Ecology of Leaf Longevity

Ecological Research Monographs

Series Editor: Yoh Iwasa

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Kihachiro Kikuzawa    Martin J. Lechowicz ●

Ecology of Leaf Longevity

Kihachiro Kikuzawa, Ph.D. Professor Ishikawa Prefectural University Nonoichi, Ishikawa 921-8836 Japan [email protected]

Martin J. Lechowicz, Ph.D. Professor Department of Biology McGill University 1205 Dr. Penfield Avenue Montreal, Québec Canada H3A 1B1 [email protected]

ISSN 2191-0707 e-ISSN 2191-0715 ISBN 978-4-431-53917-9 e-ISBN 978-4-431-53918-6 DOI 10.1007/978-4-431-53918-6 Springer Tokyo Dordrecht Heidelberg London New York Library of Congress Control Number: 2011926414 © Springer 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover  Front Cover : Leaf senescence of Fagus crenata (Japanese beech) Back Cover : Left: Bud break of Fagus crenata Center : Bud break and new leaf emergence of Mallotus japonicus Right: Bud break of Alnus hirsuta Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

The functional ecology of foliage is organized by seasonality. In temperate regions, leaves in deciduous forests often turn brilliant colors in autumn. In spring, buds of leaves burst and new shoots elongate. Similarly, in seasonal tropical environments species respond to the timing of rainy and dry periods, and in the aseasonal tropics subtle environmental cues can influence the timing of leafing and shoot growth. Detailed consideration reveals the diversity underlying such broad patterns of foliar phenology. Even in the canopy of a single forest, leaf dynamics are variable within and among species. Although at a glance leaves seem to simultaneously appear in spring and drop in autumn in a deciduous forest, some individual leaves in fact develop later in the season and some leaves fall during the growing season. The evergreen habit of trees can be achieved through leaves that persist over many years but is also maintained by overlapping cohorts of fairly short-lived leaves that keep the plant canopy evergreen. These complex patterns of leaf demography suggest the necessity of monitoring the dynamics of leaves per se, not merely describing the broad patterns of phenology at the tree or forest level. By monitoring individual leaves we can obtain estimates for a fundamental demographic parameter, that is, leaf longevity, and in this way move phenology from the realm of descriptive lore to that of a modern science providing quantitative and predictive understanding of plant function. A focus on the phenology of leaves is entirely merited if for no other reason than that leaves are the most essential of photosynthetic organs. Photosynthesis is the most important chemical reaction in the world, converting radiant energy to the chemical energy that underpins life on Earth. Among the readily observed traits that characterize leaves, arguably the most broadly relevant is leaf longevity. Leaf longevity is central to leaf function and is a critical factor deciding plant fitness in a given environment. Variations in leaf longevity create a contrast between deciduous and evergreen species that define the nature of entire biomes. Leaf longevity ­correlates with the primary production of plant communities and gains increasing importance in relationship to global climatic change. In the past several decades, scientists have accumulated information on interspecific variation in leaf longevity for thousands of species and have produced various hypotheses and theories about leaf longevity and its consequences. This monograph is an attempt to review and synthesize our present understanding of leaf longevity.

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Our own interest in leaf longevity stems from work on plant phenology that we pursued independently in the 1970s and 1980s, when the scientific study of the basis of phenological patterns was just beginning to take hold. We began our respective phenological studies on trees in the mixed-wood forests of northern Japan and eastern North America. Our interests were largely phenomenological at first, addressing questions such as why some tree species shed green leaves early in the season whereas others shed leaves only in autumn, or why some trees burst into bud earlier in spring than others. We sought explanations for these phenomena from the points of view of physiological ecology and variation in tree life history. Our thinking was drawn from phenology to more specific questions about leaf function by Brian Chabot and David Hicks’ seminal 1982 review entitled “Ecology of Leaf Lifespan” and by subsequent ecophysiological work on cost–benefit analyses, especially that of Hal Mooney and Chris Field. Gradually we gravitated to deeper explanations of variation in leaf longevity rooted in the evolution of plants through natural selection under the constraints of resource availability and teamed up to organize several symposia at international meetings in ecology and botany. Our collaborations were strengthened when M.J.L. had the opportunity to spend time with K.K. in Japan, first as a guest researcher at the Hokkaido Forest Research Institute and then as a visiting professor at Kyoto University. Through those extended visits as well as shorter ones, we carried forward an exchange of ideas that laid the framework of this book.

Box 1  Evolution Through Natural Selection

(continued)

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Box 1  (continued) In the mid-nineteenth century, Charles Darwin proposed the concept of natural selection, the foundation of modern evolutionary theory. Darwin recognized that there was some level of variation in the characteristics of individuals within a population, and that this variation in traits could affect differences in the survival and reproduction of individuals. He reasoned that over generations traits favoring greater survival and reproduction in the local environment should accumulate, or, in other words, that adaptation and fitness should increase through a process of natural selection. In Darwin’s time no one knew the genetic basis of variation in traits, but now we know that the strength of natural selection depends on the heritability of traits – the degree to which characteristics can be passed from parent to offspring. Contemporary evolutionary theory combines Darwin’s seminal idea of natural selection with our knowledge of genetics to explain everything from the origins of complex adaptations involving many interacting traits to the origins and interactions among species that create the diversity of life on Earth. In 1973, Theodosius Dobzhansky famously remarked that “nothing in biology makes sense except in the light of evolution.”

This book considers foliar phenology through the lens of leaf longevity, which we believe can yield important insights into the functional ecology of plants. Our emphasis is on woody plants, which we know best and which also are best studied, but the principles discussed often apply as well to herbaceous species. We take pains to trace the development of ideas in the literature, partly in respect of pioneering work and also because the diverse streams of research that come together to form our contemporary view are best appreciated in historical perspective. We also purposely draw on Japanese-language publications reporting work relatively little known outside Japan. The book is intended to provide a comprehensive and coherent starting point for those just embarking on research about leaf longevity and its consequences at the levels of the whole plant, plant communities, and ecosystems.

Box 2  Phenology Phenology is defined as the study of the timing of biological events and their relationship to seasonal climatic changes (Lieth 1974). People were conscious of the seasonal development and activity of organisms long before the scientific study of phenology emerged: survival depended on their knowing the (continued)

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Box 2  (continued) timing of the runs of salmon up a river or the coloring of leaves as a sign of the approaching winter. In recent centuries, more precise records of phenological events began to be kept that have proven invaluable in analysis of climate change. The record of the blooming dates for cherries in Japan stretches back over 800 years and in modern times has become an integral part of meteorological reporting much appreciated by Japanese people. Based on observations of sample trees at each weather station, blooming time is predicted as an advancing front moving gradually northward as spring arrives in Japan. Similar records document the first observation of the butterfly Pieris rapae and the first song of the bush warbler (Cettia diphone), as well as observations of the timing of leaf emergence and senescence that define leaf longevity.

Phenology and Seasonality Traditional views tie phenology to seasonality defined in terms of climatic patterns during the annual cycle defined by the planet’s transit around the sun. In middle and high latitudes where there are great differences in climate throughout the year, it is certainly reasonable to expect phenological events to reflect the responses of organisms to temporal variation in abiotic constraints on their survival and reproduction. On the other hand, climatic variation at lower latitudes can be considerably less, for example, in some equatorial forests with little or no seasonal variation in precipitation, temperature, or daylength. In these situations, and perhaps more generally, we should ­consider that the timing of biological events may have more to do with interactions among organisms than with any abiotic factors. The timing of emergence and senescence of individual leaves in a plant can be determined as much by interactions among leaves in a growing plant canopy as by seasonal variation in climatic conditions (Kikuzawa 1995). Similarly, synchronous leaf emergence by many different species in a plant community may have been favored by natural selection, not in response to climatic constraints but because this reduced the risk of herbivory (Aide 1988, 1992). The interdependence of plants and the organisms that pollinate their flowers and disperse their fruits provides countless additional examples of this phenomenon. We should not forget that interactions within and among organisms can affect phenology quite apart from the abiotic effects of seasonal climatic change.

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Box 3  Primary Production Gross primary production (GPP) and net primary production (NPP) are terms associated with ecosystem science that characterize the capture of solar energy in photosynthesis by the primary producers in the system. The total photosynthetic assimilation of carbon by a plant community is termed gross primary production (GPP), usually expressed as ton C ha−1  year−1. Some part of this assimilated carbon is used in respiration associated with growth and maintenance: the GPP minus the carbon lost to respiratory processes is termed net primary production (NPP). The annual biomass increment associated with the growth of leaves, branches, stems, roots, and reproductive structures, plus some volatile compounds and exudates, comprise NPP. Precisely estimating NPP is no easy task! Turnover of leaves and fine roots during the year, ephemeral structures such as flowers and bud scales, biomass lost to herbivores and disease, and transfers to mycorrhizal fungal symbionts all must be accounted for. At the ecosystem level, NPP can be discounted for the respiratory losses associated with the secondary production of organisms directly (herbivores, disease) or indirectly (carnivores, parasites) consuming NPP and those decomposing organic matter to obtain an estimate of net ecosystem production (NEP).

M.J.L. has enjoyed the hospitality and opportunities for intellectual growth provided by his many colleagues in Japan, but especially by K.K. This time away has been the perfect complement to the collegiality that characterizes the Department of Biology at McGill University, an academic home that could not be more congenial and stimulating. His debt is greatest, however, to his wife, friend, and colleague, Marcia Waterway, whose patient forbearance with his idiosyncrasies is exceeded only by her willingness to share her insights and ideas. M.J.L. also is grateful for enlightened and open-ended funding policies in the Discovery Grant Program at the Natural Sciences and Engineering Research Council of Canada that let him pursue research on diverse and often esoteric topics that only sometimes turn out to have practical value. K.K. would like to express his thanks to Hiromi Kikuzawa for her encouragement and assistance in fieldwork throughout this study. Colleagues in the Hokkaido Forestry Research Institute encouraged his study for more than 20 years. Students in the Center for Ecological Research and Graduate School of Agriculture in Kyoto University and in the Laboratory of Plant Ecology in Ishikawa Prefectural University helped during his fieldwork both in Japan and in Borneo. The Ministry of Education, Science, Sports and Culture of Japan provided essential financial support. Nonoichi, Japan Montreal, Canada July 2010

Kihachiro Kikuzawa Martin J. Lechowicz

Contents

1 Foliar Habit and Leaf Longevity..............................................................

1

2 Leaves: Evolution, Ontogeny, and Death.................................................

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Shoot Growth, Buds, and Leaf Emergence.................................................. Budbreak and Leaf Development................................................................ Photosynthetic Functionality in Mature Leaves........................................... Age-Dependent Decline in Photosynthetic Capacity................................... Senescence and Abscission..........................................................................

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3 Quantifying Leaf Longevity......................................................................

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Defining Leaf Longevity.............................................................................. Estimating Leaf Longevity from Leaf Turnover on Shoots......................... Estimating Leaf Longevity from Census of Leaf Cohorts over Time.......... Estimation of Leaf Longevity from Leaf Turnover at the Stand Level........ Revisiting the Basic Concept of Leaf Longevity.........................................

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4 Theories of Leaf Longevity.......................................................................

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Costs and Benefits of the Evergreen Versus Deciduous Habit..................... Leaf Longevity to Maximize Whole-Plant Carbon Gain............................. Modeling Self-Shading Effects on Leaf Longevity..................................... Carbon Balance at the Time of Leaffall....................................................... Time Value of a Leaf.................................................................................... Leaf Longevity and Leaf Turnover in Plant Canopies................................. Directions for Future Theory.......................................................................

41 43 46 48 49 52 55

5 Phylogenetic Variation in Leaf Longevity................................................

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Leaf Longevity of Ferns............................................................................... Leaf Longevity of Gymnosperms................................................................

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Contents

Leaf Longevity of Angiosperms................................................................ Evergreen Broad-Leaved Woody Species............................................. Temperate Deciduous Trees and Shrubs............................................... Tropical Trees and Shrubs..................................................................... Leaf Longevity of Herbaceous Plants........................................................

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  6 Key Elements of Foliar Function............................................................

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Photosynthesis and Foliar Nitrogen Content............................................. Assembling the Elements of Foliar Function............................................. Photosynthetic Function and Leaf Longevity............................................ Deciding the Core Set of Cardinal Traits...................................................

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  7 Endogenous Influences on Leaf Longevity............................................

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Timing of Leaf Emergence and Leaf Longevity........................................ Plant Growth Rates and Leaf Longevity.................................................... Seedling Growth and Leaf Longevity........................................................ Variation of Leaf Longevity with Timing of Leaf Emergence................... Canopy Architecture and Leaf Longevity.................................................. Canopy Heterogeneity and Leaf Longevity...............................................

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  8 Exogenous Influences on Leaf Longevity . ............................................

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Insolation and Leaf Longevity................................................................... Aridity and Leaf Longevity....................................................................... Nutrients and Leaf Longevity.................................................................... Effects of Environmental Stress on Leaf Longevity.................................. Biotic Stressors: Herbivory and Disease.................................................... Abiotic Stressors: Ozone and Natural Oxidants........................................ Abiotic Stressors: Salinity......................................................................... Abiotic Stressors: Flooding.......................................................................

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  9 Biogeography of Leaf Longevity and Foliar Habit ..............................

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Biogeography of Foliar Habit.................................................................... Contemporary Distribution of Deciduous and Evergreen Habits.............. Theory for the Geography of Foliar Habit................................................. Modeling Foliar Habit in Relationship to Climate....................................

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10 Ecosystem Perspectives on Leaf Longevity .......................................... 109 Leaf Turnover and Leaf Longevity in the Ecosystem................................ 110 Nutrient Resorption and Leaf Longevity................................................... 111 Photosynthetic Nitrogen Use Efficiency and Leaf Longevity................... 115

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Defense of Leaves and Leaf Longevity................................................. 116 Timing of Leaf Emergence, Leaf Longevity, and Leaf Defense........... 118 Linking Leaf Longevity and Ecosystem Function................................ 119 References......................................................................................................... 121 Subject Index.................................................................................................... 141 Organism Index................................................................................................ 145

Chapter 1

Foliar Habit and Leaf Longevity

Mixed wood forest in spring leafing period, Ithaca, New York, USA

K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_1, © Springer 2011

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The origins of the study of leaf longevity lie in the distinction between evergreen and deciduous plant species, which is not as simple as it first seems. The evergreen habit basically is defined by the retention of functional leaves in the plant canopy throughout the year, as opposed to the deciduous habit in which a plant is leafless for some part of the annual cycle. This simple evergreen–deciduous dichotomy most often is applied to woody trees, shrubs, and vines. Herbaceous perennials that retain leaves through winter are sometimes referred to as evergreen, or more often as wintergreen, in contrast to summergreen (Sydes 1984; Ohno 1990; Tessier 2008), but the evergreen–deciduous dichotomy has had less attention in herbaceous species than in woody plants. Box 1.1  Plant Canopy The plant canopy can be thought of as a three-dimensional array of leaves for the capture of solar energy. The term applies at two spatial scales, but in all cases it refers to an array of leaves in space. At the level of individual plants, the structure of the canopy is determined by the way that leaves are arrayed along herbaceous stems or woody branches. The canopy of individual trees is also referred to as the tree crown, the array of branches above the trunk. At the level of a plant community, canopy structure depends on the canopy architecture of neighboring plants and the way that individuals adjust their canopy architecture in response to neighbors. In grasslands, the low-growing canopy is often a heterogeneous mix of vertically oriented grasses and laterally branching, broad-leaved herbs. In forests the canopy can be multilayered, with taller trees forming the forest canopy but other, less tall, trees forming a distinct subcanopy.

Box 1.2  Foliar Habit Foliar habit refers to the common distinction between evergreen and deciduous plants, which in fact is not as straightforward as most people think. Foliar habit is a characteristic of the plant canopy as a whole, not of individual leaves. A plant is commonly referred to as evergreen if it retains at least some leaves throughout the year, in contrast to deciduous plants, which are bare of leaves for some part of the annual cycle of the seasons. Depending on the timing of emergence and fall of individual leaves and the number of leaves retained in the plant canopy, some subdivisions of the evergreen and deciduous habits are possible. Variations on the evergreen habit Leaf exchanger: Leaves are exchanged within a year; thus, leaf longevity is shorter than 1 year but there are always viable leaves in the plant canopy. (continued)

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Box 1.2  (continued) Semievergreen: Immediately after new leaf emergence, old leaves fall; leaf longevity is essentially 1 year, and a leafless period is not very apparent. Brevideciduous: Some leaves are shed during part of the year, but never more than 50% of the leaves, so the plant canopy appears evergreen. Semideciduous: More than 50% of leaves are lost at some time in the year, but the plant canopy is never completely bare. Heteroptosis: Some branches of a tree become completely leafless during unfavorable periods but others retain leaves throughout the year. Variations on the deciduous habit Summergreen: Leaves are shed in autumn, and in woody plants the canopy is completely bare through winter; this is a typical deciduous habit in temperate regions. Wintergreen: Leaf emergence occurs at the end of summer and leaves are retained through winter, but are shed at the onset of the next summer, and the plant is completely bare during summer. Drought deciduous: Leaves are shed during the dry season in tropical forests and deserts. Spring ephemeral: Plants have leaves only in early spring that wither by summer. This habit is usually found in herbaceous plants but has been recorded in a small tree (Aesculus sylvatica) in North America (DePamphilis and Neufeld 1989).

 It is important to recognize that the distinction between evergreen and deciduous species applies at the level of the entire plant canopy, not individual leaves. It is possible for a plant canopy to be evergreen by replacing relatively short-lived leaves frequently throughout the year. Of 13 evergreen species in California chaparral, 5 had leaves that survived less than a year but which maintained an evergreen canopy through a prolonged period of leaf production from early spring into summer (Ackerly 2004). Although the basic evergreen–deciduous dichotomy at the canopy level is reasonably clear, some intermediate terms have arisen to describe peculiarities in leaf turnover that can lead to differing degrees of evergreenness (Sato and Sakai 1980; Eamus 1999; Eamus et al. 1999a; Eamus and Prior 2001; Franco et  al. 2005; Saha et  al. 2005; Negi 2006; Williams et  al. 2008). Primary among these alternative terms is the recognition of a brevideciduous habit in which there is a brief period in the year when old leaves are falling and new leaves are emerging simultaneously. This intermediate habit also is referred to as “leaf exchanger” (Whitmore 1990), “incomplete deciduousness” (Hatta and Darnaedi 2005), and “semievergreen (Singh and Kushwaha 2005). The canopy in such species is never entirely leafless, even briefly, and therefore cannot be considered truly deciduous, but then neither can it be considered any more than marginally evergreen. From developmental, phylogenetic, and functional points of view, this brevideciduous

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habit appears to be more a variant of the deciduous habit rather than a true evergreen habit. Even a north temperate forest tree such as Carpinus caroliniana that is commonly perceived as unambiguously deciduous in response to harsh winter conditions is brevideciduous at the southern limits of its native geographic range (Borchert et al. 2005). The widespread tropical tree, Shorea robusta, which is usually considered evergreen, in fact shows similarly plastic rangewide responses in the timing of leaf turnover (Singh and Kushwaha 2005). There is clearly a degree of plasticity and ambiguity in what at first seems a straightforward dichotomy between the deciduous and evergreen habits. Similarly, the simple association between the deciduous habit and strongly seasonal climates is belied by its occurrence in aseasonal tropical forests as well (Hatta and Darnaedi 2005). In these same tropical forests, some of the evergreen species maintained relatively constant leaf numbers through either steady or episodic turnover of leaves throughout the year, while others were evergreen but allowed their leaf numbers to drop to only 30–60% of full canopy at some point in the year (Hatta and Darnaedi 2005). Although the evergreen–deciduous dichotomy has been recognized since ancient times, it is only in the 20th century that appreciation for the diversity in leaf demography that underlies observations at the scale of whole trees and forests has emerged to make sense of these variations within the basic dichotomy. The pioneering phytogeographic studies of Alexander von Humboldt and Aimé Bonpland (1807) were the first to stimulate scientific interest in the contrast between evergreen versus deciduous trees and forests. Western botanists already were familiar with the broad-leaved deciduous forests of central Europe and needle-leaved conifer forests of northern Europe, but Humboldt and Bonpland called attention to the somewhat surprising existence of tropical forests dominated by broad-leaved evergreen species of flowering plants. Since then, the distinction between the evergreen and deciduous habit has figured in the classification of vegetation types by phytogeographers and ecologists (Grisebach 1838, 1884; Warming 1909; Whittaker 1962; Walter et al. 2002; Woodward et al. 2004). By the late nineteenth century a complementary stream of inquiry had arisen that sought to explain the environmental basis for predominance of the evergreen habit and the frequently allied condition of small, tough, long-lived leaves referred to as sclerophylly (Beadle 1954, 1966; Loveless 1961; Monk 1966; Mooney and Dunn 1970a,b). Schimper’s (1903) classic book entitled Plant-Geography Upon a Physiological Basis consolidated the earliest work in this field and raised questions that continue to be investigated to the present day. It is these attempts to discover the adaptive value of evergreenness and sclerophylly that eventually led to the study of leaf longevity in its own right. Recognizing that the evergreen habit and sclerophylly were associated with dry and infertile sites, most of the work following Schimper (1903) focused on the evergreen and deciduous habits as alternative strategies for managing water and nutrient resources. Mooney and Dunn (1970a,b), for example, adopted a wholeplant perspective on adaptation to explain the occurrence of evergreen and deciduous species along gradients of moisture availability in the Mediterranean climates of Chile and southern California. They observed that as the summer dry period

1 Foliar Habit and Leaf Longevity

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became longer, dominance in the chaparral vegetation shifted from evergreen to deciduous species. They concluded that so long as the dry period was not too prolonged, the deeply rooted, sclerophyllous evergreens with their relatively low photosynthetic rates were more productive over the year than the shallow-rooted, mesophyllic deciduous species. Conversely, when the dry period was not long, the high photosynthetic rates typical of mesophyllic leaves conferred an advantage on the deciduous species that were better able to exploit the cool, wet winter season and to avoid water loss by being leafless in the hot, dry summer. In a related cost– benefit analysis of leaves as photosynthetic organs, Orians and Solbrig (1977) were the first to offer a functional explanation at the leaf level for sclerophylly and the evergreen habit. They postulated that plants adapted to hydric conditions should have drought-deciduous, mesophyllic leaves, photosynthesize rapidly when water was readily available, and cease photosynthetic activity quickly as conditions became drier (Fig. 1.1). On the other hand, they expected plants adapted to xeric conditions to have evergreen leaves persistent through drought periods, with relatively low photosynthetic rates even when water was readily available, but able to withstand drought through conservative stomatal regulation and low cuticular water loss associated with sclerophylly. In this their ideas followed Mooney and Dunn (1970a,b), but they also specifically suggested that the association of sclerophylly with the evergreen habit arose in the time required to recover leaf construction costs. Given the low photosynthetic capacity of sclerophyllous leaves, only an evergreen habit allowing amortization over more than a single year could recover

Fig. 1.1  The Orians and Solbrig (1977) expectations for photosynthetic activity in response to water availability as a function of different plant strategies. Soil water availability is on the abscissa, from wet to dry; photosynthetic activity is on the ordinate. Four hypothetical species are illustrated: darker shading shows the portion of the moisture gradient where each species is respectively at an advantage in terms of potential productivity. X indicates a species with xeromorphic (sclerophyllous) leaves; m indicates a species with mesomorphic leaves

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the relatively high costs of leaf construction in sclerophylls. Mesophyllic leaves, which cost less to construct and have higher photosynthetic capacity, were associated conversely with the deciduous habit. The Orians and Solbrig (1977) model embodies ideas about trade-offs in foliar design still prevalent today and stands as the first theoretical model associating leaf photosynthetic function and leaf longevity with the distinction between evergreen and deciduous plants. A seminal review a few years later by Chabot and Hicks (1982) marks a turning point in consideration of the nature and causes for the evergreen versus deciduous habits. Their review consolidated ideas emerging in the previous decade, decisively shifting the discussion from questions of resource availability and resource management at the whole-plant level to leaf longevity as a central trait in foliar function that determined whether a plant was evergreen or deciduous. Picking up on the perspective of Orians and Solbrig (1977), they presented a cost–benefit analysis of leaf carbon economy based on the premise that leaves are fundamentally photosynthetic organs, which over their lifetime must repay to the plant the carbon cost of their construction. More formally, they introduced the following equation for the carbon economy of a single leaf:

G = ∑ Pfi − ∑ Pui − C − W − H − S

(1.1)

where G is the net carbon gain by a single leaf that is exported to other parts of the plant over a year, Pfi is the carbon gain by a leaf at age i during any favorable period for photosynthesis over the year, and Pui is the net carbon exchange of the leaf during any periods unfavorable for photosynthesis. Because the photosynthetic gain during an unfavorable period is by definition zero or nearly so, the net gain during an unfavorable period typically will be negative consequent to respiratory carbon losses associated with maintenance and defense of the leaf. The term C is the construction cost to produce the leaf. Although the actual construction of a leaf occurs over some finite period of time, Chabot and Hicks (1982) imposed the cumulative construction cost at the time of leaf expansion when the leaf becomes photosynthetically active; the leaf construction cost therefore is independent of time. Similarly, any damage by wind (W) or herbivores and pathogens (H) is also accumulated and considered independent of time during the leaf lifespan. Finally, Chabot and Hicks (1982) recognized that some part (S) of the photosynthate produced by the leaf might be stored or utilized in foliar tissues rather than translocated to another part of the plant, and hence would not contribute to the net gain of the plant from that individual leaf. Reasoning in this conceptual framework and reviewing available data, Chabot and Hicks (1982) argued that leaf longevity thus should be determined by the balance between costs represented in the negative terms of (1.1) and benefits represented in the positive terms. Their conceptual framework and the literature they reviewed firmly placed the leaf in the context of the plant as a whole, inviting subsequent analyses of how variation in leaf demography contributes to the distinction between evergreen and the deciduous habits.

Chapter 2

Leaves: Evolution, Ontogeny, and Death

Bud burst of Alnus hirsuta

The evolutionary origin of leaves traces back to the gradual modification of ­branching systems in the earliest land plants. The vascular plants belonging to the phylum Rhyniophyta that first colonized land more than 400 million years ago had only simple dichotomously branching axes without organs we would recognize as either leaves or roots (Sussex and Kerk 2001). The early evolution of the land plants involved a combination of progressive changes in branching architecture (overtopping) and the associated flattening (plantation, fusion) of some branch elements to form laminar photosynthetic organs that we recognize as leaves (Sussex and Kerk 2001; Boyce and Knoll 2002; Donoghue 2005). Over the course of the Paleozoic, K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_2, © Springer 2011

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four different vascular plant lineages evolved leaves: the ferns, sphenopsids, progymnosperms, and seed plants (Boyce and Knoll 2002). The leaves of extant members of these lineages are the primary photosynthetic organs in the great majority of plant species. The earliest leaves in all four lineages were small, narrow, and single veined (“microphylls”), arrayed along highly dissected branching systems but larger and broader multiveined leaves (“macrophylls”) gradually become predominant in the fern, gymnosperm, and angiosperm lineages (Boyce and Knoll 2002). The earliest of these land plants are believed to have been evergreen, but by the early Carboniferous Archaeopteris may have had some deciduous characteristics (Addicott 1982; Thomas and Sadras 2001). The unambiguous origin of a seasonally adapted deciduous habit arose only later in the polar forests of a “greenhouse Earth” where plants had to contend with dark but warm winters and fire-prone conditions (Brentnall et al. 2005). By the Permian there is some evidence for the seasonally programmed turnover of leaves in the Glossopteris flora of polar regions (Taylor and Ryberg 2007) and strong evidence for deciduous polar forests by the Cretaceous (Taggart and Cross 2009). The leaves of contemporary plant species typically are arrayed along a stem segment to form a shoot. The basic unit of shoot construction is a metamer consisting of a leaf and bud at a node along a stem and an associated internodal stem segment (Barlow 1989). Shoots composed of some number of metamers (Fig. 2.1) can be considered the modular units of organization in the aboveground portion of plants d

b

c

a

Fig. 2.1  Growth of a plant by the accumulation of modules. (a) The shoot, a stem section with leaves, is the basic modular unit of plant vegetative growth. (b) A plant canopy grows by accumulation of modules, sometimes only by apical extension, and other times (c) by lateral branching from dormant buds. Some leaves and shoots typically will be shed as growth of the entire plant proceeds, and the developing plant canopy can take on a degree of asymmetry as shoots interact with one another and respond to their immediate microenvironment (d)

Shoot Growth, Buds, and Leaf Emergence

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(White 1979; Jones 1985; Maillette 1987; Hallé 1986; Watson 1986; Room et al. 1994). Shoot growth arises in meristematic tissues associated with the apex of the shoot (apical buds) or in terms of branching with the base of leaves (lateral buds). Buds contain a short stem with leaf primordia and embryonic leaves, essentially a partially developed, preformed shoot (Kikuzawa 1982; Jones and Watson 2001). Embryonic leaves have partially developed lamina with distinguishable venation; leaf primordia are too early in development for features of the mature leaf to be discerned. Buds are usually, but not always, enveloped by bud scales, which confer a degree of protection from dessication and herbivory (Kikuzawa 1982; Nitta and Ohsawa 1998). Shoots have a degree of autonomous regulation over their dormancy, growth, and senescence but also interact with other shoots in a coordinated way to form the plant canopy as a whole (Thomas 2002; Barthélémy and Caraglio 2007).

Shoot Growth, Buds, and Leaf Emergence

Leaf emergence of Alnus hirsuta

The emergence of leaves, the growth of shoots, and the development of buds ­containing future shoots are inextricably interlinked. At budburst, shoot extension occurs in concert with leaf emergence, and as the bout of extension growth ends, the development of buds containing future shoots ensues. This sequence is illustrated by data from Maruyama (1978) on shoot elongation of deciduous broadleaved saplings in a Japanese beech forest showing two contrasting modes of shoot growth (Fig. 2.2). One mode is illustrated by Fagus in which the shoot elongates and leaves emerge more or less simultaneously in a short burst of growth; this has been referred to as Fagus-type (Maruyama 1978), flush-type (Kikuzawa 1983,

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lus pu

ra Linde

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Po

80

Fagus

Shoot Elongation %

100

40 20 0

M

J

S J A Calendar Month

O

Fig. 2.2  Three temporal patterns of shoot elongation of tree species in a deciduous broad-leaved forest in Niigata, Japan. Shoot elongation and bud development are relativized to their maximum size (100%) and plotted against calendar months. (After Maruyama 1978; redrawn by Kikuzawa)

1984, 1988), or determinate (Kozlowski 1971; Marks 1975; Lechowicz 1984) shoot growth. Populus is an example of a succeeding-type (Maruyama 1978; Kikuzawa 1983, 1984, 1988) or indeterminate (Kozlowski 1971; Marks 1975; Lechowicz 1984) shoot growth in which the shoot elongates and leaves emerge over a relatively long period. The period of indeterminate shoot growth can be fairly short, as in Lindera, or quite extended, as in Populus (Maruyama 1978; Kikuzawa 1983). The noteworthy contrasts between these determinate and indeterminate modes of shoot growth, respectively, are (1) the episodic versus ongoing extension growth and leaf emergence and (2) the temporal separation versus overlap of bud development from extension and leaf emergence. The same two basic patterns of shoot growth prevail in tropical forests (Koriba 1947a,b, 1958; Lowman 1992), in savanna species in the western Himalayas (Zhang et  al. 2007), in herbaceous plants (Kikuzawa 2003), and in ferns (Hamilton 1990). These patterns of shoot growth and leaf emergence should be observed in any type of vegetation in the world because they arise in the developmental controls on shoot growth, not the diverse environmental factors that trigger the onset of growth (Kikuzawa et al. 1998).

Box 2.1  Bud Scale Buds are a plant structure protecting vulnerable meristematic tissues and embryonic leaves from cold or desiccation during a dormant period. The modified, scale-like leaves that form the outer layers of many buds are called bud scales. Buds form at the base of existing leaves and do not develop into leaves until the parent leaf falls.

Shoot Growth, Buds, and Leaf Emergence

11

 The basic patterns of shoot growth are also reflected in contrasting degrees of development of the embryonic shoot within the bud. In determinate species, leaves in the bud are unexpanded but already nearly completely developed before budburst, and all the leaves appear simultaneously as a flush associated with rapid stem elongation; this type of simultaneous shoot growth is always associated with fully preformed shoots (Hallé 1978). Expansion of the embryonic leaves in a preformed shoot may be arrested after their initial development for weeks or even years before budburst (Foster 1929, 1931; Garrison 1949a,b, 1955; Barthélémy and Caraglio 2007). In species with indeterminate shoot growth, in contrast, single leaves appear successively along a slowly growing shoot (Kikuzawa 1978, 2003). Leaves of species with successive, indeterminate shoot growth may be either preformed in the bud (Kikuzawa 1982) or newly produced (neoformed) during the growing season. Some trees, such as species in the genus Betula, have both determinate “short shoots” and indeterminate “long shoots” within their canopy (Kikuzawa 1983). Leaves on the short shoots and the initial leaves on long shoots are preformed in the overwintering bud, and later leaves on the extending long shoots are formed only in the season they emerge (Macdonald and Mothersill 1983; Macdonald et al. 1984; Caesar and Macdonald 1984). These patterns of simultaneous leaf emergence in species with determinate shoot growth and successive leafing in species with indeterminate shoot growth, as well as the combination of the two shoot growth syndromes in some species, are found in evergreen trees in temperate regions (Nitta and Ohsawa 1997), herbaceous plants (Yoshie and Yoshida 1989; Kikuzawa 2003), and tropical trees (Lowman 1992; Kikuzawa 1978; Miyazawa et al. 2006). There also is some relationship between the structure of buds and the nature of shoot growth and leaf emergence in deciduous broad-leaved trees (Kikuzawa 1983, 1984, 1986). Species that have buds covered by well-developed, distinct bud scales inevitably have determinate shoot growth (flushing with simultaneous leafing), but not all species with determinate shoot growth necessarily have true bud scales. For example, Styrax obassia has a naked bud, but it also has determinate shoot growth. The incipient shoot forming within the bud of any species with true bud scales is referred to as heteronomous (Fig. 2.3) because the shoot contains two types of metameric units: one forms the bud scales themselves and the other forms true leaves (Kikuzawa 1983, 1986). On the other hand, species with indeterminate shoot growth (successive leafing) generally lack true bud scales and are referred to as homonomous: all the metameric units comprising the shoot are basically identical, producing leaves that may or may not have stipules or other ancillary structures derived from the leaf lamina functioning as bud scales in the outermost metameric whorl. In Alnus hirsuta, for example, the stipules of the outermost leaf function as scales enveloping the bud as opposed to the distinct bud scales in Ulmus davidiana (see Fig.  2.3). In the Aceraceae there is a morphological series suggesting the evolutionary transition from homonomous to heteronomous buds (Sakai 1990). Dipteronia, the closest and more primitive relative of Acer, is homonomous, lacking bud scales entirely (Fig.  2.4). In Acer species with determinate shoot growth the distinction between normal leaves and bud scales is clear – the bud is fully heteronomous. In Acer species with indeterminate shoot growth, however, the distinction between heteronomy

Fig. 2.3  Cross sections of the two types of buds in deciduous broad-leaved trees. (a) Homonomous bud of Alnus hirsuta with repetitions of the same basic unit of two stipules and a lamina. (b) Heteronomous bud of Ulmus davidiana with bud scales at the outermost part of the bud distinctly different from the embryonic leaves to the interior of the bud. (After Kikuzawa 1983)

Fig. 2.4  The linkage between the development of bud scales and shoot elongation patterns (Sakai 1990). Dipteronia, the closest relative of Acer, has no bud scales. In Acer species, young bud scales have a rudimentary blade at their tips, which disappears during development, suggesting bud scales originated from normal leaves. Minute rudimentary blades exist at the tip of the inner bud scales in Acer species with indeterminate shoot growth but not in those with determinate shoot growth

Shoot Growth, Buds, and Leaf Emergence

13

and homonomy is blurred. The innermost bud scales have rudimentary leaf blades at their tips that suggest true bud scales in Acer are derived through the evolutionary modification of laminar tissues (Sakai 1990). Similar correlations between the degree of bud scale development and leaf emergence patterns can be observed in the Betulaceae (Kikuzawa 1980, 1982). The situation is somewhat similar in evergreen broad-leaved tree species, but it is less clear cut because the timing of shoot growth and bud development is not as constrained seasonally as in broadleaf deciduous trees. Three types of buds can be recognized: naked buds lacking scales, hypsophyllary buds covered with soft green leaf-like hypsophylls, and scaled buds covered by many hard imbricate brown scales (Nitta and Ohsawa 1998). Buds with well-developed scales are typically found in canopy tree species such as Castanopsis cuspidata, Quercus acuta, and Machilus thunbergii, and naked and hypsophyllary buds in subcanopy or understory species such Cleyera japonica, Eurya japonica, and Maesa japonica. Species with naked buds do not form winter buds, instead having shoot growth with acropetal production of leaves throughout the growing period. Species with hypsophyllary buds have shoot growth during April and June in the warm temperate forests of Japan, forming a terminal bud at the same time that then has no further morphological development until budbreak the following March or April. Species with bud scales have rapid shoot growth in May and June; during this stem elongation period, the shoot tip has an immature hypsophyllary bud. After the completion of stem elongation in early summer, a bud protected with many scales gradually develops through the summer, fall, and winter within this immature hypsophyllary bud (Fig. 2.5).

Fig. 2.5  Development of buds from May through October and mature bud condition in December in two evergreen broad-leaved trees: Cleyera japonica (left) and Quercus acuta (right). The hypsophyllary buds of Cleyera japonica are produced in spring within the mother bud but show no further morphological development until the following spring. In Quercus acuta, the hypsophyllary bud formed in spring develops into a scaled bud throughout the period until budburst the next year. Open bars, hypsophyllary-bud phase; closed bar, scaled-bud phase. (After Nitta and Ohsawa 1998)

14

2 Leaves: Evolution, Ontogeny, and Death

Budbreak and Leaf Development The timing of budbreak in species of temperate regions is usually in response to a combination of photoperiodic cues and spring warming (Lechowicz 2001). The control of budbreak in tropical species is less clear, but in species from seasonal climatic regimes the water balance of the plant itself serves as a cue (Borchert 1994). At the time of budbreak, embryonic leaves expand by absorbing water, in some cases with further cell divisions (Dengler and Tsukaya 2001; Barthélémy and Caraglio 2007). The duration of the period of leaf expansion depends on four factors: (1) the number of primordial cells, (2) the rate of cell division, (3) the duration of the phase of cell division, and (4) the size of the individual mature cells (Gregory1956). Newly emerged leaves often are brightly colored and only become green at full expansion (Dominy et al. 2002). Full expansion of the leaf typically requires of the order of 10–15 days from budbreak, but this timing varies substantially and is influenced by both environmental conditions and phylogenetic considerations. It should be noted that terrestrial monocotyledons with graminoid growth forms, such as sedges (Hirose et al. 1989) and grasses (Bowes 1997), as well as the gymnosperm Welwitschia, all have a different mode of leaf development in which basal meristems continuously form new leaf tissues. Hence in these plants, the leaf has different age tissues with the tip oldest and the base youngest (Mooney and Ehleringer 1997). Because leaves are the primary organs of plant productivity, the logical benchmark for leaf maturation is attainment of full photosynthetic capacity. Instantaneous rates of photosynthesis are influenced by environmental conditions such as ambient temperature, vapor pressure deficit, atmospheric CO2 level, and soil water potential, as well as plant condition and stage of development, but ultimately are most dependent on irradiance (Larcher 2001; Lambers et al. 1998). Given the very dynamic nature of photosynthetic rates, what single value might serve as an index of leaf maturation and more generally as an index of leaf function? It is reasonable to focus initially on the response of photosynthetic rate to irradiance, the flow of photons on which this biochemical process depends. Although the net photosynthetic response to irradiance varies among and within plant species, the basic shape of the response curve is consistent (Fig.  2.6). At very low irradiance, respiratory loss of CO2 is greater than photosynthetic gains, but as irradiance increases photosynthesis predominates and net gains of CO2 increase to an asymptote. This asymptotic rate of net photosynthesis under saturating irradiance and otherwise optimal conditions is referred to as photosynthetic capacity, Amax. Photosynthetic capacity is commonly taken as the cardinal value most useful in assessing foliar function and plant adaptation (Wright et al. 2004). In many, but not all, species photosynthetic capacity develops steadily after budbreak, reaching its maximal value when the leaf is fully expanded (Saeki 1959; Šesták 1981; Hodanova 1981; Castro-Diez et al. 2005; Warren 2006). This pattern is typical of relatively short-lived leaves, but in species with longer-lived leaves months can pass until full photosynthetic capacity is attained. For example, in Abies veitchii, leaves appear in June but maximum photosynthetic capacity is reached

Budbreak and Leaf Development

15

Fig. 2.6  Typical lightresponse curves of early, mid and late successional species are shown. (From Bazzaz 1979)

Photosynthesis (mg CO2 dm–2 h–1)

30 Early 20 Mid 10 0

–10

Late 3 6 Light Intensity (1000 ft-c)

9

140 slope=0.701***

Maturation period (d)

120

Ad

100 80

Cj

Ca

Cs

Cs*

Ma

60 Xm Ol

40

Qr Cu As Bn Pv

0

Qm

Mt Ns

Dp

20 0

Cp

50

Qg

Tc

100 150 LMA (g m−2)

200

250

Fig. 2.7  Leaf maturation period and leaf mass area across different evergreen broad-leaved tree species (Miyazawa et  al. 1998): Ad, Actinidia deliciosa; As, Annona spraguei; Bn, Brassica napus; Ca, Coffea arabica; Cp, Connarus panamensis; Cs, Castanopsis sieboldii; Cu, Cucumis sativus; Dp, Desmopsis panamensis; Ma, Morisonia americana; Ol, Ouratea lucens; Qr, Quercus rubra; Tc, Theobroma cacao; Xm, Xylopia micrantha. Open squares, species attaining full photosynthetic capacity before full leaf expansion; open triangles, species attaining full photosynthetic capacity at full expansion; closed squares, delayed greening; d, days

only in August (Matsumoto 1984); in Pinus pumila, full photosynthetic capacity is attained only in September or even the following spring (Kajimoto 1990). Evergreen broad-leaved trees such as Machilus thunbergii, Castanopsis sieboldii, and Quercus glauca show similar delay in foliar development (Kusumoto 1961; Miyazawa et al. 1998). In general, broad-leaved evergreen species with heavier, longerlived leaves take longer to develop their full photosynthetic capacity (Miyazawa et al. 1998; Fig. 2.7).

16

2 Leaves: Evolution, Ontogeny, and Death

Photosynthetic Functionality in Mature Leaves Once a leaf has attained full photosynthetic function, various factors constrain its performing to full capacity at all times. The overall situation is illustrated by a diurnal and seasonal record of photosynthesis in a Mediterranean shrub, Phlomis fruticosa (Fig. 2.8). The light reactions of photosynthesis obviously are precluded at night, and the diurnal trace of photosynthesis generally is in proportion to insolation from dawn to dusk if other conditions are favorable. In this evergreen Mediterranean shrub, photosynthesis is low during the summer dry season and relatively high in winter and spring when water is more available. Leaves function at their maximum photosynthetic capacity (Amax) only near midday in early June, falling well below their photosynthetic potential throughout midsummer and early fall. In the transition from late spring to early summer as soil water supplies diminish and atmospheric vapor pressure deficits increase, first midday and then late afternoon photosynthesis is depressed despite high levels of insolation (Kyparissis et  al. 1997). Such midday depression of photosynthesis in response to limited water supplies is well known in species from temperate (Ishida and Tani 2003), tropical (Zots and Winter 1994, 1996; Zots et al. 1995; Ishida et al. 1999), and even arctic (Gebauer et al. 1998) climates. These and innumerable other examples document the fact that over their lifetime leaves do not work to their full instantaneous photosynthetic capacity, Amax. Acknowledging this reality, Kikuzawa introduced the concept of the mean labor time of a leaf, the cumulative amount of photosynthesis achieved by a leaf over its lifetime compared to the potential value if a leaf were able to work to its full capacity 25

15 10

Pn (µmol m−2

20

s−1) 5 20

m

e

16

of

0 12 8

da

y

19 R AP 4 R AP 18 AR M 0 3 N JA 14 EC D 8 V2 O N 1 V1 O N 3 T2 C O 9 T1 C O 4 P2 SE 1 1 G AU 7 L1 JU L6 JU 26 N JU 7 N JU

Ti

Fig.  2.8  Diurnal and seasonal record of photosynthesis for mature leaves of an evergreen Mediterranean shrub, Phlomis fruticosa, growing at low elevation. The leaves were produced in April–May 1992 and measured from June 1992 to April 1993, just before this leaf cohort began to senesce and abscise. (From Kyparissis et al. 1997)

Photosynthetic Functionality in Mature Leaves

17

all the time (Kikuzawa et al. 2004). Mean labor time provides a complement to the use of Amax as a cardinal trait characterizing variation in leaf function. It is essentially a single, summary variable that subsumes all the environmental and ontogenetic factors that can reduce photosynthesis below its maximum value over the lifetime of a leaf. Mean labor time (m) expressed as an average per day is defined by m = 24Ga / Gh



(2.1)

where Gh is a hypothetical lifetime photosynthetic rate of a leaf, assuming that the leaf works 24 h at Amax throughout its lifetime; Ga is the actual photosynthetic rate of the leaf throughout its lifetime. This definitive equation can be decomposed into terms representing the various factors that lead to photosynthetic performance below full capacity:

m = 24

Gpclear Gp GpL Ga Ga = 24 Gh Gh Gpclear Gp GpL

(2.2)

where Gpclear is the lifetime carbon gain of a single leaf, supposing that every day through its life is a clear day. Even if a day is cloudless, the solar angle changes with time of day, hence the leaf still cannot attain maximum photosynthetic rate throughout the day; this ratio of Gpclear and Gh is designated the diel effect. The term Gp represents the lifetime carbon gain under actual weather conditions. There are cloudy days and rainy days over the lifetime of a leaf when insolation is reduced compared to a clear sky condition and the photosynthetic rate is depressed; this ratio of Gp and Gpclear is designated the overcast effect. The term GpL represents the carbon gain by a leaf under realized insolation over its lifetime, including the effects of shading by surrounding plants and self-shading of leaves within the plant canopy; this ratio of GpL and Gp is designated the shading effect. The final term is the ratio of actual photosynthesis of a leaf over its lifetime and the potential photosynthetic rate under its realized insolation regime. The ratio of Ga and GpL represents the influence of environmental factors other than insolation that suppress, such as the midday depression resulting from water balance limitations or the effects of suboptimal temperatures for maximum photosynthetic gains. This ratio of Ga and GpL is designated the depression effect. The mean labor time of leaves of Alnus sieboldiana was calculated to be around only 5 h per day on average over their lifetime (Kikuzawa et al. 2004). Estimates for herbaceous and woody species derived by various methods are similarly low: for a Cecropia species, only 1.0  h day−1; Cleyela, 1.1  h day−1; Castilla, 1.5  h day−1; Annona, 1.9  h day−1; Urera, 2.5  h day−1; Helocarpus, 2.6 h day−1; Polygtonatum, 2.7 h day−1; Fagus, 2.8 h day−1; Polygonum, 3.3 h day−1; Antirrhoea, 3.5 h day−1; Anacardium, 4.5 h day−1; and Luehea, 6.1 h day−1 (calculated from Kikuzawa et al. 2009; Kitajima et al. 1997, 2002; Ackerly and Bazzaz 1995; Kikuzawa, unpublished data). The average of all these values is 2.9 h day−1, which raises some questions about the use of Amax alone as a cardinal value for characterizing foliar function.

18

2 Leaves: Evolution, Ontogeny, and Death

Nonetheless, despite all the variability in photosynthetic rate through the day and across the growing season, there is in fact a surprisingly good correlation between the highest photosynthetic rate on a given day and the actual carbon gain on that day (Zots and Winter 1996; Rosati and DeJong 2003; Koyama and Kikuzawa 2009). Zots and Winter (1996) reported a linear relationship between daily photosynthetic gains of single leaves (Aday) and their maximum photosynthetic rate on a given day, Aˆ max (Fig. 2.9): Aday = k· Aˆ max + c



(2.3)

where k and c are constants. Note that if the value of Aˆ max is in fact the true photosynthetic capacity (Amax) and if c is zero, then the proportionality constant k equals the leaf mean labor time. This relationship within days, however, does not assure that the highest photosynthetic rate achieved by a species under ideal conditions, its true photosynthetic capacity, Amax, will in turn correlate consistently with the maximum photosynthetic rate ( Aˆ max ) achieved on a given day. The value of the mean labor time concept as a complement to the concept of photosynthetic capacity is that it emphasizes the necessity of identifying the true maximum photosynthetic rate for a species as opposed to a transient value associated with conditions over a given time interval.

ˆ

Fig. 2.9  The Aday − Amax relationship. The daily photosynthetic gain by a leaf (Aday, mmol m−2 12 h−1) on a given day plotted against the maximum net photosynthetic rate of the leaf on that day ˆ ). Note that Aˆ max here is not the true value of photosynthetic capacity (Amax) for the species, (A max but only the highest photosynthetic rate on each day of observation. (From Zots and Winter 1996)

Age-Dependent Decline in Photosynthetic Capacity

19

Age-Dependent Decline in Photosynthetic Capacity Once a leaf attains full photosynthetic capacity, Amax then gradually decreases with leaf age (Hardwick et  al. 1968; Jurik et  al. 1979; Oren et  al. 1986; Martin et  al. 1994; Mediavilla and Escudero 2003a; Castro-Diez et al. 2005; Warren 2006; Reich et al. 2009). In herbaceous plants with short-lived leaves, the decline is linear and relatively fast (Leopold and Kriedmann 1975; Šesták 1981; Hodanova 1981; Erley et al. 2002; Kikuzawa 2003). In deciduous broad-leaved trees, once full photosynthetic capacity is attained it is maintained fairly steady until immediately before leaffall and then declines quickly (Jurik 1986; Koike 1990), although in some Alnus and Betula species with fairly rapid leaf turnover the time trend is closer to that of herbaceous species (Koike 1990; Kikuzawa 2003; Miyazawa and Kikuzawa 2004). Kitajima et al. (2002) also reported this fairly rapid linear decline in photosynthetic capacity associated with high leaf turnover in two early successional tropical trees in Panama (Fig. 2.10). In five trees with longer-lived leaves in this seasonally dry tropical forest, the decline in photosynthetic capacity with leaf age was more gradual (Kitajima et al. 1997), as was also the case for tropical species in a Costa Rican plantation (Hiremath 2000). Similarly, in evergreen conifers with longer-

Urera

A (µmol CO2 .m−2.s−1)

Cecropia 30 slope = −0.287**

25

slope = −0.191**

20 15 10 5 0

No. Distal Leaves

10 slope = 0.145***

slope = 0.091***

8 6 4 2 0 0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

Leaf Age since Full Expansion (d) Fig. 2.10  Linear decline in photosynthetic capacity associated with rapid growth and high leaf turnover in early successional Panamanian trees. d, days. (From Kitajima et al. 2002)

20

2 Leaves: Evolution, Ontogeny, and Death

Photosynthetic decline rate, nmol /g/s/day

lived leaves, there is a more gradual linear decline of photosynthetic rate with age (Matsumoto 1984; Koike et al. 1994). The general tendency is for photosynthetic capacity to decrease with leaf age, but the rate of decrease lessens as leaf longevity becomes greater (Fig. 2.11). There are two hypotheses to explain the decline of photosynthetic capacity with time: (1) acclimation to the changing light regime of individual leaves as the canopy develops (Hikosaka 1998; Gan and Amasino 1997) and (2) diminished function resulting from age-related changes and senescence of foliar tissues (Guarente et al. 1998; Warren 2006). The two hypotheses are not mutually exclusive. Reduced insolation can induce translocation of nitrogen from a shaded, older leaf to a younger sunlit leaf (Hemminga et al. 1999), with consequent degradation of photosynthetic function in the older leaf. Even if the microenvironment around a leaf is stable over its lifetime, cumulative damage and reduced internal conductance of CO2 (Hensel et  al. 1993; Guarente et  al. 1998; Nooden 2004; Warren 2006) can lead to gradually lower photosynthetic capacity in older leaves.

Ha

10

Ps

Ki02 Po Ki02 Am As Ah Ki97 Bp Fc Ki97 Ki97 Ki97

1

0.1

Ki97

0.01 10

100

1000

Leaf Longevity days Fig. 2.11  Relationship between the rate of decline in photosynthetic capacity with time and leaf longevity. Data are for Acer mono (Am, Kikuzawa and Ackerly 1999), Alnus hirsuta (Ah, Kikuzawa and Ackerly 1999), Alnus sieboldiana (As, Kikuzawa 2003), five tropical tree species (Ki97, Kitajima et al. 1997), two tropical pioneer tree species (Ki02, Kitajima et al. 2002), Betula platyphylla (Bp, Kikuzawa and Ackerly 1999), Fagus crenata (Fc, Kikuzawa 2003), Heliocarpus appendiculatus (Ha, Ackerly and Bazzaz 1995), Polygonatum odoratum (Po, Kikuzawa 2003), and Polygonum sachalinensis (Ps, Kikuzawa 2003)

Senescence and Abscission

21

Senescence and Abscission Whatever may be the rate of gradual decline in photosynthetic capacity, there is a point in time for all leaves when much more rapid changes in both physiology and appearance mark their impending death and abscission (Vincent 2006; Lim et al. 2007). Leaf senescence can be triggered by exogenous factors (seasonal changes in climate, pathogen attack, herbivory) or by endogenous factors (self-shading, fruiting). Whatever the trigger, senescence is intrinsically a process of genetically regulated degradation (Nam 1997; Weaver and Amasino 2001; Nooden 2004; Vincent 2006) involving upregulation of more than 800 genes (Lim et  al. 2007). Senescence allows orderly preparations for seasonal changes in environmental conditions, including recovery of nutrients from senescing leaves and their recycling within the plant. Many senescence-associated genes encode proteins that accomplish parts of the recycling program such as proteases, nucleases, and proteins involved in metal binding and transport (Guarente et al. 1998). Senescing foliage in broadleaf deciduous forests often colors as chlorophyll degrades, no longer masking yellow and orange secondary photosynthetic pigments, and as reddish anthocyanins are produced de novo (Lee et al. 2003; Ougham et al. 2005). Coloring during senescence in species with indeterminate shoot growth is weakly developed and usually initiated within the tree crown or in the lower canopy, whereas in species with determinate shoot growth coloring is strong and tends to occur first in the upper canopy (Koike 1990, 2004). The anthocyanins confer a degree of protection against photooxidation of systems involved in the orderly breakdown and recycling of materials from the senescing leaf (Pietrini et  al. 2002). Leaf photosynthesis invariably declines strongly with the onset of senescence (Makino et  al. 1983; Hidema et  al. 1991; Hanba et al. 2004), and foliar nitrogen content decreases steadily as photosynthetic systems shut down (Mae 2004).

Chapter 3

Quantifying Leaf Longevity

Deciduous broad leaved-forest mixed with some conifers. Midori-numa, Daisetsu-san, Hokkaido, Japan

Defining Leaf Longevity Leaf longevity and “leaf lifespan” are sometimes used as equivalent terms, and at other times “leaf longevity” designates the potential longevity of leaves and “leaf lifespan” their realized longevity. To keep things simple, we here consistently refer only to leaf longevity, qualifying the context as may be necessary. With an emphasis on times when a leaf can carry out its photosynthetic function, we define leaf longevity as the period from the emergence to the fall of a leaf. Because leaf development is a continuous process, a reasonably consistent operational definition of leaf appearance and leaffall is necessary. It is impractical to include the period of leaf initiation and early development before budburst in estimations of leaf longevity, and in any case these earliest stages in leaf development are not directly relevant to photosynthetic function K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_3, © Springer 2011

23

24

3 Quantifying Leaf Longevity

(Vincent 2006). The onset of full photosynthetic function would be the most logical starting point from which to estimate leaf longevity, but this is not ­practical in broad comparative studies because of species-specific variation in the relation between foliar development and foliar function (Niinemets and Sack 2004). We generally resort to recording a phenophase consistent with records in phenological networks (Koch et al. 2007; Morisette et al. 2009) that is associated with a late stage of foliar development, such as expansion and flattening of the leaf blade in broadleaf deciduous trees (Kikuzawa 1978). Similar uncertainties are involved in scoring the timing of leaffall. Senescence of fully formed leaves is generally more drawn out than budburst and early leaf development and hence is less amenable to timing precisely (Worrall 1999). Leaf abscission, which might offer an unambiguous terminal event, is often preceded by significant declines in photosynthetic capacity as leaves change color during senescence (Diemer et  al. 1992; Hensel et  al. 1993), and some trees retain dead leaves (marcesence: Abadia et al. 1996). Any scoring system based on changing color or even abscission also can be disrupted by a stress event such as an early freeze that abruptly kills leaves outright regardless of their degree of senescence or development of their abscission layer. We review here the common methods for estimating leaf longevity, touching on ways to minimize uncertainty associated with scoring leaf emergence and leaffall when that is possible for a given method.

Box 3.1  Heterophylly

(continued)

Estimating Leaf Longevity from Leaf Turnover on Shoots

25

Box 3.1  (continued) Heterophylly refers to conspicuous differences in shape, size, or function among the leaves on a plant. For example, the leaves that appear on a shoot of Cercidiphyllum japonicum early in the season are round and heart shaped at the base, but those appearing later in the season are flat at the base and more triangular in shape. Such early and late leaves often differ not only in shape but also in longevity and physiological function.

Estimating Leaf Longevity from Leaf Turnover on Shoots The shoot is the modular unit of leaf production, and hence the natural focus for sampling coherent sets of observations to derive estimates of leaf longevity. Monitoring the emergence and fall of leaves on a particular shoot at frequent intervals over an extended time period is the definitive method for estimating leaf longevity. Counts of leaves are usually recorded at the midpoint of census intervals, so the more frequent the observations, the more precise is the estimate of leaf longevity. Frequent counts of all the leaves on a shoot are tedious, but the accumulated data are highly informative. The method gives a complete record of temporal variation in leaf production and leaf longevity, which can be especially important for species with indeterminate shoot growth (Fig. 3.1). Since the date of emergence and the date of fall are known for each individual leaf, both the mean and the variance in leaf longevity can be calculated. These demographic data can be reworked to describe the probability of leaffall as a function of leaf age (Dungan et al. 2003). Seasonal or interannual differences in leaf longevity or differences between early and late leaves in heterophyllous species can also be analyzed by partitioning the data accordingly. In principle, a census can be carried out over many years, but in practice this approach often is restricted to observations within a growing season. Data most commonly are summarized initially in a leaf survival curve (Fig. 3.2), which can illustrate in detail the differences in leaf demography that underlie the calculation of leaf longevity. The total number of leaf-days (the area under the curve showing the number of living leaves) divided by the total number of leaves produced is the mean leaf longevity over the period of observation, which typically would be one complete growing season (Kikuzawa 1983). There are various alternative calculations for estimating the mean leaf longevity from a census of the numbers of leaves emerging and falling over a time interval. A graphical framework introduced by Navas et al. (2003) helps us to understand the ways that the relative timing of leaf emergence and leaffall can affect estimates of mean leaf longevity. If for simplicity the increase (leaf emergence) and decrease (leaffall) in numbers of leaves are approximated by straight lines over time, then leaf longevity (L) can be considered in a graphical framework (Fig. 3.3) linked to the following equation (Navas et al. 2003):

(

L = tp + tL

)/ 2 + t

(3.1)

26

3 Quantifying Leaf Longevity

Fig. 3.1  Shoots of Alnus hirsuta in Bibai, Hokkaido, northern Japan, in mid-June (a) and in early July (b). By mid-June four leaves (1–4) have fully expanded and a fifth leaf (5) has protruded from the pair of bracts and is just beginning photosynthetic activity. By early July, more leaves have been produced (6–8) and the first and the second leaves (1, 2) have fallen. There are two leaf scars and six leaves (third to eighth) on the shoot; the ninth leaf is just appearing, but because it is still enclosed by bracts it is not yet counted (Kikuzawa 1980)

Fig. 3.2  Leaf survival curves for representative deciduous broad-leaved trees: Alnus hirsuta (a), Magnolia obovata (b), and Quercus mongolica var. grosseserrata (c). Open circles represent the cumulative number of leaves that have emerged through the growing season; closed circles begin with the onset of leaffall and track the number of leaves still attached at each subsequent census. The mean leaf longevity over the period of observation is the area under the line showing the number of attached leaves divided by the total number of emerged leaves

Cumulated number of leaves produced or lost

Estimating Leaf Longevity from Leaf Turnover on Shoots

a

F

E

G

E

G H

Lb

B tP

b

La

La Lb Lc

A

27

C

Lc

D

t

tL Time of leaf production or loss (d)

c

B

C

A tP

D

t

tL Time of leaf production or loss (d)

E

G

H A C tP

B

D

t tL Time of leaf production or loss (d)

Fig. 3.3  A framework for assessing the determinants of variation in leaf longevity (after Navas et al. 2003). The panels illustrate different patterns for the relative timing of leaf emergence and leaffall. This graphical framework relates the duration of the period of leaf emergence (tp, the time from the appearance of the first leaf to the last), the duration of the period of leaffall (tL, the time from the first fallen leaf to the last), and the length of the stable period t from the end of leaf emergence to the start of leaffall. If leaffall starts within the period of leaf emergence (i.e., the leaf emergence line and leaffall line overlap), then t is scored as a negative value. (a) The case when there is a time interval between the periods of leaf emergence and leaffall. (b), (c) Cases in which the emergence and fall of leaves overlap in time: t is long in (b) but short in (c). In (b), tp and tL are equivalent, but in (c) tL is far longer than tp, La, Lb, and Lc in (a) indicate the leaves in the same cohort having different longevity as a result of differences in the timing of leaffall. Symbols and calculations are explained further in the text

where tp is the duration of the period of leaf emergence (i.e., the time from the appearance of the first leaf to the last), tL is the duration of the period of leaffall (i.e., the time from the first fallen leaf to the last), and t is the length of the period from the end of leaf emergence to the start of leaffall when leaf numbers are stable. If leaffall starts within the period of leaf emergence (i.e., the leaf emergence line and leaffall line overlap), then t is scored as a negative value. Craine et al. (1999) adopt essentially the same framework. When leaf longevity is too long for the continuous observation of all the leaves on a shoot to be practical from emergence

28

3 Quantifying Leaf Longevity

to fall, we can calculate leaf longevity based on observations over any reasonable interval (Williams et al. 1989) by using this equation:

(

/

)

L = N 2 / d − (N 2 − N1 ) b (t2 − t1 )

(3.2)

where N1 is the standing number of leaves at the initial observation (t1), N2 is the sum of N1 and newly produced leaves during t2 − t1, d is the rate of leaffall during the observation period t2 − t1, and b is the rate of leaf production during this period. When N2 − N1 is equal to b, this can be reduced to the following equation (Fonseca 1994):

(

)

/

L = (N1 + b ) d − 1 (t2 − t1 )

(3.3)

These equations assume stable leaf numbers during the period of observation, which allows leaf longevity to be estimated using either the leaf production rate or the rate of leaffall. If b = d in either equation, then leaf longevity can be estimated even more simply as follows (Southwood et al. 1986; Navas et al. 2003):

L = N1

/ d

(3.4)

where t2 − t1 is 1 (year, month, day, etc.). In a situation in which the number of leaves fluctuates somewhat around an essentially stable state within the period of observation, King (1994) provides an alternative version of (3.4) utilizing the average number of leaves (Nav) instead of the initial leaf number (N1):

L = (t2 − t1 )N av

/ (0.5 (b + d ))

(3.5)

Finally, consider (3.2)–(3.5) in relationship to the graphical framework (see Fig. 3.3) introduced by Navas et al. (2003). Because b = N/t and d = N/t, the number of leaves (Ni) at any time ti is given by

{ ( )}

N i = bti − d ti − t p + t

(3.6)

and leaf longevity by

L = N / d = (b / d − 1) ti + t p + t

(3.7)

If b = d, (3.7) reduces to L = tp + t, which is the same as (3.1) from Navas et al. (2003). These various calculations of leaf longevity are all variants on a theme that arise in the juxtaposition of alternative sampling designs and interspecific contrasts in leaf demography. All the calculations use data on the relative timing of leaf emergence and leaffall in different demographic scenarios that can be visualized in the graphical framework introduced by Navas et al. (2003). In all the calculations of leaf longevity based on repeated census of leaf emergence and leaffall, the precision of the leaf longevity estimate ultimately depends on the census interval. The longer the interval between observations, the less precise will be the estimate of leaf longevity. Leaves may emerge or fall at any

Estimating Leaf Longevity from Leaf Turnover on Shoots

29

time in the period between intervals, so the common practice of referencing the data to the day midway between two sequential observations can introduce considerable error as the observation interval increases beyond a week. Up to about a week the uncertainty in the timing of leaffall or emergence is of the order of the few days potential error associated with the intrinsic ambiguity in observations of the phenophases themselves. In a study of leaf emergence and fall on the shoots of trees observed at intervals as long as a month, Dungan et al. (2003) introduced an approach to minimizing the error associated with longer intervals between observations. They observed shoots at weekly or biweekly intervals early in the seasons, so that they could fit their observations on leaf production and mortality to sigmoid growth functions. These functions can be combined to estimate the number of living leaves at any time, including times between actual observations. Fitting their leaf survivorship data to a gamma function, Dungan et al. (2003) then used failure-time analysis to estimate the probability that a leaf would survive to any given day after budburst and report leaf longevity as the age at which the probability of a leaf dying reaches 50%, the leaf half-life. Strictly speaking the leaf half-life and mean leaf longevity may not be perfectly identical because of seasonal changes in half-life, but when leaf longevity is longer than about 80 days it appears that half-life can provide a convenient surrogate for mean leaf longevity (Diemer 1998a; Dungan et al. 2003). A reanalysis of the Navas (2003) data confirmed the utility of this method and showed it to be more accurate than estimates based on the midpoint between consecutive observations (Dungan et al. 2008).

Box 3.2  Leaf Cohort Some plants produce leaves sequentially through the growing season, others all at once in a single episode early in the growing season. Any leaves emerging together at some time form an even-aged cohort: these may be all the leaves that will be produced in a year or just those produced at one time by a sequential leafing species. Following the death of individual leaves in a cohort over time provides a survivorship curve, which often yields insights into foliar function and canopy architecture. In successive leafing species, multiple cohorts of leaves coexist on the plant at any time in the season, each cohort following its own survivorship curve. A cohort produced early in the growing season has older leaves than a cohort produced in midseason, and more leaves in the older cohort may have senesced and fallen by the time the midseason cohort leaves emerge. In other words, in successive leafing species the leaves on a plant are multiaged, and the total number of leaves at any time in the season is the difference between all the leaves that have emerged across all cohorts and those that have fallen.

30

3 Quantifying Leaf Longevity

Estimating Leaf Longevity from Census of Leaf Cohorts over Time The primary alternative to following the emergence and fall of leaves on shoots is to focus on the leaves themselves, following the fate of cohorts of leaves over time. Whether estimates of leaf longevity are derived by shoot- or cohort-based methods, the calculations depend fundamentally on records of the birth and death of leaves. The cohort approach adapts methods of life table analysis well established in population biology (Krebs 2008) that provide estimates not only of leaf longevity but also age-dependent leaf mortality rates. The approach of Dungan et al. (2003) can be used in shoot-based studies to derive age-dependent probabilities for leaf death as well. The distinction between shoot-based and cohort-based approaches to estimating leaf longevity has more to do with context and sampling design than with any fundamental difference in the basis for estimation of leaf longevity. Both dynamic and static sampling designs can be used in cohort-based estimates of leaf longevity (Krebs 2008). Estimates in dynamic analyses are derived by following single cohorts of leaves from birth to death, which may impose a long and arduous sampling program. For example, Xiao (2003) provides an example of a dynamic life table analysis based on following a cohort of 1,000 leaves of Pinus tabulaeformis at annual intervals over a 5-year period (Table 3.1). The first column in the resulting life table records leaf age in years, with age zero denoting the start of the census. The second column, lx, is the number of the initial cohort surviving at age x. The third column, dx, is the mortality during age x, which is given by (lx - lx+1). Lx, the average of lx between two needle ages, is given by (lx + lx+1)/2, and defines the height of the histogram in Fig. 3.5. Tx is the summation of Lx from the older to younger age, which is equivalent to the area of the histogram, Tx = Tx+1 + Lx. Tx divided by lx represents the average expected life at age x. The line in Fig. 3.5 is the lx curve, which illustrates the survivorship of the 1,000 leaves over time. The average life expectancy at age zero is the mean longevity of leaves. In the case of this pine species, the mean leaf longevity

Table 3.1  Dynamic life table for needles of Pinus tabulaeformis (after Xiao 2003) Age (years) lx dx Lx Tx ex 0 1 2 3 4 5

1,000 760 478 232 27 3

240 282 246 205   24   3

880 619 355 130   15   1

2,000 1,120 501 146 16 1

2.00 1.47 1.05 0.63 0.59 0.33

lx, The number of the initial cohort surviving at age x; dx, the mortality during age x; Lx, the average of lx between two needle ages; Tx, the summation of Lx from older to younger age; ex, the average expected life at age x

Estimating Leaf Longevity from Census of Leaf Cohorts over Time

31

is 2.0 years. Graphically, this is equivalent to the total area of the annual histograms divided by the initial leaf number, which is essentially the same as the method shown in Figs. 3.2 and 3.3. Such long-running observation series intended for a dynamic life table analysis sometimes are stopped for practical reasons when half the leaf cohort has died (Kohyama 1980; Diemer 1998a,b); truncating the observations precludes calculation of the age-dependent probabilities of leaf death, but the observed leaf half-life provides a useful estimate of leaf longevity in its own right (Diemer 1998a; Dungan et al. 2003). On the other hand, the dynamic life table approach applies equally well to short series of observations over days, weeks, or months rather than years. Miyaji and Tagawa (1973, 1979) constructed dynamic life tables for leaves of Tilia japonica and Phaseolus vulgaris, both species with short-lived leaves. The longer observations continue, the more risk that dynamic life table analyses will be confounded by stochastic variation in the risk of mortality across the years of observation. Dynamic life table analyses are not only confounded by stochastic variation but also biased by differential rates of leaf mortality in better versus worse leaf microenvironments (Takenaka 2003). Thus even if leaves are selected randomly to establish the sampled cohort, the sample will concentrate into “better” places over time. Static life table analyses are not immune to the problem of stochastic interannual variation, but they do not suffer this sampling bias. The data required for static life table analyses are gathered in one round of sampling, which makes this approach logistically appealing. Static life table analyses do not follow a single leaf cohort over its lifetime but instead reconstruct the life table from different aged cohorts of leaves observed at a point in time. Unfortunately, the record of growth cycles in tropical regions usually is too obscure or ambiguous to apply the static life table approach with confidence. In tropical forests, the number of leaves on a branch whorl does give information about leaf emergence pattern, but the seasonal timing of leaf emergence is not fixed in species or even on branches in a single tree (Kikuzawa et  al. 1998). For example, in Araucaria araucana the mean interval between successive whorls was not exactly 1 year, and varied among individual trees depending on their light regime (Lusk and Le-Quesne 2000). On the other hand, the required sampling is relatively easy to apply with evergreen trees in boreal and temperate regions where the basic approach has a long history of use (Pease 1917). In these strongly seasonal climates, clearly visible terminal bud scars typically demarcate annual growth increments along the shoot (Fig.  3.4); it is easy to reconstruct the ages of growth segments along a branch, and hence the age of the leaves on each segment. We then can infer the number of leaves in each annual cohort by counting the number of leaves still attached and the number of leaf scars left by fallen leaves in each shoot growth increment. This static approach, however, assumes no year-to-year variation in leaf demographic parameters, which can be problematic because of interannual climatic variation, age-dependent loss of leaves to herbivory, or trade-offs in resource allocation between production and reproduction. Kayama et al. (2002), for example, found this assumption did not hold for some evergreen conifers. Interannual variation can also confound leaf longevity estimates from a dynamic life table

32

3 Quantifying Leaf Longevity Current Shoot

1-year Leaves

2-year Leaves

Fig. 3.4  Number of leaves in annual whorls of shoot growth in Osmanthus chinensis, a broadleaf ornamental evergreen tree in Japan Fig. 3.5  Decline in initial cohort of needles over time (in Xiao 2003; drawn by KK after Xiao)

analysis, as it would in a shoot-based analysis of species with long-lived leaves as well. In all the census methods for estimating leaf longevity, error variance inevitably increases with leaf longevity.

Estimating Leaf Longevity from Census of Leaf Cohorts over Time

Box 3.3  Allocation and Partitioning of Resources Both the products of photosynthesis and the mineral resources available for plant growth are in finite supply. Hence, there inevitably are limits and trade-offs imposed on plant function. Carbohydrates and mineral resources used in growth are not available for reproduction. Plants partition resources differentially to satisfy competing demands, with the result that cumulative allocations to plant parts differ. For example, biomass allocated to leaves, stems, roots, flowers, and fruits arise in the partitioning of net primary production (NPP) and reflects trade-offs imposed by the requirements for survival and reproduction in a given environmental regime.

Box 3.4  Allometry and Isometry The form and function of organisms can vary with their size. For example, the allocations to root, stem, and leaves can shift with total plant size. Such size-dependent changes can be expressed by a power function of the form A = aWb where A is a measure of some aspect of form or function, W is an appropriate measure of size, and a and b are allometric constants. This equation is mathematically equivalent to log (A) = log (a) + b log (W), which graphs as a straight line. If b is exactly unity, then A is directly proportional to W and the relationship is said to be isometric. In an isometric relationship, a twofold increase in size results in a twofold increase in form or function. In many biological cases, however, form and function change disproportionately with size: b is not unity and the relationship is said to be allometric. For example, allometric relationships are used in forest science to estimate the biomass of standing trees. The biomass of entire trees (W) or their parts such as leaves (WL), branches (WB), stems (WS), or roots (WR) all are correlated to measures of body size such as trunk diameter at breast height (D) and tree height (H). Using the equation W = aD b, we can estimate the total biomass of a tree simply by measuring its diameter at breast height. Species differ in the degree to which their total biomass changes disproportionately with size, but in all cases the relationship is allometric and b is less than unity.

33

34

3 Quantifying Leaf Longevity

Estimation of Leaf Longevity from Leaf Turnover at the Stand Level

Litter traps set on the forest understorey of Alnus japonica (Hakusan, Ishikawa, Japan)

Leaf longevity is occasionally estimated as the inverse of leaf turnover rates at the stand level. Leaf biomass, estimated by allometric methods (Clark et al. 2001) and assumed to be in steady state, is compared to the biomass of falling leaves collected in leaf traps (Tadaki 1965; Edwards and Grubb 1977; Oshima 1977; Kikuzawa et al. 1984; Takiya et al. 2006). Under the assumption of steady-state leaf numbers in the canopy, leaf longevity can be estimated as the inverse of the ratio of leaf biomass (g  m−2) to annual leaffall adjusted for the length of the growing season. For example, the standing leaf biomass in an Alnus inokumae plantation was 163  g m−2, while annual total leaf fall was 315  g m−2 during a growing season (Kikuzawa et al. 1984). This finding indicates a leaf turnover rate of about 2 over the season, and hence an average leaf longevity of the order of 93 days, about one-half the length of the growing season. There are, however, serious problems with this method. First, from a practical point of view the approach is too time consuming to acquire species-specific estimates except in monospecific stands. Second, the assumption of steady-state leaf biomass is commonly unrealistic. Third, the variance associated with the allometric estimates of canopy biomass will often be of the order of magnitude as the biomass of fallen leaves. Fourth, the biomass of individual leaves at abscission is not equal to their biomass in the canopy. This method of estimating leaf longevity is best avoided in comparisons at the species level.

Revisiting the Basic Concept of Leaf Longevity

35

Box 3.5  Photoinhibition The photosynthetic systems in leaves have two basic components, one utilizing chlorophyll and various accessory pigments to capture solar energy, and the other a series of biochemical pathways that uses the captured energy to build carbohydrates with carbon derived from atmospheric carbon dioxide. When a leaf is constructed, these two systems are created in ways suited to the environmental regime in which the leaf will function as a photosynthetic organ. Photoinhibition arises when transient environmental conditions lead to more solar energy being captured than can be utilized in the biosynthetic reactions. For example, this can occur in winter for evergreen shrubs in the forest understory when photosynthetic enzymes are inactive consequent to low temperature, but high light levels occur in the usually shaded forest understory because of leaffall in a deciduous forest canopy (Miyazawa and Kikuzawa 2004, 2006; Miyazawa et al. 2007).

Revisiting the Basic Concept of Leaf Longevity In ending this chapter, we return to the concept of leaf longevity itself, which loses its close functional connection to photosynthesis when leaves survive periods unfavorable to photosynthetic activity such as winter in high latitudes or periods of severe drought. Considering seasonal variation in conditions favorable to photosynthesis, we have proposed a concept of functional leaf longevity (Kikuzawa and Lechowicz 2006). Functional leaf longevity is the number of days when a leaf can actually carry out photosynthesis over its lifetime. In principle, functional leaf longevity is defined as leaf longevity minus unfavorable days (winter or dry season) during the leaf lifetime. In leaves of deciduous trees or annuals in temperate regions, functional leaf longevity is generally the same as leaf longevity. In other instances, a favorable period within a year can be unambiguously defined and recognized. This is the case for arctic and alpine species associated with snowbeds; the period when plants are snow covered is considered to be unfavorable for photosynthesis, although some light penetrates snow to about 30  cm (Starr and Oberbauer 2003). For example, Kudo (1992) examined the effect of differences in favorable period created naturally by the timing of snowmelt on the leaf longevity of dwarf evergreen and summergreen plants on Mt. Daisetsu, central Hokkaido. The snow-free period varied twofold, from 60 to 120 days year−1, depending on topographically induced variation in snow depth. In the case of other evergreen species, often it is not as easy to evaluate functional leaf longevity because some evergreen leaves do photosynthesize during winter. For example, understory evergreen plants in winter may suffer photoinhibition (Miyazawa et al. 2007) but still are photosynthetically active in what might at first be considered an unfavorable season. Camellia japonica, an understory evergreen tree in the deciduous forests of central Japan, actually has higher daily photosynthesis in winter than summer when the deciduous canopy is leafless (Miyazawa

36

3 Quantifying Leaf Longevity

and Kikuzawa 2004, 2006). Deciding general criteria for defining an unfavorable period caused by drought is no less straightforward than for winter cold. The different phenological adaptations of species can affect variation in the degree of unfavorable conditions for photosynthesis even among co-occurring species. For example, an unfavorable period resulting from drought in Australia has been defined as the occurrence of at least 3 consecutive months with less than 25 (or 50) mm precipitation (Eamus and Prior 2001). Eamus et al. (1999b) compared photosynthetic rates throughout a year for some tree species in a seasonal tropical forest subject to an unfavorable dry season under this criterion. Two evergreen species (Eucalyptus tetrodonta, Eucalyptus miniata) showed relatively stable photosynthetic rates with only modest declines in the dry season. In contrast, decline in the photosynthetic rate of leaves retained during the dry season on semideciduous Erythrophleum chlorostachys was greater, and in fully drought deciduous species such as Cochlospermum fraseri and Terminalia ferdinandiana was zero because they are leafless. Despite these complications, in principle it makes sense to discount leaf longevity for periods unfavorable to photosynthetic activity. Available data suggest there also are some unappreciated and potentially useful linkages between functional leaf longevity and gross primary production at the ecosystem level (Kikuzawa and Lechowicz 2006); this is apparent in the relationship between the standing biomass of foliage and foliage longevity estimated as the inverse of leaf turnover in diverse seasonal and aseasonal forests (Fig.  3.6). Considering the traditional definition of leaf longevity without regard to favorable or unfavorable conditions for photosynthesis, then leaf production rates (the slope of this relationship) in forests from seasonal and aseasonal climates appear to

Fig. 3.6  Evidence that functional leaf longevity can provide a clearer relationship to ecosystem function than leaf longevity uncorrected for time unsuitable for photosynthetic activity (Kikuzawa and Lechowicz 2006). Left: Relationship of standing leaf biomass and leaf longevity in diverse forests; the slopes differ significantly between aseasonal and seasonal forests. Right: The difference in slopes is no longer significant when functional leaf longevity is considered. Closed circles, aseasonal forest; open circles, seasonal forest

Revisiting the Basic Concept of Leaf Longevity

37

different significantly. However, if we discount periods in the seasonal climate unfavorable for photosynthetic activity, then the rate of leaf production in the system is not appreciably different between regions (Fig. 3.6). This conceptually simple adjustment in gauging the longevity of leaves has interesting implications for estimating the photosynthetic production at the ecosystem level. Gross primary production can be expressed as the product of leaf biomass and average photosynthetic capacity rate over the favorable season:

P = k · B · Amean · d

(3.8)

where P is gross primary production (g m−2 year−1), B is leaf biomass (g m−2), Amean is the average maximum photosynthetic rate (Amax) over the favorable season, and d is the duration (s year−1) of the favorable season and k is a constant. The duration can be partitioned into duration within a day (mean labor time, m h  day−1) and duration within a year (days in which plants can carry out photosynthesis within a year, the favorable period length, f days year−1).

d=m· f

(3.9)

Here we incorporate functional leaf longevity Lf into (3.8), multiply the right-hand side of (3.8) by (Lf /Lf = 1), and substitute (3.9) into (3.8) to obtain:

P = k (B / Lf ) · Amean m · Lf · f

(3.10)

The first term in (3.10), B/Lf, is the rate of daily leaf production; this is not so appreciably different among forests (Fig.  3.6). The next term, Amean m · Lf, is the lifetime photosynthetic gain by a single leaf. Thus, gross primary production (GPP) of a plant community potentially can be expressed simply as the product of only three terms: GPP = (Life time gain by a leaf ) × (daily leaf production rate ) × (favorable period length )

(3.11)

If lifetime photosynthetic gain for individual leaves can be taken as a constant across species, then gross primary production could be determined simply by the length of favorable period (f ). These rather remarkable, if speculative, possibilities are not without support in published data. Kira (1969) summarized the gross primary production data of forests in the world and concluded that gross primary production can be explained by the leaf area index (LAI) and the length of growing season (Kira 1970). Here, LAI is the total leaf area per unit land area of the forest and is equivalent to the product of leaf biomass and specific leaf area (SLA: m2 g−1). The length of the growing season is the favorable period length (f ). When we plot the relationship between gross primary production and favorable period length using Kira’s data, we obtain a significant relationship (see Fig. 3.7), suggesting the strong contribution of f in determining gross primary production. Whether or not these possibilities are sustained by further work, it is clear that the functional linkages between leaf longevity and ecosystem productivity merit close investigation.

38

3 Quantifying Leaf Longevity

Fig. 3.7  Relationship between gross primary production of forests and the favorable period length. Data were plotted from those of Kira (1969)

Box 3.6  Leaf Construction Cost The construction of leaves requires investments not only in materials but also in the energy required to acquire those materials and assemble the leaf. The constituent elements of the diverse chemicals in a leaf were acquired and assembled into foliar tissues at some cost in respiratory energy, which in turn was acquired through photosynthesis. Net primary productivity is essentially a measure of the photosynthetic gains that accrue from investments in leaves, so it only makes sense to measure the cost of those investments in a unit linked directly to photosynthesis. Thus, leaf construction cost is usually quantified by an estimate of the amount of glucose (the immediate product of photosynthesis) required to construct a unit quantity (1 g or 1 m2) of leaf tissue. Estimating the material cost of the carbon in a leaf is fairly straightforward because leaf tissues typically are about 50% carbon. Because glucose is 40% carbon, at least 1.2 g glucose can provide the carbon needed to construct each gram of leaf tissue. The more difficult problem is estimating the additional respiratory energy involved in acquiring other foliar constituents and actually assembling the leaf. These energy components include, for example, the respiratory costs of acquiring nitrogen, phosphorus, potassium, sulfur, and other mineral elements contained in biochemicals critical to leaf function such as chlorophyll and photosynthetic enzymes. There are two approaches to this problem: one is based on measurements of respiration of growing leaves and the other on analysis of the constituents of leaf tissue. Although it can be technically difficult, one can measure the respiration associated with growing leaves (Merino et  al. 1982), which can be partitioned into components (continued)

Revisiting the Basic Concept of Leaf Longevity

Box 3.6  (continued) proportional to growth rate (dW/dt) and leaf weight (W): R = r (dW/dt) + uW. Then, the parameter r multiplied by the final leaf mass gives an estimate of the respiratory energy used for construction of the leaf. Alternatively, in principle one can identify and quantify all the biochemical components of a leaf and sum up their individual costs of construction (Penning de Vries et al. 1974), but this is not very practical. A more practical variant on this approach, which has proven reliable, estimates the energy required to construct a leaf by measuring the energy released on combustion of the leaf tissue (Williams et al. 1989; Griffin 1994).

39

Chapter 4

Theories of Leaf Longevity

Leaf scars of Alnus japonica

Costs and Benefits of the Evergreen Versus Deciduous Habit The approach to theoretical work on leaf longevity is inspired by optimization models that came into vogue during the late 1960s to try to understand alternative modes of adaptation (Lewontin 1978). Reasoning in this conceptual framework and reviewing available data, Chabot and Hicks (1982) argued that leaves with higher construction cost should be longer lived because the period of photosynthetic gains to pay back the construction cost will be longer than for a leaf constructed at less cost. Using seven Mexican shrubs in the genus Piper (Piperaceae), Williams et al. (1989) set out to test this idea that leaf longevity should be determined by the time required for a leaf to pay back the costs of its construction. They found that, in contrast to Chabot and Hick’s supposition, leaf construction cost

K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_4, © Springer 2011

41

42

4 Theories of Leaf Longevity

was negatively correlated with leaf longevity, not positively. Because construction costs measured as g[glucose]·g[leaf]−1 varied relatively little among their seven Piper species, only 1.2–1.6  g g−1, they also examined the correlation of leaf longevity and leaf mass per unit area (LMA, g m−2), another presumed indicator of leaf construction cost. The LMA of the Piper species had manifold greater variation, ranging from 15 to 50 g m−2, but also no significant correlation with leaf longevity in these Piper species. These results led Williams et  al. (1989) to consider instead the ratio of cost and gain as a predictor of leaf longevity. Their proposed relationship is given by the equation: L = k ·C/a (4.1) where L is leaf longevity, C is leaf construction cost, k is a constant that prorates cost of construction to a daily basis over the leaf lifetime, and a is the mean daily photosynthetic rate of the leaf over its lifetime. They reported a significant positive correlation between this cost–benefit ratio and leaf longevity (Fig.  4.1). Sobrado (1991) reported a similar result for six deciduous and four evergreen woody species in a Venezuelan dry tropical forest using the instantaneous maximum photosynthetic rate (Amax) rather than the daily photosynthetic rate as a measure of leaf productivity. Oikawa et al. (2004) obtained a similar positive correlation between the cost/photosynthesis ratio and leaf longevity among different leaves in the fern Pteridium aquilinum.

Daily carbon gain

Construction cost

(d)

10000

1000

100

10

1 50

100

200

300

500

1000

Leaf longevity (d) Fig.  4.1  Relationship between the ratio of (leaf construction cost)/(leaf carbon gain) and leaf longevity. Symbols code different species of Piper (Piperaceae); d, days. (From Williams et al. 1989)

Leaf Longevity to Maximize Whole-Plant Carbon Gain

43

Leaf Longevity to Maximize Whole-Plant Carbon Gain Kikuzawa (1991) adopted and elaborated the idea that leaf longevity should be set not simply by the magnitude of the construction cost, but also by considering the influence of leaf production potential on the time required to recoup the cost of leaf construction. More specifically, Kikuzawa reasoned that leaf longevity should be selected to maximize lifetime net carbon gain, not for the leaf alone but more generally for the individual plant that bears the leaf (Kikuzawa 1991). In this context, consider the carbon gain by a single leaf. It has long been recognized (Šesták 1981) that, at the time of leaf maturation, the instantaneous photosynthetic rate of the leaf is at its maximum and then declines with leaf age. Let this maximum daily photosynthetic rate be a and express the daily photosynthetic rate at time t after leaf maturation as p(t ) = a · (1 − t / b)



(4.2)

where a/b is the rate of decline in photosynthetic rate with time and b is the time when the rate becomes zero. Thus, b defines the potential leaf longevity (Ackerly 1999). The cumulative net carbon gain per unit area of leaf (G) arises in the summation of photosynthetic gain per unit time (p) over the leaf lifetime minus the carbon cost of leaf construction: t

G(t ) = ∫ p(t )dt − C



(4.3)

0

where C is the cost to produce the leaf expressed as g[glucose] · m[leaf]−2. The construction cost (C) is estimated as the product of leaf mass per unit leaf area (LMA, g m−2) and a factor (c) to convert a unit weight of glucose to a unit weight of leaf tissue. This conversion factor, which is itself referred to as a construction cost in the literature, falls in the range 1.1–1.9 g[glucose] · g[leaf]−1 and can be taken as a constant value of 1.5 g[glucose] · g[leaf]−1 for most purposes (Griffin 1994; Diemer and Korner 1996; Villar and Merino 2001; Villar et al. 2006).  Box 4.1  Marginal Gain Microeconomic models used to maximize economic gain in commercial enterprises can be adapted to analyses optimizing resource gain in plants. Plants acquire, store, and allocate different kinds of resources such as carbon and nitrogen through investments in resource gain capacity such as leaf and root production (Bloom et al. 1985). In this modeling framework, plants are predicted to obtain resources at the lowest possible cost and utilize them to gain the highest possible return. Marginal gain essentially expresses the efficiency of resource gain, not simply the total amount of gain. For example, a plant should continue to acquire and invest the resources required to produce leaves and roots until the marginal gain on the investment becomes equivalent to the marginal costs of acquiring the resources. Additionally, we can expect that the plant should adjust the allocation of resources so that growth is equally limited by all required resources.

44

4 Theories of Leaf Longevity

b Gr

a

r p

Net Gain (G)

Gp

0 −C

0 0

topt

te

−C

0

topt

2topt

te

Time (t) Fig. 4.2  Leaf longevity is set by the optimal timing for replacing a leaf to maximize its cumulative photosynthetic gain at the whole-plant level. The potential photosynthetic gain by a single leaf over its lifetime is illustrated in (a). If an individual plant could retain a single leaf, the optimal time for replacing that leaf to maximize gain is topt, or the point at which the line from the origin touches the curve. C is the construction cost of the leaf and te is the timing when the instantaneous photosynthetic rate of the leaf becomes zero. The graph in (b) suggests that replacing the leaf at topt will yield a greater total gain than retaining the leaf for a second season. Gr and Gp represents the cumulative gain by a leaf when replacing (r) and persisting (p) leaves at te. (From Kikuzawa 1991)

The qualitative consequences of these relationships for leaf longevity can be illustrated graphically (Fig. 4.2). At the moment the leaf matures (time 0), there has been no photosynthetic gain but the cost of leaf construction has been incurred, so the cumulative gain curve has value (0, –C). Cumulative gain increases monotonically with time, paying back the invested cost and then achieving net carbon gains. Through the combination of decreased function with leaf age specific to a species and the annual progression of environmental conditions in a locality, we expect that generally the rate of carbon gain will diminish with time, until at some leaf age or environmental condition photosynthetic function is lost and respiratory costs associated with maintenance and defense actually lead to a net loss of carbon produced by the leaf. Thus, the point when the gain curve is a horizontal line is the time of maximum potential gain by the leaf. If we designate the time of maximum gain as te, it will be clear from (4.2) that te = b, the potential leaf longevity. So long as there are no limitations imposing a longer period of leaf retention, this is also the optimal timing for leaf turnover at the whole-plant level if photosynthetic gains are to be maximized. To better illustrate the basic logic of Kikuzawa’s model, consider a situation in which a plant can retain only one leaf at a time, and hence the optimal strategy at the whole-plant level collapses to simply replacing this single leaf. Then the optimal timing to maximize gain by the plant is not to maximize cumulative gain (G) but to maximize marginal gain (g), or

g =G/t

(4.4)

Leaf Longevity to Maximize Whole-Plant Carbon Gain

45

This optimal timing is the point that a line originating from the origin touches the cumulative gain curve. To obtain this optimal timing, topt, we differentiate g with time t, and obtain the time t when the differential becomes 0. If at this point, the second differential is negative, then this point is the maximum. The solution is given by t opt = (2·b · C / a )0.5 (4.5) This result suggests that optimum leaf longevity (topt) is determined by three para­ meters: (1) the daily photosynthetic rate of a young but fully mature leaf (a), (2) the age of the leaf when the daily photosynthetic capacity becomes 0 (b), and (3) the unit cost to produce the leaf (C). This solution, which is consistent with the conceptual model proposed by Chabot and Hicks (1982), provides a comprehensive framework for the analysis of leaf longevity; this framework also subsumes terms such as C/a that Williams et al. (1989) had earlier related to longevity through their empirical studies. Givnish (2002) criticized the focus on carbon in Kikuzawa’s (1991) model for leaf longevity, arguing that the only real constraint on leaf retention is the need to retranslocate nutrients for use in new leaves, either immediately or for storage through an unfavorable period in the annual cycle. He argued that even if leaves have only very limited potential to secure further carbon gains, it is nonetheless useful to take those gains so long as invested nutrients need not be recycled. He points out that carbon, the main element of photosynthetic gain, is mainly used to strengthen leaves through investments of cellulose, hemicellulose, and lignin in cell walls and fiber – large polymers not easily broken down and reused. Givnish (2002) would prefer a model for leaf longevity at the whole-plant level that considered jointly the economies of carbon and critical nutrients limiting leaf function (e.g., N, P), but is this really necessary to gain a fundamental understanding of variation in foliar design? At least two lines of evidence suggest otherwise: (1) foliar N and P concentrations are well correlated to photosynthetic function (Wright et al. 2004) and to one another (Han et al. 2005; Reich et al. 2009), indicating a close linkage in resource allocation and function at the leaf level, and (2) species on average recover only about half their foliar N before leaf abscission (Eckstein et  al. 1999; Hemminga et  al. 1999; Kobe et  al. 2005; Yuan and Chen 2009). The important point that determines leaf replacement is not that the nutrients concerned are or are not retranslocated, but that there is some limitation to carbon gain in retaining leaves. It is simplest to assume that allocations of N and P follow rather than determine investments of carbon and the potential for carbon gain. On the other hand, Oikawa et al. (2009) show that leaves can be shed before they have recouped their full cost of construction if recovering foliar nitrogen and investing it in new leaves confers an advantage at the whole-plant level when nitrogen is limiting in the environment. If there are limitations set by either ­endogenous or exogenous factors on the number of leaves a plant retains at a time, it is better for a plant to replace leaves; if there are no limitations, plants should retain leaves until their photosynthetic rate declines to zero. The fundamental questions about leaf longevity then have more to do with the nature of factors ­limiting or impairing leaf function as carbon-gaining organs at the leaf and wholeplant levels than with ancillary concerns about retranslocation of mineral nutrients.

46

4 Theories of Leaf Longevity

Modeling Self-Shading Effects on Leaf Longevity Self-shading in the course of canopy growth is one example of a factor at the whole-plant level that can influence leaf longevity. In the Kikuzawa (1991) model, photosynthetic rate is assumed to decline with leaf age, although not for any specific reason. If there were no photosynthetic decline with leaf age, parameter b in (4.2) and (4.5), and hence leaf longevity, would go to infinity. There is no need to replace leaves for a plant if the photosynthetic rate of leaves does not decrease with time for some reason. It may be, however, that the cause of declining photosynthetic capacity is not aging per se, but rather the progression of self-shading and a concomitant decrease of nitrogen contents in leaves caused by retranslocation to more well-lit leaves in the developing canopy (Ackerly and Bazzaz 1995; Ackerly 1999). If we assume that the number of leaves on a growing shoot is maintained constant, then leaf longevity will be given from (3.4) by

L = N /r

(4.6)

where L is leaf longevity (days), N is leaf number per shoot, and r is leaf production rate per shoot per day. Now let the photosynthetic production rate per shoot per day be Dg:

Dg = N · a

(4.7)

where a is the mean daily photosynthetic rate averaged across all leaves on the shoot. Photosynthetic carbon gain by the shoot then can be partitioned to new leaf production (Dc) and to translocation at the whole-plant level (Ds), which will be used for branch, stem, and root production and reproduction. Let the allocation ratio to foliar production be F; then

Dc = F · N · a

(4.8)

If the cost to produce one leaf is C, then leaf production rate per day (r) is given by r = Dc/C where Dc is given by

Dc = N ·C / L

(4.9)

As translocation is given by Dg − Dc, then the translocation Ds is given by

Ds = N ·(a − C / L )

(4.10)

and by substitutions, r will be given by

r = F · N ·a / C

(4.11)

The preceding two equations are focal in maximizing translocation (4.10) and shoot growth (4.11), but we have to know how mean daily photosynthetic gain (a) changes. If the instantaneous photosynthetic rate declines with time, as shown in (4.2), mean daily photosynthetic rate will be given by

A = a0 − a0 ·L / 2b

(4.12)

where a0 is the photosynthetic rate at time 0 and b is a constant. This equation is the integration of (4.2) from time 0 to time L divided by L.

Modeling Self-Shading Effects on Leaf Longevity

47

If we consider that photosynthetic rate of individual leaves is determined by the position of each leaf on a shoot, then photosynthetic rate declines linearly with position in a way analogous to decline with age in single leaves (4.2). Mean photosynthetic rate is described by the following equation: a = a0 − a0 · N / 2 p



(4.13)

where p is a constant, a0 is the photosynthetic rate of a leaf at the top of the shoot, and N is the number of leaves counted from the top of the shoot. Substitution of either (4.12) or (4.13) into either (4.10) or (4.11) gives four equations. Ackerly (1999) gave solutions for two of the four: (1) to maximize the translocation from the shoot when photosynthetic rate declines with time and (2) to maximize leaf production when photosynthetic rate declines with position. The other two cases give solutions intermediate to these two extremes. The solution of the first model maximizing translocation is L* = (2 · b · C / a0 )0.5



(4.14)

where L* is the optimal leaf longevity to maximize the translocation from the shoot. This solution is basically the same as (4.4) for a single leaf. The photosynthetic rate at L* is given by a* = a0 − (2 a0 · C / b)0.5



(4.15)

where a* is the photosynthetic rate at the time of leaffall and usually takes a positive value. In contrast, in the second case, the number of leaves that maximizes the leaf production per shoot is given by N* = p



(4.16)

and the corresponding leaf longevity is given by L* = 2 · C / a · F



(4.17)

which is equivalent to the equation given by Williams et al. (1989). The photosynthetic rate at the terminal leaf lifespan when shoot growth is maximized is then a* = 0. Box 4.2  Population Growth Rates Although leaves do not reproduce in the ways that individual plants and animals do, nonetheless the leaves in a plant canopy can be considered a population subject to the equations governing population growth. In this case, the following equation will hold: ∞

1 = ∑ li bi e −ri i =0

If a population increases without any constraints, it will grow exponentially:

N x = N 0 erx

(1) (continued)

48

4 Theories of Leaf Longevity

Box 4.2  (continued) where N0 and Nx are the number of individuals at times 0 and x, respectively, and r is the intrinsic rate of population growth. Now let the number of individuals born 1 year ago be n1; the number surviving from this cohort is then n1l1, where l is survival rate. An individual bears b1 offspring; thus, in total the cohort produces n1l1b1 offspring. Similarly, individuals born 2 years previously will produce n2l2b2 and so on. Thus, the total number of new individuals born in a year is ∞



n0 = ∑ ni li bi

(2)

0

where we consider that b0 = 0. Now consider the relationship between the total number of offspring born in this year (n0) and those born last year (n1). The growth must be exponential: n0 = n1er or n1 = e−rn0. Similarly, ni = e − ir n0 (3) and substitution of (3) into ni in (2) will give ∞

n0 = ∑ e − ir n0 li bi 0

Dividing both sides of the above equation by n0 will give the equation: ∞

1 = ∑ li bi e −ri i =0

Carbon Balance at the Time of Leaffall Which of the leaf longevities given by (4.14) and (4.17), and which of the photosynthetic rates at the time of leaffall given by (4.15) or a* = 0, are nearer to the truth? Kikuzawa (1991) held that if there were no constraints on the number of leaves that could be retained by a single individual plant at a time, then leaves should be retained for their full potential longevity and thus their photosynthetic rate at the time of leaffall should be zero. But if there are some constraints to retain a fixed number of leaves for a plant, then leaves should be shaded at the time of topt, even while photosynthetic rate is positive. Ackerly (1999) tested the two alternatives and suggested that leaf senescence is primarily a function of the position of a leaf within a canopy rather than its chronological age. He also examined the photosynthetic rates at leaf death, which were greater than zero but nearer to zero

Time Value of a Leaf

49

than expected from (4.15). Oikawa et al. (2009) reported that leaves were shed even though their carbon gain was positive, which increased the efficiency of nitrogen use in the whole plant. But when nitrogen was not limiting, leaves tended to be retained until their carbon gain became zero. Reich et al. (2009) assessed whether the daytime carbon balance at the average leaf longevity of ten Australian woodland species is positive, zero, or negative. Almost all leaves had a positive carbon balance at the time of their fall. These per-leaf carbon surpluses were of similar magnitude to the assumed whole-plant respiratory costs per leaf. Thus, the results suggest that a whole-plant economic framework may be useful in assessing controls on leaf longevity.

Time Value of a Leaf Harper (1989) was perhaps the first to consider that the value of a leaf changes with time. He recognized that the value of a leaf for a plant is not simply the lifetime summation of its photosynthetic gains but also the gains accrued through investment of organic matter translocated from the leaf. If organic matter can be translocated and used for production of new leaves earlier, this is advantageous for carbon gain at the whole-plant level compared to later translocation for production of new leaves. The situation is analogous to the process of population growth, in which individual organisms reproduce new individuals. If a population is maintained at stable numbers, then population growth rate (r) is given by

∫e

− rx

l ( x ) · m ( x ) dx = 1

(4.18)

where l(x) is the survivorship by age x, and m(x) is the rate of production of new individuals at age x per unit time dx. By analogy to age at first reproduction, young leaves cannot contribute to translocation until they are expanded and fully functional. Leaves that translocate photosynthates used for production of new leaves several days earlier thus yield an advantage in carbon gains at the whole-plant level (Harper 1989). If the photosynthate is stored for later leaf production, however, then this potential advantage is diminished or lost entirely. For example, stored photosynthates used for leaf production in the next year would confer no advantage through earlier translocation because materials from new leaves and those from old leaves do not differ in value. In trees, for example, earlier translocation is significant in successive leafing species but not in species with a simultaneous leafing habit. As a corollary, selection should favor hastened development in successiveleafing species but not in simultaneous-leafing species; delayed greening thus can be expected to occur in some simultaneous-leafing species but not in successiveleafing species.

50

4 Theories of Leaf Longevity

Box 4.3  The Monsi–Saeki Model and Its Implications Masami Monsi and Toshiro Saeki (1953) were pioneers in the development of models for ecosystem productivity. They presented a canopy photosynthesis model in which (1) light intensity decreased exponentially with accumulating leaf area and (2) canopy photosynthetic rate increased asymptotically with light intensity. Thus, in a given light regime there should be a depth in the canopy where photosynthetic gains are just balanced by respiratory losses; any deeper into the canopy respiratory losses surpass photosynthetic gains.

Monsi and Saeki predicted that in a given light regime there should be an optimum leaf area index (LAI, the area or biomass of leaves per unit ground area), although they recognized that the optimal LAI might also depend on interactions among leaf angle, leaf size, and branching architecture that influenced light interception in different species and plant communities. Monsi and Saeki’s pioneering work stimulated many studies to see how LAI varied after canopy closure within and among diverse plant community types. For example, Tadaki and Hachiya (1968) reported that the LAI in terms of leaf weight per unit land area was consistently about 3.0 ton ha−1 for temperate deciduous forests, 8.6  ton  ha−1 for evergreen broad-leaved forests, and 16 ton ha−1 for evergreen coniferous forests. Although Monsi and Saeki developed their model for plant communities, it has implications for individual plant canopies as well. If leaf biomass in a community or in the canopy of an individual plant is constant, then any new leaf production must be associated with the fall of a corresponding amount of old leaves. Light captured by a new leaf in the upper canopy will reduce the light penetrating to the deepest level of the canopy, thus tipping the balance of photosynthetic gains to respiratory losses in the most shaded leaves and (continued)

Time Value of a Leaf

51

Box 4.3  (continued) triggering their senescence. When a new leaf appears at the top of the canopy, an older, shaded leaf should fall at the bottom of the canopy in the steady state. Although leaves are fixed in their absolute position on the branch where they originated, their relative position in the canopy becomes progressively deeper as leaves develop on growing shoots at the upper and outer peripheries of the canopy. As the canopy grows over time, absolute leaf positions that once were at the growing periphery and well lighted inevitably become deeply shaded and unable to sustain a viable leaf. The change in relative position through the lifetime of an individual leaf is analogous to the change in the real position of leaves from the exterior to the interior of the canopy over time. Thus, we can speak of a canopy ergodic hypothesis that predicts the average light regime, and photosynthetic rates of leaves across positions at a moment in time are equivalent to those of a single leaf through time, at least so long as the canopy is reasonably close to a condition of steady-state growth (Kikuzawa et al. 2009).

Westoby et al. (2000) also considered the topic of the “time value of a leaf,” but went beyond Harper (1989) to formally incorporate the concept into a theory ­predicting leaf longevity. They recognized that the functional value of a leaf as a carbon-gaining organ decreases over time for a variety of reasons: intrinsic loss of function with age, shading in the course of canopy growth, the effects of damage by pathogens or herbivores, and similar considerations. In this context they assessed the trade-off between investments that could slow losses of leaf function over time and those that involved transport to create new leaves. Taking the rate of the age-dependent reduction in foliar function to be k and the organic matter transported from the leaf to other parts of the plant body as E, they then expressed the amount of transport from a unit amount of leaf over its lifetime (R) as L



(

)

R = ∫ E × SLA × e − kt dt 0

(4.19)

Integrating this relationship as

R=

E × SLA 1 − e − kL k

(

)

(4.20)

leaf longevity (L) then can be expressed as

kR   1  L =  −  n 1 −   k   E × SLA 

(4.21)

This analysis suggests that leaf longevity is a function of the lifetime amount of transported photosynthate (R), the maximum rate of transport at the time of full leaf expansion (E), the rate of decline in transport rate with leaf age (k), and specific leaf area (SLA). The analysis lets us visualize the relationship between leaf longevity

52

a

k = 0.00 mo–1

100

Leaf longevity (mo) [log scale]

Fig. 4.3  Relationship between leaf longevity and specific leaf area. Lines and curves in the panels follow from (4.18) in the text. When k = 0, the relationships are linear and when k = 0.08, they are curvilinear; the instantaneous potential translocation rate E and lifetime transportation R are parameters. (From Westoby et al. 2000)

4 Theories of Leaf Longevity

10

1

b

k = 0.08 mo–1

100

10

1 1

10

100 2

–1

Specific leaf area (mm mg ) [log scale]

and SLA (the inverse of LMA) when other factors are held constant (Fig.  4.3). When k = 0, the logarithm of leaf longevity decreases linearly with log (SLA), but if k takes a positive value, then the relationships become curvilinear and convex to the bottom. The anal­ysis makes it clear that because photosynthetic rate and thus translocation rate change with time, it is necessary to incorporate these changes in modeling of leaf longevity.

Leaf Longevity and Leaf Turnover in Plant Canopies The preceding models have focused on longevity as a leaf-level trait and invoked canopy-level influences in only a generalized way. There is another literature tracing back to a seminal paper by Monsi and Saeki (1953) on the characteristics of plant canopies that deals with leaf longevity secondarily through the rate of leaf turnover in the canopy. When a plant canopy is in steady state, leaf longevity is the inverse of leaf turnover in the canopy. The pioneering work by Monsi and Saeki (1953) focused on the concept of an optimum leaf area per unit land area, an optimal leaf area index (LAI). They used the then-novel method of stratified clipping to assess the vertical distribution of leaf area in various plant communities. These data on canopy structure stimulated development of theory predicting the aggregate characteristics of leaves in different canopy strata. Because of the close correlation between foliar nitrogen and photosynthetic capacity and the recognition that nitrogen

Leaf Longevity and Leaf Turnover in Plant Canopies

53

availability often limited plant productivity in terrestrial ecosystems, considerable attention subsequently has been devoted to the optimal distribution of nitrogen across canopy strata (Field 1983; Hirose and Werger 1987a,b). Most of this literature tracing back to Monsi and Saeki (1953) has taken a static view of the plant canopy, but recently Hikosaka (2003a,b, 2005) has turned the focus toward the dynamics of leaf turnover in the context of optimizing a stratified plant canopy. He considers that leaves are produced from the products of canopy photosynthesis and that after the canopy reaches a stable state older leaves will be shed in proportion to the production of new leaves. Simulations using Hikosaka’s model revealed the negative trends of leaf longevity on canopy light environment and on availability of soil nitrogen that have been documented in studies at the canopy level. Hikosaka’s model also showed a positive correlation between leaf longevity and leaf mass per leaf area (LMA), which is consistent with both models and observations (Fig. 4.4).

a 26

b 200 150

24

100 22

Leaf life-span (day)

50 20

0

1

2

3

4

5

0

6

0

Nitrogen uptake rate (mmol m–2 d–1)

c 30

500

1000

1500

2000

Noon PFD (µmol m–2 d–1)

d 120

N uptake rate = 0.4

N uptake rate = 0.4

25

80 20

15

N uptake rate = 4

0

0.1

0.2

N uptake rate = max.

40

0.3

Slope of Pmax−nL relationship

0

0

50

100

150

200

250 –2

Leaf mass per area (g m )

(mmol m–1 s–1) Fig. 4.4  Relationships between leaf longevity and (a) nitrogen uptake rate from soil, (b) irradiance, (c) relationship between photosynthetic capacity and foliar nitrogen, and (d) leaf mass per area. (From Hikosaka 2003a, b)

54

4 Theories of Leaf Longevity

However, because of the assumption that the respiration rate of a single leaf increases in proportion to nitrogen concentration, this model shows a curious behavior in that under higher levels of nitrogen absorption from the soil, the entire plant stand will die.

Box 4.4  Herbivory Herbivory refers to the consumption of living plant material by invertebrate and vertebrate animals. There is an extraordinary variety of modes of herbivory, from the sucking of sap to the consumption of leaves and seeds. There also are strong contrasts in losses to herbivores in terrestrial versus aquatic ecosystems. For example, leaf consumption by herbivorous animals in terrestrial ecosystems is usually less than 5% of net primary production, in strong contrast to aquatic systems, where herbivory is usually greater than 50% of net primary production (Cyr and Pace 1993). To make sense of this situation we have to consider why plants defend against herbivore losses at all. The basic answer is that the more expensive the cost of constructing the systems for primary production, the more likely are additional investments in their defense against loss to herbivores or disease. The leaves of terrestrial plants and the various ancillary structures such as roots and transport systems that sustain photosynthetic function are relatively “expensive” to construct and maintain. Terrestrial plants make substantial investments in systems for primary production that are only recovered over fairly long time periods, and hence ancillary investments in defense can ensure returns on investment in the photosynthetic function of their leaves. In contrast to terrestrial leaves, the costs associated with constructing and maintaining net primary production are much less in aquatic systems. Aquatic plants need not invest in structures for the uptake and transport of water. They can utilize buoyancy to offset the force of gravity that imposes structural costs on terrestrial plants. They can absorb nutrients from the surrounding water directly with no need of root systems. In short, the investments in systems for primary production required of aquatic plants are much lower than those in terrestrial plants, generally too low to justify diverting resources to defense. It is advantageous to produce more individuals, even if many will be lost to herbivory, to simply outgrow the risk posed by herbivory. On the other hand, there is no doubt that terrestrial plants invest in a variety of defenses against herbivory. A significant part of net primary production is allocated to plant defenses, which are usually divided into several types: 1. Physical defenses –– Hard or fibrous tissues resistant to herbivore attack (Lusk et al. 2010) –– Thorns and stinging hairs that deter herbivores (continued)

Directions for Future Theory

55

Box 4.4  (continued) 2. Chemical defense –– Quantitative chemical defense involving relatively large pools of chemicals such as phenolics that reduce tissue quality for herbivores –– Qualitative chemical defense involving small amounts of poisonous chemicals such as alkaloids that are toxic to many herbivores –– Induced chemical defenses that are produced only after herbivore attack to discourage continued feeding 3. Biological defenses involving diverse mutualisms –– Production of specialized food bodies or extrafloral nectaries on the leaf lamina or petiole to attract ants that in turn attack caterpillars which might feed on the leaf –– Production of volatile chemical signals to attract predators and parasites of an herbivore –– Specialized structures under the veins on the lower surface of a leaf for pred­ atory mites that act as guards against herbivorous mites or infecting fungi 4. Other methods to avoid herbivores –– Open leaves synchronously with other plants to satiate herbivores and reduce the risk of damage –– Reduce apparency to herbivores by mimicking less palatable tissues or species Despite these substantial and diverse investments in defense against herbivores, it still is not entirely clear why levels of terrestrial herbivory are so low relative to those in aquatic systems. The defenses enumerated here fall into a bottom-up, escape-in-time explanation for the low level of herbivory in terrestrial systems: basically, that mature plant tissues are well defended and of little value as a food resource for herbivores except in the brief period when the tissues are developing. An alternative, top-down explanation is that predatory animals, parasites, and disease keep herbivore numbers low and plant defenses have relatively little to do with the outcomes. In fact, it is likely that both top-down and bottom-up controls play a role in terrestrial as well as aquatic systems, but the relationships are complex and remain to be fully understood.

Directions for Future Theory There are at least two main lines along which theories for leaf longevity can usefully be advanced. We have already alluded to one, the consolidation of theory developed at the canopy level with that developed at the leaf level. Hikosaka (2005) has taken a

56

4 Theories of Leaf Longevity

Metabolic rate (nmd g−1 s−1)

103

Ph

oto

102

sy

10

nth

es

is

1 10

102

103

104

Longevity (days) Fig. 4.5  Longevity of individual organisms or leaves (X-axis) and metabolic rate per unit leaves. For mammals, this gradient is nearly −1.0, but for photosynthesis by leaves, the gradient is only about −0.66. The lower line parallel to photosynthesis is dark respiration. (From Reich 2001)

significant step in this direction by integrating leaf-level theory into his analysis of canopy dynamics, but until recently (Hikosaka and Osone 2009) his emphasis has been on the canopy. Although it is true that selection on foliar characteristics is contingent on plant performance that is determined at the whole-canopy level, there are constraints at the leaf level which may set limits on canopy design. For example, Shipley et al. (2006) show that the spectrum of variation in foliar design is rooted in trade-offs at the cellular and tissue levels within the leaf. There also may be some fundamental linkages of this sort that extend to the scaling of metabolic activity for all organisms (West et al. 1997; Brown et al. 2005), including plants (Reich 2001; Enquist et al. 2007; Price and Enquist 2007). Reich (2001) points out that foliar metabolism scales with leaf longevity much as animal metabolism scales with lifespan, although with a different slope (Fig. 4.5). What is uncertain is whether this scaling on leaf longevity would converge to the slope for animals if whole-plant longevity were the scaling factor. It is the give and take between functional constraints and opportunities at the canopy versus foliar levels that will decide whole-plant leaf longevities and alternative strategies for plant productivity. These interactions merit serious analysis. A fundamental understanding of the different modes of leaf longevity that underlie the evergreen versus deciduous habits and an explanation of which environments favor one or both habits is likely to be found in the interplay of foliar- and canopy-level traits. A second useful line of inquiry would be to seek a deeper understanding of the roles of herbivory and disease as factors in the selection of leaf longevity. Chabot and Hicks (1982) noted the significance of these factors, and they have been widely acknowledged in subsequent work, but without ever being explicitly incorporated into a theoretical analysis of variation in leaf longevity. We have considerable data on the effects of both herbivores and disease on leaf function as well as on the multitude of strategies for foliar defense, but no simple generalizations emerge (Jones 2006; Nunez-Farfan et al. 2007; Howe and Jander 2008; Poland et al. 2009). A more complete theoretical framework rooted in an assessment of foliar function at the whole-plant level might help make sense of the voluminous but often confounding data on plant defense against herbivores and disease.

Chapter 5

Phylogenetic Variation in Leaf Longevity

Tree fern canopy (Cyathea arborea)

There are broad patterns of variation in leaf longevity associated with plant growth form (Fig.  5.1), and leaf longevity spans more than two orders of magnitude (Fig.  5.2). Longevities as little as a few weeks are recorded for some herbaceous species and 20 years or more for some woody species (Wright et al. 2004). Lusk (2001) reported leaf longevities for a conifer in south-central Chile as long as 26.2 years in shaded sites and 21.5 years in open sites. The extensive compilation of leaf longevities by Wright et al. (2004) is primarily for woody species (79%), mostly shrubs and trees, with only a few vines; the herbaceous plants in this compilation include graminoids as well as forbs. The median value of leaf longevity in this data set is 8.5  months. Biologically noteworthy longevities are illustrated by the temporary flattening of the rank-order diagram (see Fig. 5.2) at about 3.5 months and again at 6 months. Although there is in general a highly regular and continuous variation in longevity across species, these clusters of species with similar longevities suggest the existence of some sort of limiting factor on leaf viability associated with ­longevities of these durations. We can speculate that the 6-month longevity reflects the typical

K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_5, © Springer 2011

57

5 Phylogenetic Variation in Leaf Longevity

58 Floating leaves of aquatic plants

Annual plants Perennial herbaceous plants Temperate deciduous trees 10

100 Leaf life span (days)

200

Fig. 5.1  Leaf longevity of plants of different growth forms. (From Kikuzawa and Ackerly 1999)

Leaf longevity, months

1000.00

100.00

10.00

1.00

0.10

0

200

400

600

Increasing rank Fig. 5.2  Frequency distribution of leaf longevity for leaves of diverse species from a wide variety of climate zones. (Data from Wright et al. 2004)

length of the growing season in temperate regions where many of the compiled data were taken, but what might account for the 3.5-month longevity? This cluster of species with rather rapid leaf turnover includes many fast-growing herbaceous and woody species from temperate regions, which reflects a dichotomy between deciduous species that produce only one set of leaves per season and others which produce leaves throughout the season. Even within a single climatic regime there are

Leaf Longevity of Ferns

59

alternative evolutionary outcomes in the organization of foliar phenology that involve distinct differences in leaf longevity. For some groups of plants sufficient data have been compiled (cf. Wright et al. 2004) to detect broad differences in leaf longevity, but other groups are too little studied to identify any characteristic leaf longevity. Here we briefly review what we know about patterns of leaf longevity among and within diverse groups of plants, illustrating our points with selected examples.

Box 5.1  Adaptive Radiation The diversity of species at any time in Earth’s history arises in the balance between rates of speciation and extinction. There are background rates of speciation and extinction, but occasionally events trigger a rapid increase in the rate of speciation. Such bursts of speciation are referred to as an adaptive radiation. Adaptive radiations are often associated with colonization of speciespoor environments such as an isolated oceanic island that allows colonizing species to diversify and exploit a wider variety of resources and habitats without facing strong competitive interactions from other species. A well-known example of adaptive radiation is the finches on the Galapagos Islands, which now include many species derived from a single ancestor that have diversified to use different habitats and food resources within and among the islands in this archipelago far off the coast of South America.

Leaf Longevity of Ferns The extant ferns trace their ancestry to the early Paleozoic but their current diversity to an adaptive radiation in the early Tertiary (Schneider et  al. 2004). Most species are herbaceous, but there are some woody ferns that are tropical and evergreen, with leaf longevity generally a year or longer. Leaf longevities were 328 days for Cyathea furfuraca, 525 days for C. pubescens, and 730 days for C. woodwardioides (Tanner 1983). Mean leaf longevity averaged 1.1–1.6 years for Cyathea hornei (Ash 1987) and 2–2.5 years for Leptopteris wilkesiana (Ash 1986). The herbaceous ferns are more diverse in both their climatic affinities and their leaf longevities. Sato and Sakai (1980) classified 67 herbaceous ferns in northern Japan into four groups in terms of foliar habit: evergreen, semievergreen, summergreen, and wintergreen. Evergreen species such as Lepisorus ussuriensis and Pyrrosia tricuspis produce new leaves in June and July that are shed from April to August 2 years later. Other evergreen species such as Asplenium incisum, Blechnum niponicum, and Phyllitis scolopendrium also produce leaves early in the growing season but shed them after only about 1 year. Semievergreen species such as Dryopteris crassirhizoma and Polystichum tripteron produce leaves in late May and early July that begin to senesce by

60

5 Phylogenetic Variation in Leaf Longevity

December but only completely die as new leaves are produced. Summergreen species produce their leaves in May and June and shed them in October; many species, for example, Athyrium brevifrons and Dryopteris phegopteris, share this habit with leaf longevity around a half-year. Wintergreen species such as Scepteridium multifidum var. robustum and Polypodium japonicum with a leaf longevity of about 10 months produce new leaves in late July to early September and shed their leaves in late May to early July. Yoshida and Takasu (1993) reported similar observations of leaf longevity for ferns in the warm temperate zone of central Japan. Summergreen species such as Athyrium pycnosorum, A. wardii, Coniogromme japonica var. fauriei, and Cornopteris decurrenti-alata had leaf longevities from 164 to 210 days. Among evergreen species, the leaf longevities of Polystichum retroso-paleoceum, Doryopteris polylepis, and D. lacera were around 1 year. In a semievergreen species such as P. tripteron only a few old leaves remained 300 days later when new leaves emerged. True evergreen species such as Microlepia marginata, Rumohr standishii, Athyrium otophorum, Blechnum niponicum, and Asplenium wrightii had leaf longevities longer than 1 year and old leaves coexisting with newly produced leaves. Asplenium wrightii had the longest leaf longevity, more than 1,000 days (Yoshida and Takasu 1993).

Leaf Longevity of Gymnosperms

A branch of evergreen conifer (Abies firma)

Leaf Longevity of Angiosperms

61

The extant gymnosperms, a lineage tracing back to the Middle Devonian some 365  million years ago (MYA), have their greatest diversity in the Southern Hemisphere, but it is the species in the Northern Hemisphere that are best studied (Enright and Hill 1995). Lusk (2001) reported a few leaf longevities for Southern Hemisphere species ranging from 4.2 years for Saxegothaea conspicua and 7.3 years for Podocarpus nubigena on up to 23.9 years for Araucaria araucana and 32 years for Podocarpus saligna. Species in the genera Abies, Pinus, Picea, and Larix are good examples of the northern conifers, which most often are evergreen trees with fairly long-lived needle- or scale-like leaves. In the genus Pinus, leaf longevities can range from as short as 1.5 years in Pinus taeda to more than 40 years in Pinus longaeva (Ewers and Schmid 1981; Schoettle 1990). Longevity of leaves in Pinus tabulaeformis varies with latitude, but at the extreme can be as short as 0.94 years (Xiao 2003). Needle longevity of Pinus contorta in the Rocky Mountains of Colorado was 13.1 years at 3,200 m versus 9.5 years at 2,800 m (Schoettle 1990). A similar trend also was observed in Pinus contorta in California: longevity at 15  m was 3.9 years, at 182 m was 4.2 years, and at 2,700 m was 7.9 years (Ewers and Schmid 1981). In a warm temperate region, the leaf longevity of Abies was of the order of 6–8 years (Furuno et al. 1979). The half-life of leaves of Abies mariesii ranged from 3 to 9 years and up to as long as 13 years, varying among branches within the canopy (Kohyama 1980). The mean leaf half-life is 7 years in A. mariesii and only 4 years in Abies veitchii (Kimura 1963; Kimura et al. 1968). Eight species of Asian, North American, and European Picea grown in northern Japan had leaf longevities ranging from 5 to 11 years (Kayama et al. 2007). The leaf longevity of Picea mariana was 5–8 years in Minnesota but 8–15 years in Alaska (Reich et  al. 1996). Niinemets and Lukjanova (2003) reported maximum needle longevities of 16 years in Abies balsamea, 12 in Picea abies, and 6 in Pinus sylvestris. Gower et  al. (1993) estimated leaf longevities of plantation-grown P. abies at 66  months, Pinus resinosa at 46 months, and Larix decidua at 6 months. Larix species are among the minority of conifer genera that are deciduous, unless their needles are protected under snow cover (Gower and Richards 1990). An extensive compilation of leaf longevity for coniferous trees augments these examples (Wright et al. 2004).

Leaf Longevity of Angiosperms Evergreen Broad-Leaved Woody Species Leaf longevity of evergreen broad-leaved trees in temperate regions is usually 1–5 years. Nitta and Ohsawa (1997) provide a good example for 11 species in laurel forests near the northern limit of evergeeen broad-leaved forests in

5 Phylogenetic Variation in Leaf Longevity

62

Symplocos prunifolia

Machilus thunbergii

10

10

5 4 3

5 4 3

2

2

1

1 A M J J A S ON D J F M A M J J A S ON D

A M J J A S ON D J F M A M J J A S ON D

1994

1994

1995

Month

1995

Fig. 5.3  Survivorship curves for different cohorts of leaves in two co-occurring evergreen broad­ leaf trees, Symplocos prunifolia (left) and Machilus thunbergii (right). Log leaf number is plotted against calendar months. Open circles, leaves that appeared in 1995; open squares, leaves that appeared in 1994; open triangles, leaves that appeared in 1993; inverted triangles, leaves that appeared in 1992. (From Nitta and Ohsawa 1997)

Japan. Leaf longevities ranged from 1.5 to 4.3 years, quite similar to the range of 1.4 to 3.8 years reported for 16 species of broad-leaved evergreen dwarf shrubs from Europe (Karlsson 1992). In the Japanese forest, the leaf longevity of Symplocos prunifolia was 1.5 years, with leaves emerging each spring but only being shed during spring and summer the next year (Fig. 5.3). In Machilus thunbergii with a mean leaf longevity of 2 years, the emergence of leaves in spring is more or less simultaneous with shedding of the 2-year-old leaf cohort, although the period of leaffall can be somewhat longer (Nitta and Ohsawa 1997). A similar pattern prevails in Castanopsis cuspidata, Quercus myrsinaefolia, and Quercus acuta. In S. prunifolia, Illicium religiosum, and Cleyera ochnacea, whose leaf emergence period was long, leaffall period was also long. Eurya japonica usually shows several periods of leaf emergence within a year, which are coordinated with periods of leaf shedding. This correspondence in the timing of leaf emergence and leaffall is associated with translocation of resources from senescing to emerging leaves (Nitta and Ohsawa 1997). Navas et al. (2003) studied the leaf longevity of 42 plant species in the Mediterranean region of south France, including some evergeen trees. Leaf longevities of the evergreen trees ranged from 488 to 802 days. Mediavilla and Escudero (2003b) reported leaf longevities of evergreen Quercus coccifera, Q. rotundifolia, Q. suber, and Ilex aquifolium to be between 1 and 2 years in western Spain.

Leaf Longevity of Angiosperms

63

Temperate Deciduous Trees and Shrubs The deciduous habit is characterized by the complete shedding of leaves during an unfavorable period, usually in response to freezing or drought stress. In temperate regions, deciduous (summergreen) trees that shed their leaves during winter often dominate the forested landscape. The summergreen, deciduous habit is a superficial characteristic of the tree that can mask the longevity of individual leaves during the summergreen period. All deciduous trees are superficially similar in that in spring many leaves appear on the tree and in fall leaves turn color and fall before winter. In reality, leaves emerging in spring on some species survive until autumn, but in other species all the leaves that emerged in spring have fallen by summer and been replaced by later emerging leaves that persist until autumn. For example, Kikuzawa (1983) followed leaf longevities in 41 tree species in the deciduous broad-leaved forests of Hokkaido, northern Japan. The shortest longevity was 80 days in Alnus hirsuta and the longest 160 days in Quercus crispula and Fagus crenata. Species of Alnus are well known to have short leaf longevity (Kikuzawa 1978, 1980, 1983; Kikuzawa et al. 1979; Kanda 1988, 1996; Tadaki et al. 1987). A comparable study of 16 deciduous tree species in the Great Smoky Mountains of southeastern North America (Lopez et al. 2008) found leaf longevities ranging from 116 days in Aesculus flava to 180 days in Carya cordiformis. Some shrub species in the understory of deciduous forests have an unusual summerdeciduous foliar habit. In Daphne kamtschatica, some leaves appear in early autumn (September) and overwinter, new leaves also expand the next spring (April), and then all the leaves are shed in June and July so that the plant is leafless in summer when the tree canopy casts deep shade (Kikuzawa 1984; Lei and Koike 1998).

Tropical Trees and Shrubs Even in aseasonal tropical forests, leaf longevity is not particularly long. For example, we can infer from the data of Edwards and Grubb (1977) on litterfall and leaf biomass that the leaf longevity of trees in a New Guinea forest averaged only 1.4 years. Hatta and Darnaedi (2005) surveyed leaf longevity of nearly 100 tropical tree species in Bogor and Chibotas, Indonesia. Most trees had an evergreen habit but about half had a leaf longevity less than than 1 year. Leaf longevities ranged from only 2 months in Inga edulis and Cryptocarya obliqua to more than 30 months in Cinnamomum sintoc. In the understory of the Costa Rican tropical forest some trees have leaf longevities exceeding 2 years but others less (Bentley 1979). Homolanthus caloneurus is a pioneer tree in tropical lower montane forest with leaf longevity of only 0.8 years (Miyazawa et al. 2006). In Venezuelan mangrove forests, leaf half-lives were only 60 days in Laguncularia racemosa, 100 days in Rhizophora mangle, and 160 days in Avicennia germinans (Suarez 2003). Sixteen species in the genus Psychotria, all understory shrubs in tropical forests in Panama, have a remarkable range of leaf longevities, from 119 days in P. emetica to 870 days in P. limonensis.

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5 Phylogenetic Variation in Leaf Longevity

Leaf Longevity of Herbaceous Plants

Flower and leaves of an aquatic floating-leaved plant (Nymphaea odorata)

The leaf longevity of Ambrosia trifida ranged from 20 to 90 days depending on time of emergence, averaging about 50 days (Abul-Fatih and Bazzaz 1980). Leaf longevity of other annual forbs was comparable: Xanthium canadense, 30–40 days (Oikawa et  al. 2006), Glycine max, 20–60 days (Miyaji and Tagawa 1979), and Linum usitatissimum, around 20–30 days (Bazzaz and Harper 1977). The leaf longevity of perennial herbs is not markedly different, although tending to be higher. For example, Diemer (1998a) compared leaf longevity of perennials at different altitudes in the Austrian Alps. At 600 m, leaf longevity of 13 species was 71 days, very similar to the 68-day average for 16 species at 2,600  m. The average leaf longevity of 14 herbaceous species in North American grasslands was 63 days (Craine et al. 1999). Leaf longevities in 32 Swiss grass species ranged from 19 to 29 days for annuals versus 30 to 113 days for perennials (Ryser and Urbas 2000). Compiling earlier studies, Janišová (2007) reported annual grasses having leaves with half-lives in the range of 19–29 days, short-lived perennials with 30–45 days, and long-lived perennial with 111–200 days. Tsuchiya (1991) reported the leaf longevity of floating leaves in aquatic herbs ranged from 13 to 55 days, averaging 25 days. Average leaf longevity for the floating-leaved Nymphaea tetragona and Brasenia schreberi were 30 and 25 days, respectively (Kunii and Aramaki 1987). Some floating-leaved species also produce emergent leaves with stouter petioles that have longevities from 35 to 57 days, averaging 45 days. For example, in Nelumbo nucifera the longevity of floating leaves was only 17 days, but the emergent leaves later in the season live for 30–50

Leaf Longevity of Herbaceous Plants

65

60

Leaf life span (days)

50 40 30 emergent 20 floating

10 0

MAY 1987

JUN

JUL

AUG

SEP

OCT

Date of leaf birth

Fig.  5.4  Longevity of floating (open circles) and emergent leaves (closed circles) in Nelumbo nucifera, an aquatic macrophyte that produces floating leaves throughout the season and emergent leaves held on sturdy petioles later in the season. Leaf longevity of emergent leaves is significantly longer than that of floating leaves. (From Tsuchiya and Nohara 1989)

days (Tsuchiya and Nohara 1989; Fig. 5.4). Leaf longevities of submerged plants are longer than those of floating-leaved aquatic plants and are comparable to those of herbaceous land plants. Leaf longevity ranged from 40 days for Potamogeton crispus to 100 days for Myriophyllum spicatum (Yamamoto 1994). The leaf longevities of marine seagrasses are comparable, averaging 70 days and typically ranging from 25 to 170 days (Hemminga et al. 1999; Kamermans et al. 2001).

Chapter 6

Key Elements of Foliar Function

Sclerophyllous leaves of various bog plant species K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_6, © Springer 2011

67

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6 Key Elements of Foliar Function

Leaf longevity is an integral part of a quintet of highly intercorrelated and functionally interdependent traits that organize the function of leaves as photosynthetic organs: photosynthetic capacity, Amax; leaf mass per unit area, LMA; foliar nitrogen content, N; and leaf dry matter content, LDMC (Wright et  al. 2004; Shipley et al. 2006). Photosynthetic capacity, a direct measure of foliar function, is the natural focal element in the quintet. Leaf longevity, LMA, and foliar N initially drew attention as correlates of photosynthetic capacity and only later were recognized as part of a unified set of traits characterizing overall variation in leaf function: the “leaf economic spectrum” (Wright et al. 2004). Leaf dry matter content subsequently was identified as a little-studied trait that in fact underpinned the relationships among Amax, LMA, foliar N, and leaf longevity (Shipley et  al. 2006). Considering the innumerable characteristics of leaves, including some that figure in theories of leaf longevity, what makes these the cardinal traits central in defining trends in variation of leaf function? There are basically two reasons these five traits have primacy. First, all these characteristics bear on the costs of leaf construction and the photosynthetic functions that repay those costs over the life of the leaf. Second, these traits show a wider and ecologically more consistent range of interspecific variation than other characteristics of leaves. Take leaf construction cost as an example of a foliar trait that one might well expect to be an important element in any quantification of leaf function given its central place in theories of leaf longevity. In fact, the cost of leaf construction per unit mass, which is what we can most readily measure, is a trait that turns out to be relatively invariant across both evergreen and deciduous species from a wide variety of ecosystems; hence, it is not particularly useful in interspecific comparisons of leaf function. Griffin (1994) reviewed leaf construction costs from 87 studies, which ranged from 1.08 to 2.09 g g−1 and averaged 1.54 g g−1. Reviewing 162 studies, Villar and Merino (2001) reported very similar results: an average of 1.52 g g−1 and a range from 1.08 to 1.92 g g−1. The difference in leaf construction costs between evergreen and deciduous habits within plant families is not significant (Villar et al. 2006). One might instead consider that something as simple as variation in the total area of the leaf could affect a broad range of variation in leaf longevity despite the narrow range of leaf construction costs, but this is unlikely because it is the areal rate of photosynthesis that determines the rate of recovery of costs. We must look instead to one of the cardinal traits to make sense of this situation, to LMA. By using LMA, we can convert our measured leaf construction cost (c) per unit leaf weight to an estimate of the construction cost of leaves per unit area (C) : C = c · LMA (6.1) As c varies at most twofold whereas LMA varies tenfold or more (Wright et  al. 2004), the interspecific variation of leaf architecture reflected in LMA clearly will have more influence on the time required for recovery of the cost of construction than simply the costs of the differing materials composing the leaf tissues. This concept helps illustrate why LMA is among the cardinal traits defining the principal

6 Key Elements of Foliar Function

69

Pn (mg CO2 dm

−2

−1

hr )

axes of variation in foliar design (Wright et  al. 2004) and, more generally, is an important index of plant strategies at the whole-plant level as well (Westoby 1998; Westoby et al. 2002). Specific leaf area, the inverse of leaf mass per area, was considered a key element in studies of plant productivity beginning in the early twentieth century (Blackman 1919; Clifford 1972). It was, however, only in the 1970s when traditional methods of growth analysis began to be superseded by direct measures of photosynthesis using infrared gas analysis techniques (Šesták et al. 1971) that the positive correlation between Amax and LMA (Fig. 6.1) gradually came into explicit discussion, through the interests first of plant breeders (Gifford and Evans 1981; Marini and Barden 1981) and then of ecologists (Oren et al. 1986; Koike 1988; Reich et  al. 1991). Physiological ecologists were quick to recognize how anatomical variation in leaves contributed to differences in LMA and could in turn influence photosynthetic function (Nobel et  al. 1975; Koike 1988). For example, Populus maximowiczii with a high LMA has relatively thick palisade and spongy mesophyll layers (Fig.  6.2), which facilitate high Amax in its sunny, early successional environment (Koike 1988; Hanba et  al. 1999; Terashima 2003). Conversely, Acer palmatum is a species of shaded forest understory environments with a low LMA and a thin leaf lacking extensive internal air space and having low Amax (Koike 1988). These sort of investigations firmly cemented LMA (or specific leaf weight, SLW, or its inverse, specific leaf area, SLA) as part of a growing constellation of traits critically associated with the photosynthetic capacity of leaves.

JULY 16

MAY 25

24 20 16 12

Peripheral Interiors

8

Y = 6.0+1.6x r = .83*

4 0

4

8

Y = −5.0+2.5x r = .70*

12

4

8

12

−2

SLW (mg cm ) Fig. 6.1  Relationship between photosynthetic capacity (Pn) and leaf mass per unit area (LMA) (here, SLW) for individual leaves in the interior or peripheral canopy of orchard-grown apple trees just after leaf maturation (May 25) and in midsummer (July 16). (Redrawn from Marini and Barden 1981)

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6 Key Elements of Foliar Function

Fig. 6.2  Cross sections of leaves: left, Populus maximowiczii (Pm); right, Acer palmatum (Ap). (From Koike 1988)

Photosynthesis and Foliar Nitrogen Content The relationship between photosynthetic capacity and foliar nitrogen content was brought into sharp focus in the collation by Field and Mooney (1986) of data on wild plants (Fig. 6.3) and the associated development of a theory for maximizing photosynthetic return on allocation of foliar nitrogen (Mooney and Gulmon 1979; Field 1983). Chlorophyll and photosynthetic enzymes account for the large part of foliar N (Evans 1989), so it is not surprising that photosynthetic capacity is positively correlated with foliar nitrogen content. Field’s (1983) theory for optimal allocation of nitrogen builds on the leaf-level correlation between Amax and foliar N (Fig. 6.3) to address the question of allocation of nitrogen across all the leaves on the plant. Field argued that the photosynthetic return on nitrogen investments is maximized when all leaves have the same slope [a in (6.2)] of the line tangent to the graph of daily photosynthetic gains on foliar nitrogen:

a = ∂Aday / ∂N

(6.2)

Although the optimization is scaled in terms of daily photosynthetic gains, there is a connection to the leaf-level relationship between Amax and foliar N (Field and Mooney 1986) through the linear relationship between Aday and Amax for a given leaf (Field 1991; Zots and Winter 1996; Rosati and DeJong 2003). Daily photosynthetic gain increases asymptotically with foliar N for a family of curves that originate in

Assembling the Elements of Foliar Function

71

Net CO2 uptake (nmol CO2 g−1 s−1)

1000

a

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f h

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i

k

0

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−1

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50

Leaf nitrogen (mmol g ) Fig.  6.3  The increase of photosynthetic capacity with foliar nitrogen content; each polygon bounds observations collated from different studies. (From Field and Mooney 1986)

evolved differences in foliar design among species as well as in the ecophysiological responses of single leaves in differing microenvironments within a plant canopy (Fig. 6.4). The photosynthetic return on nitrogen investment at the wholeplant level is maximized when the tangents to the point where the curves for individual leaves cross the linear leaf-level relationship between Amax and foliar N all pass through the origin (Hirose and Werger 1987a). Similarly, Koyama and Kikuzawa (2009) observed this linear relationship applied to not only Amax but also Aday in leaves of Helianthus tuberosus.

Assembling the Elements of Foliar Function By the early 1990s photosynthetic capacity was firmly linked to LMA and foliar N, but it took a seminal paper by Peter Reich and his colleagues (Reich et al. 1997) to focus attention on the high degree of coherence in the correlations among these three foliar traits. They collated data for 280 plant species to show that there were consistent correlations among Amax, LMA, and foliar N (Fig. 6.5). As any one of these traits characterizing foliar function varied from one species to another, they varied in concert, and these relationships were conserved across and within growth forms. This is compelling evidence that Amax, LMA, and foliar N are integral parts of a unified suite of traits that affects the functionality of leaves.

6 Key Elements of Foliar Function

Aday

72

Leaf N or Amax Fig. 6.4  Interrelationships among daily photosynthetic capacity (Aday), maximum photosynthetic capacity (Amax), and foliar nitrogen (N). The relationship between Aday and Amax is linear (dashed line); Aday increases asymptotically with foliar N dependent on evolutionarily constrained responses to the ambient environment of the leaf. The three asymptotic curves are examples of possible Aday–N relationships, in each case with the optimal allocation of N when the tangent lines to the curves are equivalent. When tangent lines correspond to lines originating at the origin, nitrogen use efficiency (NUE) is optimum. Because Leaf N is proportional to Amax, this relationship can be taken as a surrogate for the Aday–Amax relationship reported by Zots and Winter (1996)

Specif

100

10 7 ic lea f area (cm 2/g )

)

(m g/g

21

1000

en

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nit ro g

ro nit

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Le

ific le 10 7 af ar ea (c 2 m /g )

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af

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n

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(m

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)

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ge

sis Net photosynthe −1 −1 (nmol g s )

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sis Net photosynthe −1 −1 (nmol g s )

b

a

Herbs

Broad-leaved evergreen (leaf life-span > 1 year)

Pioneers

Needle-leaved evergreen

Broad-leaved deciduous

Fig.  6.5  Consistent relationships among three key elements of foliar function for 111 species from six biomes (a) and for 170 species reported in the literature (b). (From Reich et al. 1997)

Photosynthetic Function and Leaf Longevity Reich and his colleagues (Reich et al. 1991, 1992) also had been investigating the relationship between Amax and leaf longevity, as had others (Gower et  al. 1993; Yamamoto 1994). Their 1997 paper (Reich et al. 1997) documented not only the

Photosynthetic Function and Leaf Longevity

Lit data

1000

c

100 Field data

10

r2 =0.78 b = −0.66 ± 0.03 r2 =0.75 b = −0.69 ± 0.02

100

10 r2 =0.59 b =−0.34 ± 0.03 r2 =0.60 b =−0.32 ± 0.02

Leaf nitrogen (mg/g)

e

Net photosynthesis (nmol g−1 s−1)

Fig. 6.6  Relationships between leaf longevity (leaf lifespan) and other key elements of foliar function (Lit data, data reported in the literature). (From Reich et al. 1997)

73

1 1000

100 r2 =0.57 b =−0.46 ± 0.04 r2 =0.49 b =−0.39 ± 0.03

1

10

10

Specific leaf area (cm2/g)

f

100

Leaf life-span (months)

strong negative relationship between Amax and leaf longevity but also a negative relationship of leaf longevity with foliar N and a positive relationship with LMA (SLA in Fig. 6.6). Longer-lived leaves consistently have more mass per unit area, lower concentrations of foliar N, and lower photosynthetic capacity, which supports the inclusion of leaf longevity as a cardinal trait affecting leaf function. Leaf longevity within a single biome varies about 100-fold among species, but the broad relationships with photosynthetic capacity, foliar N, and LMA persist across biomes as diverse as lowland tropical rainforest in Venezuela, subtropical lowland shore forest in South Carolina, montane cool temperate forest in North Carolina, desert and shrubland in New Mexico, a combination of temperate forest, bogs, and prairie in Wisconsin, and a combination of alpine tundra and subalpine forest in Colorado (USA) (Fig.  6.7). These areas vary greatly in mean

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6 Key Elements of Foliar Function

Fig.  6.7  Relationships between leaf longevity and leaf traits: differences among biomes. Relationships between leaf longevity and nitrogen concentration, nitrogen content, specific leaf area (SLA), photosynthetic rate per leaf weight, and leaf area are similar among diverse biomes. The slopes are similar, but intercepts sometimes differ. (From Reich et al. 1999)

annual temperature from −3°C to 26°C and in altitude from sea level to 3,500 m. Despite the wide variations in environmental conditions among biomes, the slopes of these relationships between leaf longevity and other foliar traits do not differ significantly, but the intercepts do vary (Reich et al. 1997, 1999). The difference in intercept among biomes is the result of differences in LMA, which becomes lower when water is in good supply. For example, comparing leaves of similar leaf longevity, LMA is significantly lower in wet high-altitude regions of Colorado than in arid New Mexico. Similarly, the intercept of the relationship between leaf longevity and LMA in Australia is displaced to a lower value by aridity, but the displacement can also involve a shift up or down along the existing gradient (Fig.  6.8). The presence of relationships at the global scale does not necessarily mean the same relationships will be detected in regional data sets (Santiago and Wright 2007).

Deciding the Core Set of Cardinal Traits

75

log (leaf longevity)

Translocation towards lower soil P concentration Translocation towards lower precipitation

log (LMA) Fig. 6.8  Scheme showing translocations of relationship between leaf longevity and leaf mass area (LMA) by changes in precipitation and soil nutrient conditions. (From Wright et al. 2002; drawn after Westoby et al. 2002; redrawn by KK)

Deciding the Core Set of Cardinal Traits These emerging patterns were a stimulus to many studies that led to a much larger database against which the generality of the relationships could be tested. Peter Reich, Ian Wright, Mark Westoby, and many others (Wright et al. 2004) pooled data for more than 2,500 plant species and showed definitively that Amax, LMA, foliar N, and leaf longevity were indeed integral parts of what they called the leaf economic spectrum. Their data documented the range of values to be expected for the key traits as well as the correlations among them: Amax ranged from 5 to 660 nmol g−1 s−1, foliar N ranged from 0.2% to 6.4%, LMA ranged from 14 to 1,500 g m−2, and leaf longevity ranged from 0.9 to 288 months. They were able to compare values on a mass versus area basis and found that the correlations among traits were strongest when expressed on a mass basis. Shipley et  al. (2006) reanalyzed the relationships among four cardinal traits in the leaf economic spectrum that are highly intercorrelated (Amax, foliar N, LMA, and leaf longevity) and showed that a fifth trait in fact underpinned the relationships among these four foliar traits: leaf dry matter content (LDMC). LDMC, the ratio of leaf dry weight to fresh weight, is an index of investments in structural versus fluid cell content. Niinemets et al. (2007a) reported a strong correlation between LDMC and leaf longevity for 44 species in deciduous forests in Estonia and showed that species with higher LDMC had cell walls more resistant to deformation under turgor pressure. Compared to woody species, herbaceous species have lower LDMC, shorter leaf longevity, and greater Amax (Ellsworth et al. 2004; Wright et al. 2004; Shipley et al. 2006; Niinemets et al. 2007b).

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There is no end to the number of foliar traits that might characterize essential elements of foliar function and therefore merit inclusion in a comprehensive analysis of the leaf economic spectrum. For example, foliar phosphorus and dark respiration rate are likely candidates (Westoby et al. 2002; Wright et al. 2004), but the data supporting their inclusion are fewer than are those for the four primary traits. A subsequent analysis (Wright et  al. 2005a, b) gave further support to inclusion of foliar respiration and also suggested inclusion of photosynthetic nitrogen use efficiency (PNUE) among the cardinal traits. In the context of theory for leaf longevity, inclusion of respiration makes some sense as a possible index of the ongoing costs of foliar maintenance that could augment the initial construction cost when estimating the timing of leaf senescence. PNUE makes less sense as a unitary cardinal trait in the context of theory for leaf longevity because it is simply the ratio of two parameters already accounted for in the syndrome of traits central to foliar function. In general, it behooves us to look beyond correlations to a minimal set of traits that can be integrated in a mechanistic model of foliar function, in the present context a model that can predict leaf longevity.

Chapter 7

Endogenous Influences on Leaf Longevity

Bud break with 1-year old, sclerophyllous leaves of an evergreen tree, Camellia japonica

The functional relationships among key traits defining leaf function do not stand in isolation from functionality at the level of the whole plant. Hence, variation in leaf longevity is contingent not only on variation in foliar design, but also on trade-offs involving other aspects of plant function, which include aspects of functional organization from the level of single shoots to the entire canopy.

Timing of Leaf Emergence and Leaf Longevity In temperate regions where the length of the growing season sets a limit on leaf longevity, deciduous species with indeterminate shoot growth can be expected to have shorter-lived leaves than species with determinate shoot growth. This is the case in K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_7, © Springer 2011

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temperate deciduous broad-leaved forests, where the leaf longevity of species with determinate shoot growth such as Fagus crenata, Quercus crispula, and Carpinus cordata was 160–180 days, whereas leaf longevity in Alnus hirsuta with indeterminate shoot growth was 80–90 days (Kikuzawa 1983, 1988). Because there is no limitation set by the length of the growing period in aseasonal tropical forests, the same expectation need not apply, but in fact the leaf longevity of species with indeterminate shoot growth still tends to be less than those with determinate shoot growth. Leaf longevity was 1–4 months in Heliocarpus appendiculatus (Ackerly and Bazzaz 1995) with indeterminate shoot growth. Leaf longevity of Dendrocnide excelsa, a species in subtropical and cool temperate rainforests with indeterminate shoot growth, was about 7 months compared to 20 months in species such as Doryphora sassafras, Ceratopetalum apetalum, and Nothofagus moorei with determinate shoot growth (Lowman 1992). There is clearly endogenous organization of the timing of shoot growth and leaf turnover. In species with indeterminate shoot growth, the birth rate of a leaf (r) is given by the ratio of standing leaf number (N) on a shoot and leaf longevity (L) from (4.6) (Ackerly 1996).

r =N/L

(7.1)

Designating 1/r = P, P represents the interval between emergence of leaves, which is called the plastochron interval (Maxsymowych 1959). Using P, we can rewrite (7.1) as

L = N · P

(7.2)

Leaf longevity thus can be estimated as the product of number of leaves and the plastochron interval. Ackerly (1996) compared species with leaf longevity from 32 to 5,200 days and standing leaf number per shoot ranging from 3 to 45 (Fig. 7.1). For species with indeterminate shoot growth, leaf longevity largely depends on the rate of leaf turnover, with the oldest leaf being lost as a new leaf emerges. If the growth rate and loss rate of leaves are equivalent, the canopy will be in steady state. Moreover, if photosynthetic capacity is determined by the position of leaves as expected in (4.13), then the canopy photosynthesis at any time should be equivalent to the photosynthetic gain of a single leaf throughout its life: in other words, there appears to be an ergodic character to the functional relationships between the leaf and canopy levels (Kikuzawa et al. 2009). Leaf longevity in this steadystate condition then is determined by the appearance rate of leaves, which will reflect the shoot growth rate.

Plant Growth Rates and Leaf Longevity A negative correlation between the relative growth rate of plants and leaf longevity is expected when a tree canopy is in a stable state with new leaves produced at the same rate as leaves dropping; then, leaf longevity is determined simply by the inverse

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of the leaf production rate. Reich et al. (1992) reported the expected negative correlation in a sampling of diverse species (Fig.  7.2). Comparison of 46 species in Barro Colorado Island in Panama also showed negative correlations between the height growth rates of individual trees and some defensive characteristics of leaves such as leaf fiber content and toughness that are commonly associated with longer-lived 100

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Leaf life span (days below axis, years above) Fig. 7.1  Relationship between number of leaves on a shoot and leaf longevity for species with indeterminate shoot growth. The isoclines are plastochron intervals. Circles, pioneer species; triangles, palms; cross, woody ferns, cycads; square, mangroves. (From Ackerly 1996)

RELATIVE GROWTH RATE PER WEEK (mg/g)

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LEAF LIFE-SPAN (months) Fig.  7.2  Relationship between relative growth rate of a plant and leaf longevity. (From Reich et al. 1992)

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leaves (Coley 1983). These relationships are not considered causal in and of themselves because tree growth is affected by very many other traits, but it is clear there is a functional linkage between overall growth and leaf turnover. This linkage is also apparent in the relationship between wood density and leaf longevity. Fast-growing, early successional species on Barro Colorado Island such as Cecropia insignis with a wood density of only 0.15  g cm−3 had shorter leaf longevity than slower-growing, late successional species with wood densities in the range of 0.34– 0.64 g cm−3 (King 1994). Ishida et al. (2008) report the same trend for woody species on the subtropical Bonin Islands. Chave et  al. (2009) have characterized a “wood economic spectrum” that associates increasing wood density with slower growth rates, which suggests these relationships may prevail generally across species.

Seedling Growth and Leaf Longevity The relationship between growth rate and leaf longevity also is expressed at the seedling stage where the initial growth of current-year seedlings depends on seed size. For example, seed size varies among deciduous broad-leaved trees in northern Japan from nearly 10 g in Aesculus turbinata to less than 1 mg in Betula platyphylla (Seiwa and Kikuzawa 1989). A large-seeded species such as A. turbinata typically attains the large part of its annual height growth within a month of germination (Fig. 7.3). In contrast, the height growth of a small-seeded species such as B. platyphylla has a long lag before shoot growth takes off later in the season. The seedling shoot growth of the large-seeded species is essentially determinate, the small-seeded is essentially indeterminate, and the leaf longevities are correspondingly long and short, respectively (Seiwa and Kikuzawa 1991). The leaf longevity of seedlings, however, is shorter than that of adult trees for both large- and small-seeded species, perhaps because the costs of transport associated with each leaf are greater in adults than in seedlings (Kikuzawa and Ackerly 1999). There is, however, no significant difference in leaf longevity of saplings and adult trees (Reich et al. 2004).

Fig.  7.3  Growth curves of seedlings from germination for Betula platyphylla (Bp), a smallseeded species, and for Aesculus turbinata (At), a large-seeded species at open (open circles) and shaded (closed circles) sites. (From Seiwa and Kikuzawa 1989, 1991; redrawn by KK)

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Variation of Leaf Longevity with Timing of Leaf Emergence Leaf longevity can vary among different leaf cohorts within individual plants. In Betula species, the leaves that emerge initially in early spring and leaves that emerge successively until summer differ in morphology (Kozlowski and Clausen 1966), photosynthetic traits (Koike and Sakagami 1985; Koike 1990; Miyazawa and Kikuzawa 2004), and their parent shoot morphology (long and short shoots: Yagi and Kikuzawa 1999; Yagi 2000; Ishihara and Kikuzawa 2004). Longevity for early leaves in Betula grossa was around 160–180 days, significantly longer than the 110–130 days for late leaves (Miyazawa and Kikuzawa 2004). Similar structural differentiation of long and short shoots was also observed in Halimium atriplicifolium, but leaf longevity on long shoots of this Mediterranean subshrub was only marginally longer than on short shoots, 13.2 versus 10.6 months (Castro-Diez et al. 2005). Adenostoma fasciculatum, a shrub of Mediterranean regions in North America, also has short shoots and long shoots but with leaves on long shoots living only a year compared to 2 years on short shoots (Jow et al. 1980). Leaf longevity on the Asian vine Akebia trifolia varied from less than 10 days to more than 1 year, irrespective of emergence timing (Koyama and Kikuzawa 2008). In wild strawberry, Fragaria virginiana, leaves emerging in early spring had longevities of about 60 days compared to 130 days for those emerging in early summer and 250 days for those emerging in fall and overwintering (Jurik and Chabot 1986). Sydes (1984) observed similar contrasts in other herbaceous species between leaves produced early in the growing season with longevities about 60 days compared to 200 or even 300 days in leaves produced in fall and overwintering (Fig. 7.4). Leaf lifespan 365 Days

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Fig. 7.4  Date of leaf appearance, date of leaffall, and resulting longevity for individual leaves of Akebia trifoliata (n = 1,423). The two oblique lines are isoclines for leaf lifespan of 0 and 365 days, respectively. Shading indicates period unfavorable for photosynthesis. (From Koyama and Kikuzawa 2008)

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Box 7.1  Self-Shading and Leaf Emergence There is a dichotomy between plants that produce essentially all their leaves each year in a single burst (simultaneous-type leaf emergence) and those that produce leaves in a steady progression throughout all or part of the year (successive-leafing type). As all potential leaves appear at once at the start of a growing season in the simultaneous type, all the leaves of this type can carry out photosynthesis throughout the growing season. However, if many leaves are attached on a shoot, leaves in lower positions will be shaded by those in upper positions (self-shading), a disadvantage that can be reduced by the orientation of shoots and leaves (Kikuzawa et al. 1996). By this means, all the leaves on a shoot can receive sunlight evenly and the photosynthetic performance of the shoot will increase, although inclining the shoot will also reduce the height growth of the plant and increase biomechanical support costs. In contrast, successive leafing essentially is an alternative method to avoid selfshading within the plant canopy. The first leaf produced on a growing shoot will enjoy full sunlight until the shoot extends and the second leaf emerges and begins to shade the first leaf, and so on as successive leaves emerge. Consequently, there are some linkages among leaf phenology (leaf emergence pattern), self-shading, and shoot architecture (Kikuzawa et al. 1996; Kikuzawa2003) in deciduous broad-leaved species. Simultaneous leafing species (Fagus crenata, Quercus crispula, Tilia japonica) have strongly inclined shoots and avoid self-shading whereas successive leafing species (Alnus hirsuta, A. sieboldiana, Betula platyphylla) have upright shoots (Kikuzawa et al. 1996). Similar linkages between leaf phenology and architecture exist in herbaceous species as well (Kikuzawa 2003).

Canopy Architecture and Leaf Longevity Intrinsic controls on the development of canopy architecture determine the degree of mutual shading among different branches and leaves within a canopy and hence influence the longevity of leaves throughout the canopy. If shoot elongation is rapid and leaf turnover on the elongating shoot high, the inner canopy of the tree tends to become leafless as the outer canopy expands. The inner canopy of Alnus sieboldiana, a species that elongates upright apical shoots with short leaf longevity, illustrates this canopy-hollowing phenomenon (Shirakawa and Kikuzawa 2009). Crown hollowing incurs an increasing cost in maintaining interior branches to support the leafy shoots in the expanding outer canopy, perhaps explaining why crown hollowing occurs mostly in species that never attain heights sufficient to occupy the upper strata of mature forests. In some early successional trees canopy hollowing is diminished by production of dimorphic shoots, long shoots that expand

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the canopy periphery and short shoots that produce leaves along interior branches without elongating internodes. Long shoots function in both space acquisition and leaf display, but short shoots only play a role of leaf display. Short shoots can persist over many years along interior branches, producing only a few relatively longlived leaves and thus reducing canopy hollowing in species of Betula and Populus (Critchfield 1960; Pollard 1970; Isebrands and Nelson 1982) and some Acer species as well (Critchfield 1971; Sakai 1987). Such differentiation of leaf display and space acquisition through variation in shoot structure and leaf longevity is a general phenomenon, with the short shoot–long shoot dichotomy only a particular case of a broader range of structural variation in shoots (Takenaka 1997; Yagi and Kikuzawa 1999). For example, shifts in the relationships between bud dormancy, needle longevity, and total needle area per unit shoot length in some evergreen trees alter the balance between leaf display and space acquisition in canopy development and reduce canopy hollowing (Takenaka 1997). In some evergreen broadleaved tree species such as Cleyera japonica, leaves at the inner canopy have prolonged longevity, or burst bud only after some years of dormancy, thus avoiding canopy hollowing (Suzuki 2002). The balance between leaf display and space acquisition in canopy development is inextricably linked to leaf longevity through the feedback to leaf lifetime carbon gain. Maximizing the capture of light energy is not simply a question of growing taller to shade competing neighbors, but also a question of how effectively a plant captures light from the part of the overall plant canopy surface that it occupies. There is a trade-off between growing taller to shade neighbors and spreading laterally to claim more surface area in the upper canopy of the plant stand. For example, a tree maximizing only height growth could simply extend its apical shoots straight and upright, but many canopy tree species in mature temperate deciduous forests such as Fagus, Quercus, or Acer in fact have determinate shoot growth and apical shoots declined toward the horizontal. These trees avoid selfshading among leaves within the canopy by branch and shoot angles that allow light penetration to deeper layers of the canopy (Posada et  al. 2009). On the other hand, successional tree species with indeterminate shoot growth such as Alnus or Betula elongate their apical shoots strongly upward, growing tall more quickly but with a higher degree of self-shading in their canopy (Kikuzawa et al. 1996). Such successive leafers can attain higher photosynthetic rates by receiving full sunlight at the time of first leaf appearance. When the first leaf’s photosynthetic rate declines with aging, a second leaf appears and again receives full sunlight at the shoot apex but also shades the preceding leaf on the shoot and so forth. Thus, successive leafing, high but early decline of photosynthetic rate, and short leaf longevity are functionally linked with one another. In contrast, leaves appearing simultaneously on a determinate shoot mutually shade one another from the initial stage of leaf appearance, and thus plants avoid self-shading by more horizontal placement of shoots, branching angles, leaf angles, and the like. Simultaneous leafing, lower but persistent photosynthetic rates, relatively long leaf longevity, and a more horizontally oriented canopy structure are also parts of

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a functional syndrome (Kikuzawa 1995a, b). Similarly contrasting morphological and phenological characteristics related to light interception are found in the essentially horizontal leaves of herbaceous forb species (Kikuzawa 2003), although not in unbranched graminoid species that typically orient their leaves near vertical in turf or cespitose clumps.

Box 7.2  Impact of Deep Versus Partial Shading

The way that individual leaves react to shading depends on the light regime in which the entire plant exists. If the entire plant is subjected to low insolation, as in forest understory species, then leaf longevity is relatively long and leaves lower in the canopy do not translocate resources to less-shaded leaves higher in the canopy. Conversely, leaves on trees in the forest canopy exist in a broad range of insolation regimes from well lighted in the upper canopy to progressively more and more partially shaded deeper in the canopy. In this case, leaf longevity is shortened in proportion to shading, and resources are translocated to the upper, brighter portion of the canopy. These responses reflect a balance between optimization of resource gain and loss at the leaf level versus the whole-plant level.

Canopy Heterogeneity and Leaf Longevity The insolation regimes of leaves set by intrinsic controls on canopy architecture in a uniform and stable light regime can be disrupted by external influences that create asymmetry such as adjacent objects, forest edges, or gaps. In such instances, variation in leaf longevity within individual plants does not appear to follow the general pattern seen between individuals and species but is actually reversed: leaf longevity on shaded shoots is shortened compared to sunlit shoots. For example, Miyaji and Tagawa (1973) reported that shaded leaves in the lower canopy of a Tilia japonica

Canopy Heterogeneity and Leaf Longevity

85

sapling were shed earlier than sunlit leaves in the upper canopy. Takenaka (2000) observed individual Cinnamomum japonicum growing at more than 10%, 5% to 10%, and less than 5% full sunlight in the understory of evergreen broad-leaved forest. Each tree had some shoots in each of the three insolation classes. Takenaka (2000) compared leaf longevity on shoots in more-shaded positions of better insolated individuals, and vice versa. He found that the better insolated were individuals, the stronger was the contrast in shoot growth and leaf turnover between their well- and poorly insolated shoots. Leaves on poorly insolated shoots were shed more rapidly than on more-sunlit shoots. This situation in which faster-growing shoots inhibit slower-growing ones is a form of apical control referred to as correlative inhibition (Cline 1997; Umeki and Seino 2003). If this more rapid shedding of shaded leaves within individual plants is simply the direct consequence of the shading rather than apical control (Cline 1997), there should be a correlation between leaf longevity and plant size in a dense plant population. That is not the case. There is no significant correlation between mean leaf longevity and individual plant size and hence shading in dense plantings of Xanthium canadense; mean leaf longevity ranged from 20 to 50 days irrespective of plant size (Hikosaka and Hirose 2001). In summary, individual plants shorten leaf longevity on poorly insolated shoots when only part of the plant is shaded, but not when the entire plant is shaded. There is evidence, however, that in more mature trees the relationship between leaf longevity and insolation reverts to the norm. Mizobuchi (1989) reported that in large, open-grown Cinnamomum camphora growing on a university campus in central Japan, leaves on the better insolated southern side of the canopy had a halflife of about 1 year compared to almost 2 years on the north side. Osada et al. (2001) studied leaf longevity over more than 3 years at different heights in Dipterocarpus sublamellatus, Elateriospermum tapos, and Xanthophyllum stipitatum – trees all more than 30 m tall growing in a mature tropical rainforest. They found that leaf longevity consistently is shortest in the sunlit upper canopy of individual trees. Similar results were obtained for 15 tree species in a tropical forest that differ in maximum height (Meinzer 2003), suggesting that tree maturity rather than just tree height determines the pattern of leaf longevity with the tree canopy. Miyaji et al. (1997) studied leaf longevity in 3-m-tall cacao trees (Theobroma cacao) growing under shelter trees in a tropical plantation. Leaf longevity changed depending on the timing of leaf emergence and level in the canopy (Fig.  7.5). Longevity of upper leaves ranged from 120 to 200 days, the middle layer from 180 to 250 days, and the lower layer from 280 to 370 days; leaf longevity of bearing-age cacao trees was longer in the more-shaded, lower canopy. There may be a size-dependent shift in the degree of branch autonomy such that in the transition from saplings to trees a greater degree of branch autonomy ensues as apical control shifts from the sapling apex to individual branches in the tree crown. In this vein, we can rephrase our overall summary of the relationship between insolation and leaf longevity. When the autonomous unit organizing shoot growth is wholly shaded (an individual plant or major branch), then leaf longevity becomes longer; conversely, when a shoot is only a poorly insolated part of a larger autonomous unit, then its leaf longevity is shortened relative to the sunlit part of the autonomous system controlling shoot growth.

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7 Endogenous Influences on Leaf Longevity 800

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Jul. Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June Jul. Aug. Sept. Oct. Nov. Dec. 1983 1984

Fig. 7.5  Leaf survivorship curves in the upper (UL), middle (ML), and lower (LL) layers of the canopy in 7-year-old Theobroma cacao in a Brazilian plantation under a canopy of shelter trees. Different symbols represent cohorts of leaves emerging at different times. All leaves in the upper layer had fallen by November 1984, while some leaves still remained in the middle and lower layers. (From Miyaji et al. 1997)

Chapter 8

Exogenous Influences on Leaf Longevity

Early spring ice storm, Ithaca, New York

The normal value of leaf longevity for a species reflects functional relationships at the foliar and whole-plant level, but longevity can be both prolonged and shortened by environmental conditions. From first principles, leaf longevity is expected to increase in environments where critical resources are scarce. This generalization is rooted in a cost–benefit analysis of leaf longevity arguing that the nature of leaves in resource-limited environments imposes a long payback period on the cost of their construction (Chabot and Hicks 1982; Kikuzawa 1991). In this view, selection pressure is expected to act to prolong leaf longevity in light-, water-, or nutrientlimited environments. This expectation is consistent with observations among species and plants in differing resource environments, but not within individual plants. The expectation applies to conditions of resource limitation, not stress conditions that near or exceed the limits to a species’ survival and reproduction. Stress events such as deep drought, unseasonal frost and freezing, lengthy flooding, salinity, air

K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_8, © Springer 2011

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pollutants, and attack by herbivores or pathogens each impose qualitatively different challenges to leaf function (Kozlowski and Pallardy 2002), which we also address in this chapter.

Box 8.1  Succession Vegetation is inherently dynamic: plants grow and interact with one another while responding to changing environmental conditions. Occasionally these dynamics in a plant community are punctuated by more disruptive events that destroy some part of the plant community. Succession refers to the sequence in which plants colonize and develop in an area after such a disturbance. A successional sequence can be initiated by disturbances at large spatial scales such as volcanic eruption, windstorms, fire, flooding, and landslides or at small spatial scales by simply the death of a single tree. In the case of a big volcanic eruption such as that of Krakatau in 1883, all the vegetation on this isolated oceanic island was killed by a thick layer of ash and the succession began on barren land. Even in this extreme case plants and animals dispersed to the island within several decades, and more than 200 species were recorded on Krakatau only 50 years after the eruption. Succession typically is initiated less dramatically and involves colonization from nearby undisturbed areas. Because stochastic factors play a large role in dispersal and colonization, we cannot forecast precisely the course of succession, but we can recognize species that during early versus late stages of succession have characteristic suites of features. Early successional plant species produce abundant small seeds, have a high growth rate with low stem density, high maximum photosynthetic rates, and short leaf longevity. Late successional plant species produce fewer but large seeds, have low growth rates with high stem density, low maximum photosynthetic rates, and long leaf longevity.

Insolation and Leaf Longevity Diverse lines of evidence among and within species support the generalization that leaf longevity is relatively short in sunny compared to shaded environments. Early successional species are widely observed to have shorter leaf longevity than late successional species (Kikuzawa 1978, 1982, 1983, 1988; Koike 1988), which is consistent with the greater insolation typical of sites after disturbance. Similarly, in the understory of both tropical forests (Reich et al. 1991, 2004) and mature temperate forests (Kikuzawa 1984, 1988, 1989; Lei and Koike 1998), species typically have long-lived leaves, some surviving more than a single season. If a species occurs in both sun and shade, leaf longevity is long in the shaded environment (Kikuzawa 1989; Sterck 1999; Reich et  al. 2004). For example, in a Southeast Asian tropical forest, leaf survivorship of the

Insolation and Leaf Longevity

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Fig. 8.1  Survivorship of Elateriospermum tapos (Euphorbiaceae) leaves on the forest floor and in canopy gaps. (From Osada et al. 2003)

shade-tolerant tree Elateriospermum tapos was greater in the understory than in canopy gaps (Osada et  al. 2003; Fig.  8.1). Kai et  al. (1991) reported similar observations for the semideciduous shrub Ligustrum obtusifolium and then experimentally confirmed the role of insolation in affecting leaf longevity. They subjected cloned plants growing in a nursery to 7%, 20%, and 100% full sun; in 100% sunlight, almost all leaves were shed before mid-December, whereas in the shaded plots some leaves remained until the next autumn. The evergreen shrub Daphniphyllum macropodum normally retains leaves 4–5 years in the understory of deciduous broad-leaved forests but only 2 years in canopy gaps; a similar trend is observed in the low-growing evergreen Pachysandra terminalis (Kikuzawa 1989). Finally, leaf survivorship in the evergreen shrub Rhododendron maximum decreased for plants growing in the understory of more-open forests from 5 years under an evergreen canopy, to 4 years under a deciduous canopy, and only 3 years in canopy gaps (Nilsen 1986; Fig. 8.2). Because canopy gaps arise suddenly, existing leaves on understory species can be subjected abruptly to substantially greater insolation; understory species with fairly long-lived leaves should be more tolerant of high insolation after gap formation than those with relatively short-lived leaves (Lovelock et al. 1998). Lovelock et al. tested this expectation by assessing the degree of photoinhibition in 12 tree species from tropical rainforest, finding that species with long-lived leaves (more than 3.5 years) were more tolerant of abrupt increases in light than species with shortlived leaves (less than 2 years).

8 Exogenous Influences on Leaf Longevity

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100 Rhododendron maximum (1983)

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Fig. 8.2  Leaf survivorship curves for Rhododendron maximum in different light regimes: O canopy gap, D deciduous broad-leaved forest floor, E evergreen forest floor. (From Nilsen 1986)

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Aridity and Leaf Longevity On the assumption that leaf longevity is governed by the time required to pay back the costs of leaf construction, we can generally expect sublethal levels of water shortage to be associated with longer-lived leaves. There is a variety of experimental and observational evidence supporting this point of view at the level of individual species, but interspecific comparisons of the relationships between water availability and leaf longevity are not straightforward. The contrast between the deciduous and the evergreen habit illustrates the ambiguities of the relationship between water availability and leaf longevity. The vegetation of regions prone to water shortage can include both drought-deciduous species that drop their leaves at the onset of a dry season and evergreen species that retain leaves through the dry season. Drought-deciduous plants usually have higher maximum photosynthetic rates than evergreen plants (Comstock and Ehleringer 1986; Ackerly 2004), which is consistent with the general relationship between leaf longevity and photosynthetic rate. On the other hand, the co-occurrence of species with different foliar habits indicates that leaf longevity is only part of a larger syndrome of adaptive alternatives to water shortage. A study comparing species with short-lived versus long-lived leaves in the understory of a seasonal tropical forest in Panama illustrates this point (Tobin et al. 1999). Species with long-lived leaves had deeper root systems than species with short-lived leaves and thus could avoid drought conditions during the dry season. We cannot expect a simple pattern of leaf longevity in relationship to water stress across species, but if the cost–benefit perspective on leaf longevity (Chabot and Hicks 1982; Kikuzawa 1991) is valid, then it must apply within species.

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There is good intraspecific evidence for prolonged leaf longevity in response to aridity. For example, Encelia farinosa is a drought-tolerant shrub distributed along a precipitation gradient in Arizona and California. Its leaves become more tomentose under drier conditions, decreasing rates of transpiration but also increasing the cost of leaf construction as well as reducing photosynthetic capacity. Hence, the payback period on leaf construction is extended and leaves survive longer in the drier regions (Sandquist and Ehleringer 1998). Similar results were found when the effect of drought on leaf longevity was investigated experimentally in Cryptantha flava, a desert shrub in Utah (Casper et  al. 2001). Leaf longevity was compared between plants receiving half versus all natural precipitation. Stomatal conductance and photosynthetic rates were lower in the plants receiving less precipitation, and as expected leaf longevity became longer: leaves present at the initial census persisted 49.2 days in the dry plot versus 22.6 days in the control (Fig.  8.3). Similar trends occur in the dioecious shrub Pistacia lentiscus in southern Spain where precipitation ranges from 350 to 1,000 mm year−1 in a Mediterranean climate regime (Jonasson et al. 1997). Leaf longevity in male plants of P. lentiscus was shorter under more-arid conditions; the relationship in female plants was the same, but it was not statistically significant because of confounding effects from variation in fruit production (Jonasson et al. 1997). A severe drought extended leaf longevity in five species of deciduous trees in a Swiss forest, mostly because of later leaffall (Leuzinger et  al. 2005). In general, we can expect drought to extend leaf longevity within species but not always among species.

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Julian date Fig. 8.3  Effect of drought treatment on leaf longevity in the desert plant Cryptantha flava. Leaf longevity was prolonged by drought. (From Casper et al. 2001)

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Nutrients and Leaf Longevity The decrease in leaf longevity with higher levels of foliar nitrogen content is a well-established interspecific relationship (Field and Mooney 1986; Field 1991; Reich et al. 1991, 1992, 1994; Wright et al. 2004; Poorter and Bongers 2006), but this negative relationship may or may not apply within species or among species at a site. Observational and experimental evidence for the effect of fertility on leaf longevity in general shows that for a given species leaf longevity will be shorter at more fertile sites. For example, leaf longevities of Picea abies, P. jezoensis, and P. glehnii were longer on nutrient-poor serpentine soil compared to more fertile brown forest soil (Kayama et  al. 2002). Fertilization of the prostrate tundra evergreen shrub Ledum palustre var. decumbens increased leaf turnover (Shaver 1981). Fertilization of Pseudotsuga menziesii var. glauca and Abies grandis, coniferous trees of the Pacific Northwest in North America, reduced leaf longevity by about one-fourth (Balster and Marshall 2000). In the Hawaiian tree Metrosideros polymorpha, leaf longevity varies between 2 and 5 years and is longer on more infertile sites; fertilization decreases longevity on fertile sites but not at the infertile sites where longevity is already long (Herbert and Fownes 1999; Cordell et al. 2001). In this tree species longevity decreased as leaf nitrogen content increased across sites (Herbert and Fownes 1999). In Larrea tridentata, an evergreen desert shrub, fertilization shortened leaf longevity, and the effect was enhanced by irrigation (Lajtha and Whitford 1989; Fig. 8.4). A 100-fold increase in nutrient availability decreased leaf longevity of the perennial floating-leaved aquatic plant, Hydrocharis morus-ranae var. asiatica, from 15–20 to 10–15 days (Tsuchiya 1989); lower levels of fertilization did not significantly alter leaf longevity (Tsuchiya 1989; Tsuchiya and Iwakuma 1993).

Box 8.2  Density Dependence A density dependence in population regulation occurs whenever differences in either birth rate or death rate result in lowering of the population growth rate as the density of the population increases. If the density dependence is driven by changes in the death rate of individuals, we speak of density-dependent mortality factors. In general a population is considered to be regulated at some equilibrium density by density-dependent factors such as the reduction of birth rate resulting from short supply of food, increase in death rate from overcrowding, and similar regulatory responses. Without some sort of density-dependent factors, population numbers could not be regulated.

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Box 8.3  Growth Rate Hypothesis Short, cool growing seasons (f) are a disadvantage for plant growth. To overcome and compensate for this disadvantage, the growth rate hypothesis (GRH) predicts that natural selection will favor rapid growth in response to increases in tissue nutrient concentration, especially phosphorus (P) because of its critical role in the P-rich ribosomes required for protein synthesis (Elser et al. 2000; Kerkhoff et al. 2005).

100 75 unfertilized fertilized

50 25

Unwatered

% Leaves Remaining

100 75 50 25

6 mm/wk

100 75 50 25

25 mm/mo

M

J

J

A

S O N D J

F

M A M

J

J

A

S O

Fig.  8.4  Joint effects of fertilization and irrigation on desert evergreen plants. Open symbols, leaves emerging in spring; closed symbols, leaves emerging in autumn; wk weeks, mo months. (From Lajtha and Whitford 1989)

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Effects of Environmental Stress on Leaf Longevity The form and function of each species comprise an evolved functional design suited to a particular range of environmental conditions. In an environment within the limits of their evolved capacity, plant species generally can respond effectively to resource limitations, including through adjustments in leaf longevity of the category discussed earlier in this chapter. Environmental stress arises when conditions fall near or beyond the limits of a functional design, near the point where function can no longer be sustained. In terms of foliar function and questions of impact on leaf longevity, a stress might arise from any biotic or abiotic factor that incapacitates a leaf to the point where its production potential no longer will yield a net return on the resources invested in constructing and maintaining the leaf. In this context, expectations rooted in a cost–benefit analysis of foliar function often must be founded on analysis of carbon investments and gain at the whole-plant level, not just single leaves in isolation. We illustrate this perspective with a brief discussion of some important biotic and abiotic stressors.

Biotic Stressors: Herbivory and Disease

An herbivorous caterpillar, Actias selene gnoma

The original framework of Chabot and Hicks (1982) for cost–benefit analyses of foliar function included a term for leaf loss caused by herbivory or disease, but

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incorporating these effects into a comprehensive model of leaf longevity is not straightforward. First, there are two elements to foliar defense against herbivores and disease: constitutive and induced defenses (Karban and Baldwin 1997). Chabot and Hicks (1982), as well as subsequent cost–benefit models for leaf longevity in this vein (Kikuzawa 1991, 1995a,b; Kikuzawa and Ackerly 1999), have assessed a constitutive cost at the time of leaf construction, which cannot account for the cost of induced defensive responses to herbivore or pathogen attack. Second, the efficacy of an induced plant response is highly contingent on the ecology of the interaction between plant and attacker. For example, early abscission of gall-infested leaves can act as the density-dependent mortality factor for the gall-forming insects (Sunose and Yukawa 1979; Yukawa and Tsuda 1986), thus reducing the risk of attack for uninfested or future leaves. This sort of selective shortening of leaf longevity is illustrated by the response of Populus attacked by the gall-forming aphid (Pemphigus betae); nearly 90% of freshly fallen green leaves were gall infested, compared to less than 10% of the leaves still attached to the trees (Williams and Whitham 1986). On the other hand, infection of Populus by a rust fungus such as Melampsora medusae can result in anything from complete to only slight leaf loss (Newcombe and Chastagner 1993). Third, accounting the marginal value of a leaf at the time of attack requires assessing the return on initial investments to that point in time, the potential future return from the leaf in light of the cost and potential efficacy of any induced defenses, and integrating these costs and benefits at the wholeplant level. An effective model for the response of leaf longevity to herbivore or pathogen attack thus must scale up from the leaf to whole-plant level to address the underlying question of tolerance versus defense (Nunez-Farfan et al. 2007) as strategies for plant response to herbivory and disease.

Box 8.4  Mangroves Many tree species in five different plant families have evolved the capacity to grow in intertidal swamps along the ocean shoreline in tropical and subtropical regions. These trees, which are commonly referred to as mangroves, have converged to distinctive morphological and physiological adaptations to survive the stress associated with the twice-daily tidal alternation of saltwater versus freshwater around their roots. Mangroves are usually evergreen because their leaves are important for maintaining the metabolic and physical processes involved in salt exclusion and maintenance of stable tissue water potentials. Although mangroves have the evergreen leaf habit, the longevities of their individual leaves in fact are not very long, usually only 6–12 months (Gill and Tomlinson 1971), or sometimes up to 24 months (Tomlinson 1986).

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Abiotic Stressors: Ozone and Natural Oxidants Pollutants arising from anthropogenic sources fall outside the realm of specific adaptive responses but nonetheless can elicit generalized stress response mechanisms that have an evolutionary basis. Ozone provides a good example of this sort of preadaptation. Foliar responses to tropospheric ozone from anthropogenic sources are essentially the same as responses to UV radiation, drought, high temperatures, or other natural sources of oxidative stress (Bussotti 2008). In general, longer-lived leaves are more resistant to all oxidative stress whether natural or anthropogenic in origin. In terms of leaf longevity, the impact of ozone-induced oxidative stress, or probably most other anthropogenic pollutants as well, depends on a dose–response relationship. At lower doses in which tissue-level repair mechanisms confer sufficient resilience to maintain photosynthetic functions, leaf longevity should be extended to recover the initial leaf construction costs as well as the subsequent repair costs associated with the stress. At some higher dose, however, we can expect the leaf to be abandoned and recovery of investments shifted to shorter-lived leaves with higher production potential. Bussotti (2008) lends support to these suppositions, which invite further study.

Abiotic Stressors: Salinity The impact of salinity on leaf longevity has this same sort of dose–response dependency, at least so long as the species have at least some degree of salinity tolerance and do not simply die on exposure to saline conditions. Mangroves, a plant functional group tolerant of levels of salinity in their tidewater habitats that would be fatal for most plants, illustrate the interspecific variation and dose–response dependence in salinity effects on leaf longevity. Leaf longevities increase from only 0.36 years for Sonneratia alba and 0.65 years for Avicennia alba at the seaside on up to 2.66 years for Xylocarpus granatum at the upper edge of a mangrove swamp in Thailand (Imai et al. 2009). The leaf half-life of Avicennia germinans is 160 days in a Venezuelan mangrove swamp (Suarez 2003), but under experimental conditions in the absence of salt the half-life rises to 425 days, dropping to 195 days at 170 mol m−3 NaCl and to 75 days at 940 mol m−3 NaCl (Suarez and Medina 2005). Although Avicennia tolerates salt, it is clear that increasing salinity decreases leaf longevity. From a simplistic cost–benefit analysis, one might expect instead that increasing salinity would impair photosynthetic activity and extend leaf longevity to pay back the costs of leaf construction. This apparent contradiction is resolved at the whole-plant level because the leaves of Avicennia function not only in photosynthesis but also in salt secretion (Suarez 2003; Suarez and Medina 2005). Because the metabolic costs of salt excretion increase with leaf age and salinity, there is a point at which carbon gains at the whole-plant level are better served by shortening leaf longevity to take full advantage of the high foliar photosynthetic

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potential of young leaves before they are offset by increasing costs associated with salt excretion. In this instance, the shift from leaf to whole-plant level in resolving the stress arises not in limits to tissue repair but in competing foliar functions.

Abiotic Stressors: Flooding Flooding can impair leaf function in terrestrial plants through two effects: submersion, which cuts off access to atmospheric CO2, and anaerobic conditions in the root zone that impair root function and reduce the transpirational stream to emergent leaves (Mommer et  al. 2006; Parolin 2009). Depending on their degree of flood tolerance, species differ in the impact of flooding on leaf longevity (Terazawa and Kikuzawa 1994). Alnus japonica, a flood-tolerant riparian species, responds to flooding by developing adventitious roots near the surface and lenticels on the stem for air exchange; leaf longevity is prolonged under relatively short or shallow flooding conditions but shortened by deeper or long flooding. The response of leaf longevity is reversed in the upland, flood-intolerant Betula platyphylla var. japonica (Terazawa and Kikuzawa 1994). Similarly, in herbaceous species from wetlands, submergence in water through which light can penetrate prolongs leaf longevity, but in species from terrestrial habitats leaf longevity is shortened by submergence (Mommer et al. 2006). Most trees species submerged by the muddy floodwaters of the Amazon River immediately lose all their leaves, but others retain leaves throughout floods that can persist up to 9 months of the year; in some cases the retained leaves may actually carry on photosynthesis during submergence and in others only resume aerial photosynthesis as the flood recedes (Parolin 2009). For most of these Amazonian trees, flooding is an unfavorable period for photosynthesis, more akin to winter or prolonged drought than to an abiotic stress in which a dose–response relationship determines shifts in leaf longevity.

Chapter 9

Biogeography of Leaf Longevity and Foliar Habit

Tropical montane forest on Mt. Kinabalu, Borneo

There is, apparently, no general restriction on variation in leaf longevity per se along local and regional spatial gradients. Leaf longevity is only part of a suite of foliar traits that act in concert to ensure effective photosynthetic function in a given environmental regime (Wright et al. 2004; Shipley et al. 2006). Coordinated quantitative variation among the set of foliar traits can underpin equivalently effective photosynthetic function despite considerable variation in leaf longevity (Marks and Lechowicz 2006). As a consequence, leaf longevity typically varies substantially among species even in a single locality, a point made forcefully in earlier chapters but worth reinforcing here with another example. A careful study of 100 species representing four growth forms in the understory of a tropical montane forest (Fig. 9.1) shows the high variability in leaf survivorship curves among co-occurring K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_9, © Springer 2011

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a

b Herbs

Trees

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Diplazium cordifolium

Goniothalamus macrophyllus

Probability of leaf survival

0.8 0.6 0.4 0.2

Alocasia macrorrhiza

Cyathea contaminans

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c

d

Climbers

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Epiphytes

Elaphoglossum sp.

Piper arcuatum

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Asplenium cuneatum

Psychotria sp.

0.2 0.0

0

5

10

15

20

25

30 0

5

10

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Months after leaf flushing

Fig. 9.1  Leaf survivorship for 100 understory species co-occurring in a tropical montane forest in Indonesia. Note that by exception the Alocasia plants that were censused grew along a riverside opening less shaded than the other species. (a) Woody plants. (b) Herbaceous plants. (c) Climbing plants and (d) Epipytic plants (From Shiodera et al. 2008)

species; survivorship, in turn, is consistently correlated with elements in the leaf economic spectrum (Wright et al. 2004) as well as with aspects of foliar defense such as condensed tannin content and leaf toughness (Shiodera et al. 2008). Ideally, we might look for geographic pattern in the mean values of leaf longevity but the comprehensive, community-based samples required to do so are too scarce. We turn instead to the biogeography of foliar habit, which is rooted in leaf demography and commands not only the long-standing interest of plant geographers but also the attention of contemporary climate modelers.

Biogeography of Foliar Habit As a prelude to this discussion, we should note that the geography of ecosystems dominated by evergreen versus deciduous species has not been stable throughout Earth’s history. There are long-term influences on the patterns we see today that are set by both the evolution of the global environment and the phylogenetic history of contemporary plants. Both long-term changes in the global environment and the evolutionary diversification of the global flora have led to a temporally shifting mosaic in global land cover over Earth’s history. Although there

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have long been evergreen broadleaf forests in tropical regions, the extensive needle-leaved boreal forests that influenced the perspectives of nineteenth-century phytogeographers did not exist until recently (Taggart and Cross 2009). During most of the more than 400 million years since terrestrial plants first evolved in the Silurian, the planet has been in a “greenhouse” mode characterized by relatively warm climates worldwide that were only occasionally interrupted by episodes of cooling and glaciation (Tabor and Poulsen 2008; DiMichele et  al. 2009). During this time the deciduous habit arose, most likely as an adaptation to seasonally dark and fire-prone polar environments (Brentnall et al. 2005; Royer et al. 2003, 2005). By the Eocene, when the flora had close affinity with that of today, the distribution of evergreen and deciduous vegetation differed substantially from the present day. Broadleaf evergreen forests extended from equatorial regions to latitudes as high as 60°, and deciduous conifer forests were found in polar regions (Brentnall et al. 2005; Utescher and Mosbrugger 2007). This long period of “greenhouse” conditions is in contrast to the “icehouse” conditions that began and have persisted since the transition from the Eocene to Oligocene about 34 million years ago (MYA) when the planet became cooler and more subject to cyclic glaciation than it had been during the late Paleozoic and earlier Cenozoic (Coxall and Pearson 2007; Zachos et al. 2001). Although the contemporary phytogeography of vegetation types (Melillo et  al. 1993) has arisen in these “icehouse” conditions, on uniformitarian principles a well-grounded theory of foliar habit should predict both contemporary and paleo-distributions of evergreen and deciduous habits.

Contemporary Distribution of Deciduous and Evergreen Habits Chabot and Hicks (1982) posed the question: What can account for the bimodal distribution of evergreen forests along latitudinal gradients (Fig. 9.2), ecosystems dominated by broadleaf evergreen species at low latitudes and needle-leaf evergreen species at high latitudes (Melillo et al. 1993)? This query may, in fact, not be the best question around which to develop a theory of foliar habit. The potential problem is that despite the conceptual frameworks imposed by those interested in classifying, modeling, and mapping the broad patterns of global vegetation, plant communities only rarely, if at all, are composed entirely of evergreen or deciduous species. The norm is co-occurrence of species with these contrasting foliar habits within and among plant growth forms and vegetation strata, albeit in varying proportions. For example, the low-growing woody species of the tundra are mostly evergreen, but there are also many deciduous species of Salix. Boreal forests are dominated by evergreen conifers, but deciduous species of Populus and Betula are frequent on successional sites, Salix species widespread, and Fraxinus, Alnus, and Ulmus not uncommon trees in rich, moist sites. There is no shortage of deciduous herbs and shrubs in the boreal forest understory. The transition from boreal forests

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Fig. 9.2  Global distribution of evergreen trees. (From Woodward et al. 2004)

south to temperate broad-leaved deciduous forests transits a “mixed-wood” zone with evergreen and deciduous trees in more or less equal proportions. South of the deciduous broad-leaved forests, broad-leaved forests of evergreen oaks predominate, and further south give way to evergreen as well as deciduous forests in the subtropics and tropics. Mesic tropical forests are basically evergreen, but in seasonally dry regions tropical deciduous forests predominate. These and many other examples spanning widely divergent spatial scales illustrate a pattern of evergreen predominance at both ends of the latitudinal gradient in contemporary vegetation that, although not inviolate, is common enough to demand general explanation. We clearly need a theory addressing the fundamental basis for spatiotemporal variation in foliar habit.

Theory for the Geography of Foliar Habit Our premise is that a theoretical analysis of the geographic distribution of the evergreen and deciduous habits should be based on a theory of leaf longevity. Evergreen and deciduous habits are defined at the canopy level but set by the temporal dynamics of leaf turnover and leaf longevity. Previous theories with their intellectual heritage in the canopy-level perspectives of Monsi and Saeki (1953) have not really tried to predict conditions favoring evergreen versus deciduous species. Because this issue was an explicit motivation for the seminal review by Chabot and Hicks (1982), at least some of the existing theory for leaf longevity

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(Kikuzawa 1991, 1995a,b, 1996) has offered predictions about the factors determining foliar habit. The analysis by Kikuzawa (1991) recognizes the existence of sustained periods in the annual cycle that can be unfavorable for photosynthetic activity, and that hence would appear to compromise the raison d’etre for maintaining leaves in these unfavorable seasons. These unfavorable periods may be set, for example, by extreme cold, as in the winter of the temperate zone, or by droughts, as in the aseasonal tropics. To address the existence of the deciduous versus evergreen habits, Kikuzawa (1991, 1995a, 1996) adapted the basic theory shown by (4.3) and Fig. 4.2 to seasonal environments. Photosynthesis during the favorable period simply follows (4.3). If plants retain leaves during an unfavorable period, the leaves do not yield photosynthetic gains and in fact incur maintenance costs (respiration) during this period. Hence, (4.3) can be recast in the form:

f

G = ∫ p(t )d t + 0

1+ f

∫ 1

t

p(t )dt +  + ∫ p(t )dt − [t]

t   ∫ m(t )d t + ∫ m(t )dt +  + ∫ m(t )dt  − c 1+ f [ t ]+ f  f  1



2

(9.1)

where f is the fractional length of the favorable period within a year and t is the Gaussian notation. This equation gives photosynthetic gain by subtracting maintenance costs of leaves during the unfavorable periods from photosynthetic gains during the favorable period. Note that the maintenance costs during favorable periods are already subtracted from gross photosynthetic gain; thus, p(t) is net gain, the outcome of this subtraction. What then is the optimal replacement timing of leaves for individual plants in a seasonal environment with a period unfavorable for photosynthetic production? Much as in the aseasonal situation, the solution is obtained by finding t that maximizes g = G/t, but now G is expressed by (9.1) and follows a zigzag curve through time, increasing during summer and decreasing during winter (Fig. 9.3). The optimal timing again obtains at the point when the line from the origin touches the zigzag curve. An analytical solution is not readily available, but numerical solutions can be found through appropriate simulations. If the optimal leaf longevity under certain conditions exceeds the length of the favorable period, then the plant is predicted to be evergreen. If the solution is for leaf longevity shorter than or equal to the length of the favorable period, then the plant should be deciduous. Simulations carried out for regions differing in length of favorable period yield predictions for patterns of occurrence in evergreen and deciduous plant species (Kikuzawa 1991, 1995a, 1996). Where favorable period length (f) is equal to 1 year, all plants are expected to be evergreen, because plants can carry out photosynthesis throughout a year (Fig. 9.4). Even in such locations, however, there can be species whose leaf longevity is shorter than 1 year. The evergreen habit combined with leaf longevity less than the full favorable period suggests that a tree retains leaves throughout a year but with a high, asynchronous turnover in individual leaves. When the favorable period length becomes

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Fig. 9.3  Schematic representations to show photosynthetic production under favorable periods of different lengths. (a) Evergreen species have an advantage because of the low maintenance costs during a short unfavorable period. (b) Shedding leaves during the unfavorable period is advantageous in the longer favorable period. (c) Paying back construction costs within one short favorable period. Solid increasing line indicates net gain during favorable periods. Broken decreasing lines are maintenance costs during unfavorable periods. Broken increasing line from the origin touches the curve at the point where the marginal gain is maximum

shorter than 1 year, the deciduous habit will appear. The percentages of deciduous species increase and evergreen species decrease with the shortening of favorable period. The percentage of evergreen species reached a minimum value at an intermediate length of f (at around f = 0.5). When the favorable period length becomes still shorter, the percentage of evergreen species increases again. Various observations are consistent with this sort of interplay between the length of the favorable period and the balance between evergreen and deciduous foliar habits. Some tree species, such as Mallotus japonicus and Alnus japonica, are deciduous in central Japan but are evergreen on Okinawa in southern Japan. Some evergreen trees in Singapore such as Trema orientalis, Ficus elastica, and Duabanga sonneratioides become deciduous north along the Malay Peninsula (Koriba 1948a,b). Almost all the trees in a riparian forest in Costa Rica were evergreen compared to only about half in nearby upland forests (Frankie et al. 1974; Opler et  al. 1980). Similarly, Condit et  al. (2000) reported that the percentage of the deciduous tree species across the Isthmus of Panama increased from 14% on the Atlantic Ocean side (annual precipitation, 2,839 mm) to 28% on Barro Colorado Island (2,570 mm) and 41% on the Pacific Ocean side (2,060 mm). When the favorable period length becomes still shorter, the percentage of evergreen species increases again. Such shifts in the balance of deciduous and evergreen species can occur even in the restricted growing season of arctic regions. Of 18 plant species

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Percentages of Leaf Habit

100

50

0

1.0

0.6

Length of Favorable Period (f) (year)

0.2

Latitude

Fig. 9.4  Simulation of changes in the percentages of deciduous (open) and evergreen (shaded ) species at different length of favorable period (year) within a year (f). Dotted bar indicates the evergreen habit but shorter leaf longevity than 1 year

whose foliar habit is evergreen or wintergreen on King Christian Island at 77°50¢N, ten species were summergreen in Greenland at 72°50¢N. Similarly, seven evergreen species whose leaf longevity is longer than 2 years on King Christian Island were reported to be wintergreen with leaf longevity shorter than 2 years in Greenland (Bell and Bliss 1977). Because favorable period length often shortens from the Equator to higher latitudes, the trend on length of the favorable period shown in Fig. 9.5 might also be taken to reflect changes in percentages of deciduous and evergreen habits from the Equator to the poles. This possibility is appealing because the bimodal distribution of evergreenness on the latitudinal gradient has long puzzled ecologists (Chabot and Hicks 1982), but is this a fair interpretation of the simulations? Taking winter cold as an example of a constraint on photosynthetic production, there is some intuitive basis for interpreting the simulations in this way (cf. Fig. 9.3). When the unfavorable period is short, it can be advantageous to use overwintered leaves in the next spring by paying maintenance costs in winter rather than shedding old leaves at the end of summer and producing new leaves in spring (see Fig.  9.3a). When winter becomes longer, the cumulative maintenance costs of maintaining foliage overwinter increases, so that shedding leaves before the onset of the unfavorable period and producing new leaves with high photosynthetic capacity in the next season becomes advantageous (see Fig.  9.3b). When the unfavorable period length becomes still longer, it may be difficult for the leaves to pay back

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Fig. 9.5  Relationships between favorable period length and leaf longevity in three alpine plant species: deciduous Sieversia pentapetala (a), evergreen Phyllodoce aleutica (b), and evergreen Rhododendron aureum (c). (From Kikuzawa and Kudo 1995)

their construction costs within the short favorable period, so extended leaf longevity leads to an evergreen habit (see Fig.  9.3c). This rationale is supported by the contrasting trends in the leaf longevity of evergreen versus deciduous species reported by Wright et  al. (2005a) along global temperature gradients associated with length of the growing season. Leaf longevity of evergreen species decreased with mean annual temperature whereas that of deciduous species increased. In summary, deciduous plants are unable to retain their leaves over an unfavorable period but do prolong leaf longevity when the favorable period lengthens. Conversely, evergreen species have to prolong leaf longevity when the unfavorable period lengthens to pay back the construction and maintenance costs of leaves unproductive during the unfavorable period. Although these patterns are in accord with intuitive arguments provided by Kikuzawa (1991, 1995a, 1996) to account for the bimodal distribution of evergreen habit on latitude, the development of relevant analytical theory is desirable. In principle, these arguments should apply not only to interspecific behavior on latitudinal gradients but also to variation in leaf longevity for species at local spatial scales. We can illustrate and test the ideas using situations such as topographic variation in the timing of spring snowmelt caused by differences in winter snow depth on Mount Daisetsu in northern Japan. Kudo and Kikuzawa (Kudo 1992, 1996;

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Box 9.1  Ecosystem The concept of ecosystems emerged early in the twentieth century as ecologists began to grapple with the complex interactions defining the relationships between the biota and the abiotic environment. The concept is appealing in its generality, applying equally well from a pond to an ocean, or from a woodlot to a forest biome, or for that matter to the planet as a whole. At the heart of the ecosystem concept is the recognition that flows of energy and materials through the system sustain the interactions among its biotic and abiotic components. Ultimately all ecosystems depend either on the thermal energy and material flows associated with deep-sea vents or, most commonly, on the solar energy that is captured by photosynthetic organisms such as plants and phytoplankton – the primary producers. Other organisms in ecosystems function as consumers of primary producers or decomposers breaking down organic matter. In contrast to the emphasis of evolutionary biology on the diversity and adaptation of organisms, ecosystem science has focused more on the overall structure and nature of the flows of materials and energy through the system than on the particular organisms in the system. A contemporary challenge in ecosystem science is to understand the relationship between biodiversity and ecosystem function.

Kikuzawa and Kudo 1995) studied two evergreen species and a deciduous species associated with these snowbed habitats in which snowmelt occurred from early June through early August (see Fig. 9.5). In both evergreen species, leaf longevity declined with a longer favorable period, whereas in the deciduous species leaf longevity increased with the length of the favorable period. Leaf longevity of the deciduous species is restricted by the length of the favorable period; thus, leaf longevity necessarily is reduced in shorter favorable periods. In contrast, evergreen species can prolong leaf longevity beyond winter, thus compensating for the decrease in photosynthesis resulting from a shortened favorable period by prolonging leaf longevity and exploiting subsequent snow-free periods. By changing favorable period length in Kikuzawa’s (1991) model with other parameters held constant, Kikuzawa and Kudo (1995) simulated this pattern of decreasing versus increasing leaf longevity in evergreen and deciduous species, respectively, with a longer snow-free period (Fig.  9.5). Because leaf longevity is only one element in the suite of foliar traits affecting production potential, this snowbed community also affords an example of how the deciduous species adjust to ensure payback on leaf construction costs when the favorable period is short. Unable to extend their leaf longevity, plants growing in places subject to shorter snow-free periods instead increased their photosynthetic rates by increasing investment for photosynthetic machinery and decreasing costs such as defense. In three deciduous species in this snowbed habitat, leaf mass per area (LMA) decreased and foliar

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nitrogen increased as the favorable period length shortened across the sampled microhabitats (Kudo 1996). Consistent with Kikuzawa’s cost–benefit analysis of foliar carbon economy (Kikuzawa 1991, 1995a,b, 1996), there clearly is interplay between leaf longevity and foliar habit that should figure in analyses of the geography of foliar habit as well.

Modeling Foliar Habit in Relationship to Climate The greatest current concern in predicting the distribution of foliar habit at a global scale is in models for shifts in vegetation in response to climate change. These dynamic global vegetation models (DGVMs) draw on the distinction between evergreen and deciduous foliar habit to characterize future vegetation zones but are cast at the scale of global zonation in broadly defined plant functional types (Woodward et al. 2004; Sitch et al. 2008). The scale and the definition of DGVMs unfortunately do not allow detailed attention to the relationships between leaf longevity and foliar habit. One place where climate models of foliar habit, however, have explicitly considered the role of leaf longevity is in analyses of the possible origin of the deciduous habit in polar forests during warmer periods in earth history (Brentnall et al. 2005). In an adaptation of the University of Sheffield’s conifer forest growth model (Osborne and Beerling 2002), Brentnall and his colleagues (2005) use leaf longevity as a key driver in analyses of variation in foliar habit along simulated mid-Cretaceous climate gradients. Using wood from extant conifers, they calibrate their model with a method relating the fine structure of wood anatomy to leaf longevity (Falcon-Lang 2000a,b; Falcon-Lang and Cantrill 2001) and then test their predictions against a broad sampling of fossil wood deposits. Their analyses demonstrate the advantage of the deciduous habit in high-latitude conifers during the mid-Cretaceous when the earth was warmer and polar regions were forested (Fig. 9.6), a finding substantiated by experimental studies of extant conifers with evergreen versus deciduous habits (Royer et al. 2005).

0.5 Fractional cover

Fig. 9.6  Fractional cover of deciduous conifers (open circles) versus evergreen conifers (closed circles) as a function of latitude in simulations that consider the role of leaf longevity in affecting survival, reproduction, and competitive ability during the mid-Cretaceous. (From Brentnall et al. 2005)

0.4 0 0.2 0.1 0 60

70

80 Latitude

90

Chapter 10

Ecosystem Perspectives on Leaf Longevity

A streamside forest in fall K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity, Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_10, © Springer 2011

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As an integral part of the adaptive strategy for productivity at the level of individual plants, leaf longevity should scale up to impact flows of energy and materials at the ecosystem level. Consequently, leaf longevity and foliar habit consistently appear in enumerations of traits relevant to ecosystem function (Weiher et  al. 1999; Lavorel and Garnier 2002; Cornelissen et  al. 2003; Kleyer et al. 2008). The past decade has seen a flood of papers discussing linkages between various traits and ecosystem function: useful entry points to this literature include Lavorel and Garnier (2002), Díaz et al. (2004), Wright et al. (2005b), Quetier et al. (2007), and Suding and Goldstein (2008). Although leaf longevity and its foliar correlates clearly influence ecosystem processes (Thomas and Sadras 2001; Wright et al. 2005b; Cornwell et al. 2008), scaling up the effects of leaf longevity at the level of individual plants or species to the aggregate influence of species assembled in diverse communities across the landscape is not at all straightforward (Suding et al. 2008). Zhang and colleagues (Zhang et al. 2009) provide perhaps the best example of what is possible if one is willing to invest the effort. They followed leaf longevity on individual species in ten evergreen forests in eastern China for 5 years, calculating frequencyweighted mean leaf longevity for each forest, which was negatively correlated with mean annual temperature and positively correlated with mean annual precipitation. Very few ecosystem studies focus to this degree on leaf longevity per se at the level of individual species, or for that matter on any other species-specific traits. Some models of forest productivity incorporate an impressive amount of detail on individual species at the population level in the forest community (cf. Medvigy et al. 2009), but the focus typically remains on the forest as a whole, not the detailed analysis of individual trees and species that in aggregate decide the functional characteristics of the forest. It clearly is no easy task to assess how leaf longevity and associated traits at the species level scale up to affect ecosystem function. In any case, our goal in this closing chapter is not so much to discuss the influence of leaf longevity and foliar habit on ecosystem function, but rather the obverse – to highlight work in ecosystem ecology that may help improve theory for leaf longevity. Through perspectives drawn from ecosystem function, we basically turn the discussion of leaf longevity back to the Chabot and Hicks’ (1982) seminal review, with a focus on better accounting the costs of foliar construction and defense in predicting variation in leaf longevity. The relatively limited treatment of these factors in the initial cost–benefit models for leaf longevity (Kikuzawa 1991, 1995a,b, 1996) leaves room for improvement in our understanding of leaf longevity as a key factor in foliar function.

Leaf Turnover and Leaf Longevity in the Ecosystem The most direct connection of leaf longevity to ecosystem function is through leaf turnover because this turnover rate essentially defines a storage term for materials in the system as well as an indication of system productivity. Not surprisingly, there is a positive correlation between leaf longevity and mean retention time of

MRTB (number of growth seasons)

Nutrient Resorption and Leaf Longevity

111

6 5 4 3 2 1 0

0

1 2 3 4 5 Leaf life span (number of growth seasons)

6

Fig. 10.1  Relationship between leaf longevity and mean retention time of biomass (MRTB) in various ecosystems. (From Mediavilla and Escudero 2003b)

biomass in canopies (Fig. 10.1). The ratio of leaf biomass and leaf litter production gives an estimate of mean retention time of leaves in the canopy, and these data in turn can be used to estimate leaf longevity (cf. Chap. 3). More importantly, the voluminous data gathered by ecosystem ecologists on the quality of fallen foliage has considerable bearing on theory for leaf longevity. Foliar nitrogen in particular is not only an important determinant of decomposition and nutrient cycling at the ecosystem level (Cornwell et al. 2008) but also a critical correlate of leaf longevity and photosynthetic function (Wright et al. 2004). Construction of leaves requires investment of nitrogen that can only be acquired from the environment at some carbon cost to the plant (Givnish 2002), which contributes to the total carbon cost of leaf construction that under current theory (Kikuzawa 1991, 1995b, 1996) must be recouped over the lifetime of the leaf. If nitrogen and also phosphorus can be resorbed from senescing leaves and recycled into new leaves, then leaf construction costs may be less than if these foliar resources had to be acquired de novo in the environment.

Nutrient Resorption and Leaf Longevity Resorption of nutrients is a normal part of the leaf senescence process, but full recovery of critical and often relatively scarce nutrients such as nitrogen and phosphorus apparently is not possible. Killingbeck (2004) distinguishes potential resorption and realized resorption. Potential resorption considers all biochemically resorbable nutrients, those not so refractory as to be mobilizable only at exceedingly high metabolic costs. Potential resorption is considered a fixed value specific to each plant species and is thought to be phylogenetically dependent. In terms of realized resorption, not all nutrients that potentially could be resorbed in fact will be retranslocated from senescing leaves, so realized resorption typically is less than

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RESORPTION PROFICIENCY BASED ON NUTRIENT CONCENTRATION IN SENESCED LEAVES

COMPLETE RESORPTION

< 0.7% N

< 0.05% P

DECIDUOUS SPP.

< 0.04% P EVERGREEN SPP.

I N T E R M E D I A T E

INCOMPLETE RESORPTION

> 1.0% N

> 0.08% P

DECIDUOUS SPP.

> 0.05% P

EVERGREEN SPP.

Fig. 10.2  Resorption proficiency of nitrogen (N) and phosphorus (P). Proficiency is the ratio of mass of each nutrient to the leaf mass at leaffall. If the ratio is less than the values in the figure, resorption is complete; it is considered incomplete if the ratio is greater. SPP., species. (From Killingbeck 1996)

potential resorption. Killingbeck (1996) also distinguished resorption proficiency from efficiency (Fig. 10.2). Efficiency is the percentage difference between nutrient concentration per unit area of a green leaf immediately before shedding to the initial concentration of the green leaf. Proficiency, simply the nutrient concentration of fallen leaves, is directly relevant to biogeochemical cycling (Parton et al. 2007), whereas efficiency is more directly relevant to foliar function and leaf longevity. The value of efficiency varies greatly among species, but the value of proficiency does not vary so much (Killingbeck 1996, 2004). On average, a little less than half the nitrogen and a little more than half the phosphorus in a leaf is resorbed (Eckstein et al. 1999; Kobe et al. 2005; Yuan and Chen 2009), but in light of the huge range of interspecific variation in resorption efficiency and the lack of any correlation between nitrogen resorption efficiency (NRE) and phosphorus resorption efficiency (PRE) (Fig. 10.3), this fact provides little or no insight into alternative modes of foliar function. The apparent lack of correlation between NRE and PRE is somewhat misleading, however, because in fact tropical species are more efficient in resorbing phosphorus and temperate and boreal species are more efficient in resorbing nitrogen, which would appear to reflect latitudinal differences in soil availability of nitrogen relative to phosphorus (Yuan and Chen 2009; Fig. 10.4). The NRE also is lower and PRE higher on average

Nutrient Resorption and Leaf Longevity

113

100

Phosphorus Resorption Efficiency, %

90 80 70 60 50 40 30 20 10 0 0

10

20

30

40

50

60

70

80

90

100

Nitrogen Resorption Efficiency, %

100

Nitrogen Resorption Efficiency, %

Phosphorus Resorption Efficiency, %

Fig. 10.3  Resorption efficiencies for nitrogen (NRE, x-axis) and phosphorus (PRE, y-axis) collated by Yuan and Chen (2009) for a wide range of tree and shrub species representing all growth forms and regions

90 80 70 60 50 40 30 20 10 0 0

5 10 15 20 25 30 35 40 45 50 55 60 65 70

Latitude

90 80 70 60 50 40 30 20 10 0 0

5 10 15 20 25 30 35 40 45 50 55 60 65 70

Latitude

Fig. 10.4  Latitudinal trends of nitrogen resorption efficiency (NRE) and phosphorus resorption efficiency (PRE) for tree and shrub species in Yuan and Chen (2009) for species that had both measures of efficiency. Although overall there is no correlation between NRE and PRE for a species, this is because mid-latitude similarities mask the inverse, negative relationships at high and low latitudes

10 Ecosystem Perspectives on Leaf Longevity

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 1.0

10.0

Leaf longevity, months

100.0

Phosphorus Resorption Efficiency, %

Nitrogen Resorption Efficiency, %

114

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 1.0

10.0

100.0

leaf longevity, months

Fig. 10.5  There is no apparent relationship between leaf longevity and either NRE or PRE, although the available data are sparse and not entirely consistent (Wright et al. 2004; Yuan and Chen 2009)

in evergreen compared to deciduous woody species (Yuan and Chen 2009), which presumably has more to do with the functional ecology of the two foliar habits than with site-dependent differences in nitrogen and phosphorus availability. Considering the carbon costs of acquiring the nitrogen and phosphorus to construct a leaf (Givnish 2002), we might expect that interspecific variation in NRE and PRE would be related to leaf longevity. In principle, recovery of nitrogen or phosphorus from senescing leaves might be less costly than acquiring these resources de novo from the soil, and hence could reduce the total carbon cost of leaf construction. What little evidence there is, however, suggests there is no relationship between leaf longevity and either NRE or PRE (Fig. 10.5). Reich et al. (1992) did, however, report a significant negative relationship between the absolute amount of resorbed nitrogen and leaf longevity: the greater the amount of resorbed nitrogen, the shorter the leaf longevity. This result and the broad range of resorption efficiencies across species suggest that at least in some instances resorption may act to reduce the effective cost of leaf construction and thus might act to reduce leaf longevity. Resolving this possibility will require more studies of nitrogen and phosphorus availability at sites where NRE and PRE are determined. In this regard, it is noteworthy that longer-lived leaves generally have lower nitrogen concentration (Wright et  al. 2004) and hence decompose more slowly (Parton et al. 2007). Because leaffall and decomposition comprise a critical pathway connecting the production and decomposing functions of ecosystems (Thomas and Sadras 2001), a positive feedback may generally exist between the availability of soil resources and the frequency-weighted mean leaf longevity of ecosystems. The quality and quantity of materials in fallen leaves will affect their fragmentation and decomposition (Grime et  al. 1996). If the decomposition rate of the fallen leaves is rapid, organic matter can be decomposed to inorganic material quickly and absorbed by plant roots. Able to absorb abundant nutrients, plants could then grow vigorously, elongating shoots and shedding relatively short-lived leaves that are subject to more rapid decomposition. Conversely, longer-lived leaves are difficult

Photosynthetic Nitrogen Use Efficiency and Leaf Longevity

115

for litter invertebrates to consume, decompose slowly, and accumulate as a thick litter layer that reduces nutrient availability and slows both plant growth rates and the nutrient turnover rate in the ecosystem (Eckstein et al. 1999; Kikuzawa 2004). In short, the turnover rate of leaves is correlated with the availability of nitrogen and phosphorus in an ecosystem, and there is a positive feedback between turnover and availability that can simplify modeling the carbon cost of nitrogen and phosphorus acquisition in an improved theory for leaf longevity.

Photosynthetic Nitrogen Use Efficiency and Leaf Longevity There is a potential benefit in nitrogen resorption, but also a potential cost in lost photosynthetic capacity, so we can expect an environmentally dependent interplay among these foliar characteristics as the leaf ages. Although there are positive correlations among foliar nitrogen and phosphorus concentrations and photosynthetic capacity (Wright et al. 2004), we also need to consider the degree to which NRE and leaf longevity are conditional on photosynthetic nitrogen use efficiency (PNUE, the photosynthetic rate per unit nitrogen). Comparable arguments also may apply to phosphorus, but these are less well known because past work has emphasized species from regions where soil nitrogen availability limits productivity. Escudero and Mediavilla (2003) examined PNUE and nitrogen resorption in nine evergreen tree species from a Mediterranean climate. Although nitrogen was retranslocated immediately before leaffall, foliar nitrogen concentration was maintained rather constant throughout almost all the life of the leaf before an abrupt decline. But, because photosynthetic capacity decreased with leaf age, the PNUE also decreased linearly with time. The shorter the leaf longevity, the greater the rate of decrease in PNUE. A similar relationship between leaf longevity and the decreasing rate of PNUE was observed in 23 Amazonian tree species (Reich et al. 1991), but no significant relationship was reported in a comparison of seven tree species in an evergreen broadleaf forest in central Japan (Hikosaka and Hirose 2000). In the Amazon, tree species from various habitats were sampled, but the Japanese species came from a single forest. Plant species in the same habitat tend to have similar PNUE (Hirose and Werger 1994, 1995). We can consider this trade-off at the whole-plant level as well, which could bear on the control of resorption and longevity at the leaf level. Nitrogen use efficiency of an individual plant (NUE) is the ratio of annual net production and annual nitrogen absorption, which can be expressed as the product of nitrogen productivity (NP, the annual net production per standing nitrogen mass of the plant) and mean residence time of nitrogen (MRT) (Hirose 2002, 2003):

NUE = NP × MRT

(10.1)

Nutrient use efficiency can be expressed by the ratio of the amount of litterfall to the amount of nutrient in the litterfall (Vitousek 1982, 1984); that is to say, the inverse of the nutrient concentration in the litter expresses the nutrient use efficiency.

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If we regress nutrient use efficiency of various forests against nutrient absorption rate, we obtain a decreasing relationship between the two (Vitousek 1982). This finding suggests that in nutrient-rich forests, nutrient use efficiency (NUE) is low whereas in nutrient-poor forests the trees use nutrients efficiently. Fertilization decreased the nutrient use efficiency, indicating this relationship is not merely the outcome of autocorrelation. It is also suggested that if there is a lowest limit of nutrient amount to achieve positive net production, the relationship is not a simple decreasing function, but should be an optimum curve (Bridgham et al. 1995). When nutrient is resorbed from senescing leaves, total CO2 assimilation of the canopy can be improved by the shedding of older leaves only when the increase in photosynthesis from resorbed nitrogen (N) exceeds the photosynthesis of the leaves lost. This condition is satisfied if the ratio (100 × PNUE in the old leaves/ PNUE in the young leaves) is less than the percentage of N recovered from senescing leaves before abscission. In other words, the N of old leaf × efficiency of old leaf should be less than the retranslocation ratio × N of old leaf × efficiency of new leaf:

PNUE old / PNUE new < r

(10.2)

Otherwise, retention of the old leaves would result in a higher total CO2 assimilation for the whole-leaf biomass. Accordingly, under N limitation, maximum leaf longevity must be constrained by both the rate of decline in PNUE with leaf age and the efficiency of N resorption; the balance between these factors will determine a minimum relative PNUE for leaf retention. In agreement with the foregoing expectations, instantaneous PNUE of the leaf cohorts in nine Mediterranean tree species was usually above the predicted minimum PNUE for a leaf to be retained (Escudero and Mediavilla 2003).

Defense of Leaves and Leaf Longevity Consistent with a carbon-focused cost–benefit analysis of leaf longevity, PNUE will also be adversely affected and leaf longevity altered if the photosynthetic capacity of leaves is impaired during their lifetime. This impairment can occur not simply because of aging of tissues but also because of either damage through herbivore and pathogen attack or damage associated with abiotic factors such as wind or falling branches. The characteristics of the leaf can modulate these risks to at least some degree, and at some cost, the biotic risks through constitutive and facultative defenses and the abiotic risks through investments in stronger foliar tissue. Although Chabot and Hicks (1982) raised these points, the associated costs and benefits have not been incorporated into a theory for leaf longevity. Nor will it be easy to do so (see Agrawal (2006) for an entry into the tangled history of research on plant defense), but there are at least two possibilities among existing theories of defense for establishing links to theory for leaf longevity. Both, in turn, have links to aspects of ecosystem ecology.

Defense of Leaves and Leaf Longevity

117

Fig.  10.6  Relationship between growth and investment for defense. Each curve represents a hypothetical species; arrows indicate the maximum realized growth rate. The optimal defense differs depending on the growth rate of the plant species. For plants that have a high potential growth rate, it is advantageous to invest in growth by reducing the investments for defense, but plants with a lower potential growth rate should invest for defense. (From Coley et al. 1985)

The first possibility is the resource availability theory for plant defense (Coley et al. 1985; Agrawal 2006). This theory is predicated on the assumption that the resource environment in which a plant grows will condition its defensive investments. The theory is developed with reference to herbivores but in principle should also apply to defense against pathogens. The logic of the predictions rests on the following mathematical model (Coley et al. 1985) generating the series of growth curves shown in Fig. 10.6:

(

) (

)

g = G 1 − kD a − H − mD b

(10.3)

where g is the realized growth rate and G the potential growth rate, which represents the rate without loss by herbivory or without any defense against herbivory. D is investment for defense, H the potential herbivory, and k, m, a, and b are constants. The first term of the right-hand side of (10.2) is the growth rate, indicating that the potential growth rate (G) is reduced by the investment for defense (D). The second term is the level of leaf damage from herbivory, suggesting potential herbivory (H) is reduced by the defense. The subtraction of herbivory losses from growth gives

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the realized growth; note that the herbivore damage is not given by the percentage but by the absolute difference of the two terms. The resource availability theory predicts a close correlation between leaf longevity and defense. Plant species in resource-rich, sunny environments should invest more for growth rather than defense, replacing leaves quickly to avoid declines in aging leaves and attain vigorous growth at the whole-plant level. Nitrogen-rich leaves have this high growth potential and short longevity, on one hand, but also are attractive resources for herbivores on the other (Mooney and Gulmon 1979). Leaves in a resource-rich environment may incur more herbivore damage (Coley 1988; Coley and Barone 1996) but can tolerate losses because of the high return on investment possible in the resource-rich environment. On the other hand, plant species in resource-poor environments will have lower photosynthetic potential and hence longer-lived leaves. This situation places a premium on investments in defense over growth. To summarize, the resource availability theory predicts: (a) a negative correlation between growth and defense, and a positive correlation between growth and amount of herbivory, and (b) a positive correlation between leaf longevity and defense and a negative correlation between leaf longevity and growth. The theory does not address interspecific variation in leaf longevity among co-occurring species, but at least it has the potential to link theory for leaf longevity to environmental gradients in resource availability that affect ecosystem productivity.

Timing of Leaf Emergence, Leaf Longevity, and Leaf Defense One of the earliest theories for plant defense, the apparency theory (Feeny 1970, 1976), also has bearing on theory for leaf longevity. Apparency theory made a qualitative argument that if plants were more easily found by herbivores or pathogens because of their abundance, stature, persistence, or some similar factor, then they would be subject to more frequent and diverse attacks and should have a quantitative defense founded on reducing foliage quality for the attacker by heavy investments in tannins, fiber, and other constitutive defenses. Conversely, a less apparent plant would escape generalist herbivores or opportunistic pathogens and need only mount a less costly, qualitative defense against potential attackers specially adapted to finding the plant despite its lack of apparency. The ideas are simple but in some ways compelling and not without support (Agrawal 2006). This apparency perspective on defense is interesting for theory of leaf longevity because it might provide a link between leaf phenology and leaf longevity, and leaf phenology in turn is being altered by global warming (Parmesan 2006). Collating coherent data on the frequency and intensity of losses to herbivory and disease at the community and ecosystem levels may allow probabilistic estimates of leaf vulnerability that could be incorporated into an improved theory of leaf longevity.

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119

Linking Leaf Longevity and Ecosystem Function In summary and conclusion, there are two aspects of ecosystem studies that potentially can inform a theory for leaf longevity. First, if knowledge of ecosystem function lets us effectively quantify the carbon costs of acquiring nitrogen and phosphorus across ecosystems, then we could more explicitly assess the costs and benefits of acquiring these resources through resorption versus uptake from soil. Second, if we could effectively quantify the age-dependent risks of leaves for herbivore or disease damage across ecosystems, then we could better factor this aspect into the accounting of the carbon costs of leaf construction. In principle, both avenues hold promise, but in practice neither is likely to soon yield a firm quantitative foundation for the test of a refined theory for leaf longevity. That realization, however, does not preclude a priori refinement of the theories that qualitatively explore these relationships at the leaf and whole-plant levels.

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Subject Index

A Abscission, 21 Adaptive radiation, 59 Alkaloids, 55 Allometric relationships, 33 Anthocyanins, 21 Apparency to herbivores, 55 theory, 118 Aquatic plants, 54 Autonomous unit, 85 B Branch whorl, 31 Bud apical, 9 burst, 9 hypsophyllary, 13 lateral, 9 naked, 13 scale, 9, 10, 13 scale development, 13 timing of budbreak, 14 C Canopy architecture, 2 ergodic hypothesis, 51 hollowing phenomenon, 82 photosynthesis model, 50 structure, 2 Coloring, 21 Construction cost, 6 leaf, 38, 42, 68 Cost-benefit ratio, 42 Crown, 2 hollowing, 82

D Deciduous, 2 brevideciduous, 3 drought, 3, 90 habit, 101 incomplete deciduousness, 3 semideciduous, 3 Defense, 117 biological, 55 chemical, 55 constitutive, 95 induced, 95 induced chemical, 55 physical, 54 plant defenses, 54 Delayed greening, 49 Density dependence, 92 density-dependent mortality factor, 95 Depression effect, 17 Diel effect, 17 Disturbance, 88 Dynamic global vegetation models (DGVMs), 108 E Early, mid and late successional species, 15 Ecosystem, 107 level, 110 Efficiency, 112 Endogenous factors, 21 Even-aged cohort, 29 Evergreen broad-leaved tree species, 13 habit, 101 plant species, 2, 59 semievergreen, 3, 59 Evolution, vii Exogenous factors, 21

141

142 F Fagus type, 9 Ferns, 59 Flooding, 97 G Geographic distribution, 102 Geography of foliar habit, 102 Greenhouse, 101 greenhouse Earth, 8 Gross primary production (GPP), ix, 36, 37 Growth rate hypothesis, 93 H Herbivore, 95 herbivory, 54, 117 satiate herbivores, 55 Heteronomous, 11 Heterophylly, 25 Heteroptosis, 3 Homonomous, 11 I Icehouse, 101 Intrinsic rate of population growth, 48 Isometric relationship, 33 L LAI optimum leaf area index, 50 Leaf, 7 abscission, 24 construction cost, 38, 42, 68 decomposition rate of the fallen, 114 dry matter content LDMC, 68, 75 economic spectrum, 68 embryonic, 9 lamina, 9 mesophyllic leaves, 6 primordia, 9 Leaf emergence duration of the period of, 27 flush type, 9 period, 62 simultaneous-type, 82 successive type, 82 Leaf exchanger, 2 Leaf expansion duration of the period of, 14 full expansion, 14

Subject Index Leaffall duration of the period of, 27 period, 62 Leaf half-life, 29, 31 Leaf lifespan, 23 Leaf longevity, 23 cohort-based estimates of, 30 functional, 35, 36 potential, 43 theory for, 43, 76, 102, 116 Leaf mass per unit area (LMA), 42, 68 Leaf senescence, 21 senescence-associated genes, 21 Leaf survival curve, 25 lx curve, 30 survivorship curve, 29 Leaf traps, 34 Leaf turnover rate, 34 Litter traps, 34 M Macrophylls, 8 Mangroves, 63, 95, 96 Marcesence, 24 Marginal gain, 43 Mean labor time, 17 Mean retention time of biomass in canopy, 110–111 Metamer, 8 Microphylls, 8 Modular unit, 8, 25 N Natural selection, vii Net ecosystem production (NEP), ix Net primary production (NPP), ix, 33 Nitrogen absolute amount of resorbed, 114 foliar nitrogen (content), 52, 68, 70, 72, 115 latitudinal trends of NRE, 113 mean residence time of nitrogen MRT, 115 productivity NP, 115 resorption efficiency, 112 use efficiency of an individual plant (NUE), 115 Nutrient turnover rate, 115 O Overcast effect, 17 Ozone-induced oxidative stress, 96

Subject Index P Pathogen, 95 Phenolics, 55 Phenology, vii foliar, 59 Phosphorus latitudinal trends of PRE, 113 phosphorus resorption efficiency (PRE), 112 Photosnthesis light-response curves, 15 Photosynthetic capacity, 14, 68 daily, 72 decline with leaf age, 19, 46 maximum photosynthetic capacity, 16 Photosynthetic function onset of full, 24 photoinhibition, 35 Photosynthetic nitrogen use efficiency (PNUE), 76, 115 Photosynthetic rate at leaf death, 48 midday depression of, 16 response to irradiance, 14 Plant canopy, 2 Plastochron interval, 78, 79 R Relative growth rate, 79 Resorption of nutrient, 111 potential resorption, 111 realized resorption, 111 resorption proficiency, 112 Resource availability theory, 118 Rhyniophyta, 7

143 self-shading, 46, 82 shading effect, 17 Shoot, 8 determinate shoot growth, 10, 77 embryonic, 11 indeterminate shoot growth, 10, 77 long shoot, 11, 83 preformed, 11 seedling shoot growth, 80 short shoot, 11, 83 Snow-free period, 35, 107 Specific leaf area (SLA), 69 Specific leaf weight (SLW), 69 Spring ephemeral, 3 Standing leaf biomass, 34 Static life table analyses, 31 Steady-state leaf numbers, 34 Succeeding-type, 10 Succession early successional plant species, 88 early successional species, 80 late successional plant species, 88 late successional species, 80 Summergreen, 2, 3, 59, 63 T Terrestrial plants, 54 Trade-offs, 33 Turnover in the canopy, 52 leaf, 25 optimal timing of, 44 U Unfavorable season, 35

S Salinity, 96 Sclerophylly, 4 sclerophylls, 6 Seasonality, viii Seasonal climatic changes, vii Shade deeply shaded, 84 partially shaded, 84

V Value of a leaf, 49 W Wintergreen, 2, 3, 59 Wood density, 80

Organism Index

A Abies balsamea, 61 Abies firma, 60 Abies grandis, 92 Abies mariesii, 61 Abies veitchii, 14, 61 Acer mono, 20 Acer palmatum, 69, 70 Actias selene gnoma, 94 Actinidia deliciosa, 15 Adenostoma fasciculatum, 81 Aesculus flava, 63 Aesculus sylvatica, 3 Aesculus turbinata, 80 Akebia trifolia, 81 Alnus hirsuta, 9, 11, 20, 26, 63, 78, 82 Alnus japonica, 34, 41, 97, 104 Alnus sieboldiana, 17, 20, 82 Ambrosia trifida, 64 Annona spraguei, 15 Araucaria araucana, 31, 61 Archaeopteris, 8 Asplenium incisum, 59 Asplenium wrightii, 60 Athyrium brevifrons, 60 Athyrium otophorum, 60 Athyrium pycnosorum, 60 Athyrium wardii, 60 Avicennia alba, 96 Avicennia germinans, 63, 96 B Betula grossa, 81 Betula platyphylla, 20, 80, 82, 97 Blechnum niponicum, 59, 60 Brasenia schreberi, 64 Brassica napus, 15

C Camellia japonica, 35, 77 Carpinus caroliniana, 4 Carpinus cordata, 78 Carya cordiformis, 63 Castanopsis cuspidata, 13, 62 Castanopsis sieboldii, 15 Ceratopetalum apetalum, 78 Cercidiphyllum japonicum, 25 Cettia diphone, viii Cinnamomum camphora, 85 Cinnamomum japonicum, 85 Cinnamomum sintoc, 63 Cleyera japonica, 13, 83 Cleyera ochnacea, 62 Cochlospermum fraseri, 36 Coffea arabica, 15 Coniogromme japonica var. fauriei, 60 Connarus panamensis, 15 Cornopteris decurrenti-alata, 60 Cryptantha flava, 91 Cryptocarya obliqua, 63 Cucumis sativus, 15 Cyathea arborea, 57 Cyathea furfuraca, 59 Cyathea hornei, 59 Cyathea pubescens, 59 Cyathea woodwardioides, 59 D Daphne kamtschatica, 63 Daphniphyllum macropodum, 89 Dendrocnide excelsa, 78 Desmopsis panamensis, 15 Dipterocarpus sublamellatus, 85 Dipteronia, 11 Doryopteris lacera, 60

145

146 Doryopteris polylepis, 60 Doryphora sassafras, 78 Dryopteris crassirhizoma, 59 Dryopteris phegopteris, 60 Duabanga sonneratioides, 104 E Elateriospermum tapos, 85, 89 Encelia farinosa, 91 Erythrophleum chlorostachys, 36 Eucalyptus miniata, 36 Eucalyptus tetrodonta, 36 Eurya japonica, 13, 62 F Fagus crenata, 63, 78, 82 Ficus elastica, 104 Fragaria virginiana, 81 G Glossopteris, 8 Glycine max, 64 H Halimium atriplicifolium, 81 Helianthus tuberosus, 71 Heliocarpus appendiculatus, 20, 78 Homolanthus caloneurus, 63 Hydrocharis morus-ranae var. asiatica, 92 I Ilex aquifolium, 62 Illicium religiosum, 62 Inga edulis, 63 L Laguncularia racemosa, 63 Larix decidua, 61 Larrea tridentata, 92 Ledum palustre var. decumbens, 92 Lepisorus ussuriensis, 59 Leptopteris wilkesiana, 59 Ligustrum obtusifolium, 89 Linum usitatissimum, 64

Organism Index M Machilus thunbergii, 13, 15, 62 Maesa japonica, 13 Magnolia obovata, 26 Mallotus japonicus, 104 Melampsora medusae, 95 Metrosideros polymorpha, 92 Microlepia marginata, 60 Morisonia americana, 15 Myriophyllum spicatum, 65 N Nelumbo nucifera, 64, 65 Nothofagus moorei, 78 Nymphaea odorata, 64 Nymphaea tetragona, 64 O Osmanthus chinensis, 32 Ouratea lucens, 15 P Pachysandra terminalis, 89 Pemphigus betae, 95 Phaseolus vulgaris, 31 Phlomis fruticosa, 16 Phyllitis scolopendrium, 59 Phyllodoce aleutica, 106 Picea abies, 61, 92 Picea glehnii, 92 Picea jezoensis, 92 Picea mariana, 61 Pieris rapae, viii Pinus contorta, 61 Pinus longaeva, 61 Pinus pumila, 15 Pinus resinosa, 61 Pinus sylvestris, 61 Pinus tabulaeformis, 30, 61 Pinus taeda, 61 Piper, 41, 42 Pistacia lentiscus, 91 Podocarpus nubigena, 61 Podocarpus saligna, 61 Polygonatum odoratum, 20 Polygonum sachalinensis, 20 Polypodium japonicum, 60 Polystichum retroso-paleoceum, 60

Organism Index Polystichum tripteron, 59, 60 Populus maximowiczii, 69, 70 Potamogeton crispus, 65 Pseudotsuga menziesii var. glauca, 92 Psychotria emetica, 63 Psychotria limonensis, 63 Pteridium aquilinum, 42 Pyrrosia tricuspis, 59 Q Quercus acuta, 13 Quercus coccifera, 62 Quercus crispula, 63, 78, 82 Quercus glauca, 15 Quercus mongolica var. grosseserrata, 26 Quercus myrsinaefolia, 62 Quercus rotundifolia, 62 Quercus rubra, 15 Quercus suber, 62 R Rhizophora mangle, 63 Rhododendrom maximum, 89, 90 Rhododendron aureum, 106 Rumohr standishii, 60

147 S Saxegothaea conspicua, 61 Scepteridium multifidum var. robustum, 60 Shorea robusta, 4 Sieversia pentapetala, 106 Sonneratia alba, 96 Symplocos prunifolia, 62 T Terminalia ferdinandiana, 36 Theobroma cacao, 15, 85 Tilia japonica, 82, 84 Trema orientalis, 104 U Ulmus davidiana, 11 W Welwitschia, 14 X Xanthium canadense, 64, 85 Xanthophyllum stipitatum, 85 Xylocarpus granatum, 96 Xylopia micrantha, 15