher r) rather than conventionally defined predators. 1.2.10.3 Geographic range and density Both Bock and Ricklefs (1983) and Gaston and Lawton (1988a) found that (for their sample of birds and insects, respectively) species that are widely distributed also are generally locally abundant. As shown by Gaston and Blackburn (2000) this is a general pattern; however, there are examples

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of species with large range size that are no more abundant than species with small range sizes. The infrequency of species at high density and small range size is clearer for insects than for birds. 1.2.10.4 Geographic range and population size Gaston and Blackburn (2000) show examples for birds wherein species with larger range size exhibit larger populations. Concluding that this is a general pattern may be premature, however, because, across a broad spectrum of body size, density declines (Fig. 2.31) and range size increases in rough correlation with body size (Fig. 2.28). 1.2.10.5 Geographic range and population variation Gaston and Lawton (1988a) found that a greater fraction of species with large ranges show high levels of population variability compared to species from more restricted geographic ranges. Whether or not this is generally true remains to be shown. The reverse may be expected given the relationships between body size, population variation, and geographic range size. Counter to this would be the extinction of highly variable populations in small geographic ranges. Observed patterns would, of course, be the result of all contributing factors (Fig. 1.4) to include the balance among such opposing forces. 1.2.10.6 Global population and generation time Makarieva and Gorshkob (2004) presented a set of data with information regarding a variety of species-level characteristics important to processes involved in evolution and extinction. Among these are approximations for generation time (spanning about four orders of magnitude) and global population size (spanning about 28 orders of magnitude). These are shown in Appendix Figure 2.1.22 to illustrate the pattern involving these two species-level characteristics, even though the data represent groups of similar species rather than individual species. When reliable estimates of global population size are produced for numerous individual species, covering the full span of body size, we will see a clear pattern involving these two variables, owing to the pattern involving body size and generation time (e.g., Appendix Fig. 2.1.19) combined

log global population (individuals)

22

25 20 15 10 5 0

–3

–2 –1 0 log generation time (yr)

1

Appendix Figure 2.1.22 The correlative pattern involving total global population (individuals) and generation time (yrs.) for a variety of species ranging from bacteria and diatoms to vertebrates, all spanning several trophic levels (in log10 scale for both variables). When original data (from Makarieva and Gorshkob 2004) involved a range of options in log10 scale, the midpoint was plotted in this figure. The line is based on a fit using geometric mean regression.

with that for density and body size (Fig. 2.31). Although it is clear that species with small bodies will usually be most numerous globally, it is also likely that the variance among estimates of total population size will also be correlated with body size. This expectation is based, in part, on consideration of the pattern involving geographic range size and its variation for small-bodied species, compared to larger species (e.g., Figs 2.27 and 2.28). Collectively, this information leads to the conclusion that evaluating the total (global) population size for any species is a process that must account for generation time (and/or body size) when we are addressing management questions about a sustainable global population. 1.2.10.7 Additional patterns Further examples of patterns in bivariate frequency distributions abound. Charnov (1993) presents many graphically. These relationships are often specific to particular taxa but are represented by graphs of species-level characteristics in which each point represents a species (as in Figs 2.31 and 2.33). Examples include pairwise correlations among species-level attributes such as mortality rate, somatic growth rate, reproductive rates, age at first reproduction, sex ratios (and changes in them), rates of increase (including rate of increase per generation),

A P P E N D I X 2 .1

1.3 Three species-level characteristics

log10 (area, km2)

7 6 5 4 3 2

1.3.2 Other three-dimensional patterns In spite of the potential for hundreds of threeway patterns among recognized species-level

0

1

2 3 4 log10 (body mass, g)

5

6

Carnivores

8 7 6 5 4 3 2

1.3.1 Body size, trophic level, and geographic range Appendix Figure 2.1.23 represents the separation of North American mammal data for geographic range and generation time (Brown 1981, Fig. 2.28) into two trophic categories: herbivores and carnivores. Few species represent the combination of long generation time, high trophic level, and small geographic range (all of which contribute to elevated risk of extinction). Within the three-dimensional space of these characteristics, the density of species is maximum at an intermediate level of geographic range, small body size and low trophic level. From this peak species density declines in progressing toward combinations of small range size, large body size, and higher trophic level. Species numbers also diminish toward the opposite extremes of large range size, small body size, and lower trophic level. These patterns are not surprising based on the combined patterns in body size (May 1978, 1986, Fig. 2.1), trophic level (Fig. 2.3), and energy consumption (Fig. 2.11) along with the relationships of the latter two with body size.

Herbivores

8

log10 (area, km2)

body size, and life spans. Work on patterns involving body size seems to predominate (e.g., see the early synthesis in Peters 1983). Other studies in the ecological literature present further treatment of a variety of species-level attributes, often including body size (see Anderson 1977; Brown 1995; Gaston and Blackburn 2000; Gaston and Lawton 1988a,b, 1990a,b; Glasier 1986; Hassell and Anderson 1989; Holling 1992; Hutchinson and MacArthur 1959; Kelt and Van Vuren 2001; Lawton 1990; Marzluff and Dial 1991; Patterson 1984; Sinclair 1996; Sugihara et al. 1989; Wilson and Willis 1975; plus the many papers spawned by these). Patterns that are not related to body size should not to be forgotten (e.g., Figs 2.32 and 2.33 and anything correlated with rate of increase per generation time, and the shape of productivity curves). Other patterns not correlated with other specific species-level attributes are to be sought in macroecological research.

23

0

1

2 3 4 log10 (body mass, g)

5

6

Appendix Figure 2.1.23 The distribution of North American mammals over geographic range and body size (log transformed) according to trophic level, from Brown (1981).

characteristics (Table 2.1), graphic examples such as Appendix Figure 2.1.23 are rare in the literature. Carbone and Gittleman (2002) explore various three-way relationships among the density, numbers, and body mass of carnivores in correlation with prey biomass and productivity. Marquet (2002) showed information regarding the pattern involving trophic level, body size, and population density. Gillooly et al. (2001) present information on relationships among body size, temperature, and metabolic rates. Other similar data undoubtedly exist; their graphic presentation would help see informative structure within the relevant biotic systems—in particular for ecosystems where the species would be the entire set of species represented by populations in those systems. Qualitative descriptions for three or more characteristics are far fewer than for one or two characteristics (e.g.

24

SYSTEMIC MANAGEMENT

1.0

log (SDr)

0.5

0.0

–0.5

–1.0 –1.5 1.5 1.0 0.5

(r)

log

er (gen

e)

n tim

atio

0.0

Gaston and Lawton 1988a,b; Hanski 1990; and Orians and Kunin 1990). The next sections present more examples of data that hint at the three-dimensional patterns one would expect for entire ecosystems or the biosphere.

Appendix Figure 2.1.24 The correlations among generation time (years), intrinsic rate of increase (rmax, year–1), and standard deviation of observed r (SDr), all in log10 scales, for 26 species of mammals from Sinclair (1996).

1

log (SDr)

g

lo

0.5 0.4 0.3 0.2 0.1 0.0 –0.1 –0.2 –0.5

0

–1

1.3.2.1 Generation time, rate of increase, and population variation Appendix Figure 2.1.24 shows the interrelationships among generation time, rate of increase, and population variation for a sample of mammal species to demonstrate the general shape of patterns one would expect on the basis of the correlations between these variables and body size (Fig. 2.30, Appendix Figs 2.1.18 and 2.1.19). The overall shape of the relationship is one thing, the density of species within the relationship is another. Within this seemingly log-linear cloud of points the density of species is probably misleading owing to the lack of species with high population variation, short generation times, and high intrinsic rates of increase, especially outside the range of each of these variables as covered by this graph. The density or distribution of species within this pattern is important to the matter of distinguishing the abnormal from the normal for purposes of implementing Management Tenet 5 (Chapter 1).

–2 3 lo 2 g (d en 1 si ty )

0 –1.0

–0.5

1.5 1.0 0.5 me) i t 0.0 n atio ener log(g

2.0

Appendix Figure 2.1.25 The correlative relationships among density, population variation, and generation time for a sample of 14 mammal species in the combination of data from Damuth (1987), and Sinclair (1996).

1.3.2.2 Density, population variation, and generation time Appendix Figure 2.1.25 illustrates the relationship between population density and variation in correlation with generation time. It is based on 14 species of mammals from Sinclair (1996) with density estimates from Damuth (1987). As with Figure 2.35 and Appendix Figure 2.1.24, there is

A P P E N D I X 2 .1

25

6

log (home range)

5 4 3 2 1 0 –1 –2 3 2

lo

g(

de

ns

1

ity

)

0 –2.0

–1.5

2.0 1.5 1.0 s) s 0.5 ma 0.0 ody –0.5 b ( g –1.0 lo

an under-representation of the numbers of species with high population variation, short generation times, and high densities because such species are under-represented in relevant published research. 1.3.2.3 Density, body size, and home range size Appendix Figure 2.1.26 is similar to Appendix Figures 2.1.24 and 2.1.25 above in showing threedimensional relationships, here among body mass, population density, and home range size. There is no surprise in seeing a decline in density with an increase in home range size, although overlapping home ranges within a species would be a factor in determining the slope of this relationship.

References Anderson, C.N.K., C. Hsieh, S.A. Sandin, et al. 2008. Why fishing magnifies fluctuations in fish abundance. Nature 452: 835–839. Anderson, S. 1977. Geographic ranges of North American terrestrial mammals. American Museum Novitates 2629: 1–15. Apollonio, S. 1994. The use of ecosystem characteristics in fisheries management. Reviews in Fisheries Science 2: 157–180. Berendse, F. 1993. Ecosystem stability, competition, and nutrient cycling. In Schulze, E.D. and H.A. Mooney (eds). Biodiversity and ecosystem function, pp. 408–431. Springer-Verlag, New York, NY.

2.5

3.0

3.5

Appendix Figure 2.1.26 The relationships among body mass, population density, and home range size for 14 species of mammals common to the data sets from Damuth (1987), and Kelt and Van Vuren (2001).

Bleiweiss, R. 1990. Ecological causes of clade diversity in hummingbirds: a neontological perspective on the generation of diversity. In Ross, R.M. and W.D. Allmon (eds). Causes of evolution: a paleontological perspective, pp. 354–380. University of Chicago Press, Chicago, IL. Blueweiss, L., H. Fox, V. Kadzma, D. Nakashima, R. Peters, and S. Sams. 1978. Relationships between body size and some life history parameters. Oecologia 37: 257–272. Bock, C.E. and R.E. Ricklefs. 1983. Range size and local abundance of some North American songbirds, a positive correlation. American Naturalist 122: 295–299. Brown, J.H. 1971. Mammals on mountaintops: nonequilibrium insular biogeography. American Naturalist 105: 467–478. Brown, J.H. 1981. Two decades of homage to Santa Rosalia: toward a general theory of diversity. American Zoologist 21: 877–888. Brown, J.H. 1995. Macroecology. University of Chicago Press, Chicago, IL. Brown, J.H. and B.A. Maurer. 1987. Evolution of species assemblages: effects of energetic constraints and species dynamics on the diversification of the North American avifauna. American Naturalist 130: 1–17. Carbone, C. and J.L Gittleman. 2002. A common rule for the scaling of carnivore density. Science 295: 2273–2276. Caughley, G. 1987. The distribution of eutherian body weights. Oecologia 74: 319–320. Charnov, E.L. 1993. Life history invariants. Oxford University Press, New York, NY.

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Cohen, J.E., F. Briand, and C.M. Newman. 1990. Community food webs. Springer-Verlag, New York, NY. Crawford, R.J.M., P.G. Ryan, and A.J. Williams 1991. Seabird consumption and production in the Benguela and Western Agulhas ecosystems. South African Journal of Marine Science 11: 357–375. Damuth, J.D. 1987. Interspecific allometry of population density in mammals and other animals: the independence of body mass and population energy-use. Biological Journal of the Linnnean Society 31: 193–246. Damuth, J.D. 2007. A macroevolutionary explanation for energy equivilance in the scaling of body size and population density. American Naturalist 169: 621–631. Enquist, B.J. and K.J. Niklas. 2002. Global allocation rules for patterns of biomass partitionaing in seed plants. Science 295: 1517–1520. Fowler, C.W. and J.A. MacMahon. 1982. Selective extinction and speciation: their influence on the structure and functioning of communities and ecosystems. American Naturalist 119: 480–498. Fowler, C.W., and M.A. Perez. 1999. Constructing species frequency distributions—a step toward systemic management. NOAA Techinical Memorandum NMFSAFSC-109. US Department of Commerce, Seattle, WA. Fox, L.R. and P.A. Morrow. 1981. Specialization: species property or local phenomenon? Science 211: 887–893. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Futuyma, D.J. 1983. Evolutionary interactions among herbivorous insects and plants. In Futuyma, D.J. and M. Slatkin (eds). Cooevolution, pp. 207–231. Sinauer Associates, Sunderland, MA. Gaston, K.J. 1988. Patterns in local and regional dynamics of moth populations. Oikos 53: 49–57. Gaston, K.J. and T.M. Blackburn. 2000. Patterns and process in macroecology. Blackwell Science, Oxford. Gaston, K.J. and J.H. Lawton. 1988a. Patterns in the distribution and abundance of insect populations. Nature 331: 709–712. Gaston, K.J. and J.H. Lawton. 1988b. Patterns in body size, population dynamics, and regional distribution of braken herbivores. American Naturalist 132: 662–680. Gaston, K.J. and J.H. Lawton. 1990a. Effects of scale and habitat on the relationship between regional distribution and local abundance. Oikos 58: 329–335. Gaston, K.J. and J.H. Lawton. 1990b. The population ecology of rare species. Journal of Fish Biology 37: 97–104. Gillooly, J.F., J.H. Brown, G.B. West, V.M. Savage, and E.L. Charnov. 2001. Effects of size and temperature on metabolic rate. Science 293: 2248–2251.

Glazier, D.S. 1986. Temporal variability of abundance and the distribution of species. Oikos 47: 309–314. Glazier, D.S. 1987a. Energetics and taxonomic patterns of species diversity. Systematic Zoology 36: 62–71. Grayson, D.K. 1977. Pleistocene avifaunas and the overkill hypothesis. Science 195: 691–693. Greenwood, J.J.D., R.D. Gregory, S. Harris, P.A. Morris, and D.W. Yalden. 1996. Relationships between abundance, body size and species numbers in British birds and mammals. Philosophical Transactions of the Royal Society of London, Series B 351: 265–278. Hall, S.J. and D.G. Raffaelli. 1993. Food webs: theory and reality. In Begon, M. and A.H. Fitter (eds). Advances in Ecological Research, Vol. 24, pp. 187–239. Academic Press, London. Hanski, I. 1990. Density dependence, regulation and variability in animal populations. Philosophical Transactions of the Royal Society of London, Series B 330: 141–150. Hassell, M.P. and R.M. Anderson. 1989. Predator-prey in host–pathogen interactions. In Cherrett, J.M. (ed.). Ecological concepts: the contribution of ecology to an understanding of the natural world, pp. 147–196. Blackwell Scientific, Boston, MA. Hassell, M.P., J.H. Lawton, and R.M. May. 1976. Patterns of dynamical behaviour in single-species populations. Journal of Animal Ecology 45: 471–486. Holling, C.S. 1992. Cross-scale morphology, geometry, and dynamics of ecosystems. Ecological Monographs 62: 447–502. Hutchinson, G.E. and R.H. MacArthur. 1959. A theoretical ecological model of size distributions among species of animals. American Naturalist 93: 117–125. Kelt, D.A. and D.H. Van Vuren. 2001. The ecology and macroecology of mammalian home range area. American Naturalist 157: 637–645. Lawton, J.H. 1989b. Food webs. In Cherrett, J.M. (ed.). Ecological concepts: the contribution of ecology to an understanding of the natural world, pp. 43–78. Blackwell Scientific, Boston, MA. Lawton, J.H. 1990. Species richness and population dynamics of animal assemblages. Patterns in body size: abundance space. Philosophical Transactions of the Royal Society of London, Series B 330: 283–291. Lawton, J.H. 1992. Feeble links in food webs. Nature 355: 19–20. Lawton, J.H. 1995. Population dynamic principles. In Lawton, J.H. and R.M. May (eds). Extinction rates, pp. 147–163. Oxford University Press, New York, NY. Lawton, J.H. and M. MacGarvin. 1986. The organization of herbivore communities. In Kikkawa, J. and D.J. Anderson (eds). Community ecology: pattern and

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process, pp. 163–186. Blackwell Scientific Publications, Cambridge, MA. Makarieva, A.M. and V.G. Gorshkov. 2004. On the dependence of speciation rates on species abundance and characteristic population size. Journal of Biosciences 29: 119–128. Marquet, P.A. 2002. Of predators, prey, and power laws. Science 295: 229–230. Marzluff, J.M. and K.P. Dial. 1991. Life history correlates of taxonomic diversity. Ecology 72: 428–439. Masterson, J. 1994. Stomatal size in fossil plants: evidence for polyploidy in majority of angiosperms. Science 264: 421–424. May, R.M. 1973. Stability and complexity in model ecosystems. Princeton University Press, Princeton, NJ. May, R.M. 1978. The dynamics and diversity of insect faunas. In L.A. Mound and N. Waloff (eds). Diversity of insect faunas, pp. 188–204. Symp. R. Entomol. Soc. Lond. No. 9, Blackwell, Oxford. May, R.M. 1982. Introduction. In Anderson, R.M. and R.M. May (eds). Population biology of infectious diseases, pp. 1–12. Springer-Verlag, Berlin. May, R.M. 1986. The search for patterns in the balance of nature: advances and retreats. Ecology 67: 1115–1126. Niemalä, P., S. Hanhimäki, and R. Maunila. 1981. The relationship of adult size in noctuid moths (Lepidoptera: Noctuidae) to breadth of diet and growth form of host plants. Annales Entomologici Fennici 47: 17–20. Orians, G.H. and W.E. Kunin. 1990. Ecological uniqueness and loss of species. In Orians, G.H., G.M. Brown Jr, W.E. Kunin, and J.E. Swierzbinski (eds). The preservation and valuation of biological resources, pp. 146–184. University of Washington Press, Seattle, WA. Osmond, C.B., O. Börkman, and D.J. Anderson. 1980. Physiological processes in plant ecology: towards a synthesis with Atriplex. Springer-Verlag, Berlin. Overholtz, W.J., S.A. Murawski, and K.L. Foster. 1991. Impact of predatory fish, marine mammals, and seabirds on the pelagic fish ecosystem of the northeastern USA. ICES Marine Science Symposia 193: 198–208. Paine, R.T. 1992. Food web analysis through field measurement of per capita interaction strength. Nature 355: 73–75. Patterson, B.D. 1984. Mammalian extinction and biogeography in the southern rocky mountains. In Nitecki, M.H. (ed.). Extinctions, pp. 247–294. The University of Chicago Press, Chicago, IL.

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Pauly, D., V. Christensen, J. Dalsgaard, R. Rroese, and F. Torres Jr 1998. Fishing down marine food webs. Science 279: 860–863. Peters, R.H. 1983. The ecological implications of body size. Cambridge University Press, New York, NY. Pimm, S.L. 1982. Food webs. Chapman & Hall, London. Power, M.J., D. Tilman, J.A. Estes, et al. 1996. Challenges in the quest for keystones. Bioscience 46: 609–620. Reagan, D.P. and R.B. Waide (eds). 1996. The food web of a tropical rain forest. University of Chicago Press, Chicago, IL. Rosenzweig, M.L. 1974. And replenish the earth: the evolution, consequences, and prevention of overpopulation. Harper and Row, New York, NY. Rosenzweig, M.L. 1995. Species diversity in space and time. Cambridge University Press, New York, NY. Rothschild, B.J. 1986 . Dynamics of marine fish populations. Harvard University Press, Cambridge, MA. Schoenly, K., R.A. Beaver, and T.A. Heumier. 1991. On the trophic relations of insects: a food-web approach. American Naturalist 137: 597–638. Sinclair, A.R.E. 1996. Mammal populations: fluctuation, regulation, life history theory and their implications for conservation. In Floyd, R.B., Sheppard, A.W., and P.J. De Barro (eds), Frontiers of population ecology, pp. 127–154. CSIRO Publishing, Melbourne. Sugihara, G., K. Schoenly, and A. Trombla. 1989. Scale invariance in food web properties. Science 245: 48–52. Tanner, J.T. 1966. Effects of population density on growth rates of animal populations. Ecology 47: 733–745. Terborgh, J. 1974. Preservation of natural diversity: the problem of extinction prone species. Bioscience 24: 715–722. Thompson, J.N. 1982. Interaction and coevolution. John Wiley & Sons, New York, NY. Ueckert, D.N. and R.M. Hansen. 1971. Dietary overlap of grasshoppers on sandhill rangeland in northeastern Colorado. Oecologia 8: 276–295. Wilson, E.O. and E.O. Willis. 1975. Applied biogeography. In Cody, M.L. and J.M. Diamond (eds). Ecology and evolution of communities, pp. 522–536. The Belknap Press of Harvard University Press, Cambridge, MA. Woiwod, I.P. and I. Hanski. 1992. Patterns of density dependence in moths and aphids. Journal of Animal Ecology 61: 619–629.

Appendix 3.1

The following material is Appendix 3.1 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Species characteristics and selectivity in extinction and speciation Science has not ignored the roles of selective rates of speciation and extinction in the dynamics of species numbers and their contribution to the formation of patterns among species (exemplified by those presented in Chapter 2). Nonevolutionary factors seem to be better understood, accepted, and the focus of more research; however, selectivity at the species-level is of growing attention. It is useful here to provide access to a sampling of some of the literature in which we find consideration of the detail of selectivity in species-level dynamics with specific reference to species-level features thought to be subject to such selectivity. The objective is that of illustrating yet another aspect of science that is beyond our capacity to be exhaustive; the complexity of reality prevents our ever knowing all there is to know about the selectivity of either speciation or extinction. Does this amount to an argument to cease such studies? Absolutely not! We become convinced of the reality of such selectivity through such work. Such conviction will help substantiate in the minds of managers what scientists already know: selectivity occurs at multiple levels and is part of what contributes to the formation of integrative patterns, to be accounted for when these patterns are carefully used as the basis for management. Thus, the following is far from an exhaustive account, but provides references and a brief treatment of related arguments for a sample of work 28

dealing with the study of selective extinction and speciation. Also included is a listing of factors and species-level features considered in regard to selective extinction and speciation beyond those treated in more detail in Chapter 2. In many cases numeric data are lacking for graphic presentation of frequency distributions or patterns. Much of the literature on extinction risk points to the causeand-effect relationships between human activities and their contributions to the extinction that is occurring in today’s world (e.g., Cardillo et al. 2004, Donazar et al. 2005, Fisher et al. 2003). It must be emphasized that there are often synergistic effects among extinction risks making it difficult to see evidence for any one (e.g., Davies et al. 2004, Isaac and Cowlishaw 2004, Mattila et al 2006, Owens and Bennett 2000). Thus, in cases wherein claims are made for a particular factor contributing to risk, it is often a risk identified through analysis in which other factors are taken into account through the statistical analysis used. Thus, ‘‘all else being equal’’ (which it never is), the factors identified as sources of extinction risk often function in conjunction with others to give rise to correlative patterns such as those covered in the latter sections of Chapter 2 where more than one dimension (species-level attribute) is involved simultaneously. Small population size is a feature obviously associated with extinction risk (e.g., O’Grady et al. 2004). The volume of literature associated with this characteristic (well beyond being adequately treated here) provides a glimpse at what is in store for science focused on other species-level attributes. It is clearly a characteristic believed to contribute to the risk of extinction. Another factor clearly accepted as a risk of extinction is reduced evolutionary plasticity. When faced with environmental change, species that

A P P E N D I X 3 .1

have limited capacity to change have less chance of survival than species that are more flexible (i.e., can evolve fast, Fowler and MacMahon 1982, Maynard Smith 1989, Pease et al. 1989). Generation time is a recognized component of evolutionary plasticity (Fowler and MacMahon 1982, Freeland 1986, Lenski et al. 1991, Marzluff and Dial 1991, Maynard Smith 1976a, Pimm and Gilpin 1989, Simpson 1953, Wilson and Willis 1975). Species with large body size have long generation times (Blueweiss et al. 1978, Fenchel 1974, Peters 1983) and limited capacity for change compared to species with small bodies and shorter generation times. Thus, extinction risk tends to increase with body size (Brook and Bowman 2005, Cardillo and Bromham 2001, Coe 1980, Davies et al. 2000, del Monte-Luna and LluchBelda 2003, Diamond 1984a,b, Fagan et al. 2001, Gage et al. 2004, Hallam and Miller 1988, Isaac and Cowlishaw 2004, Murray and Hose 2005, OwenSmith 1988, Pimm et al. 1988, Raup 1986, Reynolds et al. 2005, Thomas et al. 2006, Van Valen 1973a,b, Vrba 1980, Wilcox 1980) so as to count among the factors causing the drop in species numbers with increasing body size (above an intermediate mode, Fig. 2.1). The extinction and speciation assumed as explanatory factors for observed macroecological patterns by Gaston and Blackburn (2000) relate to various life history characteristics, range size, and population variation as they are associated with body size (and clearly related to risk of extinction on their own). Cardillo et al. (2005) provide insight to the explanation of interacting factors behind the increased risk of extinction with increasing body size. The maximum rate of increase per unit time (rmax, in most literature on population biology) is a feature of species that is correlated with body size (Blueweiss et al. 1978, Peters 1983, Western 1979) and contributes to risk of extinction. Species with low rates of increase are also often (but not always) species with large body size and thus likely to experience elevated extinction risks relative to those with higher rates of increase (Dickerson and Robinson 1986). The higher rates of increase, themselves, are part of what contributes to evolutionary plasticity (Marzluff and Dial 1991). In the original analysis by Marzluff and Dial, a statistically significant relationship between extinction and fecundity

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was not found. But their results were not consistent from group to group. The results presented in Table 1 of their paper show that only four out of 22 samples showed negative correlations—between –0.10 and –0.82—which, nonparametrically, is statistically significant. Thus, as a statement for the overall sample (a sample of species), there is empirical basis for having shown a relationship between rate of increase (specifically fecundity) and extinction rates. All else being equal, extinction rates tend to be higher for species with lower rates of increase. This conclusion is supported by other work (e.g., Pimm and Gilpin 1989). The rate of increase is the Malthusian capacity for increase that prevents extinction (Bateson 1972). As argued by Pimm et al. (1988), species with low rates of increase have a higher rate of extinction because of the extra time spent at low population levels following population decline. Modeling studies suggest that higher rates of increase carry less risk of extinction (Goodman 1987a,b). Richter-Dyn and Goel (1972) found that time to extinction (specifically for colonizing species) is related to birth rates. Marzluff and Dial (1991) argue that large intrinsic rates of increase reduce rates of extinction in part due to the capacity to expand the range and recolonize areas where local populations became extinct. Species with lower rates of increase are subject to higher risks of extinction in the face of hunting pressure (by humans, Price and Gittleman 2007). Species with large litter size tend to be less prone to extinction than species with smaller litters (Cardillo 2003). Johnson (2002) found the risk of extinction to be related to reproductive rate. Evolutionary plasticity is one of the advantages of sexual reproduction (Emerson 1960, Ghiselin 1974, Lewontin 1957, Maynard Smith 1976a, Schultz 1977, Simpson 1953, Stanley 1975b, 1979, 1990a, Williams 1971). Thus, mode of reproduction serves as a simple example of the selectivity of both extinction and speciation (Fowler and MacMahon 1982, Maynard Smith 1978a, 1989, Stanley 1975b, 1979, 1990a). As Simpson and Beck (1965) summarize the matter (for species as populations in selective extinction and speciation, or subpopulations in group selection): “There is, therefore, no mystery attached to the nearly universal occurrence of sex in organisms. Those populations of

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organisms most able to vary have been those most able to survive changing conditions in the environment and those most able to evolve new ways of life as the opportunities arose. Sex is widespread because, like any other adaptation, it has promoted the long-term survival of the populations having it”. Like everything, however, there is another facet to sex: sexual selection is one of the factors under study in its contribution to evolutionary dead-ends (evolutionary suicide; Morrow and Fricke 2004, Morrow and Pitcher 2003). Trophic level is another factor in the risk of extinction, in part because species at higher trophic levels suffer higher rates of extinction because of their dependency on species at lower levels (Fowler and MacMahon 1982). Empirical information has demonstrated that species at higher trophic levels are more vulnerable to extinction than their counterparts at lower levels (e.g., Purvis et al. 2000). Work related to these concepts is found in J. Brown (1971, 1981), Davies et al. (2000), Glazier (1987a), Marzluff and Dial (1991), Pagel et al. (1991), Patterson (1984), Petchey et al. (2004), Purvis et al. (2000), Terborgh (1974), and Wilson and Willis (1975). Other forms of interdependence lend to extinction, the greater the dependence the higher the risk. Not surprisingly, the extinction of species exhibiting more interdependence has been observed to be higher than others that are less dependent. In particular, species showing symbiotic interdependence undergo extinction at higher rates than species without such strong dependence (e.g., Raup and Jablonski 1993, Rosen and Turnsek 1989). Boulter et al. (1988) provide evidence that specialist species suffer high rates of extinction among plants when compared to less specialized species. Further evidence and basis for concluding that specialist species suffer high rates of extinction when compared to generalists are found in Anstey (1978), Davies et al. (2004), Davis (1990), Diamond (1976), Eldredge (1992), Futuyma and Moreno (1988), Geist (1978), Jablonski (1986a), Koh et al. (2004), Norton (1987), Patterson (1984), Paul (1988), Raup and Jablonski (1993), Ricklefs (1976), Rosen (1981), Simpson (1953), Stanley (1984), Unwin (1988), Vermeij (1983), Vrba (1992), and Watling and Donnelly (2007).

In terms of tolerance of environmental conditions, specialization may be related to geographic ranges. As indicated by Brasier (1988) and Gaston (1990), species that have broad ecological tolerance or use a wide variety of resources will also tend to have much broader distributions and be less likely to become extinct. Other considerations of the effects of specialization (especially habitat specialization) are found in Diamond (1984a), Dunn (2005), and Safi and Kerth (2004). Specialization of various kinds has also been considered in regard to speciation (particularly cladogenesis, the splitting of a phyletic lineage to form two species; Gilinsky 1986, Vrba 1980, 1985). Beyond intermediate levels, risk of extinction may increase with numbers of species consumed, through dynamics involving factors such as population instability, thus placing limits on how many species can be consumed. This combined with tendencies toward specialization through evolutionary changes contributes to limits on connectance observed in the field of food-web analysis (May 1972, McNaughton 1978). Speciation rates are thought to vary with the extent of specialization as well as the positions in which species occur in trophic chains and symbiotic interactions. Evolutionary changes are expected among all species, including those at the first level in any such chain. Consumers of these resources are then faced with new circumstances and only those species that have the evolutionary plasticity to avoid extinction in tracking these evolutionary changes are expected to survive. Thus, species higher in such chains (or webs) of dependency would be expected to be characterized by increasing evolutionary plasticity and undergo more speciation than species upon which they depend. The idea of species evolving in reaction to each other in evolutionary systems (coevolution) is described or exemplified in the work of Benton (1987), Futuyma and Slatkin (1983a), Maynard Smith (1989), Raup (1988), Stenseth (1985), Stenseth and Maynard Smith (1984), and Van Valen (1973a) and often referred to as the Red Queen concept.1 Coevolutionary ecology/ biology (e.g., Thompson 2005) are fields of science devoted to the study of such interactions which involve evolutionary webs that permeate ecosystems and the biosphere as do food webs.

A P P E N D I X 3 .1

Species with small geographic ranges are more susceptible to extinction than species with larger ranges. Literature related to this conclusion includes island biogeographic work as well as work by paleontologists (e.g., Diamond 1984a, Gage et al. 2004, Gaston 1990, Gaston and Lawton 1990a, Glazier 1986, 1987b, Hanski 1982, Hope 1973, Jablonski 1987, Raup 1986, Rey 1984, Richman et al. 1988, Schoener and Schoener 1981, Scrutton 1988, Stanley 1989, Terborgh 1974, Terborgh and Winter 1980, Unwin 1988, Wilcox 1980). Species with large geographic ranges suffer the combined risks of increased numbers of species with which they interact and the potential of shear forces from diverging conditions in different areas to result in speciation (Glazier 1987b, Miller 1956, Rosenzweig 1995). For population variability, the many explanatory processes contributing to the observed patterns include the risks of extinction associated with small population levels (J. Brown 1971, Crowell 1973, Dennis 1989, Dickerson and Robinson 1986, Goodman 1987a, Hallam and Miller 1988, Hull 1976, Karr 1982a,b, Pielou 1977, Raup 1986, Rey 1984, Richter-Dyn and Goel 1972, Simberloff and Abele 1974, and Terborgh and Winter 1980). Part of the risk of low population levels is that of Allee effects, wherein the slope of density dependence curves changes to be positive (depensatory) at low population levels. In extreme cases, the rate of population increase at low levels is negative and there is a tendency to decline to zero (Dennis 1982, 1989, Fowler and Baker 1991, Henle et al. 2004, Lande 1988, Mosimann 1958, Odum and Allee 1954). Papers that treat population variability, related selectivity in extinction, or provide more information on related patterns include Brown (1995), Connell and Sousa (1983), Diamond (1984a), Fowler and Baker (1991), Glazier (1986), Gaston and Lawton (1988a,b), Mosimann (1958), Pagel et al. (1991), Patterson (1984), Pimm et al. (1988), Schoener (1985). Population variability can result from consumer/resource relationships in which the population level of the resource is reduced through the effects of consumption by the consumer. If such a resource species briefly escapes the effect of predation, it can grow to large population size, thus stimulating growth and higher consumption by the consumer and then experience a resulting

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population crash. Species that exhibit significant reduction in their resource species count among keystone species (Mills et al. 1993, Paine 1966, Roughgarden 1983). But, as explained, these circumstance can give rise to population fluctuation in which the magnitude of the fluctuations is related to the degree the resource species is (are) reduced by the consumer (May 1973, 1981a). Thus, one of the contributing factors in the drop in species numbers with increasing interaction strength may relate to the risk of extinction from any resulting population variability. Jonsson et al. (2006) found interaction strength to be a source of extinction risk in model systems. The kind and level of density dependence that species exhibit in their populations is related to risk of extinction (Henle et al. 2004). With no density dependence a species is doomed to extinction (Bateson 1972, Ginzburg et al. 1990, Royama 1977, Whittaker 1975). Quoting Whittaker: “In principle, a population that randomly walks in time, without some density-dependent limitations, must walk randomly to extinction. In this view, densityindependent population control is a contradiction in concepts. Influences limiting fluctuation are necessary to the long-term survival of populations”. According to Godfray and Hassell (1992): “It is a logical necessity that any population of plants or animals that persists in the environment must experience some form of density-dependent feedback on population growth . . . ”. For further treatment of this argument see Brown (1995), Godfray and Hassell (1992), Hanski (1990), Hanski et al. (1993), and Shepherd and Cushing (1990). As Hanski et al. (1993), Holyoak and Lawton (1992), Royama (1977), and Woiwod and Hanski (1992) argue, with time and increased sampling, it is expected that virtually all species will be shown to have some degree of density dependence. This conclusion is based in large part on the argument that species without, or with low levels of, density dependence suffer high extinction rates that are much higher than for species that show density dependence. However, at extreme levels of density dependence, populations run a higher risk of cyclic or chaotic behavior (Ginzburg et al. 1990, May 1975) and the risk of extinction associated with high population variability mentioned above.

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Evidence of the advantages of mobility in reducing the risk of extinction is far from rare in the scientific literature as seen in Diamond (1984a), Dickerson and Robinson (1986), Eriksson and Bremer (1991), Farnworth and Golley (1974), Janzen (1983), Marzluff and Dial (1991), Norton (1987), Owen-Smith (1988), Pimm et al. (1988), Raup (1986), Reinhardt et al. (2005), Terborgh (1974), Van Valen (1971), and Wilcox (1980). Unwin (1988) indicates that the birds of today are the principle descending survivors of the dinosaurs owing at least in part to their mobility. As indicated by Eldredge (1991), organisms that fly, swim, walk, or have their seeds carried to a new habitat can avoid changes caused by shifts in their habitat owing to climatic changes over evolutionary time. As pointed out by Davis (1990) and Roberts (1989) the survivors of the extinction processes involve many with insured seed dispersal as a means for the species to relocate during times of environmental change. Knoll (1984) also presents information and arguments indicating that plant species that have effective dispersal mechanisms survive periods of changing climate better than those that do not. Bats may be much more numerous as species than other mammals of the same size because of their mobility. Selectivity at the species level is frequently mentioned for a variety of factors beyond those mentioned above. Very few are represented by graphic illustrations of related patterns. The examples in the following paragraphs are presented here as a means to make the points that (1) in the end, the complexity of reality is such that we, as scientists, can never hope to find, account for, or understand all such factors, especially in their combination(s) (see Table 2.1), (2) there is a rich opportunity for science working on these matters to further substantiate what managers must know: selective extinction and speciation count among the factors that result in patterns that we see, and (3) the patterns we see provide information that accounts for such factors (including those we have yet to discover) when we use the resulting patterns in management. Behavior (including communication, social organization) has been seen as a factor in selectivity at the species level (Glazier 1987a, Munoz-Duran 2002). The biochemical composition of species show patterns (e.g., Woodward 1993) that may be partially explained by differential extinction, as

would be patterns in decomposition. The energetic or thermodynamic patterns in communities, ecosystems, and the biosphere are subject to a great deal of research involving microevolution and the interactions among both individual organisms and species. These, more conventional, kinds of research are being joined by work on selectivity at the species level that also contributes to observed patterns (Brown 1995, Damuth 1981, 2007, Fisher 1986, Jørgensen 1992, May 1981b). The amount, kinds, and variation of genetic material (see polyploidy below) are observed to fall in patterns (Ayala 1978, Beardmore 1983, Fisher 1986, Fowler and MacMahon 1982, Holland et al. 1982, May 1978, Raup and Jablonski 1986) that involve selectivity, not only at the chemical and individual levels, but also at the species-level. In addition to specialization measured in terms of resource specialization, habitat association and utilization involves specialization that is likely subject to selectivity in both extinction and speciation (e.g., Schoenly et al. 1991). Interaction strength (e.g., rates of consumption of resource species) involves more than population level effects. They also involve the intensity of any selectivity involved to result in ‘‘coevolutionary intensity’’ as an interaction of varying magnitude that undoubtedly lends itself to varying probabilities of either extinction or speciation (Jordano 1987). In addition to the life history traits of rates of increase, ages at first reproduction, and mortality schedules, there are species with morphological and developmental stages, and behavioral patterns such as dormancy, also subject to species-level selectivity (Bush 1975, Carlquist 1965, Cristoffer 1990, Dial and Marzluff 1989, Diamond 1974, Glazier 1980, 1987a, Herrera 1992, Jablonski and Lutz 1983, Marzluff and Dial 1991, May 1978, Maynard Smith 1989, McKinney 1990, Mertz 1971, Scheiner 1992, Spicer 1989, Stearns 1992, Sukopp and Trepl 1987, Upchurch 1989). Mimicry and chemical communication signals count among factors thought to be important at the species-level (Gilbert 1980, 1983). One of the more clearly established patterns thought to be important in selective extinction and speciation are the elements of mobility and dispersal (Brown 1995, Eldredge 1991, Gilinsky 1986, Glazier 1980, 1986, 1987a, Jablonski 1986b, 1989, Jablonski and Lutz 1983, Marzluff and Dial 1991, Pacala 1989,

A P P E N D I X 3 .1

Raup and Jablonski 1986, Stanley 1990a, Sukopp and Trepl 1987, Vermeij 1987, Wilcox 1980). In the category of behavior are specific modes of feeding or nutrient uptake thought to be subject to specieslevel selectivity (Jablonski 1989, McKinney 1990, Murray 1984, Patterson 1984, Paul 1988, Raup and Boyajian 1988) as are various sensory perceptions of the environment (e.g., visual, auditory, thermal; Fisher 1986). Phenotypic plasticity helps avoid genetic rigidity and lends to species-level advantages much as does evolutionary plasticity—a kind of genetically derived plasticity to mitigate for evolutionary inflexibility (Hoffman 1983, Pacala 1989, Parsons 1991a,b). Even the amount of genetic material shows patterns among species, initially seen in the polyploidy measured for various species (Ehrlich and Wilson 1991, Gibby 1981, Masterson 1994, Orr 1990, Rosenzweig 1974, 1975, 1995) and potentially subject to differential speciation and extinction rates. Not unrelated would be the matter of genetic variation (Lloyd and Gould 1993). In addition to the patterns in lifetime reproductive effort (Charnov et al. 2007), there may be patterns in production efficiency (May 1981b) that contribute to the changes of species-level success or failure. Both of these factors involve reproductive and mating systems which are not independent of interspecific factors such as pollination (and its concomitant interdependence, risks of extinction, and contributions to speciation) (Barrett 1989, Bond 1995, Branch 1984, Dobson and Lyles 1989, Donoghue 1989, Gill 1989, Glazier 1987a, Herrera 1992, Lieberman and Vrba 1995, Maynard Smith 1989, Orians and Kunin 1990, Sukopp and Trepl 1987). Finally, much as one might see the risk of death being associated with immaturity among individual humans (as well as individuals for most species), it appears that the age of a species is related to its risk of extinction. Age or “experience” seems to count among species as well as among individuals (Boyajian 1991, Herrera 1992, Orians and Kunin 1990).

Notes 1. Van Valen (1973a) described the process of species evolving in response to each other’s evolution by quoting from L. Carroll’s “Through the Looking Glass” (“Now

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here, you see, it takes all the running you can do, to keep in the same place”). Such processes are thus termed the Red Queen model of evolution (Maynard Smith 1988).

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Appendix 3.2

The following material is Appendix 3.2 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Evolutionary contributions to the formation of species-level patterns (frequency distributions) Extinction has only defined the groups: it has by no means made them . . . .

some species characteristics compensate for others to reduce the risk of extinction for a particular species. The structure and function of ecosystems and other species groups are then described as the result of selective extinction and speciation, in concert with all other contributing factors (Fig. 1.4)— the emergence of ecosystems involves evolution at various levels. Another section highlights the relative importance of evolutionary changes, particularly selective extinction and speciation, in the formation of frequency distributions.

—Charles Darwin

This appendix further develops the concept that patterns among species are simply natural phenomena that emerge from the complexity both of the systems within which they occur and of which they are composed. Chapter 2 began consideration of what is involved in this explanatory complexity, with examples including the abiotic environment, and the evolutionary processes of natural selection, speciation and extinction, and ecological mechanics. Chapter 3 continued that process. This appendix elaborates through consideration of how the mechanics of evolutionary processes, particularly speciation and extinction, contribute to specieslevel patterns and, in fact, may be the most important contributing factors. The bulk of what are presented here are hypothetical examples to explore the effects of extinction and speciation to include the evolution of species (the latter involving natural selection as it acts on individuals, genes, and gene combinations). These processes are examined in their contribution to the formation of species-level patterns, particularly as frequency distributions among single and multiple species characteristics. Early sections compare the effects of selectivity or nonselectivity in one or more evolutionary process. A later section describes how

1.0 Evolutionary development of species-level patterns (frequency distributions) When considering the extinction, speciation, and evolution of species, the ways species function within ecosystems is as important as their morphology (the cornerstone of taxonomy). The function of species includes their coevolutionary interactions (“ . . . .life is a dense web of genetic interactions . . . “; Lederberg 1993). From an evolutionary/ ecological perspective, species may be classified according to characteristics independent of pedigree or taxonomic relationship to other species. In other words, they may be placed in categories based on measurements like those of the species distributions shown in Chapter 2. Characteristics such as body size, trophic level, geographic range, metabolic rates, population variability, population size, and generation time are shared by all species, just as are their taxonomic links. Among higher trophic levels selectivity in consumption (e.g., selectivity by size or sex) are measurable characteristics regarding interspecific interactions. Limits to variation among species are key to the information in each species-level pattern. These limits are observable in the form of frequency distributions—often “bell” curves with tails 41

representing declines in the number of species each side of a mode. In this section, the influence of extinction and speciation in the formation of frequency distributions is described using hypothetical examples, illustrated graphically. The purpose here is to show how selective extinction and speciation combine in a trial-and-error process to produce examples of sustainability among species. This section begins by illustrating how selective extinction and speciation (coupled with the microevolution involved in speciation) influence frequency distributions for a single species-level characteristic. This requires making the unrealistic assumption that individual characteristics affect evolutionary processes independently. In nature, the simultaneous actions of selectivity along multiple dimensions create a complex of factors that contribute to the formation of species frequency distributions. Examples seen in the following are presented for patterns resulting from two or more interacting evolutionary factors in an effort to better understand this complexity. Such examples are an oversimplification of nature but reveal critical insights. Other examples further illustrate the interplay of evolutionary processes, showing how extinction, speciation, and/or evolution may be selective for some species characteristics and nonselective for others.

1.1 Effects on relative abundance of two groups for one species-level characteristic Species of any group (set, or sample) can be divided into subsets represented by bars corresponding to the portion of the sample they represent (Appendix 1.3, Fowler and Perez 1999). Examples were shown in Chapters 1 and 2 where we begin to see first approximations of probability distributions of practical utility in implementing Management Tenet 5 (Chapter 1). Species that can be divided into two categories for a single species-level characteristic can be treated the same way, as shown in Appendix Figure 3.2.1. The species-level characteristic in this illustration is generic but could apply to a specific case (e.g., one group might be species that reproduce sexually and the other asexually, or one might be of high trophic level and another low, on each side of an arbitrary midpoint).

Portion of species

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Species characteristic

Pseudoextinction

Extinction Replication

Extinction Replication

Portion of species

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Species characteristic Appendix Figure 3.2.1 The effects of selective extinction and speciation on the relative number of species in two categories of one species-level characteristic (see Table 3.1). Relative, not absolute, rates are indicated by the width of the bars on the arrows. Over a unit of time, the group represented by the white bar on the left of the top panel experiences more cladogenic replication. The group on the right (black bar) experiences more extinction and pseudoextinction (i.e., loss through anagenesis). After the combined effects of this selectivity (bottom panel), the group of species on the left outnumber those on the right.

The top panel of Appendix Figure 3.2.1 represents the starting point of a hypothetical set of species divided into two groups with the same number of species in each group. Selective extinction, as shown in this example, removes a greater fraction of those on the right than those on the left. If extinction were acting alone, the result would be a redistribution of the relative numbers of species so that the group on the left would be a larger portion of the total; the number of species on the left would change to outnumber those on the right. By contrast, nonselective extinction (which would be shown by arrows of equal width on the right and left of a graph like Appendix Fig. 3.2.1) would not, on the average, change the relative numbers of species. Each species would have the same probability of extinction and the same1 fraction of each group of species would, on average, suffer extinction.

SEL EC T I V E E X T I N C T I O N A N D SP ECI AT I O N

Extinction, however, is only part of what determines relative species numbers. Speciation is also involved. This includes species replication 2 as shown by the vertical arrows between the panels of Appendix Figure 3.2.1. The species in the category on the left have a higher species-level replication rate than those on the right. Some speciation may include anagenesis but with insufficient change to move to a different category. In the absence of extinction or anagenic interchange, the category with the highest replication rate would eventually be represented by the larger (and growing) fraction of the total number of species (which itself would be increasing). Species-level dynamics also include anagenesis or evolution to change category (pseudoextinction). In Appendix Figure 3.2.1 such a change is illustrated by the central diagonal arrows. The number of species in the category that loses a larger fraction of its (new) species to the other category exhibits a relative decline (black bar again becomes smaller than the white bar). The portion of species in each category changes as a consequence of the impact of such selective anagenesis. However, anagenic change can result in an equilibrium in such a model. This can happen because the growing category eventually contributes enough to the smaller category to make up for the latter’s loss, now in absolute numbers of species (Appendix 3.3). The relative magnitude of contributions by extinction, replication, and evolution differ over time and in regard to species characteristics, but they are simultaneous processes (Table 3.1). Equilibria3 achieved in the resulting frequency distribution of species thus contribute to the formation of ecosystem properties. In Appendix Figure 3.2.1 this is exemplified by the relative portion of species in each of the two categories. The combined effects of all three processes (extinction, species replication, and evolutionary or anagenic change) illustrated in Appendix Figure 3.2.1 result in relative4 growth of the group on the left and decline in that on the right. The growth of species numbers on the left is an example of the expression of the interplay of these processes in Combination 1 (Table 3.1). The relative effects of extinction and anagenesis are positive by contributing to an increased portion of species. These effects

43

are reinforced by cladogenic replication. The loss of species in the group on the right is an example of Combination 4 wherein the negative effects of anagenesis and extinction are reinforced by selective replication. The replication rate (vertical arrows) of the group on the left is larger than the extinction rate for the same group. The extinction rate of the group on the right is larger than on the left. Finally, the rate of conversion of species from the group on the right to that on the left is the larger of the two anagenic exchange rates. Clearly, the number of combinations of potential rates is infinite, as are the outcomes, even though the options fall into the eight categories of Table 3.1. One set of combined dynamics is noteworthy. If the extinction rates of two groups of species (as in Appendix Fig. 3.2.1) are quite different but equal to the replication rates in each case, the differences between extinction and replication rates are zero for each group. Consequently, there would be a larger turnover among species for a group with larger extinction and replication rates (expressed as relative rates or probabilities, not absolute numbers), but species numbers would not change. Species numbers would depend on the level at which speciation and extinction are in balance. It is possible, for example, that high speciation rates would be characteristic of a group with low species numbers. For purposes of illustration we can make the unrealistic assumption that we have a case wherein there is no effect from ecological mechanics and no incremental changes contributed by extinction or replication. In such cases, the ratio of species numbers between two categories would depend only on the exchange between them via evolution (i.e., anagenesis, Appendix 3.3)5. An enigma in modern biology is the preponderance of species that reproduce sexually6 because sexual reproduction is so costly to individuals.7 The observed abundance of sexually reproducing macroscopic species is easily explained by the effects of selective extinction and speciation8 (Fowler and MacMahon 1982, Appendix 3.4). This entails the combined effects of a variety of rates of extinction and replication, all in combinations with evolution that puts a drain on sexual reproducers. Such dynamics demonstrate the hierarchical potential for selective extinction and replication

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to override natural selection among individuals in anagenesis. This is especially true in Combinations 6 and 7 of Table 3.1, but also other combinations when the rates are high enough for the combined effects of cladogenesis and extinction to overpower anagenesis.

1.2 Patterns from combined selective processes (single dimension) What are the options for the dynamics in more complex situations? The example above (Appendix Fig. 3.2.1, and Appendices 3.3 and 3.4) consists of a discrete categorization of species into two groups for one characteristic. As seen in Chapter 2, most species-level characteristics are not discrete categories but involve continuous variables (e.g., body size, metabolic rates, and population variability). The spectrum of observed values for each such characteristic may be divided into numerous segments or categories (“bins” of values). As in patterns represented by the histograms of Chapter 2, a bar can be used to graphically represent the number or portion of species in each category or subdivision (Appendix 1.3, Fowler and Perez 1999). The portion of species (as well as raw species numbers, or percentages of totals) can thus be represented in a frequency distribution in a variety of ways, even continuously, as probability distributions (Fowler and Perez 1999). The added complexity seen in such cases involves the fact that for categorized groups, anagenic change can move species in either direction from any category or point. A mix of such processes occurs and involves a large variety of contributing factors, some with directional bias. The direction in which a particular species may evolve is thus not a simple matter; the probability that it will evolve in a particular direction can be considered a product of all contributing factors. Thus, each group of species along a continuum experiences extinctions, replication, exchanges with the group above (to the right in graphs like those of Chapter 2), and exchanges with the group below (to the left). As we now know, replication processes may be subdivided into three categories: 1. Species that remain unchanged.

2. Species that multiply through cladogenic splitting without sufficient anagenic change to leave a defined group. 3. Species that exhibit enough change in one or more of their other characteristics (i.e., in the categories for a different measure or dimension) to become new species. A species may stay at the same trophic level, for example, but become a new species on the basis of change in another attribute, such as body size. 1.2.1 Processes in combination One way of seeing the combined processes of selective extinction, selective replication, and selective anagenic evolution is shown in Appendix Figure 3.2.2. This would be an example of the ways a particular combination of the rates involved might reflect the effects of a hypothetical environment on a hypothetical set of species. This graph shows the combination of dynamics affecting species in their distribution across a spectrum of single-dimension measurements (e.g., any of those of the singlecharacteristic species frequency distributions in Chapter 2). In the top panel of this figure, each of the four processes is represented: extinction, replication, and anagenesis in two directions. The only process of Appendix Figure 3.2.2 that is not selective is downward anagenesis (the fraction of species that will evolve smaller values of the hypothetical characteristic). This process, downward anagenesis, occurs at a rate that is independent of the exhibited characteristic.9 The line is flat. By contrast, selectivity occurs in the upward anagenesis of the example in Appendix Figure 3.2.2 because this evolutionary process happens more often among species low in the spectrum (to the left) than those higher in the spectrum (on the right). Species in this example evolve predominantly toward higher values (i.e., toward the right),10 faster for species low (left) in the range than for those to the right. To ensure correct interpretation of similar graphs later in this appendix it is helpful to relate to a specific set of species within the spectrum of the species-level characteristic. Appendix Figure 3.2.2 shows a case where, for a specified period of time, 0.5% of the species exhibiting the smallest measure of the characteristic (i.e., those species at the far left)

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The core of selectivity in extinction and speciation involves two components:

0.07 0.06

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0.03 0.02 0.01

Replication Downward anagenesis

0.00 Species characteristic 0.10 0.08 Portion of species

45

0.06 0.04 0.02 0.00 Species characteristic

Appendix Figure 3.2.2 Selective extinction and speciation depicted in a hypothetical example. This example shows selectivity in both extinction and speciation across a continuous measure of a species-level characteristic. Here, extinction and multiplicative replication increase with increases in the species-level characteristic, and upward anagenesis decreases. Downward anagenesis is nonselective (completely so if it remains unchanged above and below the segment of species level measure represented by the abscissa). Overall, anagenesis tends to be upward in this example because the probability of upward anagenesis is greater than that for downward anagenesis for the entire range. All apply only within the range shown. Three of the four processes are selective because the rates (relative rates reflecting probabilities) at which they occur are correlated with the magnitude of the species-level characteristic.

will go extinct; 1.0% of these species will evolve to exhibit even less of the characteristic (downward anagenesis); 1.5% will replicate to produce similar species; and 4.0% will evolve toward greater measures of the characteristic (upward anagenesis). These are the rates used in the matrix in Appendix 3.5, just as are the other rates shown in the top panel, corresponding to the other measures of the characteristic. Repeated application of these rates in the matrix model of Appendix 3.5 generates the distribution shown in the lower panel.

1. The rates, or probabilities, of extinction and speciation change across the range of each specieslevel characteristic. 2. The specific nature or shape of the curves exhibiting the selectivity vary among physical and biotic environments. Evolution through processes that include natural selection among individuals are selective as expressed in selective anagenesis. The postulates of selection among species presented in Chapter 3 are graphically exemplified by the top panel of Appendix Figure 3.2.2. Included are the selective evolutionary processes involved in anagenesis. All apply as assumed for a hypothetical unspecified environment and, in reality, would change as environments change. Selective extinction, speciation, and evolution can also be represented in the form of mathematical models. The model described in Appendix 3.511 was used to produce this and similar graphs of this appendix with different sets of assumed or hypothetical parameters (rates) as shown in the top panels of the graphs. The bottom panel of Appendix Figure 3.2.2 shows the frequency distribution or relative abundance of species expected to result from the selectivity of extinction, speciation, and evolution exhibited in the top panel, when they are assumed to be the only contributing factors.12 This distribution is the balance of the interactions of all three processes (four when upward and downward anagenesis are counted separately, Appendix 3.5). In this example, species tend to be most numerous in the central part of the spectrum of possibilities. This is explained by the tendency for extinction to be greater than replication at the upper end of the range (upper panel), thereby preventing the buildup of species toward the right. Species that form in the low end tend to evolve toward the higher end more rapidly than they are replaced by replication. This prevents the buildup of species toward the left end of the spectrum. Thus, the interplay of the eight combinations of interacting forces from Chapter 3 changes continuously across the spectrum of the species level characteristic.13

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Appendix Figure 3.2.2 demonstrates the necessity of including selectivity as part of the species-level dynamics involved in the formation of species-level patterns. This figure illustrates the influence of such selectivity as it might contribute to the structure and functioning of ecosystems as assembled from species affected by these processes. Arguments restricted to consideration of individual-level selection might consider species frequency distributions to be purely fortuitous, although speciation processes, especially any anagenic trends, might not be ignored. In this example, based on natural selection among individuals alone, one would expect most species to be at the high end of the spectrum, primarily through the effects of upward anagenesis. Although the predominant direction of evolution tends to be in that direction for all categories of species, it would be more so for species at the low end of the range for the hypothetical species-level characteristic shown in Appendix Figure 3.2.2. Species at the high end tend to replicate into similar species more rapidly than at the lower end. However, the magnitude of extinction rates at the higher (right) end are enough to prevent the accumulation of species that might be expected on evolutionary arguments alone, or for that matter, pure diffusion as a random process. Consideration of the effects of any of the other processes alone can be equally misleading. Selective extinction, speciation, and evolution in combination integrate all of the evolutionary dynamics contributing to the formation of patterns (e.g., frequency distributions) among species. These are dependent on the environment in which they occur, including the biotic environment of other sets of species to which they are exposed.14 As stated before, these evolutionary dynamics contribute to the formation of patterns among species. This happens in combination with the contributions of ecological mechanics. The latter includes immigration and emigration of species through changes in their geographic ranges. Such dynamics have their consequences by adding species to, or removing species from, any relevant geographically defined set of species. 1.2.2 Further examples More hypothetical examples of selectivity in extinction and speciation are helpful in understanding

patterns among species and how these processes contribute to the characteristics of any set, group, or collection of species. Models such as that of Appendix 3.5 can be used to explore these processes and, simultaneously, appreciate both the potentials and limitations of such models. Although each example below focuses on one hypothetical species-level characteristic, it is important to remember the variety of real characteristics over which these species-level dynamics and distributions occur (Table 2.1); each example reflects the effects of both a hypothetical physical environment and the dynamics of internal biotic forces (exogenous and endogenous factors). The next example demonstrates selectivity restricted to extinction and replication through cladogenesis. Appendix Figure 3.2.3 illustrates selective extinction and speciation wherein extinction increases and replication decreases while neither form of anagenesis is selective in relation to the hypothetical species-level characteristic. In this example, both forms of anagenesis are nonzero, and equal, so that species change position only through nondirectional diffusion along the axis of the species-level characteristic. This demonstrates the ways that the characteristics of the set of species (i.e., not the individual species) within a species frequency distribution can evolve in ways where extinction and cladogenesis override anagenesis or the effects of natural selection among individuals.15 This combination in this example results in the accumulation of most species at the lower end of the scale (lower panel, Appendix Fig. 3.2.3). The pattern is represented as a product of equilibrium conditions.16 In nature, such a pattern would be seen as a collection of species tending toward such an equilibrium that would be dynamic and influenced by a multitude of other factors. Before achieving equilibrium most sets of species and the ecosystems assembled from them are faced with new environmental conditions that eventually result in a different equilibrium, often before it is achieved. The projected endpoints of such evolution keep changing as continuously “moving targets” of the evolutionary process. When environmental changes are of a magnitude that is not beyond the capacity for change in the dynamics among species (some of which result in extinction

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47

Upward anagenesis

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0.25

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0.20 0.15 0.10 0.05 0.00

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Appendix Figure 3.2.3 A hypothetical set of selective extinction and speciation processes (top panel) and the resulting species frequency distribution (bottom panel), assuming no effects of ecological mechanics. Only extinction and replicating speciation are selective within the spectrum shown. The dashed line for the species frequency distribution is the distribution resulting from no anagenesis beyond the limits of the graph to the left (a zero measure of the species-level characteristic). The solid line represents the case wherein downward anagenesis results in extinction at the same extreme (extinction would be a step function at zero where all species go extinct).

of all species in particular categories), patterns emerge. Species-level characteristics representing adaptations in response to environmental fluctuation may develop patterns that themselves show change over long time scales as cases where there is only the potential for pattern otherwise.17 One aspect of the example in Appendix Figure 3.2.3 is important in applying the methods laid out in Appendix 3.5. The lower end of the spectrum represents an endpoint of possibilities. There are at least two options. One option is that species evolve to the lower extreme and go extinct. Another option is that species stop evolving in that direction. The first drains species from the collection. The second

Species characteristic

Appendix Figure 3.2.4 A hypothetical set of selective extinction and speciation processes with selective upward anagenesis (top panel) and the resulting species frequency distribution (bottom panel). Downward anagenesis occurs but is not selective over the range shown.

terminates evolution, resulting in an accumulation of species. Both are forms of selectivity that apply only to the species at that extreme. In this example the results of both options are shown. Another hypothetical example of selective extinction and speciation is presented in Appendix Figure 3.2.4. Species near the lower end of the scale suffer extinction quite rapidly. At the upper end of the scale, and beyond an intermediate minimum, extinction rates achieve another maximum. The production of new species through replication is high for species at the low end of the scale. Selective upward anagenesis is higher for species in the intermediate ranges of the characteristic. Trends in evolution toward larger values of the characteristic decline with such increases. Downward anagenesis occurs but not selectively. This example may18 roughly approximate some of the qualities of species dynamics regarding body size. Tiny species approaching the molecular

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level in body size may suffer extinction quite rapidly owing to their simplicity and lack of options in adaptive strategies. With increasing size, extinction rates may reach a minimum above which the increased generation time, and other problems or risks associated with body size, might lower the evolutionary rate and increase the extinction rate. New species produced through replication may occur quite rapidly for the molecular-sized species.19 The selective upward anagenesis, for example, would account for Cope’s rule (Cope 1885, 1896, LaBarbera 1989, Newell 1949). Trends in evolution to larger body size decline with size as the potential evolutionary rate declines. As illustrated in the examples presented above, a variety of patterns can occur. Appreciation of this variety can be aided by exploring such examples with alternative forms of selectivity. This can, for example, involve various sets of parameters in a model such as that of Appendix 3.5. Such experience helps clarify the effects of the combination of dynamics and takes advantage of one of the useful aspects of modeling exercises.20 It must be kept in mind, however, that the resulting examples (e.g., those shown in the lower panels of Appendix Figs 3.2.2 through 3.2.4) represent situations assumed to be in equilibrium. Care must be taken in drawing more than general conclusions about such illustrative use of mathematical models because, as with all such models, the complexity of reality is beyond such oversimplification. These models, for example, do not directly account for ecological mechanics; specifically, emigration and immigration of species to any particular set of species that might be defined in geographic space are not included.21 In reality, the shape of the curves representing selective extinction and speciation (top panels of Appendix Figs 3.2.2 through 3.2.4) would be habitat-specific to include the effects of the environment, including those factors that exert their influence through ecological mechanics. The shape of the selectivity curves is also dependent on the numbers of species in addition to their characteristics. Diversity dependence (rates that also depend on total species numbers) may be more than a simple function of total numbers of species; it may also involve a complex function of not only species richness but also the characteristics

of their emergent pattern(s). This would be comparable to the influence of age distribution and genetic composition on density dependence in population dynamics. Thus, we need to appreciate the influence of selective extinction and speciation, but recognize that the complexity of ways they can operate are beyond representation by simple mathematical models such as those shown here (i.e., beyond comprehension but not to be ignored as part of reality). One element of understanding that emerges from the above is the fact that, in many cases, natural selection at the species level works in concert with the results of natural selection at the individual level. This conclusion is inherent in much of the literature listed in the citations of Chapter 3. The dynamics leading to balances among the opposing forces (especially of Combinations 2, 5, and 6 of Table 3.1) involve production of species by natural selection (primarily among individuals) that are then subject to the effects of selective extinction and speciation. The carving away of species by extinction is particularly prominent in Combinations 5 and 6 when extinction rates are high enough to prevent the accumulation of species produced by evolutionary processes acting within species.

1.3 Patterns among species in two dimensions As is obvious, and illustrated in Chapter 2, patterns in species numbers occur in relation to multiple species-level characteristics. The caveat of “everything else being equal” was an underlying assumption in the simplistic single-character examples presented above and the single- dimension examples in Chapter 2. It is possible to gain some insight into how the dynamics of species are influenced by more than one of their characteristics simultaneously. As above, consideration of two dimensions must proceed knowing that the probability of extinction or speciation is related not just to the characteristics of any particular species, but to the combined influence of all contributing factors. Nevertheless, it is instructive to proceed with the exploration of examples to better understand at least some of the dynamics behind species interacting in two-dimensional space. Expressed in terms of

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Portion of species

the dynamics of groups of species, the fraction of a group expected to go extinct or to speciate depends on the combined effects of all their characteristics. In other words, the shapes of the selectivity curves of the top panels of Appendix Figures 3.2.2 through 3.2.4 would vary in relation to other species-level characteristics. Appendix Figure 3.2.5 shows an example of a species frequency distribution along two continuous characteristics similar to several from Chapter 2, particularly the frequency distribution for body size and population density (both in log scale). In the corner of the distribution represented by species with small bodies and low densities, a high extinction rate might prevent the accumulation of species owing to the vulnerability of species with small body size to environmental variability.22 Their population variability could make them subject to higher extinction rates at low population densities. On the other hand, species in the corner represented by dense populations of large bodied species would be subject to a number of limitations.23 Lack of resistance to parasitic and predatory species that would evolve to take advantage of the biomass at such population size might lead to extinction. Anagenic pseudoextinction would occur if evolution led to lower population densities to solve such problems. Coevolution of defense mechanisms by resource species could lead to an insufficient energy base to support such

ln (

bod

y si

po n(

io

lat

pu

ze) l

Appendix Figure 3.2.5 A smoothed or fitted frequency distribution of species within two continuous species-level characteristics. For example, this might represent the portion of species or species numbers in an ecosystem (or other sample), distributed over adult body size and population density (x and y axis, in log scale).

)

ity

ns

e nd

49

densities. Moreover, there are risks of extinction by loss of resource species caused by high consumption rates. Predators consuming large quantities of prey may risk the consequences of extreme interaction strength identified in food web work (e.g., de Ruiter et al. 1995, May 1981a). The selective extinction and speciation curves that would result in distributions like Appendix Figure 3.2.5 might be similar to those in Appendix Figure 3.2.4, with the selectivity curves dependent on body size. A cross-section of the volume shown in Appendix Figure 3.2.5 could be taken at a specific population density. Such a cross-section would represent species numbers by body size at that density and would have a shape like the lower panel of Appendix Figure 3.2.4. As in the case of a single characteristic, the distribution of species over the surface defined by both dimensions in Appendix Figure 3.2.5 depends on the balance among the various interacting rates, with such balances often highly susceptible to environmental influence.24 However, the simultaneous effects of such selectivity for two characteristics show its effect within a species-level pattern wherein exchange of species involves rates of anagenesis in any direction. The simultaneous dynamics of speciation and extinction for such a case are shown in Appendix Figure 3.2.6. The grid represents the plane below a surface like that of Appendix Figure 3.2.5. Each square represents a category of species classified simultaneously for two species-level characteristics. Such categories can experience anagenic evolution that would carry an individual species into any of the neighboring categories, as shown in the lower right of this figure. An individual species, represented on the surface defined by the two dimensions, can evolve in an infinite number of directions in that plane (instead of only one of two directions along a single dimension). In other words, the vector of evolution within two dimension can carry an individual species in any direction. As in all cases, replication and extinction contribute and remove species. This is shown in the lower left of Appendix Figure 3.2.6 as an end-view of any one of the squares from the grid; the upward arrow represents an increase in species numbers and the downward arrow represents a decrease.

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Individual species

Replication

Extinction

Appendix Figure 3.2.6 Extinction, speciation and evolutionary change involving two species-level characteristics. The measure of one characteristic (dimension) is represented by the abscissa of the grid, the other by the ordinate. An individual species can evolve in any direction in this grid (single species represented by the black circle) and more collectively by the square surrounded by arrows. In the third dimension, replication adds to the numbers in any square and extinction removes them (shown by the end-view of a square in the lower left).

Selectivity, as in earlier examples, consists of rates that are dependent on the position of species, now in the two-dimensional space. Directional evolution alters the rates of change in anagenesis so that, for the group (species in any bounded category), the majority of species evolve one way. Such bias can include directional evolution in both characteristics as represented by the single species in the upper right of Appendix Figure 3.2.6. Selectivity determines how often such change occurs, as dictated by the characteristics of the species. Two-dimensional frequency distributions like that shown in Appendix Figure 3.2.5 result, in part, from a balance among rates combined with stochastic and historical influences of the environment. The potential shapes of frequency distributions for two characteristics can be infinite, just as in the simpler case of a single dimension. What we see in nature represents a finite set of such options.

Appendix Figure 3.2.5 shows a generalized representation of species numbers distributed over population density and body size. Similar graphs could be constructed to represent the frequency distribution of species over other combinations such as trophic level and population variability. Selective extinction would prevent the accumulation of species that exhibit both high trophic level and high population variability. The frequency distribution for this combination of species-level characteristics probably would be similar to that for trophic level and generation time. Most species are expected to show low population variance and low trophic level. Species numbers can be represented and studied in a variety of combinations of two characteristics. Just a dozen characteristics would reveal over 60 patterns (Table 2.1) like those of Appendix Figure 3.2.5, each with a different shape.

1.4 Frequency distributions for more than two dimensions In reality, ecosystems are, in part, products of all species characteristics that are influential in determining rates of replication, extinction, and anagenic change. A consideration of selective extinction and speciation for more than two characteristics is necessary to appreciate the complexity of ontogenetic processes behind species-level patterns and their contributions to the structure and function of ecosystems. This complexity translates to the impossibility of ever completely explaining any particular distribution. What we see in nature is the emergent; that is, the things that can be characterized and shown graphically are products of complexity (Fig. 1.4). Graphic representation of such distributions is difficult for three dimensions and nearly impossible for more, as was noted in Chapter 2. One way of illustrating the density of species within a species-level pattern in two dimensions was shown in Chapter 2 (e.g., Fig. 2.29), wherein each point represents a species so that the density points are indicative of the density of species. The relative density of points varies in the different regions of such graphs. This kind of graphic presentation can be extended to three dimensions

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such that each axis represents a species-level trait and each point represents a species in relation to the measure of each of its characteristics (e.g., Fig. 2.34). In this example, the distribution is an approximation of that expected for species plotted according to their population variability, population density, and body size. Few species exhibit large body size in combination with high population variability, and high population density. Most are small-bodied species with intermediate population density and variability. No species occur with very low population variability, such that the cloud of points in Figure 2.34 is suspended in space. Consideration of species frequency distributions as in the previous hypothetical example (Appendix Fig. 3.2.5) is a step toward understanding the “morphology” of species-level patterns (and thereby ecosystems), each as made up from populations reflecting the characteristics of a particular set of species. It also demonstrates that in spite of the potential for variety, such distributions show limits and can be evaluated through comparisons over time, among ecosystems and taxonomic groups, and across space. Although great variety in the form of species frequency distributions is obvious, limits are set by a variety of factors, including the selectivity of speciation and extinction.25 Such limits exemplify hierarchical constraint (Ahl and Allen 1996, Campbell 1974, O’Neill et al. 1986, Wilber 1995). The complexity of natural collections of species becomes increasingly apparent as one realizes that these patterns occur in multi-dimensional space (each species in its own niche) that cannot be presented graphically.

1.5 Patterns: complexity Graphic representation of selective extinction, speciation, and evolutionary rates allows for a clear distinction to be made between selectivity and nonselectivity in these processes. Nonselective processes are independent of species-level characteristics; process and characteristic are not correlated (even when the characteristic is that of an ecological process). This is exemplified by the flat line representing the probability of downward anagenesis in Appendix Figure 3.2.4.

51

However, it should never be forgotten that selective extinction and speciation are occurring for characteristics other than those being observed in any one specific frequency distribution, regardless of the number of characteristics involved. Some evolutionary processes may be more closely related to a particular species-level characteristic than another. Theoretically, changes may be confined to only the indirect effects of processes or characteristics involving other factors. In the end, such distinctions are more important for identification and understanding the complexity of processes involved than explaining outcome.26 This is because species frequency distributions of the same shape can result from a variety of selective extinction and speciation dynamics and, of course, other contributing factors (Fig. 1.4). However, additional hypothetical examples, as given below, can further illustrate the distinction. Appendix Figure 3.2.7 shows an example in which speciation rates are nonselective; extinction alone is selective. There is no directional component to anagenic change; evolution both upward and downward is equal so the lines are superimposed. Cladogenesis, in this example, is also nonselective but occurs at a rate high enough to prevent extinction from draining species numbers to zero. Because extinction is the only process in Appendix Figure 3.2.7 that is selective, the frequency distribution in the lower panel is entirely determined by this process. Species accumulate in the vicinity of the species-level characteristic where the extinction rate is at its minimum. The effects of ecological mechanics are ignored in this illustration, but not to be forgotten, as in other examples presented in this appendix.27 Ecological mechanics are also ignored (at least not considered directly) in Appendix Figure 3.2.8, where the frequency distribution of species is determined only by selective anagenesis, because extinction and cladogenic replication occur nonselectively. Selective anagenesis tends to remove species from the higher and lower regions of the range of the species-level characteristic and concentrate them toward the middle. As with all frequency distributions among species (species-level patterns), when this happens, evolution tends to concentrate species in this region as a form of “evolutionary

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Appendix Figure 3.2.7 A hypothetical set of selective extinction and nonselective speciation processes (top panel) and the resulting species frequency distribution (bottom panel). Both forms of anagenesis and replicating cladogenesis are not selective and equivalent over the range shown but can easily involve other characteristics of the species involved.

Appendix Figure 3.2.8 A hypothetical set of extinction and selective speciation processes that are not selective (top panel) and the resulting species frequency distribution (bottom panel). Both forms of anagenesis are selective. Replicating cladogenesis and extinction are not selective and nearly equivalent over the range shown.

stable strategy” (ESS, or a kind of Nash equilibrium, Nash 1950a,b) with consequences for species frequency distributions and the ecosystems drawn from them. A combination of dynamics of Appendix Figures 3.2.7 and 3.2.8 would further reinforce a concentration of species such as shown in the bottom panels of each figure. Such may be the case for density dependence (Fig. 2.21) if anagenic evolution and selective speciation reinforce each other for an ecosystem-level ESS, especially if cladogenesis is relatively nonselective. In contrast, the species frequency distribution of Appendix Figure 3.2.9 is the result of a hypothetical situation in which only replication (i.e., cladogenesis) is selective. Nonselective extinction removes species along the entire spectrum and species tend to concentrate where they are most rapidly generated by replication.

Directional speciation is influential when upward and downward anagenesis are independent of the species-level characteristic but occur at different uniform rates. This can influence both the shape and position28 of the species frequency distribution. The lower panel of Appendix Figure 3.2.9 shows two frequency distributions. The solid line corresponds to the rates depicted in the panel above. The broken line shows the frequency distribution if the two forms of anagenesis are identical in magnitude to the downward anagenesis in the panel above (i.e., both 1% per unit time). The only difference between the two cases is that the rates of anagenesis differ from each other in one case (solid line in lower panel) but not the other (dashed line in lower panel); neither is selective. For the case in which upward anagenesis is greater than downward anagenesis (solid curve in lower panel), the species frequency distribution is shifted upward

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0.025

Rate

0.020

Upward anagenesis

Extinction

0.015 0.010 0.005

Downward anagenesis

Replication

0.000 Species characteristic

Portion of species

0.25 0.20

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such forces (including nonevolutionary forces). It is likely that the collections of species in natural ecosystems are tending toward such balances as conditions change in their physical environment. This may occur without achieving equilibrium before a new set of conditions comes into play, giving rise to a “moving target” regarding what might ultimately be an equilibrium. Therefore, the observed distributions can only approximate equilibrium conditions, including those resulting from change. To the extent that they occur, such equilibria, or observed patterns, would be the ecosystem-level counterpart of an ESS (or Nash equilibria, Nash 1950a,b) at the species level.29

0.15

2.0 Risk compensation in selective extinction

0.10

The risk of extinction for an individual species is related to all of its characteristics. Each characteristic either heightens or alleviates risk according to the influence of the species’ environment. For example, a large-bodied species may be at less risk of extinction than a small-bodied species in the same habitat when we consider only its lower population variability (Fig. 2.30). A species at a high trophic level may be at less risk of extinction than one at a low trophic level if we ignore trophic level and consider only geographic range. However, there is tradeoff among risks. Thus, two species may have nearly identical total risks through the tradeoff associated with their different characteristics. Although the composite risks of extinction for species may thus be quite similar, no two species are expected to face identical risks of extinction. The shape of frequency distributions largely result from limits imposed by the combined effects produced by the risks of extinction for each characteristic. Thus, a species with high population variability, long generation time, high trophic level, small range size, and multiple interspecific dependencies would be unlikely to persist long before extinction. Such a combination would entail risks so high that they could not be overcome through low risk for another trait. Risks are multiplicative, possibly a contributing factor behind the log-normal nature of many patterns in nature (Limpert et al. 2001).30 Thus, the risk of extinction contributes to determining the configuration or shape of species-level

0.05 0.00 Species characteristic

Appendix Figure 3.2.9 A hypothetical set of nonselective extinction and selective speciation processes (top panel) and the resulting species frequency distribution (bottom panel). Both forms of anagenesis are completely equivalent over the range shown. Extinction is nonselective while replicating speciation is selective. The species frequency distribution shown by the solid line in the bottom panel corresponds to the conditions depicted in the top panel. The broken line corresponds to the case wherein both forms of anagenesis are equal to the downward form above.

(to the right) and is wider, compared to the case wherein both are the same. Collectively, the hypothetical examples of this section show that the nature of species frequency distributions can be significantly influenced by any one of the processes of selective extinction and speciation acting without selectivity in the others. In reality, of course, selectivity involves a huge variety of combinations. Selective speciation alone may be one of the more influential factors in producing some of the patterns observed in nature, while others may have their origins influenced more by selective extinction, or selective evolution (anagenesis). In some cases the species represented in any particular ecosystems may have achieved an equilibrium or balance among

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patterns, much like a container determines the shape of its fluid or gaseous contents but with limits that are observed to be “fuzzy”. Any diffusion in the process of evolution (or ecological mechanics) results in ever-increasing variability, producing “pressure” against such limits. The boundaries or edges of species frequency distributions are not so sharp or well defined as those established by a container, but are nevertheless real as constraints or limits. These limits, set in part by species being “trimmed away” through extinction, are crucial in finding what is abnormal for systemic management (Fowler and Hobbs 2002). Thus, a set of species fits within limits defined by macroevolutionary constraints as do the sets of individuals making up species (e.g., Slobodkin 1986). Beyond these boundaries, species often experience increasingly higher risks, including extinction. Evolutionary development that carries species to such combinations of characteristics results in extinction.31 Some characteristics cannot evolve owing to constraints from within (intrinsic limits); e.g., infinite life span or carbon-based protoplasm in a carbon-free environment are impossible. Bounds are also influenced by the environment and evolutionary limits on the speciation and evolutionary processes. Evolutionary processes, including selective extinction and speciation, can create only with the raw materials at hand. A species with a trophic level of ten cannot evolve in an ecosystem inhabited by species with trophic levels otherwise confined to six and below. The “clouds” of species in the multidimensional spaces formed by species characteristics (e.g., Fig. 2.34) represent a counterpart of the niche concept for individual species. The shape and position (form) of these distributions varies from habitat to habitat. Again using the metaphor of a container, the pattern of species characteristics is limited by factors that include the environment. For example, size, measured as biomass, or diversity dependence is limited by factors such as solar radiation and precipitation. For some characteristics, species undergo selective extinction and speciation to conform to the limits set by the environment. For other characteristics, species tend to converge in spaces offering minimum risk for the individuals. Within these dynamics, convergent evolution at all levels

can occur to result in similar strategies in similar settings. As is the case for Nash equilibria in general (Nash 1950a,b), what works at all levels of biological organization is represented in frequency distributions that account for complexity in general (Fig. 1.4). In the central portions of these clouds of species, where most species are located, extinction risks are likely to be roughly comparable from species to species. They experience the effects of the “tradeoff principle” noted by Rosenzweig (1995). These central locations may include species of quite different combinations of other characteristics. For example, several species may be quite similar to each other in body size, trophic level, and geographic range, but very dissimilar in population variability and numbers of prey species consumed (i.e., different from those characteristics in which the cloud is being viewed). Some combinations of factors contributing to structure and form in frequency distributions among species may even lead to an internal structure; these would be exemplified by bands or strata of species of varying concentrations within species clouds like that of Figure 2.34 (e.g., Holling 1992; note the modes of Appendix Figs 2.1.1, 2.1.12, and Fig. 2.22 of Chapter 2). Evaluating extinction risk (e.g., for endangered species) is complicated by the tradeoff among combined sources of risk. For the same reason, it is difficult to discover patterns in selective extinction and speciation in the palaeontological record. We are lucky to have comparisons that cover sufficiently broad ranges of species-level characteristics to produce at least a few cases where selectivity is empirically observed, for example those cited in Chapters 2 and 3.

3.0 Ecosystem structure and function The frequency distributions exemplified by the graphs in this appendix and Chapter 2 represent structure for the sets of species they represent. Various sets of species are represented in ecosystems and the patterns they exhibit are influenced by a large number of factors (Fig. 1.4) that include selective extinction and speciation (Fowler and MacMahon 1982). From the point of view of selective extinction and speciation, measures of

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predator-prey relationships (e.g., Figs. 2.6 and 2.7) or any other species characteristic cannot be the principal focus, nor can any one be ignored. All are important in exactly the way they are important in nature and are reflected by the related frequency distributions (Fig. 1.4, Belgrano and Fowler 2008). We are dealing here with what species frequency distributions are, what they represent, and how they originate. Thus, interactions among species based on materials and energy dynamics (i.e, ecological mechanics) receive no more a priori importance than those based on information dynamics exemplified by genetic/evolutionary interaction (e.g., coevolution, Jordano 1987, Thompson 1982). Ecosystem structures based on pollination, seed dispersal, chemical communication, behavior, or vector transmission (e.g., parasites and diseases) also emerge as patterns. Metabolic rates of the various species represented in an ecosystem contribute to its energy dynamics. The metabolic pattern among species contributes to the overall metabolic dynamics for the ecosystem. Species of different metabolic rates occur in different population densities and therefore make different contributions to the total. These are accounted for in the frequency distributions of species by density. Temporal variability is accounted for, in part, by the frequency distribution of population variation. The total is an emergent integration of the effects of selective extinction and speciation over all species-level characteristics, combined with other factors such as the effects of ecological mechanics. In dwelling on selective extinction and speciation restricted to species-level characteristics, we cannot forget the processes of selective extinction and speciation as influenced by the abiotic environment. As has been mentioned repeatedly, the shapes of real-world selectivity curves (and resulting frequency distributions) are influenced by both the physical and biotic environment. The shapes of the curves that we observe in research are certain to be influenced to some degree by short-term dynamics as well as a variety of other factors, including sampling and other statistical error. In the popular focus on biodiversity, total number of species is seen as an important characteristic of ecosystems. Total numbers represent but one measure of an ecosystem represented by the area

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or volume within species frequency distributions (e.g., the volume under the surface of Appendix Fig. 3.2.5 if it were presented in raw numbers instead of portions of a total, or the volume of the cloud in Fig. 2.34). Perhaps more important is the composition of species, or their distribution in multidimensional space, in an ecosystem, as reflected in the shapes of species-level patterns. No species-level characteristic should be forgotten, each being specific to its physical environment.32 The habitat specificity of such species collections leads to standards of reference in evaluation of ecosystems (e.g., their integrity, Karr 1990, 1991, 1992, and status, Rapport 1989a,b). The focus of this appendix is more conceptual than applied or empirical. To undertake a complete, reductionistically exhaustive, study of patterns and their formation is impossible for two reasons. First, the number of combinations of groups of more than two factors together increases by orders of magnitude (Table 2.1). The practical impossibility of any serious consideration of all such combinations is clear when the number of species characteristics exceeds a dozen or so. The handful of species characteristics studied so far is only the beginning of the potential number of characteristics that may be the focus of future studies. Recognition of more species-level characteristics (e.g., polyploidy, Masterson 1994, Orr 1990, Rosenzweig 1995) will continue to reveal the complexity of life at the ecosystem level of organization. Because of this complexity, models such as that in Appendix 3.5 are merely tools to understand and appreciate, but never to fully represent, the complexity of ecosystems and their dynamics. Second, we are only beginning to understand selectivity in extinction and speciation for currently recognized species-level characteristics. The time scale of these dynamics is often orders of magnitude longer than human life spans and the processes are of a complexity only touched upon by palaeontological sciences.

4.0 Relative importance of contributing processes The main objective of this appendix is to emphasize that among all the factors that contribute

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to the formation of species frequency distributions, evolutionary processes cannot be ignored. Importantly, they are not ignored when we use the patterns emergent from such processes to guide management. These include evolution through natural selection among individuals, among species, or among groups of either. Extinction is further emphasized because the risks of extinction are especially important in management. Having made this point, an important argument can be posed as a claim that is, and will continue to be, subject to debate in scientific circles. The argument makes two assertions; both pertain to understanding the formation of species frequency distributions and both have practical implications. These assertions are: That the formation of species frequency distributions may be more influenced by selective extinction and speciation than by the evolution of species themselves, involving natural selection among individuals. That the combination of evolutionary processes probably are much more important than ecological dynamics and mechanics in the formation of species frequency distributions. In other words, both species- and individual-level selection are more important than the nonevolutionary factors.33

●

●

While these assertions are relevant as scientific issues, they are relatively unimportant to management. We can use species frequency distributions as they represent natural variability whether or not we understand, or can agree about, how they arose.34 The actual importance of such contributing factors is inherent in the empirical information (Fig. 1.4). Origins and explanations should be debated (e.g., see Hubbell 2001) but not to the exclusion of utility. Why should we expect that selective extinction and speciation are so important? The argument is basically one of hierarchical constraint (e.g., Ahl and Allen 1996, Allen and Starr 1982, Bateson 1972, Burns et al. 1991, Buss 1988, Koestler 1978, Mayr 1982, McNeill 1993, O’Neill et al. 1986, Orians 1990, Salthe 1985). Very simply, selection at the species level occurs across the effects of selection at the individual level and ecological mechanics and,

furthermore, places limits on what is allowed both in microevolution and mechanical dynamics. Consider nonevolutionary factors first. If the population of a species varies because of seasonal variation in the physical environment (e.g., fluctuations in temperature, rainfall, or radiation), it is experiencing the effects of ecological mechanics. Both population level (numbers) and population variation are influenced by such mechanics in the short term. However, species with characteristics that resist these kinds of influences, and have reduced population variability as a result, are at an advantage over species that have not adapted. On average, the adapted species experience less risk of extinction from population variation than those vulnerable to the effects of ecological mechanics that result in variation in population numbers. Overall, the stress of mechanical dynamics results in adaptation among species just as the stress of ultraviolet radiation results in adaptation to survive its effects among individuals. The same argument can be made for biotic mechanics. For example, species that experience population variation from predator/prey interactions are, on average, more subject to extinction than species with less variation from these causes. Some species are expected to survive the extinction risks of variation by virtue of characteristics that make them less vulnerable than those with characteristics that lead to predator/prey cycles. As a result, the effects of biotic mechanics in the formation of species frequency distributions are expected to be dominated by the evolutionary effects of selective extinction and speciation. When mechanically induced characteristics pose risk of extinction, extinction tends to win. Now consider evolution through natural selection within species, exemplified by cases in which there is variation in the heritability of directional anagenesis.35 Natural selection will occur at the species level between species with heritable evolutionary trends and species having characteristics that resist trends. One or the other will be favored. Which comes to predominate depends on whether the trends lead to increased or decreased risk of extinction (Combinations 5 and 6 of the interactions described in Chapter 3, Table 3.1). For example, at the species level, characteristics have

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selective advantage if they avoid or prevent evolution toward larger body size when increasing body size leads to greater risk of extinction. Vulnerability through limited evolutionary plasticity results in the extinction of species that do not possess such characteristics. An exoskeleton that prevents insects from attaining large size, for example, may make them less vulnerable to extinction than species with endoskeletons that allow large size. The result would be what we see: larger numbers of invertebrate species than vertebrates. These arguments are not meant to reject contributions to the formation of species frequency distributions from nonevolutionary factors and microevolution. They are presented here only in superficial form, but they cannot be ignored. At this point in the history of science, many will consider the idea no more than a hypothesis. However, to the extent that these arguments have substance, nonevolutionary factors are little more than sources of short-term variation for individual measurements of species in regard to their position within species frequency distributions and varying risks of extinction. As such, the argument is meant to emphasize the need to account for the risk of extinction, including our own, in our decision making. This happens in the use of patterns to guide management which avoids the abnormal.

Notes 1. The fraction would never be exactly the same because of stochastic differences due to the complexity of process involved. They would be the same only averaged over time in the case of no long-term trend—a situation which may be rare. 2. From here forward the term replication will be used to refer to cladogenesis in which no change in category occurs (but for which there would be an increase in species numbers). It is thus analogous to birth rates at the population level and represented by the size of arrows in Appendix Figure 3.2.1. Related processes include the simple continued existence of a species in a category and species in a category which undergo anagenesis but of a form that is either (a) insufficient to change categories or (b) for an unrelated species-level measure that gives rise to a new taxonomic designation. The latter processes apply to crude rates that carry species forward in time

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and the multiplicative factors generated in the models of this appendix (to include the replication rate). 3. Because actual (especially constant) equilibria are rare in nature and entirely relative to time scale, the concept may better be characterized as one of a tendency toward equilibrium in tracking or following changing environmental conditions. It is important to distinguish the equilibria one might expect based on a model from empirically observed patterns as tendencies that emerge from the complexities of natural circumstances. 4. The term “relative” refers to the fact that one is smaller or larger than the other regardless of the absolute numbers involved in their total. This way both groups could be growing or declining in absolute numbers but one more so than the other. This would result in changes in the ratio of the number of species in one group to that of the other making the comparison relative. 5. This paragraph counters any tendency to conclude that cases with high speciation are cases in which species numbers must be high (or the reverse, to conclude that because species numbers are high speciation rates must be high). 6. The effect of body size must be accounted for here. Among the microscopic species of bacteria, viruses, etc., asexual reproduction may predominate. 7. See endnote 30, Chapter 3. 8. The combined elements of extinction, speciation and evolutionary change are not always presented together but as Eldredge (1985) says: “Sex prevails at least in large measure because it creates stable, extinction-resistant entities in nature”. The simplicity of the explanation provided by selective extinction and speciation is often expressed in similar simple statements but other works spell out the process in more detail (e.g., Blackman 1981, Buss 1988, Eldredge 1985, Gould and Eldredge 1977, Maynard Smith 1983, 1988, Stanley 1975b, 1990a, Vrijenhoek 1989). 9. If these situations are related back to Table 3.1, we see a gradation from combination 2 on the left side of Appendix Figure 3.2.2 to combination 5 on the right. In the middle, corresponding to the peak accumulation of species, is combination 1 wherein species are supplied by replication and anagenesis from below at a rate that (in combination) is not overshadowed by extinction as fast as in cases further to the right. 10. Thus, anagenesis in this example is stochastically directional because it proceeds predominantly (but not exclusively) in one direction. 11. Such models become the basis for ecosystem modeling as based on a genetic view of species frequency distributions involving species sets from which ecosystems are assembled. Such models would be expanded, in

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principal, to the many dimensions that apply as specieslevel characteristics and would involve the coevolutionary aspect of each species in its effect on the others (including the reciprocity). This is to be compared to ecosystem modeling based primarily on ecological mechanics typical of conventional ecosystem science. Models of selective extinction and speciation exhibit a parallel with models in the study of populations. This parallel concerns the methodology of continuous versus discrete variables. The approach, often attributed to Lotka (1939, e.g., Goodman, 1981), considers population birth rates and mortality as continuous functions of age. Frequency distributions of individuals by age (age distributions) are also treated as continuous. This approach in population studies is analogous to Slatkin’s (1981) for species groups wherein species characteristics are treated continuously. The method applied to species groups in Appendix 3.5 is analogous to that of Leslie (1945, 1948) for populations, with discrete groups in each case. Age-groups of populations and categories of species are considered discrete. Both Slatkin’s approach and that in Appendix 3.5 capture the basic elements of species dynamics and allow for exploration of the ways selective extinction and speciation might be realized for any particular characteristic of species. Such models differ from population models (except for models of geographic distribution) in that models of selective extinction and speciation are diffusion models (Slatkin 1981). In reality, the rates of evolution, speciation, and extinction are often likely to depend on species numbers in specific categories (character-specific diversity dependence) such that the respective rates are also functions of the species number at the corresponding level of the species-level trait. 12. Specifically, the values shown in the graph are 40 points connected by a smoothed line. The 40 points were determined by application of the procedure detailed in Appendix 3.5 using the parameter values shown in the top panel. The lower panel of this graph (and other similar graphs in this appendix) thus represent the distribution of species at an assumed equilibrium. In nature, the dynamics of the abiotic environment and other factors can be expected to prevent achievement of this precise form. It would not be surprising to find close approximations as frequency distributions track their environment as dynamic Nash equilibria (see endnote 29 of Chapter 3, and Fig. 1.4). 13. Keep in mind that these examples are to demonstrate the mechanics of selective extinction and speciation abstracted from the effects of ecological mechanics (especially current effects of human influence, and species-level movement as changes in geographic range that

would be experienced as species-level immigration/emigration by ecosystems). 14. This is parallel to embryological development. It reflects both the effects of external environments as well as internal interactions. In the embryological development of individuals, some tissues influence the growth of neighboring tissues and together are responsive to environmental circumstances exogenous to the organism. 15. This might be viewed as a set of cases wherein the eight combinations in Table 3.1 are reduced to four because anagenesis is without effect. The remaining combinations would involve the pairs 1 and 7, 2 and 8, 3 and 5, plus 4 and 6. 16. Again, found by the procedure described in Appendix 3.5 as with all the following examples. 17. Thus, we again see potential for order out of chaos consistent with the views of Prigogine (Prigogine and Stengers 1984). The stress of such change would eventually be expected to result in selective pressures to which ecosystems would respond with solutions found in species-level selection. 18. The use of the term “may” here and elsewhere relates to the examples as hypothetical examples. But it also relates to the stochasticity, complexity and uncertainty that will always exist in explanatory sciences reduced to the elements focused on by each field of science. In Chapters 4, 5, and 6, this will be revisited as basis for precautionary approaches to management. 19. Each mutation produces a new strain. Owing to the lack of sexual reproduction such mutations cannot be incorporated into the genetic code of any but the direct descendants of the parents with the mutations. Due to the short generation time for the tiny species such strains may accumulate mutations to further diversify at a rapid pace. Although this presents a taxonomist’s nightmare, it does represent rapid evolution and a cladogenic-like diversification regarding species-level characteristics. 20. These models might go so far as to include components which generate the shapes of the rate curves of the upper panels of graphs in this appendix as functions of environmental conditions (the abiotic influence on selectivity). In any case, the models shown in this appendix are simple enough to be exercised using ordinary spreadsheet software. In no case is a model the reality it represents, but is helpful in understanding and appreciating the reality of the kinds of dynamics involved. 21. If, over evolutionary time, a species adapts in such a way as to promote geographic relocation, it is much like a “mutation” at the species level (among a set of species). This kind of dynamic can result in changing total species numbers, as well as numbers within any specific category, of a species-level pattern.

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22. See the discussion of Figure 2.19 and references such as Gaston and Lawton (1988a,b), Hanski (1990), Pimm (1991), and Sinclair (1996) regarding population variability and body size. 23. The literature (e.g., Brown and Maurer 1987) regarding correlations between population density and body size should be consulted for a wide variety of explanations (and possible biases) for this relationship. Some of these were noted in Chapter 2. 24. It is within such balances, especially when they are caused by small differences in rates, that small environmental influences may have large effects. These would be the equivalent of “butterfly effects” in determining outcomes expressed in the patterns we observe (e.g., Bateson 1979, Gleick 1987, Koehl 1989 and Pennycuick 1992). 25. The limits set by extinction are easily understood in that characteristics that guarantee extinction (especially instantaneous extinction) will not exist. 26. Some processes (like extinction) may more rapidly respond to environmental forces than others so the importance of recognizing each component is not lost. 27. The ecological mechanics of conventional ecosystem models involve parameters that determine the interactions represented among populations, the density dependence of each population, and give rise to model behavior that includes population variation. This chapter and appendix deal with the evolution of such ‘parameters’ in the real world and the evolution of emergent behavior (or other characteristics) thus subject to higher level evolutionary processes through selective extinction and speciation. In this sense, selective extinction and speciation are among the processes that would be part of the answer to questions regarding the origin, existence, and relative frequencies of parameters within conventional ecosystem models. 28. As an ecosystem characteristic, the mean of specieslevel features is thereby influenced, when the set of species involved are determined by their cooccurrence in a geographic area. 29. Evidence for equilibria in species numbers, lack of evolutionary change (also referred to as stasis) and other forms of equilibria are discussed by Eldredge (1991), Stanley (1989), Webb (1987), Willims (1992), and Wright (1945). Evidence for ecosystem-level convergent evolution is seen in the similarity among ecosystems in similar habitats (e.g., deserts, rain forests, lakes, etc.). These are Nash equilibria in the sense that the advantages for both the species and its individuals play into their formation; anything disadvantageous for either will not work. 30. This is explained by the multiplicative nature of the probability of surviving independent risk. Risks of such magnitude cannot be mitigated through low risk

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attributes that are unrelated, such as sexual reproduction. However, in probabilistic terms, if such a species exists it would be expected to reproduce sexually (as well as to exhibit low risk of extinction from all of its other characteristics). 31. This would be exemplified by Appendix Figure 3.2.4 wherein upward anagenesis carries species to extinction. It can be argued that dynamics of this type (not necessarily this specific example but all combinations of types 5 and 6, Table 3.1) are more common than not (‘evolutionary suicide,’ ‘evolution to extinction,’ or ‘Darwinian extinction’; Dobzhansky 1958, Parvinen 2005, Potter 1990, Rankin and López-Sepulcre 2005). Virtually all species produced by natural selection at the level of the individual have gone extinct. 32. Saying that such things should not be forgotten is not equivalent to saying that they should be included in conventional models. The latter is impossible; in the end, it is impossible to include everything in man-made models owing to complexity. Urging that they not be forgotten is merely a matter of restating the need to be sure that they are accounted for, especially insofar as it relates to management (Management Tenet 3, Chapter 1). 33. It should be noted that this would imply that conventional ecosystem models largely miss their mark in representing ecosystems. It is not an argument that ecological mechanics are not part of what happens in ecosystems, only an argument that the patterns in ecosystem structure and function (including ecological mechanics) as we observe them are more heavily influenced by natural selection than as direct products of the mechanics alone. 34. What is being said here is simply that we can adhere to the principle of management dictating that we should maintain components of ecosystems within their normal ranges of natural variability (Tenet 5, Anderson 1991, Apollonio 1994, Christensen et al. 1996, Fowler and Hobbs 2002, Francis et al. 1999, Fuentes 1993, Grumbine 1994, Holling and Meffe 1996, Mangel et al. 1996, Moote et al. 1994, National Marine Fisheries Service Ecosystem Principles Advisory Panel 1998, Pickett et al. 1992, Uhl et al. 2000, Wood 1994) without understanding or explaining the variability. This statement is not meant to say that understanding is unimportant. For example, understanding what is normal and what is abnormal is critically important, as is understanding the matter of patterns representing the factors that contribute to their emergence (Fig. 1.4). 35. An example might be the heritability of features that either resist or promote the evolution of asexual reproduction. Early sexually reproducing species might have been divisible into two groups, those with greater range

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of options to evolve asexual reproduction than those (the other group) that had heritable characteristics that tended to resist reversion to asexual reproduction.

References Ahl, V. and T.F.H. Allen. 1996. Hierarchy theory. Columbia University Press, New York, NY. Allen, T.F.H. and T.B. Starr. 1982. Hierarchy: perspectives for ecological complexity. University of Chicago Press, Chicago, IL. Anderson, J.E. 1991. A conceptual framework for evaluating and quantifying naturalness. Conservation Biology: 347–352. Apollonio, S. 1994. The use of ecosystem characteristics in fisheries management. Reviews in Fisheries Science 2: 157–180. Bateson, G. 1972. Conscious purpose versus nature. In G. Bateson (ed.). Steps to an ecology of mind, pp. 426–439. Chandler Publishing Co., San Francisco, CA. Bateson, G. 1979. Mind and nature: a necessary unity. Dutton, New York, NY. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In S.I. Munoz (ed.). Ecology research progress, pp. 5–31. Nova Science Publishers, Hauppauge, NY. Blackman, R.L. 1981. Species, sex and parthenogenesis in aphids. In P.L. Forey (ed.). The evolving biosphere, pp. 75–85. Cambridge University Press, New York, NY. Brown, J.H., and B.A. Maurer. 1987. Evolution of species assemblages: effects of energetic constraints and species dynamics on the diversification of the North American avifauna. American Naturalist 130: 1–17. Burns, T.P., B.C. Patten, and M. Higashi. 1991. Hierarchial evolution in ecological networks: environs and selection. In Higashi, M. and T. Burns (eds). Theoretical studies of ecosystems—the network prospective, pp. 211–239. Cambridge University Press, New York, NY. Buss, L.W. 1988. The evolution of individuality. Princeton University Press, Princeton, NJ. Campbell, D.T. 1974. “Downward causation” in hierarchically organized biological systems. In Ayala, F.J. and T. Dobzhansky (eds). Studies in philosophy of biology, pp. 179–185. University California Press, Berkeley, CA. Christensen, N.L., A.M. Bartuska, J.H. Brown, et al. 1996. The report of the Ecological Society of America Committee on the scientific basis for ecosystem management. Ecological Applications 6: 665–691. Cope, E.D. 1885. On the evolution of the vertebrata. American Naturalist 19: 140–148. Cope, E.D. 1896. Primary factors of organic evolution. University of Chicago Press, Chicago, IL.

de Ruiter, P.C., A.-M. Neutel, and J.C. Moore. 1995. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269: 1257–1260. Eldredge, N. 1985. Unfinished synthesis: biological hierarchies and modern evolutionary thought. Oxford University Press, New York, NY. Eldredge, N. 1991. The miner’s canary: unraveling the mysteries of extinction. Prentice Hall Press, New York, NY. Fowler, C.W. and J.A. MacMahon. 1982. Selective extinction and speciation: their influence on the structure and functioning of communities and ecosystems. American Naturalist 119: 480–498. Fowler, C.W. and L. Hobbs. 2002. Limits to natural variation: implications for systemic management. Animal Biodiversity and Conservation. 25: 7–45. Fowler, C.W. and M.A. Perez. 1999. Constructing species frequency distributions—a step toward systemic management. NOAA Techinical Memorandum NMFSAFSC-109. U.S. Department of Commerce, Seattle, WA. Francis, R.C., K. Aydin, R.L. Merrick, and S. Bollens. 1999. Modeling and management of the Bering sea ecosystem. In Loughlin, T.R. and K. Ohtani (eds), Dynamnics of the Bering Sea, pp. 409–433. University of Alaska Sea Grant, AK-SG-99–03, Fairbanks. Fuentes, E.R. 1993. Scientific research and sustainable development. Ecological Applications 3: 576–577. Gaston, K.J. and J.H. Lawton. 1988a. Patterns in the distribution and abundance of insect populations. Nature 331: 709–712. Gaston, K.J. and J.H. Lawton. 1988b. Patterns in body size, population dynamics, and regional distribution of braken herbivores. American Naturalist 132: 662–680. Gleick, J. 1987. Chaos: making a new science. Penguin Books, New York, NY. Goodman, D. 1981. Life history analysis of large mammals. In C.W. Fowler and T.D. Smith (eds). Dynamics of large mammal populations, pp. 415–436. John Wiley & Sons, New York, NY. Gould, S.J. and N. Eldredge. 1977. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 3: 115–151. Grumbine, R.E. 1994. What is ecosystem management? Conservation Biology 8: 27–38. Hanski, I. 1990. Density dependence, regulation and variability in animal populations. Philosophical Transactions of the Royal Society of London, Series B 330: 141–150. Holling, C.S. 1992. Cross-scale morphology, geometry, and dynamics of ecosystems. Ecological Monographs 62: 447–502. Holling, C.S. and G.K. Meffe. 1996. Command and control and the pathology of natural resource management. Conservation Biology 10: 328–337.

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Hubbell, S.P. 2001. The unified neutral theory of biodiversity and biogeography. Princeton University Press, Princeton, NJ. Jordano, P. 1987. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence, asymmetries, and coevolution. American Naturalist 129: 657–677. Karr, J.R. 1990. Avian survival rates and the extinction process on Barro-Colorado Island, Panama. Conservation Biology 4: 391–397. Karr, J.R. 1991. Biological integrity: a long neglected aspect of water resource management. Ecological Applications 1: 66–84. Karr, J.R. 1992. Ecological integrity: protecting earth’s life. In Costanza, R., B.G. Norton, and B.D. Haskell (eds). Ecosystem health: new goals for environmental management, pp. 223–238. Island Press, Washington, DC. Koehl, M.A.R. 1989. Discussion: from individuals to populations In J. Roughgarden, R.M. May, and S.A. Levin (eds). Perspectives in ecological theory, pp. 39–53. Princeton University Press, Princeton, NJ. Koestler, A. 1978. Janus: a summing up. Random House, New York, NY. LaBarbera, M. 1989. Analyzing body size as a factor in ecology and evolution. Annual Review of Ecology and Systematics 20: 97–117. Lederberg, J. 1993. Viruses and humankind: intracellular symbiosis and evolutionary competition. In S.S. Morse (ed.). Emerging viruses, pp. 3–9. Oxford University Press, New York, NY. Leslie, P.H. 1945. On the use of matrices in certain population mathematics. Biometrica 33: 183–212. Leslie, P.H. 1948. Some further notes on the use of matrices in population mathematics. Biometrica 35: 213–245. Lotka, A.J. 1939. A contribution to the theory of self-renewing aggregates, with special reference to industrial replacement. Annals of Mathematical Statistics 10: 1–25. Mangel, M., L.M. Talbot, G.K. Meffe, et al. 1996. Principles for the conservation of wild living resources. Ecological Applications 6: 338–362. Masterson, J. 1994. Stomatal size in fossil plants: evidence for polyploidy in majority of angiosperms. Science 264: 421–424. May, R.M. 1981a. Models for two interacting populations. In R.M. May (ed.). Theoretical ecology: principles and applications, pp. 78–104. Sinauer Associates, Sunderland, MA. Maynard Smith, J. 1983. Current controversies in evolutionary biology. In M. Grene (ed.). Dimensions in Darwinism: Themes and counterthemes in twentiethcentury evolutionary theory, pp. 273–286. Cambridge University Press, New York, NY.

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Maynard Smith, J. 1988. Did Darwin get it right? Essays on games, sex and evolution. Chapman & Hall, New York, NY. Mayr, E. 1982. The growth of biological thought. Harvard University Press, Cambridge, MA. McNeill, W.H. 1993. Patterns of disease emergence in history. In S.S. Morse (ed.). Emerging viruses, pp. 29–36. Oxford University Press, New York, NY. Moote, M.A., S. Burke, H.J. Cortner, and M.B. Wallace. 1994. Principles of ecosystem management. Document published by the Water Resources Research Center, College of Agriculture, The University of Arizona, 14pp. Nash, J.F. 1950a. Noncooperative games. Ph.D. Thesis. Mathematics Department, Princeton University. Nash, J.F. 1950b. Equilibrium points in N-person games. Proceedings of the National Academy of Sciences of the USA 36, 48–49. National Marine Fisheries Service Ecosystem Principles Advisory Panel. 1998. Ecosystem-based fishery management. Ecosystem Principles Advisory Panel’s Report to Congress, July 6, 1998 Newell, N.D. 1949. Phyletic size increase—an important trend illustrated by fossil invertebrates. Evolution 3: 103–124. O’Neill, R.V., D.L. DeAngelis, J.B. Waide, and T.F.H. Allen. 1986. A hierarchial concept of ecosystems. Princeton University Press, Princeton, NJ. Orians, G.H. 1990. Ecological concepts of sustainability. Environment 32: 10–15, 34–39. Orr, H.A. 1990. “Why polyploidy is rarer in animals than in plants” revisited. American Naturalist 136: 759–770. Pickett, S.T.A., V.T. Parker, and P.L. Fiedler. 1992. The new paradigm in ecology: implications for conservation biology above the species level. In P.L. Jain and S.K. Jain (eds), Conservation biology: the theory and practice of nature conservation, preservation, and management, pp. 65–88. Chapman & Hall, New York. Pimm, S.L. 1991. The balance of nature? Ecological issues in the conservation of species in communities. The University of Chicago Press, Chicago, IL. Prigogine, I. and I. Stengers. 1984. Order out of chaos: man’s new dialogue with nature. Bantam Books, New York, NY. Rapport, D.J. 1989a. State of ecosystem medicine. Perspectives in Biology and Medicine 33: 120–132. Rapport, D.J. 1989b. What constitutes ecosystem health? Perspectives in Biology and Medicine 33: 120–132. Rosenzweig, M.L. 1995. Species diversity in space and time. Cambridge University Press, New York, NY. Salthe, S.N. 1985. Evolving hierarchical systems: their structure and representation. Columbia University Press, New York, NY.

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Sinclair, A.R.E. 1996. Mammal populations: fluctuation, regulation, life history theory and their implications for conservation. In Floyd, R.B., A.W. Sheppard, and P.J. De Barro (eds). Frontiers of population ecology, pp. 127–154. CSIRO Publishing, Melbourne. Slatkin, M. 1981. A diffusion model of species selection. Paleobiology 7: 421–425. Slobodkin, L.B. 1986. On the susceptibility of different species to extinction: elementary instructions for owners of a world. In B.G. Norton (ed.). The preservation of species: the value of biological diversity, pp. 226–242. Princeton University Press, Princeton, NJ. Stanley, S.M. 1975b. Clades versus clones in evolution: why we have sex. Science 190: 382–383. Stanley, S.M. 1989. Fossils, macroevolution and theoretical ecology. In Roughgarden, J., R.M. Maym, and S.A. Levin (eds). Perspectives in ecological theory, pp. 125–134. Princeton University Press, Princeton, NJ. Stanley, S.M. 1990a. The species as a unit of large-scale evolution. In Warren, L. and H. Koprowski (eds). New perspectives on evolution, pp. 87–99. John Wiley & Sons, Inc., New York, NY.

Thompson, J.N. 1982. Interaction and coevolution. John Wiley & Sons, New York, NY. Uhl, C, A. Anderson, and G. Fitzgerald. 2000. Higher education: good for the planet? Bulletin of the Ecological Society of America 81: 152–156. Vrijenhoek, R.C. 1989. Genotypic diversity and coexistence among sexual and clonal lineages of Poeciliopsis. In D. Otte, and J.A. Endler (eds). Speciation and its consequences, pp. 386–400. Sinauer Associates, Sunderland, MA. Webb, S.D. 1987. Community patterns in extinct terrestrial vertebrates. In J.H.R. Gee and P.S. Giller (eds). Organization of communities: past and present, pp. 439–466. Blackwell Scientific Publications, Oxford. Wilber, K. 1995. Sex, ecology, spirituality: the spirit of evolution. Shambhala Publications, Boston, MA. Williams, G.C. 1992. Natural selection: domains, levels, and challenges. Oxford University Press, New York, NY. Wood, C.A. 1994. Ecosystem management: achieving the new land ethic. Renewable Resources Journal 12: 6–12. Wright, S. 1945. Tempo and mode in evolution: a critical review. Ecology 26: 415–419.

Appendix 3.3

The following material is Appendix 3.3 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Selective evolution between two categories Consider N1 and N2 as the number of species in two categories. The rate at which species evolve from the first category to the second is defined as p1 N1. The term p1 represents the per-species rate of pseudo-extinction within the first category and is a probability or relative rate. The comparable rate of evolution from the second category to the first is defined as p2 N2 . Then the respective rates of change (as numerical rates, or absolute rates, not relative rates) for each group will be dN1/dt and

dN2/dt defined in terms of the relative rates and species numbers as dN1/dt = −p1N1 + p2 N2 and dN2/dt = −p2 N2 + p1N1 When equilibrium is reached in the ratio of one category to the other, both rates will be zero and therefore equal: dN1/dt = −p1N1 + p2 N2 = dN2/dt = −p2 N2 + p1N1 = 0 or p1N1 = p2 N2 which means that the ratio N1/N2 is determined by the ratio of the rates of speciation (p2 /p1) because: N1/N2 = p2 /p1

63

Appendix 3.4

The following material is Appendix 3.4 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Selective extinction, speciation, and evolution for two categories As in Appendix 3.3 we can consider N1 and N2 to be the number of species in two categories. The rate at which species evolve from the first category to the second is p1N1, and from the second to the first it is p2 N2. With the effects of speciation through replication included, species are added at the rate of r1N1 for the first group and r2 N2 for the second. Let d1N1 and d2 N2 be the extinction, or species-level death rates for the two groups, respectively. With all elements of selective extinction, speciation and evolution included, the respective rates of change for each group will be dN1/dt = r1N1 − d1N1 − p1N1 + p2 N2 and dN2 /dt = r2 N2 − d2 N2 − p2 N2 + p1N1 Assuming that the two groups neither grow to infinite numbers of species nor suffer complete extinction, but instead achieve some form of an equilibrium, the rates of change can be considered zero. This assumption allows study of the nature of the balance achieved (even if in reality such an equilibrium is dynamic). At such an equilibrium the ratio of one category (N1) to the other (N2) can be determined. The ratio can be called r ( r = N1/N2). Under these conditions dN1/dt = rr1 − rd1 − rp1 + p2 = 0 64

and dN2 /dt = r2 − d2 − p2 + r p1 = 0 From these equations it is possible to solve for ρ as a function of the “per-capita” (per-species, relative, or “instantaneous”) rates involved. This allows the determination of the ratio of species in one category to that of the other for an idea of how the various rates contribute to the relative abundance of the two groups. Several of these expressions (all of which must be simultaneously true) are: r = p2 /(p1 − r1 + d1) r = (p2 − r2 + d2)/p1 r = (d2 − r2)/(r1 − d1) r = − (r2 – d2 − 2p2)/(r1 − d1 − 2p2) Note that the first two expressions are analogous to that obtained in Appendix 3.3. They are ratios of the species-specific rates at which species are exchanged between categories but modified by the replication and extinction rates. The next-to-last expression is a ratio of the differences between these two species-specific rates for each group. With six variables (the per-species rates) that must meet the condition of balance, any one can be chosen to be determined by the other five. For example, r1 = (d1(r2 − d2) + p1(r2 − d2) − p2d2)/(r2 − d2 − p2) There are similar equations for each of the other five variables. The example of asexual (represented by N1) versus. sexual reproduction (N2) can be used to examine the implications of the interactions and balance of all the processes of selective extinction and speciation in these equations. For rates that apply over a given time span, and for a particular (here, hypothetical) physical environment, we can pick values

APPENDIX 3.4

for the respective rates to see what the resulting ratio of asexual to sexual reproducers would be. Arguments pertaining to selective extinction support a higher rate of extinction for asexual species than sexual species. Thus, the extinction rate for the sexual species can be chosen as 0.1 and that for the asexual species can be a multiple (x > 1) of the sexual species’ rate. Arguments involving selective speciation would lead to the choice of values for r2 that are greater than r1. Also r2 must be larger than d2 to replace those lost to the asexual category through pseudo-extinction. For this example the following values were chosen to illustrate the principles at play in the balance achieved: r1 = 0.05, d1 = xd2, p1 = 0.001

r2 = 0.105 d2 = 0.1 p2 = ((r2 − d2)(r1 − d1 − p1))/(r1 − d1)

Because evolutionary theory predicts shift away from the sexual category (i.e., more often than not, owing to the costs of sexual reproduction to individuals; endnote 30, Chapter 3), we want values of

65

p2 that are larger than those of p1. In this example the values of p2 are consistently about five times as great as those of p1, varying from 0.00510 to 0.00501 (compared to 0.001 for p1) for x between 1 and 5. For the values of the rates making up the selective extinction and speciation tabulated above, the ratio r is 0.1 if the extinction rates are equivalent. This ratio falls markedly as the extinction rates of the asexual species increases. Although this model suffers from the inadequacies of all models of living systems, it illustrates the way the observed ratio of asexual to sexual species may be maintained by selective extinction and speciation.1 Even when evolution tends to transform species in opposing directions, the other elements of selective extinction and speciation can counterbalance the effects to produce observed ratios.

Note 1. Keep in mind that for some kinds of species (i.e., accounting for another characteristic) the ratio may be reversed (e.g., for very small-bodied species).

Appendix 3.5

The following material is Appendix 3.5 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Selective extinction and speciation in numerous categories The case of numerous categories of species along a single dimension can be represented by dividing up the range of values for a species level characteristic in a number of adjoining categories. We can then let the number of species in the i-th category be Ni (and, of course, the portion of species in the i-th category is Ni/ΣNj). The index i (or j for the sum) extends from 1 at the lowest end of the spectrum over which the categories of species are spread, up to n at the highest end. As in the cases represented in Appendix 3.4, changes in number in the i-th category result from: (1) pseudo-extinction of its own numbers, (2) evolution of species originating in other categories to contribute species to the i-th category, along with (3) replication and (4) extinction within the category. Two rates of pseudo-extinction must be distinguished in this example because species will be evolving in both directions. Those from above can be represented by ai+1 Ni+1 and those from below by bi − 1 Ni − 1 from the i – 1 and i + 1 categories, respectively. It is assumed that the time unit chosen for application of these equations is small enough to avoid anagenic change that carries a species across a category into a second (from category i to 1 + j, where j > 1, although this can happen in nature and models to account for it can be constructed). This helps keep the model being developed as simple as possible. 66

Now, the rate of change for the i-th group can be expressed by: dNi /dt = riNi − diNi − aiNi −biNi + ai+1Ni+1 + bi−1Ni−1 For purposes of illustration, species characteristics in this chapter were divided into 40 categories, each with its own rates of replication, extinction, and anagenic exchange. With computers these equations can be dealt with using numerical methods. But another approach is satisfactory for finding equilibrium frequency distributions, especially in cases of very small changes (e.g., 3% changes per unit time, or less). This approach consists of a transition matrix that can be implemented on spreadsheet software. The categories of species can be considered as members of a vector of n numbers: N1 N2 . . . Ni−1 Ni Ni+1. Nn−1 Nn The matrix that represents the dynamics of the collection of species in these n categories can then be represented as functions of the rates specified as above. The elements of the diagonal of the matrix are multiplicative crude rates such that hidden in them is the survival of species to be carried forward from one unit of time to the next in the same category (i.e., those species that experience insufficient anagenic change to move to a different category and that do not go extinct). This matrix is

SEL EC T I V E E X T I N C T I O N A N D SP ECI AT I O N

67

e r1 − d1 − a1 − b1

1 − e − b2

0

0

···

0

0

0

0

1 − e − a1

e r2 − d2 − a2 − b2

1 − e − b3

0

···

0

0

0

0

0

1 − e − a2

e r3 − d3 − a3 − b3

1 − e − b4

···

0

0

0

0

· · 0

· · ···

· ·

· ·

· ·

1 − e − ai − 2

e ri −1 − di −1 − ai −1 − bi −1

1 − e − bi

· · 0

· · 0

· · ···

· · 0

0

···

0

1 − e ai −1

e ri − di − ai − bi

1 − e − bi+1

0

···

0

0

···

0

0

1 − e − ai

e ri+1 − di+1 − ai+1 − bi+1

1 − e − bi+ 2

···

0

· · 0

· · 0

· · 0

· · 0

· · 0

· · ···

· · 0

· ·

· ·

1 − e − an − 1

e rn − dn − an − bn

Under most circumstances, each time the vector of species numbers is multiplied by the matrix the species numbers change. Over time (i.e., repeated application of the matrix, assuming its parameters do not change—an unrealistic simplifying assumption) the frequency distribution of species begins to approach a constant form. Such distributions are exemplified in the bottom panels of Appendix Figures 3.2.2–3.2.4 and 3.2.7–3.2.9. But as changes in frequency distribution occur, changes also occur in the total number of species; species numbers within each category change as does the total among them. Eventually, both the frequency distribution of species and the rate of change in species numbers become constant. In the terminology of matrix algebra, the frequency distribution takes on values determined by the principal eigenvector and the rate of change is determined by the principal eigenvalue. The latter is a scalar (λ) that can be multiplied by the number of species in each element (Ni) determined by the eigenvector to obtain the number at the next time interval. The value of such a matrix model is the ability to explore the effects of a variety of different selective extinction and speciation regimes. The nature of dynamics in both species numbers and in achieving characteristic distributions is important. Questions regarding the speed with which equilibria are established and which forms of selective dynamics are most efficient in rapidly approaching equilibria can be addressed. Knowledge regarding

species-level characteristics that exhibit such dynamics can be used to hypothesize which ecosystem level characteristics are likely to be most robust and which most sensitive to stress. In the application of the above matrix for illustrations in this chapter (especially those of Appendix 3.2), species numbers were assumed to have experienced some form of limitation to their numbers. Thus, one form of equilibrium was assumed. It was a form of diversity dependence to establish a means of avoiding the result of no species, and the result of an infinite number of species. This was done through adjusting all extinction rates by a constant (i.e., leaving the selectivity unaltered). Rates used to produce illustrations for this chapter were therefore chosen so as to represent equilibria as explained below. A “brute force” method of finding the dominant eigenvalue and corresponding eigenvector of a “species dynamics matrix” is to simply apply the matrix to a vector of species numbers repeatedly. Through this iterative approach, both the eigenvalue and eigenvector emerge simultaneously. To prevent species numbers from increasing to unmanageably large or small levels during such iterative procedures, the total is readjusted after each iteration without disturbing the distribution (i.e., the same fraction of the total remains in each category). After having determined the eigenvalue of an original matrix, the values of this matrix can then be adjusted so that a new matrix is formed

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with an eigenvalue of 1 (condition of no net change in species numbers). In adjusting a matrix to make its eigenvalue be equal to 1, all elements of the matrix are divided by the eigenvalue of the original matrix. This preserves the values of each element in relationship to the others as each one is changed by the same factor. The elements above and below the diagonal contain only the rates of anagenesis (pseudoextinction) that are always changed from the original values in making this adjustment. Thus, 1 − e − ai ⬘ =

1 − e − ai , ␭

where ai’ is the new value of ai and similar equations apply to the values above the diagonal. The diagonal elements, however, are functions of all four types of dynamics (replication, extinction, and anagenesis both downward and upward: ri –di –ai –b1). But the rates of anagenesis (–ai –bi) are determined by the changes in the off-diagonal elements.This means that changes in replication and extinction are necessary to achieve an eigenvalue of 1—the situation needed to achieve the condition of no change in species numbers. The difference between these two rates (ri –di) must be adjusted to accomplish this end because the rates of anagenesis are already determined by the off diagonal elements. This leaves freedom of choice for which rate (speciation or extinction) to change and by how much. For the examples shown in the graphs of this chapter, the rates of extinction were adjusted to achieve these conditions; the rates of speciation were left as originally chosen. To explore the effects of various regimes of selective extinction and speciation as shown in Appendix 3.2, hypothetical relationships between each rate and measures of the species level characteristic were chosen. These have the same shapes but different values from those presented in the top panels of the figures. Iterative solutions for the principal eigenvalues and vectors were determined. The eigenvalue was used to adjust the original matrix, as described above, thus determining final values for the four processes of selective extinction and speciation. These are the numbers presented

in the top panels of the figures. This process preserves the distribution in extinction and speciation rates over the range of the chosen species level properties. In other words, the selectivity of selective extinction and speciation are unchanged in this process. Thus, in all cases, the rates shown in the top panels of the graphs of Appendix 3.2 represent those that resulted in the distribution shown in the lower panels after making the adjustments described above. In choosing rates for the matrix above, and the time unit over which the matrix applies, care must be taken to avoid evolution that, in reality, carries species across more than one category. The matrix, as described above, is based on an important assumption. As mentioned above, it is assumed that the species-level characteristic is not one that exhibits “jumping” from one character category to another by skipping a number of intermediate categories. Species characteristics such as geographic range, or population variation, violate this assumption. These situations can be dealt with by probabilities or rates in the matrix that are off the diagonal by more than one element (such as in stage matrices as applied in the study of insect population dynamics). This would not be necessary for many characteristics. Such elements would contain values corresponding to the probability that a species would, for example, undergo cladogenesis of an independent character and split to divide its range so as to form two species. These would have ranges that would both be smaller than that of the original. This would be an example of relatively dramatic “mutation” at the species level compared to the more nearly continuous evolutionary changes as traditionally understood. Any character that experiences the more sudden changes would be represented by off-diagonal elements. Other similar models may be constructed (e.g., see Slatkin 1981). Their construction and use in exploring the dynamics described in this and the last chapter is encouraged as learning exercises and research tools. It is mandatory, however, that the models never be perceived as anything other than tools for enhanced understanding (they are not the systems they represent).

Appendix 3.6

The following material is Appendix 3.6 for Chapter 3 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Alternative terms for the processes of selective extinction and speciation Over time, a variety of terms have been used to represent the concept of selective extinction and speciation as well as its components and realms of relevance. Various aspects of the processes are emphasized in each case. As is the case for the literature on the general issue of natural selection at the species level, there is inconsistent use of these terms and there is considerable debate about differences. This appendix is a brief presentation of some of the terms related to processes within selective extinction and speciation, in many cases dynamics nearly equivalent to selective extinction and speciation. It is included in the interest of adding material relevant to the history (and is confined mostly to historical developments) and documentation of the concept. Some of the debate surrounding details of the concept is found in the literature cited below; further, and more recent, information is available in sources such as Okasha (2006). For the early emphasis on extinction the term “differential extinction” was used (e.g., Guilday 1967). The term “selective extinction” was used by Brooks (1972), who also used “differential extinction”) and Vermeij (1987). Whittaker and Woodwell (1972) used the term “edited”. The community is a record of many successful species additions accumulated through evolutionary time, edited by extinctions. The process makes the community a system of interacting, niche-differentiated species.

De Vries (1905), Stanley (1975a) and others used the term “species selection”. Alexander and Borgia (1978) used the term “differential species extinction” in comparing selective extinction to group selection. The verbs “sort”, “winnow” (Davis 1990, editor’s term), “prune” (Roughgarden 1983),1 and “edit” (Whittaker and Woodwell 1972) have been used to represent the nonrandom culling process of selection in selective extinction and speciation, primarily extinction. These terms combine the concepts of nonrandomness with the extinction process. When separate terms for selectivity and extinction are used together, common adjectives and adverbs implying selectivity include: “nonrandom”, “differential”, and “preferential”. Chapter 3 and Appendix 3.2 deal with understanding macroecological or species-level patterns (and species frequency distributions in particular) as influenced by the combination of selective extinction, selective speciation and microevolutionary processes. The combined processes of selective extinction and speciation are variously named in the scientific literature. “Macroevolution” is one of the more common terms, with its emphasis on consequences for changes in groups of species with taxonomic or genealogical affinity. As a term, macroevolution originated before 1945. In 1945 Wright (1945), in one of the early uses of the term “macroevolution”, attributes the term to Dobzhansky (1937). Eldredge (1985) indicates that the term was used by Mayr (1942). The term has been developed by Stanley (1979) and many others. A sampling includes: Benton (1987), Brown and Maurer (1989), Cracraft (1985b), Damuth (1985), Dobzhansky (1937), Eldredge (1985), Eldredge and Cracraft (1980), Givnish (1989), Gould (1990), Gould and Eldredge (1977), Greenwood (1979), Hoffman (1983), Hoffman and Hecht (1986), Hull (1976), 69

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Kitchell (1985), Levinton (1983), Maynard Smith (1983), Mayr (1942, 1982), Salthe (1985), Simpson (1953), Stanley (1975a 1979), Stanley et al. (1983), Vrba (1980), Wright (1945). Scattered among the many works related to the combined processes of extinction and speciation in relation to species characteristics are a variety of other terms. The most common is “species selection”, as used by a variety of authors such as Arnold and Fristrup (1982), Cracraft (1985b), Futuyma (1986a, 1987), Gould (1982b), Gould and Eldredge (1977), Grant (1989), Grantham (1995), Hoffman (1984), Hoffman and Hecht (1986), Maynard Smith (1988, 1989), Slatkin (1981, 1983), Stanley (1975a, 1979, 1989), and Vrba (1980, 1984). “Species selection” may become the most commonly used term in the long run, partly because it helps draw the analogy with selection at the individual level so firmly entrenched in the minds of modern day biologists. Other equivalent or related expressions found in the literature include “natural selection at the level of the species” (Margalef 1975), “sorting among species” (Arnold and Fristrup 1982, Grant 1989, and Stanley 1989), “transpecific evolution” (Stanley 1975a), “interspecific selection” (Arnold and Fristrup 1982, Grant 1989, Simpson and Beck 1965, Wynne-Edwards 1962), “selection among species” (Arnold and Fristrup 1982, Grant 1989, Vermeij 1987, Vrba 1984, and Wright 1956), “differential sorting” (Cracraft 1985b), “differential speciation and extinction (rates)” (Futuyma 1987, Hoffman 1984), “selective biotic extinction” (Flessa et al. 1975), “differential (rates of, frequency of) speciation and extinction” (Gould 1982b, Marzluff and Dial 1991), “nonrandom speciation and extinction” (Marzluff and Dial 1991), “species sorting” (Maynard Smith 1989), “sorting process among species”, and “preferential extinction” (Stanley 1989). “Species dynamics” is used in some of the literature (e.g., Gaston 1988) and is equally inclusive of extinction and speciation just as “population dynamics” is usually taken to include both birth and death processes. In this book, as in Fowler and MacMahon (1982), the expression “selective extinction and speciation” is used to represent the overall concept as defined by the postulates laid out in Chapter 3. Microevolution is internal to, and an integral part of, selective

extinction and speciation. Selective extinction and speciation can apply to any species group but the emphasis in this book is on the implications for management that is sufficiently holistic to embrace larger biotic systems such as ecosystems and the bioshpere in an evolutionarily enlightened (Brown and Parman 1993) way. “Species selection” and “macroevolution” are avoided because of their historical ties (constraint) to taxonomic or genealogical consideration (Gilinsky 1986, Mayr 1982, Stanley 1975a) both of which are part of selective extinction and speciation. One of the primary objectives of Chapter 3 is to dwell on the breadth of species sets to which selective extinction and speciation apply, including ecosystems and the determinants of their structure and function. This entails expanding the emphasis from taxonomic groups to all species whether represented in ecosystems, communities, or the biosphere. Specific ties of evolution to ecosystems are to be avoided because natural selection as it applies to all levels of biological organization may be among the most promising unifying principles for all of the biological sciences (Arnold and Fristrup 1982, Bateson 1979, Dell and Goolishian 1981, Eldridge 1985, Lewontin 1970, Williams 1992). Natural selection in biotic systems, whether among genes, individuals, genomes, species, or other biotic units, fits within the general concept of selective systems failure. Self-replication distinguishes biotic systems from most others, but death, extinction, or failure are common to all. The selectivity of failure is also common to all as noted for the general notion of emergence (Morowitz 2002), and the failure among human, generated systems (e.g., Ormerod 2006 where it is also noted that failures of parts are often to the benefit to the whole, parallel to Carse’s concept of infinite games; Carse 1986). The universal or ubiquitous aspect of selective systems failure applies to systems from atoms to galaxies to include biotic systems—all of which count among the collection of elements of reality accounted for in the reality of patterns (Fig. 1.4).

Note 1. See also Morowitz (2002) for use of the term “prune” in the more general sense of selective systems failure as

APPENDIX 3.6

part of the process of emergence in and among systems at all hierarchical levels and domains of reality.

References Alexander, R.D. and G. Borgia. 1978. Group selection, altruism, and the levels of organization of life. Annual Review of Ecology and Systematics 9: 449–474. Arnold, A.J. and K. Fristrup. 1982. The theory of evolution by natural selection: a hierarchical expansion. Paleobiology 8: 113–129. Bateson, G. 1979. Mind and nature: a necessary unity. Dutton, New York, NY. Benton, M.J. 1987. Progress and competition in macroevolution. Biological Reviews 62: 305–338. Brooks, J.L. 1972. Extinction and the origin of organic diversity. In Deevey, E.S. (ed.). Growth by intussusception; ecological essays in honor or G. Evelyn Hutchinson, pp. 19–56. Transactions of the Connecticut Academy of Arts and Sciences 44: 1–433. Brown, J.H. and B.A. Maurer. 1989. Macroecology: the division of food and space among species on continents. Science 243: 1145–1150. Brown, J.S. and A.O. Parman. 1993. Consequences of sizeselective harvesting as an evolutionary game. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991, pp. 248–261 (Lecture notes in biomathematics, 99). Springer-Verlag, Berlin. Cracraft, J. 1985b. Species selection, macroevolutionary analysis and the “hierarchical theory” of evolution. Systematic Zoology 34: 222–229. Damuth, J.D. 1985. Selection among “species”: a formulation in terms of natural functional units. Evolution 39: 1132–1146. Davis, M.B. 1990. Climatic change and the survival of forest species. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 99–110. Cambridge University Press, New York, NY. De Vries, H. 1905. Species and varieties, their origin by mutation. Open Court, Chicago, IL. Dell, P.F. and H.A. Goolishian. 1981. Order through fluctuation: An evolutionary epistemology for human systems. Australian Journal of Family Therapy 2: 175–184. Eldredge, N. 1985. Unfinished synthesis: biological hierarchies and modern evolutionary thought. Oxford University Press, New York, NY. Eldredge, N. and J. Cracraft. 1980. Phylogenetic patterns and the evolutionary process. Columbia University Press, New York, NY.

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Flessa, K.W., K.V. Powers, and J.L. Cisne. 1975. Specialization and evolutionary longevity in the Arthropoda. Paleobiology 1: 71–81. Fowler, C.W. and J.A. MacMahon. 1982. Selective extinction and speciation: their influence on the structure and functioning of communities and ecosystems. American Naturalist 119: 480–498. Futuyma, D.J. 1986a. Evolutionary biology. Sinauer Associates, Sunderland, MA. Futuyma, D.J. 1987. On the role of species in anagenesis. American Naturalist 130: 465–473. Gaston, K.J. 1988. Patterns in local and regional dynamics of moth populations. Oikos 53: 49–57. Gilinsky, N.L. 1986. Species selection as a causal process. Evolutionary Biology 20: 249–273. Givnish, T.J. 1989. The roots of modern approaches to macroevolution. Ecology 70: 1552–1553. Gould, S.J. 1982a. The meaning of punctuated equilibrium and its role in validating a hierarchical approach to macroevolution. In R. Milkman (ed.). Perspectives on evolution, pp. 83–104. Sinauer Associates, Sunderland, MA. Gould, S.J. 1990. Speciation and sorting as the source of evolutionary trends, or “things are seldom what they seem”. In McNamara, K.J. (ed.). Evolutionary trends, pp. 3–27. Bellhaven Press, London. Gould, S.J. and N. Eldredge. 1977. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 3: 115–151. Grant, V. 1989. The theory of speciational trends. American Naturalist 133: 604–612. Grantham, T.A. 1995. Hierarchical approaches to macroevolution: recent work on species selection and the “effect hypothesis.” Annual Review of Ecology and Systematics 26: 301–321. Greenwood, P.H. 1979. Macroevolution—myth or reality? Biological Journal of the Linnnean Society 12: 293–304. Guilday, J.E. 1967. Differential extinction during latePleistocene and recent times. In Martin, P.S. and H.E. Wright, Jr (eds). Pleistocene extinctions: the search for a cause, pp. 121–141. Yale University Press, New Haven, CT. Hoffman, A. 1983. Paleobiology at the crossroads: a critique of some modern paleobiological research programs. In M. Grene (ed.). Dimensions in Darwinism: themes and counterthemes in twentieth-century evolutionary theory, pp. 241–271. Cambridge University Press, New York, NY. Hoffman, A. 1984. Species selection. Evolutionary Biology 18: 1–20. Hoffman, A., and M.K. Hecht. 1986. Species selection as a causal process. Evolutionary Biology 20: 275–281.

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Hull, D.L. 1976. Are species really individuals? Systematic Zoology 25: 174–191. Kitchell, J.A. 1985. Evolutionary paleoecology: recent contributions to evolutionary theory. Paleobiology 11: 91–104. Levinton, J.S. 1983. Stasis in progress: the empirical basis of macroevolution. Annual Review of Ecology and Systematics 14: 103–138. Lewontin, R.C. 1970. The units of selection. Annual Review of Ecology and Systematics 1: 1–18. Margalef, R. 1975. Diversity, stability and maturity in natural ecosystems. In W.H. van Dobben and R.H Lowe-McConnell (eds). Unifying concepts in ecology, pp. 151–160. Dr. W. Junk b. v., The Hague, the Netherlands, and the Centre for Agricultural Publishing and Documentation, Wageningen, the Netherlands. Marzluff, J.M. and K.P. Dial. 1991. Life history correlates of taxonomic diversity. Ecology 72: 428–439. Maynard Smith, J. 1983. Current controversies in evolutionary biology. In Grene, M. (ed.). Dimensions in Darwinism: Themes and counterthemes in twentiethcentury evolutionary theory, pp. 273–286. Cambridge University Press, New York, NY. Maynard Smith, J. 1988. Did Darwin get it right? Essays on games, sex and evolution. Chapman and Hall, New York, NY. Maynard Smith, J. 1989. The causes of extinction. Philosophical Transactions of the Royal Society of London, Series B 325: 241–252 Mayr, E. 1942. Systematics and the origin of species. Columbia University Press, New York, NY. Mayr, E. 1982. The growth of biological thought. Harvard University Press, Cambridge, MA. Morowitz, H.J. 2002. The emergence of everything: how the world became complex. Oxford University Press, New York, NY. Okasha, S. 2006. Evolution and the levels of selection. Clarendon Press, Oxford. Roughgarden, J. 1983. The theory of coevolution. In D.J. Futuyma and M. Slatkin (eds). Coevolution, pp. 33–64. Sinauer Associates, Sunderland, MA. Salthe, S.N. 1985. Evolving hierarchical systems: their structure and representation. Columbia University Press, New York, NY.

Simpson, G.G. 1953. The major features of evolution. Columbia University Press, New York, NY. Simpson, G.G. and W.S. Beck. 1965. Life: an introduction to biology. Harcourt, Brace and World, New York, NY. Slatkin, M. 1981. A diffusion model of species selection. Paleobiology 7: 421–425. Slatkin, M. 1983. Genetic background. In Futuyma, D.J. and M. Slatkin (eds). Coevolution, pp. 14–32. Sinauer Associates, Sunderland, MA. Stanley, S.M. 1975a. A theory of evolution above the species level. Proceedings of the National Academy of Sciences of the USA 72: 646–650. Stanley, S.M. 1979. Macroevolution, pattern and process. W.H. Freeman and Co., San Francisco, CA. Stanley, S.M. 1989. Fossils, macroevolution and theoretical ecology. In Roughgarden, J., R.M. May and S.A. Levin (eds). Perspectives in ecological theory, pp. 125–134. Princeton University Press, Princeton, NJ. Stanley, S.M., B. Van Valkenburgh, and R.S. Steneck. 1983. Coevolution and the fossil record. In D.J. Futuyma and M. Slatkin (eds). Cooevolution, pp. 328–349. Sinauer Associates, Sunderland, MA. Vermeij, G.J. 1987. The dispersal barrier in the tropical Pacific: implications for molluscan speciation and extinction. Evolution 41: 1046–1058. Vrba, E.S. 1980. Evolution, species and fossils: how does life evolve? South African Journal of Science 76: 61–84. Vrba, E.S. 1984. What is species selection? Systematic Zoology 33: 318–328. Whittaker, R.H. and G.M. Woodwell. 1972. Evolution of natural communities. In Wiens, J.A. (ed.). Ecosystem structure and function. Proceedings of the 31st Annual Biology Colloquium, pp. 137–159. Oregon State University Press, Eugene, OR. Williams, G.C. 1992. Natural selection: domains, levels, and challenges. Oxford University Press, New York, NY. Wright, S. 1945. Tempo and mode in evolution: a critical review. Ecology 26: 415–419. Wright, S. 1956. Modes of selection. American Naturalist 90: 5–24. Wynne-Edwards, V.C. 1962. Animal dispersion in relation to social behaviour. Hafner Publishing, New York, NY.

Appendix 4.1

The following material is Appendix 4.1 for Chapter 4 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 What should management applied to ecosystems include? This appendix is a brief consideration of the who, what, how, and why of “ecosystem management” as described in the literature. It demonstrates complexity and conflict when definitions are attempted on the basis of conventional science and perception of ecosystems. Conflict is experienced, often in lack of consensus. Although there are a number of common elements, surveys of the literature show that, there is no commonly accepted definition of “ecosystem management” or “ecosystembased management” (Agee and Johnson 1988, Arkema et al. 2006, Christensen et al. 1996, Clark et al. 1991, Costanza et al. 2000, Francis 1993, Francis et al. 2007, Grumbine 1994, Kay 1993, Keddy et al. 1993, Malone 1995, McCormick 1999, Moote et al. 1994, Pikitch et al. 2004, Schaeffer and Cox 1992, Stanley 1995, Toman 1993). More often than many are comfortable with, the claim is made that we are already doing ecosystem management; clearly, we are influencing ecosystems, but is our influence sustainable? Most of the literature describes or suggests desirable components of management or adds to the list of factors, elements, processes, and other things that should be taken into account (pieces of complexity, or fragments of reality stakeholders and managers are supposed to think about, consider, or discuss; top row Fig. 1.1). The desirable components of management fall into the categories of who should do “ecosystem management”, how it should be done,

what should be managed, and why. Each publication places different emphasis on each of these components and a sense of indecision emerges stemming from the complexity of “ecosystem management” as an extension of conventional management with emphasis on the transitive. There is a clear lack of consensus on how a balance can be struck in view of the diversity of opinion regarding a variety of often opposing points of view. There is frequent call for something different to deal with the impasse (e.g., Salwasser 1993). In the existing literature there is frequent mention of the need to consider the genetic effects of human activities or concern about the coevolutionary reactions among species to human influence (e.g., Brown and Parman 1993, Kendall 2007, Law 2001, Law et al. 1993, Mangel et al. 1996, NRC 2006, Orians 1990, Sutherland 1990, Thompson 2005). In actual management, however, these issues are considered less in management at all levels of biological organization than are matters of products, goods, and services and their economic value. Evolutionary/genetic effects are of even less concern in the literature when we consider ecosystems (even rejected scientifically, e.g., Golley 1993). Scientists appear to be more concerned than are managers and their concerns seem to relate more to loss of genetic diversity than change in specieslevel characteristics and their consequences. The literature identifies a wide variety of people who should be involved in “ecosystem management”. Among those are scientists from various disciplines, especially those involved in integrating and synthesizing information. Included are elected officials, their constituents, staff, executives and administrators, government agencies, international bodies and organizations, representatives of various communities (including local private interests and landowners) and people, institutions, 73

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and businesses that use resources. The breadth of involvement suggested above is intended to encompass the widely differing (often opposing) objectives of the groups represented. Collectively these are the stakeholders identified as important to the process of setting policy, establishing goals, and making decisions in their position represented in the top row of Figure 1.1. The qualities brought to management by people identified above are also deemed important. A variety of experiences, professional qualifications, and skills have been emphasized, whether for individuals or collective management groups. It has been suggested that management personnel work independently, be interdisciplinary, and show interagency, political, and economic expertise through integrated coordination. Responsibility for decisions is often emphasized. Mixes of appointed and elected participation have been suggested. The institutional inadequacy of existing management (e.g., Clark et al. 1991) is recognized and results in emphasizing inclusiveness, breadth, and commitment to abide by decisions made. The reaching of consensus and resolution of conflict is left to human design. To improve on this, special training is advocated to bridge the gap between science and management (Brosnan and Groom 2006). The variety of suggestions for what should be managed to constitute “ecosystem management” reflects the history of ecosystem science. More importantly, it reflects the prevailing concept that management is largely a matter of transitive manipulation. Such approaches often seem to be in opposition to means of ensuring long-term sustainability for future generations of humans in ecosystems exhibiting natural states and processes capable of supporting humans. This often emerges as conflict between what are called the biocentric (that value forms of life at the ecosystem level, future options and natural states) and the anthropocentric camps (that place more value on meeting human needs for the short term, Stanley 1995). Based on conventional approaches, components of ecosystems, populations, and species are often seen as options for management. Some publications emphasize control over ecosystems and their elements as an option; others focus on regulation of human activities as the approach that emerges

as the only viable option in this chapter. Processes and functions identified as subject to management include flows of nutrients and energy, and interactions and relationships among species, especially human resources. The need for holistic approaches is expressed in words such as landscape, systems, network, community, array, total, and global. Abiotic components of ecosystems are included in management of habitat (including soils) and there is concern about global warming and pollution. Although manipulation to meet human need is seen as part of “ecosystem management” (Allen and Hoekstra 1992), it is recognized that limits to such activities must exist. Although there is no agreement on how to go about establishing such limits (or find a balance, Stanley 1995), management with humans as the subject of management has been suggested as one alternative. The limitation and regulation of human activities and influence is often considered part of “ecosystem management”, and is often recognized as the only part of the system that can be controlled. On the other hand, the characteristics of managed units are often seen as available for manipulation or control. At the ecosystem level, such characteristics include variability, productivity, mean trophic level, species numbers or diversity, organization, integrity, health, homeostasis, structure, processes, patterns, composition, heterogeneity, boundaries, and equilibrium. The holistic nature of ecosystems is evidenced by the use of words such as integrity, bioregional, entirety, viability, order, dimensions, irreducible wholes (and whole systems), geographic, complexity, and interdependence. More than anything else, the literature on management has identified “what should be taken into account” in “ecosystem management”. This includes uncertainty, instability, unpredictably, evolution, dynamics, change and variability, time, area and space (including parks and preserves), flows of materials and energy, cycles, physical components and environment, extinctions (natural and human caused), aggregation, species-level issues, ecosystem responses to stress, complexity and levels of organization, nonlinear nature of interactions, interconnectedness, whole system properties (e.g., ecosystem attributes, including the fact that they are open systems and exhibit emergence), history,

A P P E N D I X 4 .1

spatial heterogeneity and fragmentation, carrying capacities, responses of ecosystems to stress, limits, overpopulation, rarity, naturalness, and extinction. Although this is only a partial list of factors that various authors have listed as important to account for in “ecosystem management”, it exemplifies the complexity of biotic systems in general (and scratches the surface of the complexity of reality, Appendix 1.1). It also illustrates the guaranteed conflict when various people or organizations support one element over another. In the end, collective consideration of such lists emphasizes that we must account for reality/complexity. Much of the existing literature recognizes that to be operational, a definition of “ecosystem management” must include specified activities. These include what managers are often asked to do: assess options, consider and integrate information, apply standards and reference points (normative information, often seen as lacking), plan, establish policy, evaluate, cooperate and coordinate, assess risks, identify and define problems, implement legislation, establish programs, educate and communicate (including journalism), authorize specific actions, and implement programs and decisions. The activities of scientists in providing information is also included: application of technologies, information integration and synthesis, measurement, analysis, experimentation, inventory, monitoring, providing data and information on natural principles (contribute to perspective, paradigms, ideas, concepts), recommending actions, educate, assess (resources, risks, benefits), interpret, identify and define problems, and conduct studies and research. In carrying out management to achieve ecosystem states, control and manipulation are frequently mentioned. Often the components of ecosystems are to be manipulated. But the main subject of effective control is the human element. Human activities should be restricted in order to protect or promote certain characteristics for resources and ecosystems. Other activities are promoted in support of humans as elements of ecosystems. These include subsidizing and mitigation for ecosystem responses, undertaking captive breeding, and directed exploitation, or alteration of environment. It is often proposed that management activities should be conducted with democratic, collaborative,

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and consensual approaches with emphasis on integration (combined or collective approaches), and holistic or global breadth. There is clear desire for predictive capacity, and the difficulty or impossibility of ever achieving it is recognized. The utility of modeling in such cases is often emphasized and in other contexts the limits of such approaches are recognized. Flexible, experimental, adaptive approaches are often emphasized (Holling 1978, Walters 1986, 1992). The magnitude of risk involved in failed trials arises in conflict with this suggestion. Motivation for developing a clear definition of “ecosystem management” stems from recognition of clearly important objectives, goals, and desired outcomes. The conflicting nature of many of the goals is clearly emphasized. Minimizing risk is often in opposition to yields, services, products, commodities, and the multiple uses of various elements of ecosystems. Preventing extinctions, maintaining biodiversity, conservation, care, and sensitivity to environmental issues are in conflict with human needs in the realms of the economy, health, and resources. Sustainability is recognized as a step toward finding a balance among these conflicting forces and is often presented with the word “development” to emphasize the need to avoid disregard for human needs. Long-term objectives are suggested to be necessary to account for the longer time scales of ecosystem dynamics. Preservation (especially of biodiversity) is seen as important but often in conflict with meeting human need. Undisturbed ecosystems are valued for their contribution to knowledge of natural baseline ecosystem characteristics. The preservation of nature is recognized as relevant to the future of humans as a species and is part of the concept of sustainability and intergenerational need. This is emphasized in the goals of renewability, continual availability, preserving options for the future, and the need for resources lasting into perpetuity as long-term objectives. These, however, are in conflict with the shortterm goals of socioeconomic and political forces in meeting day-to-day utilitarian needs. Optima are seen as balance (Fuentes 1993) wherein maintaining healthy ecosystems includes long-term human goals such as avoiding extinction. Harmony,

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altruistic, therapeutic, spiritual, ethical, and aesthetic issues arise when trying to balance use and protection, or short- and long-term objectives. To understand these conflicts as natural, one must understand the human species from biological, ecological, and evolutionary perspectives. Evolutionary forces behind the need to consume, survive, and reproduce, for example, are in opposition to the forces of ecosystems that both set limits and provide resources. Finding a balance is one of the challenges in defining an operational form of management. A variety of definitions and discussion of “ecosystem management” abound in the literature (e.g., see references in Arkema et al. 2006, McCormick 1999, Fowler 2003, and Francis et al. 2007). Because of the volume of this work, it would be difficult to find impartial representative or exhaustive listings or review of definitions. As an introduction to related concepts and to indicate the intensity of interest in the field, however, it is useful to consult a few of the relevant publications that provide the detail of what is considered important, primarily from the perspective of conventional ecosystem science and management (e.g., Agee and Johnson 1988, Allen and Hoekstra 1992, Anderson 1991, Aplet and Johnson 1993, Aplet et al. 1993, Apollonio 1994, Arkema et al. 2006, Auerbach 1981, Bakuzis 1969, Baron and Galvin 1990, Bormann and Likens 1969, Boulding 1991, Bratton 1992, Cairns 1986, 1991, Caldwell 1988, Callicott 1992, Christensen et al. 1996, Clark and Zaunbrecher 1987, Clark et al. 1991, Costanza 1992a, Costanza et al. 1992a,b, 2000, Cox 1993, Cristoffer 1990, Crystal 1989, Diamond 1980, Ehrenfeld 1992, 1993, Ehrlich 1987, Eldredge 1991, 1992, Fiedler and Jain 1992, Francis 1993, Francis et al. 2007, Franklin 1993a,b, Fuentes 1993, Gilbert 1988, Goodman 1980, 1987, Gordon 1993, Grizzle 1994, Grumbine 1994, Hannon 1992, Hargrove 1992, Haskell et al. 1992, Holling 1978, 1993, Holt and Talbot 1978, Huntley et al. 1991, Johnson and Agee 1989, Karr 1992, Kay 1993, Keddy et al. 1993, Kemf 1993, Kenchington 1990, Kerr and Dickie 1984, King 1993, Lee 1993a,b, Levine 1989, Lewis 1969, Likens 1992, Loucks 1985, Lovejoy et al. 1984, Lubchenco et al. 1991, Ludwig et al. 1993, Maerz 1994, Malone 1995, Mangel et al. 1996, Mann and Plummer 1993, Mann and Lazier 1991, McCormick 1999, McNamee

1986, McNeely 1989, Mitchell et al. 1990, Mladenhoff and Pastor 1993, Moote et al. 1994, Munn 1993, Munro and Holdgate 1991, Murawski 1991, Myers 1989, 1991, Nash 1991, National Commission on the Environment Staff 1993, Norton 1987, 1991, 1992, Noss 1990, 1993, Noss and Cooperrider 1994, Odum 1985, 1993, O’Neill 1989, O’Neill et al. 1986, Orians 1990, Orians et al. 1990, Ovington 1975, Page 1992, D. Patten 1991, Pfister 1993, Pikitch et al. 2004, Pimm 1991, Pyle 1980, Quammen 1991, Ramphal 1992, Rapport 1992, Ray and Grassle 1991, Reed 1989, Regier 1993, Roberts 1991, Romm 1993, Salwasser 1988, 1993, Salwasser et al. 1993, Sample et al. 1993, Santos 1990, Schaeffer and Cox 1992, Schaeffer et al. 1988, Scheffer 1991, Sharp 1993, Simberloff 1986, 1988, Smith 1977, Soulé 1986, Soulé and Wilcox 1980a,b, Southwick 1985, Spurr 1969, Stanley 1995, Steedman and Haider 1993, Sutherland 1990, Terborgh 1974, Thorne-Miller and Catena 1991, Toman 1993, Ulanowicz 1992, van Dobben and Lowe-McConnell 1975b, Van Dyne 1969a,b, Varley 1988, Wagner 1977, 1969, Walters 1986, Western and Pearl 1989, Westman 1990a, Wilcox and Murphy 1985, Wilson 1985a, Wood 1994, Woodley 1993, Woodley et al. 1993). A strong pattern in most of the literature listed above is the theme of expressed support for involving stakeholders, scientists and managers in decision-making in regard to setting objectives and policy as depicted in the top row of Figure 1.1. This is in contrast to the roles to which people are confined in systemic management (bottom row of Fig. 1.1) wherein posing questions, empirically observing things consonant with management questions, and carrying out management to avoid abnormality are the responsibility of all stakeholders.

References Agee, J.K. and D.R. Johnson (eds). 1988. Ecosystem management for parks and wilderness. University of Washington Press, Seattle, WA. Allen, T.F.H. and T.W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press, New York, NY. Anderson, J.E. 1991. A conceptual framework for evaluating and quantifying naturalness. Conservation Biology 5: 347–352.

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Appendix 4.2

The following material is Appendix 4.2 for Chapter 4 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Ecosystem changes Changes are often attributed to human influence (as in the case of global warming, Intergovernmental Panel on Climate Change 2007, and altered marine systems, Halpern et al. 2008). Change is one of the most documented aspects of ecosystems (Christensen et al. 1996, Colborn et al. 1997, MEA 2005a,b, Pauley et al. 1998, Silver and DeFries 1990, Turner et al. 1990, Woodwell 1990; World Conservation Monitoring Centre 1992; Vitousek et al. 1997). Changes that ecosystems experience include the influence of humans (Liu et al. 2007, Moran 2006, Steffen et al. 2004)—often seen as directly correlated with human population (Brashares et al. 2004). As part of such change, the patterns among species (seen in altered species frequency distributions) represented in ecosystems are altered. Such changes in degraded or stressed ecosystems are numerous and serve as examples of currently recognized ecosystem-level traits. Changes in the means of these distributions are often reported in the literature as a simple holistic measure of ecosystems. References that provide summaries or overviews of such changes are found in Barrett and Rosenberg (1981), Calow and Berry (1989), Cracraft and Grifo (1999), Fearnside (1990a), Freedman (1989), Gray (1989), Haskell et al. (1992), Lovejoy (1985), Mark and McSweeney (1990), Odum (1981, 1985), Rapport (1989a,b,c), Rapport et al. (1985), Sampson and Knopf (1994), Turner et al. (1990), Woodwell (1990), and World Conservation Monitoring Center (1992).

Ecosystem characteristics are interrelated such that changes in one are often interpreted to imply changes in another. Examining the variety of ecosystem characteristics that have been identified help us grasp their contribution to ecosystem structure and function. In this regard, structural properties of ecosystems seem to be more vulnerable to disruption than are functional characteristics (Odum 1985). All measures behind the conclusions in the literature listed below are based on ecosystems spatially defined by the observer. This is in contrast to what would be observed across the union of the geographic ranges of component species (e.g., those overlapping a particular point, or the range of a particular species). Structural change includes change in the shape of any species frequency distribution. The number of species (area under species frequency distributions when expressed as numbers rather than portions) is one structural characteristic. Stressed ecosystems have been documented to lose species numbers (an ecosystem can lose a species by a reduction or shift in its range). When this happens the composition of remaining species often changes (Freedman 1989, Gray 1989, Hawksworth 1990, Myers 1989, Odum 1985, van der Maarel 1975). Changes in diversity in response to stress also include losses of genetic heterogeneity (Freedman 1989, Grassle et al. 1990, Gray 1989, Haskell et al. 1992, Karr 1992, Loya 1990, Lugo and McCormick 1981, Moore 1983, Rapport 1989a, Rapport et al. 1985, Woodwell and Houghton 1990). On occasion, stress results in an initial increase in species numbers before declines are observed. During disruptive influence, more species are lost among the higher trophic levels and increases are occasionally observed among low trophic levels such that trophic chains shorten and mean trophic level drops (Odum 1985, Ulanowicz 1992, Yan and Welbourn 1990). Other interspecific dependency 83

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also declines as exemplified by the loss of species that exhibit symbiotic interactions, specialization (Davis 1990, Karr 1992, Odum 1985, Pimentel et al. 1992, Sharitz and Gibbons 1981, Woodwell 1970). Changes in rates of decomposition also occur (Freedman 1989, Gray 1989). Increased frequency of diseases and parasites (Gray 1989, McMichael 1993, Odum 1985, Rapport 1989a, Specht 1990, Webster and Walker 2003) accompanied by declines in measures of specieslevel health (Pimentel et al. 1992, Westman 1990b) have also been associated with ecosystem disturbance (see McMichael 1993, for a history of the awareness of human diseases and ecosystem disturbance). Other (related) forms of ecosystem degradation involve shifts in composition toward smaller species (Gray 1989, Haskell et al. 1992, Odum 1985, Rapport 1989a,b, Woodwell 1970, Yan and Welbourn 1990), including reductions in the size of individuals within species (Odum 1985, Rapport 1989a). Most such changes are related to the increased predominance of small-bodied, r-selected species, pests, and weedy species and loss (including extinction) of large-bodied species (Bormann 1990, Gray 1989, Kerr and Dickie 1984, Odum 1985, Rapport 1989b, Rapport et al. 1985, Regier 1973, Specht 1990, Westman 1990b, Woodwell 1970, 1990, Woodwell and Houghton 1990, Yan and Welbourn 1990). Various population-level characteristics of species undergo changes in disturbed ecosystems. At the ecosystem level, the mean level of population variability increases as a form of destabilization (Apollonio 1994, Gray 1989, Rapport 1989b, Rapport et al. 1985, Regier 1973, Regier and Hartman 1973, Rosenzweig 1971, Woodwell 1970, Yan and Welbourn 1990). Some of this may relate to increased mortality rates (Odum 1985). Humans are not immune to the effects of ecosystem change (e.g., Redman 1999). Frequency distributions of density and population size are altered (Harmsen 1983). Populations for some species become reduced (Valiela 1990). Exceptions include small-bodied pests and pathogens as mentioned above. Changes similar to reductions in population size occur through changes in the spatial distribution of biomass (Freedman 1989, Lugo and McCormick 1981), and numbers of

species in various range size categories (Mark and McSweeney 1990, Specht 1990). The physiological or metabolic nature of ecosystems also change in response to various influences. As examples of functional aspects of ecosystems, these attributes may not be altered as much as structural properties. This may be explained by changes within the altered system wherein the switching of categories among species in structural change serves partially to compensate for changes that would otherwise be observed in total ecosystem-level rates. Frequency distributions of productivity levels (including efficiency of photosynthesis) are altered such that mean productivity declines with stress (Freedman 1989, Gray 1989, Haskell et al. 1992, Lugo and McCormick 1981, Naeem et al. 1994, Odum 1981, Rapport 1989b, Sharitz and Gibbons 1981, Woodwell 1970). Typically, community-level metabolism declines (Freedman 1989, Odum 1985, Rapport et al. 1981, 1985). The efficiency of energy resource use declines (Odum 1985). Other frequency distributions that respond to stress include those for nutrient cycling, nutrient content, and contributions to system totals for these dynamics. Specific rates at which nutrients (including carbon and oxygen) are cycled usually increase (Freedman 1989, Lugo and McCormick 1981, Naeem et al. 1994, Odum 1985, Rapport et al. 1981, 1985, Vitousek et al. 1981). As a result, changes in nutrient pools are observed (Haskell et al. 1992, Rapport 1989b, Rapport et al. 1985). These are usually losses, especially through increased rates of loss (Freedman 1989, Odum 1985, Rapport et al. 1985, Woodwell 1970). In contrast, in polluted ecosystems, there is a shift toward higher occurrence of species with large loads of toxic materials, along with declines in ability to detoxify (Rapport 1989a, Rapport et al. 1981). When ecosystem structure and function are modified, new selective pressures arise such that coevolutionary dynamics within ecosystems are altered (e.g., Stenseth 1989). Evolutionary changes occur within species subject to direct influence of human activities (e.g., fishing, pesticide use, antibiotics; Bloom and Murray 1992, Blythe and Stokes 1993, Brown and Parman 1993, Cohen 1992, Curson 1989, Garrett 1994, Grey 1993, Kirkpatrick 1993,

APPENDIX 4.2

Lappe 1994, Law and Rowell 1993, Law and Grey 1989, Law et al. 1993, Levinton 1992, Nelson and Soulé 1986, Neu 1992, Orians 1990, Pimentel et al. 1993, Policansky 1993a,b, Reznick 1993, Rijnsdorp 1993, Rowell 1993, Ryman and Utter 1987, Schaffer and Elson 1975, Slatkin 1983, Smith et al. 1991, Sutherland 1990, Trippel 1995). When one species undergoes evolutionary change it produces new selective pressures to which other species respond. These result in evolutionary ripple effects and can be initiated by human influence. As Jordano (1987) indicates, little attention has been given to such dynamics especially in comparison to the attention historically paid to ecosystem mechanics (e.g., predator-prey or consumer-resource interactions). Such evolutionary changes are observed (see Grime 1989). Some are significant enough to result in extinction as one of the reactions to ecosystem change (Myers 1989, van der Maarel 1975). Such extinctions could easily be the result of increased population variability or any number of the many other changes observed for ecosystems under stress as listed above (e.g., declining health of component species, Westman 1990b).

References Apollonio, S. 1994. The use of ecosystem characteristics in fisheries management. Reviews in Fisheries Science 2: 157–180. Barrett, G.W., and R. Rosenberg (eds). 1981. Stress effects on natural ecosystems. John Wiley & Sons, New York, NY. Bloom, B.R. and C.J.L. Murray. 1992. Tuberculosis: commentary on a reemergent killer. Science 257: 1055–1064. Blythe, S.P. and T.K. Stokes. 1993. Size-selective harvesting and age-at-maturity. I: some theoretical implications for management of evolving resources. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991 (Lecture notes in biomathematics, 99), pp. 222–231. Springer-Verlag, Berlin. Bormann, F.H. 1990. Air pollution and temperature forests: creeping degradation. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 25–44. Cambridge University Press, New York, NY.

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Brashares, J.S., P. Arcese, M.K. Sam, P.B. Coppolillo, A.R.E. Sinclair, and A. Balmford. 2004. Bushmeat hunting, wildlife declines, and fish supply in West Africa. Science 306: 1180–1183. Brown, J.S. and A.O. Parman. 1993. Consequences of sizeselective harvesting as an evolutionary game. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991 (Lecture notes in biomathematics, 99), pp. 248–261. Springer-Verlag, Berlin. Calow, P. and R.J. Berry (eds). 1989. Evolution, ecology and environmental stress. Academic Press, New York, NY. Christensen, N.L., A.M. Bartuska, J.H. Brown, S.R. Carpenter, C. D’Antonio, R. Francis, J.F. Franklin, J.A. MacMahon, R.F. Noss, D.J. Parsons, C.H. Peterson, M.G. Turner, and R.G. Woodmansee. 1996. The report of the Ecological Society of America Committee on the scientific basis for ecosystem management. Ecological Applications 6: 665–691. Cohen, M.L. 1992. Epidemiology of drug resistance: implications for the post-antibiotic era. Science 257: 1050–1055. Colborn, T., D. Dumanoski, and J.P. Myers. 1997. Our stolen future: are we threatening our fertility, intelligence, and survival?: a scientific detective story. Penguin Group, New York, NY. Cracraft, J. and F.T. Grifo (eds). 1999. The living planet in crisis: biodiversity science and policy. Columbia University Press, New York, NY. Curson, P. 1989. Paradise delayed: epidemics of infectious disease in the industrialized world. In J.I. Clarke, P. Curson, S.L. Kayastha, and P. Nag (eds). Population and disaster, pp. 157–179. Basil Blackwell, IGUCPG, Cambridge, MA. Davis, M.B. 1990. Climatic change and the survival of forest species. In Woodwell, G.M. (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 99–110. Cambridge University Press, New York, NY. Fearnside, P.M. 1990. Deforestation in Brazilian Amazonia. In Woodwell, G.M. (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 211–238. Cambridge University Press, New York, NY. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Garrett, L. 1994. The coming plague: newly emerging diseases in a world out of balance. Farrar, Straus and Giroux, New York, NY.

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Grassle, J.F., N.J. Maciolek, and J.A. Blake. 1990. Are deepsea communities resilient? In G.M.Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 385–394. Cambridge University Press, New York, NY. Gray, J.S. 1989. Effects of environmental stress on species rich assemblages. Biological Journal of the Linnnean Society 37: 19–32. Grey, D.R. 1993. Evolutionarily stable optimal harvesting strategies. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991. (Lecture notes in biomathematics, 99), pp. 176–186. Springer-Verlag, Berlin. Grime, J.P. 1989. The stress debate: symptoms of impending synthesis? Biological Journal of the Linnnean Society 37: 3–17. Halpern, B.S., S. Walbridge, K.A. Selkoe, et al. 2008. A global map of human impact on marine ecosystems. Science 319: 948–952. Harmsen, R. 1983. Abundance distribution and evolution of community structure. Evolutionary Theory 6: 283–292. Haskell, B.D., B.G. Norton, and R. Costanza. 1992. Introduction: What is ecosystem health and why should we worry about it? In R.Costanza, B.G. Norton and B.D. Haskell (eds). Ecosystem health: new goals for environmental management, pp. 3–20. Island Press, Washington, DC. Hawksworth, D.L. 1990. The long-term effects of air pollutants on lichen communities in Europe and North America. In Woodwell, G.M. (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 45–64. Cambridge University Press, New York, NY. Intergovernmental Panel on Climate Change 2007. Cllimate change 2007: the physical science basis. http:// www.ipcc.ch/SPM2feb07.pdf Jordano, P. 1987. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence, asymmetries, and coevolution. American Naturalist 129: 657–677. Karr, J.R. 1992. Ecological integrity: protecting earth’s life. In R. Costanza, B.G. Norton, and B.D. Haskell (eds). Ecosystem health: new goals for environmental management, pp. 223–238. Island Press, Washington, DC. Kerr, S.R., and L.M. Dickie. 1984. Measuring the health of aquatic ecosystems. In Cairns, V.W., P.V. Hodson, and J.O. Nriagu (eds). Contaminant effects on fisheries., pp. 279–284. John Wiley & Sons, New York, NY. Kirkpatrick, M. 1993. The evolution of size and growth in harvested natural populations. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of

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MEA (Millennium Ecosystem Assessment). 2005b. Ecosystems and human well-being: biodiversity synthesis. World Resources Institute, Washington, DC. Moore, P.D. 1983. Ecological diversity and stress. Nature 306: 17. Moran, E.F. 2006. People and nature: an introduction to human ecological relations. Blackwell, Malden, MA. Myers, N. 1989. Extinction rates past and present. Bioscience 39: 39–41. Naeem, S., L.J. Thomson, S.P. Lawler, J.H. Lawton, and R.M. Woodin. 1994. Declining biodiversity can alter the performance of ecosystems. Nature 368: 734–737. Nelson, K. and M.E. Soulé. 1986. Genetical conservation of exploited fishes. In Ryman, N., and F. Utter (eds) Population genetics and fishery management, pp. 345–368. Washington Sea Grant, University of Washington Press, Seattle, WA. Neu, H.C. 1992. The crisis in antibiotic resistance. Science 257: 1064–1073. Odum, E.P. 1981. The effects of stress on the trajectory of ecological succession. In Barrett, G.W. and R. Rosenberg (eds). Stress effects on natural ecosystems, pp. 43–48. John Wiley & Sons, New York, NY. Odum, E.P. 1985. Trends expected in stressed ecosystems. Bioscience 35: 419–422. Orians, G.H. 1990. Ecological concepts of sustainability. Environment 32: 10–15, 34–39. Pauly, D., V. Christensen, J. Dalsgaard, R. Rroese, and F. Torres Jr. 1998. Fishing down marine food webs. Science 279: 860–863. Pimentel, D., H. Acquay, M. Biltonen, et al. 1992. Environmental and economic costs of pesticide use. Bioscience 42: 750–758. Pimentel, D., H. Acquay, M. Biltonen, et al. 1993. Assessment of environmental and economic costs of pesticide use. In Pimentel, D. and H. Lehman (eds). The pesticide question: environment, economics, and ethics, pp. 47–84. Chapman & Hall, New York, NY. Policansky, D. 1993. Evolution and management of exploited fish populations. In Kruse, G., D.M. Eggers, R.J. Marasco, C. Pautzke, and T.J. Quinn II (eds). Proceedings of the international Symposium on management strategies for exploited fish populations, pp. 651–664. Alaska Sea Grant College Program Report No. 93–02, University of Alaska, Fairbanks. Policansky, D. 1993. Fishing as a cause of evolution in fishes. In Law, R., J.M. McGlade and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991. (Lecture notes in biomathematics, 99) pp. 2–18. Springer-Verlag, Berlin.

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Rapport, D.J. 1989a. State of ecosystem medicine. Perspectives in Biology and Medicine 33: 120–132. Rapport, D.J. 1989b. What constitutes ecosystem health? Perspectives in Biology and Medicine 33: 120–132. Rapport, D.J. 1989c. Symptoms of pathology in the Gulf of Bothnia (Baltic Sea): Ecosystem response to stress from human activity. Biological Journal of the Linnnean Society 37: 33–49. Rapport, D.J., H.A. Regier, and C. Thorpe. 1981. Diagnosis, prognosis, and treatment of ecosystems under stress. In G.W. Barrett, and R. Rosenberg (eds). Stress effects on natural ecosystems., pp. 269–280. John Wiley & Sons, New York, NY. Rapport, D.J., H.A. Regier, and T.C. Hutchinson. 1985. Ecosystem behavior under stress. American Naturalist 125: 617–640. Redman, C.L. 1999. Human impact on ancient enviornments. University of Arizona Press, Tucson, AZ. Regier, H.A. 1973. Sequence of exploitation of stocks in multispecies fisheries in the Laurentian Great Lakes. Journal of the Fisheries Research Board of Canada 30: 1992–1999. Regier, H.A. and W.L. Hartman. 1973. Lake Erie’s fish community: 150 years of cultural stresses. Science 180: 1248–1255. Reznick, D.N. 1993. Norms of reaction in fishes. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991 (Lecture notes in biomathematics, 99), pp. 72–90. Springer-Verlag, Berlin. Rijnsdorp, A.D. 1993. Selection differentials in male and female North Sea plaice and changes in maturation and fecundity. In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991 (Lecture notes in biomathematics, 99), pp. 19–36. Springer-Verlag, Berlin. Rosenzweig, M.L. 1971. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171: 385–387. Rowell, C.A. 1993. The effects of fishing on the timing of maturity in North Sea cod (Gadus morhua.). In Law, R., J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991 (Lecture notes in biomathematics, 99), pp. 44–61. Springer-Verlag, Berlin. Ryman, N., and F. Utter. 1987. Population genetics and fishery management. University of Washington Press, Seattle, WA. Sampson, F. and F. Knopf. 1994. Prairie conservation in North America. Bioscience 44: 418–421.

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Schaffer, W.M. and P.F. Elson. 1975. The adaptive significance of variations in life history among local populations of Atlantic salmon in North America. Ecology 56: 577–590. Sharitz, R.R. and J.W. Gibbons. 1981. Effects of thermal effluents on a lake: enrichment and stress. In G.W. Barrett and R. Rosenberg (eds). Stress effects on natural ecosystems, pp. 243–260. John Wiley & Sons, New York, NY. Silver, C.S. and R.S. DeFries (eds). 1990. One earth, one future: our changing global environment. National Academy Press, Washington, DC. Slatkin, M. 1983. Genetic background. In Futuyma, D.J. and M. Slatkin (eds). Coevolution, pp. 14–32. Sinauer Associates, Sunderland, MA. Smith, P.J., R.I.C.C. Francis, and M. McVeagh. 1991. Loss of genetic diversity due to fishing pressure. Fisheries Research 10: 309–316. Specht, R.L. 1990. Changes in the eucalypt forests of Australia as a result of human disturbance. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 177–198. Cambridge University Press, New York, NY. Steffen, W., A. Sanderson, P.D. Tyson, et al. 2004. Global change and the earth system: a planet under pressure. Springer-Verlag, Berlin. Stenseth, N.C. 1989. On the evolutionary ecology of mammalian communities. In D.W. Morris, Z. Abramsky, B.J. Fox, and M.R. Willig (eds). Patterns in the structure of mammalian communities, pp. 219–228. Texas Tech University Press, Lubbock, TX. Sutherland, W.J. 1990. Evolution and fisheries. Nature 344: 814–815. Trippel, E.A. 1995. Age at maturity as a stress indicator in fisheries. Bioscience 45: 759–771. Turner, B.L., II, W.C. Clark, R.W. Kates, J.F. Richards, J.T. Mathews, and W.B. Meyer (eds). 1990. The earth as transformed by human action; global and regional changes in the biosphere over the past 300 years. Cambridge University Press, New York, NY. Ulanowicz, R.E. 1992. Ecosystem health and trophic flow networks. In R. Costanza, B.G. Norton and B.D. Haskell (eds). Ecosystem health: new goals for environmental management, pp. 190–206. Island Press, Washington, DC. Valiela, I. 1990. Suitably large scales for study of marine ecosystems. Ecology 71: 2031. van der Maarel, E. 1975. Man-made natural ecosystems in environmental management and planning. In

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Appendix 4.3

The following material is Appendix 4.3 for Chapter 4 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press

4. Survey or monitoring, analysis, and assessment should precede planned use and accompany actual use of wild living resources. The results should be made available promptly for critical public review.

1 Principles of “ecosystem management”

In 1994, these were expanded with seven principles developed at the second Airlie House meeting (paraphrased from Mangel et al. 1996):

Much effort has been spent in defining “ecosystem management” (Appendix 4.1, Arkema et al. 2006, McCormick 1999) and the principles of management in general. The list of tenets presented in Chapter 1 represents a distillation of this work (Fowler et al. 1999, Fowler 2002, Fowler and Hobbs 2002). The quest for sustainability has a long history (Rockford et al. 2008). In principle, long-term sustainability is one of the main issues facing managers who are involved in resource utilization and faced with environmental problems (e.g., Christensen et al. 1996, Holt and Talbot 1978, Mangel et al. 1996, Oliver et al. 1995). Wallace (1994) and other sources listed in Chapter 1 demonstrate the degree to which these principles have been translated to legal mandate. Holt and Talbot’s (1978) four principles are: 1. The ecosystem should be maintained in a desirable state such that: (a) consumptive and nonconsumptive values could be maximized on a continuing basis, (b) present and future options are ensured, and (c) risk of irreversible changes or long-term adverse effects as a result of use is minimized. 2. Management decisions should include a safety factor to allow for the fact that knowledge is limited and institutions are imperfect. 3. Measures to conserve a wild living resource should be formulated and applied so as to avoid wasteful use of other resources.

1. Sustainability is inconsistent with unlimited growth of human consumption of, and demand for, resources. 2. Present and future options are to be achieved by maintaining biological diversity at genetic, species, population and ecosystem levels. Neither the resource nor other components of the ecosystem should be perturbed beyond natural boundaries of variation. 3. Assessment (including ecological and sociological effects) of resource use should precede both proposed use and proposed restriction or expansion of ongoing use of a resource. 4. Management must be based on an understanding of the structure and dynamics of ecosystems while accounting for both ecological and sociological factors. 5. The full range of knowledge and skills from the natural and social sciences must be brought to bear in dealing with conservation problems. 6. Effective conservation requires understanding and taking account of the motives, interests, and values of all users and stakeholders, but not by simply averaging their positions. 7. Effective conservation requires communication that is interactive, reciprocal, and continuous. Similar elements of “ecosystem management” are found in the Ecological Society of America’s report (Christensen et al. 1996) that specifies

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that “ecosystem management” includes eight elements: 1. Sustainability. Ecosystem Management does not focus primarily on “deliverables” but rather regards intergenerational sustainability as a precondition. 2. Goals. Ecosystem Management establishes measurable goals that specify future processes and outcomes necessary for sustainability. 3. Sound ecological models and understanding. Ecosystem Management relies on research performed at all levels of ecological organization. 4. Complexity and connectedness. Ecosystem Management recognizes that biological diversity and structural complexity strengthen ecosystems against disturbance and supply the genetic resources necessary to adapt to long-term change. 5. The dynamic character of ecosystems. Recognizing that change and evolution are inherent in ecosystem sustainability, Ecosystem Management avoids attempts to “freeze” ecosystems in a particular state or configuration. 6. Context and scale. Ecosystem processes operate over a wide range of spatial and temporal scales, and their behavior at any given location is greatly affected by surrounding systems. Thus, there is no single appropriate scale or time frame for management. 7. Humans as ecosystem components. Ecosystem Management values the active role of humans in achieving sustainable management goals. 8. Adaptability and accountability. Ecosystem Management acknowledges that current knowledge and paradigms of ecosystem function are provisional, incomplete, and subject to change. Management approaches must be viewed as hypotheses to be tested by research and monitoring programs. Francis et al. (2007) present “ten commandments” for ecosystem-based fisheries science as a basis for management: 1. Keep a perspective that is holistic, risk-averse, and adaptive. 2. Question key assumptions, no matter how basic. 3. Maintain old-growth age structure in fish populations.

4. Characterize and maintain the natural spatial structure of fish stocks. 5. Characterize and maintain viable fish habitats. 6. Characterize and maintain ecosystem resilience. 7. Identify and maintain critical food web connections. 8. Account for ecosystem change through time. 9. Account for evolutionary change caused by fishing. 10. Implement an approach that is integrated, interdisciplinary, and inclusive. Similar lists are presented in McCormick (1999), Dale et al. (2000), and Fowler (2003). Other such lists are found in much of the literature referred to in work exemplified by Arkema et al. (2006) and Appendix 4.1. As pointed out in Appendix 4.1, a common thread behind this work is a strong tendency to retain stakeholders in their position in the top row of Figure 1.1. In this role, thought, rather than observation, is the emphasis in decisionmaking. The lack of objectivity inherent to this role is retained in conventional management. This is one of the most significant differences between conventional and systemic management. In converting to systemic management, the conventional role for stakeholders is brought to a halt and replaced by using stakeholders in the role of asking management questions so that the appropriate research can be carried out to answer those questions objectively (Belgrano and Fowler 2008, Hobbs and Fowler 2008).

References Arkema, K.K, S.C. Abramson, and B.M. Dewsbury. 2006. Marine ecosystem-based management: from characterization to implementation. Frontiers in Ecology and the Environment 4: 525–532. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In S.I. Munoz (ed.). Ecology research progress, pp. 5–31. Nova Science Publishers, Hauppauge, NY. Christensen, N.L., A.M. Bartuska, J.H. Brown, et al. 1996. The report of the Ecological Society of America Committee on the scientific basis for ecosystem management. Ecological Applications 6: 665–691. Dale, V.H., S. Brown, R.A. Haeuber, et al. 2000. Ecological principles and guidelines for managing the use of land. Ecological Applications 10: 639–670.

APPENDIX 4.3

Fowler, C.W. 2002. Sustainability. In W.F. Perrin, B. Würsig, and H.G.M. Thewissen (eds). Encyclopedia of marine mammals, pp. 1205–1208. Academic Press, San Diego, CA. Fowler, C.W. 2003. Tenets, principles, and criteria for management: the basis for systemic management. Marine Fisheries Review 65: 1–55. Fowler, C.W. and L. Hobbs. 2002. Limits to natural variation: implications for systemic management. Animal Biodiversity and Conservation. 25: 7–45. Fowler, C.W., J.D. Baker, K.E.W. Shelden, P.R. Wade, D.P. DeMaster, and R.C. Hobbs. 1999. Sustainability: empirical examples and management implications. In Ecosystem approaches for fisheries management, pp. 305–314. University of Alaska Sea Grant, Fairbanks, Alaska, AK-SG-99-01. Francis, R.C., M.A. Hixon, M.E. Clarke, S.A. Murawski, and S. Ralston. 2007. Ten commandments for ecosystem-based fisheries scientists. Fisheries 32: 217–233. Hobbs, L. and C.W. Fowler. 2008. Putting humans in ecology: consistency in science and management. Ambio 37: 119–124.

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Holt, S.J. and L.M. Talbot. 1978. New principles for the conservation of wild living resources. Wildlife Monographs 59: 5–33. Mangel, M., L.M. Talbot, G.K. Meffe, et al. 1996. Principles for the conservation of wild living resources. Ecological Applications 6: 338–362. McCormick, F.J. 1999. Principles of ecosystem management and sustainable development. In Peine, J.D. (ed.). Ecosystem management for sustainability: principles and practices illustrated by a regional biosphere reserve cooperative, pp. 3–21. Lewis Publishers, Boca Raton, FL. Oliver, C.H., B.J. Shuter, and C.K. Minns. 1995. Toward a definition of conservation principles for fisheries management. Canadian Journal of Fisheries and Aquatic Sciences 52: 1584–1594. Rockford, L.L., R.E. Stewart, and T. Dietz (eds). 2008. Foundations of environmental sustainability. Oxford University Press, New York, NY. Wallace, R.L. 1994. The marine mammal commission compendium of selected treaties, international agreements, and other relevant documents on marine resources, wildlife, and the environment. US Marine Mammal Commission, Washington, DC. (Three Vols.).

Appendix 4.4

The following material is Appendix 4.4 for Chapter 4 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 The Bayesian interpretation of selective extinction and speciation To appreciate the interpretation of species frequency distributions (Fig. 1.4) as Bayesian integrators of complexity, consider the following scenario. Imagine a statistician being asked to undertake a Bayesian analysis to address management questions plagued by conflict, complexity, and uncertainty. In such an approach a computer model that mimics a species could be repeatedly used to try alternative parameters relating to a specific management question. With such models, we could, for example, address the question of how much biomass (or alternatively how many individuals) might optimally be removed from a resource species. Such a statistician could start with a population model of a consuming species, with a set of randomly chosen values for population parameters (e.g., density dependence, age specific mortality, and birth rates, etc.). Consumption rates would be included as part of the model and the behavior of the model would be compared with empirical data. Such data could include the population dynamics of observed consumer species from field studies. Models that do not conform to reality (especially those that result in extinction) would indicate that the respective parameter combination is unrealistic and they would be given low probability. However, a simple population model is insufficient; there are dynamics of interactions to take into account. The population model, therefore, could 92

be embedded in an ecosystem model. Additional people would be hired to contribute to the exercise. The enlarged team would build models that would be based on parameters and measures of the environment and other species with which the focal species interacts. There would be population models of the other species embedded in the overall model. Producing such models would require accumulation of a great deal of information. Various interactions would be included, but of special importance would be the predator-prey interaction and processes related to consumption rates, and competition. To achieve as much realistic representation as possible, resource populations would be represented in the model to incorporate responses to consumers, especially for the focal species (primarily its consumption rates, still one of the main focuses of the exercise). Numerous applications of the model would be tried with different parameter combinations. Consumption rates corresponding to model formulations (parameter combinations) exhibiting population dynamics in which levels of less than one organism (extinction) for the consumer species would not be viable options, nor would be other parameters in combinations resulting in extinction. Such combinations of parameters would be rejected as unrealistic. Similarly, parameter combinations that resulted in model behavior showing population variation higher than observed would be rejected or given low weight. Other combinations of parameters would be rejected on the bases of other unrealistic behavior, including such things as unrealistic fluctuations in the age structure of the consuming species population or unrealistic birth rates. Critics of such an approach might point out that various factors, processes, dynamics or interactions were not taken into account and the model should

APPENDIX 4.4

be expanded to incorporate at least some of them. Further enlargement would account for greater complexity (including more species of competitors, other species in food chains, and more of their interactions with their environments), all matters of ecological mechanics, but never, of course, all of them. However, a different level of complexity, another realm of consideration, is still missing entirely. Further review in attempts to publish the results in early phases of such a study could easily bring out the fact that evolutionary dynamics were not included. This would extend the level of consideration beyond (but would include as fundamentally important) early attempts to consider ecological mechanics. Evolutionary biologists would be added to the team and the population model might be made to have at least a few parameters that are subject to various elements of selection. Individual organisms could be added to the model (after hiring on experts in individual-based modeling) and individuals in the model would be made subject to selective mortality and reproduction. Coevolutionary biologists (especially those interested in employment and joining the elite project) might find fault with this situation if it is not extended to the other species in the system and all of their interactions, both with other species and their interactions with their environments. The complexity of such a model would probably prohibit the analysis on all but the largest of existing computers in view of the number of iterations required to sample the parameter space at the heart of the original question. The data required for such an exercise, and estimating the variance of these data, would be prohibitively difficult to obtain. What started as a one person exercise now requires a major team and budget and the politicians involved speak highly of the progress and economic stimulus it represents. However, even unskilled critics would be able to find fault with various parts of the model. It would never be clear whether or not the correct model formulation had been chosen. But because of its complexity and the volume of information used, it would be seen as an honest and well intentioned effort from which something should be learned, in spite of the costs (perhaps because of the costs) and in spite of the

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fact that consideration of elements, molecules, chemistry, hormones, behavior, and atomic particles has not yet been entertained. At this point a new set of issues might arise. Perhaps they would originate with the addition of a new, relatively uneducated, or inexperienced, member of the growing team of experts, all brought to bear on the original question. Someone might suggest that the models of individual organisms embedded in the larger model should account for the fact that individual organisms in reality are made up of cells, molecules, atoms, tissues, organs, and have behavior, and interact with each other as well as the individuals of other species. Pheromones are part of both interspecific and intraspecific interactions. The physical environment might be recognized as inclusive of astronomical factors influencing day length, tidal cycles, and weather associated with the movement of the sun and moon. Carbon dioxide, oxygen, nitrogen, and all of the other elements would be brought into the model. To adequately integrate (account for) such factors would require more than had been anticipated at the outset of such a project. In the frustration of the realization that no model can be so complex as to fully represent reality (or even the whole of such a complex system), one of the seasoned members of the team might realize that to account for certain aspects of reality, the model would have to be physical, much like hydrological models or airplanes used in wind tunnels. In contemplating the fanciful world of what a Bayesian analysis would be if it were based on physical models, the newly arrived naive team member might stimulate a brainstorming mode of thinking, noting that it would be interesting to know what it would be like if an extra-terrestrial with the capacity to introduce billions of genetically engineered species might do as a Bayesian exercise with physical species rather than computer models. After all, they would be injected into a world with all of the factors and processes about which the critics were concerned. Upon hearing the query, a paleontologist (recently added to the team to help account for long time frames and extinction processes) might react to this hypothetical prospect with the suggestion that they consider what has happened

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through the processes of selective extinction and speciation as just that: trial-and-error production and testing of species as a Monte Carlo process resulting in a natural Bayesian integration process. The results of the process would be represented in the species frequency distributions among the sets of species found in nature. These would correspond to the probability distributions from conventional Bayesian analysis. This would also take into account that species are made up of individuals because the physical models of species would be made up of physical individuals (with cells, physiological processes, environmentally influenced genetic design, and subject to selective mortality and reproduction). Further consideration of this possibility would reveal that the “data” ordinarily used in Bayesian integration would be the complex of real factors and conditions to which species (as physical Bayesian integration models) are exposed. In Bayesian approaches, the probabilities with which statisticians work are the probabilities of the models given the data (rather than probabilities of the data given the model for conventional frequentist statistics). The integration represented by species frequency distributions are probabilities of species characteristics given the reality to which they are exposed and the reality of what they are composed, along with all interrelated processes over all scales of time and space. This reality, of course, is impossible to sample completely, to study for full understanding or explanation, or to represent in models adequately. But the probability of that reality is one (1.0—a critical assumption: reality exists; Appendix 1.1). The sciences that study particular phenomena are only conceptual models of pieces of reality. A frequentist statistician in the group might lean back in his chair, after witnessing this history, and wonder to himself if his Bayesian oriented friends hadn’t just come full circle to join him in a frequentist approach with data from direct observations at the species level—with direct practical application if the observations dealt with a specific issue of importance to management. The term “useful reductionism” might enter his mind. The Bayesian approach to statistical parameter estimation (system characterization) uses computer

models. These artificial models of natural systems are constructed with parameters that are allowed to vary randomly in numerous applications of the model (often hundreds of thousands or more). Such a model is compared to the real world by assessing the characteristics and behavior of the model against data. Those parameter combinations that fail to produce realistic “simulation” or representation of the system are discarded (or given low weight). Among the parameter combinations that work, some are more realistic than others and are given higher weight. The probability that a particular parameter value is realistic is measured primarily on the basis of the frequency with which it resulted in acceptable models. Parameter combinations are evaluated on the basis of preselected criteria and weightings based on knowledge of the real system. Frequency distributions of values for each parameter are thus produced based on the number of times (portion of trials) specific values resulted in acceptable models. Part of the parallel or analogy to be drawn between selective extinction and speciation on the one hand, and Monte Carlo (randomized experimental) aspect of Bayesian statistics on the other, involves the trial-and-error nature of both. A variety of models (parameter combinations) are involved in Bayesian analyses; a variety of species (and combinations of DNA coded characteristics) are involved in natural selection at the species level. The trial-and-error aspects of selective extinction and speciation were developed in Chapter 3 and its appendices. In nature, species take the place of computer models (are analogues of computer models but are real rather than abstract). Species are actual physical entities with a DNA code rather than models with a computer code as used in Bayesian analyses. Rather than being tested against data collected by human observers from the real world, species are tested against the real world itself. This testing is carried out, therefore, in the full spectrum of complexity and not subject to the error of measurement inherent in data nor errors and inadequacies in the specification of models. Those combinations of code (DNA) that do not meet the criteria for success in nature are removed (rejected) as the species that contain them go extinct, just as certain parameter

APPENDIX 4.4

combinations for Bayesian models are rejected as unrealistic. Thus, the genetic code of extinct species is not represented among species to be duplicated for further testing within the constraints of nature. Within species are individuals and individuals undergo a similar trial-and-error process of natural selection. Thus, evolution is taken into account as one of the characteristics or processes of relevance for the physical Bayesian models we call species. Thus, existing species are nature’s trial-and-error models of success, as tenuous as each one is. These successes are determined as functions of all the factors to which they are exposed plus their history of such exposure and the variable circumstances

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involved (Fig. 1.4). Therefore the probability distribution represented by species frequency distributions (examples of which were shown in Chapter 2) present extremely useful information. In part, this information is in code form (DNA) in parallel with the computer code used in Bayesian models. Thus, species frequency distributions, as probability distributions, reflect the constraints known to operate in natural systems. In other words, existing species represent an integration of all factors in their environment. They are products of natural selection brought about by these factors, including selective extinction and speciation. They represent information of practical use, or guidance, for systemic management as developed in this book.

Appendix 5.1

The following material is Appendix 5.1 for Chapter 5 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Science is inadequate for management The following is a list of references and statements regarding the inadequacy of science and the need for an improved conceptual foundation or new paradigm to meet our needs. In reading through these observations, keep in mind that part of what is being identified here, is the problem of mismatch between science and management, or the lack of consonance between patterns science can observe and the management questions to which they correspond. The authors are not necessarily conscious of this problem as much as they are expressing symptoms of it. Problems are being identified in the conventional approach depicted in the top row of Figure 1.1. Another part of what is being treated is the fact that there are not fields of science to explore many of the components, relationships, and dynamics of complex systems. The lack of a full acceptance of natural selection at the species level is part of this problem. The following comments also relate to the impossibility of achieving the integrative quality of consonant patterns by other means. We are always confined to reductionism, and attempts to recombine nonconsonant reductionistic information always encounter the impossibility of such efforts (the Humpty Dumpty syndrome described elsewhere in this book). Allen and Hoekstra (1992): “Business as usual has been distinctly incremental in recent years. Theory has been barely relevant to field practitioners, who have therefore been forced to take 96

up the slack as best they can.” “. . . complexity is hard to manage not because there are too many parts to track, but because of our limitations in conceiving the whole.”

Botkin (1990): “It is imperative to develop what I have referred to elsewhere as the new science of the biosphere as well as other ecological sciences.”

Brown (1981): “Despite two decades of intensive investigation, Hutchinson’s question remains largely unanswered.” “The relationship between species diversity, functional organization, and stability of communities remains a challenging problem.” Many community ecologists “ . . . seem to feel that ecological theory has promised far more than it has delivered”.

Brown and Maurer (1989): “ . . . ecology has become increasingly microscopic and experimental in its approach.” “Without a complimentary emphasis on large-scale phenomena, there is little basis for determining which results simply reflect the idiosyncrasies of individual species in particular sites and which reflect the operation of more universal processes.”

Costanza et al. (1992b): “At least some theory is necessary if we are to explain why the measures we choose to indicate states of ecosystem health are not arbitrary.”

Ehrlich (1989): “A lack of both agreement on models and of empirical tests has limited the application of mathematical community ecology to practical problems.” “But, if we do not begin to shape . . . theory, the future of technological Homo sapiens seems dim indeed. Theory, remember, is not just useful when it is providing specific predictions, but also when it supplies an overall frame work that is useful in policy making.”

A P P E N D I X 5 .1

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Haefner (1977):

Roughgarden (1989):

“Until systems ecologists produce a theory that explains the evolution of the biological units they describe, it does not appear feasible for them to use the theorems and modeling constraints provided by evolutionary theory.”

“Future theory will not be simple extension of present models. We must remain alert for altogether new metaphors.”

Hagen (1992): “The final report on the I.B.P. . . . in 1975 concluded that no major breakthrough in ecological theory or biome level perspectives had resulted . . . to date.”

Kareiva (1989): “Past mistakes and the fact that no grand unified theory is on the horizon should not detract from the contributions models are likely to make to ecology.”

Lawton (1974): “Ecology suffers from a surfeit of fascinating but apparently unrelated observations, superimposed upon an acute shortage of general theories.”

Levin (1989): “We must develop a theory for the response pattern of different ecosystems to stresses. We must develop standards of comparisons among ecosystems, based on the identification of common, functionally important processes and properties. Such understanding can emerge only from theoretical syntheses based on a comprehensive program of microcosm research and experimental manipulation coupled with the retrospective studies.”

Margalef (1963): “Present day ecology is extremely poor in unifying and ordering principles.”

Maynard Smith (1988): “For some years, people have been measuring energy flow in ecosystems, but, so far, what has emerged is a description, not a causal theory.”

Norton (1986): “It was overwhelmingly agreed that the lack of scientific knowledge regarding ecosystem functioning is deplorable.” (See also McGowan 1990.)

Orians and Paine (1983): “It is evident that we suffer from a poverty of both theory and data.”

Smith (1977): “ . . . if ecologists fail to examine ecosystems as wholes and fail to determine their emergent properties (emergent not in the computer but in the higher level of organization being considered), then they will fail to advance ecosystem theory.”

References Allen, T.F.H. and T.W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press, New York, NY. Botkin, D.B. 1990. Discordant harmonies. Oxford University Press, New York, NY. Brown, J.H. 1981. Two decades of homage to Santa Rosalia: toward a general theory of diversity. American Zoologist 21: 877–888. Brown, J.H. and B.A. Maurer. 1989. Macroecology: the division of food and space among species on continents. Science 243: 1145–1150. Costanza, R., B.G. Norton, and B.D. Haskell. 1992. Introduction: What is ecosystem health and why should we worry about it? In R. Costanza, B.G. Norton, and B.D. Haskell (eds). Ecosystem health: new goals for environmental management, pp. 3–22. Island Press, Washington, DC. Ehrlich, P.R. 1989. Discussion: Ecology and resource management—is ecological theory any good in practice? In J. Roughgarden, R.M. May, and S.A. Levin (eds). Perspectives in ecological theory, pp. 306–318. Princeton University Press, Princeton, NJ. Haefner, J.W. 1977. Generative grammars that simulate ecological systems. In G. Innis (ed.). New directions in the analysis of ecological systems, Part 2, pp. 189–211. The Society for Computer Simulation, La Jolla, CA. Hagen, J.B. 1992. An entangled bank: the origins of ecosystem ecology. Rutgers University Press, New Brunswick, NJ. Kareiva, P. 1989. Renewing the dialogue between theory and experiments in population ecology. In J. Roughgarden, R.M. May, and S.A. Levin (eds). Perspectives in ecological theory, pp. 68–88. Princeton University Press, Princeton, NJ. Lawton, J.H. 1974. Review of J.M. Smith’s models in ecology. Nature 248: 537. Levin, S.A. 1989. Challenges in the development of a theory of community and ecosystem structure and function. In J. Roughgarden, R.M. May and S.A. Levin

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(eds). Perspectives in ecological theory, pp. 242–255. Princeton University Press, Princeton, NJ. Margalef, R. 1963. On certain unifying principles in ecology. American Naturalist 97: 357–374. Maynard Smith, J. 1988. Did Darwin get it right? Essays on games, sex and evolution. Chapman & Hall, New York, NY. McGowan, J.A. 1990. Species dominance–diversity patterns in oceanic communities. In Woodwell, G.M. (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 395–421. Cambridge University Press, New York, NY. Norton, B.G. 1986. Epilogue. In Norton, B.G. (ed.). The preservation of species: the value of biological

diversity, pp. 268–283. Princeton University Press, Princeton, NJ. Orians, G.H. and R.T. Paine. 1983. Convergent evolution at the community level. In D.J. Futuyma and M. Slatkin (eds). Coevolution, pp. 431–454. Sinauer Associates, Sunderland, MA. Roughgarden, J. 1989. The structure and assembly of communities. In J. Roughgarden, R.M. May and S.A. Levin (eds). Perspectives in ecological theory, pp. 203–226. Princeton University Press, Princeton, NJ. Smith, F.E. 1977. Comments revisited—or, what I wish I had said: In G.S. Innis (ed.). New directions in the analysis of ecological systems, Part 2, pp. 231–235. The Society for Computer Simulation, La Jolla, CA.

Appendix 5.2

The following material is Appendix 5.2 for Chapter 5 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Asking management and research questions In conventional management we force ourselves to convert information unrealistically. Consider, for example, a situation in which we knew beyond doubt that fishing caused observed declines in fur seals, sea lions, birds, and walleye pollock in the Bering Sea (directly or indirectly). This is, of course, hypothetical (science rarely fully proves anything). However, it serves to makes a point: such knowledge would do little more than further validate a principle that is already accepted. That principle is: there are interconnections and relationships between and among parts of complex systems. Interconnections are part of the complexity to be taken into account in the implementation of Management Tenet 3. However, there is another important point. Proof that fishing is causing population declines for various species (and maybe increases in others) in the Bering Sea does not tell us how much walleye pollock to harvest each year. Human judgement might lead to the conclusion that we should reduce fishing, but by how much? Such proof would not tell us how much biomass we can sustainably remove each year from the eastern Bering Sea ecosystem, from any species within the ecosystem, or from any particular set of species (e.g., the sets defined as the resource species consumed by fur seals or sea lions). Observed declines in populations such as fur seals, sea lions, or walleye pollock lead to posing

a clear management question (or questions). What is a sustainable harvest of pollock in the eastern Bering Sea? What is a sustainable consumption of biomass in the eastern Bering Sea? If there is the remotest thought that population declines might be related to global warming or pollution: At what rate can CO2 be produced sustainably by our species? The questions we ask must start with clear management questions. This appendix is an initial treatment of the asking of questions and the relationship between management questions and questions for research (science questions). One management question leads to many others—all part of the experience of complexity, hierarchy, and interconnectedness, and taking them into account. When science exposes a problem, it leads to posing management questions. Any known or suspected connection between human agency and identified problems in the systems with which we interact is grounds for asking a management question. Management questions always address what we humans should do, or what we can do sustainably as humans (combining all Management Tenets, from Chapter 1, but especially 1, 2, and 9). What is sustainable in our interactions and relationships with other species, ecosystems, and the biosphere? Management questions lead to science questions. Scientists are responsible for producing the information used in management. In systemic management, there must be an isomorphism between the science question and the management questions (there must be identical units, logical typing, and circumstances to avoid any need for conversion). There must be a one-to-one mapping between the management question and the research question it generates. Thus, the posing of a clear management question itself defines the corresponding research question. This results in science best 99

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suited to providing the most useful information.1 The asking of questions is where all stakeholders play a very important role (bottom row, Fig. 1.1) in leading to objective information to guide the process of setting goals and policies. The process of asking questions may be driven by politics, special agendas, emotions, economic interests, both short-term and long-term bias, personal preferences, and religious beliefs. Such factors are not the basis for management; they provide motivation for asking clear questions (an important distinction between the top row and the bottom row of Fig. 1.1). In other words, concerns expressed by anyone give rise to thinking that helps pose management questions; all questions are of very real concern to management. Once clearly posed, however, each management question specifies a research question. The resulting research leads to information that is consonant with the management question. Such information is the basis for establishing goals and policies through the implementation of Management Tenet 5. The process leads to a great deal more objectivity than is found in conventional management. The role of stakeholders (including managers and scientists) in decision making is converted from a process of converting/translating information (top row of Fig. 1.1) to that of asking good management questions paired with consonant research questions (bottom row of Fig. 1.1). Some management questions involve components of an original question. Among these are questions related to implementation of guiding information relevant to the initial question. Still others are meta-level questions of which the original question is a component question. This appendix considers the treatment of the additional questions raised by any particular focal question—always accompanied by consonant research questions. The process of generating more management questions involves three elements: (1) asking entirely new questions, plus (2) refining, and (3) expanding related questions. The following will deal with the refinement and expansion of any given question. These correspond to accounting for lower (components or parts) and higher levels (inclusive, larger wholes or hierarchical levels) of systemic organization. The more detailed component questions include information necessary to provide guiding

information for implementation. In the process, entirely new questions always arise and any question can be refined or expanded. This appendix ends with consideration of management questions at the individual level in contrast to the specieslevel bias of the book.

2 Refining questions Questions important to management can be identified by breaking any one management question into component questions and taking advantage of correlative information. Component questions are crucial to implementation—finding ways to carry out the change necessary to achieve the goal established for the original question. Each question, however, occurs as part of a pair—there is always a second question related to research or observation in which guiding information is produced. The guiding information is similar to information used in navigation wherein the path (between a current location and a desired location) is established based on the experience of past trial-and-error exploration of channels and waterways—things that are observed to work.

2.1 Component questions If we decide to harvest the population of a fish, there is more to the issue than the amount of biomass or numbers that can sustainably be removed (consumed or harvested). There are also questions regarding time, location, age, and size composition. Spatial and temporal allocation are questions to be addressed—all related to the overall question of how much of any resource can sustainably be consumed (water, energy, nitrogen). Totals are only a start toward dealing with complexity in systemic management. Asking about sustainability in the total number of resource species to consume leads to questions regarding allocation. What portion of the total harvest from the full set of resource species should be taken from each of the individual species (addressed with data on interspecific selectivity, or composition of the diet, with data such as those shown in Fig. 2.8)? Component questions are particularly important for guidance on methodology—the how of

APPENDIX 5.2

management. Here, as in navigation, the path must be specified after attaining information on the goal and current location. The specifics of implementation involve components. In fisheries management, these involve such things as choice of gear, mesh size, time of day, net type—complexity again, but now involving and relying more on patterns of direct human experience. The legal and regulatory details are also to be established on the basis of years of experience in finding ways that work.

2.2 Correlative information The matter of using correlative relationships was considered in this chapter (and Chapters 2–4) as one of the four ways in which complexity is taken into account in systemic management. This is a matter of directly (or overtly) accounting for such things as human characteristics (e.g., body size, metabolic rate, trophic level), factors we decide to ignore (e.g., the decision to take adult fish in a fishery when most other species take juveniles), and environmental factors (e.g., mean temperature, climatic variation, and other environmental circumstances) when the information is available. Statistically, this is a matter of breaking down the overall variation into its components by accounting for “explanatory” correlations (e.g., see Fig. 2.31 wherein the overall variation in population density is partially “explained” by body size). Correlative information is brought to bear in the refinement of management questions, some of which were exemplified in Chapter 2. So, rather than stopping at a simple question (such as: “What is a sustainable harvest of individual fish stocks in the eastern Bering Sea?”), refinement results in dealing with more complexity (directly) by asking more detailed questions (such as: “What is a sustainable harvest of individual fish stocks characterized by the adult body size of walleye pollock in the eastern Bering Sea?”). Here is where relevant information is involved in a different kind of conversion from that of conventional management. Rather than letting stakeholders interpret such information, it is used as correlative identification of the most useful consonant information. The correlative variables are integral parts of the overall patterns used in management.

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2.3 Implementation Having an objective in mind requires information about how to achieve it. If the sustainable harvest of fish from a marine ecosystem can be established as outlined in this book, then there is the matter of how to implement management. Past experience in management is invaluable. Laws and regulations can be established, enforcement can be carried out, management councils can function, and fishermen can operate as they have learned to operate in the past. If protected areas, closed season, or single resource species quotas are established with systemic information, these objectives become matters to be achieved through practices we recognize as proven options for implementation. Many of the mini-objectives of implementation become a matter of systemic management based on the variety of human experiences whether they involve individuals, industry, government, or society. In at least some cases, the challenge is less a matter of how to carry out the management than it is that of achieving objectives that have been identified— confining ourselves to workable limits. This always involves more expansive questions in regard to more inclusive systems such as ecosystems and the biosphere.

3 Expanding questions Refining questions is not enough to deal with complexity. It is crucial that we also expand questions. Through expansion we account for ecosystems and the biosphere among the levels of biological organization that include humans as a component. Humans interact with, and influence, all systems of which we are a part. Thus, we can start by asking what the sustainable level of harvest from walleye pollock might be. However, we must also be guided by answers to questions such as “Should we harvest walleye pollock at all; should pollock be among the species that we harvest?” Predators in an ecosystem do not consume from all species in the ecosystem. The harvest of pollock is part of the total harvest for an ecosystem, and abnormality must be avoided in the total harvest from an ecosystem? Abnormality must also be avoided in the numbers of species which we harvest. Here, we

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confront the management question of how many species we should harvest: “What is the total number of species that can be sustainably harvested?”. This question is added to those regarding the allocation of harvest across alternative species (Fowler 1999), the geographic range over which harvests are taken, the total take and allocation across tropic levels, and the allocation of take over seasons. Expanding the scope of inquiry, like refining, generates an unending list of questions (i.e., again humanly inexhaustible). Further expansion involves harvests at the level of the biosphere. Harvesting fish in the Bering Sea is part of the total consumption by humans in the biosphere and opens the door to even more questions, all of which are part of systemic management. Again, we face human limits and deal with information we have in hand. This is done while we obtain further, more consonant, information with more research for pressing questions for which we lack consonant information. We continue with the responsibility of developing as many questions as possible. Known questions for which we lack consonant information is basis for research—a great deal of research. At every step, asking the research question that is consonant with each management question is of crucial importance. Thus, when the management question is: “What is a sustainable harvest of fiveyear-old walleye pollock in the eastern Bering Sea during summer months?”, the consonant research question is: “What is the consumption rate of fiveyear-old walleye pollock in the eastern Bering Sea during summer months for marine mammals of human body size?” Consonance is achieved in addressing consumption, a specific age-class of walleye pollock, identifying walleye pollock (with all of its characteristics), specifying location and season, and recognizing the taxonomy and body size of the predator (humans). In all cases, species are part of the consonance; humans (about which the management question is being asked) and marine mammals (the empirically observed examples of sustainability). As mentioned earlier, with the results of such research, no further translation, interpretation, or combination of information is required in establishing goals after adhering to Management Tenet 5. Setting goals this way removes the vulnerability of the decision-making

process to the effects of politics, economic factors, emotions, human limitations, bias, hubris, arrogance, or other human qualities. These play their role in leading to meaningful management questions, followed by consonant research questions, consonant research, and consonant action (bottom row Fig. 1.1). In addressing questions regarding things that other species do not do, both component and expanded questions are particularly important. For example, we face the question of how many computers to produce (or at what rate to produce them). First we face the fact that other species produce no computers—information. If we decide to produce computers anyway, it is important to address component questions exemplified by energy consumption, use of individual materials (copper, mercury, silicon, petroleum, etc.), waste production, and temporal allocation of human activities that are affected by the use of computers. What portion of each 24-hour day can sustainably be spent in activities devoid of interaction with other species and open air? What portion of each 24-hour day can sustainably be spent devoid of the aesthetic qualities of nature, gathering food, resting, interacting with other humans, witnessing the risks of nature, or sitting versus walking/running?

4 Questions at the individual level This section addresses questions applicable to us as individuals to help provide insight regarding the asking of management questions that lead to research—all in the practice of producing scientific information best suited for management. The field of ecopsychology has raised concerns about our “disconnect” with nature (actually abnormal relationships with other elements of nature because we cannot be seen as anything but a part of reality). These concerns can be used as motivation for specific management questions. For example, “What portion of the day should we be spending in procuring food?” Research in response to this question would involve individuals in other cultures, conducted to find the allocation of time for various activities, including that of finding, harvesting, and preparing food. Information from ancient and long-standing cultures would be

APPENDIX 5.2

evidence of what works. However, research would not be confined to humans and would include individuals of other species so that consistency would be achieved at all levels. The research questions would be: “What portion of the day is spent procuring food by individuals in other cultures and other mammalian species of our body size?” Such research would provide objective scientific information to illustrate the abnormality of modern western cultural human life at the individual level—most of the production, distribution, and acquisition of food is industrial. Individuals in such cultures spend very little time during which the experience of hunting and gathering provide them with first-hand ecological insight, spiritual connection with other species, and a sense of limits set by ecological systems. A related question involves the diversity of species in our immediate surroundings. Here, the abnormal relationships we have achieved in modern civilized cultures often involves city dwelling, life confined largely to time spent living in rooms, or surrounded by monocultures of commercially valuable species. What would be a normal diversity of species in our environment with increasing distance from each of us as individuals? Again, research would involve indigenous cultures, and include other species. The research questions would be: What is the pattern in diversity of species in the environment of individuals, expressed as a function of distance? Various measures provide options, including the simple count of species included in an expanding circle or sphere around individuals. The relative lack of other species in the immediate surroundings of people in civilized cultures would become apparent; most other species and many individuals of indigenous cultures would show a much higher diversity of species within 100 meters than westernized humans. Core to the concerns of ecopsychology is the psychological effect of this lack of diversity. The list of questions of the kind introduced above is again an endless expression of complexity. What is a normal average distance to the nearest individual of a nonhuman species of human body size? What is the pattern in average distance to individuals of other species as a function of body size? With a multitude of other species within one’s

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field of vision on a more continuous basis, the world-view of people of civilized cultures would be entirely different; our values would be different, our mental state would be different, we would almost undoubtedly feel more of a sense of well being. The field of medicine also raises concerns; exercise has been recognized as important to physiological, physical, and emotional health. What is a normal distance traveled in a lifetime? What is the pattern in lifetime distance traveled for mammalian species of human body size? How much energy should be spent in exercise? What is the pattern in energy spent in exercise for individuals among mammalian species of our body size? What portion of the average day should be spent getting exercise? What is the pattern in portion of time spent in exercise for individuals in mammalian species of our body size? How much exercise should be achieved at various pulse rates? Within each day, what is the pattern of time spent with the heart beating at various rates for individuals of nonhuman mammalian species of our body size? What portion of one’s life should be spent in getting exercise on machines, sidewalks, swimming pools, or pavement? What is the pattern in portion of exercise experienced on soil or rock, in lakes or rivers, and in proximity to a diversity of other species when measured for other mammalian species of our body size? Other fields of science (e.g., evolutionary ecology, outdoor recreation, and the behavioral sciences) can be the source of questions similar to those exemplified above—both for us as a species and for us as individuals. Agricultural scientists can ask: What would the normal diversity of species be where we now have monocultures of corn, rice, wheat, and other agricultural crops? Research can be conducted to take advantage of the few remaining spots of seminatural grasslands to begin to find the patterns consonant with such questions. Others can ask: What is a sustainable population size for cattle, sheep, hogs, goats, chickens, or turkeys? Research to reveal patterns similar to that shown in Figure 5.1 can be conducted to answer the question for each species (i.e., with data for mammalian or avian species of similar body size).

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It is the responsibility of scientists to both help ask such questions and then to conduct the science to reveal patterns that differentiate the normal and abnormal so that answers to management questions are produced with information that needs no conversion.

Note 1. The matter of providing the best scientific information for management has long been debated, and often involves consideration of the quality of science rather than the choice of information. Thus, we are often left with guidelines for producing such information rather than a clear definition of what the best information is (NRC 2004). Part of what systemic management accomplishes is a clear definition of the scientific information best suited for management. It is information consonant with the management question. In conventional management there is a form of alchemy involved in converting a mixture of nonconsonant, fragmented, and incomplete information to answers to management questions (usually ill-defined questions if they are asked at all, top row

of Fig. 1.1). In systemic management consonance between empirical pattern and management question means that there is no need for conversion; the best scientific information prevents the need for conversion. The quality of science is still a factor and it is essential that good science be brought to bear, but the choice of information that good science produces is the key factor in the definition of best scientific information available for management. It involves the clear distinction between relevance and consonance. Confined to mere relevance we cannot help but make errors; consonance serves us well when expressed in terms of variation that serves to identify the abnormal.

References Fowler, C.W. 1999a. Management of multi-species fisheries: from overfishing to sustainability. ICES Journal of Marine Science 56: 927–932. NRC (National Research Council). 2004. Improving the use of “best scientific information available” standard in fisheries management. National Academy Press, Washington, DC.

Appendix 6.1

The following material is Appendix 6.1 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere.Oxford University Press 1 Identifying human elements in needed management To apply systemic management, the tenets listed in Chapter 1 (Christensen et al. 1986, Fowler 2003, Fowler and Hobbs 2002, Fowler et al. 1999, Holt and Talbot 1978, Mangel et al. 1996, McCormick 1999; see also Appendix 4.3) cannot be ignored. The principles behind these tenets are clear and their requirements are to be fulfilled— simultaneously, consistently, and fully. No matter what form of management is being used, problems are to be identified through assessment, causes are to be ascribed (especially potential human contributions), and options for action identified. In systemic management, frequency distributions (patterns) revealing abnormalities would be developed, whether they expose problems over which we have no control, or related problems over which we do have a measure of control (ourselves). The management questions will always be questions regarding how humans can fit in rather than how to make already overtaxed systems meet unrealistic human needs. The complexity to be accounted for would simultaneously involve individuals, species, ecosystems, and the biosphere, because none can be ignored. The identification of problems and tracing their origins to human contributions where we can take action are processes exemplified in the literature as reviewed below. At the ecosystem level, changes observed are often ascribed to humans activities (Appendix 4.2) and emphasize the need to manage differently.

Species are going extinct as has been the case over geological history owing to dynamics such as climate change, plate tectonics, or bolide impact with the earth (e.g., Raup 1989). Now, current extinction rates are arguably abnormal. Whether or not they are abnormal, they are being compared to mass extinction events that have occurred over geological history (e.g., Brown 1995, Diamond 1984b, 1989, Knoll 1984, Raup 1984, Raven and Cracraft 1999, Simberloff 1986b, Wilson 1985a,b). Loss of forests and grasslands are seen as responses to human influence. Reduced geographic ranges, increased abundance among pests, and problems related to diseases, parasites, and other micropredators are often seen as anthropogenic, as are declines in body size and trophic level, and increases in population variation. The observed changes may well represent abnormality for these ecosystems (e.g., MEA 2005a,b, Turner et al. 1990, Woodwell 1990a, World Conservation Monitoring Centre 1992). In terms of human values, the benefits to humans of such change are usually interpreted as negative impacts on ecosystems. The impacts on ecosystems are often actively sought as a means of meeting short-term, local, or individual human needs. These include the production and distribution of agricultural products (especially food for humans, often locally in surplus), and space for living and transportation of the current population of humans. Risks, as we evaluate them, are exemplified by loss of goods and services, and the effects of pests and parasites in altered ecosystems, especially in the long term. These entail not only the easily recognized negative (in human value systems) effects of starvation, pain, death, and any resulting social disruption. However, they also involve the real risk of extinction—not only that which we observe among other species, but also that of our own. Over 99% 105

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of all other species have gone extinct and there is little reason to expect that humans are different in this regard (Boulter 2002), especially in view of a history of human societies that have suffered extinction or near-extinction (Brown 1995, Catton 1980, Costanza et al. 2007, Diamond 2004, Lollar 1991, Whitmore 1980). Knowledge of the risks and how we may be increasing them is important (even if only to emphasize that such risks are accounted for in the integrative nature of natural patterns). Through regulation to avoid abnormality for our species, the tradeoffs among various risks are dealt with to achieve as much sustainability as possible for all systems involved. There are costs, both as changes scientists observe and as problems identified in human value systems. These are incurred in both modifying ecosystems and then in maintaining them in modified form (Allen and Hoekstra 1992, Pimentel et al. 1993)—resisting the homeostatic forces of such systems. Some of these costs are documented by Pimentel et al. (1992). Pest control, for example, is a form of population level management that attempts to maintain an ecosystem state necessary for agricultural monocultures. The manufacture and application of pesticides is costly ($20 billion/ yr worldwide in 1990; Pimentel et al. 1993). It is estimated that more direct costs from the effects of pesticides include 20,000 human deaths annually worldwide. Associated human health problems include mental impairment and immune dysfunction, a problem that extends to domestic livestock. Pesticides destroy other nontarget species including natural predators and parasites of the target pests and species of more direct benefit to humans such as honey bees and other pollinating insects of importance to food production (Perkins 1982). Also affected are aquatic systems and associated organisms. Micropredators and microherbivores that use humans and their resource species as hosts (or as prey) evolve antibiotic and pesticide resistant strains (Garrett 1994, Georghiou 1990). Ecosystems that have been modified react over evolutionary time scales (periods that may be only a few years or even months for short-lived species) to produce changes, some of which will be forms that are more difficult to control resulting in greater

costs in human life, energy, and effort. The genetic effects of our harvest and agricultural production of resources have unintended consequences, most of which have yet to be documented, and many of which will remain unknown. Increased virulence can be expected to evolve in pests associated with monocultures of agricultural species (Farnworth and Golley 1974). Thus, as the human species itself becomes more of a monoculture through increasing density, especially in cities, it is not surprising to see increasingly challenging problems caused by diseases (Garrett 1994) and the threat of more (Webster and Walker 2003). More than a thousand species prey on humans and will undergo evolutionary modifications some of which will involve increased impact (Table 6.3). In addition, there are numerous species capable of attacking humans that occur (yet to be identified or discovered) in the ecosystems of the world with which humans are increasingly coming into contact (and modifying). As with other species in this regard, the human relationship with ecosystems entails at least two components. One is phenotypic (ecological mechanics) wherein disease organisms limit human numbers. The other is genetic with at least two components: other species and humans. Disease species evolve to exhibit greater effects (evolutionary processes raising risks of dramatic human population declines as those experienced in the past and even extinction). Diseases are also involved in the process of natural selection; humans without genetically resistant immune systems are selectively more vulnerable. Increasing incidence of diseases as the human population increases is already being noted. Human diseases that have recently challenged epidemiologists includes Legionnaires disease, Lyme disease, Bengal cholera, E. coli O157, Hantavirus, Ebola virus, Cryptospiridium (intestinal parasite), streptococcal bacteria, new strains or multidrug resistant forms of Staphylococcus aureus (MRSA), filovirus (Africa, Philippines, Germany), west Nile virus, tuberculosis, and the new strains of diseases, like flu and severe acute respiratory syndrome, that appear each year (see Curson 1989, Dowdle 1993, Garrett 1994, Gibbons 1993, McMichael 1993, Morse 1993a,b,c, Webster and Walker 2003). Over the course of history, diseases have dramatically

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reduced various populations of humans, some apparently to extinction.1 Today, the emergence of new diseases through mutations, sharing genes for antibiotic resistance or virulence (e.g., by way of plasmid transfer and conjugation among bacteria), and chronic exposure to antibiotics is a source of serious concern to epidemiologists in light of human population densities, mobility, and compromised immune systems (Garrett 1994, Webster and Walker 2003). Mutation resulting in AIDS, Ebola, or other virus capable of being transmitted by minimal physical contact or in aerosol media (sneezing, etc.) could produce effects at least as dramatic as past epidemics. Evolution of an extremely virulent influenza virus (much as happened during World War I, Garrett 1994) could do likewise.2 Increased contact with pre-existing species of diseases and parasites is related to increasing population size and resulting expansion of geographic range. The evolution of new strains is also not unusual and is expected more often in species with short generation times. If new strains are arising at increasing rates, however, a different ecosystem-level change is in effect. The increasing numbers of treatment resistant strains and microorganism-based diseases may represent such a possibility (Garrett 1994). Their numbers and abilities to develop new forms threaten to overwhelm existing disease control efforts and certainly to increase the monetary costs of such “control” (Garrett 1994, Webster and Walker 2003). Significant changes in the world’s ecosystems are not only posing problems for humans, both as individuals and as a species, but are also problems for other species. Nearly all scientific evaluations of the current state of the world’s ecosystems express concern regarding observed changes (e.g., MEA 2005a,b, Turner et al. 1990, Union of Concerned Scientists 1992, Woodwell 1990a, World Conservation Monitoring Centre 1992). There is motivation for identifying the causes of the changes observed in the world’s ecosystems—specifically the human contributions wherein we have a modicum of control. One of the main contributors to observed ecosystem changes is, to most people, obvious: humans.3 Forests, grasslands, and steppes are modified for agricultural purposes by humans. Deforestation,

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overharvesting commercial fisheries, pollution, habitat destruction, and introduction of exotic species (especially pest species), are primarily human activities. The acidification of marine waters, global warming, acid rain, and human-related changes in ozone layers, in their impact on ecosystems are human-caused changes in the physical environment. Many changes in the marine environment are attributable to humans (Halpern et al. 2008) through overfishing, CO2 production, and pollution. Housing, roads, airports, cities, businesses, factories, irrigation canals, and other such manmade structures displace other species, reduce their geographic ranges and population sizes, and introduce gaps in their habitat and distribution. Coe (1980) suggests that man affects “ . . . more in Africa than animal distribution and abundance; he is disrupting and destroying entire habitats. . . . . It would be foolhardy to suggest that man has been solely responsible for the formation of the savannah, but it would not seem unreasonable to suggest that he has been the primary agency in the erosion of the forest and dense woodland margins and, therefore, has been a primary force in the extension of the Sahel zone”. Andrewartha and Birch (1984) indicate that “ . . . cause for extinctions seems to be the destruction of habitat by burgeoning populations of Homo sapiens”. Allen and Hoekstra (1992) say that “The nonequilibrial nature of the modern world is something that emerges when we try to describe ecological systems in terms of the old parts of the biosphere that existed before the human population explosion. . . management itself explicitly holds the system away from equilibrium”. Most human activities (i.e., consumption, waste production, occupation of space) are natural functions in any ecosystem as for all species. The problem is not the mere presence and activities of the human species. The problem is the abnormal magnitude of so many of these processes and, unavoidably, their effects. As a species, humans are (and human values maintain that we should remain) a natural part of ecosystem. This is a requisite of Management Tenet 1; but this tenet is not basis for ignoring Management Tenet 5—being an abnormal part is a problem. The magnitude of impacts are important to address,4 and cannot be dealt with

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independently of the population issue. As Ehrlich and Ehrlich (1990) and others5 have stressed, the magnitude of human impact includes the effects of: 1) human numbers and 2) the extent of per capita contributions. Both species- and individuallevel issues are involved. Life style, consumption rates, and technology contribute to determining the extent to which humans exert influence on their environments. Consumption is not alone; production of chemicals, pollutants, CO2, and waste are also involved but do not complete the list of such factors. Technology entails the expression of human tool-using capacities supported by energy, primarily from hydroelectrical sources, petroleum, and coal.6 Technology is not related simply to population numbers, but it would seem that certain intermediate population levels might support more per capita technology than either very small or very large ones.7 Regardless of the technological magnification, the more humans, the greater the total impact. The impact of simple numbers is increased by per capita influence in cultures with technology. Again, both the size of the human population and per capita elements are important. The concept that the human population may be too large and is one major contributing factor in environmental deterioration is nothing new in the minds of most scientists (Appendix 6.2) nor over human history (Costanza et al. 2007, Redman 1999). A variety of scientific organizations have published resolutions identifying the size of the human population as a basic problem. In 1970, for example, the American Society of Mammalogists (Am. Soc. Mamm. 1970) published a “Resolution on Population Growth”. In 1960, and again in 1991, the American Association for the Advancement of Science (AAAS) passed resolutions regarding overpopulation. The 1991 resolution states “ . . . growth of the human population contributes directly to human suffering throughout the planet, impedes sustainable economic development, increases international tensions, and exacerbates environmental degradation that endangers the survival of the human and many other species . . . ” (Lollar 1991). The Union of Concerned Scientists (1992) published a warning to humanity regarding the effects of overpopulation (Ehrlich and Daily 1993).

Calls for international response to the problem of overpopulation have been generated jointly by a variety of conservation associations, environmental organizations, and more specialized groups long concerned about the issue of overpopulation (Alper 1991). The examples of abnormality in this chapter include population size as one issue for management. Systemic management would not be complete without taking action that results in population reduction, although doing so directly may be less advisable, from a systemic perspective, than letting the systems of which we are a part function so as to result in a reduction. Similar standards would apply if we were concerned about underpopulation and the risk of extinction from too few individuals.

Notes 1. “McNeill’s law” refers to the disease facilitated spread of imperialistic peoples (Crosby 1986). Diseases such as smallpox, measles, typhus, scarlet fever, and venereal diseases reduced native populations to such low levels that there was little resistance to occupation of their lands by explorers. The Guanches of the Canary Islands appear to have been driven to extinction by imported diseases after their population increased beyond the capacity of the land to support them. Another example of such an extinction may have been the Arawaks of the West Indies. Other reductions in human populations have resulted from epidemics of endemic diseases. Black Death, a plague, may have killed one-third of the population of Europe during the mid-1300s, with death rates highest in crowded cities (Crosby 1986, Garrett 1994). In Norway and Iceland, two-thirds of the population may have died. The population of China declined by nearly 50% (see also, Catton 1980, Costanza 1995, Diamond 1986, Ehrenfeld 1993, Ponting 1991, Tainter 1988, and Yoffee and Cowgill 1988). 2. Perhaps of more concern is the risk that such things will happen by purposeful human design in biological warfare (or bio-terrorism exemplified in a number of novels published in the 1990s, and real-world events witnessed in the early part of the 21st century). 3. If there is any doubt concerning the impact attributable to humans, a reading of many of the references found in the bibliography of this book should be convincing (e.g., the appendices of this chapter, Appendix 4.2; Andrewartha and Birch 1984, Arizpe et al. 1994, Bloom

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1995, Brown 1995, Christensen et al. 1996, Coe 1980, Diamond 1984b, 1989, Diamond and Case 1986, Ehrlich 1986, Ehrlich and Ehrlich 1981, Ehrlich and Wilson 1991, Eldredge 1991, Freedman 1989, Gibbons 1993, Green and Sussman 1990, Gregg 1955, Karr 1994, Law et al. 1993, Lawton and May 1995, Mark and McSweeney 1990, Martin 1986, McMichael 1993, Meffe 1994, Meyer and Turner 1994, Owen-Smith 1988, Pimentel et al. 1992, Pimm 1991, Pimm et al. 1995, Policansky 1993a, Ponting 1991, Postel 1994, Potts and Behrensmeyer 1992, Rapport et al. 1985, Singh 1995, Sisk et al. 1994, Skole et al. 1994, Southwick 1985, Specht 1990, Trotter and McCulloch 1984, Turner et al. 1990, Union of Concerned Scientists 1992, Vitousek 1994, Woodwell 1990a, World Conservation Monitoring Centre 1992). 4. An analogy that may be useful here is that of an individual being a natural member of a family. Any individual born into a family is a natural member. If that individual murders the rest of the family, the magnitude of his or her actions and their effects are abnormal in regard to sustainable participation within the family. 5. Numerous works make repeated reference to the I = PAT of Ehrlich and Holdren (1974) in which I is the impact, P is the population, and AT is the per capita amplification of individual impact through affluence (A) and technology (T). See, for example, Kummer and Turner (1994), Mangel et al. (1996), and Smith (1995). Note that this both oversimplifies and underestimates the problem, at least in most cases, as there are a variety of dimensions over which impact can be measured and one, human caused extinction rates, shows evidence of being heavily non-linear (Appendix 6.6). 6. In the vein of selective extinction and speciation (Chapter 2) it is important to note that natural gas, petroleum, and coal are the remains of extinct species. Dependency on a species with high extinction risk is one thing, dependency on species that are already extinct is yet another. Other sources of energy, such as solar energy and hydroelectric power, are sources from the abiotic environment that can be used in ecosystem modification and maintenance that is not limited by the capacity of humans to ingest and undertake muscular work. This is a limit that other species experience in not having machines at their disposal and one of the major contributing factors to the structure and functioning of ecosystems (Fig. 1.4). For the most part, using sources of energy other than energy that is ingested, is highly abnormal for mammalian species of our body size especially when such sources involve nuclear energy. 7. For example, a human population of 500 individuals would, as individuals, spend most of their lives ensuring

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that they meet their basic needs (food and shelter). Little latitude would exist in such a population for specialization (e.g., to run computers, fly airplanes, manage fisheries, train physicians, publish books, administer universities, build satellites, construct solar energy collectors, or drill oil wells). To the extent that these relationships are more general, then, the per capita influence humans are capable of exerting shows an increasing relationship to population size. (Note that populations such as that of China have not realized the potential for per capita influence that have materialized in other countries.) There must be a maximum to this relationship beyond which human endeavor is increasingly channeled to deal with the negative impacts of excessive population (problems associated with physical and mental health, social upheaval and unrest, conflict over resource utilization, food production and distribution, and fighting diseases and parasites).

References Allen, T.F.H. and T.W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press, New York, NY. Alper, J. 1991. Environmentalists: ban the (population) bomb. Science 252: 1247. American Society of Mammologists. 1970. Resolution on population growth. Journal of Mammalogy 51: 856. Andrewartha, H.G., and L.C. Birch. 1984. The ecological web: more on the distribution and abundance of animals. University of Chicago Press, Chicago, IL. Arizpe, L., M.P. Stone, and D.C. Major (eds). 1994. Population and environment: rethinking the debate. Westview, Boulder, CO. Bloom, D.E. 1995. International public opinion on the environment. Science 269: 354–358. Boulter, M. 2002. Extinction: evolution and the end of man. Columbia University Press, New York, NY. Brown, J.H. 1995. Macroecology. University of Chicago Press, Chicago, IL. Catton, W.R., Jr 1980. Overshoot: The ecological basis of revolutionary change. University of Illinois Press, Chicago, IL. Christensen, N.L., A.M. Bartuska, J.H. Brown, et al. 1996. The report of the Ecological Society of America Committee on the scientific basis for ecosystem management. Ecological Applications 6: 665–691. Coe, M. 1980. African wildlife resources. In M.E. Soulé and M.A. Wilcox (eds). Conservation biology: an evolutionary-ecological perspective, pp. 273–302. Sinauer Associates, Sunderland, MA. Costanza, R. 1995. An unbalanced debate. Bioscience 45: 633–634.

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Crosby, A.W. 1986. Ecological imperialism: the biological expansion of Europe, 900–1900. Cambridge University Press, New York, NY. Curson, P. 1989. Paradise delayed: epidemics of infectious disease in the industrialized world. In J.I. Clarke, P. Curson, S.L. Kayastha, and P. Nag (eds). Population and disaster, pp. 157–179. Basil Blackwell, IGUCPG, Cambridge, MA. Diamond, J.M. 1984b. Historic extinctions: a Rosetta stone for understanding prehistoric extinction. In P.S. Martin, and R.G. Klein (eds). Quaternary extinctions: a prehistoric revolution, pp. 824–862. University of Arizona Press, Tucson, AZ. Diamond, J.M. 1986. The environmentalist myth. Nature 324: 19–20. Diamond, J.M. 1989. Overview of recent extinctions. In D. Western, and M. Pearl (eds). Conservation for the twenty-first century, pp. 37–41. Oxford University Press, New York, NY. Diamond, J.M. 2004. Collapse: how societies choose to fail or succeed. Viking Books, New York, NY. Diamond, J.M. and T.J. Case. 1986. Overview: introductions, extinctions, exterminations, invasions. In J. Diamond and T.J. Case (eds). Community ecology, pp. 65–79 Harper and Row, New York. Dowdle, W. 1993. The origins of plagues. Science 261: 1610–1611. Ehrenfeld, D.J. 1993. Beginning again: people and nature in the new millennium. Oxford University Press, New York, NY. Ehrlich, P.R. 1986. Extinction: what is happening now and what needs to be done. In D.K. Elliott (ed.). Dynamics of extinction, pp. 157–164. John Wiley and Sons, New York. Ehrlich, P.R. and A.H. Ehrlich. 1981. Extinction: the causes and consequences of the disappearance of species. Random House, New York, NY. Ehrlich, P.R. and A.H. Ehrlich. 1990. The population explosion. Simon and Schuster, New York, NY. Ehrlich, P.R. and E.O. Wilson. 1991. Biodiversity studies: science and policy. Science 253: 758–762. Ehrlich, P.R. and G.C. Daily. 1993. Science and the management of natural resources. Ecological Applications 3: 558–560. Ehrlich, P.R. and J.P. Holdren. 1974. Impact of population growth. Science 171: 1212–1217. Eldredge, N. 1991. The miner’s canary: unraveling the mysteries of extinction. Prentice Hall Press, New York, NY. Farnworth, E.G. and F.B. Golley. 1974. Fragile ecosystems: evaluation of research and application in the neotropics. Springer-Verlag, New York, NY.

Fowler, C.W. 2003. Tenets, principles, and criteria for management: the basis for systemic management. Marine Fisheries Review 65: 1–55. Fowler, C.W. and L. Hobbs. 2002. Limits to natural variation: implications for systemic management. Animal Biodiversity and Conservation 25: 7–45. Fowler, C.W., J.D. Baker, K.E.W. Shelden, P.R. Wade, D.P. DeMaster, and R.C. Hobbs. 1999. Sustainability: empirical examples and management implications. In Ecosystem approaches for fisheries management, pp. 305–314. University of Alaska Sea Grant, Fairbanks, Alaska, AK-SG-99-01. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Garrett, L. 1994. The coming plague: newly emerging diseases in a world out of balance. Farrar, Straus and Giroux, New York, NY. Georghiou, G.P. 1990. Overview of insecticide resistance. In M.B. Green, H.M. LeBaron, and W.K. Moberg (eds). Managing resistance to agrochemicals: from fundamental research to practical strategies, pp. 18–41. American Chemical Society, Washington, DC. Gibbons, A. 1993. Where are “new” diseases born? Science 261: 680–681. Green, G.M. and R.W. Sussman. 1990. Deforestation history of the eastern rain forests of Madagascar from satellite images. Science 248: 212–214. Gregg, A. 1955. A medical aspect of the population problem. Science 121: 681–682. Halpern, B.S., S. Walbridge, K.A. Selkoe, et al. 2008. A global map of human impact on marine ecosystems. Science 319: 948–952. Holt, S.J. and L.M. Talbot. 1978. New principles for the conservation of wild living resources. Wildlife Monographs 59: 5–33. Karr, J.R. 1994. Protecting aquatic ecosystems: clean water is not enough. In W.S. Davis, and T.P. Simon (eds). Biological assessment and criteria: tools for water resource planning and decision making, pp. 7–13. Lewis Publishers, Boca Raton, FL. Knoll, A.H. 1984. Patterns of extinction in the fossil record of vascular plants. In M.H. Nitecki (ed.). Extinctions, pp. 21–68 .The University of Chicago Press, Chicago, IL. Kummer, D.M. and B.L. Turner, II. 1994. The human causes of deforestation in Southeast Asia. Bioscience 44: 323–328. Law, R., J.M. McGlade, and T.K. Stokes (eds). 1993. The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991. (Lecture notes in biomathematics, 99). SpringerVerlag, Berlin.

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Lawton, J.H. and R.M. May (eds). 1995. Extinction rates. Oxford University Press, New York, NY. Lollar, C. (ed.). 1991. Inside AAAS. Science 252: 586–587. Mangel, M., L.M. Talbot, G.K. Meffe, et al. 1996. Principles for the conservation of wild living resources. Ecological Applications 6: 338–362. Mark, A.F. and G.D. McSweeney. 1990. Patterns of impoverishment in natural communities: case history studies in forest ecosystems-New Zealand. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 151–176. Cambridge University Press, New York, NY. Martin, P.S. 1986. Refuting late Pleistocene extinction models. In D.K. Elliott (ed.). Dynamics of extinction, pp. 107–130. John Wiley and Sons, New York. McCormick, F.J. 1999. Principles of ecosystem management and sustainable development. In J.D. Peine (ed.). Ecosystem management for sustainability: principles and practices illustrated by a regional biosphere reserve cooperative, pp. 3–21. Lewis Publishers, Boca Raton, FL. McMichael, A.J. 1993. Planetary overload; global environmental change and the health of the human species. Cambridge University Press, New York, NY. MEA (Millennium Ecosystem Assessment). 2005a. Ecosystems and human well-being: synthesis. Island Press, Washington, DC. MEA (Millennium Ecosystem Assessment). 2005b. Ecosystems and human well-being: biodiversity synthesis. World Resources Institute, Washington, DC. Meffe, G.K. 1994. Human population control: the missing awareness. Conservation Biology 8: 310–313. Meyer, W.B. and B.L. Turner (eds). 1994. Changes in land use and land cover: a global perspective. Cambridge University Press, New York, NY. Morse, S.S. 1993. Examining the origins of emerging viruses. In S.S. Morse (ed.). Emerging viruses, pp. 10–28. Oxford University Press, New York, NY. Morse, S.S. (ed.). 1993. Emerging viruses. Oxford University Press, New York, NY. Morse, S.S. (ed.). 1993. The evolutionary biology of viruses. Raven, New York, NY. Owen-Smith, R.N. 1988. Megaherbivores: The influence of very large body size on ecology. Cambridge University Press, New York, NY. Perkins, J.H. 1982. Insects, experts and the insecticide crisis: the quest for new pesticide management strategies. Plenum Press, New York, NY. Pimentel, D., H. Acquay, M. Biltonen, et al. 1992. Environmental and economic costs of pesticide use. Bioscience 42: 750–758. Pimentel, D., H. Acquay, M. Biltonen, et al. 1993. Assessment of environmental and economic costs of

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pesticide use. In D. Pimentel and H. Lehman (eds). The pesticide question: environment, economics, and ethics, pp. 47–84. Chapman & Hall, New York, NY. Pimm, S.L. 1991. The balance of nature? Ecological issues in the conservation of species in communities. The University of Chicago Press, Chicago, IL. Pimm, S.L., G.J. Russell, J.L. Gittleman, and T.M. Brooks. 1995. The future of biodiversity. Science 269: 347–350. Policansky, D. 1993. Fishing as a cause of evolution in fishes. In R. Law, J.M. McGlade, and T.K. Stokes (eds). The exploitation of evolving resources: Proceedings of an international conference held at Julich, Germany, Sept. 3–5, 1991, pp. 2–18. (Lecture notes in biomathematics, 99). Springer-Verlag, Berlin. Ponting, C. 1991. A green history of the world: the environment and the collapse of great civilizations. Sinclair-Stevenson, London. Postel, S. 1994. Carrying capacity: the Earth’s bottom line. In L.A. Mazur (ed.). Beyond the numbers, pp. 48–70. Island Press, Washington, D.C. Potts, R., and A.K. Behrensmeyer. 1992. Late Cenozoic terrestrial ecosystems. In A.K. Behrensmeyer, J.D. Damuth, W.A. DiMichele, R. Potts, H.-D. Sues, and S.L. Wing (eds). Terrestrial ecosystems through time: evolutionary paleoecology of terrestrial plants and animals, pp. 419–541. University of Chicago Press, Chicago, IL. Rapport, D.J., H.A. Regier, and T.C. Hutchinson. 1985. Ecosystem behavior under stress. American Naturalist 125: 617–640. Raup, D.M. 1984. Death of species. In M.H. Nitecki (ed.). Extinctions, pp. 1–19. The University of Chicago Press, Chicago, IL. Raup, D.M. 1989. The case for extraterrestrial causes of extinction. Philosophical Transactions of the Royal Society of London, Series B 325: 421–435. Raven, P.H. and J. Cracraft. 1999. Seeing the world as it really is: global stability and environmental change. In J. Cracraft and F.T. Grifo (eds). The living planet in crisis: biodiversity science and policy, pp. 287–298. Columbia University Press, New York, NY. Redman, C.L. 1999. Human impact on ancient environments. University of Arizona Press, Tucson, AZ. Simberloff, D. 1986. Are we on the verge of a mass extinction in tropical rain forests? In Elliott D.K. (ed.). Dynamics of Extinction, 165–180. John Wiley & Sons, New York, NY. Singh, S.P. 1995. India’s population problem, a global issue of environment and sustainability: an appeal for cooperation. Bulletin of the Ecological Society of America 76: 55–59.

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Sisk, T.D., A.E. Launer, K.R. Switky, and P.R. Ehrlich. 1994. Identifying extinction threats. Bioscience 44: 592–604. Skole, D.L., W.H. Chomentowski, W.A. Salas, and A.D. Nobre. 1994. Physical and human dimensions of deforestation in Amozonia. Bioscience 44: 314–322. Smith, C.L. 1995. Assessing the limits to growth. Bioscience 45: 478–483. Southwick, C.H. (ed.). 1985. Global ecology. Sinauer Associates, Sunderland, MA. Specht, R.L. 1990. Changes in the eucalypt forests of Australia as a result of human disturbance. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 177–198. Cambridge University Press, New York, NY. Tainter, J.A. 1988. The collapse of complex societies. Cambridge University Press, Cambridge. Trotter, M.M., and B. McCulloch. 1984. Moas, men and middens. In Martin, P.S., and R.G. Klein (ed.). Quaternary extinctions: a prehistoric revolution, pp. 708–727. University of Arizona Press, Tucson, AZ. Turner, B.L., II, W.C. Clark, R.W. Kates, J.F. Richards, J.T. Mathews, and W.B. Meyer (eds). 1990. The earth as transformed by human action; global and regional changes in the biosphere over the past 300 years. Cambridge University Press, New York, NY.

Union of Concerned Scientists. 1992. World scientists’ warning to humanity. Available UCS Headquarters, 26 Church Street, Cambridge, MA 02238 Vitousek, P.M. 1994. Beyond global warming: ecology and global change. Ecology 75: 1861–1876. Webster, R.G., and E.J. Walker. 2003. Influenza. American Scientist 91: 122–129. Whitmore, T.C. 1980. The conservation of tropical rain forest. In M.E.Soulé and M.A. Wilcox (eds). Conservation biology, an evolutionary-ecological perspective, pp. 303–318. Sinauer Associates, Sunderland, MA. Wilson, E.O. 1985. The biological diversity crisis. Bioscience 35: 700–706. Wilson, E.O. 1985. The biological diversity crisis: a challenge to science. Issues in Science and Technology 2: 20–29. Woodwell, G.M. (ed.). 1990. The earth in transition; patterns and processes of biotic impoverishment. Cambridge University Press, New York, NY. World Conservation Monitoring Centre. 1992. Global biodiversity. Chapman & Hall, New York, NY. Yoffee, N. and G.L. Cowgill (eds). 1988. The collapse of ancient states and civilizations. University of Arizona Press, Tucson, AZ.

Appendix 6.2

The following material is Appendix 6.2 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Overpopulation as contributing cause to environmental degradation The following table contains a sample of statements (primarily from the ecological/ecosystem literature) regarding human overpopulation or population growth seen as one of the main factors in the causes of altered (usually evaluated as degraded) ecosystems. Allen and Hoekstra (1992): “The nonequilibrial nature of the modern world is something that emerges when we try to describe ecological systems in terms of the old parts of the biosphere that existed before the human population explosion.”

Andrewartha and Birch (1984): “. . . cause for extinctions seems to be the destruction of habitat by burgeoning populations of Homo sapiens . . .”

Bateson (1972): “ . . . the more basic causes of the current rash of environmental troubles. The present testimony argues that these basic causes lie in the combined action of (a) technological advance; (b) population increase; and (c) conventional (but wrong) ideas about the nature of man and his relation to the environment.”

Brown and Maurer (1989): “Within the last few centuries the exponentially growing population of Homo sapiens has changed in the rules of resource allocation. Human beings currently use 20 to 40% of the solar energy that is captured in organic material by land plants . . . Never before in the history of the earth has a single species been so widely distributed

and monopolized such a large fraction of the energetic resources. An ever-diminishing remainder of these limited resources is now being divided among the millions of other species. The consequences are predictable: contraction of geographic ranges, reduction of population sizes, and increasing probability of extinction for most wild species; expansion of ranges and increased populations of the few species that benefit from human activities; and loss of biological diversity at all scales from local to global.”

L. Brown (1971): “ . . . I conclude that we have already, at some time in the past, exceeded our optimum population level in the United States.”

Catton (1980): “Famine in the modern world must be read as one of several symptoms reflecting a deeper malady in the human condition—namely, diachronic competition, a relationship whereby contemporary well-being is achieved at the expense of our descendants.” “We are already living in an overloaded world. Our future will be a product of that fact; that fact is a product of our past.” “Barring human extinction, there will never come an end to man’s need for enlightened self-restraint—the conservation ethic, as Leopold understood it.”

Christensen et al. (1996): “We must also address such daunting issues as human population growth, poverty, and human perceptions regarding the use of energy and natural resources.”

Cox (1993): “. . . conservation ecology has emerged because of a basic need: the human population stands on the verge of causing the massive extinction of species throughout the biosphere.”

Diamond (1989): “As regards the future, consideration of the main mechanisms of human-caused extinctions (over hunting, 113

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effects of introduced species, habitat destruction, and secondary ripple effects) indicates that the rate of extinction is accelerating. The basic reason is that there are now more humans than ever before, armed with more potent destructive technology and encroaching on the world’s most species-rich habitats: the continental tropical rain forests.”

Ehrlich (1980): “Three key assertions can be made about this growing human impact on the biosphere. First, unless these trends can be reversed, the most ingenious tactics on the part of the conservation movement will, at best, slightly delay an unhappy end to the biotic armageddon now underway. . . . . a non trivial consequence of the failure to reverse these trends will be the disappearance of civilization as we know it.” “Continued human population growth and conservation are fundamentally incompatible.” “It goes almost without saying that the conservation movement must join even more whole heartedly in the population control movement.”

Ehrlich (1985): “The human population must gradually be reduced to a size that can be sustained in the long-term with every body living a decent life. . . . the only way to go.”

Eldredge (1991): “But there is equally no doubt that our threat is the greatest one, at least over the short term: we seem to be able to affect more environmental change per unit of time than any other factor ever proposed as a cause for serious bouts of extinction, with the sole exception of the most catastrophic of bolide impact scenarios.” “It has been said thousands of times that it is our own unbridled growth—growth and utilization and exploitation of resources, leading to the most important aspects of all, growth of our own populations—that poses the greatest threat to the global ecosystem, and thus, ironically, to our own survival. High population numbers generally help insulate against extinction, but that is for species that have remained integrated into a variety of different local ecosystems.”

Freedman (1989): “ . . . the size of the human population remains a root cause of the degradation of our environment.”

Jenkins (1985): “ . . . human species is lurching and stumbling toward a biological catastrophe of the first order.”

Koshland (1992): “ . . . the environment is threatened by a population growth that is proceeding largely unchecked.”

Lollar (1991): In 1960 the AAAS board supported a statement on overpopulation signed by Nobel laureates and others for submission to the United Nations calling for international action: The “ . . . AAAS is concerned that continued rapid growth of the human population contributes directly to human suffering throughout the planet, impedes sustainable economic development, increases international tensions, and exacerbates environmental degradation and endangers the survival of the human and many other species; . . . .”

Mangel et al. (1996): “Maintenance of healthy populations of wild living resources in perpetuity is inconsistent with unlimited growth of human consumption of and demand for those resources.” “It is almost certain that the only practicable way to reduce human per capita resource demand is to stabilize and then decrease the human population.”

May (1990): “Human activities are destroying natural habitats, and the associated biota, at rates that are probably without precedent in the history of life on Earth.” “The scale and scope of human activity are now so large that they rival the natural processes that created and maintained the biosphere as a place where life can flourish.” “The clock ticks faster and faster as human numbers continue to grow, and each year 1–2% of the tropical forests are destroyed.”

Odum (1972): “Controlled management of the human population together with the resources and the life support system on which it depends as a single, integrated unit now becomes the greatest, and certainly the most difficult, challenge ever faced by human society.”

Paddock (1971): “Based on the limitations of our agriculture, too many people now live in the United States. Our optimum population size is, therefore, less than our current 205 million people.”

Pimentel and Dodds (1999): “Environmental quality and Earth’s capacity to support people will only diminish given current trends in both per capita resource use and human population growth.”

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Pimentel, Stachow et al. (1992): “However, with the escalation of human numbers, the movement of humans into wild areas, and industrialization, a decline in species diversity . . . is associated with the destruction of ecosystems.” “These trends are accelerated by the ever burgeoning rates of human population growth: a quarter-million humans added each day to the world’s population of 5.3 billion . . . ” “ . . . In developed countries the use of natural resources may be 100- to 600-fold more per capita than in developing countries.” “ . . . Humans have destroyed approximately 44% of the world’s tropical forests . . . ” “The deterioration of current agriculture land, combined with the increasing population, results in approximately 15 million ha of new agriculture land being needed each year to satisfy human food needs.”

Pimm (1991): “The expected catastrophic extinction of species (already well underway in many places) will alter the planet’s biological diversity so profoundly that, at known rates of speciation, it will take millions of years to recover it.” “ . . . I predict that there will be at least 10 billion [humans], dying from many causes each of which is orders of magnitude more important than the genetic causes the human genome sequencing will uncover. If we do not understand ecological processes better than at present, these 10 billion humans will be destroying our planet more rapidly than we are now.”

Pimm and Gilpin (1989): “The human species needs desperately to find a new way to navigate its ship of technology and population. One problem is that we are drowning too many other species in our wake.”

Risser et al. (1991): “Disturbing examples of environmental problems around the world lead to the unescapable conclusion that human activities have begun to threaten the ability of Earth to support even current human life-styles. . . . A few years ago, statements that Earth’s ability to sustain human populations as threatened might have been dismissed as unsubstantiated assertions from pessimistic, emotion-driven environmentalists. Now, however, that conclusion comes from the broader scientific community.” “If Earth’s ability to support both humans and natural functions of the biosphere is in jeopardy, then there is no higher priority for the attention of society.”

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Rosenzweig (1974): “Overpopulation and ecological insanity are not likely to produce the total extinction of man. Instead, the result will be treatable calamity: massive increase in famine and disease coupled with destruction of our way of life and its replacement by a sparse, bleak, marginal existence in which disease and deprivation will culminate in a permanently higher death rate—especially among infants and children. Surely most people will agree that is worth avoiding.” “We do not predict overpopulation; we are observing it.”

Singer (1971): “We have by far exceeded an optimum level of population.”

Southwick (1985): “ . . . by 2000 the world’s human population may be within only a few generations of reaching of the entire planet’s carrying capacity.”

Soulé and Wilcox (1980b): “A green mantle of earth is now being ravaged and pillaged in a frenzy of exploitation by a mushrooming mass of humans and bulldozers.”

Talbot, L.M. (2008): “ . . . the exponential growth of human numbers has brought a corresponding exponential rise in environmental impacts which, in turn have been amplified by increasing technology.”

Tudge (1989): “Our population cannot continue to expand at its present rate for much longer, and the examples of many other species suggest that expansion can end in catastrophic collapse.” “Survival beyond the next century in a tolerable state seems most unlikely unless all religions and economies begin to take account of the facts of biology. This, if it occurred, would be a step in cultural evolution that would compare in import with the birth of agriculture.” “Human numbers are, of course, staggering. There is an ecological law—a simple extrapolation of bedrock physics which says that large, predatory animals are rare. We break that law . . . “

Whitmore (1980): “Man’s dependence on other organisms and especially upon plants is such that unless this attack on

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them is moderated, man’s own continued existence is threatened.”

Whittaker (1975): “The Malthusian problem has not . . . been escaped, but delayed, changed in implication, and probably intensified. . . . the effects of overgrowth are now being felt by world society, . . . .”

Woodwell (1990a): “The earth’s complement of living systems is being reduced now more rapidly than at any time previously by the spread of human influences. The changes are global; no part of the earth is unaffected, no natural or human-dominated community immune.”

Woodwell (1990b): “The cause of the disruption is a single species, Homo sapiens, which has escaped the normal limitations that keep the numbers of individuals of each species in check and has swarmed over the earth as no species has ever done previously. Homo has also succeeded in developing the capacity to turn other species and countless things into resources that favor the further expansion of the populations of Homo. The effect is a series of drastic changes in the biosphere that threaten all life.”

References cited Allen, T.F.H. and T.W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press, New York, NY. Andrewartha, H.G. and L.C. Birch. 1984. The ecological web: more on the distribution and abundance of animals. University of Chicago Press, Chicago, IL. Bateson, G. 1972. Conscious purpose versus nature. In G. Bateson (ed.) Steps to an ecology of mind, pp. 426–439. Chandler Publishing Co., San Francisco, CA. Brown, J.H. and B.A. Maurer. 1989. Macroecology: the division of food and space among species on continents. Science 243: 1145–1150. Brown, L.R. 1971. Food supplies and the optimum level of population. In S.F. Singer (ed.). Is there an optimum level of population?, pp. 72–88. McGraw-Hill Publishing Co., New York, NY. Catton, W.R., Jr 1980. Overshoot: The ecological basis of revolutionary change. University of Illinois Press, Chicago, IL. Christensen, N.L., A.M. Bartuska, J.H. Brown, et al. 1996. The report of the Ecological Society of America Committee on the scientific basis for ecosystem management. Ecological Applications 6: 665–691.

Cox, G.W. 1993. Conservation ecology. Wm. C. Brown Publishers, Dubuque, IA. Diamond, J.M. 1989. Overview of recent extinctions. In Western, D. and M. Pearl (eds). Conservation for the twenty-first century, pp. 37–41. Oxford University Press, New York, NY. Ehrlich, P.R. 1980. The strategy of conservation, 1980–2000. In M.E. Soulé and M.A. Wilcox (eds). Conservation biology, an evolutionary-ecological perspective, pp. 329–344. Sinauer Associates, Sunderland, MA. Ehrlich, P.R. 1985. Ecosystems and ecosystem functions: implications for humankind. In R.J. Hoage (ed.). Animal extinctions; what everyone should know, pp. 159–173. Smithsonian Institution Press, Washington, DC. Eldredge, N. 1991. The miner’s canary: unraveling the mysteries of extinction. Prentice Hall Press, New York, NY. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Jenkins, R.E. 1985. The identification, acquisition, and preservation of land as a species conservation strategy. In R.J. Hoage (ed.). Animal extinctions: what everyone should know, pp. 129–145 . Smithsonian Institution Press, Washington, DC. Koshland, D.E. 1992. The dimension of the brain. Science 258: 199. Lollar, C. (ed.). 1991. Inside AAAS. Science 252: 586–587. Mangel, M., L.M. Talbot, G.K. Meffe, et al. 1996. Principles for the conservation of wild living resources. Ecological Applications 6: 338–362. May, R.M. 1990. How many species? Philosophical Transactions of the Royal Society of London, Series B 330: 293–304. Odum, E.P. 1972. Ecosystem theory in relation to man. In J.A. Wiens (ed.). Ecosystem structure and function, pp. 11–24. Oregon State University Press, Eugene, OR. Paddock, W.C. 1971. Agriculture as a force in determining the United States’ optimum population size. In S.F. Singer (ed.). Is there an optimum level of population?, pp. 89–95. McGraw-Hill Publishing Co., New York, NY. Pimentel, D. and W. Dodds. 1999. Human resource use, population growth, and environmental destruction. Bulletin of the Ecological Society of America 80: 88–91. Pimentel, D., U. Stachow, D.A. Takacs, et al. 1992. Conserving biological diversity in agricultural/ forestry systems: most biological diversity exists in human-managed ecosystems. Bioscience 42: 354–362. Pimm, S.L. 1991. The balance of nature? Ecological issues in the conservation of species in communities. The University of Chicago Press, Chicago, IL. Pimm, S.L. and M.E. Gilpin. 1989. Theoretical issues in conservation biology. In J. Roughgarden, R.M. May,

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and S.A. Levin (eds). Perspectives in ecological theory, pp. 287–305. Princeton University Press, Princeton, NJ. Risser, P.G. (ed.). 1991. Long-term ecological research: an international perspective. Scope 47. John Wiley & Sons, New York, NY. Rosenzweig, M.L. 1974. And replenish the earth: the evolution, consequences, and prevention of overpopulation. Harper and Row, New York, NY. Singer, S.F. 1971. Is there an optimum level of population? McGraw-Hill Publishing Company, New York, NY. Soulé, M.E. and B.A. Wilcox. 1980. Conservation biology: its scope and its challenge. In M.E. Soulé and M.A. Wilcox (eds). Conservation biology, an evolutionary ecological perspective, pp. 1–8. Sinauer Associates, Sunderland, MA. Southwick, C.H. (ed.). 1985. Global ecology. Sinauer Associates, Sunderland, MA. Talbot, L.M. 2008. Introduction: the quest for environmental sustainability. In L.L. Rockford, R.E. Stewart, and T. Dietz (eds). Foundations of environmental

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sustainability, pp. 3–24. Oxford University Press, New York, NY. Tudge, C. 1989. The rise and fall of Homo sapiens sapiens. Philosophical Transactions of the Royal Society of London, Series B 325: 479–488. Whitmore, T.C. 1980. The conservation of tropical rain forest. In M.E. Soulé and M.A. Wilcox (eds). Conservation biology, an evolutionary-ecological perspective, pp. 303–318. Sinauer Associates, Sunderland, MA. Whittaker, R.H. 1975. Communities and ecosystems (2nd ed.). McMillan Publishing Co., New York, NY. Woodwell, G.M. 1990. The earth under stress: a transition to climatic instability raises questions about patterns of impoverishment. In G.M. Woodwell (ed.). The earth in transition; patterns and processes of biotic impoverishment, pp. 3–8. Cambridge University Press, New York, NY. Woodwell, G.M. (ed.). 1990. The earth in transition; patterns and processes of biotic impoverishment. Cambridge University Press, New York, NY.

Appendix 6.3

The following material is Appendix 6.3 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Conventional assessment of human population size Attempts that have been made to estimate the magnitude of overpopulation have been based primarily on simplistic approaches that consider isolated subsets of the factors actually involved in the systemic determination of population size within ecosystems and the biosphere. Such approaches are exemplified by consideration (e.g., conscious thought, models, or meetings) of factors such as resource requirements, space, food, water, and soils, usually individually, sometimes in small sets, but never in a way that exhausts the complete list to include those things about which we do not yet know. Examples of such approaches are compiled in Table 6.11 and Figure 6.20 (from Cohen 1995b).2 As is obvious, the perceived degree of overpopulation varies. The FAO estimate in Table 6.1 indicates that the human population of the earth is roughly half of sustainable levels (“overpopulation” index of about 0.6). Cohen (1995b) found even more extreme estimates. These include a 1978 estimate indicating one trillion people are supportable through technology; another was an estimate indicating that a billion billion people could coexist on this planet! The human population in the early 1980s was about 13 times too large according to Catton (1980).3 The more extreme estimates of overpopulation are those attributed to Soulé by Tudge (1989) in which the earth was evaluated to have 54 times too many humans in the mid 1980s and that of Walker (1984), in which humans were 118

estimated to be overpopulated by a factor of 206 (by considering humans as carnivores that would occupy particular habitat types). The mean index of overpopulation from Table 6.1 is 18.6 (indicating that there are 18.6 times more people than are sustainable). In contrast, the mean estimated sustainable human population for the earth is about 2.01 million. In other words this collection of scientific opinion would indicate that an optimal human population would be between 5% and 35% of recent numbers (about 6 billion). In contrast we see a different assessment in the mean of estimates of the earth’s carrying capacity for humans represented by Figure 6.20 (55.02 billion, based on the sampling of the literature from Cohen 1995b). This would indicate that the world is not overpopulated yet, and our population could continue to grow almost tenfold and still be sustainable. There is a difference of note between these two sets of assessments. It is the degree to which estimates in Table 6.1 are based on consideration of combinations of ecological factors (or simple systems approaches) while Figure 6.20 is based primarily on consideration of single factors (in few cases are more than two or three factors considered simultaneously) or single species population models for humans—often simple projections based on recent history. Approaches based on many population models involve the assumption that the human population will find its own natural carrying capacity; they assume that because the population is growing it must be below carrying capacity.4 Lag effects and the complexity of systems with their long-term lag effects in evolutionary feedback are not considered. Most important in considering this information collectively, is the degree to which conventional applications of science gives rise to variety in estimated optimum level for the human population.

APPENDIX 6.3

Conventional approaches have not been particularly helpful in providing advice in dealing with what is clearly considered a problem both because of variance (uncertainty) and results inconsistent with measures of the symptoms of what is perceived as a major contributing problem.

1.1 Symptomatic measures Most evaluations of human overpopulation examine symptoms (e.g., the variety of changes observed in ecosystems, including evolution of pesticide resistant pests, habitat loss, etc., Appendix 6.1). Individually and systemically, each of these observed phenomena accounts for the synergistic nature of combined effects (including overpopulation, belief systems, evolution; Belgrano and Fowler 2008) leading to its origin. However when considered as an index of overpopulation, this set of factors replaces the set of factors accounted for in population size itself—the set of factors to be accounted for in evaluation of population size and its sustainable levels. Even so, such approaches lead to the understanding that solutions to overpopulation will involve constraints on individuals as part of the solution to the problem (Holling and Meffe 1996). For direct (consonant) evaluation, the factors involved in these constraints need to be compared themselves to the corresponding patterns among individuals (both within and among species). A very important point here is that observation of any problem (abnormality), symptomatic of overpopulation or not, leads to the addressing of a multitude of real management questions as we trace known or suspected interconnections (Appendix 5.2). Growth, rather than magnitude, is often identified as the main problem (see Appendix 6.2) in many attempts to characterize overpopulation. The Earth’s human population was experiencing a growth rate of about 3.1 people per second in 1993. This translates to the population of the United States being added every 31 months. Growth is clearly a problem if the population is already too large. In other words, in correlative patterns, the fact that our population is growing is abnormal given the extent to which our population is itself too large in comparison to that of other species

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(Fig. 5.3, Fowler 2008). Population growth rates are important when the management question is “At what rate can a population grow sustainably?”— the answer to which is, of course, 0.00. The question being addressed here, however, is “What is a sustainable population for our species?” It is population size that is of concern. If the human population were 500 individuals and growing at 4% per year, it would be normal for the circumstances. There would be little effect compared to current effects of a world population numbering over 6 billion people and growing by about 3% per year. The number of people currently added each second could also be achieved with a human population of 146 billion growing at 0.1% per year. If growth were the only problem, a zero growth rate and a population of 146 billion would be of no concern. Obviously, such is not the case.5 If the mountain gorilla population were 6.6 billion and there were 800 humans to pass judgment on the gorillas, the consensus would surely be that the gorillas represented a pestilence of major proportions. Direct treatment of human population size helps guide attention to its contribution to the magnitude of related impacts, each of which would be reduced if the human population were reduced.6 Several are shown in this chapter; another is the production of fecal material. At the current size of the world population, fecal material is produced by humans at the rate of 12 cubic meters per second (over 300 cubic feet per second).7 There are about 133 billion kg of human biomass (146 million tons—133 million metric tons: Fig. 6.25) that respire about 4.2 million tons (3.8 × 109 kg, Fig. 6.14) of carbon dioxide each day. Thus the “size” of the human species translates to products and processes that have innumerable ecological effects. These include extraction of coal and oil, maintenance of agricultural areas for raising food and producing fiber, harvesting of living resources, and occupation of space for these activities. But the ecological complexity of net effects and long-term implications are not considered by these indices without evaluating them with the directly corresponding (consonant) information on limits to natural variation—asking individual management questions and using the corresponding consonant pattern in each case.

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1.2 Human density Knowing the numbers and density of humans is important, but this information cannot be evaluated without normative frames of reference. The following is a brief presentation of human population data used in this chapter in applying normative information derived from observed variation in the density of other species. This is information that is consonant with the management question: “What is a sustainable density for the population of Homo sapiens?” To determine human population density, one must consider habitable area. In the approach that follows it is assumed that suitable human habitat can be found in 20% of the earth’s terrestrial surface—an unrealistically large portion (see the discussion regarding Fig. 6.198). The total human population for the earth was estimated at about six billion at the turn of the century (6.6 billion in 2007; Population Reference Bureau, Inc. 2007). The surface of the earth is about 0.51 billion square kilometers (e.g., Whittaker 1975, World Almanac 1998). Thus, the average density of humans, if spread over the entire planet, was about (11.8) humans per square kilometer in 1996. However, the earth is only about 20% land (excluding Antarctica), so the mean density of humans on dry land was 55.7 per square kilometer (about 1.3 hectares or 4.2 acres per person). However, not all land on the earth’s surface can sustain humans. As mentioned above, only about one-fifth of the earth’s land might be considered suitable for agricultural purposes,9 ignoring the question of whether or not agriculture is a sustainable practice. This is land of the quality that can meet the needs for human habitation, complete with the production of food. Thus, spread over the land assumed suitable for human habitation, the human population has a density of over 288 per square km. This amounts to less than 0.35 hectares (about 0.9 acre) per person. Such densities are abnormal (Fig. 6.21).

1.3 Normative information from historic population levels Historical population levels can be considered indicative of typical circumstances for a species like

humans. Comparison to historic levels involves the consonance of population to population comparisons. It is also, however, an application of the self-referential approach to assessment (considered in Chapter 4) and is consistent with evaluation as applied to individuals, species, or ecosystems in general with risk that there is systemic abnormality on a larger scale. Following this approach, nevertheless, the state of a system at any one time can be compared to historical states. More realistic comparisons involve other systems of the same kind and at the same level of biological organization (individuals, species, or ecosystems). Comparisons of the latter kind were carried out in the main body of the text of this chapter. Human influence on ecosystems has increased over time (Talbot 2008). In the last 20,000 years of human history, what are considered “primitive” modes of life such as hunter-gathering were transformed to systems largely dependent on energy from outside the living ecosystem (Mannion 1991, Ponting 1991). The early influence of humans on ecosystems depended on energy restricted to sources within the living ecosystem (e.g., food humans consumed). The use of fire brought with it a significant increase in the ability to change ecosystems—to maintain them in states somewhat different from what would be expected with fires originating only from nonhuman sources and an early example of human ingenuity contributing to abnormal conditions. The energy used for practices such as cooking and firing pottery was derived from living ecosystems with a dependence on specific species (primarily woody plants). Domestication of beasts of burden and draft animals (6–12,000 years ago) brought about more directed use of energy but still from within ecosystems—a form of interspecific dependence that has its own natural limits. Eventually, human-induced changes in ecosystems were based on energy from outside the living ecosystem (i.e., noningested sources of energy such as water power, nuclear power, coal, and oil) and the technology that magnifies the impact of humans on ecosystems today. With energy from outside living ecosystems, humans have temporarily escaped many limits, especially the short-term limitation from complete dependence on ecosystems (Catton 1980). Much of

APPENDIX 6.3

today’s energy use is focused on maintaining ecosystems in abnormal states for the production of food: pesticides, fertilizers, cultivation, irrigation, and protection of monocultures of various kinds. Other technological advances have provided temporary reprieve from the limiting effects of diseases and predation (e.g., see Ausubel 1996), again with abnormal production, use, and effects. Human numbers and, thereby, their increasing influences, are not presently limited to historic levels by dependence on, and limitations of, the ecosystems of which they are a part. However, the dependence on extinct species (oil, gas, and coal) guarantees long-term consequences because these resources are not renewable in the time scales they are being used (Raven and Cracraft 1999). Being dependent on a species that may go extinct is one problem, being dependent on species that are already extinct is yet another. The concept of carrying capacity (the number of individuals of a particular species that are sustainable) is a matter of balance expressed as tendencies within population variation. Such variation is limited and limited to be expressed as patterns determined by the full suite of factors involved. The list of these factors is immense (infinite, Fig. 1.4) but includes the positive effects of resources and other services provided by the ecosystem and the negative effects of consumer species, diseases, parasites, and the limits to renewable resources—especially in sustainable competition with other consumers. There is tradeoff between the ability of the environment to support and to limit the population of any particular species. These tradeoffs include the effects of a species on its environment and the responses among the supporting and limiting processes. Included are all interactions among species, being more supportive at low population levels and more suppressive at high population levels (and the resulting phenomenon of density dependence). Species fall into at least two groups depending on their sources of energy. Lithotrophic species live independently of light (microbial species that may represent more biomass and species than others; Pace 1997) while phototrophic species (of which humans are one) depend on light via the photosynthetic process. Humans, primary producers, and lithotrophic species have energy sources

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that do not depend on biological sources (ingestion). Humans, as a species evolved to ingest the bulk of their energy, have now subsidized their supplies with energy from sources outside living ecosystems—an abnormal situation. Naturally occurring populations vary, and the carrying capacity of a wildlife population is the mean population level, determined over a period of time10 under normal natural circumstances (i.e., undisturbed by petro/technologically supported human influence that is itself outside the normal range of natural variation). The carrying capacity is a frame of reference against which to evaluate current population levels. Owing to complexity we are unable to evaluate ecosystems (e.g., a species’ relationships with other species) to estimate either historic or current carrying capacity for any particular species. In lieu of such an estimate, historic population levels (or population levels free of abnormal human impact in systems largely recovered from such influences) can be assumed to represent the carrying capacity under normal circumstances; they reflect an integration of all prevailing factors to serve as a measure of carrying capacity in the absence of abnormally high human impacts (e.g., harvests, CO2 production, pollution). Consistent with species seen as Monte Carlo or Bayesian estimators (themselves capable of change, see Chapters 4 and 5), historic population levels are used as frames of reference (as has been done in assessing marine mammal populations for classification as depleted by the US National Marine Fisheries Service under terms of the Marine Mammal Protection Act (16 U.S.C. 1361(6)). A very similar approach has been used to assess whale populations by the scientific committee of the International Whaling Commission (IWC 1977). Such an approach is a means for assessing the population of any species. Current population levels are compared to levels prior to the pervasive influence of human numbers and their technology. Humans evolved in the same ecosystems as other species and must be regarded as subject to the same kinds of criteria for evaluation and assessment as any other species. One way of approaching the evaluation of population is to compare the size of the current human population with historical

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human populations under conditions more like those in which our species evolved. Hassan (1981) presents a compilation of information relating to the density of humans under conditions of prehistorical hunter-gatherer societies. The mean population density for hunting and gathering peoples from Hassan’s compilation is 0.672 humans per square kilometer with a minimum of 0.01 and a maximum of 9.5 (standard deviation of 0.820). The current estimated human density of 288 per spare km is 428 ( = 288/0.672) times higher than the mean from Hassan’s work. These density estimates are for habitats specifically known to support humans and do not include areas that have been modified agriculturally. Taking the approach outlined above, 428 serves as a measure of overpopulation; a human population of about 14 million would be more likely to be sustainable than the current six billion. But this assumes that humans at such a population level, with current standards of living, would have no more influence on their environment than did hunter-gatherers. Another approach to estimating overpopulation factors is to compare simple human population numbers rather than densities. Today’s human population of over 6 billion is more than 1000 times larger than the estimated population of about 5 million 10,000 years ago (ranges from 2–20 million, Catton 1980, Coale 1974, Cohen 1995b, Dumond 1975, Ehrlich 1995, Freedman 1989, Hassan 1981, Santos 1990).

Notes 1. In their initial form, some of these estimates were expressed as average density (regardless of location); others were for total numbers. Some were estimates for specific areas (such as the United States). In cases where estimates were for specific areas, it was assumed that similar densities would apply worldwide. 2. The data used here do not include one estimate of one billion billion and several for which there was only a bound (either lower or upper but not both). When ranges were presented in the original literature, the midpoint was used. 3. This is not a direct estimate but an interpretation of Catton’s work, i.e., derived from information he provides. 4. As pointed out by Catton (1980), what we are seeing in human population growth, however, is analogous to

outbreaks by pest species. Following further destruction of the environment the population is likely to decline rapidly for combinations of reasons—some given in various parts of this chapter. To use some of the simple population models (e.g., logistic) fitted to the data for population growth of humans over this eruption is similar to fitting the same model to data for the populations of pests confined to the eruptive phase of their population growth. We must deal with the risk that a systemic reaction will result in a catastrophic decline if we do not take action soon enough (Pimentel and Dodds 1999). 5. This is not meant to deny the fact that growth makes the problem worse. See the parts of this chapter where growth is a species-level characteristic that is also abnormal. This exemplifies the importance of consonance, or correct match, between management question and empirical pattern. 6. This raises the matter of objectivity. Some of these changes are changes that humans would judge to be positive, others would be seen as negative. Such evaluations as basis for decision-making are a perpetuation of conventional management/thinking and fail to find sustainability free of the potential for being an evolutionary dead-end (or on the path of evolutionary suicide, Table 3.1). 7. This can be compared to the mean discharge of the following rivers (country and discharge in cubic meters/ second in parentheses, Showers 1979): Itapicuru (Brazil, 17), Flinders (Queensland, Australia, 16), Castlereagh (New South Wales, Australia, 8), Grand (South Dakota, USA, 8), Souris (Manitoba, Canada, 13), South Platt (Nebraska, USA, 6), Powder (Montana, USA, 17), White (South Dakota, USA, 15), Little Missouri (North Dakota, USA, 16), and Gudena (Denmark, 16). 8. This value (about 20 million square kilometers) is chosen only for illustrative calculations. By comparison, as explained in other parts of this chapter, other species of human body size occupy less than 6% of the earth’s terrestrial surface on average. It is also a very generous area, as pointed out in the section dealing with geographic range directly (Fig. 6.19), when using information on the limits to natural variation among all mammalian species as a basis for evaluation. On more conventional grounds, of course, there are a variety of ways to evaluate the lands humans would be able to occupy. Lands are being opened up for human use in the tropics while others are being overused and abandoned. According to Ponting (1991) “ . . . about 11% of the world’s surface is now used for growing crops and there is little land left suitable for agriculture. . . . About a quarter of the world’s surface has been taken over for

APPENDIX 6.3

grazing animals and although arable land could extend into this area the net increase in food production would be small . . . ”. Sisk et al. (1994) indicate that 13.6 percent of the terrestrial portions of the earth are used in agriculture and that about 5% is grasslands. Ehrlich and Ehrlich (1996) say that the Food and Agriculture Organization has classified over a third of the land surface of the earth as cropland or permanent pasture. Pimentel et al. (1992) indicate that about 50% of the area of the earth’s land is under agricultural management. Whittaker (1975) indicates that temperate grass lands and cultivated land comprise 9 million and 14 million square kilometers, respectively. 9. Keep in mind that the exercise here is one of conventional approaches estimating range size. Thus it ignores the information on the limits to natural variation in geographic range size among species. Progress in conventional thinking would recognize that land such as that covered by glaciers and deserts cannot support humans without immense energy expenditure. The amount of land that is habitable is debatable (and would be in conventional thinking, see previous endnote). The World Conservation Monitoring Centre (1992) indicates that the total of all forests, woodlands, grasslands, pastures, and croplands is less than 25% of the earth’s land surface (a total of 24.7 million square kilometers). Using one-fifth of the earth’s land area as hospitable to humans seems generous (it is twice the amount under cultivation as reported by Whittaker in 1975) in that not all of the forests and woodlands would be as habitable as the grass and croplands. Using such a number oversimplifies the adaptability of humans but is a conventional attempt to strike a balance between the low densities of such areas with the higher densities supportable in cropland areas. This point, and the debate it involves, are moot when range size itself is considered directly (Fig. 6.19, again the importance of consonance). 10. This needs to be a period of time sufficient to capture the range of fluctuation to avoid randomly measuring a population at levels that are low or high relative to the mean, and to integrate the effects of positive factors involving food and space, and the negative factors of limited food and space along with those of predators, pests, diseases, and parasites. Carrying capacity varies with time but shows a pattern exemplified by the relationship between density and body size (Fig. 6.21).

References Ausubel, J. 1996. Can technology spare the earth? American Scientist 84: 166–178.

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Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In S.I. Munoz (ed.). Ecology Research Progress, pp. 5–31. Nova Science Publishers, Hauppauge, NY. Catton, W.R., Jr 1980. Overshoot: The ecological basis of revolutionary change. University of Illinois Press, Chicago, IL. Coale, A.J. 1974. The history of the human population. Scientific American 231: 40–51. Cohen, J.E. 1995b. How many people can the earth support? Norton, New York, NY. Dumond, D.E. 1975. The limitation of human population: A natural history. Science 187: 713–721. Ehrlich, P.R. and A.H. Ehrlich. 1996. Betrayal of science and reason. Island Press, Washington, DC. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Hassan, F.A. 1981. Demographic archeology. Academic Press, New York, NY. Holling, C.S. and G.K. Meffe. 1996. Command and control and the pathology of natural resource management. Conservation Biology 10: 328–337. IWC (International Whaling Commission). 1977. Report of the sperm whale meeting. Report of the International Whaling Commission 27: 240–252. Mannion, A.M. 1991. Global environmental change. Longman Scientific and Technical, Essex, England. Pace, N.R. 1997. A molecular view of microbial diversity and the biosphere. Science 276: 734–740. Pimentel, D. and W. Dodds. 1999. Human resource use, population growth, and environmental destruction. Bulletin of the Ecological Society of America. 80: 88–91. Pimentel, D., H. Acquay, M. Biltonen, P. Rice, M. Silva, J. Nelson, V. Lipner, S. Giordano, A. Horowitz, and M. D’Amore. 1992. Environmental and economic costs of pesticide use. Bioscience 42: 750–758. Ponting, C. 1991. A green history of the world: the environment and the collapse of great civilizations. SinclairStevenson, London. Population Reference Bureau. 2007. 2000 world population data sheet. http://www.prb.org/pdf07/07WPDS_Eng.pdf Raven, P.H. and J. Cracraft. 1999. Seeing the world as it really is: global stability and environmental change. In J. Cracraft and F.T. Grifo (eds). The living planet in crisis: biodiversity science and policy, pp. 287–298. Columbia University Press, New York, NY. Santos, M.A. 1990. Managing planet earth: perspectives on population, ecology, and the law. Bergin and Garvey, New York, NY. Showers, V. 1979. World facts and figures. John Wiley and Sons, New York, NY.

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Sisk, T.D., A.E. Launer, K.R. Switky, and P.R. Ehrlich. 1994. Identifying extinction threats. Bioscience 44: 592–604. Talbot, L.M. 2008. Introduction: the quest for environmental sustainability. In L.L. Rockford, R.E. Stewart, and T. Dietz (eds). Foundations of environmental sustainability, pp. 3–24. Oxford University Press, New York, NY. Tudge, C. 1989. The rise and fall of Homo sapiens sapiens. Philosophical Transactions of the Royal Society of London, Series B 325: 479–488.

Walker, A. 1984. Extinction in Hominid evolution. In M.H. Nitecki (ed.). Extinctions, pp. 119–152. The University of Chicago Press, Chicago, IL. Whittaker, R.H. 1975. Communities and ecosystems (2nd ed.). McMillan Publishing Co., New York, NY. World Almanac. 1998. The world almanac and book of world facts, 1994. St. Martins Press, New York, NY. World Conservation Monitoring Centre. 1992. Global biodiversity. Chapman and Hall, New York, NY.

Appendix 6.4

The following material is Appendix 6.4 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 The human population evaluated by interspecific comparisons Information on the limits to natural variation among nonhuman species that can be used to assess the human population measured either in terms of density or total population (Fowler 2005). Density is considered first.

1.1 Density Peters (1983) argued that the observed relationship between density and body size (as was shown in Fig. 2.31, and Fig. 6.21) can provide estimates of population density when body size is known (for racoons in his example). Alternatively, one can evaluate a population with known density. In particular this can be done for humans (Cohen 1997, Fowler 2005). Species with either very sparse or very dense populations may be compared to normative values within the density/body size relationship to determine the degree to which they depart from such levels—the comparison is consonant. As mentioned in Chapter 2, relationships between body size and density has been examined and debated in a number of studies (Blackburn et al. 1993, Brown 1995, Damuth 1981, 1987, Lawton 1990, Peters 1983, Schmid et al. 2001). This approach can be applied to humans, by comparing human population density with the mean population density of species the same size as humans (Cohen 1997, Fowler 2005, Fowler and Perez 1999). Damuth’s (1987) data for herbivorous

mammals were reanalyzed using geometric mean regression resulting in the linear relationship shown in Figures 2.31 and 6.21.1 Using a mean adult human body weight of about 68 kg (150 pounds),2 and the equation representing the relationship between density and body size, the mean density of herbivorous species that exhibit the body size of humans was estimated to be about 2.3 per square kilometer (6 per square mile). Spread over the entire surface of the earth, the current human density of 11.7 per square kilometer (Appendix 6.3) is 5.1 times the mean expected for herbivores the size of humans (Table 6.2). Of all the species in Damuth’s (1987) sample, 85% were found at population densities that, for their size, were less than that of humans spread over the entire surface of the earth.3 Restricted to terrestrial areas except Antarctica, the mean density of humans (57.7 per square kilometer) is over 24 times the mean for similar-sized species of herbivorous mammals. Of the 368 species of herbivores in Damuth’s work, only 1.6% (6) were more densely populated for their body size than humans dispersed over all land areas. Finally, at over 288 people per square km of agricultural land, the current human population is overpopulated by a factor of over 120 when compared to the mean of herbivorous mammals of the same body size. A normal human population consistent with herbivores of similar body size would be about 48 million people if we allow ourselves 20% of the earth to live on. Yet there are over 130 times that many. Although there may be exceptions when local densities are considered, no species in Damuth’s sample is found at such high average densities for their body size. This is true even if 40% of the earth’s land surface is considered habitable by humans. Humans are the most densely populated species of mammal for their body size and exhibit clear 125

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abnormality in comparison to mammalian herbivore species (Fig. 6.22). Because humans are not strictly herbivores, estimates of overpopulation based on comparison with herbivores are biased. Due to our higher trophic position, the optimal human population would probably occur at even lower density. To demonstrate, we consider a relationship between body size and population density for carnivores (Marquet 2002, Peters 1983). Table 6.2 shows overpopulation indices for a number of density and body-size relationships, including that for carnivores.4 Thus, for similarly-sized carnivorous mammals, the human population is 2470 times more dense than expected as more optimal. Compared to a mixture of species from a variety of trophic levels, the overpopulation factor is 578. Tudge (1989) stated: “There is an ecological law—a simple extrapolation of bedrock physics which says that large, predatory animals are rare. We break that law: we are large and we have a penchant for predation, and our population now stands at 5 billion . . . ”. The comparison made here examines the extent to which we “break this law” to exhibit abnormality in violation of one of the tenets of management (Tenet 5, Chapter 1) and underlying principles laid out in Mangel et al. (1996). The pattern in distribution of species across density is very asymmetrical. Without log transformation, most species occur at densities well below the mean. More than 73% of Damuth’s sample for herbivorous mammals exhibit densities below the mean of about 119 per square kilometer. Another way to evaluate human overpopulation is to contrast human population density with the modal (most frequent) densities for other species.5 Such a comparison should again account for the recognized effects of body size. To do so, population density can be expressed as a multiple of the value expected from the regression in Figure 6.21. Figure 6.22A is the frequency distribution of the subsample of species that fall in the range of 0 to 2 for such multiples as compressed into one bar in the graph of Figure 6.22B. An evaluation of human overpopulation in this way results in even more extremes. Figure 6.22B covers values beyond the multiple of two to include humans. The human population is about 600-fold

more densely populated than the most common kinds of herbivorous species. This mode occurs at about 20% of the expected levels for their body size as shown in Figure 6.22A (as a result of the skewed nature of this species frequency distribution). Figure 6.22C shows our species’ location in log10 scale where we find ourselves at over 120 times the population density of other species after accounting for body size. Figure 6.23 shows a comparison of the estimates of human population size sustainable for the earth as a whole, based on three approaches. One (top panel) is based on conventional scientific considerations (derived—the combination of data from Fig. 6.20 and Table 6.1). The second (middle panel) is an interspecific comparison (empirical) assuming we can sustainably occupy about 20 million square kilometers of the earth’s surface as vegetarians (i.e., the density information from Fig. 6.22 converted to numbers). The third (bottom panel, Fig. 6.23), shows the population size that approximates the mode of geographic range size for species of our body size (a geographic range of approximately 2 million sq. km., Figs 2.14 and 2.28). It adds another order of magnitude to the difference between measures based on conventional approaches and those based on interspecific comparison. It also corresponds to the estimated population level of about 5 million for prehistoric humans and is within the range of variation observed for populations of other large mammals (Freedman 1989, see below) of our body size. We must remain mindful, however, that trophic level has not been adequately accounted for in that the data used to present the distributions for the bottom panels is based on data from herbivores. As a world society, we may wish to retain the capacity to consume some meat. Even though there are obvious further refinements needed in this process, the bottom panel of Figure 6.23 would have to serve as a better basis for management (e.g., establishing goals, and points of reference for measuring progress in solving the problem of overpopulation) than either of the top panels. To avoid having any component of ecosystems exhibiting abnormality (Tenet 5, Chapter 1, Mangel et al. 1996) and avoid the combination of risks involved, our species would be much better off with a population within the range of the bottom

APPENDIX 6.4

panel. It might be argued that it would be preferable to be near the mode as the example of sustainability represented by most species as it would be consistent with attempts to maximize sustainability. But, as always, things are not as simple as this and a precise location within the normal range of natural variation is debatable (a point visited in Chapter 5 in consideration of maximizing biodiversity). A point estimate cannot be entertained as an option if we are to integrate into management the consideration of data such as that of Figures 2.20–2.22. A population with no variation is exhibiting abnormal population variation—no species has a constant population. Now, however, debate over which metric and statistical distributions are most important for accurately providing guidance is overshadowed by the degree to which humans have departed from all of them at this point in time. To account for trophic level, the human species would arguably be most risk free at even lower population levels than accounted for by comparisons with herbivores. Figure 6.23 illustrates the difference (about three orders of magnitude) between results obtained in comparing conventional approaches to evaluating our population with those based on information on the limits to natural variation—to integrate a much more complete consideration of complexity. It also indicates that the overpopulation factors in Table 6.1 are underestimates by an order of magnitude or more. This graph brings us through a more complete appraisal of overpopulation by humans when based on density. It accounts for bias in the initial calculation of density based on assumed habitat following conventional approaches. But geographic range size must be dealt with directly (as a distinct management question) and the advantages of direct comparisons with population numbers per se are clear.

1.2 Total population Complications arise in comparisons such as those above, when we address a question with nonconsonant information; the question of sustainable density is better addressed with information on density and addressing questions of sustainable total population with density information involves

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assumptions, models, and calculations that inject errors, bias, and uncertainty. In the case of density, geographic range size is a complicating factor when it is not known on a species-by-species basis. In the above, it is not clear what the normal geographic range might be for humans. This emphasizes the importance of making comparisons between humans and other species with measurements as consonant as possible (identical units, and categories, with isomorphic information) to the question being addressed. To assess human population size directly, it should be compared to information on the limits to natural variation among the mean population size of other species, rather than density. Density can be used directly on an area-by-area basis. Most countries of the world have densities higher than the mean or modes of frequency distributions above (Appendix 6.5). As a total population, however, the human population is also the largest for its body size (Freedman 1989, Nowak 1991). The human population is at least two orders of magnitude larger than the largest populations for other large mammals (Fig. 6.24). The crabeater seal (Lobodon carcinophagus) has a population of less than 15 million (MacDonald 19846). The white tailed deer (Odocoileus virgianus) has a total population perhaps as large as 28 million. Mule deer (Odocoileus hemionus) may number as many as 6 million. Other similar sized species with large populations include wildebeest (Chonnochaetes taurinus, about 3.1 million), pronghorn antelope (Antilocapra americana, approximately 1 million), several species of dolphins (Stenella, each less than 20 million), and northern fur seals (Calhorinus ursinus, about 2 million). Humans are over two orders of magnitude more numerous and two to three orders of magnitude more densely populated than these extremes. However, these extremes are limited basis for comparison if the question involves sustainability. Current circumstances include the effects of many human abnormalities (a few of which are exemplified in this chapter). Some of these species (e.g., white tailed deer, crabeater seal) may exhibit large populations because of the disrupting influence of humans—many in ways we are outside the normal range of natural variation. Some species of nonhuman mammals are in marine environments

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that collectively make up about 70% of the earth’s surface, compared to the 20% represented by terrestrial surfaces outside the Antarctic. Population sizes, taken individually, are poor standards of reference, especially our own in its current state in that the integrative power of a pattern of multiple observations is lost. The point of mentioning these extremes is that all other species of mammals of our body size have much smaller populations than that of humans (Fig. 6.24). For most species, of course, populations are much smaller than these extremes. The mean population size for species of body mass similar to that of humans, shown in Figure 6.24, is about 2.34 million. The current human population is about 2500 times that large or over three orders of magnitude more numerous than this mean. The geometric mean of these populations is 157 thousand (i.e., when based on log10 transformations). Based on comparison with this mean the human population is 36.7 thousand-fold larger than the mean (over four orders of magnitude larger). Such comparisons are also subject to bias if the question is: What is a sustainable human population in the absence of abnormal human impact? Again, this is because of the reduced nature of many of the populations as contributed to by the host of historical anthropogenic effects. Many species included in Figure 6.24, plus even more that are not, are endangered largely as a result of human influence. Whether with or without abnormal human influence, the extent of human overpopulation evaluated with the preliminary comparisons of total populations are comparable to results based on density as well as those based on estimated prehistorical population levels. The human population is approximately 1000-fold overpopulated if assessed as falling between the two categories in the lower right of Table 6.2. These two categories are: species in general and carnivores more specifically. Humans are assumed to have a habitat involving 20% of the earth’s terrestrial surface. This approximate 1000-fold overpopulation assessment is not unlike those obtained from Figure 6.24, even is we account for human influence through assumption. For example, if we assume that our influence on the populations of other species has been to reduce them, on the average, to 10% of normal levels, the

approximately four orders of magnitude difference between human and the mean (based on log transformations) of other population levels is reduced to three. Our population is about 700-fold larger than that which would maximize diversity based on population size (Fowler 2008). It is difficult to escape the conclusion that our population is close to three orders of magnitude too large—there are about a 1000-fold too many of us. This is a problem that, like our CO2 production and energy consumption (among others as shown in this chapter) are problems much larger than previously recognized.

Notes 1. This form of regression analysis takes into account the fact that there is variability in the estimates of adult body mass as well as in the estimates of population density and better represents the underlying relationship between the two variables. Debate and discussion of the issue of regression techniques is found in a number of related papers (see Blackburn et al. 1993, LaBarbera 1989, McArdle 1988, Ricker 1973, 1984). Also, the data used in this regression do not include domestic species. 2. The body mass used here may be slightly large compared to the world average of mean adult body size of humans (65 kg from Nowak 1991). It is not clear what mean body weight would reflect that of the human as a species independent of petro/technical influence. The average weight of women and men in the 30–39 age group for Americans (1994 World Almanac) is 152 lbs (68.9 kg) based on the midpoints of the size ranges (170 lbs— 77.1 kg—for men, 134 lbs—60.8 kg—for women). It can be assumed that at least part of a desirable index of standard of living is reflected in body weight. Thus, in looking for sustainable population size one option that serves as a standard of reference is the body weight (mass) of Americans although it is potentially biased. 3. Species that occur under any line parallel to the regression line in Figure 6.21 exhibit densities less than some fixed multiple of the expected density represented by the line. Eighty-five percent (313 of 368) of the species in Damuth’s (1987) work showed densities that place them under such a line located to pass through the point where humans are represented as spread over the entire surface of the earth. 4. These comparisons ignore the effects of ordinary linear regression as conducted in the original analysis and presented here without reanalysis. The effects of

APPENDIX 6.4

geometric mean regression would be to accentuate estimated overpopulation. 5. This corresponds to what may be the peak of overall risk aversion represented by naturally occurring species treated as nature’s Monte Carlo sampling procedure or examples of natural Bayesian integration (Fig. 1.4). This is to be contrasted, however, with values that maximize biodiversity (Fowler 2008) which are higher than statistical measures of central tendency. 6. Recent populations have been at least 7 million (Boveng 1993, Erickson and Hanson 1990). Kooyman (1981) emphasizes the uncertainty in estimates of the population for this species with estimates ranging from 2 to 75 million.

References Blackburn, T.M., V.K. Brown, B.M. Doube, J.J.D. Greedwood, J.H. Lawton, and N.E. Stork. 1993. The relationship between abundance and body size in natural animal assemblages. Journal of Animal Ecology 62: 519–528. Boveng, P.L. 1993. Variability in a crabeater seal population and the marine ecosystem near the Antarctic Peninsula. Ph.D. Dissertation, Montana State University, Bozeman, MT. Brown, J.H. 1995. Macroecology. University of Chicago Press, Chicago, IL. Cohen, J.E. 1997. Population, economics, environment and culture: an introduction to human carrying capacity. Journal of Applied Ecology 34: 1325–1333. Damuth, J.D. 1981. Population density and body size in mammals. Nature 290: 699–700. Damuth, J.D. 1987. Interspecific allometry of population density in mammals and other animals: the independence of body mass and population energy-use. Biological Journal of the Linnnean Society 31: 193–246. Erickson, A.W. and M.B. Hanson. 1990. Continental estimates and population trends of Antarctic ice seals. In K.R. Kerry and G. Hemple (eds). Antarctic ice ecosystems: ecological change and conservation, pp. 253–264. Springer Verlag, Berlin. Fowler, C.W. 2005. Sustainability, health, and the human population. EcoHealth 2: 58–69. Fowler, C.W. 2008. Maximizing biodiversity, information and sustainability. Biodiversity and Conservation 17: 841–855.

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Fowler, C.W., and M.A. Perez. 1999. Constructing species frequency distributions—a step toward systemic management. NOAA Techinical Memorandum NMFSAFSC-109. U.S. Department of Commerce, Seattle, WA. Freedman, B. 1989. Environmental ecology: the impacts of pollution and other stresses on ecosystem structure and function. Academic Press, New York, NY. Kooyman, G.L. 1981. Crabeater seal, Lobodon carcinophagus (Hombron and Jacquinot, 1842). In S.H. Ridgeway and R.J. Harrison (eds). Handbook of marine mammals, Vol. 2, Seals, pp. 221–235. Academic Press, New York, NY. LaBarbera, M. 1989. Analyzing body size as a factor in ecology and evolution. Annual Review of Ecology and Systematics 20: 97–117. Lawton, J.H. 1990. Species richness and population dynamics of animal assemblages. Patterns in body size: abundance space. Philosophical Transactions of the Royal Society of London, Series B 330: 283–291. Mangel, M., L.M. Talbot, G.K. Meffe, et al. 1996. Principles for the conservation of wild living resources. Ecological Applications 6: 338–362. Marquet, P.A. 2002. Of predators, prey, and power laws. Science 295: 229–2230. McArdle, B.H. 1988. The structural relationship: regression in biology. Canadian Journal of Zoology 66: 2329–2339. McDonald, J.N. 1984. The reordered North American selection regime and late Quaternary megafaunal extinctions In P.S. Martin and R.G. Klein (eds). Quaternary extinctions: a prehistoric revolution, pp. 404–439. University of Arizona Press, Tucson, AZ. Nowak, R.M. (ed.). 1991. Walker’s mammals of the World (5th edn). Johns Hopkins University Press, Baltimore, MD. Peters, R.H. 1983. The ecological implications of body size. Cambridge University Press, New York, NY. Ricker, W.E. 1973. Linear regression in fishery research. Journal of the Fisheries Research Board of Canada 30: 409–434. Ricker, W.E. 1984. Computation and uses of central trend lines. Canadian Journal of Zoology 62: 1897–1905. Schmid, P.E., M. Tokeshi, and J.M. Schmid-Araya. 2001. Relation between population density and body size in stream communities. Science 289: 1557–1560. Tudge, C. 1989. The rise and fall of Homo sapiens sapiens. Philosophical Transactions of the Royal Society of London, Series B 325: 479–488.

Appendix 6.5

1 The challenge of population at the national level The problem of overpopulation is not restricted to any one country (Appendix Fig. 6.5.1). There are countries that have populations that are close to the mean among nonhuman herbivorous species, but the complexity of reality dictates that we not rush to conclusions as those countries that have the least people often include a great deal of uninhabitable land, such as deserts. Canada is a sparsely populated country by comparison to many but requires a great deal of energy to heat homes and maintain what are considered an acceptable life

style today (assuming that such a life style is sustainable independent of population size, which of course it is not). As with all systemic issues (and all issues are systemic) the question of sustainable population must be addressed with information on habitat characteristics (rainfall, mean annual temperature and temperature variation, primary production, latitude, and other abiotic factors), and density of nonhuman species with human 0.25 Portion of species

The following material is Appendix 6.5 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press

Mammalian carnivores

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Appendix Figure 6.5.1 The frequency distribution of the population densities of the world’s countries in comparison to the mean population density of nonhuman herbivorous mammals of human body size from the lower panel of Appendix Figure 6.5.2. (Data from the Population Reference Bureau, 1875 Connecticut Ave., NW, Suite 520, Washington, DC 20009.) 130

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Appendix Figure 6.5.2 The frequency distribution is shown in Figure 2.36, here shown with the density of humans in comparison with carnivorous species of mammals of a similar body size. Note the larger gap between humans and carnivorous mammals than observed when compared to herbivorous species as shown in Figure 6.21.

APPENDIX 6.5

qualities (e.g., body size and trophic level). Because carnivores show lower population density than do herbivores of similar body size (Marquet 2002), factoring in the matter of trophic level would widen the gap between humans and the mean for other species as shown in the distribution of Appendix Figure 6.5.2. Are problems solved in redistributing people? It is easy to see that such mitigation, as with all mitigation, only transfers problems from one realm to another—here from one area to a different area, from one nation to another. For example, one country might export people to make its density correspond to the mean for density among nonhuman species. But doing so moves people to other countries to result in higher densities, densities that were

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already above that for nonhuman species. Thus, problems are solved in one place and magnified in others. There is no way to move people from one country to another so as to change the gap between the mean of the distribution of humans among countries and the mean among nonhuman species. Only through a cooperative international recognition of the problem in general can we hope for progress. The economic, political, religious, social, and ethnic components to such progress (if it can happen at all) attest to the complexity involved.

Reference Marquet, P.A. 2002. Of predators, prey, and power laws. Science 295: 229–230.

Appendix 6.6

The following material is Appendix 6.6 for Chapter 6 of: Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford University Press 1 Human contribution to extinction The influence of a species in contributing to extinction is another aspect of species-level involvement in ecosystems that may be measured. We are far from developing information on the limits to natural variation for extinction rates caused by individual species to see any pattern(s) among species. Nevertheless, an initial assessment of human contribution to worldwide extinction can be achieved through comparison with average rates of extinction caused by other species—rates that can be estimated in very rough approximations. An analogous exercise at the population level would be the mortality (e.g., murder, or cannibalism, for humans) caused by a specific individual compared to that caused by other members of the same species. Mathematically, we can represent the current crude extinction rate (extinctions per year) by E, the current instantaneous extinction rate by ε′, the background extinction rate (normal overall extinction rate) by ε, and the current number of species (total for the earth) by No. The ratio of ε′ to ε (a measure of departure from normal) can be represented by m. Thus, an estimate of ε′ is ε′ = ln((No−E)/No) and ε′/m is an estimate for ε in situations where we have information on the value of m. If extinctions were caused exclusively by biotic causes (which they are not), the mean contribution by each individual species to the total background extinction rate ε, would then be estimated 132

by ε/No. The total background extinction rate, ε, would be the sum of the individual contributions by each species none of which would necessarily be equivalent to the mean (ε/No). If the current excess of extinctions is due to human activities (Diamond 1989, Ehrlich and Ehrlich 1981, Ehrlich and Wilson 1991, Hern 1993, Kerr and Currie 1995, Raup 1984, Simberloff 1986a, Stanley 1985), then the human contribution can be estimated by (ε′–ε) or ε(m–1). From this we can calculate a ratio of human caused extinction to that which is the mean contribution of other individual species: (ε(m−1))/(ε/No) = No(m−1) If every species showed such effects on their environment (i.e., if the total effect of all species were No times as large as the effect of humans), life as we know it would disappear quite rapidly. We can estimate how rapidly by multiplying the human contribution by No then using t = −(ln(1/No))/(No(ε′−ε)) to find an estimate of the time to achieve a reduction of species numbers to one final species. The average duration of existence for an individual species under background conditions would be approximated by 1/ε. From May et al. (1995), the extinction rate among mammals and birds is two to four orders of magnitude higher than “background” rates (normal or average extinction rates, see also Wilson 1985a). The current (or soon to be realized) rates of extinction are estimated at levels from 1000 to 100,000 species per year (Ehrlich 1988, Janzen 1986, Myers 1989, Pimentel, Stachow et al. 1992, Simberloff 1986b). Ehrlich and Wilson (1991) estimate that in excess of 4000 species per year are going extinct. Based on this information, Appendix Table 6.6.1 shows: (1) estimated time to achieve extinction to

APPENDIX 6.6

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Appendix Table 6.6.1 Implications of the current rates of extinction (species per year), estimates of total numbers of species on earth, and the current extinction rate expressed as a multiple of the background rate (implied temporal average for long geological time scales) Current extinction rate (species per year)

Ratio1

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30

Days until one species remains2 10 594.25 634.76 100 59.43 63.48 1000 5.94 6.35 10,000 0.59 0.63 Human influence expressed as billion-fold that for the mean of other individual species3 100 0.99 2.97 1000 9.99 29.97 10,000 99.99 299.97 Mean duration of individual species (million years) at implied background rate4 10 100 100.00 300.00 1000 1000.00 3000.00 10,000 10,000.00 29,999.99 100 100 10.00 30.00 1000 100.00 300.00 10,000 1000.00 3000.00 1000 100 1.00 3.00 1000 10.00 30.00 10,000 100.00 300.00 10000 100 0.10 0.30 1000 1.00 3.00 10,000 9.99 30.00

50

653.59 65.36 6.54 0.65 4.95 49.95 499.95 500.00 5000.00 50,000.00 50.00 500.00 5000.00 5.00 50.00 500.00 0.50 5.00 50.00

The three panels of this table show the time expected until the demise of all except the last species (in days) if all species had effects comparable to humans (top panel), human influence expressed as a multiple of the mean effect of other species (billion-fold, middle panel), and the average duration of a species expected from the implied background extinction rate (expressed in millions of years, bottom panel). 1

The ratio of total current rate of extinction to normal background rates of extinction. Time (days) to the obliteration of life (last species) if all species exhibited the same effects in causing extinction as do humans. It is independent of the ratio in the second column. 3 Ratio of human caused extinction to mean species-by-species contribution to background rate of extinction (billion-fold). It is independent of the absolute current extinction rate. 4 Average duration of species at implied normal background rates of extinction (million years) as dependent on current extinction rates, ratio of current to mean rates, and species numbers. 2

lose all but one species if other species were having the same impact as humans, (2) human-caused extinction as a multiple of extinction caused by the mean of other species under typical circumstances, and (3) the implied average duration of individual species under typical conditions. Within the range of rates represented in the literature as covered in this table, life would disappear in less than two years if other species, on the average, were having the impact that humans have.

The subsection of Appendix Table 6.6.1 devoted to species durations is closely approximated by Nom/E, and is shown because any information on average species duration helps narrow down the options being considered. For example, in combination with species numbers estimated at about 45 million, the average durations of 1–10 million years (based on marine invertebrates, Lawton and May 1995, Pimm et al. 1995, Stanley 1985), helps restrict realistic possibilities in this table to the lower right

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quadrant in each subsection of the table. This implies that human impact (middle section of the table) may be 10–12 orders of magnitude greater than normal (measured as the mean for other species). These calculations are primitive and, in many ways, ignore a great deal of the complexity of reality. However, they do serve to provide first approximations helpful in assessing human impact. It should not be ignored that these estimates, as sobering as they may be, are probably conservative. For example, under circumstances free of such extensive human influence, some fraction of extinction is undoubtedly due to abiotic forces (Raup and Boyajian 1988) and not the impact of other species (i.e., not biotic [Vermeij 1987], thus reducing the contribution of individual nonhuman species and making human contributions even more atypical). Species turnover may occur at a much higher rate than indicated by data for marine invertebrates (owing to the rapid dynamics we might expect for the numerous terrestrial microorganisms, for example, thus emphasizing the focus on values in the lower right quadrants of the table). Finally, the mode of the species frequency distribution for species-specific contributions to extinction is probably less than the mean. If the mode is any indication of maximized sustainability, it would be maximized through minimized risk of extinction involving feedback from effects on supporting ecosystems. This exercise also exposes potential oversimplicity (but serves to emphasize rather than detract from the intended message) in the I=PAT calculations (Ehrlich 1991, 1994, Ehrlich and Holdren 1974, Kummer and Turner 1994, Smith 1995; I is impact, P is population, A is a measure of affluence, and T is a measure of technology). If human-caused extinctions (one measure of human impact) are ten orders of magnitude larger than the mean of other species, there is much left to be accounted for. As pointed out in other parts of this chapter (and Fowler 2008), the human population may be three orders of magnitude beyond sustainable levels. If energy use is a measure of AT (affluence times technology), it may account for two more orders of magnitude. There remain four to five (maybe more) orders of magnitude! This implies that human impact (measured in terms of life destroying effects) is a nonlinear

function of population multiplied by energy use, probably a power function of both. It undoubtedly involves synergism among the various factors associated with both population and energy use. This emphasizes the need to know if it is population (P) or amplification (AT) that is most nonlinear to prioritize management action (see Kerr and Currie 1995). Furthermore, if extinction rates caused by humans are 5–10 billion times those of the mean rates caused by other species, this implies that the energy use and technological amplification of today’s human society makes each individual human, on the average, roughly equivalent, in “toxic effects” at the ecosystem level, to that of the mean among other species as entire species! In other words, each human, on the average, would have an ecological influence (or extinction producing “footprint”) roughly equivalent to an entire species among the nonhuman.

References Diamond, J.M. 1989. Overview of recent extinctions. In D. Western and M. Pearl (eds). Conservation for the twenty-first century, pp. 37–41 . Oxford University Press, New York, NY. Ehrlich, P.R. 1988. The loss of diversity: causes and consequences. In E.O. Wilson (ed.) Biodiversity, pp. 21–27. National Academy Press, Washington, DC. Ehrlich, P.R. 1991. Population diversity and the future of ecosystems. Science 254: 175. Ehrlich, P.R. 1994. Energy use and biodiversity loss. Philosophical Transactions of the Royal Society of London, Series B 344: 99–104. Ehrlich, P.R. and A.H. Ehrlich. 1981. Extinction: the causes and consequences of the disappearance of species. Random House, New York, NY. Ehrlich, P.R. and E.O. Wilson. 1991. Biodiversity studies: science and policy. Science 253: 758–762. Ehrlich, P.R. and J.P. Holdren. 1974. Impact of population growth. Science 171: 1212–1217. Fowler, C.W. 2008. Maximizing biodiversity, information and sustainability. Biodiversity and Conservation. 17: 841–855. Hern, W.M. 1993. Is human culture carcinogenic for uncontrolled population growth and ecological destruction? Bioscience 43: 768–773. Janzen, D.H. 1986. The future of tropical ecology. Annual Review of Ecology and Systematics 17: 305–324.

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Kerr, J.T. and D.J. Currie. 1995. Effects of human activity on global extinction risk. Conservation Biology 9: 1528–1538. Kummer, D.M. and B.L. Turner, II. 1994. The human causes of deforestation in Southeast Asia. Bioscience 44: 323–328. Lawton, J.H. and R.M. May (eds). 1995. Extinction rates. Oxford University Press, New York, NY. May, R.M., J.H. Lawton, and N.E. Stork. 1995. Assessing extinction rates. In J.H. Lawton and R.M. May (eds). Extinction rates, pp. 1–24. Oxford University Press, New York, NY. Myers, N. 1989. Extinction rates past and present. Bioscience 39: 39–41. Pimentel, D., U. Stachow, D.A. Takacs, et al. 1992. Conserving biological diversity in agricultural/ forestry systems: most biological diversity exists in human-managed ecosystems. Bioscience 42: 354–362. Pimm, S.L., G.J. Russell, J.L. Gittleman, and T.M. Brooks. 1995. The future of biodiversity. Science 269: 347–350. Raup, D.M. 1984. Death of species. In M.H. Nitecki (ed.). Extinctions, pp. 1–19. The University of Chicago Press, Chicago, IL.

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Raup, D.M. and G.E. Boyajian. 1988. Patterns of generic extinction. Paleobiology 14: 109–125. Simberloff, D. 1986. Are we on the verge of a mass extinction in tropical rain forests? In D.K. Elliott (ed.). Dynamics of Extinction, pp. 165–180. John Wiley & Sons, New York, NY. Simberloff, D. 1986. The proximate causes of extinction. In D.M. Raup and D. Jablonski (eds). Patterns and processes in the history of life, pp. 259–276. Springer-Verlag, Berlin. Smith, C.L. 1995. Assessing the limits to growth. Bioscience 45: 478–483. Stanley, S.M. 1985. Extinction as part of the natural evolutionary process: a paleobiological perspective. In R.J. Hoage (ed.). Animal extinctions; what everyone should know, pp. 31–46. Smithsonian Institution Press, Washington, DC. Vermeij, G.J. 1987. The dispersal barrier in the tropical Pacific: implications for molluscan speciation and extinction. Evolution 41: 1046–1058. Wilson, E.O. 1985. The biological diversity crisis: a challenge to science. Issues in Science and Technology 2: 20–29.

Plate 1.1 Water has a variety of physical properties and exemplifies one of the many substances that flow among the components of ecosystems and the biosphere to result in interconnectedness that we cannot fully take into account in current management practices. This aspect of complexity is one of many that must be taken into account in determining how much water can be used sustainably.

Plate 1.2 Grant’s gazelle (Gazella granti) is a species characterized as a primary consumer, and has an adult body mass of about 55 kg. It exhibits a measurable population density, population variation, and home range size; it has a life history strategy involving its own characteristic birth rate, mortality rate, and mean age at first reproduction. Relationships among such characteristics result in patterns scientists discover and explain.

Plate 2.1 Many species are migratory. Exemplified by the sandhill crane (Grus canadensis), these species underline the reality of interactions among ecosystems. Such species spend one part of their lives in one ecosystem and other parts of their life cycle in other ecosystems. What happens in one of these ecosystems influences what happens in the others—interconnectedness.

Plate 2.2 The behavior of various species is part of what lends to their influence on other species and how other species influence them. The elk (Cervus canadensis) exhibits mating behavior, as do many species, that may serve to draw the attention of predators. This can happen through displays that involve color, motion, sound, or odors. Predators can take advantage of the concentration of individuals during times when they are gathered together for reproduction. Such concentrations also intensify the consumption of resources in, or near, such locations.

Plate 3.1 The redheaded woodpecker (Melanerpes erythrocephalus) exemplifies a species as a biological system emergent as a product of all contributing factors as depicted in Figure 1.4. Both the individuals, the species, and the ecosystems of which they are a part are such systems. This species is a part of various groups of species, each of which exhibit patterns that are also emergent from the complexity of contributing factors. The genomes of such species represent information about that complexity (Photo courtesy of, and copyright by, Bruce Fowler).

Plate 3.2 Poppies (Papaver spp.) are represented by over one hundred species and, as a group, the challenge of taxonomy in identifying just what constitutes a species. Individual species often have their names changed; two or more species are often lumped, later to be split again. Inconsistency in common names presents even more frustration as is experienced repeatedly by bird enthusiasts.

Plate 4.1 Insects such as this cicada are often considered lesser than humans. Such human value systems are brought to decision making in conventional management. In a fully ecosystem-based approach to management all species must be considered and given consideration without bias—either anthropocentric or biocentric.

Plate 4.2 Predator control has been a major component of conventional management. This kind of mis-directed reductionism is not unique in today’s world. Any research showing that lions compete with more popular species is verification of competition, not basis for reducing lion populations.

Plate 5.1 Systemic management accounts for factors such as the evolution of thermoregulation (exemplified by large surface areas of body parts such as the ears of the jack rabbit, Lepus townsendii) in management based on consonant patterns. This happens without explicit information because of the contribution of such factors to any pattern and explicitly when factors such as ear size are considered in correlative relationships within patterns (Photo courtesy of, and copyright by, Bruce Fowler).

Plate 5.2 The ghost crab, Ocypode gaudichaudii, typifies species for which the tide is an important factor. The tide, in turn, is influenced by the gravitational forces of the sun and moon. The latitudinal differences in the influence of the sun are also well known in the amount of energy that passes through primary producers and on to primary consumers—all factors that contribute to patterns among species and, as such, are factors that get taken into account in the use of patterns to guide management.

Plate 6.1 The common milkweed (Asclepias syriaca) is a species for which the energy in air currents provide a means of distribution. This is not energy used to achieve a conscious purpose. Evolution has led to success among plants and animals that have seeds or larvae small enough to be transported by air and water currents—among the many effects of the physical environment.

Plate 6.2 The manatee ( Trichechus manatus) is an endangered species with a population size reflective of such things as its specialized habitat, diet, and body size. Among the many things involved in determining the population size of this species are the cumulative effects of human impacts—thus, factors reflected in the population size we observe for this species today. The pattern in population size among mammalian species with the manatee’s body size accounts for human impact.

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1,000

160° W

Plate 6.4 Map of the eastern Bering Sea showing the density of marine mammal species found by counting the species with geographic ranges that overlap each point in the system (N = 21, from Fowler and Johnson in prep.). This is the same kind of map as shown for the bearded seal (Plate 6.3) but here involves the entire eastern Bering Sea to include the bearded seal in the total count.

Plate 7.1 Anyone can notice change, and change can include the decline or increase in the size of a glacier anywhere. If there is the least suspicion by anyone that a receding glacier might be related to our production of CO2, directly or indirectly, there are grounds for looking at the pattern in CO2 production among other species to see what is normal.

Plate 7.2 Over the eons of time, history has left behind clues as to what happened. Some are found in rock forced up from the bottoms of ancient lakes and seabeds by plate tectonics. We know that everything has a history—everything has an explanation. Everything reflects the complete set of factors behind its emergence. This sacred quality of what we see involves seeing patterns that show us what works.

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