J. Settele L. Penev T. Georgiev R. Grabaum V. Grobelnik V. Hammen S. Klotz M. Kotarac I. Kühn
The Atlas combines the main outcomes of the large European project ALARM (performed by 68 partner organisations from 35 countries from Europe as well as other continents) with some core outputs of numerous further research networks. A total number of 366 authors from more than 180 institutions in 43 countries provided information and contributed to the Atlas. The Atlas is addressed to a wide spectrum of users. Scientists will find summaries of well-described methods, approaches and case studies. Conservationists and policy makers will use the conclusions and recommendations based on academic research output and presented in a comprehensive and easy-to-read way. Lecturers and teachers will find good examples to illustrate the main challenges in our century of global environmental changes. The Atlas is an indispensible tool to any library or institution in biodiversity and environmental sciences. Finally, all people concerned with environmental issues will find the Atlas a powerful weapon in their fight for saving the life on our Planet!
IVE ER RIISK SK A T L A S O F B I O D IV RSSIT ITY R
The present Atlas of Biodiversity Risk is the first of its kind to describe and summarise in a comprehensive, easy-to-read and richly illustrated form the major pressures, impacts and risks of biodiversity loss at a global level. The main risks identified are caused by global climate and land use change, environmental pollution, loss of pollinators and biological invasions. The impacts and consequences of biodiversity loss are analyzed with a strong focus on socio-economic drivers and their effects on society. Three scenarios of potential futures are the baseline for predicting impacts and explore options for mitigating adverse effects at several spatio-temporal scales. Elements of these futures are modeled, tested and illustrated. The Atlas is divided into chapters which mostly deal with particular pressures. It furthermore is based on case studies from a large set of countries, which are completed by introductory and concluding texts for each chapter.
ATLAS of Biodiversity Risk Edited by Josef Settele, Lyubomir Penev, Teodor Georgiev, Ralf Grabaum, Vesna Grobelnik, Volker Hammen, Stefan Klotz, Mladen Kotarac & Ingolf Kühn
ATLAS OF BIODIVERSITY RISK
ATLAS
of Biodiversity Risk Edited by Josef Settele, Lyubomir Penev, Teodor Georgiev, Ralf Grabaum, Vesna Grobelnik, Volker Hammen, Stefan Klotz, Mladen Kotarac & Ingolf Kühn
Sofia-Moscow 2010
ATLAS OF BIODIVERSITY RISK Edited by: Josef Settele, Lyubomir Penev, Teodor Georgiev, Ralf Grabaum, Vesna Grobelnik, Volker Hammen, Stefan Klotz, Mladen Kotarac & Ingolf Kühn Linguistic editor: M. Sykes
The “Atlas of Biodiversity Risk” originated from the Project ALARM, Assessing LArge-scale environmental Risks for biodiversity with tested Methods (www.alarmproject.net), funded by the European Commision under its Sixth Framework Programme and coordinated by the Helmholtz Centre for Environmental Research – UFZ in Halle, Germany. It is published in cooperation with associated biodiversity projects, such as ALTER-NET, COCONUT, DAISIE, EUMON, MACIS, MODELKEY, RUBICODE, and other research initiatives.
Cartography and GIS:
Photographs used in the overall atlas design: Vlada Peneva, Nicolas J. Vereecken, Albert Vliegenthart, Peter Ginzinger, Teodor Georgiev, Mark Kenis, Sergej Olenin, Marten Winter, Sonja Knapp, Cornelia Baessler & Eva Völler
First published 2010 ISBN 978-954-642-446-4 (print) ISBN 978-954-642-447-1 (e-book) Pensoft Publishers Geo Milev 13a 1111 Sofia, Bulgaria e-mail:
[email protected] www.pensoft.net Design: Zheko Aleksiev Layout: Teodor Georgiev
Citation: Settele J, Penev L, Georgiev T, Grabaum R, Grobelnik V, Hammen V, Klotz S, Kotarac M, Kühn I (Eds) 2010. Atlas of Biodiversity Risk. Pensoft Publishers, Sofia-Moscow, 280 pp.
© PENSOFT Publishers All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the copyright owner.
Printed in Bulgaria, May 2010
CONTENTS
,
Foreword
vii
KARL FALKENBERG
Atlas of Biodiversity Risk: Editorial JOSEF SETTELE, LYUBOMIR PENEV, TEODOR GEORGIEV, RALF GRABAUM, VESNA GROBELNIK, VOLKER HAMMEN, xiv STEFAN KLOTZ, MLADEN KOTARAC & INGOLF KÜHN
Chapter 1. Biodiversity Baseline Information
1
The Availability and Usage of High Quality, Cross-Scale Baseline Information for Risk Assessment INGOLF KÜHN, LYUBOMIR PENEV & JOSEF SETTELE
2
European Plant Diversity in the Global Context JENS MUTKE, HOLGER KREFT, GEROLD KIER & WILHELM BARTHLOTT
4
A Pan-European Species directories Infrastructure (PESI) YDE DE JONG, LOUIS BOUMANS, JULIANA KOUWENBERG, HENRIK ENGHOFF, PHILLIP BOEGH, NIHAT AKTAÇ, SELÇUK YURTSEVER, CHARLES HUSSEY, ROGER HYAM, MARK COSTELLO, THIERRY BOURGOIN, WALTER BERENDSOHN, ECKHARD VON RAAB-STRAUBE, ANTON GÜNTSCH, WARD APPELTANS & BART VANHOORNE 6
Assessment of Ecosystem Services PAULA A. HARRISON, GARY W. LUCK, CHRISTIAN K. FELD & MARTIN T. SYKES
8
The ALARM Scenarios: Storylines and Simulations for Assessing Biodiversity Risks in Europe JOACHIM H. SPANGENBERG, STEFAN FRONZEK, VOLKER HAMMEN, THOMAS HICKLER, JILL JÄGER, KIRSTI JYLHÄ, INGOLF KÜHN, GLENN MARION, LAURA MAXIM, ILIANA MONTERROSO, MARTIN O’CONNOR, INES OMANN, ISABELLE REGINSTER, BEATRIZ RODRÍGUEZ-LABAJOS, MARK ROUNSEVELL, MARTIN T. SYKES, MARCO VIGHI & JOSEF SETTELE
10
Drivers, Pressures, Impacts: DPSIR for Biodiversity LAURA MAXIM, JOACHIM H. SPANGENBERG & MARTIN O’CONNOR
16
European Biodiversity and Its Drivers – an “Inter-national” Analysis JAAN LIIRA, JOSEF SETTELE & MARTIN ZOBEL
18
A Vision of the Availability of High Quality, Cross-Scale Baseline Biodiversity Information INGOLF KÜHN, LYUBOMIR PENEV & JOSEF SETTELE
22
Chapter 2. Research Approaches into the Interactions between Impact Factors and Biodiversity
23
Research Approaches into the Interactions between Impact Factors and Biodiversity STEFAN KLOTZ & MARK FRENZEL
24
“Exploratories” for Functional Biodiversity Research SIMONE PFEIFFER, SONJA GOCKEL, ANDREAS HEMP, KONSTANS WELLS, DANIEL PRATI, JENS NIESCHULZE, ELISABETH K.V. KALKO, FRANÇOIS BUSCOT, K. EDUARD LINSENMAIR, ERNST-DETLEF SCHULZE, WOLFGANG W. WEISSER & MARKUS FISCHER 26
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Biodiversity Experiments: What Have We Learnt about Biodiversity–Ecosystem Functioning Relationships? ALEXANDER J.F. FERGUS & BERNHARD SCHMID
30
Observing Biodiversity Changes in Europe KLAUS HENLE, BIANCA BAUCH, SANDRA BELL, ERIK FRAMSTAD, MLADEN KOTARAC, PIERRE-YVES HENRY, SZABOLCS LENGYEL, VESNA GROBELNIK & DIRK S. SCHMELLER 34
Assessing LArge-scale environmental Risks for biodiversity with tested Methods – the ALARM project JOSEF SETTELE, JOACHIM H. SPANGENBERG, VOLKER HAMMEN, ALEXANDER HARPKE, STEFAN KLOTZ, SILKE RATTEI, ANNETTE SCHMIDT, OLIVER SCHWEIGER & INGOLF KÜHN 38
The ALARM Field Site Network, FSN VOLKER HAMMEN, JACOBUS C. BIESMEIJER, RICCARDO BOMMARCO, EDUARDAS BUDRYS, TORBEN R. CHRISTENSEN, STEFAN FRONZEK, RALF GRABAUM, PREDRAG JAKSIC, STEFAN KLOTZ, PAULINA KRAMARZ, GYÖRGY KRÖEL-DULAY, INGOLF KÜHN, MICHAEL MIRTL, MARI MOORA, THEODORA PETANIDOU, JOAN PINO, SIMON G. POTTS, AGNÈS RORTAIS, CHRISTIAN H. SCHULZE, INGOLF STEFFAN-DEWENTER, JANE STOUT, HAJNALKA SZENTGYÖRGYI, MARCO VIGHI, ANTE VUJIC, CATRIN WESTPHAL, TORSTEN WOLF, GONZALO ZAVALA, MARTIN ZOBEL, JOSEF SETTELE & WILLIAM E. KUNIN 42
The ALARM Field Site Network: a Continental-Scale Test Bed for Questions Related to Major Drivers of Biodiversity Change JACOBUS C. BIESMEIJER, JENS DAUBER, CHIARA POLCE, WILLIAM E. KUNIN, VOLKER HAMMEN & JOSEF SETTELE
46
Socio-Economic Research within a Field Site Network Established by Ecologists – Pragmatic Approaches to Create Added Value JOACHIM H. SPANGENBERG, NANCY ARIZPE, BEATRIZ RODRIGUEZ-LABAJOS, ROSA BINIMELIS, JOAN MARTÍNEZ-ALIER, 48 LAURA MAXIM & JEAN-MARC DOUGUET
Assemblages of Social Wasps in Forests and Open Land across Europe – an ALARM-FSN Study LIBOR DVOŘÁK, EDUARDAS BUDRYS, ALEKSANDAR ĆETKOVIĆ & SIMON SPRINGATE
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From FSN to LTER-Europe MICHAEL MIRTL, KINGA KRAUZE, VOLKER HAMMEN & MARK FRENZEL
52
Assessing Risks for Biodiversity with Bioclimatic Envelope Modelling OLIVER SCHWEIGER, MIGUEL B. ARAÚJO, JAN HANSPACH, RISTO K. HEIKKINEN, INGOLF KÜHN, MISKA LUOTO, RALF OHLEMÜLLER & RAIMO VIRKKALA 54
Statistical Aspects of Biodiversity Risk Assessment GLENN MARION, STIJN BIERMAN, ADAM BUTLER, STEPHEN CATTERALL, ALEX R. COOK, RUTH DOHERTY, INGOLF KÜHN, BJÖRN REINEKING, OLIVER SCHWEIGER & PHILIP E. HULME 58
Structuring Future Biodiversity Research and Its Community – the Role of Infrastructures WOUTER LOS
62
Chapter 3. Climate Change Impacts on Biodiversity
63
Climate Change, Species and Ecosystems MARTIN T. SYKES & THOMAS HICKLER
64
Current Climatic Conditions and Observed Trends in Europe KIRSTI JYLHÄ, TIMOTHY R. CARTER & STEFAN FRONZEK
66
Scenarios of Climate Change for Europe STEFAN FRONZEK, TIMOTHY R. CARTER & KIRSTI JYLHÄ
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Forest Fire Risk in Spain under Future Climate Change JOSÉ M. MORENO, GONZALO ZAVALA, MARÍA MARTÍN & AMPARO MILLÁN
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Observed Climate-Biodiversity Relationships GIAN-RETO WALTHER, LASZLO NAGY, RISTO K. HEIKKINEN, JOSEP PEÑUELAS, JÜRGEN OTT, HARALD PAULI, JUHA PÖYRY, 74 SILJE BERGER & THOMAS HICKLER
Projected Climate Change Impacts on Biodiversity in Mediterranean Ecosystems JOSEP PEÑUELAS, MARC ESTIARTE, PATRICIA PRIETO, JORDI SARDANS, ALISTAIR JUMP, JOSÉ M. MORENO, IVÁN TORRES, BLANCA CÉSPEDES, EDUARD PLA, SANTI SABATÉ & CARLOS GRACIA 76
Climate Change Impacts on the Future Extent of the Alpine Climate Zone LASZLO NAGY, HARALD PAULI, MICHAEL GOTTFRIED & GEORG GRABHERR
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Risk of Disappearing Sub-Arctic Palsa Mires in Europe MARGARETA JOHANSSON, STEFAN FRONZEK, TORBEN R. CHRISTENSEN, MISKA LUOTO & TIM R. CARTER
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Climate Impacts on High Latitude Lakes MATS JANSSON, PER ASK, JENNY ASK, PÄR BYSTRÖM, JAN KARLSSON & LENNART PERSSON
80
The Big Trek Northwards: Recent Changes in the European Dragonfly Fauna JÜRGEN OTT
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Effects of Climatic Changes on Odonata: Are the Impacts likely to be the Same in the Northern and Southern Hemispheres? JÜRGEN OTT & MICHAEL J. SAMWAYS
84
Modelling the Range Expansion with Global Warming of an Urticating Moth: a Case Study from France CHRISTELLE ROBINET, JÉRÔME ROUSSELET, FRANCIS GOUSSARD, JACQUES GARCIA & ALAIN ROQUES
86
Moorland Wildfires in the UK Peak District SARAH LINDLEY, JULIA MCMORROW & ALETTA BONN
88
South America: Climate Monitoring and Adaptation Integrated across Regions and Disciplines STEPHAN HALLOY, KARINA YAGER, CAROLINA GARCÍA, STEPHAN BECK, JULIETA CARILLA, ALFREDO TUPAYACHI, JORGE JÁCOME, ROSA ISELA MENESES, JIM FARFÁN, ANTON SEIMON, TRACIE SEIMON, PAMELA RODRIGUEZ, SOLEDAD CUELLO & ALFREDO GRAU 90
Climate Change, Ecosystem Services and Biodiversity – Risks and Opportunities KARIN ZAUNBERGER & MARTIN SYKES
96
Chapter 4. Land Use Changes and Their Impacts
97
Land Use, Its Change and Effects on Biodiversity RICCARDO BOMMARCO
98
Land Use Change Scenarios for Europe ISABELLE REGINSTER, MARK ROUNSEVELL, ADAM BUTLER & NICOLAS DENDONCKER
100
Evaluating Land Use Changes in and around Natura 2000 Sites: a Proposed Methodology IOANNIS N. VOGIATZAKIS, STUART P.M. ROBERTS, MARIA T. STIRPE & SIMON G. POTTS
106
Life History Traits in Insects and Habitat Fragmentation RICCARDO BOMMARCO, ERIK ÖCKINGER & AVELIINA HELM
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Where Have All the Flowers Gone? From Natural Vegetation to Land Use – Land Cover Types: Past Changes and Future Forecasts LASZLO NAGY, NICOLAS DENDONCKER, ADAM BUTLER, ISABELLE REGINSTER, MARK ROUNSEVELL, GEORG GRABHERR, MICHAEL GOTTFRIED & HARALD PAULI 110
Future Land Use Related Challenges for Biodiversity Research and Conservation RICCARDO BOMMARCO
112
Chapter 5. Environmental Chemicals and Biodiversity
113
Assessing the Impacts of Environmental Chemicals on Biodiversity and Ecosystems MARCO VIGHI & DAVID SPURGEON
114
MODELKEY: European Rivers under Toxic Stress WERNER BRACK, JOOP F. BAKKER, ERIC DE DECKERE, DICK DE ZWART, TIMO HAMERS, MICHAELA HEIN, PIM LEONARDS, URTE LÜBCKE-VON VAREL, CLAUDIA SCHMITT, MECHTHILD SCHMITT-JANSEN & PETER C. VON DER OHE 116
Sources and Fate of PAHs in an Urban Environment IAN T. COUSINS, KONSTANTINOS PREVEDOUROS, MARIA UNGER & ÖRJAN GUSTAFSSON
119
Nitrogen Deposition – a Major Risk for Biodiversity FRANZ-W. BADECK & TILL STERZEL
120
Is Atmospheric Nitrogen Deposition a Cause for Concern in Alpine Ecosystems? LASZLO NAGY, FRANZ-W. BADECK, SVEN POMPE, MICHAEL GOTTFRIED, HARALD PAULI & GEORG GRABHERR
122
Predicted Environmental Concentrations of Organic Pollutants on a European Scale as a Basis for Risk Assessment SANDRA MEIJER, ALEX PAUL & ANDY SWEETMAN
124
Ecotoxicological Risk Assessment of Pesticides Considering Different Geographical Scales and Evolution through Time SERENELLA SALA & MARCO VIGHI
126
Chemical Effect Assessment within ALARM: Identifying Habitats in which Microbial Function may be Impacted by Metal Pollution DAVID SPURGEON, SARA LONG, RYSZARD LASKOWSKI & SANDRA MEIJER
128
Risk for Chemicals on Biodiversity: Which Future is to be Expected? MARCO VIGHI & DAVID SPURGEON
Chapter 6. Biological Invasions
130
131
Are the Aliens Taking Over? Invasive Species and Their Increasing Impact on Biodiversity PHILIP E. HULME, MONTSERRAT VILÀ, WOLFGANG NENTWIG & PETR PYŠEK
132
DAISIE: Delivering Alien Invasive Species Inventories for Europe PHILIP E. HULME, WOLFGANG NENTWIG, PETR PYŠEK & MONTSERRAT VILÀ
Biological Pollution of Aquatic Ecosystems in Europe SERGEJ OLENIN, DAN MINCHIN, DARIUS DAUNYS & ANASTASIJA ZAIKO
136
Pathways of Aquatic Invasions in Europe SERGEJ OLENIN, DAN MINCHIN, DARIUS DAUNYS & ANASTASIJA ZAIKO
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Risk Assessment of Aquatic Invasive Species Introductions via European Inland Waterways VADIM E. PANOV, BORIS ALEXANDROV, KESTUTIS ARBACIAUSKAS, ROSA BINIMELIS, GORDON H. COPP, MICHAL GRABOWSKI, 140 FRANCES LUCY, ROB S.E.W. LEUVEN, STEFAN NEHRING, MOMIR PAUNOVIĆ, VITALIY SEMENCHENKO & MIKHAIL O. SON
Distribution of Alien Bleak Alburnus alburnus (Linnaeus, 1758) in the Northeastern Iberian Mediterranean Watersheds: Past and Present ALBERTO MACEDA-VEIGA, ADOLFO DE SOSTOA, EDGAR SOLORIO-ORNELAS, MARIO MONROY, DOLORS VINYOLES, 144 NUNO CAIOLA, FREDERIC CASALS, EMILI GARCIA-BERTHOU & ANTONI MUNNÉ
Mapping Invasion by Alien Plants in Europe PETR PYŠEK, MILAN CHYTRÝ, JAN WILD, JOAN PINO, LINDSAY C. MASKELL & MONTSERRAT VILÀ
146
European Plants in Southern South America – Unwanted Visitors? EDUARDO UGARTE, NICOL FUENTES & STEFAN KLOTZ
148
The Hogweed Story: Invasion of Europe by Large Heracleum Species PETR PYŠEK, JAN PERGL, ŠÁRKA JAHODOVÁ, LENKA MORAVCOVÁ, JANA MÜLLEROVÁ, IRENA PERGLOVÁ & JAN WILD
150
Terrestrial Alien Vertebrates in Europe WOJCIECH SOLARZ, WIESŁAW KRÓL, SVEN BACHER, WOLFGANG NENTWIG & DANIEL SOL
152
The Exotic Mammals of Argentina RICARDO A. OJEDA, AGUSTINA NOVILLO & FERNANDA CUEVAS
154
The ALARM Field Site Network, an Outstanding Tool for the Survey of Invasive Insects Infesting Seeds of Wild Roses in Europe MARIE-ANNE AUGER-ROZENBERG, EDUARDAS BUDRYS, THEODORA PETANIDOU, MILKA GLAVENDEKI, RICCARDO BOMMARCO, SARA BONZINI, GYÖRGY KRÖEL-DULAY, JARA ANDREU URETA, MARI MOORA, SIMON G. POTTS, AGNÈS RORTAIS, JANE STOUT, IVÁN TORRES, CATRIN WESTPHAL, HAJNALKA SZENTGYÖRGYI, SÉBASTIEN DESBOIS, PHILIPPE LORME, JEAN-PAUL RAIMBAULT, PATRICK PINEAU & ALAIN ROQUES 156
The Rapid Colonization of the Introduced Black Locust Tree by an Invasive NorthAmerican Midge and Its Parasitoid MILKA GLAVENDEKIĆ, ALAIN ROQUES & LJUBODRAG MIHAJLOVIĆ
158
A Stowaway Species from the Balkans – the Horse Chestnut Leafminer, Cameraria ohridella SYLVIE AUGUSTIN, MARC KENIS, ROMAIN VALADE, MARIUS GILBERT, JACQUES GARCIA, ALAIN ROQUES & CARLOS LOPEZ-VAAMONDE 160
Invasion of the Harlequin ladybird, Harmonia axyridis, in Europe: When Beauty Becomes the Beast MARC KENIS, PETER M.J. BROWN, REMY L. WARE & DAVID B. ROY
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The Siberian Moth, Dendrolimus sibiricus – a Potential Invader in Europe? YURI BARANCHIKOV, NADEZHDA TCHEBAKOVA, NATALIYA KIRICHENKO, ELENA PARPHENOVA, MIKHAIL KORETS & 164 MARC KENIS
How to Deal with Invasive Species? A Proposal for Europe PHILIP E. HULME, WOLFGANG NENTWIG, PETR PYŠEK & MONTSERRAT VILÀ
Chapter 7. Decline of Pollinators and Its Impact
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Pollination – a Key Service Regulating Ecosystems THOMAS TSCHEULIN, THEODORA PETANIDOU & SIMON G. POTTS
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Methods for Quantifying Pollinator Loss CATRIN WESTPHAL, RICCARDO BOMMARCO, GABRIEL CARRÉ, ELLEN LAMBORN, NICOLAS MORISON, THEODORA PETANIDOU, SIMON G. POTTS, STUART P.M. ROBERTS, HAJNALKA SZENTGYÖRGYI, THOMAS TSCHEULIN, BERNARD E. VAISSIÈRE, 170 MICHAŁ WOYCIECHOWSKI, JACOBUS C. BIESMEIJER, WILLIAM E. KUNIN, JOSEF SETTELE & INGOLF STEFFAN-DEWENTER
Cavity-Nesting Hymenoptera across Europe: a Study in ALARM Project Field Site Network Sites Using Small Trap-Nests on Trees and Buildings EDUARDAS BUDRYS, JARA ANDREU URETA, AUŠRA BRILIŪTĖ, ALEKSANDAR ĆETKOVIĆ, SILKE HEINRICH, GYÖRGY KRÖEL-DULAY, MARI MOORA, SIMON G. POTTS, AGNÈS RORTAIS, ERIK SJÖDIN, HAJNALKA SZENTGYÖRGYI, IVÁN TORRES, MARCO VIGHI, 172 CATRIN WESTPHAL & ANNA BUDRIENĖ
Assessing the Impact of Pollinator Shifts on Wild Plants ANDERS NIELSEN, JENS DAUBER, WILLIAM E. KUNIN, ELLEN LAMBORN, BIRGIT MEYER, MARI MOORA, SIMON G. POTTS, JOSEF SETTELE, VIRVE SOBER, INGOLF STEFFAN-DEWENTER, THOMAS TSCHEULIN, DANIELE VIVARELLI, JACOBUS C. BIESMEIJER & THEODORA PETANIDOU 174
Drivers of Pollinator Loss – a Case Study from Germany BIRGIT MEYER & INGOLF STEFFAN-DEWENTER
176
Domesticated Bumblebees HAJNALKA SZENTGYÖRGYI, DAWID MOROŃ, MANDY ROHDE, ELŻBIETA ROŻEJ, MARTA WANTUCH, JOSEF SETTELE, ROBIN F.A. MORITZ & MICHAŁ WOYCIECHOWSKI 178
A Geometric Morphometric Tool for the Conservation of the Black Honeybee in Europe AGNÈS RORTAIS, MICHEL BAYLAC, GÉRARD ARNOLD & LIONEL GARNERY
180
A New Enemy of Honeybees in Europe: the Asian Hornet, Vespa velutina AGNÈS RORTAIS, CLAIRE VILLEMANT, OLIVIER GARGOMINY, QUENTIN ROME, JEAN HAXAIRE, ALEXANDROS PAPACHRISTOFOROU & GÉRARD ARNOLD 181
Beekeeping and the Conservation of Native Honeybees in Europe RODOLFO JAFFÉ & ROBIN F.A. MORITZ
182
Severe Declines of Managed Honeybees in Central Europe SIMON G. POTTS, STUART P.M. ROBERTS, ROBIN DEAN, GAY MARRIS, MIKE BROWN, RICHARD JONES & JOSEF SETTELE
184
The Future of Pollinators? SIMON G. POTTS, THEODORA PETANIDOU & THOMAS TSCHEULIN
186
Chapter 8. Socio-Economics and Its Role in Biodiversity Loss
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Mankind as the Driver behind Global Change and Socio-Economics as a Research Discipline to Find Solutions? JOACHIM H. SPANGENBERG, JOAN MARTÍNEZ-ALIER & MARTIN O’CONNOR
188
Monetary Valuation of the Pollination Service Provided to European Agriculture by Insects NICOLA GALLAI, JEAN-MICHEL SALLES, GABRIEL CARRÉ, NICOLAS MORISON & BERNARD E. VAISSIÈRE
190
Climate Change Mitigation and Adaptation Measures and Biodiversity PAM BERRY & JAMES PATERSON
194
Socio-Economic Modelling of the ALARM Scenarios. Results for Europe INES OMANN, ANDREA STOCKER & JILL JÄGER
196
Chronicle of a Bioinvasion Foretold: Distribution and Management of the Zebra Mussel (Dreissena polymorpha) Invasion in Spain BEATRIZ RODRÍGUEZ-LABAJOS, ROSA BINIMELIS, CARLES CARDONA, KRISTOFER DITTMER, JOAN MARTÍNEZ-ALIER, ILIANA MONTERROSO & ANTONI MUNNÉ 198 x
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“The Farmer’s Terror”: Glyphosate Resistant Johnsongrass in Argentina ILIANA MONTERROSO, ROSA BINIMELIS & WALTER PENGUE
202
Future Contributions of Socio-Economic Research to the Conservation of Biodiversity JOACHIM H. SPANGENBERG, LEWIS AKENJI, ALAIN AYONG LE KAMA, TOM BAULER, ROSA BINIMELIS, JEAN-MARC DOUGUET, BIRGIT BEDNAR-FRIEDL, JILL JÄGER, KRZYSZTOF KAMIENIECKI, PIRET KULDNA, JYRKI LUUKANEN, JOAN MARTÍNEZ-ALIER, LAURA MAXIM, MARTIN O’CONNOR, KAJA PETERSON, BEATRIZ RODRIGUEZ-LABAJOS, LARS RYDEN, KARLHEINZ STEINMÜLLER, 204 UNO SVEDIN, SERGIO ULGIATI, MEELIS UUSTAL, JEROEN VAN DER SLUIJS & JOSEF SETTELE
Chapter 9. The Combined Effects of Major Drivers and Pressures on Biodiversity
207
Designing Projects for Integrated Research – the ALARM Experience JOSEF SETTELE, MARTIN ZOBEL, JOACHIM H. SPANGENBERG, STEFAN KLOTZ, VOLKER HAMMEN & INGOLF KÜHN
208
Mapping Plant-Invader Integration into Plant-Pollinator Networks MONTSERRAT VILÀ, IGNASI BARTOMEUS, ANKE DIETZSCH, THEODORA PETANIDOU, INGOLF STEFFAN-DEWENTER, JANE STOUT & THOMAS TSCHEULIN 210
Palms (and other Evergreen Broad-Leaved Species) Conquer the North GIAN-RETO WALTHER & SILJE BERGER
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Modelling the Potential Expansion as a Result of Global Warming of the Invasive Pinewood Nematode in China LILIN ZHAO, JIANGHUA SUN, ALAIN ROQUES & CHRISTELLE ROBINET
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Will Interacting Species Still Co-Occur in the Future? OLIVER SCHWEIGER, INGOLF KÜHN, OTAKAR KUDRNA, STEFAN KLOTZ & JOSEF SETTELE
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How to Evaluate Effects of Pesticides in Terrestrial Ecosystems STEFANIA BARMAZ, CLAIRE BRITTAIN, SERENELLA SALA, SIMON G. POTTS & MARCO VIGHI
218
Do Declines in the Use of the Organotin (TBT), Used as an Antifoulant, Result in an Increase in Aquatic Alien Species Establishment? DAN MINCHIN
220
The Effect of Heavy Metal Pollution on the Development of Wild Bees DAWID MOROŃ, HAJNALKA SZENTGYÖRGYI, IRENA GRZEŚ, MARTA WANTUCH, ELŻBIETA ROŻEJ, JOSEF SETTELE, SIMON G. POTTS, RYSZARD LASKOWSKI & MICHAŁ WOYCIECHOWSKI 224
Agricultural Land Use Shapes Biodiversity Patterns in Ponds TOM DE BIE, ROBBY STOKS, STEVEN DECLERCK, LUC DE MEESTER, FRANK VAN DE MEUTTER, KOEN MARTENS & 226 LUC BRENDONCK
Mapping Relative Risk to Biodiversity from the Application of Pesticides, Focussing on Pollinators PETER BORGEN SØRENSEN, STEEN GYLDENKÆRNE, SIMON G. POTTS, CLAIRE BRITTAIN & MARIANNE THOMSEN
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Integration in Large-Scale Research: on the Art and Science of Coordination JOSEF SETTELE, JOACHIM H. SPANGENBERG, VOLKER HAMMEN, ALEXANDER HARPKE, STEFAN KLOTZ, SILKE RATTEI, 229 ANNETTE SCHMIDT, OLIVER SCHWEIGER, SUSANNE STOLL-KLEEMANN, KARIN ZAUNBERGER & INGOLF KÜHN
Chapter 10. The Future of Biodiversity and Biodiversity Research
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Aspects of the Future of Biodiversity and Biodiversity Research MARTIN T. SYKES, THOMAS HICKLER & JOSEF SETTELE
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Priority Setting for Nature Conservation KATRIN VOHLAND, THOMAS HICKLER, JANE FEEHAN, MARLIES GUMPENBERGER, MIGUEL B. ARAÚJO & 234 WOLFGANG CRAMER
Vegetation on the Move – Where Do Conservation Strategies Have to be Redefined? THOMAS HICKLER, KATRIN VOHLAND, LUIS COSTA, WOLFGANG CRAMER, PAUL A. MILLER, BENJAMIN SMITH, JANE FEEHAN, INGOLF KÜHN & MARTIN T. SYKES 238
Ecological Networks as One Answer to Climate Change KATRIN VOHLAND, STEFAN KLOTZ & SANDRA BALZER
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Establishing a Volunteer-Based Butterfly Monitoring Scheme – the German Experience ELISABETH KÜHN, ALEXANDER HARPKE, NORBERT HIRNEISEN, REINART FELDMANN, PATRICK LEOPOLD & JOSEF SETTELE 242
Managing Alien Aquatic Species DAN MINCHIN
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Biological Control Ecosystem Services in Tropical Rice KONG LUEN HEONG, ROBERT J. HIJMANS, SYLVIA VILLAREAL & JOSIE LYNN CATINDIG
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Policy Options to Protect Biodiversity under Climate Change JAKE PIPER & ELIZABETH WILSON
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Biodiversity Risk Assessment for Europe – Putting It All Together GLENN MARION, RALF GRABAUM, VOLKER GRESCHO, ADAM BUTLER, STIJN BIERMAN, JEAN-MARC DOUGUET, VOLKER HAMMEN, THOMAS HICKLER, PHILIP E. HULME, LAURA MAXIM, INES OMANN, KAJA PETERSON, SIMON G. POTTS, 252 ISABELLE REGINSTER, JOSEF SETTELE, JOACHIM H. SPANGENBERG & INGOLF KÜHN
Future Biodiversity Research – Targets, the Human Factor and Lessons Learned JOSEF SETTELE, INGOLF KÜHN, MARTIN SHARMAN, ALLAN WATT & JOACHIM H. SPANGENBERG
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Foreword
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It is hard to imagine a more important task than protecting biodiversity and the ecosystem services that it provides. Biodiversity is fundamentally important to humans, not least because it provides food, fuel, clean water and a habitable atmosphere and climate. Humans are part of biodiversity and depend on it, yet many human activities – unsustainable development – are the main driver for biodiversity loss. At the same time, we are still discovering more about the fascinating complexity of biodiversity and the benefits it provides to people. It is clear that biodiversity loss erodes the integrity of ecosystems and their capacity to adapt in a changing world. It represents a serious risk to human existence on this planet and a squandering of current assets and future opportunities. The year 2010 is the International Year of Biodiversity and therefore the Atlas of Biodiversity Risk is very timely. The wealth of information provided is impressive, covering risks related to climate change, biological invasions, pollinator loss, environmental chemicals, land use changes and socio-economic aspects. Thus the atlas will be a great source of knowledge to support the development and implementation of the post 2010 biodiversity policy. A particular merit of the atlas is its attempt to provide an integrated view, which is essential as the many risks to biodiversity don’t act in isolation, but interfere with one another. Better understanding of the linkages, feedbacks and synergies will help to develop efficient, integrated measures to tackle biodiversity loss. Biodiversity loss is not a thing “out there” but an integral part of the way present human societies work. We will not be able to halt biodiversity loss by treating it as an independent issue. Biodiversity loss and accelerated change – including climate change – are intimately bound into our economies and societies. We need a realistic view about the relationship between the economy and the environment. Clearly, biodiversity and ecosystem services are essential elements of many other policy areas such as climate change and food security. Protecting and enhancing ecosystem resilience through biodiversity and ecosystem service conservation, are amongst the best and most cost effective ways of tackling both the causes and consequences of climate change. In the ‘Message from Athens’ 1 it reads that “We cannot halt biodiversity loss without addressing climate change, but it is equally impossible to tackle climate change without addressing biodiversity loss.” Biodiversity and ecosystem services are not just the victims of our mismanagement, but are our ally in dealing with the problems of global environmental change. Managing, restoring and protecting biodiversity and ecosystem services provide multiple benefits to human society. These ecosystem-based approaches contribute to protecting and restoring natural ecosystems by conserving or enhancing carbon stocks, reducing emissions caused by ecosystem degradation and loss, and providing cost-effective protection against some of the threats that result from climate change. Maintaining genetic diversity is fundamental to food security and the provision of raw materials and it is best preserved within species’ natural habitats. Sustainability concerns, first and foremost, the maintenance of the biological capacity of the planet to support human demands. Without biodiversity there is nothing – no society, and certainly no economy. Therefore policies in all con-
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cerned sectors need to address biodiversity issues and be integrated with each other – such a cross-sectoral approach is essential if the benefits derived in one area are not to be lost or counteracted in another. Better understanding the complexity of biodiversity and ecosystem services is a challenge to science as it is to policy makers and civil society. At the Athens Conference, Commission President Jose Manuel Barroso said that “we must develop a clearer global understanding of ‘why biodiversity really matters’ and we need to improve our scientific understanding particularly since the drivers for biodiversity loss are more complex than for climate change, and the direct impacts are harder to measure”. He went on to point out that “we should include biodiversity concerns when we make the shift to more resource-efficient economies.” For most of our sectoral policies, it is fundamental that biodiversity is mainstreamed into policy development and implementation in order to obtain sustainable results. Science is a key player in the realisation of this mainstreaming as it can inform policy makers about assessing vulnerabilities and risks, identifying response options. Building a policy-science dialogue is essential. Evidence based on rigorous research needs to be translated into policy relevant language and placed into the policy process. I hope that the International Year of Biodiversity will see the setting up of an Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) on all appropriate scales, able to give authoritative and peer reviewed advice to catalyse debate and improve policy response. In addition we need research into the long-term survival of species, their genetic diversity, and the ecological integrity and functionality of habitats and ecosystems and the long-term provision of ecosystem services. Further priorities include mitigation and adaptation to global change including climate change and prevention and reduction of environmental pressures and risks while developing viable, sustainable economic activities. This also includes support for innovative technology and products derived from living systems and learning from nature. To generate the knowledge necessary to bring human societies into a sustainable and mutually beneficial relationship with the living world, we need a constructive and forceful collaboration between natural and social sciences as well as infrastructures for monitoring and assessment, open databases, and virtual institutes for data exchanges and analysis. To encourage the uptake and use of research results in decision making, appropriate stakeholders need to be involved in the projects and significant resources devoted to communication. The co-operation initiated through ALARM and other research projects are invaluable assets, as we need this type of interdisciplinary research and approaches to help to address the global challenges of the 21st century.
KARL FALKENBERG Director General of DG Environment, European Commission January 2010
The Message from Athens. High-level Conference on Biodiversity Protection beyond 2010, April 2009. See http://ec.europa.eu/environment/nature/biodiversity/conference/ index_en.htm FO R E WO R D
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ATLAS OF BIODIVERSITY RISK Editorial
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JOSEF SETTELE, LYUBOMIR PENEV, TEODOR GEORGIEV, RALF GRABAUM, VESNA GROBELNIK, VOLKER HAMMEN, STEFAN KLOTZ, MLADEN KOTARAC & INGOLF KÜHN
Research in Europe and worldwide has created a huge amount of information about the living things around us. As this information, in particular the newer developments, is, on the one hand, overwhelming and, on the other, very scattered, it is the aim of this Atlas of Biodiversity Risk to present an overview of a wide variety of research aspects relating to biological diversity and its major drivers. This is done in relation to contemporary and future threats biodiversity faces, in order to disentangle the driving forces of change and the role of humankind. To achieve this, our atlas is not simply a collection of different maps. We follow the concept of thematic environmental atlases, where maps, figures, graphs and pictures are combined to tell multi-facetted stories. There is no doubt among scientists and an increasing awareness in society that biological diversity is at great risk. The changes in species and ecosystems we are already experiencing – and which we may experience even more in the future – should therefore be our primary concern. The magnitude of the problems we face at global and local levels, however, should by no means paralyse our efforts to mitigate the negative impact of biodiversity loss. Conservation of biodiversity at all relevant scales can, however, be achieved only if society becomes involved and future generations are educated in such a way that protecting biodiversity becomes an integral part of their perception of the world. That is why this atlas follows the concept of story-telling, i.e., for most of the major threats to and pressures on biodiversity we have tried to select relevant, current examples. We want to describe and illustrate them in a concise and useful way. We hope that the design of the Atlas and the objective language in which it is written make it suitable for use in public relations, as well as for teaching at different levels, such as high schools and universities. The first chapters introduce the topic, starting with examples of global/continental/large-scale species and pressures inventories and the services biodiversity and ecosystems provide to mankind. This is followed by an overview of contemporary biodiversity research approaches which form the baseline for gaining new insights. We then treat major pressures on biodiversity, first in isolation (chapters 3-7), while in the later parts we attempt to present some examples of how these are linked to social backgrounds (chapter 8) and how they “perform in concert” (chapter 9). Chapter 10 deals with the future of biodiversity, again exemplified with case studies, where in the last contribution we try to summarize some core messages. This atlas was initiated through the project ALARM (Settele et al., this atlas, pp. 38ff.). During the concept development and after intensive exchange of ideas with many colleagues working in related projects and networks (Alter-Net, COCONUT, DAISIE, EUMON, Exploratories, LIFEWATCH, LTER, MACIS, MODELKEY, PESI, RUBICODE, etc.), we decided to use the Atlas as a focus to bring together several core elements of contemporary biodiversity and environmental research. Nevertheless, the spirit of ALARM is apparent throughout the Atlas, since its structure follows to a large extent the project’s modular architecture, which in turn reflects the major drivers and/or pressures on biodiversity, namely climate change, land use change, environmental chemicals, biological invasions and loss of pollinators. ALARM is short for “Assessing LArge-scale environmental Risks for biodiversity with tested Methods”. It was an Integrated Project (IP) established within the 6th Framework Programme of the European Commission (EC). To develop and test methods and protocols to assess large-scale environmental risks for biodiversity, ALARM has integrated the research results of more than 250 scientists from 68 institutions in 35 countries. Their analyses formed and still form the basis for policy recommendations, in an attempt to strengthen evidence-based decision making for and within biodiversity policies. The challenge was how to integrate multiple disciplines, dimensions, perspectives, spatial and temporal scales, based on diverse knowledge sources, tools and methods, under the conditions of prevailing uncertainty, high stakes and urgent decisions. xiv
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To achieve this, we needed to create a common language between disciplines to set up a sound communication basis within the established body of expertise. This knowledge – through integration – has led far beyond the previous state of the art, and was finally linked to action (the science-policy interface): decision makers needed to understand how the policies they adopt could impact upon biodiversity in the future, and in turn impact upon ecosystem services. The ALARM project has aimed to do this at the European level and beyond, by contributing to (a) the integrated assessment of socio-economic drivers affecting biodiversity and (b) integrated, longterm oriented measures to mitigate them. Two steps were essential in this respect: 1) developing a common set of scenarios for future policy and describing, both qualitatively and quantitatively, the impact of these policies on key pressures for biodiversity; 2) assessing the impacts on biodiversity and ecosystem functioning in terrestrial and freshwater ecosystems, resulting from these changes. The mechanisms analysed include the risks arising from the major drivers mentioned above. The project was truly multi-disciplinary, involving socio-economists and natural scientists from a diverse range of different backgrounds. The task of summarising the scientific conclusions first through a common comprehensible scientific language, and then in a policy-relevant format has therefore been a real challenge. This atlas is an attempt to make the achievements of the ALARM project and other contemporary research initiatives known to a broader audience, by focussing on a large number of case studies and biodiversity related stories. It is intended to show how impact assessment may look like on a more sectoral basis as well as how Integrated Assessment may work a) across natural-science disciplines and b) with the inclusion of policy aspects. Key concepts that formed the background of ALARM and consequently also the present Atlas are scenarios and biodiversity risk assessment as tools of research and dissemination. Scenarios To assess the impacts of potential future developments of biodiversity and their interplay with the socio-economic context, a set of scenarios has been developed and frequently applied within ALARM and beyond (Spangenberg et al., this atlas, pp. 10ff.). Scenarios are not predictions; on the contrary, they are used when the system to be analysed is too complex for predicting anything with certainty or a quantified probability (as in ecological and socio-economic systems). Scenarios help to answer “if ... then” questions, i.e., to better understand the potential impacts that arise if certain decisions are taken and implemented. Thus, scenarios provide a set of reasonable assumptions to help one’s thinking about possible futures and the impact of current decisions on future development. They illustrate what could be the consequences of different change trajectories, induced by human decisions on how to organise their societies and economies and their relation to the environment. In this sense, they are policy recommendations themselves, illustrating the pros and cons of certain directional decisions. At the same time, they represent tools for analysing drivers and pressures at a finer grained scale within each of these broad directions. Both effects serve to inform political decision making, in the ALARM scenarios with special emphasis on halting the loss of biodiversity. Biodiversity risk assessment Risk assessment was chosen as the background idea of the Atlas, as it might be able to bring many of the results closer to application and/or implementation. Risk analysis can be defined as “a multi-stage process that includes the identification/characterization of a hazard or risk factor, assessment of the likelihood of occurrence, evaluation of impacts associated with that hazard, evaluation of mitigation measures (risk management), and communication of risks” (OIE, 2000). A hazard is the potential of a risk source to
cause an adverse effect. Risk assessment may thus consist of four stages (as elaborated by the International Standard for Pest Management; Figure 1), which can be translated for our purposes as follows: 1. Hazard identification: The aim is to identify the main environmental factors which impact on biodiversity and which should be considered for risk analysis concerning a focal area (e.g., climatic factors representing climate as a whole on a European scale, which is equally applicable to other geographic scales such as biogeographic regions, nations, counties or even plots).
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ment decision can be clearly demonstrated. This final step is a critical one as it ensures that all parties understand the scientific, regulatory (e.g., legal), and other bases for the recommendations. The Atlas as a risk communication tool This sequential listing of steps does not imply chronology. Risk communication, in particular, is a process that should be implemented from the beginning of the process. Whatever the method used, the results of a risk analysis must be understandable, useful, credible, and tailored to the problem envisaged. This is exemplified in the present atlas, which is an integral part of risk communication, while presenting all of the steps included, hidden or obvious, dependant on the respective case study. We very much hope that the present atlas will find its readers among environmental scientists of different disciplines and the present and next generation of environmentally oriented citizens and that it will find its way into many organisations, into the public at large, and – directly or indirectly – into decision making processes of the policy sphere.
R I S K C O M M U N I C AT I O N
Halle, Sofia, Leipzig, Ljubljana, Munich May 2010
Figure 1. Steps in risk analysis (OIE, 2000).
2. Risk assessment: This is the characterisation of risk using estimations of the likelihood of an adverse event, its consequences, and the associated uncertainty. An “adverse event” may, for instance, be a change in the distribution (expansion, retraction or a combination of both) of a species. A typical procedure of risk assessment would hence consist of three interrelated steps, e.g.: ◙ assessing the probability of distributional change (how likely this is); ◙ assessing potential consequences (differences in distribution changes under different scenarios compared to baseline data); ◙ quantifying numerically or in the form of broadly ranked classes, the uncertainty attributed to each of these steps. This can finally lead to the categorization of species into risk classes considering a certain pressure (or groups of pressures). 3. Risk management: It refers to the analytical process used to identify risk mitigation options and evaluate these for efficacy, feasibility and impacts in order to decide or recommend the most appropriate means to mitigate risks that are found to be unacceptable. The uncertainty noted in the assessments of potential consequences and probabilities of, e.g., distributional change are also considered and included in the selection of options for conservation and/or management. 4. Risk communication: The final step is to communicate findings in terms that are clear to all stakeholders. The whole process from hazard identification to risk management should be sufficiently documented so that when a review or a dispute arises, the sources of information and rationale used in reaching the manage-
Acknowledgements We greatly appreciated the support of: ◙ European Commission (FP 6) Integrated Project ALARM (Assessing LArgescale environmental Risks with tested Methods; GOCE-CT-2003-506675; www.alarmproject.net) and (FP 6) Scientific Support to Policy project MACIS (Minimization of and Adaptation to Climate change impacts on biodiverSity; 044399 (SSPI); Kühn et al. 2008); ◙ Karin Zaunberger, Astrid Kaemena, Dov Sax, and Susanne Stoll-Kleemann for very constructively accompanying ALARM; ◙ Members of the ALARM Advisory Board and Consultative Forum; ◙ All members and friends of the ALARM consortium (see list of names in the reproduction of the ALARM flyer on page 41 of this atlas) as well of all the other consortia and authors involved in the writing of this atlas; ◙ Annette Schmidt, Silke Rattei, Ellen Selent, Ursula Schmitz, and Brigitte Grosser, who also throughout the last years considerably contributed to the success of the ALARM project and the making of this atlas; ◙ Helmholtz Centre for Environmental Research – UFZ, in particular Georg Teutsch and Andreas Schmidt. References KÜHN I, SYKES MT, BERRY PM, THUILLER W, PIPER JM, NIGMANN U, ARAÚJO MB, BALLETTO E, BONELLI S, CABEZA M, GUISAN A, HICKLER T, KLOTZ S, METZGER M, MIDGLEY G, MUSCHE M, OLOFSSON J, PATERSON JS, PENEV L, RICKEBUSCH S, ROUNSEVELL MDAR, SCHWEIGER O, WILSON E, SETTELE J (2008) MACIS: Minimisation of and Adaptation to Climate Change Impacts on BiodiverSity. Gaia-Ecological Perspectives for Science and Society 17: 393-395. OFFICE OF INTERNATIONAL EPIZOOTICS (OIE) (2000) International Animal Health Code.
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The Availability and Usage of High Quality, Cross-Scale Baseline Information for Risk Assessment
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INGOLF KÜHN, LYUBOMIR PENEV & JOSEF SETTELE
Introduction The assessment of risks for biodiversity and ecosystems at any scale inevitably demands high quality baseline data on the biodiversity (or selected components of it), environment, and scenario projections. This obvious pre-condition for reliable risk assessment is still frequently being neglected or even ignored in public funding schemes and biodiversity-oriented strategic policy planning. Without knowledge of current or historical biodiversity patterns, as well as states of environmental conditions, it is impossible to make inferences about current processes and temporal dynamics. This knowledge, however, is crucial when deducing potential impacts derived from the change in state (see Maxim et al., this atlas, pp. 16f.) of important environmental drivers as imposed by global change and its components (e.g., climate change, land use change, environmental chemicals, invasive species or declining pollination services). In Europe, there is a long tradition of making inventories of plants and animals which would allow the assessment of temporal dynamics as a consequence of climate change. There is a plethora of data available, some of them dating back to the 19th century (Figure 1). Only a few sources, however, offer data gathered through standardised sampling protocols that are consistent and adequately documented, and, hence, can readily be used. Depending on the spatial scale and the aim of sampling, we can distinguish at least four different sources of biodiversity information which could potentially be used as baseline starting points for risk assessment. Sources of biodiversity baseline information Small scale field observations These are observations in restricted areas of limited extent, usually at a small resolution (grain, plot size). Such data are often not collated in a systematic way and frequently not easily accessible, nor are they available in online open access. Especially natural history data are useful but they are often published in mostly regional periodicals. In addition, many data collected for bachelors’, masters’ or even PhD theses fall also into this category. Though they are often more accessibly published, the information is scattered across many different sources. This kind of data is of a very heterogeneous nature, i.e., they have different taxonomic concepts, different resolutions and extent, and vary in sampling effort and methods, therefore analyses across such data are not straightforward. There is an urgent need to collate and digitize such data in order to obtain high quality small scale datasets across a larger geographical extent. An ideal way would be, for instance, to aggregate and/or index these data by large international organisations, e.g., GBIF (http://www.gbif.org). An important additional incentive for data creators would be to provide the option to publish data in “data sections” of specific journals, or even “data journals” providing publication record, dissemination, storage of, and access facility to datasets. Authors would benefit by having their data properly published and cited, as well as by opening possibilities for collaboration with other data holders. Society would benefit by multiplying investments in data collection and research through future use, re-use of data (Costello 2009, Penev et al. 2009). Nevertheless, such data aggregation would require substantial pre-processing and homogenization prior to analysis. This kind of aggregation of locally distributed data sources would lead directly to the last source of information on this list: collation of existing data. Regional or national mapping schemes These are usually compiled at larger scales, that is to a larger extent and resolution. There is a plethora of mapping schemes in many countries and for many taxonomic groups (see Henle et al., this atlas, pp. 34ff.). Ideally, across the whole range of mapping schemes, data collection should follow the same protocols and, hence, would be reasonably homogeneous in quality across the whole extent of a region or taxon range. In Europe, probably the most prominent are the mapping schemes of European butterflies, birds and mammals. Additionally, there are many national mapping schemes for a variety of taxonomic groups. They are frequently accessible to the public, e.g., Atlas Florae Europaeae Database (free software with encrypted data: http://www.fmnh.helsinki.fi/english/botany/afe/publishing/database.htm), the Biological Record Centre (www.brc.ac.uk) and the National Biodiversity Network (www.nbn.org.uk) in the United Kingdom or FloraWeb (www.floraweb.de) in 2
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Germany. Other sources are not publicly available in a digital format (such as European data on herptiles: Gasc et al. 1997, birds: Hagemeijer & Blair 1997, mammals: Mitchel-Jones et al. 1999, or butterflies: Kudrna 2002). Unfortunately, as the distribution of volunteers, on whom such undertakings very much depend, vary across regions, sampling effort and data quality is spatially heterogeneous. Additional heterogeneity caused by temporal variation cannot easily be deduced from this kind of data. Monitoring schemes Monitoring schemes usually work over a large extent but at small resolution for (ideally) unlimited time. They can not cover large territories as do large-scale mapping schemes, but provide much more detail on the actual landscapes or communities at the size of a single monitoring plot or transect, which are often measured in a few square metres. Since the protocols for monitoring schemes like this are quite strict, data are comparable across space and in time. As most of the monitoring schemes repeat their data collection annually, they are ideally suited for the analysis of temporal dynamics, i.e., population dynamics over many years and sometimes even temporal dynamics and phenological events within and across years. Prominent monitoring schemes are Biodiversity Monitoring Switzerland (www.biodiversitymonitoring.ch), covering a wide range of taxonomic groups, Butterfly Monitoring schemes (www.bc-europe.eu) in the United Kingdom, The Netherlands and Germany (Kühn et al., this atlas, pp. 242f.), or the Pan-European Bird Monitoring Scheme (http://www.ebcc.info). Large scale collation of existing data Lastly, there are currently several initiatives dealing with digitisation, aggregation and collation of data across large scales, up to global scale. Owing to the heterogeneity in data input, resolution and quality of the collated data is heterogeneous across space. Compiling such data from various sources (e.g., local and regional floras and faunas; digitising and processing of medium to large scale distribution maps) is a very laborious task. For this reason, such data have therefore been compiled only for a few popular taxonomic groups, such as plants (Mutke et al., this atlas, pp. 4f.) or birds (Orme et al. 2005, Jetz et al. 2007). In addition, most of such data are not publicly accessible; nevertheless, they receive considerable attention from scientists because of their potential for global analysis of biodiversity patterns and their most important drivers. Collated data of this kind are most valuable for assessing the impact of global change at large geographical scales. Other initiatives assemble various datasets from various regions at different scales (see Los, this atlas, p. 62), and so the coverage across space, time and taxonomic groups of collated datasets can be very heterogeneous. One of the advantages is that these data are usually publicly available. Probably the best known network is the Global Biodiversity Information Facility (GBIF: www.gbif.org). GBIF aims at discovering, mobilising, indexing and opening biodiversity data to everyone, in the service of science, the Convention on Biological Diversity among other international conventions, and the public good. GBIF also aims to become a preferred gateway, either through its main data portal or national nodes, to a comprehensive, distributed array of primary biodiversity (i.e., species by occurrence) data. Data stems from a variety of sources, but the main focus is on digitisation and mobilisation of natural history collections. Over 60% of the data records accessible through GBIF are thus observational records. One of the promising, though still little known, contributors is an initiative called “PANGAEA: Publishing Network for Geoscientific & Environmental Data” (www.pangaea.de). The information system PANGAEA operates as an open access library aiming at archiving, publishing and distributing geo-referenced data, collected mostly in earth sciences. Although PANGAEA focuses mostly on abiotic data, such as water, sediment, ice and atmosphere, one can find almost 7000 biodiversity datasets and more than a million records easily accessible. One of the large remaining problems connected to such data is not only that they are spatially and temporally heterogeneous but, in addition, they vary considerably in the taxonomic concepts used. And as soon as factual data (such as distribution, traits, population dynamics, etc.) are attached to species names, one cannot simply synonymize the names according to a certain taxonomic checklist, but has to account for the differences in taxonomic concepts (e.g., Berendsohn 1995) in order
to keep the information available and correctly assigned. Among the European initiatives working on the infrastructure accounting for this kind of problem is the Pan-European Species-directories Infrastructure (PESI: de Jong et al., this atlas, pp. 6f.). At a global level, some initiatives simply index the names such as GNI (Global Names Index, www.globalnames.org) while others are able to incorporate different taxonomic concepts, such as uBio (www.uBio.org) or GNUB (Global Name Usage Bank) using GNA (Global Names Architecture, http://www.gbif.org/informatics/ name-services/global-names-architecture) services. All these projects aim to establish and implement a complete and integrated taxonomic framework for all taxonomic names in order to facilitate interoperability and linkage of biodiversity data (see Los, this atlas, p. 62, for further aspects). Beyond biodiversity All data like these are valuable in their raw formats but their value multiplies tremendously in further aggregated and collated formats, allowing assessment of the state of biodiversity, expected (projected) impacts and risks. A very specific aggregation and transformation Figure 1. Botanic-geographic map of Europe by Heinrich K. W. Berghaus (1840) (Botanisch-geographisch-statistische Karte von Europa; Verbreitung der vornehmsten of such data that has recently drawn Phanerogamen, Lauf der Temperatur-Kurven des wärmsten und des kältesten Monats). In: Physikalischer Atlas oder Sammlung von Karten, auf denen die hauptsächlichincreased attention is the analysis of sten Erscheinungen der anorganischen und organischen Natur nach ihrer geographischen Verbreitung und Vertheilung bildlich dargestellt sind: zu Alexander von Humboldt, Kosmos – Entwurf einer physischen Weltbeschreibung. Gotha: Justus Perthes. functional aspects. Here, the idea is not only to analyse patterns of species richness or species turnover across space and time, but also to analyse the functions of cation tool. In Europe, the DPSIR (Driving Forces – Pressures – State – Impact – species in an ecosystem and the services of ecosystems provided to society. Still, Responses) framework, has recently attracted increased attention. Due to the intuthere are two approaches here: (i) the analysis of relationships between functional itive concept and its easy application, we have adopted the system in the ALARM traits and the environment and (ii) the analysis of ecosystem services. While in (i) project (Maxim et al., this atlas, pp. 16f.). generalizations can be made across species, which provide some more functional insights into ecosystem processes, in (ii) the focus is on the benefits (or maybe even A first example on how such biodiversity baseline data and its relationships to detriments) provided for human society (Harrison et al., this atlas, pp. 8f.). Each of drivers relate to socio-economy, climate and land use change is provided by Liira these two strains has yielded some fairly conceptual background and especially the et al. (this atlas, pp. 18ff.). The scale of this analysis is rather coarse as, in contrast analysis of functional properties has gained considerable attention for the past two to many biodiversity data, any socio-economic data are available only for larger decades or so. The second strain has come into focus much more recently, mostly administrative units. For good reasons, however, plants and animals tend to ignore as a result of the demand to demonstrate the benefit of biodiversity and ecosysthese sorts of structures. As a compromise between availability of appropriate tems in a socio-economically challenging environment. Unfortunately, it seems that data and sensitivity of cross-driver analyses, the relationship between species richthese two strains are currently rather disconnected and some conceptual backness of vascular plants, mammals, birds, reptiles and butterflies, respectively, was ground on how functional properties can provide (or translate to) ecosystem servanalyzed for most of the countries in Europe. Further analyses of combined ices remains a vast new field for exploration. effects (combining different groups of drivers or interactions among responding taxa) are provided in chapter 9. While most of the baseline data outlined so far is data on current (or recent historical) states of biodiversity and ecosystems, for impact assessment of possible future developments input data of the projected development (or plausible environmental References trajectories) are necessary. Therefore, the outputs of scenario exercises have to be BERENDSOHN WG (1995) The concept of “potential taxa” in databases. Taxon 44: 207-212. COSTELLO MJ (2009) Motivating online publication of data. BioScience 59: 418-427. an integral part of risk analysis and form a crucial input into such studies. Only GASC JP, CABELA A, CRNOBRNJA-ISAILOVIC J, DOLMEN D, GROSSENBACHER K, HAFFNER P, LESCURE when all baseline data, being biodiversity, current or recent historical environmental J, MARTENS H, MARTINEZ RICA JP, MAURIN H, OLIVEIRA ME, SOFIANIDOU TS, VEITH M, conditions, and scenarios are being processed in a harmonized way (e.g., having ZUIDERWIJK A (1997) Atlas of amphibians and reptiles in Europe. Societas Europaea common formats, resolution, extent. etc.), will it be possible to reach consistent proHerpetologica & Museum National d’Histoire Naturelle, Paris. jections of possible future developments. This requires the underlying assumptions HAGEMEIJER WJM, BALIR MJ (1997) The EBCC Atlas of European Breeding Birds. Poyser. JETZ W, WILCOVE DS, DOBSON AP (2007) Projected impacts of climate and land-use change on the and socio-economic conjectures to be consistent and harmonized. This all calls for global diversity of birds. PLoS Biology 5(6): e157 (doi: 10.1371/journal.pbio.0050157). and will lead to integrated scenarios (Spangenberg et al., this atlas, pp. 10ff.). These KUDRNA O (2002) The distribution atlas of European butterflies. Apollo Books, Stenstrup, Denmark. are scenarios and quantified projections of them across many different disciplines, MITCHELL-JONES G, AMORI G, BOGDANOWICZ W, KRYSTUFEK B, REIJNDERS PJH, SPITZENBERGER F, all based on the same standards, protocols and assumptions. Having such a set of STUBBE M, THISSEN JBM, VOHRALIK V, ZIMA J (1999) The Atlas of European Mammals. scenarios elaborated through a consistent methodology is one of the main outPrinceton University Press Princeton, USA. ORME CDL, DAVIES RG, BURGESS M, EIGENBROD F, PICKUP N, OLSON VA, WEBSTER AJ, DING TS, comes of the ALARJM project and forms a sound basis for the success and conRASMUSSEN PC, RIDGELY RS, STATTERSFIELD AJ, BENNETT PM, BLACKBURN TM, GASTON KJ, sistency of biodiversity and ecosystem risk assessment. Analysing changes, communicating consequences and proposing possible measures and policies to act calls for an efficient conceptual framework and communi-
T H E
AVA I L A B I L I T Y
A N D
U S AG E
O F
H I G H
Q UA L I T Y,
OWENS IPF (2005) Global hotspots of species richness are not congruent with endemism or threat. Nature 436: 1016-1019. PENEV L, ERWIN T, MILLER J, CHAVAN V, MORITZ T, GRISWOLD C (2009) Publication and dissemination of datasets in taxonomy: ZooKeys working example. ZooKeys 11: 1-8 (doi: 10.3897/zookeys.11.210).
C RO S S - S C A L E
BA S E L I N E
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F O R
R I S K
A S S E S S M E N T
3
European Plant Diversity in the Global Context
,
JENS MUTKE, HOLGER KREFT, GEROLD KIER & WILHELM BARTHLOTT
ther macroecological analyses (e.g., Mutke & Barthlott 2005, Kreft & Jetz 2007, Figures 2, 4),
DZ 9
DZ 3
200-500 spp.
DZ 7
2,000-3,000 spp.
DZ 4
500-1,000 spp.
DZ 8
3,000-4,000 spp.
4,000-5,000 spp. Nees Institute for Biodiversity of Plants University of Bonn
> 5,000 spp.
Figure 1. World map of species richness of vascular plants (Barthlott et al. 2005, Mutke & Barthlott 2005). The map is based on species richness figures for ca. 1,400 geographical units word-wide.
4
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1
0
1,500-2,000 spp. DZ 10
00
1,000-1,500 spp.
DZ 6
40°
5,
DZ 5
20-200 spp.
20°
0
< 20 spp.
0°
00
DZ 1 DZ 2
20°
3,
W. Barthlott, G. Kier, H. Kreft, W. Küper, D. Rafiqpoor & J. Mutke 2005
40°
0
Diversity Zones (DZ): Number of species per 10,000 km2
60°
00
Generating a world map of plant diversity Plant diversity is documented in thousands of inventories such as floras and checklists world-wide. Based on this literature, we compiled a dataset with numbers of native plant species of ca. 3,000 operational units such as countries, provinces, islands, mountain ranges, and conservation areas. As these units differ very much in area, only a subset of ca. 1,400 of these was used for our mapping approach. Species richness figures of the selected units were standardized using classical models of the relation of area and species richness. To interpolate between areas with suitable raw data, additional datasets of environmental parameters have been used (Barthlott et al. 2005, Mutke & Barthlott 2005, Figure 1). The same dataset was the basis for fur-
Environmental and historical controls of plant diversity Plant richness changes systematically along environmental and latitudinal gradients (Mutke & Barthlott 2005, Kreft & Jetz 2007, Figure 2). At high latitudes, where temperature and the length of the thermal vegetation period are limiting factors, species richness is closely correlated with measures of thermal energy like potential evapotranspiration (PET). On the other hand, water availability and the spatial heterogeneity of the environment appear to be more important at lower latitudes (Kreft & Jetz 2007, compare Figure 4). In addition to richness-environment relationships, the regional history of the environment, especially of the climate, has considerably influenced today’s diversity patterns. Due to severe impacts of the harsh climate during the ice ages, the woody plant flora of Central Europe is highly impoverished compared to similar vegetation in East Asia or eastern North America. The same holds true
80°
1,
Global centres of plant diversity Global centres of species richness are located in the humid tropics and subtropics, especially in areas with a high heterogeneity of the abiotic environment (“geodiversity”) like mountains and regions of steep climatic gradients. Five global centres of plant species richness reach species densities of more than 5,000 vascular plant species per 10,000 km² (compare Table 1, Figure 1). In total, there are 20 centres of plant diversity with more than 3,000 species per 10,000 km². Important extra-tropical centres are the Mediterranean-type climate areas of the world with hot and dry summers and cool, wet winters: the Mediterranean Basin, California, central Chile, the South African Cape Region, and South and Southwest Australia. These regions are characterized by comparatively diverse and highly endemic floras and are considered as Biodiversity Hotspots by Conservation International. High numbers of endemic species can be found on the oceanic islands of
the world. Some 70,000 species of vascular plants or 20 % of the world’s flora are endemic to islands – thus, occurring nowhere else (Kreft et al. 2008).
Latitude
The distribution of plant diversity across the Earth is highly uneven (Barthlott et al. 2005, Kier et al. 2005, Mutke & Barthlott 2005, Figure 1). For instance, the small South American country of Ecuador which has a surface area comparable to the British Isles harbours some 30-40 % more species than continental Europe.
Species / 10,000 km2 Figure 2. Latitudinal gradient of vascular plant diversity in Europe and Africa. Each dot represents the number of native plant species of a geographic unit (e.g., flora, checklist). Species numbers were standardized for disparities in area size (modified from Mutke & Barthlott 2005).
for the number of native vascular plant families in Europe (169) which is much lower as compared to North America (excl. Mexico: 210) or China (260). European plant diversity Europe is home to some 11,500 of the estimated 320,000 vascular plant species on earth. The Flora Europaea lists ca. 10,600 flowering plants, ca. 160 of the ca. 13,000 global fern species, and ca. 40 of ca. 1,000 species of gymnosperms. While these species numbers are much lower than those of the top global centres of plant diversity listed in Table 1, parts of the European mountain regions or the Mediterranean show a level of richness comparable with, e.g., tropical Africa (Figure 2). The non-vascular plant flora of Europe is relatively well documented compared to many other regions. It harbours more than 30,000 documented species of bryophytes, algae, fungi, and lichens. Especially in the groups of algae and fungi, there are still many species to be discovered. On a European scale, highest species richness can be found in the Mediterranean and the Caucasus. Especially the geodiverse mountainous areas surrounding the Mediterranean like the Balkans region, the Alps, the Pyrenees and the mountain ranges of SE Spain are important centres of plant species richness and endemism (Araújo et al. 2005). The Balkan, the Iberian Peninsula, and Italy have been
Table 1. Global and European Centres of Plant Diversity (modified after Barthlott et al. 2005). 2
Centre
Area (km )
Total spp.
Endemism spp.
Percent protected
%
The top 5 Global Centres of Plant Diversity 1
Costa Rica-Chocó
78,000
≥ 12,500
5,500
44%
18,8%
2
Tropical Eastern Andes
62,000
10,000
3,000
30%
19,1%
3
Atlantic Brazil
50,000
≥ 6,000
4,500
75%
6,3%
4
Northern Borneo
57,000
9,000
3,500
39%
7,7%
5
New Guinea
87,000
≥ 6,000
2,000
33%
1,8%
European Centres of Plant Diversity Mediterranean basin
2,085,000
22,500
11,700
52%
4,3%
Caucasus
440,000
6,400
1,600
25%
2,8%
Alps
200,000
5,500
350
6%
23 %
important refugia where many plant species survived during the ice ages. Especially the Mediterranean is a centre of origin of important crop species including grape vine (Vitis vinifera), beet (Beta vulgaris), carrot (Daucus carota), rape seed (Brassica napus), and the garden pea (Pisum sativum). The laurel tree (Laurus nobilis) and the olive tree (Olea europaea) are characteristic plants of the region. Several important species of spices are native to the Mediterranean, such as marjoram, rosemary, thyme, or sage. Priorities for nature conservation Major drivers of biodiversity loss at the global scale are habitat conversion, over-exploitation, pollution, invasive species, and climate change as documented by the Millennium Ecosystem Assessment published in 2005 under the leadership of the UN Environment Program (UNEP). At the European level, habitat loss has highest importance, mostly due to intensification of agriculture, urbanisation, and infrastructure development.
Although only the Mediterranean basin and the Caucasus belong to centres of plant species richness at the global scale, Europe houses a large amount of specific and unique biological diversity. Vegetation types like the “European-Mediterranean montane mixed forests”, the “Caucasus mixed forests”, and the “Fenno-Scandia alpine tundra and taiga” have been selected as priority regions for the Global 200 Ecoregions by WWF. Some 3,500 vascular plant species are endemic to Europe, occurring nowhere else. Typical vegetation such as European beech forests, bogs, and species rich grasslands plays an important role for ecosystem functioning and provide crucial ecosystem services. This includes essential ecological functions such as nutrient cycling, provision of clean water, wood, and genetic resources, but also the recreational and aesthetic values of our landscapes and their biological diversity. Due to the long history of human settlement and agriculture in Europe, specifically adapted ecosystems have developed. Nature conservation in these mosaic-like landscapes has therefore to
High Energy
Low Energy
Species richness
10,000
integrate forms of traditional extensive agriculture as well. Good knowledge about our flora and fauna, well trained conservation managers and staff, a good infrastructure, and a comprehensive political framework are the basis for effective nature conservation in Europe. As global action is needed to tackle the challenges of the different aspects of global environmental change, European countries are needed as promoters for effective conservation and management of the environment. Additionally, in a globalized economy, all decisions and actions may also have important impacts on other continents. Thus, the conservation of our environment, landscapes, and biological diversity not only has an important impact on the well-being of Europeans, but on a global scale, as well. References ARAÚJO MB, THUILLER W, WILLIAMS PH, REGINSTER I (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography 14: 17-30. BARTHLOTT W, MUTKE J, RAFIQPOOR MD, KIER G, KREFT H (2005) Global centres of vascular plant diversity. Nova Acta Leopoldina 92: 61-83. KIER G, MUTKE J, DINERSTEIN E, RICKETTS TH, KÜPER W, KREFT H, BARTHLOTT W (2005) Global patterns of plant diversity and floristic knowledge. Journal of Biogeography 32: 1107-1116. KREFT H, JETZ W (2007) Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences of the USA 104: 5925-5930. KREFT H, JETZ W, MUTKE J, KIER G, BARTHLOTT W (2008) Global diversity of island floras from a macroecological perspective. Ecology Letters 11: 116-127. MUTKE J, BARTHLOTT W (2005) Patterns of vascular plant diversity at continental to global scales. Biologiske Skrifter 55: 521-537.
r2=0.30***
1,000
r2=0.00n.s.
100
r2=0.64*** 0
r2=0.03n.s. 1,000
Potential Evapotranspiration
2,000
0
100
200
300
Wet Days
Low Energy; PET < 505 mm
High Energy; PET > 505 mm
Figure 3. Global and European Centres of Plant Diversity: Tropical Andes, Caucasus, South African Cape Region, Mediterranean. Photos: N. Köster, J. Mutke.
Figure 4. Global relationship between environmental predictors and species richness of vascular plants in low-energy regions (blue dots) and high-energy regions (red dots). Each dot represents one of more than 1,000 geographic regions for which species numbers have been derived from the literature. In regions with low thermal energy (PET < 505 mm) a close relationship with species richness is observed. In contrast, PET is a non-significant predictor in high-energy regions where water availability is a strong predictor (modified from Kreft & Jetz 2007)
EURO P EA N
P L A N T
D I V E R S I TY
Figure 5. Complex species rich central European landscapes including human land use and semi-natural vegetation in the Mosel valley, Germany and north of Budapest, Hungary. Photos: J. Mutke.
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5
A Pan-European Species directories Infrastructure (PESI) YDE DE JONG, LOUIS BOUMANS, JULIANA KOUWENBERG, HENRIK ENGHOFF, PHILLIP BOEGH, NIHAT AKTAÇ, SELÇUK YURTSEVER, CHARLES HUSSEY, ROGER HYAM, MARK COSTELLO, THIERRY BOURGOIN, WALTER BERENDSOHN, ECKHARD VON RAAB-STRAUBE, ANTON GÜNTSCH, WARD APPELTANS & BART VANHOORNE
,
Introduction The urgency of global problems related to conservation and sustainable use of biological resources is generally acknowledged. Obstacles to the proper development and implementation of environmental management systems include poor access to reliable biodiversity information. Part of this problem lies in the lack of standardisation in taxonomic reference systems. Other parts of the problem concern the quality and completeness of taxonomic data sets, and the absence of an integrated access to taxonomic information. PESI will contribute to the solution of this impediment by improving the European e-infrastructure through the strengthening of the respective
name registers that underpin the management of biodiversity in Europe. PESI will integrate the three main alltaxon registers in Europe, namely the European Register of Marine Species, Fauna Europaea, and Euro+Med PlantBase in coordination with EU-based nomenclators and the network of EU-based Global Species Databases. It is a standards-based, quality-controlled, expert-validated, open access infrastructure for research, education and resource management. Action plan PESI defines and coordinates strategies to enhance the quality and reliability of European biodiversity information by integrating the infrastructural compo-
The organisation of national and regional focal point networks as projected not only assures the efficient access to local expertise, but is also important for the synergistic promotion of taxonomic standards throughout Europe, for instance to liaise with national governmental bodies on the implementation of European biodiversity legislation. In addition, PESI will coordinate the integration and synchronisation of the European taxonomic information systems into a joint e-infrastructure. This follows the running initiatives for the creation of a Global Names Architecture for the efficient and unambiguous cross-referencing of taxon names, the progress on a joint
INFRASTRUCTURAL NETWORKS Expertnetworks
Focal point networks
Authority files & Standards
Data e-Infrastructure
e-Services
COMMUNITY NETWORKS
Zoological Community
Botanical Community
Marine Community Mycological Community Phycological Community
Figure1. Four community networks (horizontal) will be integrated in five categories of coordination effort (vertical) in PESI.
scientific, social, political, technological, and information capacities in Europe, needed for a proper biodiversity assessment. Objectives Because the correct use of names and names relationships is essential for biodiversity management, the availability of taxonomically validated standardised nomenclatures is fundamental for biological e-infrastructures. PESI is the next step in integrating and securing taxonomically authoritative species 6
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nents of five major community networks on taxonomic indexing and their respective knowledge infrastructures, namely those of marine life, terrestrial plants, fungi, algae and animals, into a joint work program. This will result in functional knowledge networks of taxonomic experts and regional focal points, which will collaborate on the establishment of standardised and authoritative taxonomic (meta-) data and the development of approaches to their long-term maintenance.
RISK
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Internet Platform for Cybertaxonomy within EDIT, and the setting-up of a common user-interface disseminating the pan-European checklists and associated user services. Coordination activities A crucial part of this project will be the involvement of the expert community to work collaboratively on the PESI tasks following common work formats. The development of national and regional focal point networks assures the efficient access to local expertise.
PESI also makes a start on the geographic expansion of the European networks to eventually cover the entire Palaearctic biogeographic region. As an important first step, the cooperation is intensified with partners from Turkey, Georgia, Ukraine and Russia. User communities A range of initiatives has been taken within the European Research Area (ERA) to develop information systems assembling and integrating biological species information for various purposes. A prerequisite of these initiatives is the support of scientists and infrastructures that provide standardised and authoritative taxonomic information. PESI will coordinate the delivery of this information to stakeholders through the interoperation of the existing data infrastructures and expert networks. Technology does not work in isolation, and requires parallel development in contributor and user practices. PESI will explore the user needs allowing users to comment and provide feedback on the system performance via an end user forum and a user feedback system, which will also allow other experts to communicate on the quality of the taxonomic data. International aspects PESI supports international efforts on the development of the Global Names Architecture by building a common intelligent name-matching device in consultation with the principal initiatives (GBIF, TDWG, EoL). PESI contributes to the development of a unified cross-reference system and provides high quality taxonomic standards. PESI will further involve the Europe-based nomenclatural services and link the planned joint European taxonomic e-infrastructures middle layer to the global e-gateway. The intention is that PESI form a component of a broader initiative to be known as ‘EU-nomen’ that will service the long-term needs of the biodiversity community in Europe for taxonomic data standards and by ensuring an integrated access to European and Palaearctic authoritative taxonomic digital resources. References PESI: www.eu-nomen.eu/pesi Fauna Europaea: www.faunaeur.org ERMS: www.marbef.org/data/erms.php Euro+Med PlantBase: www.emplantbase.org/ home.html
Alpine North Alpine South Boreal Atlantic Continental Pannonian
'
Mediterranean
'
no data
'
' ' ' '
' ' '
' ' ' '
'
'
'
' '
'
'
'
' ''
' '
' '
'
'
'
'
' '
'' ' Figure 2. PESI Focal Points network.
''
'
' '
'
'
'
'
'
'
' ' '
ERMS Eu+Med FaEu
Figure 3. Draft outline of the future PESI interface.
A
PA N -EURO P EA N
SP EC I ES
D I RE C TO R I E S
I N FR A S TRU C TU R E
(PE S I )
7
Assessment of Ecosystem Services
,
PAULA A. HARRISON, GARY W. LUCK, CHRISTIAN K. FELD & MARTIN T. SYKES
Introduction Ecosystem services are the benefits that humans obtain from ecosystems. They support, directly or indirectly, our survival and quality of life. The Millennium Ecosystem Assessment (MA) conducted an extensive scientific review on ecosystem services between 2001 and 2005, involving 1300 researchers from 95 countries (www.millenniumassessment. org). The MA concluded that 60 % of ecosystem services are being degraded or used unsustainably, often resulting in significant harm to human well-being. The MA categorised ecosystem services into four classes:
Freshwater ecosystem services River ecosystems – in a broader sense – encompass river channels and floodplains. Both form a diverse mosaic of habitats with the riparian area at the transition zone between the land and water. During flood events, water and sediment are transported onto the floodplain and provide the nutrients that render river ecosystems highly productive. Conversely, floodplains (and other wetlands) constitute important sinks of river nutrients and sediments and, hence, contribute substantially to a river’s self-purification. They act as a sponge and regulate the water
River Fresh water Self-purification Water regulation Recreation Primery production Water cycling Nutrient cycling
R I V E R E C O S Y S T E M S E RV I C E S
Floodplain/ Wetland
ent and water cycling) services provided by freshwater ecosystems. Ecosystem services are sometimes valued in monetary terms for use in policy- and decision-making. This is relatively straightforward for provisioning services such as water and timber supply where market values exist. However, it is more difficult and often controversial for many regulatory and supporting services for which the direct benefits to people are not as clear. Nevertheless, several studies have provided values for river and floodplain ecosystem services. The Danube floodplain and wetlands, especially their regulatory role as a
nutrient sink, have been valued at 650 Million Euro per year (Gren et al. 1995). On a global scale, an annual total value of 4,879 Trillion US$ has been estimated for wetlands and 3,231 Trillion US$ for floodplains (including swamps) or, altogether, around 24 % of the total annual ecosystems services’ value on Earth (Costanza et al. 1997). Mapping freshwater ecosystem services The contribution that the protection of ecosystem services will make to biodiversity conservation is being explored through broad-scaled map-
a
Timber Water purification Water regulation Flood control Climate regulation CO2 sequestration Air quality regulation Recreation Soil formation Water cycling Nutrient cycling Photosynthesis
Riparian Area Water purification Nutrient buffer Water regulation Erosion regulation Recreation Soil formation Carbon supply
b
Figure 1. Major ecosystem services provided by rivers, riparian areas and floodplains/wetlands in Europe.
◙ Provisioning services which are the products obtained from ecosystems, such as food, water, fuel and materials for building. ◙ Regulatory services which are the benefits obtained from the regulation of natural processes, such as air quality regulation, climate regulation, water/flood regulation, disease and pest control, pollination and water purification. ◙ Cultural services which are the non-material benefits people obtain from ecosystems and landscapes through spiritual enrichment, recreation and aesthetic enjoyment. ◙ Supporting services which are necessary for the production of all other ecosystem services, such as soil formation, nutrient and water cycling, and photosynthesis. The importance of ecosystem services for humankind is illustrated for freshwater ecosystems. 8
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volume, as they cut off flood peaks and release water during low-flow conditions. Floodplains, especially the riparian areas, provide the river channel with carbon (organic matter) which is essential for sustaining riverine plant, animal and micro-organism communities in many regions of Europe. Looking more precisely at the specific services provided by river ecosystems, their important role for human well-being becomes obvious. Nearly everywhere on Earth, people depend on rivers for fresh water supply and sanitation purposes. But there are many more services linked with rivers and floodplains besides these fundamental human needs. Figure 1 provides an overview of the major provisioning (e.g., fresh water and timber supply), regulatory (e.g., water and erosion regulation, self-purification), cultural (recreation and ecotourism) and supporting (e.g., soil formation, nutri-
RISK
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1
MHP
ESP
BCP
MLP
0
1,000
2,000 km
Figure 2. Priorities for the protection of ecosystem services and biodiversity in European freshwater ecosystems: (a) ecosystem services compared with freshwater biodiversity; and (b) ecosystem services compared with other biodiversity conservation schemes. Areas are split into four categories: mutual-high priorities (MHP) for protection of both ecosystem services and biodiversity; high priorities for protecting ecosystem services (ESP); high priorities for protecting biodiversity (BCP); and mutual-low priorities (MLP) for protecting both ecosystem services and biodiversity. Other areas are not included in the analysis.
1. IDENTIFICATION
2. QUANTIFICATION
3. APPRAISAL
{ { {
Define the ecosystem service: • Identify the ecosystem service beneficiaries (ESB) • Identify the spatial and temporal scale of service delivery • Identify the ecosystem service providers (ESP) Quantify the ecosystem service demand: • Determine the net level of demand/need for the service Quantify the service providing unit (SPU): • Determine the characteristics of organisms necessary for service provision • Quantify the relationship between SPUs and service supply • Quantify the components of biodiversity that support the SPU Value the service as provided by the SPU
Identify and value alternative ways of providing the service
Evaluate options: • Compare valuations and examine trade-offs • Determine implications for biodiversity conservation, policy and sustainable livelihoods
Figure 3. RUBICODE framework for the identification and quantification of an ecosystem service.
ping. The spatial distribution of three key freshwater ecosystem services (water provision, flood prevention and carbon storage) has been mapped for Europe by Luck et al. (2009a). Areas were ranked in order of priority for investment in service protection and compared with an index of freshwater biodiversity and rankings based on established biodiversity conservation prioritisation schemes (Conservation International biodiversity hotspots, World Wildlife Fund Global 200 ecoregions and Birdlife International endemic bird areas). These schemes identify regions across the globe that are priorities for protecting biodiversity. Examining the spatial patterns for ecosystem services and biodiversity conservation priorities reveals four possible trends: (i) mutual-high priorities (MHP) – where areas have high priority for both biodiversity conservation and ecosystem service protection; (ii) mutual-low priorities (MLP) – where areas have low priority for both biodiversity conservation and service protection; (iii) ecosystem service pri-
orities (ESP) – where areas have high priority for service protection only; and (iv) biodiversity conservation priorities (BCP) – where areas have high priority for biodiversity conservation only. Figure 2 shows the locations of areas in the above categories. For example, when comparing ecosystem services with the index of freshwater biodiversity, areas such as Vistula (Poland), Odra (Poland) and Tagus (Portugal and Spain) are ranked as MHPs, whereas the Danube and Rhine are only priorities for ecosystem service protection (Figure 2a). The classification varies slightly when comparing ecosystem services with the other biodiversity conservation schemes (Figure 2b). These results serve to inform European conservation and development agencies of priority locations where return-on-investment in service protection and biodiversity conservation is greatest. Quantifying the contribution of organisms to ecosystem services Information on how ecosystem services are provided is limited. Identifying the
organisms and their characteristics that provide services is crucial to developing policies which will protect them. The European Commission project, RUBICODE (www.rubicode.net), has developed a framework showing the steps that need to be undertaken to identify and quantify an ecosystem service (Figure 3). The steps can be divided into three stages of analysis: (i) identify the human beneficiaries of the service and the biological organisms that provide it; (ii) quantify demand and supply of the service; and (iii) appraise the service value and implications for management and policy. We need to know which sections of the human community use the service (known as the Ecosystem Service Beneficiaries, ESB) and at what level is it required, what components of the ecosystem provide the service (known as the Ecosystem Service Providers, ESP), and what characteristics of these components are required to provide the service at the desired level (termed the Service Providing Unit (SPU), Luck et al. 2009b). The relevant SPU characteristics which need quantifying depend on the service in question and the organism(s) that supply it and may include population size, distribution, diversity, behaviour or functional traits. The framework is applied to two case studies in Table 1 to illustrate its application. Information on how ecosystem service supply changes as the characteristics of SPUs change is fundamental to policy-makers and land managers who need to decide between trade-offs attached to different management strategies (e.g., protecting habitat for service providers vs. clearing a certain proportion for production). Indeed, it is this quantitative information that is
Table 1. Case studies for quantifying ecosystem services. Service
Nutrient and sediment regulation by riparian buffers
Recreation in the Stockholm National Urban Park, Sweden
Description
Riparian vegetation (Figure 4) regulates the flow of water, nutrients and sediment from uplands to the stream through reducing surface runoff and promoting infiltration. It filters both surface runoff (nutrients, pollutants and sediment) and groundwater runoff (nutrients, pollutants).
The Stockholm National Urban Park (Figure 5) is an oak forest which forms the largest green area in northern and eastern Stockholm. It is 26 km2 in area with a unique and well-known biodiversity with many rare species. The park is protected by law and the area has to be maintained in its natural state or at least essentially unchanged.
Ecosystem Service General public Beneficiaries (ESB) Ecosystem Service Providers (ESP)
Quantification of the Service Providing Unit (SPU) Valuation
Appraisal
Figure 4. In agricultural landscapes, mixed riparian buffers composed of trees and grass strips can effectively retain sediment from surface run-off and nutrients from the upper groundwater layer. River Nuthe in Brandenburg, Germany. Photo: Christian Feld.
of most value to policy-makers and land managers because it facilitates specific rather than vague management guidelines, which ensure the sustainability of ecosystem services. References COSTANZA R, D’ARGE R, GROOT RD, FARBER S, GRASSO M, HANNON B, LIMBURG K, NAEEM S, O’NEILL RV, PARUELO J, RASKIN RG, SUTTON P, BELT M.V.D. (1997) The value of the world’s ecosystem services and natural capital. Nature 387: 253-260. DOSSKEY MG (2001) Toward quantifying water pollution abatement in response to installing buffers on crop land. Environmental Management 28: 577-598. GREN I-M, GROTH K-H, SYLVÉN M (1995) Economic values of Danube floodplains. Journal of Environmental Management 45: 333-345. HOUGNER C, COLDING J, SÖDERQVIST T (2006) Economic valuation of a seed dispersal service in the Stockholm National Urban Park, Sweden. Ecological Economics 59: 364-374. LUCK GW, CHAN KMA, FAY JP (2009a) Protecting ecosystem services and biodiversity in the world’s watersheds. Conservation Letters 2: 179-188. LUCK GW, HARRINGTON R, HARRISON PA, KREMEN C, BERRY PM, BUGTER R, DAWSON TP, DE BELLO F, DIAZ S, FELD CK, HASLETT JR, HERING D, KONTOGIANNI A, LAVOREL S, ROUNSEVELL M, SAMWAYS MJ, SANDIN L, SETTELE J, SYKES MT, VAN DE HOVE S, VANDEWALLE M, ZOBEL M (2009b) Quantifying the contribution of organisms to the provision of ecosystem services. Bioscience 59: 223-235.
The park is an important recreational area being the most visited urban park in Sweden by both locals and tourists.
The multi-species-multi-zone riparian plant community (i.e. different The oak forest provides a direct service to humanity. The Eurasian Jay (Gartrees, shrubs, herbs and grasses in the area, located in different zones rulus glandarius; Figure 6) provides a seed dispersal service for the oaks. It of a sufficient width to provide the service). collects and hides acorns during the autumn for later winter consumption at the ideal depth for germination (and reduced predation). Such dispersal also enhances the gene pool of the oaks where 85% of the oaks are estimated to regenerate naturally. The service depends on the number of constituent zones and the Minimum species abundance is 12 pairs of jays for the park (the current density and width of the buffer (e.g., 30 m of mixed riparian buffer jay population is estimated at 42 pairs). This results in the establishment of removes 92-100 % of ground water nitrate and 5-20 m grass strips 33,148 oak saplings per year (over a 14-year period), which is required for retain 40-100 % of sediments; Dosskey 2001). forest maintenance (Hougner et al. 2006). Calculation of replacement costs if the service were to be provided Alternatives to the service provided by the jays include humans actively seedby conventional waste water treatment systems: Removal of NO3: 15- ing acorns, planting saplings and promoting natural regeneration through 30 € (E a)-1 (per person equivalent and year); removal of PO4: 1-3 € felling of trees and some sort of disturbance. Seeding methods would cost (E a)-1; removal of C, N and P together: 45-75 € (E a)-1 (figures based 11,560 € per year, whilst planting by humans would cost 50,390 € per year. on Emscher Water Board, Ruhr Metropolitan Area, Germany). Thus the replacement cost of losing the jays would be 16,880 €/pair. Restoration of riparian buffers is unavoidable to meet the demands of There is widespread public support for the maintenance of the park.The park the Water Framework Directive, as there are no practical alternatives received formal status in 1995 and is now classified in the Swedish Environavailable. The lack of intact riparian buffer strips has severe negative mental code as an area of national interest. New developments in the area implications for river water and habitat quality. Both nutrients and are allowed but only if they can be carried out without intruding on the park sediments also severely impact the riverine fauna and flora and may landscape and without affecting negatively the natural and cultural values of have additional implications at the landscape level. To meet a good the area. Continued investment in management that safeguards the jay popuecological status of rivers, an extensive restoration of riparian areas lation at a level suitable for the continued and successfully regeneration of oak forest in the Stockholm NUP is required. along river ecosystems is necessary.
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Figure 5. Stockholm National Urban Park. Photo: Peter Schantz.
Figure 6. The Eurasian Jay (Garrulus glandarius). Photo: L.G.M. Schols.
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The ALARM Scenarios: Storylines and Simulations for Assessing Biodiversity Risks in Europe JOACHIM H. SPANGENBERG, STEFAN FRONZEK, VOLKER HAMMEN, THOMAS HICKLER, JILL JÄGER, KIRSTI JYLHÄ, INGOLF KÜHN, GLENN MARION, LAURA MAXIM, ILIANA MONTERROSO, MARTIN O’CONNOR, INES OMANN, ISABELLE REGINSTER, BEATRIZ RODRÍGUEZ-LABAJOS, MARK ROUNSEVELL, MARTIN T. SYKES, MARCO VIGHI & JOSEF SETTELE
,
Why scenarios? Biodiversity is influenced by a combination of natural processes (e.g., evolution, competition, changing environments) and anthropogenic pressures (e.g., land use, nitrogen deposition, climate change, alien species invasions). Changes in biodiversity have impacts on ecosystem structure and function and therefore on the possibility of human societies and economies to yield ecosystem services. Thus assessments of the possible future development of biodiversity and their interplay with the socioeconomic context constitute an important input into policy formulation processes; scenarios are tools for generating such assessments. Scenarios are not predictions. On the contrary, when the system to be analysed is too complex for predicting anything with certainty or a quantified probability (as it is the case for largescale eco- and socio-economic systems), scenarios help to answer “if ... then” questions, i.e. to better understand the potential impacts that arise if certain decisions are taken and implemented. Thus scenarios provide
a set of reasonable assumptions to help thinking about possible futures and the impact of current decisions on future development, illustrating what could be the consequences of different change trajectories, induced by human decisions on how to organise their societies and economies and their relation to their natural environment. In this sense they are in themselves policy recommendations, illustrating the pros and cons of certain directional decisions, and they are tools with which to analyse the more detailed factors within each of these broad directions. Both effects serve to inform political decision making, in the ALARM scenarios with special emphasis on halting the loss of biodiversity. Using storylines and model runs, this can be done before deciding upon the course of action and the policy framework needed for that (“look before you leap”). Following the “Storyline And Simulation” SAS method (Alcamo 2001), we have chosen to distinguish scenario narratives and simulations (both publicly often referred to as scenarios) from what
Shock – Scenarios, Wild Cards
BAMBU-CANE GRAS-CUT BAMBU-SEL
Economic Shock
Social Shock
Climate Shock
NARRATIVES, STORYLINES
BAMBU: Business as Might Be Usual
GRAS: Growth Applied Strategy SEDG: Sustainable European Development Goal
Figure 1. The ALARM scenarios (Source: ALARM scenario team).
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Figure 2. In BAMBU, end-of-pipe solutions to environmental problems dominate: waste incineration plant in Barcelona, Spain. Photo: J.H. Spangenberg.
we call scenarios, with each scenario consisting of these two elements. Scenario story lines or (synonymous) narratives are qualitative descriptions of the options analysed, i.e. the “if ” part (see Explanation Box “Storylines”). Simulations refer to quantitative model simulations helping us to assess the “then” for the aspects a specific model covers (see Explanation Box “Modelling”). Storylines are the core and backbone of any scenario as they fulfil several functions: they formulate the “if ” question, fill the gaps between complementary modelling efforts, and help to reconcile diverging modelling results by putting them into perspective. Modelling is the tool used to illustrate certain aspects of scenarios and enrich the storylines with quantitative data (to be interpreted in the narrative context) (Alcamo 2001). This interpretation is of particular importance if a diversity of models is used to illustrate aspects of the same narrative. A major challenge is to ensure that the assumptions used by the various disciplines in their respective research programmes are consistent. Many projections with regard to future distributions of species have relied on the use of statistical methods such as bioclimatic species envelope or niche-based models to identify possible future ranges and thus assess risk to biodiversity in general.
However, climate change is one important, but not the only factor causing biodiversity loss. Thus developing effective strategies for biodiversity preservation requires the analysis of all major pressures affecting biodiversity and their interaction. Scenarios developed for this purpose must be broadly based, addressing production, consumption and administration patterns and attitudes alike. This requires scenarios which deal with the effects of physical and social, of quantitative and qualitative factors in an integrative way. In ALARM, scenarios are based on storylines, and include model simulations with a range of different models to assess the impacts of multiple pressures on biodiversity. “Yet even well-crafted scenarios can fail to have their intended policy impact if they present irrelevant information, lack support from relevant actors, are poorly embedded into relevant organisations or ignore key institutional context conditions” (EEA 2009). To avoid this effect, the ALARM scenarios have been developed in a permanent dialogue with the ALARM research teams, to improve their robustness, and with the ALARM Consultative Forum (decision makers from different levels and different walks of life, plus independent scientists from different disciplines), the Commission, the EEA and others to enhance their policy relevance.
ALARM: Three storylines ... The ALARM storylines represent a set of possible development directions, all starting from the status quo but representing different basic philosophies, leading to diverging policies and results. In doing so, they illustrate the fact that human societies have options to chose from, that biodiversity loss can be minimised, but that this requires political decisions now and in the future. In the 2005 Environment Outlook, the European Environment
its 2007 Pan-European Environment Report, it adds geo-politics and international co-operation, globalisation and trade, migration, and natural resources. This illustrates how broad a comprehensive narrative should be. The three ALARM storylines cover social, economic, environmental, agricultural, foreign and other policies (see Table 1): ◙ “Business As Might Be Usual” (BAMBU) is a policy-driven scenario, i.e. a scenario extrapolating the expected trends in EU decision
Explanation box “Storylines” A storyline (or narrative) provides a comprehensive, internally coherent (i.e. free of contradictions) and plausible description of a possible future. As they are based on human societies’ decisions, which are a priori non-predictable, they have to make their assumptions explicit (thus permit a review of the plausibility), but cannot provide probability figures for the one or the other option. Each storyline describes in qualitative, and sometimes semi-quantitative terms a possible direction of socio-economic decision making and its biodiversity impacts. In order to influence decision makers from all walks of life (politics, administrations, NGOs, business, ...), the narrative must resonate with them, i.e. they must find the story told to be plausible, as in “yes, we – or some colleagues – might have reacted that way”. Only then does the outcome of the scenario exercise become important for their decisions. One way of making sure this effect emerges is to involve representatives of the target groups (e.g. administration, politics, civil society, business) in the storyline formulation process, allowing them to modify the initial draft story. In ALARM this was done in the Consultative Forum, with representatives from civil society and policy consultancy, from the local, national and European level. Usually different storylines are developed and their outcomes compared, each of them standing for a different direction of development. Such directions can be pursued by policy strategies differing in many details. Each storyline represents one such strategy, as a kind of ‘ideal type’ for the respective direction. As in the interpretation phase these ideal type storylines are compared as stand-ins for the directions they represent, it is necessary that the comparison is based on robust outcomes, i.e. on differences which would not easily vanish with a different scenario narrative representing the same kind of basic directional choice. Only then can the results of the storylines be used as a basis for decision making by applying external, politically defined criteria to the scenario outcomes and using the result as decision aid as to which direction of development to pursue in present policies.
Agency EEA identifies as determinants of the state of the environment: the socio-economic context, demography, macro-economy, technological developments, consumption patterns, energy and transport; agriculture, waste and material flows. In
making and assessing their sustainability and biodiversity impacts. Policy decisions already made in the EU are implemented and enforced. However, BAMBU is no business as usual scenario, based on trend extrapolation, since
recent or upcoming changes in EU policies would have been ignored that way. At the national level as well, deregulation and privatisation continue except in “strategic areas”. Internationally, there is free trade. Environmental policy is perceived as another technological challenge. ◙ “GRowth Applied Strategy” (GRAS) is a coherent liberal, growth-focussed policy scenario. It includes deregulation, free trade, growth and globalisation as policy objectives actively pursued by governments. Environmental policies will focus on damage repair and limited prevention based on cost-benefit calculations, with no emphasis on biodiversity beyond the preservation of ecosystem services ESS. ◙ “Sustainable European Development Goal” (SEDG) is a backcasting (inverse projection) scenario, and as such is necessarily normative, designed to meet specific goals and deriving the necessary policy measures to achieve them, e.g., a stabilisation of GHG emissions. It aims at enhancing the sustainability of societal development by integrated social, environmental and economic policy. Policy priorities under SEDG are a competitive economy and a healthy environment, gender equity and international co-operation. SEDG represents a precautionary approach, taking measures under uncertainty to avoid not yet fully known future damages.
sions used for sustainability concepts, the environmental, the economic and the social, one shock is defined, as illustrated in Figure 1.
... plus three shocks However, assuming a gradual development, i.e. no surprises, is probably the most implausible vision of the future. Thus three potential shocks (see Explanation Box “Shocks”) were added to the scenarios, assuming disturbances with widespread consequences considered extreme at the time of writing. In each of the three dimen-
They are: ◙ Cooling Under Thermohaline collapse (GRAS-CUT) is the environmental shock. It describes a collapse of the Atlantic ocean water circulation (the most familiar part of it being the Gulf Stream); and the resulting relative cooling of Europe. ◙ Shock in Energy price Level (BAMBU-SEL) describes the economic shock of a permanent quadrupling of the energy price, as expected when Peak Oil, the global maximum of oil production, has been passed (we had a taste of that in 2008). ◙ ContAgious Natural Epidemic (BAMBU-CANE) is the social shock, a pandemic out of control. Again, we had a taste of that, with the Chinese bird flu in 2006 and the Mexican swine flu in 2009.
Table 1. ALARM Scenarios: diverging policies in areas central for biodiversity pressure generation.
Scenario GRAS Climate envelope fits with the IPCC SRES-A1FI storyline and its assumptions
SEDG SRES-B1 scenario (lowest SRES scenario available, 450 ppm not in SRES. B1 and SEDG story lines differ significantly)
CAP
Spatially explicit support structure to maintain (organic) agriculture throughout the landscape (only 2nd pillar transfers) Focussed on local green development and opportunities, education and employment Aiming at ¾ reduction of CO2-emissions by 2050 through savings, changing consumption patterns and renewables Transport reduction priority, plus modal split change (through pricing and infrastructure supply), technical improvements
EU Funds Energy Policy
Transport Policy
Chemicals Policy Trade Policy
BAMBU SRES A2 (the best fitting available SRES scenario at the time of calculation – of all SRES scenarios, SRES A1B would have fitted best with past emission trajectories) Dismantling payments for production and Shift 1st to 2nd pillar results in polarisation: for 2nd pillar (rural development & environ- intensification of high yielding locations, ment) neglect of low yielding ones. Phasing out, considered as subsidies Focussed on infrastructure development and growth in poor regions Efficiency, some renewables based on cost Efficiency, aiming at 20 % reduction of GHG calculations emissions by 2020, 80 % 2080. Increase nuclear and renewables. Increased efficiency due to market pressure, Technological improvements and changing no policy to shift the mode or even reduce the share of different modes of mobility transport (walking, cycling, trains, cars, boats, planes) (modal split) Focus on innovation and competitiveness. REACH implemented REACH not consequently implemented Strong support for WTO and free trade Promoting free trade except in “strategic areas”
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REACH plus; filling gaps e.g. for metals, nanomaterials, endocrine disruptors. Global sourcing reduced due to cost reasons; phytosanitarian controls
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Explanation box “Shocks” A shock is any event that comes unexpectedly and has the capability to change the development trajectory of a system. Only then a new direction of development emerges, starting at the shock point and distinguishing the shock scenario from its origin, the base scenario. Thus in the pre-shock period, shock scenarios (synonymous with “Wild Cards”) are identical to the base scenarios and then diverge from them along a new, hazard-induced trajectory. The “surprise factor” sounds simple, but is complex: its reasons usually consist of a mix of different factors such as the lack of knowledge, the inherent uncertainty of future developments in complex systems, or plain human ignorance. In other words: ignoring emerging threats in decision making can reduce the resilience and enhance the vulnerability of a system, potentially turning what could have been a minor additional pressure into a veritable shock. As they assume deviations from the linear development trend, shocks are not as easily modelled as other scenarios. Often they are mainly storylines with limited illustrative modelling available. Nonetheless shock scenarios can support the development of survey systems for the identification of new threats and for dealing with them adequately from the very beginning of their emergence.
Modelling: from storylines to scenarios Scenarios are more than storylines: they require modelling to illustrate certain aspects of the narrative (see Explanation Box “Modelling”). In order to integrate the conservation of
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Figure 4. Links covered in ALARM by quantitative modelling (straight black arrows), and those which are not (dotted red arrows). The latter had to be addressed by qualitative discussion within the storylines to generate a comprehensive picture of the interactions (Source: ALARM scenario team).
Figure 3. In GRAS, global growth is a top priority, and increasing car traffic tolerated, as here in Hong Kong. Photo: J.H. Spangenberg.
biodiversity into the larger policy context in the scenarios, it is essential to make sure that the drivers described in the story lines are adequately taken into account when choosing the parameters for the models used to illustrate the narrative. Only then it is possible to compare different scenarios regarding the full range of their expected impacts on biodiversity, and to derive suitable policy suggestions. In illustrating the ALARM storylines, we combined, for each of them ◙ climate scenarios from the set of those used by the IPCC (the SRES scenarios), selected to offer the best fit with the expected climate development and to represent climate model uncertainty (see Fronzek et al., this atlas, pp. 68ff.); climate scenarios cover the period 2001-2100 and are spatially explicit across Europe (10' x 10' grid); ◙ a narrative-specific run of MOLLUSC, a spatially explicit land use scenario generator (see Reginster et al., this atlas, pp. 100ff.); and ◙ a specific set of parameters for a run of GINFORS, a highly endogenised econometric input-output model (see Omann et al., this atlas, pp. 196f.).
used in the economic model. Thus the inputs and outputs of the econometric and the land use model were integrated, while the land use model in addition takes the projected climate change into account – the maximum integration that was possible in ALARM. In Figure 4, the black arrows illustrate which integrative links of the different models could be realised in the simulation process, and which additional integration steps had to be left to the storyline and its interpretation, in order to generate a comprehensive picture. Figure 5 shows the kinds of models used, and how their results are integrated. Although the models used cover the global scale, the focus of the analysis is Europe, and how changes there affect the world (and vice versa).
Economic development trends cannot be spatially disaggregated to a sub-national level based on the available data, but to assess their impacts we have developed rules to spatially differentiate population density, migration, income disparities and income development based on reasoning plausible in the context of the scenario narrative. These were applied in the land use model, and their aggregates conform with the data 12
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The situation is different with the shock scenarios, as their assumptions stress the limits of what current models can accommodate: ◙ GRAS-CUT can be modelled in the climate models, but not in the socio-economic one. Thus in the narrative interpretation, the impact on different sectors is assessed, based on the climate data, by plausible reasoning based on past model experience: it is marginal. ◙ BAMBU-SEL can be modelled, but stretches the limits of model capabilities. We expect fast economic recovery due to trade, poverty/income redistribution and severe environmental impacts from a priority for agrofuel production. ◙ BAMBU-CANE is not modelled but argued; it is the only scenario which might possibly result in economic collapse.
Explanation box “Modelling” The term “scenarios” frequently is misunderstood to refer to quantitative computer simulations. However, modelling – although a useful way to synthesize information gathered independently about components of a larger system – suffers from the unpredictability of system behaviour as much as from the lack of models of the same level of complexity as the systems to be analysed. Models are neither an end in themselves nor a ‘crystal ball’ suitable for forecasting the future with exactitude. Models are necessarily based on simplifying assumptions (which should be made explicit), but through this reduction of complexity are able to provide quantitative data complementing the storylines. For instance, models such as GLOBIO integrate socio-economic and environmental factors, but have to make simplifying assumptions e.g. regarding the effects of multiple pressures to keep the models manageable (MNP 2006). Against all intuition, the closer a model comes to representing the complexity of reality, the less useful it becomes: a model that combines too many different pieces of information becomes unwieldy and difficult to interpret because results cannot easily be attributed to particular processes. In that sense, the lack of integrated models and the resulting need to derive and interpret information from separate models and model runs can be considered not a deficit, but a benefit. Consequently, models can only be used to illustrate certain aspects of the functioning and the interactions of complex systems. To illustrate the scenarios in a coherent manner with different simulation models, it is necessary to compare and – where necessary and possible – to reconcile the model assumptions, a task not made easier by the different time horizons, levels of uncertainty and spatial resolutions. Maps are an important tool for integrating the results.
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Some results Averaged over Europe, the ALARM scenarios describe changes in mean annual temperature by the end of the 21st century relative to 1961-1990 that range between 3.0 and 6.1 °C (Fronzek et al., this atlas, pp. 68ff.). Changes in annual precipitation are between -1 and 6 % with wetter conditions in northern Europe in winter and drier conditions in southern Europe in summer. The economic research results (for more details see Omann et al., this atlas, pp. 196f.) confirm the limited direct economic impacts of this climate change in the simulation period of the economic model (i.e. until 2050).1 Even the indirect effects such as increasing risk of water deficits (see map) have no significant economic impact on the macro scale of national economies the model represents. However, the user of such models should be aware of their limits: societies and economies are complex evolving systems, with system elements (agents) able to reflect system trends and adjust their behaviour accordingly. The result is a system with changing structures and unpredictable behaviour in the medium to long term. Thus econometric models (at least those which allow for structural change) can be meaningfully run only over a limited time period of 20 years or less. In ALARM, the economic scenario runs only until 2020, with some key variables projected (i.e. without taking structural change into account) until 2050. Furthermore, econometric models can only reflect changes which are expressed in economic parameters (in the case of GINFORS also energy and material flows inside the econom1
Nordhaus finds the loss of 3 to 8 months of economic growth over a 50- to 100year period. Stern, on the other hand, expects economic damage worse than a world war: economic scenarios depend heavily on the assumptions made.
Qualitative storyline
Climate change scenarios LPJmL Socio-economic model (GINFORS)
Land use model
Nitrogen deposition and CO2 scenarios
Maps
Maps
Maps Alarm ecosystem & biodiversity models
Maps
Figure 5. Illustrating the storylines with model simulations: Interaction of models and the central role of maps (Source: ALARM climate team).
ic system, but not ecological processes). As only human agents and their decisions can be addressed, natural feedbacks from ecosystems and processes in those are not integrated or modelled (see Figure 4). For instance, temperature and precipitation change must be translated into e.g. agricultural losses or health cost before they can be taken into account. Shortages of resources are expressed as price increases, but absolute limits to their availability cannot be modelled (Scrieciu 2007). ◙ Economic modelling suggests that adaptation might happen quite easily in the business sector, as the speed of change in the economic system is so much higher than in the bio-geosphere that it can relatively easily accommodate these changes of the environment into the investment planning of the business cycle. ◙ Thus it is in vain to hope that the market or the business sector would act on their own behalf due to cost reasons. ◙ Instead dedicated political decisions are needed to set the framework right for climate mitigation. ◙ Public expenditures, from coastal protection to health care services, may be significant, but often spread over many years. ◙ Mitigation must be a global effort, as even a radical mitigation policy in Europe will result in nothing more than a delay in global warming of a few years, if other parts of the world do not follow suit. The results of the land use scenario development show different quantities and spatial patterns of land use change for the three scenarios, although the basic land use change trends are the same for each of them (for more details see Reginster et al., this atlas, pp. 100ff.).
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◙ Some of the largest changes involve the abandonment of agricultural land (cropland and grassland) with greater changes being observed for GRAS (due to imported substitutes) than for BAMBU and the least for SEDG (a result of assumed policies against large scale land abandonment). ◙ Some of the abandoned agricultural land is used for agrofuels and forestry, however without going to extremes regarding the pressure to extend agrofuel areas, with mixed impacts on biodiversity. ◙ Regardless of these transitions, important areas of surplus land result from the assumed agricultural productivity increases for all three scenarios, with larger surplus areas again occurring in GRAS (more imports) than in BAMBU (leading to a polarization between highly fertile and less productive
Figure 6. Under GRAS-CUT, the climate in Europe cools down relative to the trend of global warming. One result might be that the frozen Baltic sea, as seen here in Jurmala, remains a frequent view. Photo: J.H. Spangenberg.
areas) and in SEDG (assuming a transition to organic agriculture and no complete abandonment of any region). The results of the shock scenarios differ due to the different kinds of shocks analysed. As far as land use is concerned, each shock leads to various effects on global land use changes and spatial patterns. The most important effects on quantities are due to the climate shock and the most important effect on patterns are due to the pandemic. After all three kinds of shocks, lower values of abandonment of agricultural land occur. However, the major land use changes that have been modelled in these
explorations concern the first decade after each shock. After this, it was assumed that a come back of regulation and control authorities or new adaptation strategies are expected to produce a stabilisation of the effects of the shocks on socio-economy and land uses. Nevertheless, it is important to highlight the fact that some land use impacts after these three shock are irreversible. ◙ For GRAS-CUT, since the warming was of limited economic effect, so is the interim cooling (if it materialises after 2050 – nowadays the shock would be significant, but this is not a plausible scenario). After the cooling, the decrease of crop-
Figure 7. Sugar cane (Kenya, close to Lake Victoria) is one of the preferred sources of agrofuels, which are a prominent response to the fuel price crisis in BAMBUSEL. Photo: J.H. Spangenberg.
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GRAS compared with SEDG
BAMBU compared with SEDG 62 %
49 % 38 %
12 %
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Large increase Small increase No difference Small decrease Large Decrease 51 % 4%
4% 0%
Figure 8. Results from a questionnaire addressing the scientist participating in ALARM: risk of biodiversity loss, comparison of scenarios. As compared to BAMBU, GRAS causes an increasing risk of losses, while SEDG decreases the risk. Unaffected species include those not at risk today (Source: ALARM risk assessment team).
land is slightly smaller compared to the continuous GRAS scenario due to the decrease of yields of crops. A large use of surplus areas is assumed. ◙ The quadrupling of the oil price in BAMBU-SEL first sounds like a safe recipe for an economic disaster, and so it is (grossly minus a fifth of the GDP) – for less than five years. Then the economic growth bounces back to the old level (or possibly even more), since due to international trade the money that has flowed out of the importing countries comes back in the form of product orders. As a result, the economic crisis is limited in time (but twice as long as the 2008-2010 reces-
sion). However, since a large bill has to be paid for imports, the social impact is serious and lasting, resembling the wave of poverty resulting from the East Asian economic crisis a few years ago, and still pertaining. What would be the most plausible policy response? For Europe, we assumed a massive investment in agrofuels, resulting in a significant pressure on agricultural land, a reduction of surplus area, and a pressure on protected areas for at least a decade, leading to losses of biodiversity in particular as a result of converting abandoned land and grassland into intensive agrofuel agriculture areas. Furthermore, agrofuel production
Figure 10. Local supply systems are boosted in the SEDG scenario (here a market in Versailles). However, as the photograph illustrates, this does not eliminate the supply of tropical fruit. Photo: J.H. Spangenberg.
in the South tends to produce significantly more CO2 than is saved by using it, while the balance in Europe is at best slightly positive (in both cases N2O emissions tend to make the overall climate balance negative). So what looked like an economic crisis turns out in BAMBU-SEL2 to be a predominantly social one, and the policies to mitigate it create an environmental disaster (while reducing GHG emissions at best to a very limited extent). The EU policy has already begun to develop according to this scenario, although the 2008 oil price increase was significantly below the BAMBU-SEL figures. The expected negative result indeed materialised. Internationally the EU targets, under the current WTO regime, contributed to increasing food prices undoing the successes of 10 years of development efforts. ◙ The pandemic described by the BAMBU-CANE scenario results either in an economic transforma2
Figure 9. A pandemic as is assumed to happen in BAMBU-CANE leads to the limitation of transport and travel, as the medical control during the Mexican swine flu epidemic illustrates. Boarding aircraft was permitted only after careful checks. Photo: J.H. Spangenberg.
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This would have been even more intensive under a GRAS scenario, with its free trade preference.
tion with some sectors losing and others winning, with an overall reduction of GDP of more than 10 % and an early rebound, or leads to the total collapse of the economy. The latter would be the case if about a fifth of the population or more dropped out of the production and distribution processes – a few dead or on sick leave, many busy caring for sick relatives, but most of them trying to escape infection by avoiding all events where many people meet (as observed in the bird flu epidemic in China). For instance, in the recent swine flu epidemic in Mexico, restaurants, cinemas, museums, shopping malls and sporting events were closed down, public transport was reduced and even religious services were postponed, with tangible impacts on economic activities. The cost incurred, and the burden of personnel withdrawn from their ordinary tasks to serve in the emergency plans, are a significant burden on the national economy. The spatial patterns after the pandemic show a large perturbation of land use. The urban changes assessed for the time after the
pandemic are important, with many new urbanised areas in rural regions. As in the BAMBU-SEL shock scenario, protected areas are affected, this time by relocated settlements resulting in urban land use expansion after the pandemic and a pressure on protected areas.
Change in %
Policy conclusions Biodiversity protection needs to get out of the conservation policy niche to be effective; the key challenge is to integrate biodiversity concerns into the day-to-day working mechanisms of state, business and society, beyond end-of-the-pipe solutions and compensations such as establishing protected areas. Instead economic incentives and legal frameworks across societal sectors have to account for biodiversity as a fundamental aspect of sustainable development. Developing effective strategies for biodiversity conservation is an international policy priority; the European Union (the Commission and the Council, i.e., the heads of states and governments) have even set the target to end biodiversity loss in the EU by 2010. Nonetheless, the EU’s biodiversity action plan interim assessment clearly shows that despite some progress the overall aim will not be reached. Achieving it soon after (as the title of the EU biodiversity communication “2010 and beyond” indicates) will require significant policy changes, addressing production, consumption, administration patterns and attitudes alike. The ALARM scenarios analyse such policy options. Numerous ALARM studies and the results from a questionnaire addressing the ALARM experts (see Marion et al., this atlas, pp. 252f.; Figure 8) show that: ◙ GRAS consistently provides the least desirable outcome for biodiversity in Europe – across different biomes, and for most ecosystems and species. ◙ “Muddling through” along the BAMBU path, although probably slowing down biodiversity losses, will systematically fail to meet the EU target to end the loss of biodiversity, by 2020 and beyond. ◙ From a biodiversity point of view, SEDG represents a significant step in the right direction, although not sufficient in every respect (in some biomes some species and ecosystems are still lost, see Marion et al., this atlas, pp. 252f.). For the EU policies this implies that although certain species and ecosystems may be stabilised under the EU policies as modelled in the BAMBU scenario, the current policies
T H E
A L A R M
-49.99 to -40.00
40.01 to 50.00
-39.99 to -30.00
50.01 to 60.00
-29.99 to -20.00
60.01 to 70.00
-19.99 to -10.00
70.01 to 80.00
-9.99 to 0.00
80.01 to 90.00
0.01 to 10.00
90.01 to 100.00
10.01 to 20.00
100.01 to 150.00
20.01 to 30.00
150.01 to 250.00
30.01 to 40.00
Figure 11. Change in water deficit relative to the period 1961-1990 under SEDG 2021-2050 using the HadCM3 GCM. The annual water deficit is calculated as the annual sum of the monthly differences between potential evapotranspiration (PET) and precipitation for those months when PET exceeds precipitation, positive values denoting drier conditions (Source: ALARM climate team).
will not be able to deliver on the 2010 target, not even with delay. This general trend is unambiguous, despite significant differences between different species groups and between different ecosystems in different biomes. As most species and ecosystems will benefit from a policy trajectory change towards a more rigorous sustainability policy (SEDG), mainstreaming sustainable development in EU policies has to be considered a necessary condition for biodiversity conservation, but current sustainability policies have to be adjusted to better integrate biodiversity conservation necessities.
S C E N A R I O S :
S T O RY L I N E S
A N D
Acknowledgements The authors are grateful to J.-M. Douguet, S. Giljum, J. Martinez Alier, B. Meyer, R. Binimelis and the members of the ALARM Consultative Forum for their contributions to the development of the ALARM scenarios.
References ALCAMO J (2001) Scenarios as tools for international environmental assessments. EEA Expert Corner Report Prospects and Scenarios No. 5. Office for the Official Publications of the European Communities. Luxembourg.
S I M U L AT I O N S
F O R
A S S E S S I N G
ALLEN PM (1998) Evolutionary Complex Systems and Sustainable Development. – In: van den Bergh JCJM, Hofkes MW (Eds) Theory and Implementation of Economic Models for Sustainable Development. Kluwer Academic Publishers, Dordrecht: 67-100. EEA European Environment Agency (2009) Announcement of the report “Looking back on looking forward: a review of evaluative scenario literature”, 29 April 2009, EEA, Copenhagen. MNP Netherlands Environmental Assessment Agency (2006) The International Biodiversity Project, MNP, Bilthoven/Netherlands. SCRIECIU SS (2007) The inherent dangers of using computable general equilibrium models as a single integrated modelling framework for sustainability impact assessment, Ecological Economics 60: 678-684
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Drivers, Pressures, Impacts: DPSIR for Biodiversity
,
LAURA MAXIM, JOACHIM H. SPANGENBERG & MARTIN O’CONNOR
DPSIR (Driving Forces – Pressures – State – Impact – Responses) is a framework for the communication of environmental information. According to this terminology, social and economic developments (Driving Forces, D) exert Pressures (P) on the environment. As a consequence, the State (S) of the environment changes, leading to Impacts (I) on ecosystems, human health, and society. These may elicit a societal Response (R) that feeds back on Driving Forces, on State or on Impacts via various mitigation, adaptation or curative actions (Smeets & Weterings 1999, Gabrielsen & Bosch 2003). The DPSIR is a helpful tool for structuring communication between scientists and end-users of environmental information. This is acknowledged by its wide use by the European Environment Agency and by Eurostat. However, it is inappropriate as an analytical tool, because it proposes an apparently linear causal description of environmental issues, which inevitably downplays the uncertainty and interlinked dimensions of causality inherent in complex environmental and socio-economic systems. Furthermore, our analysis of former uses of DPSIR shows that the relationships between the environ-
mental and the human systems can be described very heterogeneously. The same phenomenon may be characterised as a Driving Force, Pressure, State or Response, by different researchers (see Figure 1). This creates ambiguity and low comparability between descriptions and indicators issued from different studies. Consequently, in ALARM, the socio-economic team has worked on reframing the DPSIR, using a complex system methodology based on the distinction between four ‘dimensions’ of sustainability (environmental, economic, social and political) (see Figure 2). Ensuring a respect for conditions of natural and social system viability, upon which long term economic activity depends, appears as a key precept for sustainability policy (Spangenberg 2005). Governance for sustainable development therefore centres not only on the enhancement of economic performance but: ◙ on the regulation of the economic sphere in relation to the two other spheres in order to assure the simultaneous respect for quality/ performance goals pertaining to each of the three spheres and ◙ the respect for one sphere in relation to another (O’Connor 2007).
Therefore, the pair-wise interface aspects between each two dimensions are characterised, in this ‘tetrahedral’ model, through investigation of the ‘demands’ and ‘supply’ of each sphere relative to the others (see Table 1). This analytical approach is guided by a definition of sustainability as being the “coevolution of economic, social and environmental systems respecting a dynamic ‘triple bottom line’ – the simultaneous satisfaction of quality/performance goals pertaining to each of the three spheres” (O’Connor 2007, p. 1). To each of the intersections between the four ‘spheres’ of sustainability, we associate one category of the DPSIR framework. This process of attribution obviously involves simplifications, which are however necessary for didactic clarity. Within the resulting conceptual framework, each of the five D, P, S, I and R concepts are specified, for application in integrative analysis of relationships between policy, society, economy and biodiversity in the ALARM project. The formulations issued from this process are presented below: ◙ DRIVING FORCES are changes in the social, economic and institu-
State of knowledge
Complex, non-linear, self-organising systems
◙
◙
◙
◙
tional systems (and/or their relationships) which trigger, directly and indirectly, Pressures on biodiversity. PRESSURES are consequences of human activities (i.e. release of chemicals, physical and biological agents, extraction and use of resources, patterns of land use, creation of invasion corridors) which have the potential to cause or contribute to adverse effects (Impacts). The STATE of biodiversity is the quantity of biological features (measured within species, between species and between ecosystems), of physical and chemical features of ecosystems, and/or of environmental functions, vulnerable to (a) Pressure(s), in a certain area. IMPACTS are changes in the environmental functions, affecting (negatively) the social, economic and environmental dimensions, and which are caused by changes in the State of the biodiversity. A RESPONSE is a policy action, initiated by institutions or groups (politicians, managers, consensus groups, etc.), which is directly or indirectly triggered by [the societal perception of] Impacts and their
Socio-economic and political stakes associated to scientific results
S D
I
System P
Observer
R
Framing the study (objective-dependent): space and time scales, financial, human and time resources, data availability
The observer: risk perception, values, competence, disciplinary background
Figure 1. Factors influencing the use of the DPSIR framework.
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Different terminologies, methods and conceptual approaches of disciplines involved in the study
Table 1. Methodological intersections between DPSIR representation of environmental issues and the tetrahedral framework for sustainability analysis.
SOCIAL
ECONOMIC
ENVIRONMENTAL
POLITICAL
Forms of collective Identity and Community: SOCIAL
THE SOCIAL SPHERE DRIVING FORCE OPPORTUNITIES & IMPACTS:
Performance, Products and Output:
“The economy versus the community”
THE ECONOMIC SPHERE
DRIVING FORCE
DRIVING FORCE
LIVING WITH(IN) NATURE:
ENVIRONMENTAL FUNCTIONS:
Energy, matter, natural cycles & biodiversity:
Pressures on services of the environment
THE ENVIRONMENTAL SPHERE
IMPACTS
PRESSURES
STATE
SOCIAL POLICY:
ECONOMIC POLICY:
ENVIRONMENTAL POLICY:
(Capacity of communities; citizen/public participation)
(Shaping the rules and limits of markets)
(Regulation of what counts as an environmental value)
THE POLITICAL SPHERE
RESPONSE
RESPONSE
RESPONSE
DRIVING FORCE
ECONOMIC
ENVIRONMENTAL
Meanings,Values & Risks: sustaining what and for whom?
POLITICAL
causes, attempting to prevent, eliminate, compensate, reduce or adapt to them and their consequences. These definitions have been empirically tested for applicability on different aspects of biodiversity loss addressed by ALARM (environmental chemicals, climate change, biological invasions and pollinator loss) (Maxim et al. 2009). We therefore consider that the definitions described below are robust for biodiversity loss description using the DPSIR scheme. In literature, the uses of the DPSIR scheme mainly refer to relationships between economy and the environment, but they rarely take into consideration social or political aspects. Nevertheless, these can have an important influence on risks for biodiversity. For example, inefficient policies, or inappropriate institutions or practices for implementing them can play a role of Driving Forces, but they are usually ignored by the DPSIR style descriptions. The model presented here highlights a fourth category of organisation, the political sphere. This allows differentiation of aspects referring to relationships between economy, society and environment which are relevant for the governance of biodiversity.
The tetrahedral framework enlarges the question of choice about “what should be done” from the policy (or economic) to the societal level. By this, the model evidences the tensions existing between different criteria of choice, which must be acknowledged by political decision-
making in order to insure its legitimacy and efficiency. Bringing system analysis into the DPSIR model contributes to structuring information of different natures (economic, social…) needed for describing causal chains. By this reframing of the DPSIR model, the
SYSTEM REGULATION VIA POLITICAL ORGANISATION
Coordination, Power and Governance:
contribution of different scientific disciplines involved in research for biodiversity can be accommodated. Bio-sciences mainly deal with identifying and measuring Pressures, State and changes in the State of biodiversity. Social sciences contribute to the understanding of Driving Forces, of socio-economic Impacts, of Pressures and of Responses. Having this conceptual framework as background helps to focus on the points of interaction between bio-sciences and social sciences, where work in cooperation is needed to understand both sides of the coin…
NATURAL SYSTEMS ORGANISATION
References
SOCIAL ORGANISATION
ECONOMIC ORGANISATION
GABRIELSEN P, BOSCH P (2003) Internal working paper Environmental Indicators: Typology and Use in Reporting. European Environment Agency, Copenhagen, 20 pp. MAXIM L, SPANGENBERG J, O’CONNOR M (2009) An analysis of risks for biodiversity under the DPSIR framework. Ecological Economics 69: 12-23. doi:10.1016/j.ecolecon.2009.03.017 O’CONNOR M (2007) The “four spheres” framework for sustainability. Ecological Complexity 3: 285-292. SMEETS E, WETERINGS R (1999) Environmental indicators: Typology and overview. Technical report No. 25, European Environment Agency, Copenhagen, 19 pp. SPANGENBERG JH (2005) Die ökonomische Nachhaltigkeit der Wirtschaft. Edition Sigma, Berlin, 312 pp.
Figure 2. The tetrahedron for sustainability studies (Source: O’Connor 2007).
D R I V ER S,
P R ESSUR ES,
I M PAC TS :
D PS I R
FO R
BI O D I V E R S I TY
17
European Biodiversity and Its Drivers – an “Inter-national” Analysis
,
JAAN LIIRA, JOSEF SETTELE & MARTIN ZOBEL
During the last few centuries, global biodiversity has been changing at an unprecedented rate as a complex response to several anthropogenic changes in the environment. Human alteration of the global environment has caused widespread changes in the global distribution of habitats and has become one of the most important
climate change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration (Sala et al. 2000). Intensification of land use, in particular mineral fertilizer driven eutrophication and an increase in pesticide use, are, however, claimed as leading to a decline in biodiversity and a loss of ecosystem services (Tilman et al. 2001).
drivers shaping regional and local patterns of biodiversity and ecosystem function. The last decade has been characterized by serious attempts to prevent further loss of biodiversity. Land-use change as well as habitat loss and fragmentation are expected to have the largest effect on biodiversity during the next century, followed by
Drivers
Biodiversity
Drivers
1.0
1.0 a
Biodiversity
b Fertilizers Latitude
Birds
0.5
0.5
Forest%
Log(LandArea)
Veg.Period
0.0
PCA-4:12.6%
PCA-221.7%
ProtArea%
Mammals
Log(Pop.Density)
Butterflies and moths
Longitude
Fertilizers
Log(Pop.Density) Reptiles
Latitude
Reptiles Vascular plants
Agri.Land%
-0.5
-0.5
Vascular plantsa
Birds
Longitude
Mammals
-0.5
ProtArea% Butterflies and moths
Log(LandArea)
Agri.Land%
-1.0 -1.0
Forest%
Veg.Period
0.0
0.0
0.5
1.0
-1.0 -1.0
-0.5
0.0
0.5
1.0
PCA-3:14.1%
PCA-1:34.2%
Figure 1. PCA analysis of environmental and anthropogenic drivers of biodiversity and the correlation vectors of diversity of five taxonomic groups (a: axis 1 & 2; b: axis 3 & 4).
Table 1. List of data sources used in analyses. Driver/Trait Population density Forest area %
Agricultural land %
Total Fertilizer kg/ha Protected areas
Vascular plants
Mammals
Birds
Reptiles
Butterflies and moths
18
Sources Population density in a country in late 90s (inh/km²) 1) World Resources Institute (2005). EarthTrends: The Environmental Information Portal. (http://earthtrends.wri.org/index.cfm). 2) Food and Agriculture Organization of the United Nations (FAO) (2006). FAOSTAT Online Statistical Service. Rome: FAO. (http://apps.fao.org/). The proportional area of forest land. 1) Food and Agriculture Organization of the United Nations (FAO) (2005). Global Forest Resources Assessment 2005: Progress towards sustainable forest management. FAO Forestry Paper 147. Rome: FAO. (http://www.fao.org/forestry/foris/webview/forestry2/index.jsp?siteId=101&langId=1). The proportional area of rotational and permanent agricultural land. 1) Food and Agriculture Organization of the United Nations (FAO) (2006). FAOSTAT Online Statistical Service. Rome: FAO. (http://apps.fao.org/). 2) World Resources Institute. 2005 EarthTrends: The Environmental Information Portal. (16 February 2004; http://earthtrends.wri.org/index.cfm). Fertilizer consumption kg/ha of arable land (data of 1998-2001). Total fertilizer is sum of consumption of various plant nutrients (N, P2O5 and K2O). 1) Food and Agriculture Organization of the United Nations (FAO) (2006). FAOSTAT Online Statistical Service. Rome: FAO. (http://apps.fao.org/). The proportional area of protected areas. 1) United Nations Environment Programme – World Conservation Monitoring Centre (UNEP-WCMC) (2006). World Database on Protected Areas (WDPA). Data set available on CD-ROM. Cambridge, U.K: UNEP-WCMC. (http://www.unep-wcmc.org/wdpa/). Number of vascular plant species in a spontaneous flora of a country 1) World Conservation Monitoring Centre of the United Nations Environment Programme (UNEP-WCMC) (2004). Species Data (unpublished, September 2004). Cambridge, England: UNEP-WCMC. (http://www.unep-wcmc.org). 2) CBD (2001). In Global biodiversity outlook. Montreal: Secretatiat of the Convention on Biological Diversity. Number of mammal species in a spontaneous fauna of a country 1) World Conservation Monitoring Centre of the United Nations Environment Programme (UNEP-WCMC) (2004). Species Data (unpublished, September 2004). Cambridge, England: UNEP-WCMC. (http://www.unep-wcmc.org) 2) Wilson DE, Reeder DM (eds) (1993). Mammal species of the World. Washington, DC: Smithsonian Institution Press. Number of bird species in a spontaneous fauna of a country 1) World Conservation Monitoring Centre of the United Nations Environment Programme (UNEP-WCMC) (2004). Species Data (unpublished, September 2004). Cambridge, England: UNEP-WCMC. (http://www.unep-wcmc.org) 2) LePage D (2004). Avibase: The World Bird Database. Port Rowan, Ontario: Bird Studies Canada. Available on-line at http://www.bsc-eoc.org/avibase/avibase.jsp. Number of reptile species in a spontaneous fauna of a country 1) World Conservation Monitoring Centre of the United Nations Environment Programme (UNEP-WCMC) (2004). Species Data (unpublished, September 2004). Cambridge, England: UNEP-WCMC. (http://www.unep-wcmc.org). 2) European Molecular Biology Laboratory (EMBL) (2004). The EMBL Reptile Database. Heidelberg, Germany: EMBL. (http://www.embl-heidelberg.de/~uetz/ LivingReptiles.html). Number of butterfly and moth species in a spontaneous fauna of a country 1) De Prins W (ed) (2005) Lepidoptera. Fauna Europaea version 1.2. (http://www.faunaeur.org). 2) Karsholt O, Razowski J (1996) The Lepidoptera of Europe, a distributional checklist. Apollo Books, Stenstrup. 3) Solodovnikov IA, Dovgailo KE, Rubin NI (2003) The butterflies (diurnal Lepidoptera) of Belarus. Pensoft Publishers, Sofia–Moscow.
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Although there have been numerous studies of the effects of local-scale changes in land use on the abundance of groups of organisms, broader continental analyses, addressing the same issues, are still largely absent (Gaston et al. 2003), or concentrate mostly on alternative single pressure variables. Great variation in biodiversity, as well as in the diversity of potential drivers, makes Europe a suitable area for largescale analysis. General information on the state of biodiversity and on its drivers is urgently needed on national scales, at which most conservation-related decisions are made. For this purpose the approach using large-scale biodiversity indicators was suggested by The 2010 Biodiversity Target (Balmford et al. 2005). Here we analyze large-scale patterns of biodiversity of some of these indicator groups. We assume that the observed general relationships between biodiversity and anthropogenic drivers in Europe may reveal large-scale mechanisms of biodiversity change.
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Methods The analysis was carried out in two stages. First, we identified potential drivers and studied their interrelations in Europe by Principal Component Analysis. Second, we calculated the correlation between biodiversity and anthropogenic drivers. We used European countries as observational units and included those 33 countries, about which we were able to find coherent data. Although political sampling units – countries – may not be optimal for studying natural biodiversity drivers, they are, due to their unique socio-economic history, relevant for tracing the large-scale impact of anthropogenic drivers. We used the total number of species per country of those taxonomic groups, for which reliable information is available: vascular plants, birds, mammals, reptiles and butterflies. Anthropogenic pressure was characterized by four main groups of variables according to the indicator system suggested by The 2010 Biodiversity Target (Balmford et al. 2005). First, habitat loss was measured as the extent of human-dominated land or availability of indicator habitat. We used two complementary parameters: i) the relative area of forests as a surrogate for the proportion of natural areas, and ii) the relative area of agricultural land under arable cultivation
Table 2. Spearman Rank Order Correlations (Marked correlations are significant at p <0.05).
Latitude Longitude ProtArea% Log(LandArea) Forest% Log(PopDens) Agri.Land% Fertilizers Veg.Period
Vasc.Plants
Mammals
Birds
Reptiles
-0.780 0.099 -0.016 0.232 0.030 0.122 0.279 -0.204 0.402
-0.390 -0.398 0.272 0.528 -0.028 0.427 0.289 0.228 0.460
0.071 -0.757 0.327 0.540 -0.062 0.402 -0.054 0.624 0.416
-0.932 -0.046 -0.145 0.004 -0.156 0.282 0.151 -0.086 0.695
and permanent crops, as a surrogate for the extent of frequently disturbed agricultural areas. Second, intensification of land use is associated with use of fertilizers, pesticides and irrigation. We adopted the use-rate of total fertilizer as the parameter characterizing land use intensity. Third, human population density is a substantial constituent of the so-called human footprint concept (Sanderson et al. 2002). Fourth, in order to describe special conditions favouring biodiversity, we also included the percentage of areas under nature protection. Geographic position – the latitude and longitude coordinates of the centroid of the country – was used as a surrogate for natural biodiversity driver in Europe. The length of vegetation period was also included in the data matrix to consider direct climatic effects. In order to take into account the effect of country area, the log of a country’s land area was included in the analysis. Variation of drivers in European countries PCA analysis of environmental and anthropogenic drives shows strong intercorrelation among natural and anthropogenic factors (Figure 1). According to the descriptive power, we selected four principle components. The first PCA axis describes 34 %, the second 22 %, the third 14 % and the fourth 13 % of total variation. Results point to the fact that several anthropogenic factors, and thus also their effects, are confounded by natural factors (latitudinal, longitudinal and/or climatic) gradient. First of all, there is an increase in the length of vegetation period from north-east toward oceanic south-west. Concerning anthropogenic drivers, the most revealing is negative correlation of population density and the use of fertilizers with geographical longitude of the country, and also their positive correlation to the length of vegetation period. These are quite clear results because of the high intensity of land use in western-Europe and particularly in regions of milder climatic conditions and higher population density. Among positive drivers of biodiversity, the proportion of protected areas shows a decreasing trend with
Butterflies and moths -0.246 -0.139 0.399 0.453 0.097 0.284 0.308 0.095 0.118
Latitude
Longitude
ProtArea%
1.000 -0.037 0.104 0.166 0.119 -0.238 -0.163 0.192 -0.681
-0.037 1.000 -0.410 -0.084 0.029 -0.466 0.115 -0.745 -0.626
0.104 -0.410 1.000 0.060 0.267 0.322 0.015 0.268 0.067
longitudinal position of a country. The negative correlation between the proportion of forest areas and agricultural land was expected, as was the increase of forest land towards northern countries (although the last is not statistically significant). The third and fourth axes (Figure 1b) are mostly dominated by the effect of the forest-rich mountainous countries in southern Europe and by the land area of the country. Variation of biodiversity in European countries Placing biodiversity data into the multivariate space of drivers (Figure 1), we were able to demonstrate a strong correlation of biodiversity with geographical location of the country and climatic conditions. In particular, the well known latitudinal gradient of biodiversity (Gaston 2000) was most evident for reptiles and plants. In addition, the trend of biodiversity form east to west become evident for birds. Mammals showed a pattern of biodiversity increase towards the southwest of Europe. In the multi-dimensional space of factors, after considering natural drivers, anthropogenic factors such as land use intensity and availability of habitats did not appear to be strongly correlated with biodiversity. Though the variables indicating the intensity of agricultural land use have frequently been shown to be important on landscape or farm scales, at country-scale, the relative effect of biogeographic location and climatic conditions seems to be stronger than the effect of land-use related drivers. Consequently, land-use driven changes in biodiversity are not yet overwhelming in Europe and they are confounded by bio-geographical gradients. Alternatively, at the countryscale, these relationships might also be hidden by successful nature protection activities. The diagram (Figure 1b) illustrates positive relationship between countryscale biodiversity and the proportion of protected areas in a country. Specifically, this is evident for butterflies and moths, birds and mammals, indicating the overall importance of large protected areas from those highly mobile and wide home-range taxonomic groups. This conclusion is also
EURO P EA N
Log (LandArea) 0.166 -0.084 0.060 1.000 0.085 -0.197 0.190 0.031 -0.092
Forest% 0.119 0.029 0.267 0.085 1.000 -0.434 -0.322 -0.153 -0.216
supported by the fact that diversity of those three taxonomic groups was also strongly correlated with the land area of a country. The finding that the proportion of protected areas in a country has a positive relationship with the richness of all taxonomic groups shows that biodiversity conservation in Europe seems to have positive effects, providing good arguments for a further extension of protected areas. This process has already started in Europe, e.g. the establishment of the so-called “Natura 2000” areas network according to the EU Birds Directive and the Habitats Directive (79/409/EEC; 1979; 92/43/EEC 1992). However, the high correlation with a few certain taxonomic groups indicates that further extension of the protected areas should be planned carefully. References BALMFORD A, BENNUN L, BRINK BT, COOPER D, CÓTE IM, CRANE P, DOBSON A, DUDLEY N, DUTTON I, GREEN RE, GREGORY RD,
Log (PopDens) -0.238 -0.466 0.322 -0.197 -0.434 1.000 0.331 0.389 0.439
Agri.Land%
Fertilizers
Veg.Period
-0.163 0.115 0.015 0.190 -0.322 0.331 1.000 -0.402 -0.011
0.192 -0.745 0.268 0.031 -0.153 0.389 -0.402 1.000 0.368
-0.681 -0.626 0.067 -0.092 -0.216 0.439 -0.011 0.368 1.000
HARRISON J, KENNEDY ET, KREMEN C, LEADER-WILLIAMS N, LOVEJOY TE, MACE G, MAY R, MAYAUX P, MORLING P, PHILLIPS J, REDFORD K, RICKETTS TH, RODRIGUEZ JP, SANJAYAN M, SCHEI PJ, VAN JAARSVELD AS, WALTHER BA (2005) The Convention on Biological Diversity’s 2010 Target. Science 307: 212-213. GASTON KJ (2000) Global patterns in biodiversity. Nature 405: 220-227. GASTON KJ, BLACKBURN TM, GOLDEWIJK KK (2003) Habitat conversion and global biodiversity loss. Proceedings of the Royal Society of London – Biological Sciences 270: 1293-1300. SALA OE, CHAPIN FS, ARMESTO JJ, BERLOW E, BLOOMFIELD J, DIRZO R, HUBER-SANNWALD E, HUENNEKE LF, JACKSON RB, LEEMANS R, LODGE DM, MOONEY HA, OESTERHELD M, POFF NL, SYKES MT, WALKER BH, WALKER M, WALL DH (2000) Global biodiversity scenarios for the year 2100. Science 287: 1770-1774. SANDERSON EW, JAITEH M, LEVY MA, REDFORD KH, WANNEBO AV, WOOLMER G (2002) The human footprint and the last of the wild. BioScience 52: 891-904. TILMAN D, FARGIONE J, WOLFF B, D’ANTONIO C, DOBSON A, HOWARTH R, SCHNIDLER D, SCHLEISINGER W H, SIMBERLOFF, D, SWACKHAMER, D (2001) Forecasting agriculturally driven global environmental change. Science 292: 281-284.
Forest area (%) 9-10 11-20 21-30 31-40 41-50 51-70
Figure 2. Maps of various drivers and species richness of taxonomic groups in Europe.
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Agricultural area (%)
Fertilizers (total) Intensity of use
3-10 11-20 21-30 31-40 41-50 51-70
0-100 101-200 201-300 301-400 401-600
Population density
Vascular plants
pers/km2
Species richness
0-100 101-200 201-300 301-400
950-1,000 1,001-2,000 2,001-3,000 3,001-4,000 4,001-5,000 5,001-6,000
Figure 2. Continued.
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Birds
Mammals
Species richness
Species richness
0-300 301-400 401-500 501-600
50-70 71-90 91-110 111-130 131-150
Reptiles
Butterflies and moths
Species richness
Species richness
0-10 11-20 21-30 31-40 41-50 51-70
0-1,000 1,001-2,000 2,001-3,000 3,001-4,000 4,001-5,000 5,001-6,000
Figure 2. Continued.
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A Vision of the Availability of High Quality, Cross-Scale Baseline Biodiversity Information
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INGOLF KÜHN, LYUBOMIR PENEV & JOSEF SETTELE
High quality and up-to-date data on biodiversity and environment are indispensable for assessing impacts of global change and other human interventions on the ecosystems and services provided by them. Data have to be as accurate as possible. Different processes, hence different impacts on the ecological systems act at different scales. It is also necessary, therefore, to collect and provide baseline information at relevant scales. This, in fact, calls for multi-scale inventories of biodiversity. Admittedly, it is not always possible or necessary to sample every scale across the complete range covered by a sampling or monitoring scheme. For reliable assessments, however, it is crucial to have cross-scale data available through representative sampling of the landscapes considered. Especially in heterogeneous landscapes, nested sampling from the scale of ten or one hundred square metres to several (hundred) square kilometres will largely increase our ability to understand ecological processes, assess potential impacts and provide adequate management options and policy recommendations. To undertake large-scale basic and applied ecological research, as well as integrated impact assessments, the availability and accessibility of data in an integrated way would greatly facilitate the research process and would drastically reduce costs, if compared to the collation of locally dispersed, heterogeneously stored data. This means that integrated assessments call for integrated data. Information technology is, at present, sufficiently advanced to offer alternative solutions for storing all data within one ”super database” or information system which, in turn, needs to be mirrored at several institutions. Rather, it is already technically possible to develop a common interface provided via the World Wide Web which acts as a one-stop shop and directs data requests and queries to dispersed databases around Europe, or even the globe. This is the approach used, for instance, by the Global Biodiversity Information Facilities (GBIF, http://www.gbif.org) and the Lifewatch infrastructure (Los, this atlas, p. 62). In such a system, it would not be necessary physically to give data away, hence data amendments and corrections could be made by data holders at any time, thus providing accurate and always upto-date, ”last-version” datasets. Such a user-friendly Web interface should be based on metadata cataloguing, providing the information where, how and by whom data have been collected, stored and retrieved. The Integrated Publishing Toolkit (IPT, http://ipt.gbif.org) of GBIF is an example of this type of interface, used to transform information from different sources into a common exchange format so that the data can be provided in an integrated way to potential users. Appropriate exchange protocols and data standardization formats, such as DIGIR (Distributed Generic Information Retrieval), TAPIR (TDWG Access Protocol for Information Retrieval), DarwinCore (http://www.tdwg.org/activities/darwincore), Veg-X (an exchange standard for plot-based vegetation databases; http://wiki. tdwg.org/Vegetation) and others (see http://www.tdwg.org for details) are currently being developed and implemented by several international working groups and organisations. The first to be mentioned here are the Taxonomy Database Working Group (TDWG, http://www.tdwg.org), the Global Biodiversity Information Facilities (GBIF, http://www.gbif.org), the Global Names Index and Global Names Architecture (GNI and GNA, http://www.globalnames.org), as well as some other taxon- or domain-based data aggregators, e.g., Fauna Europea, (http://www.faunaeur.org), The World Register of Marine Species (WoRMS, http://www.marinespecies.org), FishBase (http://www.fishbase.org), VegBank (http://www.vegbank.org) and others. Two issues are closely linked to this kind of approach: costs of creation and maintenance of data aggregators and intellectual property rights. The analyses
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of ecological patterns and processes, as well as the assessment of global change impacts and risks, are of primary public concern. We need biodiversity and ecosystems as both resources and service providers to society. Society will therefore need to share in providing the necessary funds. Furthermore, individuals, companies and the economy as such externalize specific costs at the expense of natural systems and their properties of resistance, resilience and provisioning. Hence, it seems fair to redirect funds from such sources to the scientific examination of biodiversity. Intellectual property rights are a very sensitive issue. Nevertheless, the scientific community has shown a rapid and visible progress towards open data publishing during the last few years. One of the fundamental principles of the open access movement states that tax payers should not pay twice for the same thing: first to pursue some study and then to have the results of the same study available in public or academic libraries. Since data collection, collation and provision are usually paid for from public funds, the data should in principle be made publicly available. In addition, data publication opens enormous possibilities for future use and re-use of original or collated datasets, multiplying society’s investment in this way. Further, publication of data gives data collectors the opportunity to register their priority, to be properly published, cited and acknowledged for their efforts. Many volunteers, for example, are less concerned about refund of their travel expenses than who will get the data they have collected and whether they will be appropriately acknowledged and recognized by the people using their data. The legal framework for open publication of data is evolving and we already have international publishing licenses at hand, such as the Science Commons Database Protocol (http://sciencecommons.org/resources/faq/database-protocol) and Open Data Commons (http://opendatacommons.org). On the other hand, it is only fair that those institutions or consortia that have applied for a specific project are given a head start on scientific exploration of such data over other groups. Therefore, while in principle publicly funded data and their acquisition and provision will have to be open access, this could be delayed for a limited period (e.g., three years) to allow exploitation by those groups that made an effort to collect such data and make it accessible. We can therefore summarize our vision in a few theses: ◙ It is beneficial to individuals and organizations to share collected or collated data in a commonly accepted format and make them freely available. This will facilitate cooperation, new knowledge and scientific progress rather than depriving the owners of their monopoly of data exploration and publication. It will increase scientific and public reputation of the data owners and increase their publication record. ◙ Data collected, collated, and/or processed using public funds need to be stored in a commonly accepted format and to be made available free of charge (if necessary after some lag period). ◙ Increasing the accessibility to such data should be of primary interest to public funding agencies at any administrative level. Whether it is necessary, beneficial or even counterproductive, physically to combine such data sources remains to be assessed in each particular case. A promising first step would be an inventory of existing data being made available through ”metadatabases”. In a second step, such decentralised and heterogeneous databases could be made accessible via a common gateway and exchange format.
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RESEARCH APPROACHES INTO THE INTERACTIONS BETWEEN IMPACT FACTORS AND BIODIVERSITY
Research Approaches into the Interactions between Impact Factors and Biodiversity STEFAN KLOTZ & MARK FRENZEL
While the previous chapter focussed on the availability of baseline data for research on biodiversity and its impacting factors, we wish here to give some impressions of core research approaches in the field across different spatial and temporal scales. As stated by Fergus & Schmid (this atlas, pp. 30ff.), concerns over biodiversity loss have triggered nearly two decades of experiments contributing to a canon of research linking biodiversity and ecosystem functioning. Three main methods have been used to investigate the effect of biodiversity on ecosystem functioning: monitoring studies, field removal experiments and experiments using artificial assemblages of species. These methods can be grouped according to contrasting assembly processes. Both monitoring studies and field removal experiments are carried out in natural communities. In contrast, artificially assembled communities are usually put together by random draw from an experimental species pool, often based on functional groups such as grasses, small herbs, legumes, etc. Experiments are the only way to detect biodiversity processes and they are urgently needed in order to deal with the restrictions of correlative approaches, which are based purely on observation and monitoring. The main restrictions of experimental approaches are their limitation as to the number of parameters considered and in spatial and temporal scales. Observation and monitoring approaches open opportunities for analyzing biodiversity patterns at larger spatial and temporal resolutions and to gather information on larger areas. The combination of both approaches is the best way to achieve a comprehensive picture of biodiversity patterns and processes, and on the functioning of ecosystems. Additionally, modelling and scenario approaches are not only important for developing experimental setups and for evaluating experimental, observational und monitoring data. As explained by Schweiger et al. (this atlas, pp. 54ff.) and Marion et al. (this atlas, pp. 58ff.) the construction of mechanistic or process-based, mathematical models as well as bioclimatic envelope modelling, based upon climate and land use scenarios, provides us with powerful tools for biodiversity research in general and for risk assessment studies in particular. Experiments Only by directly manipulating species richness under constant abiotic factors can specific ecosystem responses be attributed to changes in biodiversity (Fergus & Schmid, this atlas, pp. 30ff). Artificial assemblage experiments therefore focus on the feedback from biodiversity to ecosystem functioning. The majority of such experiments manipulate terrestrial plant communities, the basis for a number of fundamental ecosystem processes. Aboveground productivity is the common metric for measuring ecosystem function; it provides a good proxy for services such as carbon storage, but is not a surrogate for all ecosystem functions. Fergus & Schmid focus on experiments that have manipulated species richness in artificial grasslands, where productivity responses gauge biodiversity effects. They conclude that across countries, species pools, evolutionary histories, and even within the genetic code of a single species, evidence suggests that biodiversity has significant impacts on the production of biomass and associated ecosystem processes. As a result of manipulating community species richness and functional group composition, increased biodiversity has been shown to impact positively on nutrient retention, soil sustainability and carbon cycling. But there are limitations to artificial assemblage experiments; we can draw conclusions about the feedback from biodiversity to production, but excluding most of the natural processes. The authors also argue for more long-term studies including more and different response variables, as focusing on individual processes may underestimate the biodiversity necessary for ecosystem functioning. This is particularly necessary as the most recent species richness-productivity results suggest that we may 24
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have underestimated the impact of species richness and in turn species loss on ecosystem functioning. Observations and monitoring Henle et al (this atlas, pp. 34ff.) emphasize that biodiversity monitoring, the longterm and close observation of our natural environment, is imperative if we are to determine the state and trend of animal and plant populations and their habitats. Only this information allows us to draw a detailed image of large-scale effects of habitat fragmentation, climate change, pollution, translocation of species, or other pressures on biodiversity. And without biodiversity monitoring, conservation decision makers will not be able to comprehend the effectiveness of nature conservation policies put into action. Based on the results of the European EuMon project, Henle et al. conclude that there are manifold and intensive monitoring activities being undertaken, particularly in Europe. More than 120,000 volunteers invest large amounts of their spare time in biodiversity monitoring. Despite the value of all this data, its accessibility needs to be improved so that analyses can be made of the best possible dataset. Only then can a detailed image of the state and trend of biodiversity be drawn on a large number of species and habitats. Here, governmental bodies should assist to provide the infrastructure to complete such a huge task by those dedicated to the monitoring and conservation of the biodiversity heritage of Europe. Observation and monitoring approaches not only include species monitoring schemes but also data on the state of ecosystems, abiotic conditions, and different anthropogenic impacts. The combined evaluation of these data sets not only leads to the description of trends but also to the analysis of the role of the main drivers and pressures on biodiversity. Combined approaches Model experiments indicate that ecosystem processes respond to changes in biodiversity in complex ways. However, such responses are only beginning to be studied in real landscapes. Therefore, interdisciplinary research projects addressing the relationships between land use, biodiversity and ecosystem processes in the landscape context are necessary for both scientific and societal reasons. Accordingly, the complex relationships of land use intensity with the diversity of different taxa and the functional role of diversity for ecosystem processes is investigated within the “Biodiversity Exploratories” summarized by Pfeiffer et al. (this atlas, pp. 26ff.) These exploratories combine long-term monitoring of biodiversity and ecosystem processes with experimental studies in real landscapes. This provides a study design that allows the investigation of causal and mechanistic interpretation of patterns and processes together with modelling approaches that combine large arrays of biotic and abiotic parameters across various scales. The project considers biodiversity at its various levels of biological organisation, including genetic diversity within species, species diversity and composition, interactions between species, and landscape diversity. The study design involves gradients of land use intensity in forests and grasslands in all exploratories. Moreover, the exploratories contribute to a profound understanding of land use effects on biodiversity and of functional consequences of changes in biodiversity for ecosystem processes. The merging of different approaches is intended as a significant step towards a more profound understanding of the causes and consequences of biodiversity change. Large scale research and modelling approaches Another approach, briefly introduced by Settele et al. (this atlas, pp. 38ff.), which is heavily observation-based and has a strong focus on statistical modelling, was chosen by the ALARM project, from which a large number of studies is presented in this Atlas of Biodiversity Risk.
ALARM stands for “Assessing Large scale environmental Risks for biodiversity with tested Methods”. It was an Integrated Project (IP) within the 6th Framework Programme of the European Commission (EC). The ultimate aim of the ALARM project was to develop and test methods and protocols for the assessment of large-scale environmental risks to biodiversity. The analyses of the ALARM scientists form(ed) the basis for policy recommendations, in an attempt to strengthen evidence-based decision making on issues relevant to biodiversity. Research focused on assessment and forecast of changes, particularly in biodiversity but also in structure, function, and dynamics of ecosystems. This related to ecosystem services and included the relationship between society, economy and biodiversity. In particular, risks arising from climate change, environmental chemicals, biological invasions and pollinator loss in the context of current and future European land use patterns have been assessed. Risk assessments in ALARM was hierarchical and examined a range of organisational (genes, species, ecosystems), temporal (seasonal, annual, decadal) and spatial scales (habitat, region, continent) determined by the appropriate resolution of current case studies and databases. Socio-economics as a cross-cutting theme contributed centrally to the integration of driver-specific risk assessment tools and methods and developed instruments for communicating risks to biodiversity to end users, and to indicate policy options to mitigate such risks One approach for large scale observations is the ALARM field site network (FSN; Hammen et al., this atlas pp. 42ff.). After establishing the research sites across Europe, standardised and detailed field protocols were designed for use across the site network. It serves as a continental-scale test-bed for questions related to major drivers of biodiversity change (Biesmeijer et al, this atlas, pp. 46f; Dvorak et al., this atlas, pp. 50f.; and further studies presented within this atlas). Spangenberg et al (this atlas, pp. 48f.) furthermore show how the FSN can be used pragmatically for research in socio-economy to create added value of an infrastructure created by ecologists. In the meantime a process has been started within ALARM in order to find a future embedding for the FSN beyond ALARM. Mirtl et al. (this atlas, pp. 52f.) explore further the combination of international LTER (Long Term Ecosystem Research) networks and FSN and conclude that FSN sites can be integrated into LTER Europe by becoming members of the respective national networks. In the case of sites in countries without national LTER networks (e.g., in Russia, Greece), these sites could even form the starting points for building national LTER networks. LTER-Europe provides support (e.g., best practice guidelines) to further foster the integration.
ments of the implications of making forecasts with a single model or using combinations of outputs from several models, assessments of the consequences of using incomplete species distributional data, potential improvements of model performance by using additional environmental data such as land use variables or variables reflecting biotic interactions. Schweiger et al conclude that bioclimatic envelope models provide useful ‘first filters’ for identifying locations and species that may be at greater risk and provide first approximations as to the impacts of climate change on species’ ranges. The questions of uncertainties, already mentioned, lead directly to the statistical aspects of biodiversity risk assessment, dealt with by Marion at al. (this atlas, pp. 58ff.). In their examples they focus on (i) species atlas data, where they pay special attention to the assessment of ecological niches and to the accounting for spatially varying non-detection probabilities; (ii) spatio-temporal modelling of the spread of invasive species; (iii) quantification of uncertainty in model-based projections of biodiversity impacts, and (iv) projections of future global vegetation carbon. Future research approaches and infrastructures According to Los (this atlas, p. 62), we face serious problems in the understanding and managing of the biodiversity system. Various examples in this atlas indicate that the system properties cannot be described by scaling up from the simple sum of its components and relations. But the functioning of the biodiversity system is also hard to unravel by continuing with experiments on only a few parameters mainly related to primary producers, since this reductionism fails to capture a wider picture of the full complexity. This holds for all levels of biological life, on the cellular level, the organism level, and the ecological level. In addition, these subsystems operate on different spatial and temporal scales, which cannot easily be interrelated. We need a different scientific methodology in order to overcome this reductionism.
A typical example of the statistical modelling component within ALARM and many other contemporary research projects are bioclimatic envelope models, as elaborated by Schweiger et al. (this atlas, pp. 54ff.). These models are based on and complement observational data and are used, for instance, to assess future range shifts of species under climate change. These modelling strategies assess the relationships of current species distributions with contemporary climate variables, and then use these relationships to project future distributions of species under different climate change scenarios.
Los elaborates on the links from systems biology over an infrastructure for biodiversity research to collaborative networks to the structuring of the scientific community. He states that biodiversity data providers, laboratories, universities, conservation groups, etc. are increasingly involved in collaborative activities with other organisations in and outside the biodiversity domain to share competencies and resources. Here the LifeWatch research infrastructure is going to provide ICT (Information and Communication Technologies) supported mechanisms for such collaborations. Increasingly, computer networks serve as a communication/interaction infrastructure. If virtual laboratories or services also have to function as collaborative networks, a wide variety of issues must be resolved (interactions, roles, trust), apart from technicalities to work collectively together by accessing and sharing data, software and computation. The traditional way of engineering infrastructures, relying on dedicating the hardware for a single purpose and a single user group, must be replaced by sharing resources and social interactions in virtual environments (which, for example, also formed the basis of projects like ALARM, see pp. 38ff. in this atlas, or Networks like AlterNet, www.alter-net.info and LTER-Europa, www.lter-europe.net).
Schweiger et al. show several examples of how envelope models can be applied, but also discuss thoroughly, the manifold sources of uncertainties. Some originate in more technical aspects such as the model building and evaluation procedure, while others result from the nature, quality and structure of the species distributional data and the variables used to calibrate the models. The authors show examples from a series of case studies within ALARM which addressed many of these uncertainties and limitations. These studies included issues of model validation when making projections of species range shift under climate change, assess-
Settele et al (this atlas, pp. 38ff.) conclude that research projects of the size of ALARM offer opportunities for productive partnerships. If scientists are given sufficient choice, new and productive partnerships emerge, and their success increases with project size and collaboration options. Integrating different experiences particularly with EU-funded research, they favour more projects of variable sizes, organized through work plans and accompanied by model agreements – including a reasonable proportion of large integrated projects to create opportunities for interdisciplinary and productive partnerships.
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“Exploratories” for Functional Biodiversity Research SIMONE PFEIFFER, SONJA GOCKEL, ANDREAS HEMP, KONSTANS WELLS, DANIEL PRATI, JENS NIESCHULZE, ELISABETH K.V. KALKO, FRANÇOIS BUSCOT, K. EDUARD LINSENMAIR, ERNST-DETLEF SCHULZE, WOLFGANG W. WEISSER & MARKUS FISCHER
Wilderness areas have become extremely scarce in Germany. However, managed cultural landscapes differing in land use intensity and landscape heterogeneity also offer diverse habitats for organisms. The maintenance of stable and functional ecosystems is clearly a high priority, especially as humans depend on them for their contributions to the provision of fertile soils, clean water, clean air, pollination of crops and wild plants and other ecosystem services. Model experiments indicate that ecosystem processes respond to changes in biodiversity in
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with modelling approaches that combine large arrays of biotic and abiotic parameters across various scales (Symstad et al. 2003). The project considers biodiversity at its various levels of biological organisation, including genetic diversity within species, species diversity and composition, interactions between species, and landscape diversity. The study design involves gradients of land use intensity in forests and grasslands in all exploratories. As implemented in their design, the exploratories contribute to a profound understanding of land use effects on
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Figure 1. Conceptual framework of the Biodiversity Exploratories. While biodiversity in cultural landscape is largely shaped by society and its way of using the various habitats, ecosystem functions are tightly linked to biodiversity and, in turn, determine the ecosystem services provided to society. Such feedback loops at the various levels of biodiversity are the focus of the Exploratories (Source: Fischer et al. 2010).
complex ways (Hector & Bagchi 2007). However, such responses are only beginning to be studied in real landscapes. Therefore, interdisciplinary research projects addressing the relationships between land use, biodiversity and ecosystem processes in the landscape context are necessary for both scientific and societal reasons (Sala et al. 2000). Accordingly, the complex relationships of land use intensity with the diversity of different taxa and the functional role of diversity for ecosystem processes is investigated in our recently established longterm project, the Biodiversity Exploratories (Fischer et al. 2010, www.biodiversity-exploratories.de). In 2006 the German Research Foundation (Deutsche Forschungsgemeinschaft DFG) granted the establishment of three large-scale research sites called exploratories. The exploratories combine long-term monitoring of biodiversity and ecosystem processes with experimental studies in real landscapes. This provides a study design that allows the investigation of causal and mechanistic interpretation of patterns and processes together 26
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biodiversity and of functional consequences of changes in biodiversity for ecosystem processes (Figure 1). Design and methods Due to their various land use systems and pronounced gradients in land use intensity, exploratories have been established in the biosphere reserve Schorfheide-Chorin (study area of 1,300 km2), the National Park Hainich and its surroundings (1,560 km2) and the biosphere area Schwäbische Alb (670 km2) (Figure 3). These landscapes are situated in three different geographical regions of Germany, the Northern lowlands, the Central hill country, and the Southern mountain ranges. The exploratories cover most of the variation in land use intensity in German grasslands and forests, ranging from largely unmanaged and protected land in nature reserves to commercially and intensively used land. Between 2006 and 2008 a local implementation team established the study sites and plots in each exploratory in accordance and collaboration with local land owners, authorities and various stakeholders (Figure 2). Each exploratory comprises 1,000 so-called “grid plots” (500 in grassland and 500 in forest) where land use systems and their intensity, soil and vegetation were inven-
toried in order to cover overall regional diversity and to provide study sites in a landscape context. Along a land use gradient from near-natural to intensively managed grassland and forests, 100 plots were selected out of these 1,000 grid plots as “experimental plots” in each exploratory where most research activities take place (Figure 3). A sub-set of 18 plots (9 in grassland and 9 in forest, called “very intensive research plots”) in each exploratory is used for particularly labour-intensive work such as molecular work on microorganisms. All plots remain under regular management by the land owners, requiring close contact and information exchange with all stakeholders. Detailed planning of all research activities within subplots allows simultaneous work by many research groups and synergistic integration of data from various disciplines. Experimental plots are equipped with environmental monitoring stations that continuously measure air and soil temperatures, relative air humidity and soil moisture. Land use gradients In Germany, grassland use ranges from low-intensity small-scale farming as found in the Schwäbische Alb to farms with more than 500 ha as found in
Experimental plots in forest and grassland
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Figure 2. Selection procedure of grid and experimental plots in grassland and forest of the Biodiversity Exploratories. Grid plots were selected from all types of grassland and forest habitats. To focus on land use effects, experimental plots of different land use intensity were selected from grid plots on comparable soil. To allow for simultaneous activities of various research groups experimental plots were partitioned into subplots (Source: Jörg Hailer, University of Ulm).
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Schorfheide-Chorin. Grasslands include mowed, grazed and both mowed and grazed examples (see Figure 4 for an example of the Schwäbische Alb). Intensity of grassland use is mainly reflected by different schemes of fertilisation, which are codetermined by different farming types (e.g., organic versus conventional, private versus company farming), and subsidized culturallandscape programmes.
isms to landscapes (Figure 5). The Exploratories have an open platform structure offering new research projects the chance to join in. Currently, six central projects and 34 contributing projects are participating, involving more than 250 scientists, technical staff and students. The projects range from the monitoring and diversity assessment of various taxa (of plants, fungi, vertebrates,
Forests range from natural oldgrowth beech forests with trees older than 100 years, through mixed forests to intensively managed pine and spruce monocultures (Figure 4). Interdisciplinary research groups The Exploratories offer a study design for a large number of research groups studying all scales from single organ-
invertebrates, and micro-organisms), studies of interactions among organisms and ecosystem processes including pollination, pools and fluxes of carbon, nitrogen and phosphate, to remote sensing and modelling approaches. Experimental manipulations such as seeding, soil surface disturbance or exclusion of herbivores and insectivorous predators will reveal causal diversity-functioning mechanisms.
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Figure 3. Map of the three Biodiversity Exploratories with a total of 300 experimental plots across land use gradients. Half of the plots are located in forest (dark green) and half in grasslands (light green).
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A) in grassland Pastures
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Figure 4. Land use gradients which are covered by experimental plots in the Schwäbische Alb Exploratory. Photos: K. Wells.
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In particular, the functional role of soil and its large content of biodiversity have not been investigated in detail so far. Soil fauna, microorganisms, mycorrhiza and many other organisms contribute to a complex ecosystem with unique features, e.g. in nutrient cycling and food webs. Intensive cooperation among projects in the Exploratories allows scientists to overcome the difficulties of investigating such complex systems across disciplines. For example, a group from the Max-Planck Institute for Biogeochemistry headed by E.D. Schulze and the central soil project coordinated a joint soil sampling in Spring 2008. More than 1 200 soil samples were subdivided for as many as 17 participating projects of various disciplines, allowing for the first time the study of various aspects of soil biology with common replicated samples from many sites. The partitioning of soil samples plays the same important role for synergistic cooperation on questions addressing below-
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ground mechanisms as the partitioning of the experimental plots into subplots does for aboveground mechanisms. Central coordination A central database called BExIS (short for Biodiversity Exploratories Information System) was designed using state-of-theart web technologies. It is the platform 1) for storing all project data, 2) for allowing information exchange between the research groups and 3) for documenting field work, logistics and available infrastructure. It will further facilitate complex and interdisciplinary meta-analyses. The database is very flexible and supports many data formats for internal project up- and download of data. A central office coordinates various activities of the entire project, fosters coherence in the implementation of the common study design in all three Exploratories and serves as an information platform for researchers, press and public.
Potential extension beyond Germany This new approach of studying the relationships between land use, biodiversity, and ecosystem processes is also of interest outside Germany. In a European Science Foundation workshop in Summer 2008, scientists from twelve European countries agreed that the conceptual multi-taxon, multi-process, and multi-approach framework and type of research platform exemplified by the Biodiversity Exploratory could possibly serve as a starting point for an international network of research sites for functional biodiversity research across Europe.
have so far operated largely separately, is intended as a significant step towards a more profound understanding of the causes and consequences of biodiversity change. References FISCHER M, KALKO EKV, LINSENMAIR KE, PFEIFFER S, PRATI D, SCHULZE E-D, WEISSER WW (2010) Exploratories for large-scale and long-term functional biodiversity research. – In: Müller F, Baessler C, Schubert H, Klotz S (Eds) Long-term ecological research – between theory and application. Springer (in press). HECTOR A, BAGCHI R (2007) Biodiversity and ecosystem multifunctionality. Nature 448: 188-190. SALA OE, CHAPIN FS, ARMESTO JJ, BERLOW E, BLOOMFIELD J et al. (2000) Biodiversity – Global biodiversity scenarios for the year 2100. Science 287: 1770-1774. SYMSTAD AJ, CHAPIN FS, WALL DH, GROSS KL, HUENNEKE LF, MITTELBACH GG, PETERS DCP, TILMAN D (2003) Long-term and large-scale perspectives on the relationship between biodiversity and ecosystem functioning. Bioscience 53: 89-98.
Conclusion The Exploratories are research platforms linking functional biodiversity research of all kinds with various ecosystem-process research approaches in the real landscape context. The merging of these disciplines, which
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Figure 5. A large number of methods are applied in the Exploratories. a – assessment of pollinators, b – growth experiments from soil seed banks, c – traps for flying arthropods in the forest, d – observation of plant-animal interactions, e – assessment of herbivory on oak leaves, f – soil sampling, g – measurement of environmental data in a pasture, and h – seeding experiment in the experimental plots in grasslands. Photos: C. Börschig (d), I. Mai (c), J. Müller (b, g, h), I. Schöning (f), K. Wells (e), M. Werner (a).
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Biodiversity Experiments: What Have We Learnt about Biodiversity– Ecosystem Functioning Relationships? ALEXANDER J.F. FERGUS & BERNHARD SCHMID
Background to biodiversity experiments Concerns over biodiversity loss have triggered nearly two decades of experiments contributing to a canon of research linking biodiversity and ecosystem functioning. General anxiety regarding biodiversity loss relates to its magnitude across the globe and to the potential consequences on the goods and services that ecosystems provide humanity (Balvanera et al. 2006). More specifically, the concerns of ecologists are focused on how biodiversity losses will impact ecosystem properties such as productivity, carbon storage, and nutrient cycling. How to investigate the role of biodiversity? Three main methods have been used to investigate the effect of biodiversity on ecosystem functioning: monitoring studies, field removal experiments and experiments using artificial assemblages of species (Figure 1) (Diaz et al. 2003). These methods can be grouped based on contrasting assembly processes. Both monitoring studies and field removal experiments are carried out in natural communities, incorporating
important natural processes. The biodiversity and composition of natural communities is determined by dispersal, the ability to establish under local environmental conditions (abiotic filtering), and by the interaction of incoming species with the biotic community (biotic filtering). In contrast, artificially assembled communities are usually put together by random draw from an experimental species pool. However, this pool is usually carefully selected to include only species that would naturally occur in the same community (Schmid & Hector 2004). There are concerns that random assembly – which translates into random extinction – underestimates the effect of natural processes, and contrasts typically nonrandom extinction patterns (Leps 2004). The influence of random assembly must be taken into account, but only by directly manipulating species richness under constant abiotic factors can specific ecosystem responses be attributed to changes in biodiversity. Artificial assemblage experiments therefore focus on the feedback from biodiversity to ecosystem functioning. The majority of such experiments manipulate terrestrial
plant communities, the basis for a number of fundamental ecosystem processes (Balvanera et al. 2006). Aboveground productivity is the common metric for measuring ecosystem function, it provides a good proxy for services such as carbon storage, but is not a surrogate for all ecosystem functions. Grasslands are typically used as model ecosystems because they are easily manipulated and productivity can be measured by mowing, which corresponds to either the normal management regime or grazing by herbivores. This contribution focuses on experiments that have manipulated species richness in artificial grasslands, where productivity responses gauge biodiversity effects. Using artificially assembled communities Biodiversity has been manipulated at many scales, with different species pools, at various locations around the world. The set up of the Jena Experiment (one of the largest artificial assemblage experiments) provides a good example of the approach (Figure 2). The Jena Experiment was established in 2002 to investigate the
Species richness increases on a logarithmic scale: 1, 2, 4, 8, and 16 species, and nearly all possible combinations of species richness x functional group composition occur in the experiment (Figure 4). The composition of each of the plots is maintained by intensive weeding and occasional herbicide application. Species richness-productivity theory If increasing species richness positively affects productivity, then we expect both to increase together. Conversely, reductions in species richness should lead to declines in productivity. Sampling/selection effects and complementarity effects are two general mechanisms proposed to explain this relationship, and respectively relate to single- vs. multi-species processes (Cardinale et al. 2007). The net biodiversity effect is the combination of the two. Sampling/ selection effects are species-specific impacts on biomass, thought to occur when the most productive species have a greater chance of being included and eventually dominating the biomass of species-rich polycultures (Cardinale et al. 2007). The terms sampling and selec-
control over species diversity control over species composition required artificial maintenance relevance to non-random extinction importance of ecological history Community assemblage Random draw
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Figure 2. The Jena Experiment on the floodplain of the Saale river, Thuringia, Germany (Photo: Jena Experiment consortium).
Figure 1. Comparing different approaches to studying biodiversity–ecosystem functioning relationships (modified from Diaz et al. 2003). (a) Monitoring studies in the BIOLOG project (Franconian Forest, Germany); (b) field removal experiments on boreal island ecosystems (Lake Hornavan, Sweden); artificial assemblage experiments in the field (c) and in microcosms (d) (Zürich, Switzerland). Photos: Juliane Specht (a), Alexander Fergus (b, c), and Yann Hautier (d).
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effect of biodiversity on element cycling and trophic interactions (Roscher et al. 2004). The Jena Experiment species pool is comprised of 60 plant species common to Central European Arrhenatherion grasslands, artificial communities range in richness from 1-16 species, and contain between 1 and 4 functional groups (Figure 3).
tion are often used interchangeably, but the sampling process is shared by both selection and complementarity effects (Loreau & Hector 2001). A community with increased species richness is more likely to contain either single species with particular trait values (selection effects) or a group of species with complementary traits (complementarity
effects). Complementarity effects can be seen as the portion of the net biodiversity effect not attributable to any single species. Niche complementarity suggests that greater productivity with increasing species richness results from differences between species in resource requirements, and spatial and temporal resource and habitat use (Tilman et al. 2001). But complementarity effects also include the balance of all forms of niche partitioning that might impact biomass, and all forms of indirect and non-additive species interactions (Cardinale et al. 2007). Experimental results One of the first artificial assemblage experiments manipulated both plant and animal biodiversity by creating microcosms of low, intermediate and high species richness (Naeem et al. 1994). These microcosms, housed in the Ecotron system of controlled environmental chambers, revealed that species-rich communities consumed more CO2 than species poor communities and produced more plant biomass. This trend of increased productivity with species richness was also found at the Cedar Creek field site, a nitrogen poor Minnesota grassland (Tilman et al. 1996). Experiments at Cedar Creek have also shown increased species richness to increase both soil nutrient use efficiency (more sustainable nutrient cycling) and stability of primary production (Tilman et al. 1996). Plant biodiversity was experimentally manipulated in a number of ways at Cedar Creek, but it was increasing species richness and functional group composition that emerged as the major determinants of increasing productivity (Tilman et al. 2001). The Biodepth experiment increased the generality of these results by testing the biodiversity-productivity relationship across a range of European grasslands (Hector et al. 1999). Results from 8 sites across 7 countries demonstrated that decreasing species richness resulted in a log-linear decline in productivity, whereby reductions in complementarity effects appeared to be responsible (Figure 5). These results deepened the debate over whether complementarity effects or selection effects were generating positive species richness-productivity relationships. In response, Loreau & Hector (2001) devised a method using additive partitioning to separate the two effects. When the Biodepth experiment was re-analysed using this partitioning method, complementarity effects were shown to be positive overall. Following the first decade of artificial assemblage experiments, designs were adapted to address methodological criticisms. Claims that positive species richness-productivity relationships are dependant on legumes were rejected as assemblages without legumes BIODIV ERS IT Y
also detected positive relationships (van Ruijven & Berendse 2003) and complementarity effects were found between species belonging to non-legume functional groups (Loreau & Hector 2001, Tilman et al. 2001). Concerns over random assembly have been addressed with two stage experiments that first delimit the species pool by inducing experimental extinction (Schmid & Hector 2004). By first applying high-intensity management as an extinction filter, the productivity of the resulting species poor assemblages were shown to decrease almost as much as in randomly assembled communities (Schläpfer et al. 2005). In experiments conducted over a longer period, the positive species richness-productivity relationship increases, and complementarity effects have a progressively greater impact on ecosystem functioning (Tilman et al. 2001). This is supported by a recent metaanalysis summarising 44 experiments where plant species richness was manipulated (Cardinale et al. 2007). On average across these 44 experiments, polycultures produced 1.7 times more biomass than monocultures, and were more productive in 79 % of experiments. Transgressive overyielding – which describes how the total biomass of a polyculture exceeds that produced by the highest yielding component species in monoculture – was found to occur in only 12 % of experiments (Cardinale et al. 2007). Transgressive overyielding can only result from complementarity effects; hence, positive net effects of biodiversity without overyielding have sometimes been interpreted as evidence for selection effects. But lack of transgressive overyielding does not necessarily conflict with positive species complementarity. Estimates suggest it takes the most diverse polyculture 1750 days before transgressive overyielding begins (Cardinale et al.
Figure 3. A high diversity 16-species plot in the Jena Experiment. Photos: Jena Experiment consortium (main image) and Alexander Fergus (inset image).
2007). Because most experiments run for an average of 730 days, it is likely that complementarity effects have so far been underestimated.
recruitment of more diverse plant communities. Complementarity between functional groups is thought to have generated the result, which suggests that functional diversity of pollinator networks may well be critical to ecosystem stability (Fontaine et al. 2006). Recent results from the Jena Experiment also expand our understanding of which ecosystem functions and processes respond most to changes in biodiversity. Analyses of 520 variables revealed carbon measures to be influenced more by species loss than variables associated with the nitrogen cycle, reiterating the role of biodiversity in mitigating climate change (Allan et al. submitted). To include another measure of biodiversity, the genetic diversity of populations of a single species has recently been manipulated (Crutsinger et al. 2006). The genotype diversity of Tall Goldenrod, Solidago altissima, was manipulated by creating populations with the same number of individuals
Current directions Because biodiversity spans a range of biotic scales, from genetic variation within a species to biome distribution across the planet, recent experiments have explored other measures of both biodiversity and ecosystem functioning. This is a necessary step, as maintenance of an increasing number of ecosystem processes has been shown to require more species (ecosystem multifunctionality) as different species often influence different ecosystem functions and processes (Hector & Bagchi 2007). Plant-pollinator interactions represent another key ecosystem function where biodiversity has recently been manipulated across trophic levels (Figure 6) (Fontaine et al. 2006). Manipulating the functional diversity of both plants and pollinators was shown to increase
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but containing 1, 3, 6, or 12 genotypes (Figure 7). Aboveground productivity increased with plant genotype diversity, and was 36 % higher in 12- genotype vs. single-genotype plots (Figure 8). Extending beyond the productivity function, a positive relationship was also found between genotype diversity and the diversity of associated consumers. The number of arthropods was on average 27 % higher in 12genotype vs. single genotype plots, and not simply because of increased plant
productivity (Figure 8). Most recently ecologists have asked how the evolutionary relationships among species predict how biodiversity impacts productivity (Cadotte et al. 2008). The phylogenetic diversity of communities was found to explain more variation in plant productivity than species richness. Therefore in artificial assemblages there is a greater effect of biodiversity on productivity when plant species are more distantly related to one another (Cadotte et al. 2008).
Figure 6. Manipulating biodiversity across trophic levels; cages containing different functional diversity combinations of both plants and pollinators. Inset: monitoring pollinator behaviour. Photos: Colin Fontaine.
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Biodiversity conclusions Across countries, species pools, evolutionary histories, and even within the genetic code of a single species, evidence suggests that biodiversity has significant impacts on the production of biomass and associated ecosystem processes (Hector et al. 1999, Crutsinger et al. 2006, Cadotte et al. 2008). As a result of manipulating community species richness and functional group composition, increased biodiversity has been shown to positively impact nutrient
retention, soil sustainability and carbon cycling (Tilman et al. 1996, Allan et al. submitted). But there are limitations to artificial assemblage experiments; we are seeing the feedback from biodiversity to production, but without the incorporation of most natural processes (Schmid & Hector 2004). We now have a good idea how grassland systems operate, and the role of biodiversity within them, but the incorporation of natural processes may still generate unexpected results. More long-term
Figure 7. Populations of Tall Goldenrod, Solidago altissima, assembled so that each population is made up of 1, 3, 6, or 12 genotypes. Inset: sampling arthropod diversity as a response to Tall Goldenrod genotype diversity. Photos: Gregory Crutsinger.
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experiments are required in grasslands and experiments in general must expand into other systems dominated by species with different life forms and life histories. Encouragingly, biodiversity experiments with tree species are underway in Borneo, China, France, Finland, Germany, and Panama, but such systems will take time to generate results. More and different response variables must also be measured, as focus on individual processes may underestimate the biodiversity necessary for ecosystem functioning (Hector & Bagchi 2007). These recommendations echo the most recent species richness-productivity results, which suggest if anything, we may have underestimated the impact of species richness and in turn species loss on ecosystem functioning.
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ALLAN E, et al. Generalising the biodiversity – ecosystem functioning relationship based on 517 measures from a single experiment. Submitted. BALVANERA P, PFISTERER AB, BUCHMANN N, HE JS, NAKASHIZUKA T, RAFFAELLI D, SCHMID B (2006) Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecology Letters 9: 1146-1156. CADOTTE MW, CARDINALE BJ, OAKLEY TH (2008) Evolutionary history and the effect of biodiversity on plant productivity. Proceedings of the National Academy of Sciences 105: 17012-17017. CARDINALE BJ, WRIGH JP, CADOTTE MW, CARROLL IT, HECTOR A, SRIVASTAVA DS, LOREAU M, WEIS JJ (2007) Impacts of plant diversity on biomass production increase through time because of species complementarity. Proceedings of the National Academy of Sciences of the United States of America 104: 18123-18128. CRUTSINGER GM, COLLINS MD, FORDYCE JA, GOMPERT Z, NICE CC, SANDERS NJ (2006) Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313: 966-968. DIAZ S, SYMSTAD AJ, CHAPIN FS, WARDLE DA, HUENNEKE LF (2003) Functional diversity revealed by removal experiments. Trends in Ecology & Evolution 18: 140-146. FONTAINE C, DAJOZ I, MERIGUET J, LOREAU M (2006) Functional diversity of plant-pollinator interaction webs enhances the persistence of plant communities. Plos Biology 4: 129-135. HECTOR A, BAGCHI R (2007) Biodiversity and ecosystem multifunctionality. Nature 448: 188-190. HECTOR A, SCHMID B, BEIERKUHNLEIN C, CALDEIRA MC, DIEMER M, DIMITRAKOPOULOS PG, FINN JA, FREITAS H, GILLER PS, GOOD J, HARRIS R, HOGBERG P, HUSSDANELL K, JOSHI J, JUMPPONEN A, KORNER C, LEADLEY PW, LOREAU M, MINNS A, MULDER CPH, O’DONOVAN G, OTWAY SJ, PEREIRA JS, PRINZ A, READ DJ, SCHERERLORENZEN M, SCHULZE ED, SIAMANTZIOURAS ASD, SPEHN EM, TERRY AC, TROUMBIS AY, WOODWARD FI, YACHI S, LAWTON JH (1999) Plant diversity and productivity experiments in European grasslands. Science 286: 1123-1127. LEPS J (2004) What do the biodiversity experiments tell us about consequences of plant species loss in the real world? Basic and Applied Ecology 5: 529-534. LOREAU M, HECTOR A (2001) Partitioning selection and complementarity in biodiversity experiments. Nature 412: 72-76. NAEEM S, THOMPSON LJ, LAWLER SP, LAWTON JH, WOODFIN RM (1994) Declining
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Number of plant genotypes Figure 8. Increased arthropod biodiversity (a, b) and plant productivity (c) in response to increasing genotype diversity of Tall Goldenrod, Solidago altissima (Source: Crutsinger et al. 2006). Photos: G. Crutsinger.
Biodiversity Can Alter the Performance of Ecosystems. Nature 368: 734-737. ROSCHER C, SCHUMACHER J, BAADE J, WILCKE W, GLEIXNER G, WEISSER WW, SCHMID B, SCHULZE ED (2004) The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic and Applied Ecology 5: 107-121.
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SCHLÄPFER F, PFISTERER AB, SCHMID B (2005) Non-random species extinction and plant production: implications for ecosystem functioning. Journal of Applied Ecology 42: 13-24. SCHMID B, HECTOR A (2004) The value of biodiversity experiments. Basic and Applied Ecology 5: 535-542. TILMAN D, WEDIN D, KNOPS J (1996) Productivity and sustainability influenced by
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biodiversity in grassland ecosystems. Nature 379: 718-720. TILMAN D, REICH PB, KNOPS J, WEDIN D, MIELKE T, LEHMAN C (2001) Diversity and productivity in a long-term grassland experiment. Science 294: 843-845. VAN RUIJVEN J, BERENDSE F (2003) Positive effects of plant species diversity on productivity in the absence of legumes. Ecology Letters 6: 170-175.
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Observing Biodiversity Changes in Europe KLAUS HENLE, BIANCA BAUCH, SANDRA BELL, ERIK FRAMSTAD, MLADEN KOTARAC, PIERRE-YVES HENRY, SZABOLCS LENGYEL, VESNA GROBELNIK & DIRK S. SCHMELLER
Introduction Biodiversity monitoring, the close observation of our natural environment, is imperative to determine the state and trend of animal and plant populations and their habitats. Only this information allows drawing a detailed image of large-scale effects of habitat fragmentation, climate change, pollution, translocation of species, or other pressures on biodiversity. And without biodiversity monitoring conservation decision makers will not be able to compre-
hend the effectiveness of nature conservation policies put in action. In recognition of this importance, monitoring of species and habitats is a priority in many national and international conservation strategies and legislations (Green et al. 2005). For example, signatory countries to the Convention on Biological Diversity committed themselves to monitor their biodiversity. In Europe, monitoring species and habitats is a legal obligation as set out in article 17 of the Habitats Directive (more formally Professionals
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known as Council Directive 92/43/ EEC). Member States have to report about the status and trends of all habitats and species listed in Annexes I and II every six years to the European Commission. In consequence, biodiversity monitoring has to be considered a centrepiece of nature conservation across the globe. Information on status and trend of biodiversity needs to be collected by properly designed monitoring systems (Pereira & Cooper 2006). Recently, much work has been focused on describing the desirable properties of monitoring systems or the indicators proposed to measure large-scale trends in biodiversity (Weber et al. 2004, Heer et al. 2005). Despite these recent developments and the importance for nature conservation, monitoring activities are not centrally coordinated, vastly differ in their monitoring targets, sampling designs, geographic and taxonomic coverage, and manpower involved (Henry et al. 2008, Kull et al. 2008, Lengyel et al. 2008a,b, Marsh & Trenham 2008). Until recently, any overview on who is doing what, how, and why in biodiversity monitoring was completely lacking. The EuMon project (EU-wide monitoring methods and systems of surveillance for species and habitats of Community interest) funded under the 6th Framework Program of the European Union was set-up to compile such an overview and to provide recommendations about species and habitat monitoring in Europe (Schmeller 2008, Schmeller & Henle 2008; eumon.ckff.si). Recently, a first overview of biodiversity monitoring schemes has also been published for North America (Marsh & Trenham 2008). The recommendations developed by EuMon focus on the involvement of volunteers (Bell et al. 2008, Podjed & Mursic 2008) and on a framework that supports standardization, integration, and coordination of new and existing European biodiversity monitoring programs (Henry et al. 2008, Kull et al. 2008, Lengyel et al. 2008a,b, van Swaay et al. 2008). For the sake of generality, all European species and habitats were covered. In addition, EuMon put a special effort on species and habitats of Community interest. EuMon further compiled methods to develop an efficient network of protected
areas and analysed gaps and biases in the NATURA 2000 network (Gruber et al. in prep.). EuMon also developed methods to evaluate national responsibilities of EU Member states for the conservation of species (Schmeller et al. 2008a,b). National responsibility is a measure of the importance of the assessed region (usually country) for the global survival of a species and can contribute to priority setting in species monitoring (Schmeller et al. 2008c). In this publication we present selected results for four major aspects important for biodiversity monitoring: the involvement of volunteers to reduce costs, the accessibility of monitoring data, the coverage of monitoring, and the setting of monitoring and conservation priorities. Volunteers in biodiversity monitoring Monitoring of species and habitat requires the participation of large numbers of people that far outstrip the capacity of professional scientists. There is a great deal of variation in the amount and types of volunteer-based monitoring and the organizations, in which it takes place. We define these organizations as Participatory Monitoring Networks (PMN), a broad term that includes a host of different arrangements and involves collaboration between a range of nature specialists, both professional and amateur. The involvement of volunteers in monitoring varies largely across different species and habitat groups (Figure 1). However, EuMon showed that volunteer involvement is a good trade-off solution and extremely important and valuable for biodiversity monitoring because data from participatory monitoring networks are not less, and may be even more, informative than those collected in professional schemes (Schmeller et al. 2009) The research of EuMon revealed six predominant factors that influence the effectiveness and the sustainability of PMNs (Bell et al. 2008): ◙ Socio-political background influences levels of participation. This accounts for variation in the spread of PMNs in Europe. Post-communist countries may experience a deficit in PMNs because of historical impediments
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to the development of an overall ethos of civic voluntarism. Different strategies are needed for recruitment and retention of volunteers. PMNs need to balance the degree of effort needed to bring in new contributors with the effort required to retain existing volunteers. Good communication in the form of interpersonal interactions is a key attribute of vibrant PMNs. Volunteers should be kept informed about how the data that they collect is used. Biological records collected by amateur volunteers are personalised to some degree, because they hold unique meanings. PMNs need to inform volunteers about the fate of their data and consult with them over decisions relating to that data. Several factors motivate volunteers. The motivations of volunteers involve a combination of wanting to learn, passion for nature, and the desire to be with other like-minded people. PMNs need to cater for the combination of these factors. Careful consideration for relations between professional and amateurs. While professionalization can benefit certain types of PMNs, potential negative effects need to be acknowledged and managed to create a balanced relationship between professional and volunteers so that neither
category of people feel undervalued or isolated. ◙ Collaboration with other PMNs adds value to monitoring. Collaboration with other organisations has many benefits, particularly in terms of efficiency and cost-effectiveness. Accessibility of monitoring data Data on biodiversity are scattered and diverse in Europe, but they contain a huge amount of information on biodiversity changes and drivers of these changes. This information would be much more valuable for biodiversity assessment if it were more easily accessible, e.g., assembled in metadatabases instead of remaining hidden in disconnected datasets. Increased data integration should benefit all interested parties of biodiversity monitoring. Researchers analyzing biodiversity changes should benefit from the increased levels of complexity they can study and understand, as well as from an increase in predictive power and range of inference of their conclusions. Environmental policy makers should obtain information of broader generality and robustness, at more relevant geographical and temporal scales, thus securing a sound scientific support to their policy decisions (Henry et al. 2008, Lengyel et al. 2008b, Schmeller & Henle 2008b, van Swaay et al. 2008). Integration should provide environmental managers with assessments of the general impact of
Figure 2. Structure of the BioMAT tool.
management actions, and evaluations of their own impact. Finally, individuals and organizations engaged in monitoring activities would benefit from an increased awareness about, and legitimacy of their activity with a better recognition of their role as major data providers for biodiversity assessments.
To contribute to a better accessibility and integration of biodiversity monitoring data, EuMon made a comprehensive inventory of European monitoring practices and designed a meta-database that provides information on the characteristics, coverage, and coordinators of species and habitat monitoring schemes in Europe.
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This database called DaEuMon is accessible via an Internet portal (eumon.ckff.si). A second database covers organizational aspects of volunteer based monitoring schemes and is also available via the same portal. Both databases will be maintained beyond the lifetime of EuMon, with new schemes added regularly. If you have not registered your monitoring schemes, please do so for the benefit of biodiversity monitoring and conservation in Europe. EuMon developed a tool to support an assessment of the coverage and characteristics of the monitoring schemes, called BioMAT. Its primary aim is to provide a coherent and consistent framework for assessing the state and trends in selected components of biodiversity. It covers essentially three aspects in three main modules (Figure 2):
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BioMAT leads the users through a number of different steps relevant to the main issues of the three modules. At the relevant end points, recommendations for action are provided as well as references to additional background information on the topic at hand. Coverage of biodiversity monitoring In total, the EuMon consortium has collected over 4,000 contact details of potential monitoring organizations (including fish and hunting associations as well as local, regional, and national governmental bodies). The input to the database still continues, making the database the most comprehensive one in Europe. As of March 2009, the survey yielded 615 monitoring schemes conducted by 246 monitoring institutions across Europe (Figures 2 and 3). Of these schemes, 445 are species monitoring schemes and 170 habitat schemes. About 4,000 species are covered by the monitoring programs in the database. Vertebrate and especially bird monitoring schemes clearly dominate (Figure 4a). Schemes moni-
toring birds and mammals showed small biases (relative to the number of publications listed in the Zoological Record; evaluated in 2008), but some groups, especially fishes, lichens, and fungi, still seem to be underrepresented (Schmeller et al. 2009, Figure 4b). Forests were the most frequently monitored habitat type, followed by marine, grassland, and coastal habitats, whereas bogs and fens, heaths and scrubs, and especially agricultural areas are monitored less often (Figure 4c). Not all countries are equally well represented (Figure 5). Despite these biases, our survey yielded a wide geographical coverage with at least one scheme from any country in geographical Europe and also adequately covers central and eastern (Poland, Hungary, Lithuania) and western European countries (France, Belgium, Germany). The EuMon survey also suggests that both species and habitat monitoring activities are fragmented (Kull et al. 2008, Lengyel et al. 2008a). Monitoring projects are scattered, data collection methods are not standardized and, thus, processed information is not easily accessible for decision-makers and stakeholders (Henry et al. 2008, Lengyel et al. 2008a). Most reported schemes have been started only recently, while 15 have been initiated already at least half a century ago. While the majority of schemes are small in geographical scope and operate on small spatial scales, 243 schemes have a national or international coverage, with the percentage of national/international schemes being significantly larger for species monitoring compared to habitat monitoring (χ2 = 32; α < 0.001). Whereas approximately 50 % of the schemes monitor ≤ 10 species, 47 monitor > 100 species. In species monitoring schemes, only about 30 % consider detection probability, despite inferences form schemes that do not account for detection probability being difficult. National responsibilities and policy support EuMon also made some recommendations how to prioritize conservation and developed a new approach to assist decision makers (Schmeller et al. 2008c). Such a methodology becomes necessary as the threat status does not always reflect actual conservation needs and can be very different from actual conservation priorities. Therefore, red lists may at best be a suboptimal tool for setting conservation priorities in a country or region. EuMon provided a new method that integrates the two concepts of conservation status and a measure of the international importance of a
Conclusion More than 120,000 volunteers in Europe invest a lot of their spare time in monitoring biodiversity. Despite the value of all this data, its accessibility needs to be improved so that analyses can be done on the best possible dataset. Only then a detailed image of the state and trend of biodiversity can be drawn on a large number of species and habitats. Here, governmental bodies should assist to provide the infrastructure to complete such a huge task by those dedicated to the monitoring and conservation of the biodiversity heritage of Europe.
population for the global survival of a species, but keeps them conceptually separate. The main benefit of the EuMon method is that it can be applied across variable geographical scales, such as regions, countries, and even continents. Furthermore, it allows for better recommendations for applied conservation and conservation policy development than the two concepts in isolation (Schmeller et al. 2008a-c). Regarding monitoring, recommendations suggested by the new method are a close monitoring of species highly threatened especially in those nations, in which they are relatively abundant, frequent monitoring for species vulnerable or near threatened and concentrated in a certain nation, and the set up of new monitoring programs for species of which little data is available. In case, a species is rare in a certain country, but not globally / internationally threatened, practitioners could redirect resources from such monitoring programs to others in more urgent need. Such a method, if applied internationally, would further allow for a standardized priority setting in species conservation, would be highly comparable between countries, and would lead to a more efficient use of the limited financial and human resources for monitoring and conservation of biodiversity.
Acknowledgements This paper is a result of the EU-project EuMon (http://eumon. ckff.si), funded by the EU-Commission (contract number 6463). We would like to thank other EuMon-colleagues for constructive and inspiring discussions and all coordinators of monitoring schemes that provided us valuable information. References BELL S, MARZANO M, CENT J, KOBIERSKA H, PODJED D, VANDZINSKAITE D, REINERT H, ARMAITIENE A, GRODZINSKAJURCZAK M, MURSIC R (2008) What counts? Volunteers and their organisations in the recording and monitoring of biodiversity. Biodiversity and Conservation 17: 3443-3454.
Number of habitats schemes
GREEN RE, BALMFORD A, CRANE PR, MACE GM, REYNOLDS JD, TURNER RK (2005) A framework for improved monitoring of biodiversity: responses to the World Summit on Sustainable Development. Conservation Biology 19: 56-65. HEER M DE, KAPOS V, TEN BRINK BJE (2005) Biodiversity trends in Europe: development and testing of a species trend indicator for evaluating progress towards the 2010 target. Philosophical Transactions of the Royal Society. Biological Sciences 360: 297-308. HENRY P-Y, LENGYEL S, NOWICKI P, JULLIARD R, CLOBERT J, CELIK T, GRUBER B, SCHMELLER DS, BABIJ V, HENLE K (2008) Integrating ongoing biodiversity monitoring: potential benefits and methods. Biodiversity and Conservation 17: 3357-3382. KULL T, SAMMUL M, KULL K, LANNO K, TALI K, GRUBER B, SCHMELLER D, HENLE K (2008) Necessity and reality of monitoring threatened European vascular plants. Biodiversity and Conservation 17: 3383-3402. LENGYEL S, DÉRI E, VARGA Z, HORVÁTH R, TÓTHMÉRESZ B, HENRY P-Y, KOBLER A, KUTNAR L, BABIJ V, SELISKAR A, CHRISTIA C, PAPASTERIADOU E, GRUBER B, HENLE K (2008a) Habitat monitoring in Europe: a description of current practices. Biodiversity and Conservation 17: 3327-3339. LENGYEL S, KOBLER A, KUTNAR L, FRAMSTAD E, HENRY P-Y, BABIJ V, GRUBER B, SCHMELLER D, HENLE K (2008b) A review and a framework for the integration of biodiversity monitoring at the habitat level. Biodiversity and Conservation 17: 3341-3356. MARSH DM, TRENHAM PC (2008) Current trends in plant and animal population monitoring. Conservation Biology 22: 647-655. PEREIRA HM, COOPER HD (2006) Towards the global monitoring of biodiversity change. Trends in Ecology and Evolution 21: 123-129. PODJED D, MURSIC R (2008) Dialectical relations between professionals and volunteers in a
biodiversity monitoring organisation. Biodiversity and Conservation 17: 3471-3483. SCHMELLER D (2008) European species and habitat monitoring: where are we now? Biodiversity and Conservation 17: 3321-3326. SCHMELLER DS, GRUBER B, BAUCH B, HENLE K (2006) EuMon – Arten- und LebensraumMonitoring in Europa. Naturschutz und Landschaftsplanung 12: 35-36. SCHMELLER DS, GRUBER B, BAUCH B, LANNO K, BUDRYS E, BABIJ V, JUSKAITIS R, SAMMUL M, VARGA Z, HENLE K (2008a) Determination of national conservation responsibilities for species in regions with multiple political jurisdictions. Biodiversity and Conservation 17: 3607-3622. SCHMELLER DS, GRUBER B, BUDRYS E, FRAMSTAD E, LENGYEL S, HENLE K (2008b) National responsibilities in European species conservation: a methodological review. Conservation Biology 22: 593-601. SCHMELLER DS, BAUCH B, GRUBER B, JUSKAITIS R, BUDRYS E, BABIJ V, LANNO K, SAMMUL M, VARGA Z, HENLE K (2008c) Determination of conservation priorities in regions with multiple political jurisdictions. Biodiversity and Conservation 17: 3623-3630. SCHMELLER DS, HENRY P-Y, JULLIARD R, GRUBER B, CLOBERT J, DZIOCK F, LENGYEL S, NOWICKI P, DÉRI E, BUDRYS E, KULL T, TALI K, BAUCH B, SETTELE J, VAN SWAAY CAM, KOBLER A, BABIJ V, PAPASTERGIADOU E, HENLE K (2009) Advantages of volunteer-based biodiversity monitoring in Europe. Conservation Biology 23: 307-316. VAN SWAAY CAM, NOWICKI P, SETTELE J, VAN STRIEN AJ (2008) Butterfly monitoring in Europe: methods, applications and perspectives. Biodiversity and Conservation 17: 3455-3469. WEBER D, HINTERMANN U, ZANGGER A (2004) Scale and trends in species richness: considerations for monitoring biological diversity for political purposes. Global Ecology and Biogeography 13: 97-104.
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Assessing LArge-scale environmental Risks for biodiversity with tested Methods – the ALARM project JOSEF SETTELE, JOACHIM H. SPANGENBERG, VOLKER HAMMEN, ALEXANDER HARPKE, STEFAN KLOTZ, SILKE RATTEI, ANNETTE SCHMIDT, OLIVER SCHWEIGER & INGOLF KÜHN
ALARM stands for “Assessing LArgescale environmental Risks for biodiversity with tested Methods”. It was an Integrated Project (IP) within the 6th Framework Programme of the European Commission (EC). The ultimate aim of the ALARM project was to develop and test methods and protocols for the assessment of large-scale environmental risks for biodiversity. The analyses of the ALARM scientists form(ed) the basis for policy recommendations, in an attempt to strengthen evidence based decision making on biodiversity relevant issues. Project objectives and summary Based on a better understanding of terrestrial and freshwater biodiversity and ecosystem functioning, ALARM developed and tested methods and protocols for the assessment of large-scale environmental risks for biodiversity in order to minimise negative direct and indirect human impacts. Research focused on assessment and forecast of changes particularly in biodiversity but also in structure, function, and dynamics of ecosystems. This related to ecosystem ser-
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vices and included the relationship between society, economy and biodiversity. In particular, risks arising from climate change, environmental chemicals, biological invasions and pollinator loss in the context of current and future European land use patterns have been assessed. There is an improved understanding on how these environmental risks subsequent to each of these impacts act individually and affect living systems. Whereas the knowledge on how they act in concert is poor and ALARM was the first research initiative with the critical mass needed to deal with such aspects of combined impacts and their consequences. Risk assessments in ALARM was hierarchical and examined a range of organisational (genes, species, ecosystems), temporal (seasonal, annual, decadal) and spatial scales (habitat, region, continent) determined by the appropriate resolution of current case studies and databases. Socio-economics as a cross-cutting theme centrally contributed to the integration of driver-specific risk assessment tools and methods and developed instruments to communicate
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ALARM partnership The ALARM consortium is co-ordinated by the Helmholtz-Centre for Environmental Research – UFZ. In its first years it combined the expertise of 54 partner organisations from 26 countries (state as published in Settele et al. 2005), while in the course of the project the partnership was enlarged in early 2007 and some complementary expertise included from so called Third-Targeted Countries (TTC), which lead to a total partnership of 68 partner organisations from 35 countries spread around the globe (see Figure 1), but with an emphasis in Europe. In total around 250 scientists were involved in ALARM (see Figure 3). ALARM approach The ALARM approach is summarized in Figure 2. The four modular environmental pressures studied within ALARM were climate change, environmental chemicals, biological invasions and pollinator loss (which to some
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AT – Austria, BY – Belarus, BE – Belgium, BG – Bulgaria, CZ – Czech Republic, DK – Denmark, EE – Estonia, FI – Finland, FR – France, DE – Germany, GB – Great Britain, GR – Greece, HU – Hungary, IE – Ireland, IL – Israel, IT – Italy, LT – Lithuania, NL – Netherlands, PL – Poland, PT – Portugal, RO – Romania, RS – Serbia, SI – Slovenia, ES – Spain, SE – Sweden, CH – Switzerland, UA – Ukraine
A ! DK A LT! ! A ! A! A BY A! A NL ! A ! A ! A A! ! GB ! A ! A A A ! ! A PL ! A! ! A A! ! A A ! UA A! ! A A !CZ ! A A ! BE A ! A ! A ! A ! ! A A DE ! A ! ACH AT HU FR A! ! RO A SI! AA ! A ! ! RS BG ! IT A A ! A ! ES A ! GR A !
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risks to biodiversity to end users, and to indicate policy options to mitigate such risks
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A ! A! A A ! ! A! ! A A! ! A ! A ! A A A ! A A! A! ! A! ! AA! ! A! ! A ! A ! A! ! A ! A A ! A ! A ! A A ! A! ! A! ! A A ! ! A A ! A A A A ! ! A ! A! ! A! ! A A ! A ! A A! ! A! A !
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Figure 1. ALARM partners.
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extend also led to the layout of chapters in the present atlas). The impacted biodiversity was classified in a nested way from genes via populations or species to ecosystems. Indicators of Environmental impacts are on the genetic (e.g., hybridisation due to cross-breeding with invasive species), populations or species (e.g., decline of species numbers or abundance), and on the ecosystem level (e.g., change in species composition). To quantify the impacts of the pressures ALARM used combined risk likelihood and risk consequences scores throughout to identify low, medium or high risks consequent on the respective pressure(s). This approach was used for single as well as multiple pressures. Scenarios have been applied to simulate future environmental threats and to quantify risks subsequent on these (see Spangenberg et al., this atlas, pp. 10ff.). Results of these different risk assessment approaches have been communicated to stakeholders as tested methods for broader application – e.g., through the ALARM Risk Assessment Toolkit (see Marion et al., this atlas, pp. 252f.). Socio-economics as a cross-cutting theme (see Spangenberg et al., this atlas, pp. 188f.) contributed to the integration of driver-specific risk assessment tools and methods and developed instruments to communicate risks to biodiversity to end users, and indicate policy options to mitigate such risks. In the context of ALARM largescale risk assessment refers to processes which have an impact on a large scale, but could affect biodiversity and ecosystems from a local to a continental scale. This includes natural processes as well as anthropogenically triggered change or direct impacts of socio-economic systems in the EU and globally. Some major achievements of ALARM To highlight some of the accomplishments of ALARM we have combined elements of the review report with some of our own thoughts. During the 60 months of project duration, ALARM has been successful in accomplishing al of the key goals and milestones. In addition, the project has provided a large number of benefits that were not initially planned. This can be seen as a result of the willing-
Impact of ALARM on future research Based on the working atmosphere which developed in the course of ALARM, many new initiatives built on established trans-disciplinary contacts from within ALARM (while at the same time taking new partners on board) and due to the positive experience adopted the coordination and
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Environmental Pressures
Climate Change Environmental Chemicals
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Indicators of Environmental Pressures on Biodiversity
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Size and coordination matters: A subjective outlook Some key statements have previously been published on this topic (Settele et al. 2007, 2008). But we would like to highlight some of the major aspects for the readership of this atlas – and herewith we also like to put it in new perspectives. Projects of the size of ALARM offer opportunities for productive partnerships. It is a bit like locking scientists in a room and expecting them to get along – which Paul Jeffrey is quoted as saying that this won’t happen (Nature News Feature ‘With all good intentions’; Nature 452, 682–684; 2008). But our experience in ALARM is that, if you give scientists sufficient choice, new and productive partnerships emerge, and their success increases with project size and collaboration options. Our large consortium also included many leading scientists, who increasingly appreciate the opportunities offered through a project of such size and scope, e.g., by forming new teams conducting inter- and trans-disciplinary research. This is exactly what is urgently needed in science, as expressed by Carpenter et al. (2006): “Meeting the research needs described will require new coalitions among disciplines that traditionally have been isolated….The [Millennium Ecosystem Assessment] has provided a road map; now, we need to start the journey.” We think that large integrated projects have the clear potential to fulfil these requirements. By initiating the IP instrument, the European Commission created considerable support to get the journey started. We very much support the idea to maintain the opportunity to have consortia of such a size also for the future – but the EC now seems to hesitate? Integrating different experiences particularly with EU funded research, we favour more projects of variable sizes, organized through work plans and accompanied by model agreements – including a reasonable proportion of large integrated projects to create opportunities for interdisciplinary and productive partnerships. But surely the key issue especially in larger undertakings is the science and
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Highlights of results Research Findings: Until early 2009 ALARM has contributed to more than 1,000 scientific publications, with a number of papers which may well develop into citation classics and which might shape the way science is conducted in the future. Training: Activities within ALARM encompassed the development of taxonomic/systematic expertise, molecular methods and statistical/quantitative approaches needed to solve biodiversity issues over the next century. This was achieved through funding of graduate and postgraduate level research, conducting formal training sessions, and through the synthesis activities of the many scientific gatherings. Infrastructure: This includes the development of strong interdisciplinary teams, data that will be used in many subsequent analyses, and tools and toolkits that will be adopted within future research. Media Outreach: Research findings have little opportunity to influence policy if the policymakers themselves are not influenced by an increasingly informed public. That is why the media outreach performed in ALARM has been so important. It has included TV and radio shows, coverage in magazines, and newspapers. Last but not least the present atlas to a very large extend is an output of ALARM with exactly the aim to inform the public and policymakers. Also the foundation of the new open access journal “BioRisk” has to be mentioned here (http://pensoftonline. net/biorisk/index.php/journal)
management structure (see Settele et al. 2005). Also the establishment of the above-mentioned focal site network (FSN; Hammen et al. 2010) might have a potentially important long-term impact derived directly from ALARM, as due to its pragmatic structure it should have a high chance to prevail for long time after the end of the ALARM project, e.g. within the LTEREurope network (see Mirtl et al., this atlas, pp. 52f.).
Stakeholder Solutions ECNC, EPPO, EEA
ness of partners to take advantage of unplanned opportunities. Good indicators for success have been the sense of community that could be developed among the various partners, the many collaborations that have been initiated, and the solutions that have been developed for problems that have arisen along the way. ALARM has made a number of advances in building infrastructure, has developed expertise for future generations of scientists, and last but not leas has made important research findings. ALARM has been successful at gaining the attention of key stakeholders and in general of raising awareness of threats to biodiversity in Europe.
Ecosystems
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Figure 2. ALARM scheme to describe the relationships among the four main environmental pressures and the development of methods for Integrated Risk Assessment for the different levels of biodiversity. Socio-economic pressures and indicators form the general background of the ALARM approach (blue arrows: principal effects; fine arrows: additional/indirect impacts; Source: Settele et al. 2005).
art of coordination. Often downgraded as being managers and administrators we think that coordinators at first are scientists with the challenging task to conceptualise new promising approaches. It is the coordinators setting the scenes, developing the proposals and putting together elements which potentially have totally new value to the scientific community. A further challenge is to keep the diversity of characters and the disciplines they represent together in order to maintain the opportunities for productive interactions. Administrative means, like e.g. the consortium agreements required for integrated projects hereby may play a key role. For ALARM, they were signed by all partners before the project started. Laying down rules may seem unnecessary as members cooperate to avoid adverse consequences. But this may be different without such rules. But at the end it is the direct contact and open-mindedness one has to maintain in order to keep a tanker manoeuvrable – and that’s rather an element of art or psychology than of science. Acknowledgements The authors are indebted to the members of the ALARM consortium for active and especially passive contributions to the analysis presented here, namely by presenting the proof of concept that multidisciplinary integration to tackle cross-cutting issues is possible if there is enough freedom to nurture an atmosphere which makes scientist very willing to achieve integration.
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This publication is the result of work in the FP 6 Integrated Project ‘ALARM” (Assessing LArge-scale environmental Risks for biodiversity with tested Methods; grant number GOCECT-2003-506675; Settele et al. 2005, 2007; see www.alarmproject.net). References CARPENTER SR, DEFRIES R, DIETZ T, MOONEY HA, POLASKY S, REID WV, SCHOLES RJ (2006) Millennium Ecosystem Assessment: Research Needs. Science 314: 257-258. HAMMEN VC, BIESMEIJER JC, BOMMARCO R, BUDRYS E, CHRISTENSEN TR, FRONZEK S, GRABAUM R, JAKSIC P, KLOTZ S, KRAMARZ P, KROEL-DULAY G, KÜHN I, MIRTL M, MOORA M, PETANIDOU T, POTTS SG, RORTAIS A, SCHULZE CH, STEFFANDEWENTER, STOUT J, SZENTGYÖRGYI H, VIGHI M, VILÀ M, VUJIC, WOLF T, ZAVALA G, SETTELE J, KUNIN WE (2010; in press). Establishment of a cross-European field site network in the ALARM project for assessing large-scale changes in biodiversity. Environmental Monitoring and Assessment 164: 337-348. SETTELE J, HAMMEN V, HULME P, KARLSON U, KLOTZ S, KOTARAC M, KUNIN W, MARION G, O’CONNOR M, PETANIDOU T, PETERSON K, POTTS S, PRITCHARD H, PYSEK P, ROUNSEVELL M, SPANGENBERG J, STEFFANDEWENTER I, SYKES M, VIGHI M, ZOBEL M, KÜHN I (2005): ALARM – Assessing LArge-scale environmental Risks for biodiversity with tested Methods. Gaia-Ecological Perspectives For Science And Society 14/1: 69-72. SETTELE J, KUHN I, KLOTZ S, HAMMEN V, SPANGENBERG J (2007) Is the EC afraid of its own visions? Science 315: 1220. SETTELE J, SPANGENBERG J, KÜHN I (2008) Large projects can create useful partnerships. Nature 453: 850.
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ALARM provides coherent scenarios of socio-economic, climate, land use and other biodiversity-relevant trends, exploring the framework conditions for biodiversity pressures. An innovative element will be the combination of long term trend and short term shock scenarios, allowing a sensitivity analysis of currently predominating trend projections.
ALARM
Assessing large scale environmental risks for biodiversity with tested methods
Illustration of the complete scenario framework of ALARM
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SEDG – Sustainable European Development Goal
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BAMBU – Business-As-Might-Be-Usual Shocks: BAMBU-SEL (Shock in Energy price Level) BAMBU-CANE (ContAgious Natural Epidemic)
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GRAS - GRowth Applied Strategy Shock: GRAS-CUT – GRAS with Cooling Under Thermohaline collapse
ALARM is an “’Integrated Project” under the 6th EU Framework Programme and lasts from 2004 until 2009 (Contract number: GOCE-CT-2003-506675) Website: www.alarmproject.net Project Co-ordination: Josef Settele, Ingolf Kühn, Volker Hammen, Stefan Klotz, Annette Schmidt, Silke Rattei Helmholtz Centre for Environmental Research - UFZ, Theodor-Lieser-Str. 4, 06120 Halle, Germany E-mail:
[email protected]
The objectives of ALARM
To focus on risks consequent on climate change, environmental chemicals, rates and extent of loss of pollinators and biological invasions in the context of current and future European land use patterns
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To develop a research network across different environmental drivers and pressures and across different spatial and temporal scales of ecosystem and biodiversity changes
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To establish socio-economic risk indicators related to the drivers of biodiversity pressures as a tool to support long-term oriented mitigating policies and to monitor their implementation
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Figure 3. Reproduction of ALARM flyer.
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Atlas of climate change impacts on biodiversity (Araujo et al., 2006 ff.; see http://www.biochange-lab.eu/resources/ data)
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Assessment of parallel declines in pollinators and insect pollinated plants in North-Western Europe (Biesmeijer et al., Science 313: 351-354; 2006)
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Data base on pollinators of Europe (with data on 180.000 plant species; the decline of pollinators poses a major threat to human food supply)
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Quantification of importance of pollinators in changing landscapes for world crops (Klein et al., Proc. Roy. Soc. B 274: 303-313; 2007)
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Review on the effects of pollution on biodiversity (2007)
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Foundation of a Biodiversity Risk Assessment publication series (2007/2008)
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Identification key to European bee genera (2008)
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Development of EU policy scenarios (2008)
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RAT – Risk Assessment Toolkit for biodiversity (2009)
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ALARM atlas of biodiversity risks (2009)
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ALARM scheme to describe the relationships among the four main environmental drivers and pressures and the development of methods for Integrated Risk Assessment for the different levels of biodiversity. Socio-Economic pressures and indicators form the general background of the ALARM approach
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Establishment of a European site network for ecological research (2005 ff.; in the long term planned to be integrated into the system of LTER-Europe)
Biological Invasions
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The ALARM consortium combines the expertise of 68 partners from 35 countries. ALARM encompasses 7 SMEs as full partners with central responsibilities
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To develop an integrated large scale risk assessment to biodiversity as well as terrestrial and freshwater ecosystems
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Helmholtz-Centre for Environmental Research - UFZ (UFZ) – Germany
Estonian Institute for Sustainable Development (SEIT) – Estonia
Potsdam Institute for Climate Impact Research (PIK) – Germany
Annette Schmidt, Bernd Gruber, Carsten Dormann, Elisabeth Kühn, Eva Küster, Heinz-Ulrich Neue, Ingolf Kühn, Jan Hanspach, Jeroen Everaars, Joachim Spangenberg, Josef Settele, Karin Ulbrich, Mark Frenzel, Oliver Schweiger, Ralf Seppelt, Silke Rattei, Stefan Klotz, Sven Pompe, Sylvia Ritter, Ursula Nigmann, Volker Hammen
Helen Poltimäe, Jaan Luig, Kaja Peterson, Piret Kuldna, Valdur Lahtvee
Alberte Bondeau, Franz W. Badeck, Katrin Vohland, Wolfgang Cramer
Centre for Cartography of Fauna and Flora (CKFF) – Slovenia
Jagiellonian University (UJAG) – Poland
GeoBiosphere Science Centre, Lund University (ULUND) – Sweden
University of Leeds (LEEDS) – Great Britain
Anna Amirowicz, Anna Stefanowicz, Dawid Moroń, Elżbieta Rożej, Hajnalka Szentgyörgyi, Irena Grześ, Maciej Maryański, Maria NiIklińska, Marta Wantuch, Michal Woyciechowski, Paulina Kramarz, Renata Śliwińska, Ryszard Laskowski, Stanislaw Knutelski, Tomasz Skalski
Margareta Johansson, Martin T. Sykes, Thomas Hickler, Torben R. Christensen
Bill Kunin, Chiara Polce, Jens Dauber, Koos Biesmeijer, Laura Harrison
Bourgas University (LMC) – Bulgaria
Catholic University of Louvain (UCL) – Belgium
Biomathematics & Statistics Scotland (BioSS) – Great Britain
Ali Šalamun, Branko Ivanek, Katja Poboljšaj, Marijan Govedič, Mladen Kotarac, Vesna Grobelnik
Ovanes Mekenyan, Saby Dimitrov Isabelle Reginster, Mark Rounsevell
Adam Butler, Glenn Marion, Stijn Bierman
Lancaster University, Environmental Science Dept. (ULANC) – Great Britain Andy Sweetman, Kevin Jones
National Environmental Research Institute (NERI) – Denmark
OLANIS Expert Systems GmbH (OLANIS) – Germany
Marianne Thomsen, Martin Holmstrup, Paul Henning Krogh, Peter Borgen Sørensen, Philipp Mayer, Steen Gyldenkærne, Ulrich Karlson, Vibeke Simonsen
University of Milano Bicocca (ConISMa-UNIMIB) – Italy Antonio Finizio, Marco Rosso, Marco Vighi, Maura Calliera, Roberto Verro, Sara Bonzini, Sara Villa, Serenella Sala, Stefania Barmaz
Natural Environmental Research Council, Centre for Ecology and Hydrology (NERC) – Great Britain Annie Truscott, Chris Preston, Claus Svendsen, Dan Osborn, David Roy, David Spurgeon, Julian Wright, Peter Hankard, Phil E. Hulme, Rodolphe Gozlan, Sara Long
Barry Muijs, Chiel Jonker, Joop Hermens
Finnish Environment Institute (SYKE) – Finland
University of Stockholm (ITM) – Sweden
Juha Pöyry, Mikko Kuussaari, Miska Luoto, Niko Leikola, Raimo Virkkala, Risto K. Heikkinen, Susanna Kankaanpää, Stefan Fronzek, Timothy R. Carter, Varpu Mitikka
University of Bern (UB) – Switzerland
Costas Prevedouros, Ian Cousins, Örjan Gustafsson
Martin Schmidt, Sven Bacher, Wolfgang Nentwig
Finnish Meteorological Institute (FMI) – Finland Andrea Vajda, Ari Venäläinen, Kirsti Jylhä
Klaipeda University, Coastal Research and Planning Institute (KUCORPI) – Lithuania
University of Castilla-La Mancha (UCLM) – Spain
Sergej Olenin
Helena Fernández Castro, Gonzalo Zavala, Iván Torres, José M. Moreno, Juan Angel Gracía, María Martín
University of Hannover (GEOBOT) – Germany
Institute of Botany, Academy of Sciences of Czech Republic (IBOT) – Czech Republic
Utrecht University (IRAS-TOX) – Netherlands
Ralf Grabaum, Thomas Meyer
Zoological Institute of the Russian Academy of Sciences (ZIN RAS) – Russia Vadim Panov
Gian-Reto Walther, Silje Berger
Irena Perglová, Jan Pergl, Martin Hejda, Martin Křivánek, Milan Chytrý, Petr Pyšek, Vojtěch Jarošík
CAB International (CABI) – Great Britain
University of Umea (UMU) – Sweden
Georg-August University of Göttingen (GAUG) – Germany Arno Kuhn, Birgit Meyer, Catrin Westphal, Farina Herrmann, Gitte Hornemann, Ingolf Steffan-Dewenter, Jennifer Kröger, Julia Dolezil, Kristin Krewenka
University of the Aegean (AEGEAN) – Greece
Mats Jansson
Christelle Péré, Marc Kenis, Matthew Cock, Rüdiger Wittenberg
Swiss Federal Institute of Technology (ETHZ) – Switzerland
Marine Organism Investigations (MOI) – Ireland
Center for Ecological Research and Forestry Applications (CREAF) – Spain Bartomeus Nacho, Daniel Sol, Eduard Pla, Jara Andreu, Joan Pino, Jordi Sardans, Josep Peñuelas, Núria Gassó, Santi Sabaté
Annita Logotheti, Andrew Grace, Ellen Lamborn, Michalis Vaitis, Olivia Messinger, Theodora Petanidou
Sustainable Europe Research Institute (SERI) – Austria
University of Evora (UE) – Portugal
Andrea Stocker, Fritz Hinterberger, Gabi Christler, Ines Omann, Jill Jäger, Joachim Spangenberg
Dan Minchin
Björn Reineking, Harald Bugmann
University of Reading, School of Agriculture (Reading) – Great Britain Claire Brittain, Emily Chambers, Ioannis Vogiatzakis, Simon G. Potts, Simon Springate, Stuart Roberts, Thomas Tscheulin
National Institute of Agronomic Research (INRA) – France
David Nogues, Miguel B. Araújo, Wilfried Thuiller
Alain Roques, Bernard E. Vaissière, Christelle Robinet, Gabriel Carre, Nicola Gallai
University of Vienna (VEGVIE) – Austria
Croatian Natural History Museum (CNHM2) – Croatia
Georg Grabherr, Harald Pauli, Laszlo Nagy, Michael Gottfried, Sonya Laimer
Martina Sasic
Martin-Luther-University Halle-Wittenberg (MLU) – Germany
Procter & Gamble European Technical Centre (P&G) – Belgium
Institute of Zoology - Chinese Academy of Sciences (IOZ-CAS1) – China
Mandy Rohde, Robin Moritz, Stephan Wolf
Diederik Schowanek, Joanna Jaworska
Jianghua Sun, Lili Zhao
Swedish University of Agricultural Sciences (SLU) – Sweden
The Natural History Museum, London (NHM) – Great Britain
Universidad Mayor de San Andres (FUND-ECO/ UMSA1) – Bolivia
Carol Högfeldt, Janne Bengtsson, Riccardo Bommarco
Andrew Polaszek, Paul H. Williams
Alfredo Grau, Jorge Jacome, Stephan Beck, Stephan Halloy
University of Haifa (HAIFA) – Israel
University of York (YORK) – Great Britain
Amots Dafni, Gidi Ne’eman
Chris Thomas, Ralf Ohlemüller
Latin American Faculty of Social Sciences (FLACSO1) – Guatemala
University of Versailles Saint Quentin en Yvelines (UVSQ) – France
University of Bayreuth, Department of Animal Ecology I (UBT) – Germany
Franck Legrand, Jean-Marc Douguet, Laura Maxim, Martin O’Conor, Philippe Lanceleur
Catrin Westphal, Gitte Hornemann, Ingolf Steffan-Dewenter
Enrioco Virgilio Reyes, Iliana Monterroso
University of Stellenbosch (SU1) – South Africa Autonomous University of Barcelona (UAB) – Spain Beatriz Rodriguez-Labajos, Iliana Monterroso, Joan Martinez Alier, Rosa Binimelis
PENSOFT Publishers (PENSOFT) – Bulgaria
Colleen Louw, John Simaika, Michael Samways
Institute of Cytology and Genetics SB, RAS, Novosibirsk (ICiG1) – Russia Alexander Blinov, Olga Novikova
Lyubomir Penev, Teodor Georgiev
Katholieke Universiteit Leuven (KUL) – Belgium Frank Van de Meutter, Luc Brendonck, Luc De Meester, Robby Stoks, Tom De Bie
Institute of Nature Conservation, Polish Academy of Sciences (INC PAS) – Poland Wieslaw Krol, Wojciech Solarz
University of Tartu, Institute of Botany and Ecology (UT) – Estonia Jaan Liira, Kersti Püssa, Mari Moora, Martin Zobel, Meelis Pärtel, Tiit Teder, Virve Sõber
Institute of Biological Research (ICBC) – Romania
V. N. Sukachev Institute of Forest, Krasnoyarsk (SIFSB-RAS1) - Russia Elena I. Parfenova, Nadezda M. Tchebakova, Natalia I. Kirichenko, Yuri N. Baranchikov
El Colegio de la Frontera Sur (ECOSUR1) – Mexico Bernhard Kraus, Daniel Sanchéz, Remy Vandame
Centre for the Balkan Biodiversity Conservation (FSUNS1) – Serbia Andrijana Andrić, Ante Vujić, Boža Pal, Dragana Obreht, Dubravka Polić, Edita Suturović, Goran Anačkov, Jadranka Luković, Jasmina Ludoški, Lana Krstić, Sanja Veselić, Sergey Popov, Smiljka Šimić, Snežana Radenković, Vesna Milankov, Zorica Nedeljković
Laszlo Rakosy, Vlad Dinca
L.U.P.O. GmbH (LUPO) – Germany Jürgen Ott
Odessa Branch Institute of Biology of Southern Seas, National Academy of Sciences of Ukraine (OBIBSS1) – Ukraine Boris Aleksandrov, Marina Kosenko
Centre for Ecological Research, Polish Academy of Sciences (CBE) – Poland Andrzej Tatur, Janusz Uchmanski, Karol Kram, Krassimira Ilieva-Makulec, Marek Rzepecki
University of Concepcion (UDEC) – Chile
Institute of Zoology, National Academy of Sciences (IZ-NAS1) – Belarus Mikhail Moroz, Mikhail Pluta, Sergey Mastitsky, Vasil Vezhnovetz, Viktor Ryzevsky, Vitalij Semenchenko, Vladimir Razlutsky
International Rice Research Institute (IRRI1) – Philippines Greg Fanslow, Kong Luen Heong, Yolanda Chen
Centre National de la Recherche Scientifique (CNRS2) – France Agnès Rortais, Gérard Arnold
University of Dublin, Trinity College, School of Natural Science (Dublin2) – Ireland Jane Stout, Karl Duffy
Institute of Ecology of Vilnius University (Vilnius2) – Lithuania Eduardas Budrys
ALTER-Net: A Long-Term Biodiversity, Ecosystem and Awareness Research Network (ALTER-Net2) – Great Britain Allan Watt, Andrew Sier, Michael Mirtl, Terry Parr
Biological Station Doñana (EBD-CSIC2) – Spain Montserrat Vilà
Butterfly Conservation Europe (BCE2) – Netherlands Chris Van Swaay, Dirk Maes, Martina Sasic, Irma Wynhoff, Martin Warren, Rudi Verovnik, Theo Verstrael
Institute of Ecology and Botany of the Hungarian Academy of Sciences (IEB-HAS2) – Hungary Bálint Czúcz, Edit Kovács-Láng, György Kröel-Dulay, László Somay, Zoltán Botta-Dukát
Umweltbundesamt (UBA2) – Austria Christian Schulze, Erich Pötsch, Michael Mirtl
Eduardo Ugarte, Nicol Fuentes
Argentinean Institute (IADIZA), National Council for Science and Technology (CONICET) – Argentina Ricardo Ojeda
Institute for Biological Research “Sinisa Stankovic”, University of Belgrade (IBISS1) – Serbia
St. Petersburg State University, (SPBSU) - Russia Vadim Panov
Aleksandra Grozdanovic, Dragana Vujanovic, Miodrag Petrovic, Momir Paunovic, Predrag Jaksic, Zoran Krivosej
1 2
TTC partner associated partner
Figure 3. Continued.
A S S ES S ING
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The ALARM Field Site Network, FSN VOLKER HAMMEN, JACOBUS C. BIESMEIJER, RICCARDO BOMMARCO, EDUARDAS BUDRYS, TORBEN R. CHRISTENSEN, STEFAN FRONZEK, RALF GRABAUM, PREDRAG JAKSIC, STEFAN KLOTZ, PAULINA KRAMARZ, GYÖRGY KRÖEL-DULAY, INGOLF KÜHN, MICHAEL MIRTL, MARI MOORA, THEODORA PETANIDOU, JOAN PINO, SIMON G. POTTS, AGNÈS RORTAIS, CHRISTIAN H. SCHULZE, INGOLF STEFFAN-DEWENTER, JANE STOUT, HAJNALKA SZENTGYÖRGYI, MARCO VIGHI, ANTE VUJIC, CATRIN WESTPHAL, TORSTEN WOLF, GONZALO ZAVALA, MARTIN ZOBEL, JOSEF SETTELE & WILLIAM E. KUNIN
Introduction European biodiversity research lacks the infrastructure of a cohesive site network for testing, monitoring and mapping impacts of different largescale environmental pressures on biodiversity. This infrastructure can help answer questions of high ecological and political importance about impacts on biodiversity such as where,
how, and to what degree global change and climate change is impacting European biodiversity. Complex and numerous relationships and their changes due to anthropogenic pressures have to be described, risks assessed and impacts measured, but previously the answers have often been provided only for bilateral relationships, on a local scale or for a few
European countries or areas. But climate change impacts on biodiversity need a large-scale site network, where assessments of these impacts can be investigated, spanning all important biogeographical regions of the continent. Most research sites are investigated for a short term only or for a specific research topic, and complete coverage of European biogeographi-
cally zones is not given. Size and numbers of investigated sites are usually small and detection of species ranges and their shifts in Europe is rare. Risks and their impacts on species populations can only then be assessed. As the first research initiative with the critical mass needed, ALARM deals with such aspects of combined impacts and their consequences.
Biogeographical Regions ! !
Alpine North
Abisko
Alpine South Boreal Atlantic Continental Pannonian Mediterranean no data
Uppsala
!
!
!
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Galway Berkshire !
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Krakow
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Ile de Frace
Gumpenstein
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Meolo Avignon
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Fruška Gora
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Toledo
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! Lesvos !
!
Figure 1. Realized FSN across Europe. The larger dots represent the examples which are shown on the following pages.
42
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Field sites
Figure 2. Field site Abisko (Sweden). Photos: T.R. Christensen.
Goals of the field site network Setting up the field site network was a response to two central issues: first, the establishment of a site network as a framework within which separate and interacting effects of the ALARMconcerned multiple pressures were investigated, and second, as a testing ground for the risk assessment tools developed during the project. The field site network FSN acts as the necessary testing ground for all aspects
of the four natural science modules of the ALARM research, and was established with a comprehensive geographical and environmental coverage of Europe. It allows detailed monitoring of environmental variables, species distributions and ecological interactions in a locally intensive but geographically extensive manner, providing a unique continent-wide perspective on these issues. The FSN enables the use of long-term field sites in estimating risk factors for biodiversity.
The FSN network thus plays a central role in the joint research within the ALARM modules and integrates modular research on the same site for investigating multiple impacts on biodiversity. All sites include terrestrial as well as freshwater habitats, including both lentic and lotic environments. The network covers most European climates and biogeographic regions, from subarctic environments through boreal and central European zones to the Mediterranean.
The ALARM field site network has four objectives: A) To establish field experimental plots within paired field sites; B) To continue collecting current and historical remote-sensing data and associated information; C) To develop detailed field protocols for use across the site network; D) To organise field teams to perform replicated research protocols at all sites; and E) To collect and analyse field data and feed it back to the main research modules.
AYIA PARASKEVI
ALTINOVA
MITILINI
POLIKHNITOS
PLOMARION
Source: EEA
Source: EEA Mystegna Mytilini Discontinuous urban fabric Industrial or commercial units Olive groves Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural vegetation Coniferous forest Natural grasslands Sclerophyllous vegetation Transitional woodlandshrub Sea and ocean Field site boundary
0
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Figure 3. Field site Lesvos (Greece). Photo: T. Pedanidou.
T HE
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! ( ! (
Ballindooly
GALWAY
! (
Carnmore
! ( ! (
! (
! ( Clarinbridge
! ( ! ( ! (
! (
Craughwell
! (
Loughrea
! (
Kiltiernan
! ( ! ( ! (
Kilchreest
Ardrahan
Figure 4. Field site Galway (Ireland).
Establishing experimental plots To provide the necessary testing ground for ALARM research, a network of field sites had to be established with a comprehensive environmental and geographical coverage of Europe. These areas are arranged across Europe in a nearly regular grid, approximately 800 km apart from each other, and allowing slight variations to fit the irregular shape of the continent. Each of the areas in focus consists of two study sites, each 4x4 km and within 50 km of each other. Paired study sites are chosen to be as similar as possible in abiotic conditions, such as geology, type of landforms and soils, elevation, hydrology, and other environmental parameters so that human disturbance and land-use intensity are the main distinguishing characteristics between the two sites: 1) Areas dominated by extensive agriculture, with remnants of semi-natural vegetation. The “disturbed” site is located here; 2) Extensive areas dominated by semi-natural vegetation, with some agriculture, the “natural” site. The FNS played an important role for research integration in ALARM in the years 2006 and 2007. By the year 2007, 16 field sites had been selected and assigned to the core field site network of ALARM. All these FSN sites
are in principle open to any researcher outside ALARM. The site selection process was started by gathering information using questionnaires on (a) the field sites where partners work, (b) the field site characteristics necessary and sufficient to carry out research, (c) the projects planned or thought of. The selection process not only involved ALARM partners’ field sites but also partners and their input from Greenveins, AlterNet, LTER-Europe [1,2], and LTERGermany [3]. This has led to the incorporation of a Greenveins’ site and an LTER-site in the ALARM field site network FSN in 2007 plus three more sites from new partners in ALARM complementing the FSN network. Compared to previous EU projects the FSN has many sites and a more complete coverage of environmental heterogeneity within the continent. The size of each field site also enables research on landscape-scale heterogeneities and habitat / land-use types. Standardisation of site selection is a major advantage leading to a higher comparability. Selected field sites and site information ALARM research has been carried out on 16 field sites. (Figure 1 and Table 1).
Table 1. Field sites, their country and biogeographical region (North to South).
Field site name Abisko Uppsala Tartu Maisiagala Galway Berkshire Göttingen Ile de France Krakow Gumpenstein Kiskun Meolo Fruška Gora Garraf Toledo Lesvos
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Biogeographical region Alpine Boreal Boreal Boreal / Continental Atlantic Atlantic Continental Atlantic Continental Alpine Pannonian Continental Continental Mediterranean Mediterranean Mediterranean
Sweden Sweden Estonia Lithuania Ireland UK Germany France Poland Austria Hungary Italy Serbia Spain Spain Greece
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Figure 5. Climate scenarios for the site Lesvos, Greece (Source: S. Fronzek).
The field site in Abisko (Northern Sweden) does not have a disturbed site due to a lack of anthropogenic influence in the area. All other sites show the paired design. Most sites are managed by ALARM partners. The site managers prepared information on the FSN sites, then collected and distributed them within the project. Information on soil, climate, topography, geomorphology, land use, as well as pictures were essential for researchers in ALARM to develop research protocols and tests. Maps were processed for the sites, as topographic maps or LandSat satellite pictures and also as digital elevation maps, to give remote researchers an impression of the site area without scientists having to visit the sites. The disturbed and undisturbed sites were identified by red and green frames for which more detailed information and maps were then provided. Photographic images and contact details for each field site provided complementary information. Climate change research plays a key role in ALARM. We developed climate scenarios for the grid cells of each focal area for the parameters mean temperature, mean precipitation and growing degree days for the time period 2001 to 2100. The climate scenarios are available as graphs and as data for the specific location. We used selected climate and
SRES socio-economic scenarios for the field sites as well as an additional scenario for the break down of the Thermohaline Circulation of the North Atlantic Ocean. This provides research at the field sites with scenarios of potential future climates (Figure 5). Field research and outlook After establishing the research sites, standardised and detailed field protocols were designed for the use across the site network. Senior researchers, representing the research modules of ALARM, assisted the field site programme. A number of research teams within the ALARM consortium proposed specific research protocols for that year, with precise descriptions of the methods to be employed. A process was started within ALARM in order to find a future for the FSN beyond ALARM. Furthermore, the LTER sites and the ALARM sites plus other research sites all together should be used as a backbone for future research projects, where research consortia are integrating all or specific sites into their proposal depending on research topics. References [1] http://www.lter-europe.ceh.ac.uk/index.htm [2] http://www.ilternet.edu/ [3] http://www.lter-d.ufz.de/
VITTORIO VENETO PORDENONE
CONEGLIANO
BASSANO DEL GRAPPA
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Altitude [m] 1420
Maize Potatoes
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Source: EEA
Livenza
Source: EEA
Meolo
Discontinuous urban fabric Industrial or commercial units Mineral extraction sites Non-irrigated arable land Vineyards Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural vegetation Broad-leaved forest Coniferous forest Mixed forest Natural grasslands Transitional woodland-shrub 0
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Figure 6. Field site Meolo (Italy). Photos and GIS maps: M. Vighi.
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The ALARM Field Site Network: a Continental-Scale Test Bed for Questions Related to Major Drivers of Biodiversity Change JACOBUS C. BIESMEIJER, JENS DAUBER, CHIARA POLCE, WILLIAM E. KUNIN, VOLKER HAMMEN & JOSEF SETTELE
The ALARM Field Site Network (FSN) was established in order to meet the demand of a cohesive site network for European biodiversity research (Hammen et al. 2010 and this atlas, pp. 42 ff.). It provides researchers with the infrastructure for testing, monitoring and mapping impacts of different largescale environmental pressures on biodiversity on a Continental-wide scale. The FSN was extensively deployed as a testing ground by the ALARM researchers. The sites have been used for 30 research protocols during the 2006 and 2007 field seasons, and cover all ALARM’s natural and socio-economic science modules, incl. cross-cutting themes (Table 1). More than half of the ALARM partners have been involved in research and fieldwork at FSN sites. In 2006, 13 research projects made use of the FSN sites, and several of those were continued in 2007. Additional projects were started in 2007, for a total of 20 projects (Table 1). More than half of the projects involved a single module, whereas the others involved more than one module. A huge variety of ecological methods were applied in the FSN research
activities (Figure 1), including a range of surveying methods (e.g., pan traps, pitfall traps, transects, social science surveys), experimental plots (e.g., for tree germination), and monitoring (e.g., using temperature loggers). Since land use differences are among the main foci of the ALARM FSN work we needed a sound way of defining landscape structure qualitatively and quantitatively. Therefore the BIOHAB protocol, which is likely to become one of the standard methods for landscape surveillance and monitoring and has the advantage of being compatible to many other systems while still being sufficiently detailed, was applied for our purposes (Figure 2). The main aim was to get a good measure of landscape structure for all FSN sites, by sampling 1 km2 in each site and to provide background data for other FSN experiments, including pitfall traps, potted plants, pan traps. For the second field season we aimed at projects cutting across the main research pillars of ALARM. This led to the CITIRAT and PACRAT projects. The CITIRAT (Climate Interaction with Terrestrial Invasions Risk Assessment Tool) project assessed
whether climate change enhances or hampers terrestrial invasions. We focussed on plants, ants and pollinators. The PACRAT (Pollinators affected by Agro-Chemicals as a Risk Assessment Tool) project assessed links between the use of agro-chemicals, mainly pesticides, and pollinator abundance and diversity across European agricultural landscapes. Some other projects in brief: The Rose hip chalcid survey aimed at assessing the present distribution range of invasive seed chalcid wasps, their relationships with the native species and their impact on the potential of natural regeneration of the host plants (see Auger-Rozenberg et al., this atlas, pp. 156f.). The large-scale tree transplanting experiment aimed at testing speciesspecific ecological limits across their range. Experimental plots were established to assess germination and growth of two tree species to test some modelling predictions. Palm germination was possible in a wider range than the simulated potential range. The Merodon–Ornithogalum interaction study explored the relationship between hoverflies of the genus Merodon and its
Table 1. Overview of ALARM field site research.
Year 2006-01 2006-02 2006-03 2006-04 2006-05 2006-06 2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 2007-05 2007-06 2007-07 2007-08 2007-10 2007-10 2007-10 2007-13 2007-14 2007-15 2007-16 2007-17 2007-18 2007-19
Project Rose hip seed predators Invertebrate diversity Tree transplanting Pollinator abundance Pollination success Tree recruitment Micro/macro climate Landscape structure Socio-economic survey Horse chestnut miner Wasp communities in woodland Pollinator Transect surveys Rose hip seed predators Tree transplanting Merodon–Ornithogalum Socio-economic survey CITI-RAT Plant survey CITI-RAT Temperature CITI-RAT Pan traps CITI-RAT Ant traps PAC-RAT Pesticide sampling PAC-RAT Honeybee pollen sampling PAC-RAT Crops: field survey & interviews PAC-RAT Pollination services PAC-RAT Pan trapping pollinators PAC-RAT Trap nesting bees and wasps PAC-RAT Pollinator Transect surveys PAC-RAT Bumblebee genetics / parasites PAC-RAT Climate change & seed germination PAC-RAT Wasp communities in woodland
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Module Invasives Invasives & Climate Climate Pollinators Pollinators Climate (Invasives) Climate All (background info) Socio-Economic Invasives Pollinator loss Pollinator loss Invasives Climate Pollinator loss Socio-economic Invasives & Climate Invasives & Climate Climate Invasives & Climate Env. Chem. Pollinator loss Env. Chem. Pollinator loss Pollinator loss Pollinator loss Pollinator loss Pollinator loss Climate Pollinator loss
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Leader A. Roques W. Nentwig G.-R.Walther J. Biesmeijer C.Westphal C.Thomas J. Biesmeijer J. Biesmeijer B. Rodrigues-Labajos C.Vaamonde L. Dvorak J. Settele A. Roques G.-R.Walther T. Petanidou B. Rodrigues-Labajos W. Kunin W. Kunin J. Biesmeijer J. Dauber M.Vighi M.Vighi M.Vighi R. Bommarco J. Biesmeijer E. Budrys J. Settele I. Steffan-Dewenter J. Moreno L. Dvorak
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Partner INRA Bern UBT Leeds Bayreuth York Leeds Leeds UAB INRA Associated partner UFZ INRA UBT AEGEAN-FSUNS UAB LEEDS LEEDS LEEDS LEEDS ConISMa-UNIMIB ConISMa-UNIMIB ConISMa-UNIMIB SLU LEEDS Vilnius UFZ UBT UCLM Ass.
larval host plant, Ornithogalum spp across the Mediterranean. The Socio-economic survey aimed at understanding the driving forces behind the pressures on biodiversity. It revealed that energy consumption, material flows and land use intensity are quantitative aggregate drivers behind many pressures on the macro level, whereas habitat fragmentation is a major qualitative driver. The previously mentioned CITIRAT temperature and plant survey assessed to what extend plant species were found in different microclimates and whether the hottest sites might be more amenable to alien invasion. Available temperature data (from widely interspersed meteorological stations) average out across landscapes and have limited ecological meaning. We used digital elevation models and computer algorithms to calculate fine-scale temperature differences (Figure 3 for description of the methods). The microclimatic pattern of mean temperature where then validated using temperature probes (Figure 4). The Pan trap survey as part of the CITIRAT project aimed to assess whether the abundance and diversity of pollinators in the landscape is different in hot and cold areas in the semi-natural vegetation in the field sites. The ant trap survey assessed whether microclimate leads to differences in the abundance, community composition and diversity of ants. Pesticide sampling aimed at linking small-scale pesticide pressure information to data on pollinator abundance and diversity. The pollination services are studied for their differences in land use intensity, different habitat types, and climatic differences across the European gradient. As part of the PACRAT study, we also assessed the abundance and diversity of pollinators in relation to habitat and landscape structure as well as pesticide pressures resulting from agriculture. The trap nesting survey assessed the relative abundance and diversity of wood-nesting pollinators and wasps. The pollinator transect survey of butterflies and bumblebees aimed at assessing species inventories, estimating relative densities of butterflies and bumblebees. The bumblebee sampling study aimed at assessing whether land use intensity and climatic gradient have an impact on the genetic diversity and parasite load of bumblebees.
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Figure 1. Examples of some of the methods applied in FSN research activities. Pitfall trap (a). Plot for tree transplanting project (b). Pan traps in situ and up close (c). Wild mustard plants in the pollination services project (d). Horse chestnut leaf with mines (e). Infected rose hip seeds (f). iButtons used for temperature registration in project (g). Photos: W. Nentwig (a), J. Biesmeijer (b, g), I. Torres (c), C. Westphal (d), C. Lopez-Vaamonde (e), A. Roques (f).
Figure 2. Results from detailed landscape mapping using the BIOHAB protocols developed in the FP5 project BIOHAB. This example shows the pairs of landscapes of the Göttingen FSN site. The small-scale landscape with extensive semi-natural area (Rossbach) and the disturbed site dominated by large crop fields (Barterode). The listed codes refer to different categories of land use which are described in the BIOHAB field handbook.
Seed germination experiment characterizes the germination response of six widely distributed species from varied provenances across Europe’s environments when exposed to different germination conditions. The social wasp community study is assessing the diversity of social wasps in European forests.
References
Results show that FSN is a valuable tool for (1) testing for spread of invasive or expanding species and for assessment of potential ranges under climate change; (2) a controlled assessment of the impact of disturbance at landscape and habitat level on biodiversity at a European scale.
DTM slope aspect
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MOORA M, PETANIDOU T, PINO J, POTTS SG, RORTAIS A, SCHULZE CH, STEFFANDEWENTER I, STOUT J, SZENTGYÖRGYI H, VIGHI M, VUJIC A, WESTPHAL C, WOLF T, ZAVALA G, ZOBEL M, SETTELE J, KUNIN WE (2010) Establishment of a cross-European field site network in the ALARM project for assessing large-scale changes in biodiversity. Environmental Monitoring and Assessment 164: 337-348.
BENNIE J, HUNTLEY B, WILTSHIRE A, HILL MO, BAXTER R (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecological Modelling 216: 47-59. HAMMEN VC, BIESMEIJER JC, BOMMARCO R, BUDRYS E, CHRISTENSEN TR, FRONZEK S, GRABAUM R, JAKSIC P, KLOTZ S, KRAMARZ P, KRÖEL-DULAY G, KÜHN I, MIRTL M,
Potential Solar Potential SolarRadiation Radiation clear sky sk conditions) 2 (i.e. under clear from r.sun (GRASS GIS)
Potential t map From Radiation Temperature: From Radiationtoto Temperature:
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3 empirical relation tested on grassland (Bennie et al. 2008)
Corine LC data
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Maisiagala (Lithuania)
High : 15.29
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Figure 3. Scheme of the methodology used for the generation of maps of average annual temperature for the FSN sites.
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Figure 4. Examples of temperature surfaces (average annual temperatures in degrees Celcius) calculated using the methods explained in Figure 3.
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Socio-Economic Research within a Field Site Network Established by Ecologists – Pragmatic Approaches to Create Added Value JOACHIM H. SPANGENBERG, NANCY ARIZPE, BEATRIZ RODRIGUEZ-LABAJOS, ROSA BINIMELIS, JOAN MARTÍNEZ-ALIER, LAURA MAXIM & JEAN-MARC DOUGUET
Socio-economists as strangers in ecologists’ paradise: Concept and analytical approach The ALARM Field Site Network has been established on existing sites selected by bio-scientists to analyse major drivers of biodiversity change (see Biesmeijer et al., this atlas, pp. 46f.). Analysing such field sites regarding their socio-economic characteristics is not a usual procedure. For an empirical socio-economic analysis (as
Figure 1. Traditional land use on churchyard and along hedges in Düren, Germany. Photo: J.H. Spangenberg.
opposed to using the theoretical models preferred e.g., by most economists), sites with significantly different socio-economic characteristics would have been chosen to analyse the resulting impacts. However, as the network was already established (see Hammen et al., this atlas, pp. 42ff.), for the socio-economic analysis a conceptual exercise was an unavoidable starting point, and fuzzy data were to be expected. In order to contribute to improved biodiversity conservation, in the conceptual phase the socio-economic team had first to identify the kind of assessment needed. Within biosciences indicators and monitoring systems are being developed, tested and fine tuned (see Westphal et al., this atlas, pp. 170f.) which intend to measure the state of biodiversity, either on a micro level (including endangered species) or on the level of ecosystem processes (e.g., for pollinator loss; see Nielsen et al., this atlas, pp. 174f.). However, such efforts tend to underestimate the relevance of anthropogenic processes (traditional or modern ones, with positive or negative impacts, see Figures 1 and 2) as compared to natural. Thus they do often not reflect human impacts sufficiently, at least in the con48
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clusions for conservation (anthropogenic drivers are often taken as a fixed external condition and not as a system parameter which can be influenced). These facts point to a gap in the science-policy interface: scientific insights need to be formulated in a way that the can bear fruit in the policy process, and this requires to extend the horizon of the research questions beyond the biosciences. Measurements on the biological micro and the ecological macro level can signal fatal trends, and in some cases be the basis for emergency measures, but they have only limited capacity to guide long term preventive policies (Spangenberg et al., this atlas, pp. 188f.). Pressure monitoring The next level to be taken into account (following loosely the DPSIR concept and its systematic specification for biodiversity, see Maxim et al., this atlas, pp. 16f.) was the pressures on ecosystems which cause the impacts usually monitored. Focussing on pressures brings the analysis one important step closer to policy relevance: pressures are man made interventions causing impacts on biodiversity, and they can be changed by policy intervention. Thus pressure monitoring can be the basis of decisions to intervene or not, and give hints how to do it. Policy measures guided by pressure measurements will usually be curative, addressing specific current causes of biodiversity loss. However, many, if not most
pressures are not intentionally caused, but side effects of policies and strategies designed and introduced for other purposes. These must be changed, either in their implementation, their substance or even their purpose if not the pressures – after implementing curative interventions – are to reemerge in the same or a similar form. Driver analysis Thus what is needed for an effective reduction of negative impacts, and for a proactive policy formulation, is an analysis of the drivers behind the pressures. This can be used for a more cause-oriented policy formulation, taking biodiversity into account ex ante, and not only ex post when damages have been registered (which can be much later than when they occurred). This is the domain of socio-economic analysis, and where socio-economic indicators for biodiversity pressures and loss have to be formulated (Spangenberg 2007). Caveats due to the complexity of society However, the society (and the economy as a part of it) is a complex system. This has two main implications: on the one hand, it requires a differentiation of various levels; in this respect we loosely followed the classification into organisation, mechanisms and orientations known from political sciences, and analysed drivers on all three interacting levels.
Figure 2. Modern land use on agricultural land in Düren, Germany. Photo: J.H. Spangenberg.
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On the other hand, we are aware that the behaviour of complex evolving systems is unpredictable, as an inherent system characteristic. Thus indicators on this level can serve as early warning signals that there is a risk that unsustainable pressures on biodiversity might be caused by certain trends, decisions or structures, but there is no prediction of what will happen (what might happen can be illustrated by scenarios; see Spangenberg et al., this atlas, pp. 10ff.). Instead, such measurements are intended to help focusing the attention on factors that might make achieving politically defined objectives illusionary, such as halting the loss of biodiversity, and to call for closer scrutiny and a reassessment of past decisions. They also indicate which direction must be taken to minimise the risk, but again without a prediction what will be achieved when and how. This may be inconvenient, but it is close to the reality of complex systems. Starting from this point, a literature survey provided a list of known impacts on biodiversity and ecosystem services from the four pillars of ALARM, i.e. from land use and climate change, chemicals, invasive species and pollinator loss (an impact as well as a pressure for the insect pollinated plants). The list was analysed and clusters were formed to identify the drivers on different levels. Figure 3 uses some of the examples found to illustrate the application of the formal scheme for the analysis of biodiversity pressures. Down to Earth: a protocol to be applied in the FSN This consolidated list was the basis for the questionnaire, the SE FSN protocol, aiming at a socio-economic characterisation of the FSN sites. It included an application of the DPSIR scheme, and a reference to the concept of ecosystem services as structuring principles, in addition to general socio-economic and geographical questions. From the answers it was obvious that the ecosystem services concept as well as the DPSIR scheme were not familiar to the field staff (at least not as a tool to apply on the ground), but that the latter was intuitively understandable, although the information required was considered hard to access. Ecosystem services were more familiar as terms, but the answers proved that there was no experience in applying the concept to the local circumstances.
This provides useful hints for future education and training activities, but also for academic teaching. The questions regarding the more general characterisation of the sites included general economic (business, income), social (employment, population) and legal information (the ownership structure, considered potentially relevant for the “tragedy of the commons”). Their backgrounds are some hypotheses from economic theory, postulating that poverty leads to higher environmental impacts or that private property is better protected than public property, and admitting that enhanced economic activity may cause environmental impacts. Disciplinary background is the key of success and failure To the field site assistants gathering the data, these questions were less familiar, and considered not easy to deal with – a complete surprise to the socio-economists who had considered this part of the questionnaire as rather trivial. Lesson learnt: differences in the familiarity with certain data sources due to the respective disciplinary background can cause unexpected difficulties in data gathering. For instance, economic and social data are (easily?) accessible at the statistical departments of the local administration, ownership structures are documented in the land register offices in charge of the respective community, and to find out about the perceived relevance of agents and their respective roles a call to a local newspaper could be the optimal source of information. However, this kind of data gathering proved to be a major hurdle to staff members used to generate data by on-site measurements. The resulting profiles were analysed regarding systematic differences between disturbed and undisturbed sites, and thus to test of some of the drivers hypothesised in the previous work could be detected and shown to be at work in the FSN system. Although the feedback from the field sites was far from being complete for the reasons explained earlier, they provided enough data for an analysis, and for the detection of significant differences between the two kinds of sites. The parameters for which significant results were obtained are listed in Table 1. In general, disturbed sites are characterised by more inhabitants, more jobs, more industry and more agriculture. This is no surprise, but confirms that the disturbed sites are representative for disturbances widely spread in Europe. As compared to the disturbed ones, the “undisturbed” sites have more tourism, and more open access to nature (a causality in either direction can be hypothesised but is not proven). One possible explanation is that there
were no significant restrictions to access for nature conservation purposes, but quite some for commercial purposes which – although partly “green businesses” like open air sport and recreation sites and golf courses) – are not particularly supportive for biodiversity; other restrictions were due to industrial use. It was interesting to see that for both kinds of sites information availability on environment and nature protection regulations was considered as fully given, and that the level of enforcement of rules was estimated to be above 90 % in both cases: the disturbance is systemic and not a result of misconduct by whatever agent. The implementation deficit, a hot topic at the EU level, was obviously not felt locally. For the ownership structures we received no data.
Disturbed
?
Undisturbed
Disturbed
< Undisturbed
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Fragmentation, eutrophication
Primery driving forces Management level
Anthropogenic activities like road-building, settlement building, or intensive agriculture
Secondaty driving forces Policy level
Trade, Transport, Regional planning, Agricultural policy
Tertiary driving forces
Ideology, paradigms, lifestyle and livelihood, consumption
Base driving forces
‘Short-term exogenous’ human patterns, like population
account. Scientists are thus well advised to reveal their intentions and interests, enhancing the transparency of their position and thus strengthening their credibility (“we support safeguarding this xyz, and we do so for good reasons. They are...”). If these were the agents of protection, which were the forces wrecking havoc for the disturbed sites? For this question, we had no groups of agents (responsibilities were considered too difficult to identify, in particular since they often overlap). Instead we followed the differentiation of drivers described earlier, applying it to the FSN sites. The responses were a bit unsystematic, and a clear distinction of secondary and tertiary drivers was not possible from the data. Thus the relevant drivers were classified as shown in Table 2. Lessons learned First of all, the socio-economic characteristics of the field sites match the expectations for disturbed and undisturbed sites in Europe. Thus the sites chosen seem to be typical for the European situation not only regarding their ecological profile, but also regarding the kind of socio-economic pressures they are exposed to. These pressures, and thus the driving forces causing them, are correlated to disturbance, most probably contributing to the loss of biodiversity. Thus for deriving conservation strategies from biodiversity research it is useful to combine ecological and socio-economic analyses to be able to address the full range of drivers leading to biodiversity loss (this supports the ecosystem management approach developed by IUCN and Table 2. Driver analysis – most relevant drivers. Primary drivers Energy use Land use intensity Resource use / waste generation
* Access restrictions usually not for conservation purposes, but for exclusive use (sport/golf, leisure, industry).
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Figure 3. Examples for the categories, from the literature analysis. Source: Beatriz Rodriguez-Labajos.
Table 1. Differences between disturbed and undisturbed FSN sites. of sites Undisturbed Undisturbed Undisturbed Undisturbed Undisturbed Undisturbed Undisturbed
Habitat destruction
SOCIO-ECONOMIC DRIVERS
This includes two surprises: many of the relevant protectors are not, or at least not necessarily, local agents, but act from higher system levels. This seems to indicate that for any successful conservation strategy, a multi-level governance approach is necessary. Secondly, scientists are perceived as one agent amongst the top four: not by means of the information they provide for other agents, but as an active group with an agenda of its own, as conservation advocates. To the selfperception of being a group of people arguing in a fact-based, neutral manner, not having a hidden agenda in doing so, this is a serious blow. It might undermine the credibility of scientific argumentation, if the discrepancy between self-perception and public impression is not properly taken into
Relation Disturbed > Disturbed < Disturbed > Disturbed > Disturbed < Disturbed = Disturbed =
Species loss
Biological processes Anthropogenic pressures
Scientists as biased agents? Going deeper into the issue of agents, the local staff identified five groups as particularly important for the protection of the quality of (more undisturbed) sites. They are (in this order): ◙ Environmental departments; ◙ Private societies, NGOs, conservation organisations; ◙ Inhabitants; ◙ Scientists; ◙ Nature protection managers.
Pressure factor Population density Unemployment Intensive agriculture Industry Tourism Forestry Regulation & enforcement > 90 % Ownership structure Open access*
Biological patterns
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Secondary & tertiary drivers Policies (environmental, industrial, social) Technological change Consumption patterns
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adopted by the CBD). This is obvious already from the limited data base resulting from the pragmatic approach chosen in the ALARM FSN work. For future research this implies that it is highly advisable to further develop the European network of long term ecological research sites LTER by including socio-economic research aspects, as already done in a part of the network, the long term socio-ecological research sites LTSER (see Mirtl et al., this atlas, pp. 52f.). However, our experience shows that just adding some socio-economic research protocols to a setting designed according to ecological criteria is beneficial, but not enough. What is needed is an adequate training of staff, learning about the relevance of societal factors as part of the ecology education, and interdisciplinary cooperation in implementing the protocols (to overcome the methodological difficulties). In the longer run, data generated by socio-economic field site research could serve two purposes: on the one hand, they could be used to test economic hypotheses used in decision making, confirm their applicability or falsify them, thus improving the basis for fact based decision making. On the other hand, they could help to identify knowledge gaps, the need for information not available from the existing sites, and thus lead to recommendations how to complement the network with sites which help underpinning decision making by providing relevant information. References HABERL H, WINIWARTER V, ANDERSSON K, AYRES RB, BOONE C, CASTILLO A, CUNFER G, FISCHER-KOWALSKI M, FREUDENBURG WR, PURMAN E, KAUFMANN R, LANGTHALER E, LOTZE-CAMPEN H, MIRTL M, REDMAN CI, REENBERG A, WARDELL A, WARR B, ZECHMEISTER H (2006) From LTER to LTSER. Conceptualising the socio-economic dimension of long-term socio-ecological research. Ecology and Society 11: 13. SPANGENBERG JH (2007) Biodiversity pressures and the driving forces behind. Ecological Economics 61: 146-158.
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Assemblages of Social Wasps in Forests and Open Land across Europe – an ALARM-FSN Study LIBOR DVOŘÁK, EDUARDAS BUDRYS, ALEKSANDAR ĆETKOVIĆ & SIMON SPRINGATE
Introduction Social wasps (Vespidae) are a notable and fascinating component of European biodiversity. They fulfil important functions and services in ecosystems – such as pest control (Donovan 2003), but some species of social wasps can also be pestiferous insects that damage fruit, sting people and their animals, and nest within or on human habitations (e.g., Edwards 1980). Maximum pest status is in late summer, when their colony sizes are peaking for the season. Some wasps are pests in orchards and vineyards in late summer
when they feed on ripe fruit and are a stinging hazard to agricultural workers. They also are of concern in apiaries where they feed on honey and prey on honey bees, and in some food processing plants where they are a hazard to workers as they scavenge for food. In some areas, they are also a danger to people at many outside venues such as fairs, campsites and picnics when they are attracted to food items and garbage. Wasp workers forage both for proteinrich foods that are fed to larvae and for sugar-rich foods that they consume and feed to larvae. The foraging habits of
Figure 1. An example of the trapping bottle from the ALARM site Garraf. Photo: Jara Andreau.
some wasp species bring them into frequent contact with people, when they are attracted to meats, fruits, sweets, and even garbage. However, the foraging habits of different species of wasps vary. This can influence the susceptibility of their abundances to changes in food supply as affected by increased pesticide use and loss of floral resources in intensified agriculture (Archer 2001). Loss of nesting sites such as hedges and banks in intensive agricultural areas can also affect the occurrence and abundance of social wasps (Archer 2001).
Figure 2. View on disturbed part of the ALARM site Garraf. Photo: Jara Andreau.
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Methods For this survey we applied a very simple but efficient sampling method (Dvořák 2007): clear PET bottle filled with 0.5 litre of a beer was installed on the branch of a tree or bush or on a stake in open habitats respectively (Figure 1). The beertrap was exposed for ca. 1 month between mid- July and mid- August. We RISK
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Results Altogether eight social wasp species were trapped during the survey at ALARMFSN sites. The species assemblage (the abbreviations will be used in the text and
Figure 3. The structure of the natural part of the ALARM site Abisko. Photo: Olga Bohuslavová.
Wasp foraging behaviour also provides opportunities to develop lures and traps for capturing and killing wasps, either for research purposes or for population reduction (see Dvořák & Landolt 2006 for more details). In our study, we tested the method of beer-trapping for the evaluation of social wasp species assemblages. The main aims of this study were: (i) to compare species assemblage of forests and open sites within natural vs. disturbed landscapes using the ALARM fieldsite network (ii) to compare results obtained from the ALARM FSN-sites with the results from more than 100 other localities in the whole of Europe. A special focus was on the distribution of Dolichovespula media, a species which increased its distributional range over the last few years.
Figure 4. ALARM FSN sites with dates of wasp trapping.
focussed on forest ecosystems in 2006 and on both forests and open stands in 2007. Both forests and open stands were located in paired “natural” and “disturbed” sites of the ALARM field site network in 16 European countries (Figures 2 and 3). Three sites were studied in 2006 only, four in 2007 only, and nine sites in both years (Figure 4).
figures) can be divided into two groups. The first group comprises commonly and regularly trapped species such as Vespa crabro – Hornet (Vcra; Figure 5), a species of lowland forests, parks, and bushes; Vespula germanica – German wasp (Vger; Figure 6), a species of open and urban stands that is more common in lower altitudes and, moreover, more common towards the south; Vespula vulgaris – Common wasp (Vvul), a species common everywhere except for the Mediterranean region; and Dolichovespula media – Median wasp (Dmed) a species of forests and bush-land, formerly rare, but becoming increasingly common. The second group involves species that are occasionally and irregularly trapped with bait traps such as Vespula rufa – Red wasp (Vruf), a predominantly forest species, which can inhabit bushy stands; Dolichovespula saxonica – Saxon wasp (Dsax), a forest species; Polistes dominulus – paper wasp (Pdom), a species of open stands; and finally Polistes nimpha – paper wasp (Pnim), a species of open stands, preferring warmer biotopes. During the complete forest survey (2006 and 2007), seven species were trapped. Among those, only V. crabro
and V. vulgaris (partly also D. media) were common. V. crabro was visibly more common at natural sites while V. germanica, V. vulgaris and D. media were more common at disturbed sites. Altogether, seven species were trapped during the open stands survey in 2007. The most common species was V. vulgaris, followed by V. crabro and V. germanica. Again, V. crabro was visibly more common at natural sites while V. germanica and V. vulgaris were more common at disturbed sites. In total, the most commonly trapped species were V. vulgaris and V. crabro, partly also V. germanica and D. media. V. crabro and V. rufa were visibly more common at natural sites while V. germanica and V. vulgaris at disturbed sites. Conclusions The four species (Vcra, Vger, Vvul, Dmed) are the only social wasp species regularly attracted by beer in temperate Europe. Of course, several other species can be trapped irregularly or accidentally. Practically the same assemblage was found at more than 100 sites across Europe in 2006 and 2007 (Dvořák 2007, Dvořák et al. 2008). There are no significant differences between the countries – the four most common species can be trapped practically everywhere. The slightly thermophilous V. germanica is relatively rare in the north and in mountains while the slightly psychrophilous form is rather rare towards the south. This result is also supported by the panEuropean research conducted by Dvořák (2007) and Dvořák et al. (2008). The only significant difference in the proportion of species occurrences in natural vs. disturbed sites was observed in forest stands with V. crabro having a visibly higher percentage proportion in natural sites. V. rufa has a higher representation in natural sites and V. germanica, and D. media in disturbed sites. The situation in open stands in contrast seems to be clearer. V. crabro had a visibly much higher proportion in natural sites while V. germanica and V. vulgaris had a higher proportion in disturbed sites. Perhaps this fact of high disproportions of V. crabro, V. germanica, and V. vulgaris in natural vs. disturbed sites can be used to evaluate the landscape quality in large-scale research. At least one or two years of additional research are needed to support this idea. Note: the discovery of V. vulgaris from the Toledo site in 2006 was published as the first record of this species for the Ciudad Real province of Spain (Dvořák & Castro 2007). Acknowledgements The authors wish to thank ALARMFSN site coordinators for their help on the project, Jacobus Bieesmeier for the coordination of the whole research at ALARM-FSN sites and Jens Dauber for helpful comments to the manuscript. A S S EMB LAGES
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Figure 5. Worker of British form of Hornet, Vespa crabro. Photo: Alan Phillips.
References DONOVAN BJ (2003) Potential manageable exploitation of social wasps, Vespula spp. (Hymenoptera: Vespidae), as generalist predators of insect pests. International Journal of Pest Management 49: 281-285. DVOŘÁK L (2007) Social wasps (Hymenoptera: Vespidae) trapped with beer in European forest ecosystems. Acta Musei Moraviae,
Scientiae biologicae 92: 181-204. DVOŘÁK L, CASTRO L (2007) New and noteworthy records of vespid wasps (Hymenoptera: Vespidae) from the Palaearctic region. Acta Entomologica Musei Nationalis Pragae 47: 229-236. DVOŘÁK L, LANDOLT PJ (2006) Social wasps trapped in the Czech Republic with syrup and fermented fruit and comparison with similar studies (Hymenoptera
Total
Total Vger 3.37 %
Vvul 35.02 %
Vcra 46.72 %
Natural
Dsax 0.17 %
Dmed 7.08 %
Vruf 0.49 %
Natural
Dmed 12.54 %
Pnim 0.21 %
Dsax 0.31 %
Dmed 7.28 %
Vruf 0.86 %
Vvul 36.55 %
Vvul 51.09 %
Pnim 0.21 %
Dsax 0.00 %
Dmed 6.97 %
Vruf 0.31 %
Pdom 0.31 %
Figure 7. Results of wasp trapping at forest stands in 2006 and 2007.
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Figure 8. Results of wasp trapping at open stands in 2007.
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E U RO PE
Vvul 45.80 %
Vger 13.17 %
Vcra 30.58 %
Vcra 23.20 %
Dmed 14.55 %
I N
Pnim 0.09 %
Vruf Dmed 1.18 % 10.35 %
Disturbed
Vger 17.90 %
Vruf 0.36 %
WA SP S
Vcra 45.14 %
Vvul 37.42 %
Dsax Pdom 0.18 % 0.64 %
Disturbed
Pdom 0.18 %
Vger 5.00 %
Pdom 0.86 %
Disturbed
Vcra 43.45 %
Dsax Pdom 0.08 % 0.42 %
Vvul 42.18 %
Vcra 38.76 %
Vruf 1.41 %
Vruf Dmed 0.69 % 9.99 %
Natural Vger 9.85 %
Vvul 33.70 %
Vger 4.91 %
Pnim 0.11 %
Pdom 0.49 %
Vcra 49.53 %
Vvul 42.27 %
Vcra 36.72 % Pnim 0.14 %
Vger 2.04 %
Pdom 0.47 %
Vger 9.72 %
Vvul 48.18 %
Vcra 28.33 %
Dmed 13.47 %
Vespidae). Bulletin of Insectology 59 (2): 115-120. DVORÁK L, CASTRO L, ROBERTS SPM (2008) Social wasps (Hymenoptera: Vespidae) trapped with beer bait in European open ecosystems. Acta Musei Moraviae, Scientiae biologicae 93: 105-130. EDWARDS R (1980) Social wasps. Their biology and control. Rentokil Ltd., East Grinstead, 398 pp. Total
Vger 15.29 %
Vruf 0.93 % Pdom 0.34 %
S OCIA L
Figure 6. Female of German wasp, Vespula germanica. Photo: Libor Dvoˇrák.
Vruf Dmed 0.33 % Pnim 9.73 % 0.13 % Dsax Pdom 0.00 % 0.26 %
Figure 9. Results of wasp trapping at ALARM FSN sites in 2006 and 2007.
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From FSN to LTER-Europe MICHAEL MIRTL, KINGA KRAUZE, VOLKER HAMMEN & MARK FRENZEL
FSN at the end of ALARM The field site network of the ALARM project demonstrated the power and the success of a pan-European network operating on a large geographic scale according to distinct research questions and detailed protocols. However, after finishing the project, there is a demand for an infrastructure setting a frame for continuation of the established network structure and supporting network-based research projects. This gave reason to the idea to integrate the ALARM FSN into
the Long Term Ecosystem and Research network LTER-Europe (www.ltereurope.net) to make it available as part of a permanent network of test sites. History of LTER LTER-Europe is one of the regional networks of ILTER, the international umbrella organisation for LTER (www. ilternet.edu). ILTER is operating since 1993 and aimed at meeting the growing need for global communication and collaboration among long-term ecological
researchers. The future development of ILTER is outlined in the strategic plan (http://www.ilternet.edu/about-ilter/keydocuments). The LTER-community in Europe is rapidly growing and developing (Figure 1), as there is a strong demand from science, policy and the public in the light of global change to have information about ecological long-term trends at hand. LTER data are the base for prognoses about the future development of ecosystems which may be influenced by political counteractions (Gee 2001). Reasons for building LTER-Europe Up to 2007 LTER in Europe was divided in a Central/East-European and a West-European branch. Supported by the EU project ALTER-Net, they were merged into LTER-Europe. The reason for building this regional network was to strengthen and better integrate ecosystem research activities and infrastructure in Europe (Mirtl & Krauze 2007, Mirtl 2010). A general drawback of all ecological monitoring and research activities is in the use of different schemes and methods, and the unavailability of common or exchangeable databases. The vision of LTER-Europe is to overcome these drawbacks by creating a framework for streamlining research questions and providing metadata and data to create common ground for operation.
Figure 1. Development of LTER-Europe. Until 2007 separation in western (blue) and eastern (green) European branch, afterwards joined to LTER-Europe (red).
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From LTER to LTSER Considering the complex interactions between human society and ecosystems across all scales in space and time as well as organizational levels the concept of LTSER (Long-Term SocioEcological Research, Redman et al. 2004, Haberl et al. 2006) has been adopted by LTER-Europe as an integral part of its structure. The elements of LTSER platforms represent the main habitats, land use forms and practices relevant for broader socio-ecological regions (10010,000 km²). LTSER platforms should represent economic and social units or coincide/overlap with such units where adequate information on land use history, economy and demography is available. About 25 European LTSER platforms have been established as hot spot regions for socio-ecological research. Amongst the first was the LTSER platform Eisenwurzen, a region of about 5,000 km2 in the central part of Austria (Figure 2). Most current trends of the socio-economic system and the environment can only be understood by taking the development over past cen-
Table 1. Criteria for LTER-sites. Commitment of hosting institutions / site coordinators Formal criteria Continuity
Agreed support (staff, infrastructure) Site coordinator Permanent staff with distinct definition of tasks Data sharing Agreement on (meta)data exchange Common language English Communication Maximum response time 10 days (email) Data criteria Documentation Up-to-date/current documentation in the LTER InfoBase Storage Minimum standard (Excel, Access); shared database InfoBase (at least for metadata) Sharing Availability of data (bylaws for sharing) Time series Depending on topics; at least two data sets with a sufficient time interval Frequency Rules for measurements considering thresholds for discontinuity
turies into account. Featuring one of the highest densities of existing research infrastructure, covering the dominant habitats, several large scale protected areas and the transition between industrial, urban and rural areas, the region was in 2004 selected as a LTSER platform in Austria. The common goal is to investigate the entire socio-ecological fingerprint of the region and the most pressing questions as identified by the actor groups ranging from local decision makers and regional developers to researchers. The LTSER platform process has led to the establishment of a unique hot spot (van der Werf et al. 2008) of infrastructure, data and expertise with numerous scientific projects working in a highly integrated way. Basic elements of LTER-Europe LTER-Europe consists of national networks, LTSER platforms and LTER sites. It is governed by the executive and the coordinating committee. Additional bodies are annually meetings of the national networks representatives and the scientific site co-ordinators. LTER-Europe is composed of actually 20 (2009) national networks comprising more than 150 official LTER-sites (Figures 3 and 4). Quality control in LTER-Europe There is a strong need for rules and criteria to ensure the quality and the consistency of the network. Such criteria have been developed for LTER
sites, LTSER platforms and national LTER networks. Some of the criteria for the site level are listed in Table 1. (1) Regular sites fulfil all criteria, (2) intermediate sites have no sufficient time series of long-term data, but they are starting to collect time series and (3) starting sites are newcomers, just starting to collect long-term data. FSN in LTER-Europe FSN sites can be integrated into LTEREurope by becoming members of the respective national networks (Figure 1). In case of sites in countries without national LTER networks (e.g., in Russia, Greece), these sites could even form the starting points for building national LTER networks. LTER-Europe provides support (e.g., best practice guidelines) to further foster the integration. Outlook LTER-Europe has a high potential to become a major component for ecological research in Europe, covering its environmental and socio-economic heterogeneity (Metzger et al. 2010). Special strengths are the synergy of monitoring and research, facilitating the complete chain of activities from data collection to analysis. This provides the basis for sound synthesis, building alliances for problem solving, dissemination of know-how, supporting decision makers, identifying current and future threats to environment and humans, helping to define management and policy priorities, and capacity building in order to mitigate environmental hazards in the face of global change. References GEE D (2001) Late Lessons in Early Warning. European Environment Agency, Copenhagen. HABERL H, WINIWARTER V, ANDERSSON K, AYRES R, BOONE C, CASTILLO A, CUNFER G, FISCHER-KOWALSKI M, FREUDENBURG WR, FURMAN E, KAUFMANN R,
Figure 2. Map of the Eisenwurzen region.
Figure 3. Map of Europe with official LTER sites (blue dots) and FSN sites (red dots).
KRAUSMANN F, LANGTHALER E, LOTZECAMPEN H, MIRTL M, REDMAN CL, REENBERG A, WARDELL A, WARR B, ZECHMEISTER H (2006) From LTER to LTSER: Conceptualizing the socio-economic dimension of long-term socio-ecological research. Ecology and Society 11(2): 13. METZGER MJ, BUNCE RGH, VAN EUPEN M, MIRTL M (2010) An assessment of long term ecosystem research activities across European socio-ecological gradients. Journal of Environmental Management 91: 1357-1365. MIRTL M (2010) Introducing the next generation of ecosystem research in Europe: LTEREurope´s multifunctional and multiscale
approach. – In: Müller F, Baessler C, Schubert H, Klotz S (Ed.) Long-Term Ecological Research – Between Theory and Application. Heidelberg, Berlin. Springer (in press). MIRTL M, KRAUZE K (2007) Developing a new strategy for environmental research, monitoring and management: The European Long-Term Ecological Research Network´s (LTER-Europe) role and perspectives. – In: Chmielewski TJ (Ed.) Nature conservation management – From idea to practical results. ALTERnet. Lublin-LodzHesinki-Aarhus, 36-52. REDMAN CL, GROVE JM, KUBY LH (2004) Integrating Social Science into the Long-
Term Ecological Research (LTER) Network: Social Dimensions of Ecological Change and Ecological Dimensions of Social Change. Ecosystems 7: 161-171. VAN DER WERF B, ADAMESCU M, AYROMLOU M, BERTRAND N, BOROVEC J, BOUSSARD H, CAZACU C, VAN DAELE T, DATCU S, FRENZEL M, HAMMEN V, KARASTI H, KERTESZ M, KUITUNEN P, LANE M, LIESKOVSKY J, MAGAGNA B, PETERSEIL J, RENNIE S, SCHENTZ H, SCHLEIDT K, TUOMINEN L (2008) SERONTO: A Socio-Ecological Research and Observation oNTOlogy. – In: Weitzman AL, Belbin L (Eds) Proceedings of TDWG (2008), Fremantle, Australia.
Figure 4. Map with habitat classification of a typical LTER site.
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Assessing Risks for Biodiversity with Bioclimatic Envelope Modelling OLIVER SCHWEIGER, MIGUEL B. ARAÚJO, JAN HANSPACH, RISTO K. HEIKKINEN, INGOLF KÜHN, MISKA LUOTO, RALF OHLEMÜLLER & RAIMO VIRKKALA
Bioclimatic envelope modelling Several habitats and species have already been affected by 20th century climate change and 21st century changes are expected to have even greater impact on species distributions. The assessments of future range shifts are currently mostly based on implementations of bioclimatic envelope models. These modelling strategies assess the relationships of current species distributions with contemporary climate variables, and then use these relationships to project future distributions of species under different climate change scenarios. When applied with caution, bioclimatic envelope models provide useful ‘first filters’ for identifying locations and species that may be at greater risk and provide first approximations as to the impacts of climate change on
species ranges. Within ALARM, several studies used bioclimatic envelope models to provide assessments of biodiversity risk to climate change. Examples Plants, general. A European assessment of the future distributions of 1,350 plant species (nearly 10 % of the European flora) under various emission scenarios indicated that more than half of the modelled species could become vulnerable, endangered, critically endangered, or committed to extinction by 2080 if they were unable to disperse (Thuiller et al. 2005). Under the most extreme climate change scenario (GRAS, A1FI, see Spangenberg et al., this atlas, pp. 10ff.), and assuming that species could track their climate envelope through dispersal, 22 %
-11 to -3 -2 to 0 1 to 3 4 to 6 7 to 15
of the species modelled would become critically endangered, and 2 % could be committed to extinction if they were unable to survive in suboptimal climate conditions. According to these analyses, 21st century plant species distributions are very likely to expand northward and contract in the southern European mountains and in the Mediterranean Basin. Woody plants. Both the amount and the spatial distribution of suitable climate conditions have crucial implications for the survival of species under future climate change. Large areas of suitable climate space might be of no use to a species if these areas are too far away for the species to disperse. Likewise, small areas with suitable climate might be sufficient as rescue locations if they are nearby and can easily
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d Figure 1. Projected losses and gains in suitable climatic niche space for amphibians (a,b) and reptiles (c,d) for a moderate (SEDG; a,c) and a high emission scenario (GRAS; b,d) for 2050 based on generalised linear models (GLMs). (Data source: http://www.biochange-lab.eu).
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be reached by a species under changing climate conditions. When these components of risk were quantified for seventeen European woody species (Ohlemüller et al. 2006a), it was shown that species of boreal and temperate deciduous forests are predicted to face higher risk from loss of climatically suitable area than species from warmer and drier parts of Europe by 2095, using both a moderate and a severe emission scenario (BAMBU, B1 and GRAS, A1FI; see Spangenberg et al., this atlas, pp. 10ff.). However, the average distance from currently occupied locations to areas predicted suitable in the future is generally shorter for boreal species than for southern species. Areas currently occupied will become more suitable for boreal and temperate species than for Mediterranean species whereas new suitable areas outside a species’ current range are expected to show greater increases in suitability for Mediterranean species than for boreal and temperate species (Ohlemüller et al. 2006a). Herptiles. Another study in ALARM, used an ensemble of climate envelopes to assess potential changes in the distributions of European amphibians and reptiles. Results indicated that the majority of amphibian (45 % to 69 %) and reptile (61 % to 89 %) species could expand their ranges under various emission scenarios if dispersal was unlimited (Araújo et al. 2006). However, if unable to disperse, then the range of most species (>97 %) would become smaller, especially in the Iberian Peninsula and France. Species in the UK, south-eastern Europe and southern Scandinavia were also projected to benefit from a more suitable climate, although dispersal limitations might prevent them from occupying new suitable areas (Figure 1). Birds. In Northern Europe, Virkkala et al. (2008) projected changes in the distributions of 27 northern-boreal land bird species, using bioclimatic envelope models and two climate change scenarios (SEDG, B1 and BAMBU, A2; see Spangenberg et al., this atlas, pp. 10ff.). Their results showed that over two thirds of the studied bird species may lose most of their climatically suitable space by 2080 in both of the scenarios (Figures 2 and 3; Photo Anthus cervinus). The projected climate change-induced threats are of particular importance because the Arctic Ocean represents a natural barrier for northward movement of species. Butterflies. Settele et al. (2008) modelled the climatic niche of ca 300
a 1971-1990
b 2051-2080
a 1971-1990
b 2051-2080
Figure 2. Predicted bird species numbers of the 27 northern bird species in 1971-1990 (a), and in 2051-2080 (b) based on climate scenario BAMBU in northernmost Europe based on bioclimatic envelope modelling. (Source: Virkkala et al. 2008).
Figure 3. Predicted distribution for the red-throated pipit (Anthus cervinus) in 1971-1990 (a) and in 20512080 based on scenario BAMBU (b). (Source: Virkkala et al. 2008).
European butterflies on the basis of four climate variables (growing degree days, soil water content, range in annual temperature, range in precipitation). The study shows clearly that climate change poses a considerable additional risk to European butterflies. Model projections to three future scenarios (SEDG, B1; BAMBU, A2; GRAS, A1FI) for 2050 and 2080 revealed mostly northward range shifts and often serious reductions in the potential future distributions (e.g. Figure 5). Between 3 % and 24 % of the modelled species were projected to lose more than 95 % and between 54 % and 70 % of the species may lose more than 50 % of their present climatic niche by 2080 when dispersal is assumed to be highly limited, while only 6 % of the species can be rated as being at lower risk. The results also show that there is a considerable time lag in the effects of climate change on European butterflies. Until 2050, the effects across different scenarios are still moderate, while they heavily intensify until 2080 (see e.g. Figures 5 and 6).
for evaluation of the models were available and results from individual models were surprising: the ability of all projections to predict the direction of shifts was “no better than tossing a coin”. However, by choosing consensus models that best represented the variation in the projections from individual models, Araújo et al. (2005) showed considerable improvements in the accuracy of predictions. A theory of ensemble forecasting is now being developed and several new studies are exploring the properties of consensus forecasting in bioclimatic envelope modelling. Additional variables – land use. A series of ALARM studies conducted in boreal landscapes showed that the integration of land cover information into pure bioclimatic envelope models has the potential to increase the predictive accuracy of the models for many bird species. This increase appeared to be scale-dependent, most discernible at spatial resolutions of 10-km and 20-km, and not any more at 40-km or 80-km resolution (Luoto et al. 2007). Especially at the 10-km resolution the distribution patterns of boreal birds reflects the interplay between habitat availability and climate. Additional variables – elevation range. In a broad-scale modelling study, Luoto & Heikkinen (2008) showed that the inclusion of elevation range increased the predictive accuracy of species-climate-only models for 86 of the 100 studied European butterfly species. The inclusion of elevation range in the models resulted in clear differences in the projected future distributions and the projected regional losses in butterfly species (Figure 7) in comparison to climate-only model projections, the most notable differences
Uncertainties and improvements of bioclimatic envelope modelling Sources of uncertainties in bioclimatic envelope modelling are manifold. Some originate in more technical aspects such as the model building and evaluation procedure, while others result from the nature, quality and structure of the species distributional data and the variables used to calibrate the models. A series of case studies within ALARM addressed many of these uncertainties and limitations. Studies included issues of model validation when making projections of
species range shift under climate change, assessments of the implications of making forecasts with a single model or using combinations of outputs from several models, assessments of the consequences of using incomplete species distributional data, potential improvements of model performance by using additional environmental data such as land use variables or variables reflecting biotic interactions. Data quality. To describe the climatic envelope adequately, calibration data should cover the whole range of a species and thus the full niche. A study on the impact of climate change on the distribution of plants in Germany showed that depending on modelling algorithm 42-100 % of the species not yet occurring in Germany would find suitable climatic conditions in the future (Pompe et al. 2008). Thus, ignoring species and climatic niches outside a specific study area may lead to significant overestimation of climatic risks. Inter-model variability. Several analyses in ALARM demonstrated that pre-eminence of any single model differing in the model building procedure was not guaranteed. Indeed it was shown that projections by alternative models can be so variable as to compromise even the simplest assessment of whether species distributions should be expected to contract or expand for any given climate projection. For example, Araújo and colleagues (2005, 2006) applied several well documented bioclimatic modelling techniques to standardised data sets of birds in the UK and amphibians and reptiles in Europe and compared consistency in projections under current and future climates. Results varied significantly across modelling techniques. In the UK bird study, independent data ASSESSI N G
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being observed for mountainous areas. Disregarding topographical heterogeneity may thus cause a significant source of error in broad-scale bioclimatic modelling. Additional variables – species traits. In boreal butterflies, and elsewhere in Europe, the accuracy of the bioclimatic envelope models has been shown to
Figure 4. The red-throated pipit (Anthus cervinus). Photo: P. Kelly and A. Kelly.
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be affected by species traits and their geographical attributes. Using national atlas data on butterflies in Finland, Pöyry et al. (2008) showed that species traits may also have a strong impact on model performance, and certain butterfly trait groups can be inherently difficult to model reliably. For example, species with a long flight period and high mobility were modelled less accurately than species with a short flight period and low mobility. In addition, species’ geographical attributes, such as latitudinal range, prevalence and
clumping of occurrences, may also account for a major deal of the variation in the accuracy of the pure bioclimatic envelope models. In general, species with clumped distributions, having low prevalence and occurring at the margin of their range are modelled best. Additional variables – biotic interactions. It has been increasingly argued that also biotic interactions should be taken into account in bioclimatic modelling, and two ALARM studies were among the first ones making a contribution in
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this field (Araújo & Luoto 2007, Heikkinen et al. 2007). Heikkinen et al. (2007) showed that the incorporation of woodpecker distributions into species-climate-land cover models developed for boreal owls improved significantly the explanatory power and the predictive accuracy of the models. Woodpeckers excavate cavities in trees, which provide nesting sites for owls; this link appears to be visible also at macro-ecological scales. The study by Araújo & Luoto (2007) showed that the projections of the suitable current and future ranges for the clouded apollo butterfly (Parnassius mnemosyne) can be significantly altered when the current and projected future distribution of its host plants are taken into account in the bioclimatic envelope models. Schweiger et al. (2008) extended this approach and found that differences in climatic dependencies can lead to increasing mismatches of trophically interacting species. They showed that
Figure 6. Peacock (Aglais io). Photo: P. Ginzinger.
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Figure 5. Even very common butterflies such as the Peacock (Aglais io, see Photo) (a) are projected to lose large areas of current suitable climate (b). While until 2050 a time lag effect moderates the range shift, it is highly pronounced until 2080. Red circles, observed distribution; orange, areas modelled as having suitable climate; grey, lost areas; brown, future suitable areas. (Source: Settele et al. 2008).
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future overlaps of climatically suitable areas of the monophagous butterfly Boloria titania and its larval host plant Polygonum bistorta virtually disappear in the current distributional range of the butterfly and allow co-occurrence only in distantly located areas, whereby the ability to colonise these areas is questionable for both plant and butterfly (see Schweiger et al., this atlas, pp. 216f.). Nonetheless, considering biotic interactions for bioclimatic envelope models remains a challenge. Most interactions are much more complex and include more than two species. Due to highly individualistic response of interacting species, significant changes in the composition of species assemblages can be expected. Consequently, existing interactions may be altered or even disrupted, while the potential of new interactions (of both adverse such as competition or predation and facilitative such as pollination) may also result in shifted ecological niches.
Species-independent climatic risk surface. In the light of these various sources of uncertainties associated with bioclimatic envelope modelling, a novel approach can avoid some of these uncertainties. The basis of this approach is to model the climate risk for a location independently from the species occurring at that location (Ohlemüller et al. 2006b). This enabled to identify areas in Europe which have a current and future climate similar to the current climate of any target location in Europe (Figure 8). Locations which have large areas elsewhere in Europe with similar climate conditions can be considered as low risk locations because species living here potentially have large areas to disperse and migrate to where conditions at the target location to become unsuitable; locations with only small areas of analogous climate conditions in Europe can be considered as high risk locations because species living at these locations only have small areas to which they could potentially disperse or migrate to. When this approach was applied to a European grid identifying for each grid cell the degree of climate risk, it was shown that in particular for northern European regions, areas with prewarming analogous climate conditions will be smaller and further away in the future than they currently are, potentially indicating a high risk for the biota of these regions (Figure 8; Ohlemüller et al. 2006b). This climate risk framework was also applied to biodiversity of several taxa to investigate the relationship between climate rarity and species rarity for Western Hemisphere birds and European plants and butterflies. Here, regions with high numbers of rare (small-ranged) species are generally characterised by unusual climate conditions with limited spatial extent. These patterns of coincidence of climatic and species rarity was consistent for all three taxa investigated. Thus, regions of high climatic rarity are likely to lose disproportionally large areas of analogous climate space under future climate change compared to regions with a more common climate. References ARAÚJO MB, LUOTO M (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16: 743-753. ARAÚJO MB, THUILLER W, PEARSON RG (2006) Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography 33: 1712-1728. ARAÚJO MB, WHITTAKER RJ, LADLE RJ, ERHARD M (2005) Reducing uncertainty in extinction risk from climate change. Global Ecology and Biogeography 14: 529-538. HEIKKINEN RK, LUOTO M, VIRKKALA R, PEARSON RG, KORBER JH (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecology and Biogeography 16: 754-763. LUOTO M, HEIKKINEN RK (2008) Disregarding topographical heterogeneity biases species
a Climate
b Climate-topography
Species loss (%) 0-8 >8-16 >16-32 >32-48 >48-100
Figure 7. Projected species loss of 100 selected European butterfly species based on climate-only (a) and climate-topography (b) models under the climate scenario BAMBU. Topography measured as elevation range. (Source: Luoto & Heikkinen 2008).
turnover assessments based on bioclimatic models. Global Change Biology 14: 483-494. LUOTO M, VIRKKALA R, HEIKKINEN RK (2007) The role of land cover in bioclimatic models depends on spatial resolution. Global Ecology and Biogeography 16: 34-42. OHLEMÜLLER R, GRITTI ES, SYKES MT, THOMAS CD (2006a) Quantifying components of risk for European woody species under climate change. Global Change Biology 12: 1788-1799. OHLEMÜLLER R, GRITTI ES, SYKES MT, THOMAS CD (2006b) Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931-2100. Global Ecology and Biogeography 15: 395-405.
POMPE S, HANSPACH J, BADECK F, THUILLER W, KÜHN I (2008) Climate and land use change impacts on plant distributions in Germany. Biology Letters 4: 564-567. PÖYRY J, LUOTO M, HEIKKINEN RK, SAARINEN K (2008) Species traits are associated with the quality of bioclimatic models. Global Ecology and Biogeography 17: 403-414. SETTELE J, KUDRNA O, HARPKE A, KÜHN I, VAN SWAAY C, VEROVNIK R, WARREN M, WIEMERS M, HANSPACH J, HICKLER T, KÜHN E, VAN HALDER I, VELING K, VLIEGENTHART A, WYNHOFF I, SCHWEIGER O (2008) Climatic Risk Atlas of European Butterflies. BioRisk 1: 1-710.
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SCHWEIGER O, SETTELE J, KUDRNA O, KLOTZ S, KÜHN I (2008) Climate change can cause spatial mismatch of trophically interacting species. Ecology 89: 3472-3479. THUILLER W, LAVOREL S, ARAÚJO MB, SYKES MT, PRENTICE IC (2005) Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences of the United States of America 102: 8245-8250. VIRKKALA R, HEIKKINEN RK, LEIKOLA N, LUOTO M (2008) Projected large-scale range reductions of northern-boreal land bird species due to climate change. Biological Conservation 141: 1343-1353.
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Change in mean distance to 1945-analogous cells [km] no analogous grid cells <-1,000 -999 to -800 -799 to -600 -599 to -400 -399 to -200 -199 to 0 1 to 200 201 to 400 401 to 600 601 to 800 801 to 1,000 >1,000
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Figure 8. The spatial distribution of 1945-analogous and 1945-non-analogous conditions for three periods (I–III): (I) 1945; (II) average European mean annual temperature (Tm) has changed by c. 2 °C (HadCM3, A1FI scenario, 2035); (III) average European Tm has changed by c. 4 °C (HadCM3, B2 scenario, 2095). Black areas indicate vanishing climates, i.e. areas without 1945-analogous conditions elsewhere in the study area. (a) Total 1945-analogous area (I) and proportional change in 1945-analogous area compared to 1945 (II and III) with green colours indicating an increase and purple colours indicating a decrease in 1945-analogous area; (b) average distance to all 1945-analogous grid cells (I) and change in average distance (II and III) with green colours indicating a decrease and purple colours indicating an increase in distance to areas with 1945-analogous conditions (Source: Ohlemüller et al. 2006b).
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Statistical Aspects of Biodiversity Risk Assessment GLENN MARION, STIJN BIERMAN, ADAM BUTLER, STEPHEN CATTERALL, ALEX R. COOK, RUTH DOHERTY, INGOLF KÜHN, BJÖRN REINEKING, OLIVER SCHWEIGER & PHILIP E. HULME
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Figure 2. Observed species distribution (50 x 50 UTM grid; open circles) and modelled actual (predicted current) distribution of the climatic niche (orange area) of Aglais urticae. The model performed very well since there is high agreement between actual distribution and the modelled one (AUC = 0.8) (Source: Settele et al. 2008).
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(www.floraweb.de containing records of vascular plant species in Germany), and the National Biodiversity Network Gateway (www.nbn.org.uk containing records of 3,705 vascular plants and over 5,000 other species in the United Kingdom). The problem with species atlas data is that they are formed largely from submitted observations, or records, for each species in the database, and the number and nature of these submissions is not controlled according to a statistical design. Therefore such data can contain statistical biases e.g. the timing, location and nature of submitted records can reflect where interested people (e.g., amateur botanists) live, leading to spatial variation in species detection probabilities (i.e., the probability that a given species will be recorded at a given location when it is in reality present there). However, it is infeasible to conduct designed surveys at national scales and therefore a great deal of expert knowledge and effort goes into the process of collating these submitted records to produce presence/absence maps which represent a consensus about the geographic distribution of each species in the atlas for a given time window.
Annual temperature range
Minimum Swc
( ( ( ( (
Species atlas data Species atlas data are an important source of information about the spatial distribution of biodiversity, typically at the national scale, often covering a wide range of species. Examples of webbased species atlases are FLORKART
Small (33%) Swc
human activity (Wilson 2001). In order to understand the true nature of this threat and minimize its consequences we must learn to better quantify impacts on biodiversity using the varied but incomplete sources of informa-
to handling data in practice require the input of expert knowledge, and risk assessments from complex mechanistic models can be considerably enhanced through the use of data. In this article we discuss four examples. The first two illustrate the use of empirical models for spatial data arising from species atlases which are an important source of information on biodiversity (see Schweiger et al., this atlas, pp. 54ff.). The third illustrates a complete integration of statistical and process-based modeling approaches using a relatively simple process-based model for the spread of alien species across a landscape. The fourth example describes how to apply statistical methods to an existing complex deterministic mechanistic model for the development of global natural vegetation cover under future climate scenarios.
Large (66%) Swc
Figure 1. Small Tortoiseshell (Aglais urticae). Photo: K. Veling.
tion available. In this article we focus on ongoing developments in methods that allow the statistical quantification of both the magnitude of risk (see Marion et al., this atlas, pp. 252f.) and our uncertainty in estimating it. Broadly speaking there are two classes of information used in assessing risks to biodiversity; empirical or observational data, and expert scientific knowledge. Uncertainties in our knowledge of how biodiversity responds to changing pressures makes the direct use of data appealing, but purely empirically based risk assessments are limited by the range of environmental conditions for which observations are available; e.g. many future scenarios of climate change take climatic conditions outside observed ranges. On the other hand, considerable expert scientific knowledge about relevant mechanisms and processes is often available, and, for example, this can be used (although it is not straightforward) in the construction of mechanistic, or process-based, mathematical models. There are proponents of both empirical and process-orientated modeling approaches, but in reality all (useful) biodiversity risk assessments combine both data and expert knowledge to some degree. As we see below empirically driven statistical approaches
Maximum Swc
The great ecologist E.O. Wilson has convincingly argued that we are currently living through the sixth great mass-extinction of life on Earth, and one which represents an unprecedented rate of loss of species and, in contrast to earlier extinctions, is driven by
Annual precipitation range
0
2,000
Gdd
4,000 0
2,000
Gdd
4,000 0
2,000
Gdd
4,000 0
2,000
4,000
Gdd
Figure 3. Four-dimensional climatic niche of Aglais urticae. Occurrence probability defined by accumulated growing degree days until August (Gdd) and soil water content (Swc) for combinations of minimum, lower tercile, upper tercile and maximum values of annual temperature range and annual precipitation range. Climate variables were ecological relevant and selected as the least correlated variables by means of cluster analysis. Orange, unsuitable; green suitalbe climatic conditions. Black line, modelled threshold (Source: Settele et al. 2008).
Assessing ecological niches from atlas data Given biological atlas data and information on the spatial patterns in environmental variables one might seek to estimate the relationship between them. Although any such relationships are correlative they might reflect underlying environmental determinants of distribution, for example revealing the climatic conditions required by a given species which could be used for modeling its climatic niche and subsequently for assessing risks of range contraction and extinction under future climate change scenarios (see Fronzek et al., this atlas, 68ff.). To demonstrate such ideas we apply the classical approach of climatic niche modeling to the common butterfly Small Tortoiseshell (Aglais urticae; Figure 1). Before modelling begins, potentially relevant climatic variables are selected. There are several methods to reduce the number of variables prior to model building, but the challenge is to retain the most biologically meaningful, while, taking care to remove high levels of correlation between climatic variables that would seriously decrease model reliability. Once this problem is solved, the selected variables are related to the distributional data of the species (open circles in Figure 2) by means of regression analysis or other statistical methods. A further reduction of the variables to the essential ones-according to measures of goodness of fit, such as Akaike’s information criterion,which aim to ensure that models are as simple as possible but remain consistent with the data – will then result in an adequate mathematical description of the multidimensional niche. Such a model provides probabilities of occurrence under all combinations of the selected climatic variables. An example of the four-dimensional niche of A. uricae is depicted in Figure 3. This model can then be used to project the modelled climatic niche space to current conditions, and agreements between observed presences and absences and projected distributions provide information about the quality of the model (Figure 2). Finally, the developed climatic niche model can be projected to future climate change scenarios (Figure 4) to assess potential risk for a particular species or whole species groups (for the European butterflies see Settele et al. 2008). It is crucial to note that the depicted current and future climatic niches do not represent predictions of future species distributions but in fact they are projected distributions of areas with potentially suitable climate for a particular species. Under all the scenarios considered for the currently widespread A. urticae this approach revealed an increasing risk of range reduction predominantly in the south, and only little potential for range expansions in
the north as the severity of climate change increases over time (Figure 4). Accounting for spatially varying non-detection probabilities in species atlas data One potential problem with atlas data is that species-environment relationships may be obscured by spatial variation in species detection probabilities, due to for example spatial variation in numbers of submitted records discussed above. Here we describe an approach that adapts methods for restoring corrupted images (Bayesian Image Restoration, or BIR) as the basis for a general statistical framework to parameterize species distribution models using species atlas data in a manner that accounts for different hypothesized spatial patterns in mapping intensities. This is achieved by relating detection probabilities to variables (such as the control variable shown in Figure 5, which was constructed from the number of species which are thought to be ubiquitous) that are hypothesized to be correlated with mapping intensities, and then inferring both the detection probabilities and the underlying species distribution from the atlas data. The resulting ‘restored’ distribution maps (which combine observed presences where available and inferred probabilities of presence where no observation was made; note that this means that errors from false positive sightings are ignored), and the estimated detection probabilities can then be re-assessed by experts for plausibility, and the scenarios rejected or adapted accordingly. Here we demonstrate the implementation of this BIR framework by fitting species distribution models to maps of recorded presences of plant species of the German atlas of vascular plants (FLORKART see http://www. floraweb.de), and estimated speciesand location-specific detection probabilities based on the control variable shown in Figure 5. Figure 6 shows the restored map for Inula conyzae (Ploughman’s Spikenard – see botanical illustration in Figure 7). The BIR method can enhance the utility of species atlas data when it is used to investigate the existence of hypothesized spatial patterns in detection probabilities and as a formal expert knowledge tool to test the reliance of inferences on the distribution of species to assumptions concerning detection probabilities. Spatio-temporal modelling of the spread of invasive species A statistical approach for parameter estimation has recently been developed for simple process-orientated models of dispersal and colonisation by invasive species across landscapes. To date statistical regression techniques, used for example in climate envelope modelling (see above), have outstripped such sto-
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 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! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !! ! ! ! !! ! ! !! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! !! ! ! ! ! ! !! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! !! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! !! ! !! ! ! ! ! !! ! ! !! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! !! !! ! !! !! ! ! !! ! !! ! !! ! !! ! !! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! !! ! ! ! ! ! ! !! ! ! !! !! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! !! !! ! !! !! ! ! ! !! ! !! ! !! ! !! !! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! !! ! !!! !!!!!! ! ! ! ! !! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! !! ! ! !! ! ! !! !! !! !! ! ! !! ! ! ! ! !! ! !! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! !! ! ! ! ! !! !! !! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! !! ! ! ! !! ! ! ! ! ! ! !!!! !! !!! ! ! !! ! ! !! ! ! ! ! ! ! ! ! !! !!! !! ! !! ! ! !! ! ! ! ! ! ! ! ! ! !! !! !!!!
e F gure 4 Mode ed u u e d bu on o c ma ca y u ab e n che pace o he yea 2050 a c e and 2080 b d and he h ee d e en u u e cena o SEDG a b BAMBU c d and GRAS e o Ag a u ae O ange ema n ng n che pace g ey o n che pace b own ga ned n che pace Sou ce Se e e e a 2008
chas c spa o- empora mode s n he r ab y o hand e nforma on abou env ronmen a fac ors such as c ma e and anduse However our work Cook e a 2007 has recen y addressed h s prob em by mak ng use of a range of covar a es descr b ng such b ogeograph ca fea ures of he andscape F gure 8 shows an examp e of he da a commony ava ab e for a en spec es ha have been expand ng he r range over a number of decades The f gure shows hree
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maps of he recorded presences of an nvas ve p an G an Hogweed H ra um man gazz anum – see Pyšek e a h s a as pp 150f n hree success ve a ases The cons derab e effor ha goes n o co a ng he nforma on ha goes n o such spec es a ases see above enab es us o assume ha h s nvas ve spec es co on ses par cu ar oca ons here 10 × 10 km squares or hec ads a some po n n me be ween say he f rs a as when was no
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Figure 5. a) Map of 9 categories of grid cells (‘control groups’; see below) A recording probability was estimated for each category of grid cell. In this way, we were able to test the hypothesis that recording probabilities would decrease with increasing control group number. b) The number of grid cells in each of the 9 different control groups. As a proxy for mapping intensity of each grid cell the number of observed species out of 50 species believed to be present in every grid cell, were yielding 9 different ‘control’ groups of grid cells, with the following number of control species out of a total of 50 present: 50 (group1), 49 (group 2), 48 (group 3), 47 (group 4), 46 (group 5), 44-45 (group 6), 40-44 (group 7), 20-39 (group 8), and 0-19 (group 9). Note that recording probabilities were estimated using the BIR model assuming no prior knowledge on these probabilities (Source: Bierman et al. 2010).
Figure 6. A ‘restored’ map of Inula conyzae (Ploughman’s Spikenard) showing the recorded presences of the species in the grid cells (crosses), and the probabilities (estimated using the BIR model) that species were present in grid cells where the species were not recorded (green-tones in grid cells without crosses) (Source: Bierman et al. 2010).
observed, and the second when it was. We have recently extended the methods presented in Cook et al. (2007) to account for the uncertainty in our knowledge of the colonisation times that is inherit in the use of such species atlas data using Markov chain Monte Carlo sampling within a Bayesian statistical framework. The direct use of species atlas data opens the door to more widespread application of these methods. Relative to standard regression techniques, our models are more transparent in their representation of biologi-
cal processes. They also explicitly model changes over time, and therefore, unlike standard statistical approaches, do not require the assumption that the species’ distribution has already reached equilibrium (which is clearly not the case for an expanding invasive species). In order to illustrate the use of such techniques we apply them to the analysis of species atlas data shown in Figure 8 detailing the spread of Giant Hogweed across Britain in the 20th Century using georeferenced data describing local temperature, elevation and habitat type. The
use of Markov chain Monte Carlo sampling within a Bayesian statistical framework enables the mapping of probabilistic predictions of habitat suitability (Figure 9) and future spread (Figure 10) that account for uncertainty in model parameters as well as variability in the processes of dispersal and colonisation estimated from species atlas data. Unfortunately such complete integration of statistical methods and process models is currently only possible for relatively simple models and therefore below we describe an approach to which quan-
Figure 7. Illustration of Inula conyzae (Ploughman’s Spikenard) originally from Köhler’s Medicinal Plants (1887), but sourced here from Wikimedia Commons (http://commons.wikimedia.org/).
Figure 8. Maps showing the distribution of Giant Hogweed (Heracleum mantegazzianum) across hectads (10 × 10 km squares) in the UK at three points in time (from left to right 1970, 1987, 2000) corresponding to species atlases published by the Botanical Society of the British Isles (www.bsbi.org.uk/html/atlas.html). The data were obtained from the National Biodiversity Network Gateway, and is compiled from numerous sources including the Countryside Council for Wales, Bristol Regional Environmental Records Centre, The Scottish Wildlife Trust, and Scottish Borders Biological Records Centre (see www.nbn.org.uk for details).
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tifies uncertainty through the application of statistical methods to the outputs of existing process-based models. Quantifying uncertainty in model-based projections of biodiversity impacts Process-based models that describe ecosystem functioning can be used to quantify the impacts of climate change and other environmental pressures upon biodiversity and ecosystem services. Such models also allow us to make quantitative statements about
future ecological trends for example by using meteorological inputs based on simulations of future climate scenarios produced by General Circulation Models (GCMs). Climate scientists have developed a number of state-ofthe-art GCMs, which differ in the ways in which atmospheric processes and atmosphere-ocean-biosphere interactions are represented within them. Statistical methods can, at least in principle, provide a natural and powerful framework for quantifying this uncertainty, although there are substantial philosophical and practical challenges in actually doing so. These challenges become even more profound when we go on to examine the impact of this uncertainty upon ecological systems. Projecting future global vegetation carbon A widely used ecosystem model, known as the Lund-Potsdam-Jena dynamic vegetation model (LPJ, Sitch et al. 2003), has been used to generate a range of projections of future vegetation carbon stocks under the IPCC’s SREs A2 scenario of future greenhouse gas emissions (Figure 11). The climate projec-
tions are generated using different GCMs, and for several of the models multiple simulations are used where the individual GCM has been run under slightly different initial conditions. The range of GCM projections therefore represents part (but only part) of the uncertainty associated with our knowledge of the climate system. These climate projections drive the LPJ DGVM. These deterministic projections of carbon cycling can then be combined, using a variant of the statistical technique known as “model averaging”, into a single probabilistic projection. In order to do this, the approach requires us to specify the degree of weight that we will assign to each projection; the values of these weights can either be determined a priori using expert knowledge, estimated empirically (based on the ability of the model to reproduce present-day vegetation carbon stocks), or based on a combination of prior knowledge and past performance. These different weighting schemes reflect differing assumptions about the relationship between current and future climate, and our work (Figure 12) has demonstrated that they can lead to markedly different projections of
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Figure 9. Colonisability by Giant Hogweed (H. mantegazzianum) under spatio-temporal spread model (described in the text) based on estimated habitat suitability and the response to temperature and altitude. This represents the overall suitability of each hectad for the invasive plant.
Figure 10. Probability of colonisation with Giant Hogweed (H. mantegazzianum) by 2050 under the model of spatio-temporal spread and establishment described in the text. This risk map accounts for both variability in the modelled process of invasive spread and uncertainty in the parameters estimated from data. Grey squares indicate hectads that are already colonised by 2000.
future trends in the amount of carbon that is stored within vegetation (for details see Butler et al. 2008). In simple terms, we obtain a narrow prediction when we assume that past performance is a good guide to future performance, alternatively the range of plausible predictions is much wider when relatively greater emphasis is placed on expert knowledge (as represented in the alternative climate models) than on data used to assess past performance.
ing non-detection probabilities. Ecography doi: 10.1111/j.1600-0587.2009.05798.x BUTLER A, DOHERTY RM, MARION G (2008) Model averaging to combine simulations of future global vegetation carbon stocks. Environmetrics 20: 791-811. COOK A, MARION G, BUTLER A, GIBSON G (2007) Bayesian inference for the spatiotemporal invasion of alien species. Bulletin of Mathematical Biology 69: 2005-2025. SETTELE J, KUDRNA O, HARPKE A, KÜHN I, VAN SWAAY C, VEROVNIK R, WARREN M, WIEMERS M, HANSPACH J, HICKLER T, KÜHN E, VAN HALDER I, VELING K, VLIEGENTHART A, WYNHOFF I, SCHWEIGER O (2008) Climatic Risk Atlas of European Butterflies. BioRisk 1: 1-710. SITCH S, SMITH B, PRENTICE IC, ARNETH A, BONDEAU A, CRAMER W, KAPLAN JO, LEVIS S, LUCH W, SYKES MT, THONICKE K, VENEVSKY S (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9: 161-185. WILSON EO (2001) The Diversity of Life, Penguin books.
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Figure 11. LPJ simulations of global annual vegetation carbon stocks for the 20th and 21st centuries. Carbon stocks are measured in gigatonnes of carbon (gtC), and are reported as anomalies relative to the mean value for a thirty year reference period (1961-1990). Climate inputs to the baseline run (black) are based on gridded observational climate data, with inputs to the remaining eighteen runs (coloured lines – see legend) provided by outputs from nine different general Circulation Models, some of which have multiple runs (Source: Butler et al. 2008).
BIERMAN S, BUTLER A, MARION G, KÜHN I (2008) Bayesian Image Restoration to analyze biological atlas data with spatially varying non-detection probabilities. BIERMAN S, BUTLER A, MARION G, KÜHN I (2010) Bayesian Image Restoration to analyze biological atlas data with spatially vary-
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Figure 12. Predictive distributions for global vegetation carbon stocks during the twenty-first century, based on model averaging which reweighs the deterministic models (left) according to their ability to represent past vegetation carbon stocks, or keeps their (in this case equal) prior weights fixed (right). 2.5 %, 50 % and 97.5 % quantiles (red: 2000-2100) of the predictive distribution are shown, together with the baseline run (thick red: 19002000) and GCM-based runs (black). Stocks are reported as anomalies relative to the mean value for the period 1961-1990 (Source: Butler et al. 2008).
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Structuring Future Biodiversity Research and Its Community – the Role of Infrastructures WOUTER LOS
We face serious problems in the understanding and managing of the biodiversity system. Various examples in this atlas indicate that the system properties cannot be described by scaling up from the simple sum of its components and relations. But the functioning of the biodiversity system is also hard to unravel by continuing with experiments on only a few parameters, since this reductionism fails in capturing a wider picture of the full complexity. This holds for all levels of biological life, on the cellular level, the organism level, and the ecological level. In addition, these subsystems operate on different spatial and temporal scales which cannot easily be interrelated. We need a different scientific methodology in order to further limit this reductionism. Examples shown by Pfeiffer et al. (this atlas, pp. 26ff.) or Fergus & Schmid (this atlas, pp. 30ff.) move in the right direction, but are also bound to have limitations if these are to provide scientific evidence to the policy domain for making management decisions. Systems biology Biological systems are characterized by self-organization resulting in a high variety of diversity and complexity in order to adapt to external constraints (environments). This is known for the variation of configurations of single proteins up to ecological communities (Guill & Drossel 2008). It is not possible to understand a biological system by extrapolating from the known behaviour of single units which constitute the system. It is inevitable to apply different methodological views on the system by analysing the correlation properties of all (ensembles of) units with statistical methods (Dhar 2007). Such a “Systems Biology” approach requires the integration of large data sources (data ensembles), data accessibility, software, and computation. This may assist in detecting patterns of strong correlations with evidence for “collective organisations”, which in turn can be further analysed to understand the processes resulting in such patterns (Conti et al. 2008). An infrastructure environment bringing together data, software and computation power at a sufficiently large and integrated scale will support the analysis and modelling of biodiversity systems. CETAF GBIF GEO BON GEOSS LifeWatch LTER MARS
Consortium of European Taxonomic Facilities http://www.cetaf.org/ Global Biodiversity Information Facility http://www.gbif.org/ Global Earth Observation Biodiversity Observation Network http://www.earthobservations.org/geobon.shtml Global Earth Observation System of Systems http://www.earthobservations.org/geoss.shtml Infrastructure for Biodiversity Research http://www.lifewatch.eu Long Term Ecological Research network http://www.lter-europe.net Marine Research Stations Europe http://www.marsnetwork.org/
An infrastructure for biodiversity research Scientists have been working in the last two decades on essential components of such an infrastructure for biodiversity research (Los & Hof 2007). It resulted in LifeWatch as an e-Science research infrastructure designed to explore, describe and understand the complexity of biodiversity (LifeWatch 2008). The LifeWatch infrastructure will allow research teams to create ‘e-Laboratories’ or to compose ‘e-Services’ for various user groups. Data repositories, sensor data, analytical and modelling software tools and computational capacity become available through a service-oriented architecture. The architecture will allow for linkages to external resources and associated infrastructures such as the Global Biodiversity Information Facility (GBIF). As such, LifeWatch represents a new generation of research infrastructures operating in a cooperating fabric of supporting or client infrastructures. The architecture of LifeWatch is modular with several modules connecting to supporting external resources and to the user communities benefiting from the LifeWatch capabilities. ◙ The User Layer provides facilities for creating ‘virtual labs’ where researchers may work together in experimenting with work flows and by controlling and monitoring of supporting tasks. ◙ The Composition Layer supports the intelligent selection and combination of application services in order to complete tasks. Semantic interoperability is a challenge. ◙ The e-Infrastructure Layer provides mechanisms for enabling the sharing of the resources as generic services in a distributed environment spread across multiple external domains. 62
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◙ The Resource Layer contains a variety of conditional resources, such as data repositories, sensor and human observation networks, modelling software and computational capacity. Most of these will by provided by other existing facilities. Collaborative networks Biodiversity data providers, laboratories, universities, conservation groups, etc. are increasingly involved in collaborative activities with other organisations in and outside the biodiversity domain to share competencies and resources. The LifeWatch research infrastructure is going to provide ICT supported mechanisms for such collaborations. Increasingly, computer networks serve as a communication/interaction infrastructure (Camarinha-Matos & Afsarmanesh 2004, 2008). If virtual laboratories or services also have to function as collaborative networks, a wide variety of issues must be resolved (interactions, roles, trust), apart from technicalities to work collectively together by accessing and sharing data, software and computation. The traditional way of engineering infrastructures, relying on dedicating the hardware for a single purpose and a single user group, must be replaced by sharing resources and social interactions in virtual environments (which e.g. also formed the basis of projects like ALARM, see pp. 38ff. in this atlas, or Networks like AlterNet, www.alter-net.info). Structuring the scientific community Knowledge discovery on the complexity of biodiversity systems will be promoted when scientific actors can cooperate at a much larger scale with a common research agenda. As for example, biodiversity data should not only originate from a few locations, but preferably represent all parts of a fine-mazed grid covering a continental area or sea, or even better the whole planet. In this respect, Europe is in the relative good position with a variety of networked organisations which cooperate on data capture with common protocols, like e.g. the Long-Term Ecological Research Network LTER-Europe (see also Mirtl et al., this atlas, pp. 52f.), the Marine Research Stations Network MARS, and the Consortium of European Taxonomic Facilities CETAF. Other networks bring together the observation activities of ten thousands of “citizen scientists”; experienced volunteers who send their (GPS supported) species observations in defined areas at a regularly basis to a central shared data repository (see e.g. Kühn et al., this atlas, pp. 242f.). We also see the potential of autonomous operating wireless environmental sensors or smart hand-held devices for transmitting observations and accelerating data generation with more reliability and cheaper in the long term. The ambition of the LifeWatch developers is to create the technologies and “virtual” environments to enable and serve large scale cooperation. LifeWatch will also contribute to GEO BON, the initiative to add a biodiversity and ecosystem observation component to GEOSS, the Global Earth Observation System of Systems (Scholes et al. 2008). It is expected that large scale cooperation will attract new and large funding opportunities for the cooperating research communities. Experiences of established research infrastructures in other disciplines show that such developments are a reality; and it also has been shown for biodiversity research e.g. by the comparatively small field site network (FSN) of ALARM (see Hammen et al., this atlas, pp. 42ff. and further FSN-related chapters of this atlas). References CAMARINHA-MATOS LM, AFSARMANESH H (2004) The emerging discipline of collaborative networks. Proceedings of PRO-VE’04 - Virtual Enterprises and Collaborative Networks, Kluwer Academic Publishers, ISBN 1-4020-8138-3, pp 3-16. CONTI F VALERIO MC, ZBILUT JP, GIULIANI A (2007) Will systems biology offer new holistic paradigms to life sciences? Syst Synth Biol 1: 161-165. DAHR PK (2007) The next step in biology: a periodic table. J Biosci 32: 1005-1008. GUILL C, DROSSEL B (2008) Emergence of complexity in evolving niche-model food webs. J Theor Biol 251: 108-120. LIFEWATCH (2008) e-science and technology infrastructure, http://www.lifewatch.eu. LOS W, HOF CHJ (2007) The European Network for Biodiversity Information. Biodiversity databases. pp. 5-12. Editors G.B. Curry & C.J. Humphries. Taylor & Francis / CRC Press. SCHOLES RJ, MACE GM, TURNER W, GELLER GN, JÜRGENS N, LARIGAUDERIE A, MUCHONEY D, WALTHER BA, MOONEY HA (2008) Towards a Global Biodiversity Observing System. Science 321: 1044-1045.
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CLIMATE CHANGE IMPACTS ON BIODIVERSITY
Climate Change, Species and Ecosystems
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MARTIN T. SYKES & THOMAS HICKLER
Introduction Climate, more than any other factor, controls the broad-scale distributions of plant and animal species as well as ecosystem structure and functioning. This is true of present-day climate, but also of past climates as they still influence for example current vegetation patterns, not least because generation times for many species, especially trees, can be hundreds of years. At finer scales other factors such as local environmental conditions including micrometeorology, soil nutrient status, pH, water-holding capacity and the physical elements of aspect or slope influence the potential presence or absence of a species. However, intra- and inter- specific interactions, such as competition for resources (light, water, nutrients), ultimately determine whether an individual species is actually found at any particular location. In this section of the Atlas we present some of the studies undertaken in ALARM with regard to climate change impacts on biodiversity, presenting the responses of a wide range of species and ecosystems. Current and future climates Jyhla et al. (this atlas, pp. 66f.) set the scene with regard to current climate condition and observed trends. They briefly describe how various climatic variables such as temperature and precipitation can be developed into indices that classify spatially different regions of Europe by their climate and how the borders between different climatic zones are being altered by recent climate change. They show that temperatures in Europe in the 20th century were the warmest since 1,500 AD and at the same time there have been complex and regional changes in precipitation. Climate change has also affected the frequency and intensity of extreme events with increases in high temperature related events and also in heavy precipitation, at the same time as there has been a decrease in the occurrence of cold events. Predicting what may happen to biodiversity and ecosystems in the future usually requires some sort of description or scenario of the possibilities. Fronzek et al. (this atlas, pp. 68ff.) introduce the scenarios that were developed and applied throughout the ALARM project. These involve general circulation model (GCM) predictions downscaled and gridded for Europe to a produce datasets that include both historical climate data and predictions about climate to the end of the 21st century. Each scenario is then associated to various storylines that are alternative pathways of key driving factors that may affect biodiversity in the future. These scenarios are based on the IPCC SRES scenarios (IPCC 2000) but include updated and different socio -economic and policy strategy assumptions including some “shock” events such as the collapse of the thermohaline circulation or a mass epidemic. The scenarios include key elements including uncertainties that are required in any study of rapid climate change effects on biodiversity. Disturbances and climate change Changing climate is likely to lead to changing frequencies in the disturbances that occur naturally within ecosystems. Fire as a disturbance has close interactions with climate and changes in climate will lead to changes in natural and non-natural fire regimes. For example in Spain forest fires play a dominant role in the landscape (Moreno et al., this atlas, pp. 72f.), though there is a great deal of variability between regions. As the climate warms and the potential fire season becomes longer so natural fires may increase and at the same time the influence humans have in igniting fires either by accident or design is also likely to become greater. Fire is also important in highly managed landscapes both as a tool for management through prescribed burning but also as a risk to biodiversity. Lindley et al. (this atlas, pp. 88f.) discuss this in in the context of the landscapes of the UK upland peak District. They conclude protecting the highest risk areas under climate change is especially important where a longer growing season may lead to greater biomass available for burning probably in drier conditions. Though the largest uncertainties are around the role of human ignition. Impacts in high alpine or high arctic zones The arctic and high alpine regions are generally expected to be among the areas most affected by climate change including changes in biodiversity, treelines and 64
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biome shifts. Kaplan & New (2006), for example, using 6 GCMs with 4 emission scenarios and the BIOME4 equilibrium vegetation model project large biome changes with a 2 °C warming. Forest area between 60-90°N could increase by 55 % (3 × 106 km2) with a reduction in tundra of 42 %. Tundra vegetation moves north but with a significant loss in prostrate dwarf-shrub tundra. More specifically in the Barents region (northern Scandinavia, Russia, Novaya Zemlya, Svalbard and Franz Josef Land) model predictions indicate an increase in boreal needle leaved evergreen forest northwards and up mountains, increased net primary production and leaf area index (Wolf et al. 2008). In the ALARM project Nagy et al. (this atlas, p. 78) explored the changing extent of the high alpine zones as temperatures warm and treeline moves upwards and discuss the likely varied responses among species that inhabit the current high alpine zones throughout Europe. Similarly other types of ecosystems in more extreme climates are likely to be affected. Johansson et al. (this atlas, p. 79) show that sub-arctic palsa mires may well completely disappear, by the end of the century. This is caused by changes in the active layer, the soil layer above the permafrost, that thaws and refreezes every year, as it becomes deeper and permafrost disappears completely. This will influence not only biodiversity, but also important biogeochemical feedbacks to the atmosphere. Climate change not only affects terrestrial ecosystems, for example Jansson et al. (this atlas, pp. 80f.) studied climate impacts of high latitude lake ecosystems. They showed that biodiversity and productivity are likely to be reduced in these lakes due to the increased influx of leached organic carbon from increasingly productive terrestrial ecosystems around these lakes, similarly warmer waters may lead to invasions of fish from warmer climates. Climate and biodiversity relationships Interactions between biodiversity and climate change are usually explored in three ways: observation, experimentation and modelling and Penuelas et al. (this atlas, pp. 76f.) describe applications of these methodologies in a Mediterranean context, though they can be equally applied in most regions. Two important processes; phenology and range shifting; can be studied by these methods and it can be shown how species respond to changing climate. Phenology is the timing of events over the annual cycle of plants and animals and often is a response to changing temperature, moisture and light levels that occur through the year. In plants phenological events include easily observed events such as leaf emergence, flowering and leaf drop. In animals they can be related to the timing of migration, egg laying etc. The earliest known long-term phenological records in Europe were kept by the Marsham family in England from 17361947. They recorded the phenology of more than 20 different species of plants and animals (Sparks & Carey 1995). The oldest known records are however from AD 801 from Japan and record the flowering of cherry trees connected with the timing of the annual blossom festival (Anono & Kazui 2008). The other important process induced by climate change is range shifting and under warming is likely to lead to suitable climate space becoming available north of current range boundaries for species that require warmer conditions. Changes, related to both latitude and altitude, are possible and these can be both expansions and contractions. There is a substantial body of evidence that show that species have already responded to climate change by range shifting and these are summarised in a number of reviews e.g., Parmesan & Yohe (2003). In the northern hemisphere changes among the most studied groups, butterflies and moths, show range expansion in the north and sometimes contraction in the south (Parmesan 2006). Walther et al. (this atlas, pp. 74f.) describe a number of studies that explore these different processes. They include poleward and altitudinal expansions and range contractions. They conclude that poleward expansions are easier to observe than equatorward contractions as resilient older generations may survive (without regeneration) long after the climate has changed.
In this chapter a number of examples of range shifts are included. Ott (this volume) describes recent changes in European dragonfly fauna, including some changes that were already obvious more than 20 years ago. Of course as species move north in Europe so species currently beyond Europe’s boundaries and Ott (this volume) describes invasions from Africa and discusses what this might mean for indigenous species. Robinet et al (this atlas, pp. 86f.) explores the range expansions of the pine processionary moth as a modelling case study in France. The moth range boundary has already shifted 87 km northwards in the last 30 years and under various climate scenarios this expansion is likely to continue, though extreme climatic events such as the 2003 heatwave are likely to moderate such expansions. In summary climate change is likely to change spatially the range or envelope that a species can occupy. Ranges may expand polewards in high- latitudes as most warming is likely in these latitudes. It could lead to low-latitude boundaries that are contracting with possibilities of extinctions. In order to survive, a species may therefore need to be able to disperse to new and more suitable climates. Depending on the speed of climate change, the species-specific dispersal capabilities, the degree of landscape fragmentation and the possibility of human management this may or may not be possible. Climate and biodiversity outside Europe Finally Halloy et al (this atlas, pp. 90ff.) as one of the global partners summarises ALARM research in South America. Here a network of monitoring sites has been
developed in the Argentinean Andes linking into the GLORIA network (www.gloria.ac.at) of alpine observation sites. Additionally studies on a variety of themes including mammals, reptiles, amphibians, soil bacteria, glacial retreat, agriculture have also been initiated. This chapter in the Atlas summarises only some of the studies that have been carried out in the ALARM project. It aims however to give a flavour of the varying impacts of climate change on species and ecosystems that are occurring and likely to occur in the near future. References AONO Y, KAZUKI K (2008) Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. International Journal of Climatology 28: 905-914. IPCC (2000) Emissions Scenarios 2000. Cambridge University Press, Cambridge, UK. KAPLAN JO, NEW M (2006) Arctic climate change with a 2 ºC global warming: Timing, climate patterns and vegetation change. Climatic Change 79: 213-241. PARMESAN C (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics 37: 637-669. PARMESAN C, YOHE G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37-42. SPARKS TH, CAREY PD (1995) The response of species to climate over two centuries: an analysis of the Marsham phenological record, 1736-1947. Journal of Ecology 83: 321-329. WOLF A, CALLAGHAN TV, LARSON K (2008) Future changes in vegetation and ecosystem function of the Barents Region. Climatic Change 87: 51-73.
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Current Climatic Conditions and Observed Trends in Europe
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KIRSTI JYLHÄ, TIMOTHY R. CARTER & STEFAN FRONZEK
Characterising Europe’s present-day climate The average climatic conditions in Europe that are of relevance for biodiversity are typically defined by variables such as air temperature, precipitation, humidity, wind speed and solar radiation. These variables are observed on a daily basis at thousands of meteorological stations across Europe, and timeaveraged statistics are commonly interpolated from these locations onto a regular grid to enable mapping, comparison with models and many other applications. Maps of monthly, seasonal and annual climate are available from national meteorological services and other sources, and are not reproduced here. Instead, it can be instructive to characterise the present-day European climate by using indices that combine several climatic variables. One such index is the Köppen climate classification (Figure 1). The Köppen classification has been mapped based on interpolated climatic observations for 1961-1990. It illustrates how a moist temperate climate type with long summers (Cfb) is most
abundant in western and central Europe, with mild winters, at least four months warmer than 10 ºC and precipitation in all seasons. A climate type with approximately similar summers but colder winters, with the average temperature of the coldest month below -3 ºC, prevails in eastern Europe (Dfb). Cold winters and short summers, with only one to three months above 10 ºC, constitute the dominant climate type (Dfc) north of 60ºN. The Mediterranean Europe is characterized by four subtypes of temperate climates, with wetter (Cf) or drier (Cs) warm (b) or hot (a) summers. Areas assigned to a cold snow climate type (ET), with the mean temperature of the warmest month below 10 ºC but above 0 ºC, can be found in the Scandinavian mountains and the Alps. However, in line with recent climate change (see below), the borders between climatic zones are shifting, as the area occupied by boreal climates in Europe has decreased since the mid 1960s whilst areas assigned to temperate climates have expanded (Beck et al. 2006).
Recent trends in mean climate The Intergovernmental Panel on Climate Change (IPCC) reports that global warming has proceeded at an average rate of 0.07±0.02 °C per decade over the last 100 years (IPCC 2007). The warmest years in the instrumental record of global surface temperatures are 1998 and 2005. Years 2002-2004 are the 3rd, 4th and 5th warmest ones in the series since 1850. Based on temperature reconstructions, the average Northern Hemisphere temperatures during the second half of the 20th century were very likely1 higher than during any other 50-year period in the last 500 years and likely1 to have been the highest in at least 1,300 years. In Europe as well, the 20th century was the warmest since 1,500 (Figure 2). The annual averaged mean surface air temperature increased by 0.08±0.03 °C per decade within the 20th century and the period from 1974 to 2003 was about 0.45 °C warmer than the second warmest 30-year period in the 18th century (Luterbacher et al. 2004).
Increases in temperature lead to increased water-holding capacity of the atmosphere, altering the hydrological cycle and thus also precipitation events. The pattern of precipitation changes is complex in space as well as seasonally, some regions experiencing drier conditions while others have become wetter. In Europe, significantly increased precipitation has been observed in northern Europe, whilst drying has been observed in the Mediterranean region (IPCC 2007). However, due to the large variation, significant regional trends in precipitation are generally more difficult to detect than temperature trends (e.g., BACC 2008). Moreover, estimates of precipitation changes often suffer from a scarcity of data and lack of homogeneous observational records. Recent trends in climate extremes Climate extremes are rare events that fall in the tails of the distribution of variables such as daily temperature or precipitation. In order statistically to detect any trends in the frequency and magni-
Table 1. Change in extremes for meteorological phenomena over the specified region and period, with the level of confidence1 (Source: IPCC 2007). Phenomenon Low-temperature days/nights and frost days High-temperature days/nights Cold spells/snaps (episodes of several days) Warm spells (heat waves) (episodes of several days) Cool seasons/ warm seasons (seasonal averages) Heavy precipitation events (that occur every year) Rare precipitation events (with return periods > ~10 yr)
Drought (season/year) Tropical cyclones
ET
Dfc
Dfb
Cfc
Cfb
Cfa
Csb
Csa
Extreme extratropical storms
BS
Figure 1. Average spatial distribution of climatic types in Europe in 1961-1990. The patterns are based on the Köppen climate classification and deduced from European monthly mean temperature and precipitation data provided by the Climate Research Unit (CRU) at the University of East Anglia, UK (New et al. 2002, Mitchell et al. 2003). For the classes, see below. ET: Cold snow climate (tundra); Df: Moist boreal snow climates, with shorter (Dfc) or longer (Dfb) summers; Cf: Temperate wet-all-seasons climates, with shorter (Cfc) or longer (Cfb) warm summers or hot (Cfa) summers; Cs: Temperate dry-summer climates, with long warm (Csb) or hot (Cfa) summers; BS: Dry semiarid (steppe).
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Small-scale severe weather phenomena
1
Change Decrease, more so for nights than days
Region Period Over 70 % of global 1951-2003 (last land area 150 years for Europe and China) Over 70 % of global 1951-2003 land area
Increase, more so for nights than days Insufficient studies, but daily temperature changes imply a decrease Increase: implicit Global evidence from changes of daily temperatures Central Europe Some new evidence for changes in interseasonal variability
Confidence Very likely
Very likely
1951-2003
Likely
1961-2004
Likely
Increase, generally beyond that expected from changes in the mean Increase
Many mid-latitude regions (even where reduction in total precipitation) Only a few regions have sufficient data for reliable trends (e.g., UK and USA)
1951-2003
Likely
Various since 1893
Increase in total area affected Trends towards longer lifetimes and greater storm intensity, but no trend in frequency Net increase in frequency/intensity and poleward shift in track Insufficient studies for assessment
Many land regions of the world Tropics
Since 1970s
Likely (consistent with changes inferred for more robust statistics) Likely
Northern Hemispheric land
Since about 1950
Since 1970s
Likelihoods are defined by the IPCC as: Very likely > 90 %, Likely > 66 %.
Likely; more confidence in frequency and intensity Likely
European summer mean temperature in 1500-2004
European winter mean temperature in 1500-2004
1 0 -1 -2 -3
1500
1600
1700
1800
1900
2000
Temperature anomaly (°C wrt 1961-1990)
Temperature anomaly (°C wrt 1961-1990)
Temperature anomaly (°C wrt 1961-1990)
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-4
European annual mean temperature in 1500-2004 2
2
3
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-2
1500
1600
1700
1800
1900
1
0
-1
-2
2000
1500
1600
1700
1800
1900
2000
Year
Year
Year
Figure 2. Variations of winter (DJF), summer (JJA), and annual averaged mean European temperature in 1500-2004 (anomalies relative to the 1961 to 1990 average), defined as the average over the land area 25°W to 40°E and 35°N to 70°N (thin white line). The values for the period 1500 to 1900 are reconstructions; data from 1901 onward are based on observations. The thick red line shows 30-year running averages. The winter y axis uses a different scale (Data source: Luterbacher et al. 2006).
tude of extreme weather situations, longer observed time-series are required compared to analyses of changes in the mean climate. A global analysis with a large set of indices of daily climate extremes such as a warm spell duration index, the number of frost days or the occurrence of very wet days was conducted by Alexander et al. (2006) for the period 1951-2003. They found significant increases in daily minimum and maximum temperatures across the globe and increases in precipitation extremes over many areas, although much less spatially coherent than for temperature. Table 1 gives an overview of observed changes in extremes and the level of confidence. Observed changes in the frequency of temperature-related extremes generally show increases in heat and heavy precipitation events and decreases in cold events. In Europe, particularly in regions belonging to the temperate wet-all-season climate type (Figure 1), the growing season length has increased by 3-9 days per decade during the period 19462007, or even more, while the number of days with maximum temperature exceeding 25 ºC has also increased (Figure 3). Even wider European areas are characterized by decreasing trends in the annual number of frost days. The frequency of wet days has increased, particularly in north-eastern Europe. Based on Figure 4, sites where there is a suggestion of an increasing (though often not statistically significant) trend in the occurrence of very heavy precipitation events are more common than those indicating a decreasing trend. At most stations no long-term trends in the number of consecutive dry days have been observed.
RUSTICUCCI M, VAZQUEZ-AGUIRRE JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research 111: D05109. doi: 10.1029/2005JD006290 THE BACC AUTHOR TEAM (2008) Assessment of Climate Change for the Baltic Sea Basin. Springer 2008, 469 pp. BECK C, GRIESER J, KOTTEK M, RUBEL F, RUDOLF B (2006) Characterizing Global Climate Change by means of Köppen Climate Classification. Klimastatusbericht, 2005, 139-149. IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate a
Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (Eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. KLEIN TANK AMG, COAUTHORS (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology 22: 1441-1453. doi: 10.1002/joc.773 LUTERBACHER J, DIETRICH D, XOPLAKI E, GROSJEAN M, HEINZ WANNER H (2004) European Seasonal and Annual Temperature Variability, Trends, and Extremes Since 1500. Science 303: 1499-1503. b
LUTERBACHER J, COAUTHORS (2006) European Seasonal Temperature Reconstructions. IGBP PAGES/World Data Center for Paleoclimatology. Data Contribution Series # 2006-060. NOAA/NCDC Paleoclimatology Program, Boulder CO, USA. Data available at ftp://ftp.ncdc.noaa.gov/pub/data/paleo/ historical/europe-seasonal.txt MITCHELL TD, CARTER TR, JONES PD, HULME M, NEW M (2003) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre Working Paper 55: 29. NEW M, LISTER D, HULME M, MAKIN I (2002) A high-resolution data set of surface climate over global land areas. Climate Research 21: 1-25. c
8 6 4 2 0 -2 -4 -6 -8
Figure 3. Linear trends in temperature-related indices at a selection of European meteorological stations over the period 1946 to 2007. Annual number of frost days (daily minimum air temperature < 0 ºC) (a). Growing season length (the number of days between the first occurrence of at least 6 days with mean temperature > 5 ºC and the first occurrence after 1 July of at least 6 days with mean temperature < 5 ºC) (b). Annual number of summer days (daily maximum temperature > 25 ºC) (c). Solid circles denote a trend (unit: 1/decade; see the legends) with a significance level of 5 %. Sites where there is a suggestion of an increasing or a decreasing (but not statistically significant) trend are shown by coloured open circles. The stations indicating no trend are denoted by small black dots. Data source: Klein Tank et al. (2002), the European Climate Assessment & Dataset (ECA&D) project. a
b
c
8 6 4 2 0 -2 -4 -6 -8
References ALEXANDER LV, ZHANGE X, PETERSONN TC, CAESAR J, GLEASONN B, KLEIN TANK AMG, HAYLOCK M, COLLINS D, TREWIN B, RAHIMZADEH F, TAGIPOUR A, RUPA KUMAR K, REVADEKAR J, GRIFFITHS G, VINCENT L, STEPHENSON DB, BURN J, AGUILAR E, BRUNET M, TAYLOR M, NEW M, ZHAI P,
Figure 4. Same as Figure 3 but for precipitation-related indices. Annual number of wet days (daily precipitation amount ≥ 1 mm) (a). Annual number of very heavy precipitation days (daily precipitation amount ≥ 20 mm) (b). Annual maximum number of consecutive dry days (c). Unit: 1/decade. Data source: Klein Tank et al. (2002), the European Climate Assessment & Dataset (ECA&D) project.
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Scenarios of Climate Change for Europe
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STEFAN FRONZEK, TIMOTHY R. CARTER & KIRSTI JYLHÄ
the past century. Climate models are customarily used to simulate future climate under alternative scenarios of greenhouse gas emissions, facilitating the development of climate scenarios.
Picture by NASA – The Earth seen from Apollo 17 (high resolution image: http://upload.wikimedia.org/ wikipedia/commons/9/97/The_Earth_seen_from_ Apollo_17.jpg; copyright: public domain, see http:// www.jsc.nasa.gov/policies.html#Guidelines).
One of the primary tasks of the ALARM project is to study the risks of climate change for biodiversity in Europe. In order to characterise these risks, it is necessary to provide a description of how the climate has changed historically and is expected to change in the future under increasing atmospheric concentrations of greenhouse gases. Instrumental climate observations are available from many locations to describe aspects of the climate during
Modelling Europe’s future climate The Intergovernmental Panel on Climate Change (IPCC) estimates that global mean temperature will rise during the 21st century by between 1.1 and 6.4 °C, depending on alternative pathways of socio-economic and technological developments (IPCC 2007).1 European temperatures are projected to increase more than the global mean with the largest warming in northern Europe in winter and in the Mediterranean area in summer. Mean precipitation is projected to increase in northern Europe, especially in winter, but decrease in southern Europe throughout the year as well as in central Europe in summer. These estimates are based on coupled atmosphere-ocean general circulation models (AOGCMs), which are the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations.
AOGCMs depict the climate using a three dimensional grid over the globe, typically having a horizontal resolution of several hundred km. Many physical processes, such as those related to clouds, occur at smaller scales and cannot be properly modelled by GCMs. Instead, their known properties must be approximated over the larger scale in a technique known as parameterization. This is one source of uncertainty in GCM-based simulations of future climate. Others relate to the simulation of various feedback mechanisms. For example, warming enhances the Earth’s hydrological cycle and increases atmospheric water vapour, which is itself a greenhouse gas and hence promotes additional warming. Snow cover cools the earth by reflecting solar radiation back to space, but greenhouse gas-induced warming decreases snow cover, reducing its cooling effect and hence warming the climate further. Other feedback processes concerning cloud properties, ocean circulation and land surface characteristics are also difficult to represent. Given these complexities, different AOGCMs may simulate quite different responses to the same forcing,
Table 1. Climate scenarios provided for ALARM.
Scenarios
SRES Forcing 1
GRAS GRAS-CUT BAMBU SEDG
A1FI A1FI A2 B1
Climate models (2001-2100) CSIRO2 HadCM3 AOGCM 3 AOGCM 3 X (scaled) X (scaled) X X X (scaled)
NCAR-PCM AOGCM 3
X
RCA3 RCM 4
X
1
Radiative forcing of the atmosphere due to greenhouse gas and aerosol concentrations based on SRES.
2
Mean atmospheric CO2 concentrations computed using the Bern-CC model (IPCC 2001).
3
AOGCM: Atmosphere-Ocean General Circulation Model projections (2001-2100). Models – NCAR-PCM: National Center of Atmospheric Research, USA; CSIRO2: Commonwealth Scientific and Industrial Research Organisation, Australia; HadCM3: Hadley Centre for Climate Prediction and Research, UK.
4
RCM: Regional Climate Model (1961-2100). Model – RCA3: Rossby Centre Regional Atmosphere Model, Sweden.
Table 2. Summary of the ALARM scenarios for three time periods in the future showing mean annual temperature and precipitation change averaged over Europe relative to 1961-1990 and atmospheric carbon dioxide (CO2) concentration at the end of each period. The GRAS-CUT scenario does not extend to 2100.
2021-2050 CO2 concentration (ppm) Temperature change (°C) Precipitation change (%) 2051-2060 CO2 concentration (ppm) Temperature change (°C) Precipitation change (%) 2071-2100 CO2 concentration (ppm) Temperature change (°C) Precipitation change (%)
1
68
BAMBU (3-model range)
SEDG
GRAS
GRAS-CUT
522 1.2 – 1.9 0–1
482 1.9 -1
555 2.2 -1
555 2.2 -1
568 1.7 – 2.9 0–3
503 2.5 -1
625 3.7 -1
625 2.1 -14
836 3.0 – 5.0 0–6
540 3.3 -1
958 6.1 -1
-
Estimated change by 2090-2099 relative to 1980-1999 with a 90 % likelihood for six alternative scenarios of greenhouse gas emissions.
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simply because of the way certain processes and feedbacks are modelled. The ALARM scenarios were designed to represent aspects of these uncertainties. With their coarse spatial resolution, AOGCMs are not capable of resolving local details of the climate, such as the effects of topography, coasts, and surface characteristics. These details can be crucial for understanding how organisms, physical systems and human activities are likely to respond to a changing climate at regional scale, so researchers have developed techniques for downscaling AOGCM projections to the finer scales of relevance for studying impacts. The two most common techniques are dynamical downscaling, using high resolution numerical models nested within AOGCMs, and statistical downscaling, applying statistical relationships between observed local climate and large-scale atmospheric circulation to derive future local climate from AOGCM projections. In addition to AOGCM projections, the ALARM scenarios also make use of dynamically downscaled information for Europe from high resolution, regional climate models (RCMs). All model projections are related to historical observations of the European climate. The ALARM climate scenarios Historical observations of climate interpolated to a regular grid system, developed by the Climatic Research Unit (CRU) at the University of East Anglia, UK, provide high-resolution information for key climate variables in monthly time steps throughout the 20th century (New et al. 2002, Mitchell et al. 2003). The datasets consists of five variables: mean surface temperature, diurnal temperature range, precipitation, vapour pressure and cloudiness. Projected changes in climatic variables from AOGCMs were used to construct the core set of ALARM climate scenarios for Europe that continues the historical time series through to the end of the 21st century. Each climate scenario is associated with one of three ALARM storylines – BAMBU, GRAS and SEDG – named symbolically after plants and constructed to describe alternative future pathways of key driving factors affecting biodiversity (see Box on page 71). Other drivers include population, economic development, technology and land-use change (see Omann et al., this atlas, pp. 196f. and Reginster et al., this atlas, pp. 100ff.). Together, quantified scenarios of these drivers permit a multi-pressure assessment of future risks to biodiversity.
The climate scenarios for each of the storylines can be traced back to a common set of global scenarios of the underlying driving factors of environmental change, based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES − Nakićenović et al. 2000, see Table 1). They have been selected to provide an impression of two sources of uncertainty in climate projections: uncertainty in future emissions and uncertainty in future climate. Projections from a common AOGCM (HadCM3) are used to represent the uncertainty attributable to future emissions, spanning the SRES range from GRAS (HadCM3)
basic scenarios in Figure 1 and 2. The strongest warming by the end of the 21st century is projected for the GRAS scenario. In this scenario, winter warming shows a gradient from south-western to north-eastern Europe with the smallest increases of about 3 °C over the Iberian peninsula and the largest increases of more than 10 °C in northern Finland, while summer warming is strongest in the Mediterranean countries. The pattern of winter precipitation changes for the same scenario shows wetter conditions over nearly all of central and northern Europe and drier conditions in southern Europe, compared to the 1961-1990 mean. Summer precip-
A1FI (highest emissions), through A2 (moderately high) to B1 (lowest). For one of the ALARM scenarios, BAMBU (A2 emissions), projections from two other AOGCMs are used alongside HadCM3 to represent model uncertainties in projected climate. Averaged over Europe, the ALARM scenarios describe changes in mean annual temperature by the end of the 21st century relative to 19611990 that range between 3.0 and 6.1 °C. Changes in annual precipitation are between -1 and 6 % (Table 2). The spatial patterns of simulated changes in mean annual temperature and precipitation are shown for the five SEDG (HadCM3)
BAMBU (PCM)
1
2
3
4
5
itation in this scenario decreases over a large part of Europe with the only exceptions being Fennoscandia and parts of the Baltic countries. The ALARM climate scenarios only span a part of the uncertainty range of climate model projections, though the AOGCM projections (Table 1) were selected to be representative of a larger ensemble of temperature and precipitation projections for Europe. Figure 3 shows the changes in temperature and precipitation of the three BAMBU scenarios (orange, green and brown points) and of the full ensembles of climate model projections used by the IPCC from the recently pub-
BAMBU (CSIRO2)
6
7
8
BAMBU (HadCM3)
9
Figure 1. Winter (December-January-February, above) and summer (June-July-August, below) temperature change (°C) for the ALARM scenarios by 2071-2100 relative to 1961-1990.
GRAS (HadCM3)
SEDG (HadCM3)
BAMBU (PCM)
-25
-20
-15
-10
-5
0
BAMBU (CSIRO2)
5
10
15
BAMBU (HadCM3)
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Figure 2. Winter (December-January-February, above) and summer (June-July-August, below) precipitation change (%) for the ALARM scenarios by 2071-2100 relative to 1961-1990.
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the North Atlantic thermohaline circulation (THC) that could cause a major cooling over north-western Europe. There has been much discussion about the possibility of a THC collapse triggered by the introduction of increased quantities of freshwater due to melting ice and enhanced river discharges. If this were to happen, it could be expected to cause major disruptions in regional climate over northwest Europe, with associated impacts on biodiversity. No AOGCM experiments with realistic forcing has shown such a shut-down; merely a weakening (IPCC 2007). However, a collapse cannot be ruled out on theoretical grounds and the possible implications for global climate (including a cooling over Europe) have been shown in “hosing experiments”, which assume a sudden freshening (reduced salinity) in the North Atlantic (Vellinga & Wood 2007). To address these concerns, we have also provided a THC collapse scenar-
Source: http://en.wikipedia.org/wiki/Image: Stormclouds.jpg, high resolution image: http://upload. wikimedia.org/wikipedia/en/b/b9/Stormclouds.jpg, copyright under GNU Free Documentation License.
lished Fourth Assessment Report (AR4 – IPCC 2007). GRAS-CUT: A scenario of the collapse of the North Atlantic thermohaline circulation An additional climate scenario explores the possibility of a sudden collapse of
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io, labelled GRAS-CUT. This assumes that rapid emissions under the A1FI scenario trigger a shutdown of the THC around 2050, and the climate suddenly cools over much of Europe. The AOGCM simulation we are applying was conducted with the HadCM3 AOGCM assuming a greenhouse gas forcing of the atmosphere described by the IPCC IS92a emissions scenario up to 2049, whereupon freshwater was suddenly introduced to the North Atlantic (Vellinga & Wood 2007, updated). The patterns of summer and winter temperature and precipitation changes across Europe are shown for the decade 2050-59 relative to the previous decade 2040-2049 (Figure 4). The figure indicates that climate cools sharply over much of Europe in the years immediately following the THC collapse, but that cooling is most intense over northwest Europe, dropping to levels well below those observed during 1961-
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Figure 3. Comparison of the ALARM climate scenarios (large symbols) with later projections reported in the IPCC Fourth Assessment Report (AR4 – coloured dots) for northern Europe (NEU – upper panels) and southern Europe (SEU – lower panels) in winter (DJF – left panels) and summer (JJA – right panels). Average rates of air temperature (°C per century) and precipitation change (% per century) are based on atmosphere-ocean general circulation model (AOGCM) and regional climate model (RCM) projections for the last 30-years of the 21st century relative to simulated present-day climate under different SRES scenarios of greenhouse gas and aerosol emissions. Regional definitions differ slightly: for the AR4 projections: NEU = 48.0°N - 75.0°N, 10.0°W - 40.0°E and SEU = 30.0°N - 48.0°N, 10.0°W - 40.0°E (data from Isaac Held, personal communication); for the ALARM scenarios: NEU = 47.5°N - 67.5°N, 10.0°W - 40.0°E and SEU = 30.0°N - 47.5°N; 10.0°W - 40.0°E (data from Ruosteenoja et al. 2003 and Kjellström et al. 2005).
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Assessing changes in extreme weather events with Regional Climate Models In order to offer information at a higher spatial (approximately 50 km) and temporal (daily) resolution than the AOGCM-based scenarios, and to provide the possibility to account for changes in both the mean climate and inter-annual variability, information is also being made available from a regional climate model (RCM) run over a spatial domain covering Europe. The Rossby Centre in Sweden has recently conducted a transient climate projection with their RCA3 RCM for 1961-2100 using the A2 emissions scenario and lateral boundaries from the ECHAM4/ OPYC3 AOGCM (Kjellström et al. 2005). Outputs from this model have been provided as the core RCM-based scenario for ALARM because it is consistent with the BAMBU scenario and because it spans all of the time slices selected for ALARM. The variables provided from this simulation were mean, maximum and minimum surface air temperature, precipitation, relative humidity, snow water equivalent, shortwave net radiation, total cloud cover, and wind components. Daily climate information simulated with RCMs can be used to study extreme events, changes of which could have significant impacts in Europe. For example, Figure 5 shows the projected number of frost days in Europe by the end of the 21st century in comparison to 1961-1990 observed and simulated information. This information is of potential importance for the survival of certain plant and animal species. Conclusions The scenarios described here are designed to be consistent with socioeconomic, and land use scenarios developed in parallel for ALARM. They embrace a number of key climate and related variables required for studying climate change impacts, offer continental coverage, capture a range of uncertainties in future European climate including a low probability, high impact “climate surprise” scenario, and address alternative mitigation policies.
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1990 (not shown). There is also drying in most seasons.
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References HAYLOCK MR, HOFSTRA N, KLEIN TANK AMG, KLOK EJ, JONES PD, NEW M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 113: D20119. IPCC (2001) Climate Change (2001) The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton JT, Ding Y, Griggs DJ,
Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (Eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881 pp. IPCC (2007) Climate Change (2007) The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (Eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. JYLHÄ K, FRONZEK S, TUOMENVIRTA H, CARTER TR, RUOSTEENOJA K (2008) Changes in frost and snow in Europe and Baltic sea ice by the end of the 21st century. Climatic Change 86: 441-462. KJELLSTRÖM E, BÄRRING L, GOLLVIK S, HANSSON U, JONES C, SAMUELSSON P, RUMMUKAINEN M, ULLERSTIG A, WILLÉN U, WYSER K (2005) A 140-year simulation of European climate with the new version of the Rossby Centre regional atmospheric climate model (RCA3). Reports Meteorology and Climatology 108, SMHI, SE-60176 Norrköping, Sweden, 54 pp. [http://www. smhi.se/sgn0106/if/biblioteket/rapporter_ pdf/RMK108.pdf] MITCHELL TD, CARTER TR, JONES PD, HULME M, NEW M (2003) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre Working Paper 55: 29.
NAKIĆENOVIĆ N, ALCAMO J, DAVIS G, DE FRIES B, FENHANN J, GAFFIN S, GREGORY K, GRÜBLER A, JUNG TY, KRAM T, LA ROVERE EL, MICHAELIS L, MORI S, MORITA T, PEPPER W, PITCHER H, PRICE L, RAIHI K, ROEHRL A, ROGNER H-H, SANKOVSKI A, SCHLESINGER M, SHUKLA P, SMITH S, SWART R, VON ROOIJEN S, VICTOR N, DADI Z (Eds) (2000) Emissions Scenarios. A Special Report of
Figure 4. Surface temperature change (°C) and precipitation change (%) in the first decade (2050-2059) after a hypothetical THC shutdown in 2049 relative to the previous decade (2040-2049) during winter (December-JanuaryFebruary, left panels) and during summer (June-July-August, right panels).
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BAMBU: Business-As-Might-Be-Usual scenario. A continuation into the future of currently known (and near future) socio-economic and policy strategies. => Climate and CO2 concentration are consistent with the SRES A2 scenario GRAS: GRowth Applied Strategy scenario. A future world orientated toward economic growth. => Climate and CO2 concentration are consistent with the SRES A1FI scenario SEDG: Sustainable European Development Goal scenario. A normative scenario focused on the achievement of sustainable development; CO2 stabilisation at 550 ppm => Climate is consistent with the SRES B1 scenario GRAS-CUT scenario: A variant of the GRAS scenarios, where rapid climate change is triggered by an abrupt collapse of the North Atlantic thermohaline circulation, affecting the climate over Europe => Climate for SRES A1FI until 2049 followed by THC collapse (adapted from Vellinga & Wood 2007)
observed 1961-1990
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world regions: an intercomparison of model-based projections for the new IPCC emissions scenarios. The Finnish Environment 644. VELLINGA M, WOOD R (2007) Impacts of thermohaline circulation shutdown in the twenty-first century. Climatic Change 91: 43-63.
June-August temperature change
December-February temperature change
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Concise description of the ALARM storylines (details are presented in Spangenberg et al., this atlas, pp. 10ff.): •
Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 599 pp. NEW M, LISTER D, HULME M, MAKIN I (2002) A high-resolution data set of surface climate over global land areas. Climate Research 21: 1-25. RUOSTEENOJA K, CARTER TR, JYLHÄ K, TUOMENVIRTA H (2003) Future climate in
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Figure 5. Average number of annual frost days: a) observed during 1961-1990 based on a daily dataset (Haylock et al. 2008), b) simulated for 1961-1990 based on the RCA3 regional climate model nested in ECHAM4/OPYC AOGCM and c) simulated for the period 2071-2100 using the same RCA3 RCM with A2 forcing.
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Forest Fire Risk in Spain under Future Climate Change
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JOSÉ M. MORENO, GONZALO ZAVALA, MARÍA MARTÍN & AMPARO MILLÁN
Forest fires play a dominant role in Spanish landscapes. During the last three decades, there were over 14,000 fires per year, which swept through ca. 200,000 ha. These fires burned a total of nearly 6 Mha, of a forested area of ca. 25 Mha. Fires occurred virtually everywhere throughout the country, except in the valleys of the large rivers, where agriculture landscapes dominate (Moreno et al. 1998) (Figure 1). Fire incidence, however, varied greatly between regions. Particularly “hot” areas were the North-west (the region of Galicia), and the mountains along the Mediterranean Sea, of the Central System, and of the southern ranges. Based on data from the last decade, most fires are caused by people (Vázquez & Moreno 1998) (97 %), lightning causing only 3 %. Of the human-induced fires, the majority of them were lit intentionally (ca. 60 %). Negligence and other accidental causes accounted for less than 20 % of all fires, and the rest were caused by unknown sources. Climate is a major determinant of the occurrence of forest fires and of fire regime across the globe. Climate determines the vegetation of any given place, its primary productivity and, in combination with physiographic features, the land-use of the area, which affects the type of human-influenced vegetation apt to burn. For a fire to ignite and spread, the appropriate conditions are critical. These include, high temperatures and low air relative
humidity, dry soils and litter, and wind. In Spain, where most of the country has a Mediterranean-type climate, the summer months are particularly critical. This is reflected in the high values of some fire danger indices used by the forest services to alert people to the risk of fire. One index that reflects this risk well is the Canadian Fire Weather Index set of codes. Of these, the drought code (DC, a measure of the seasonal drought effect on fuels) and the FWI (a measure of the intensity of a spreading fire), are highest in summer time (Moreno 2005) (lefthand side panels of Figure 3). Accordingly, the majority of fires in the country occur between May and October, with July and August being the critical months. In some areas (North and North-west) fires in spring are also important, and are linked to vegetation burning for pastures. The course of ignitions during the day reflects the course of the daily weather and of the FWI. The peak of fire break-outs occurs at 4 p.m., declining thereafter. However, intentional fire break-outs do not decline markedly until past 10 p.m. In countries where fires are not natural, man plays a critical role, overriding that of climate (Pausas & Vallejo 1999) and determining where and when fires occur, since the majority of them are intentionally lit. Indeed, whether a fire will occur or not in an area with very hazardous vegetation or very dangerous weather conditions
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Figure 1. Number of fires/decade (a); and area burned/decade (b) in Spain during the years 1975-2000. Cell size is 50 × 50 km. Source: EGIF Forest Fires Database, Ministry of Environment, Government of Spain.
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depends on the availability, intentionally or not, of a source of ignition. And, in areas with vegetation ordinarily not very prone to burn, man can select those few particular occasions when weather is favorable to start a fire. Not surprisingly, the relationship between the number of fires, or the surface burned, and the mean FWI or DC for the fire season (May to October)(both being measures of the susceptibility of an area to sustain fire, given the appropriate vegetation) represented by 50 × 50 km cells in which peninsular Spain can be divided, yields no significant relationships. That is, across the country, fire danger indices do not provide a good basis to infer fire occurrence. This is in accordance with the fact that the region of Spain with the greatest fire incidence is the Northwest. This area is characterized by an Atlantic climate, with a cool and moist summer, and non-Mediterranean vegetation (Vázquez et al. 2002). Knowing this, inferring future fire activity in Spain solely from climate is not possible, unless people’s behavior can be incorporated in fire-occurrence models. Nevertheless, within a given 50 × 50 km cell the relationship between monthly values of FWI or DC during the fire season (May to October) and fire occurrence through the years yields significant correlations for most cells (85 to 91 % for DC and FWI, respectively, and for the number of fires or area burned), albeit in most cells the relationship is fairly weak (mean Spearman non-parametric correlation coefficient of about 0.35 for DC and 0.45 for FWI, for both number of fires or area burned). That is, fire occurrence is partially explained by whether a year is drier (DC) or more dangerous (FWI), even though the potential of any of these indices to explain the total fire activity is low, given the uncertainty linked to the number of ignitions caused by people. Therefore, for a given area, the larger the FWI or the DC was in previous decades, the more important it was in determining the number of fires that occurred each year, or the total surface burned. One additional point signaling at the role of climate across Spain and its interaction with forest fires was that the relationship between the Gini coefficient of fires, a measure of the inequality in fire sizes, and the FWI for each of the 50 × 50 km cells used was positive. That is, the larger the mean FWI the
Figure 2. Details of a large forest fire (12,000 ha), started in Riba de Saelices (prov. of Guadalajara, Spain), on July 16th, 2005, as a result of negligence. When the fire broke out, at 2 p.m. the FWI had a value of 66.6, which exceeded the 95-percentile of the historic series. The fire raced through some rugged terrain, burning a mixture of old and young pine woodlands and shrublands, and caused 11 deaths among fire-fighters. Photos: José Moreno.
greater the coefficient, thus the more unequal the size of fires. Most cells (88 %) showed high Gini coefficient (> 0.5), meaning high inequality in fire sizes, whereas few (12 %) cells presented values under 0.5, which means more equality in the distribution of fire sizes. Greater inequality in the size of fires means that more large fires occur. These fires are the ones with greater catastrophic potential and cover most of the area burned per year. Therefore, increases in FWI can severely impact Spanish landscapes by changing the proportion of large fires. The climate of Spain by the end of this century will be characterized by much higher temperatures during the summer and reduced precipitation, according to the several regional climate models. These models reproduce the general conditions of present climate in the country reasonably well. The projected changes in climate towards the end of this century (2071-2100) will produce increases in
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Figure 3. Observed (a, d) and modeled (b, e, c, f) values for the Drought Code (DC)(a, b, c)(a measure of the seasonal drought effect on fuels) and Fire Weather Index (FWI)(d, e, f)(a measure of the intensity of a spreading fire) during a 30 year period for a fixed fire season (May to October) in Spain. Observed values are based on daily data of the MARSSTAT database from the Joint Research Centre of the EC at Ispra (IT), and the period 1975-2004. Modeled data are the median of the A2 and B2 SRES scenarios of 5 Regional Climate Models with daily data for the period 2071-2100, made available by the Spanish Institute of Meteorology (Madrid, Spain). Cell size is 50 × 50 km.
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References
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MORENO JM (2005) Riesgos de Origen Climático: Impactos sobre los Incendios Forestales. – In: Moreno JM (Ed.), Evaluación Preliminar de los Impactos en España por Efecto del Cambio Climático. Ministerio de Medio Ambiente, Madrid, 581-615. MORENO JM, VÁZQUEZ A, VÉLEZ R (1998) Recent history of forest fires in Spain. – In: Moreno JM (Ed.), Large Forest Fires, Backhuys Publishers, Leiden, The Netherlands, 159-185. PAUSAS J, VALLEJO R (1999) The role of fire in European Mediterranean ecosystems. – In: Chuvieco E (Ed.), Remote sensing of large Wildfires. Springer-Verlag, Berlin, 2-16. VÁZQUEZ A, MORENO JM (1998) Patterns of lightning and people-caused fires in peninsular Spain. International Journal of Wildland Fire 8: 103-115. VÁZQUEZ A, PÉREZ B, FERNÁNDEZ-GONZÁLEZ F, MORENO JM (2002) Forest fires characteristics and potential natural vegetation in peninsular Spain during the period 19741994. Journal of Vegetation Science 13: 663-676.
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the fire danger indices, either of the DC (greater drought on average) or of the FWI (greater fire intensity). This will be the case for lower emission scenarios (B2) or for larger emission scenarios (A2), the differences not being very great (centre and right-hand side panels of Figure 3). Not only will fire danger indices increase but the length of the fire season will also increase, as shown for the period of alert or the period of risk (Figure 4). This means that the fire fighting services will have to be in place earlier in the year and until later, and be prepared for a greater number of days of high risk within the fire season. In summary, anticipating how many fires will occur in Spain as a result of climate change, or the surface they will burn, is difficult given the importance of human activity in providing ignition sources. However, the prospect for increased fire occurrence and fire impacts is quite considerable. A longer and more dangerous season implies that the potential for fires caused by accidents will increase, provided that the same number of ignitions occurs. Therefore, unless efforts are made to effectively deter these sources their capacity to increase fire risk is very likely. Since the potential to have larger fires is related to fire danger indices, the greater they are the more likely to have a large fire, this implies that large fires, which are more catastrophic, will tend to increase. Finally, the extension of a longer and more dangerous fire season into new areas implies that, unless the patterns of ignition are changed, the potential for having catastrophic fires in these areas is possible, thence extending this potential through new areas of the country.
120 180 240 300 365
Figure 4. Observed (a, d) and modeled (b, c, e, f) values for the Period of Alert (a, b, c)(number of days between the first and last day during the year that FWI ≥ 15 continuously for a week) and Period of Risk (d, e, f)(number of effective days during the PA in which FWI ≥ 15) during a 30 year period in Spain. Observed values are based on daily data of the MARSSTAT database from the Joint Research Centre of the EC at Ispra (IT), and the period 1975-2004. Modeled data are the median of the A2 and B2 SRES scenarios of 5 Regional Climate Models with daily data for the period 2071-2100, made available by the Spanish Institute of Meteorology (Madrid, Spain). Cell size is 50 × 50 km.
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Observed Climate-Biodiversity Relationships
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GIAN-RETO WALTHER, LASZLO NAGY, RISTO K. HEIKKINEN, JOSEP PEÑUELAS, JÜRGEN OTT, HARALD PAULI, JUHA PÖYRY, SILJE BERGER & THOMAS HICKLER
Altitudinal range expansions Advance of species’ ranges and vegetation belts towards higher elevation have been observed in European mountains. On altitudinal gradients, shifts in forest belts are reported from north-eastern Spain. A comparison of present day and early twentieth century photographs (Figure 7) shows that the European beech (Fagus sylvatica) forest in the Montseny Mountains (Catalonia, NE Spain) has not only become denser at its upper limit but the treeline has extended upwards with the establishment of new, vigorous outpost trees (Peñuelas et al.
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! A ! A ! A ! A A ! A ! A! Figure 1. Historic (left) and updated (right) 0 °C-January-Isoline (blue line) and distribution of Ilex aquifolium (green area and red dots) in northern Central Europe and southern Scandinavia (Source: Walther et al. 2005a, updated).
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Northward range expansions With climate warming, species in the northern hemisphere are expected to be able to extend their range northward and/or towards higher altitudes. There is increasing evidence with a widening range of taxonomic groups that such shifts are already in progress. Holly (Ilex aquifolium), an evergreen broad-leaved shrub or small tree species, is a classic example of a cold-limited species. In the last few decades, the species has expanded its range in northern Germany and southern Scandinavia in parallel with the expansion of its potential climatic range (Figure 1) (Walther et al. 2005a). The same is true for insects, such as dragonflies. In southern Europe, some species of African origin have reached northern Italy (Selysiothemis nigra, Trithemis annulata) or have even crossed the Pyrenees in France (Trithemis annulata) (Ott submitted), while Mediterranean species, such as the Scarlet Darter (Crocothemis erythraea) (Figure 3), have colonised Germany from south to north in only about two decades (Figure 2). In the mid 1980s, the Scarlet Darter was found indigenous only in the south-west of Germany; however, it has since been recorded across the entire country including the northernmost states. Its
northward expansion goes along with the trend of increasing temperatures (Ott 2007) and comparable range expansions of the species can be observed all over central Europe. Some birds and butterflies have also expanded their distributions recently at their northern range margins (e.g., Brommer 2004, Mitikka et al. 2008). A comparison of the occurrence of 48 butterfly species in the 10-km grid system used in the Finnish National Butterfly Recording Scheme (NAFI) between 1992-1996 and 2000-2004, showed that the ranges of 39 of the species have moved towards the north of Finland. Maximum shifts of over 300 km have been observed for three species, including the Poplar Admiral (Limenitis populi) (Figures 4 and 5). However, not all species have migrated in the wake of a changing climate. Species whose range expanded most were mobile generalist species of nonthreatened status, whereas the distributions of some red-listed butterfly species were rather stationary (Figure 6).
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A number of species in a variety of ecosystems have been observed to respond to the climatic warming that has occurred in the last few decades (e.g., Parmesan 2006). A series of case studies elaborated within ALARM contribute to these findings as illustrated in the following with some examples of species’ range shifts based on field data.
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first block: federal state (Germany) second block: time before 1970, third block etc.: decades from 1970 onwards until present o = species present, ● = autochthonous populations, ▲ = increasing populations, () = probably Figure 2. Range expansion of Crocothemis erythraea in Germany in the course of the last decades of the twentieth century (Source: Ott 2007, updated).
2007). In the Alps, several mountain peaks have shown an increase in species richness as a consequence of climate warming (Walther et al. 2005b). In the course of the twentieth century, warmer temperatures have allowed species from lower areas to move upslope and reach the summits (Figure 9). Range contractions Whereas the trend for range expansions has become increasingly visible, fewer examples exist for range contractions (caused by the deterioration of growth conditions in the former, pre-warming range of a species). Some stenoecious insects (species that have a narrow ecological range) that formerly had been much more widespread than today (e.g., Coenagrion hastulatum, Aeshna juncea, Leucorrhinia dubia (Figure 8) have nearly become extinct in the Palatinate Forest Biosphere Reserve, Germany, a reference area to detect the effects of climate
change on dragonflies within the ALARM-project. These species have also shown a strong decline on a regional (federal state) level as a consequence of the lack of precipitation in the last few years (Ott submitted). In north-eastern Spain, recruitment rates of beech at its lower range limit have been three times lower in the last few decades than those of the dominant species downslope, the Holm oak (Quercus ilex), a Mediterranean species (Peñuelas et al. 2007, see also Arrieta & Suarez (2006) for holly). In the
Figure 3. The Scarlet Darter (Crocothemis erythraea). Photo: J. Ott.
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Figure 4. The Poplar Admiral (Limenitis populi). Photo: J. Heliölä.
Figure 10. Photo pairs of permanent plots at the alpine-nival ecotone of Schrankogel (Tyrol, Austria; approx. 3000 m a.s.l.) in mid-growing season. Left and centre: the subnival to nival Saxifraga bryoides (light-green cushions, blooming on left side) showing a decrease in cover between 1994 and 2004. Right: the alpine pioneer grass Oreochloa disticha was among the species showing an increase in cover (Pauli et al. 2007). White bars indicate 10 cm.
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Figure 5. Distribution of the Poplar Admiral (Limenitis populi) in Finland in 1992-1996 and 20002004 (source: the Finnish National Butterfly Recording Scheme “NAFI”), and the isotherm of 1000 of the mean annual growing degree days (Gdd5) in 1992-1996 and 2000-2004.
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Figure 6. The relationship between the range shift (km) between 1992-1996 and 2000-2004 of 48 butterfly species in Finland and their Red list status. Boxplots show the median, quartiles, and outlier values within a category (Source: the Finnish National Butterfly Recording Scheme “NAFI”).
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Austrian Alps, permanent plots across the alpine-nival ecotone have shown that species that predominantly occur in the nival zone (upper part of the ecotone) have decreased in cover, whereas species that mainly grow in the alpine belt (lower part of the ecotone) have remained constant or even increased in cover (Figure 10) between 1994 and 2004. Despite the changes in cover, which may be signals of ongoing range contractions and expansions, an actual shrinkage of the distribution range of individual species was not able (yet) to be detected in the observed area (Pauli et al. 2007).
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Recent climate change has already affected a variety of species in various habitats and ecosystems in Europe and globally. Species respond to species-specific (combinations of) pressures and an in-depth mechanistic understanding is necessary to link observed range shifts to the relevant ecological drivers. Shifts at the rear, or retreating end of the distribution of species are considered to be of critical importance
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Figure 8. White-faced Darter (Leucorrhinia dubia). Photo: J. Ott.
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Figure 7. Altitudinal upward shift of European beech forest towards the top (ca. 1700 m) of the highest summits in the Turó de l’Home-Les Agudes ridge in the last century (for details see Peñuelas et. al. 2007). Photos: M. Boada and J. Peñuelas.
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Gdd5 isotherm of 1,000 1992-1996 2000-2004
(Hampe & Petit 2005) for overall range shifts. There is less evidence of contemporaneous range contractions (compared to range expansions) and several reasons may explain such lags at the southern/lower range margins in response to climate change. Climate effects at the front edge may immediately be visible with the establishment of young populations beyond former range margins. At the rear end, climate first affects demography: regeneration becomes sparse, while a resilient old generation persists. The lack of regeneration and other factors may first cause a formerly contiguous distribution to fragment, but still occupy the same distribution periphery. These factors and processes may explain why the response of species is faster and more easily traceable at the upper/northern limit than at the opposite range margins. We are at the very beginning of both the expected warming of climate but also the understanding of the ecological responses and their complexity (Walther 2007), linked to climateinduced dynamics of biodiversity change. Species are nested in ecological networks with complex temporal, spatial, and trophic interactions. As a consequence, if single species changes
Figure 9. Three consecutive floristic surveys within a century of the same summits reveal a pronounced increase of the number of vascular plant species on mountain peaks of the Swiss Alps from early 20th century to early 21st century (Source: Walther et al. 2005b, modified).
O B SERV ED
occur, the entire network is potentially influenced and expected subsequently to respond. With continued warming, one might expect not only an increase in the number of affected species but also in the variety of responses, which may unravel so far hidden consequences on higher trophic and complex levels of climate-biodiversity relationships. References ARRIETA S, SUAREZ F (2006) Marginal holly (Ilex aquifolium L.) populations in Mediterranean central Spain are constrained by a low-seedling recruitment. Flora 201: 152-160. BROMMER JE (2004) The range margins of northern birds shift polewards. Annales Zoologici Fennici 41: 391-397. HAMPE A, PETIT RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecology Letters 8: 461-467. MITIKKA V, HEIKKINEN RK, LUOTO, ARAUJO MB, SAARINEN K, PÖYRY J, FRONZEK S (2008) Predicting range expansion of the map butterfly in Northern Europe using bioclimatic models. Biodiversity and Conservation 17: 623-641. OTT J (2007) The expansion of Crocothemis erythraea (Brullé, 1832) in Germany – an indicator of climatic changes. – In: Tyagi BK (Ed.), Biology of dragonflies – Odonata. Scientific publisher, Jodhpur, 201-222. OTT J (submitted) Effects of climatic changes on dragonflies – results and recent observations in Europe. BioRisk. PARMESAN C (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecology Evolution and Systematics 37: 637-669. PAULI H, GOTTFRIED M, REITER K, KLETTNER C, GRABHERR G (2007) Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994– 2004) at the GLORIA master site Schrankogel, Tyrol, Austria. Global Change Biology 13: 147-156. PEÑUELAS J, OGAYA R, BOADA M, JUMP AS (2007) Migration, invasion and decline: changes in recruitment and forest structure in a warming-linked shift of European beech forest in Catalonia (NE Spain). Ecography 30: 829-837. WALTHER G-R (2007) Tackling ecological complexity in climate impact research. Science 315: 606-607. WALTHER G-R, BERGER S, SYKES MT (2005a) An ecological ‘footprint’ of climate change. Proceedings of the Royal Society B-Biological Sciences 272: 1427-1432. WALTHER G-R, BEISSNER S, BURGA CA (2005b) Trends in the upward shift of alpine plants. Journal of Vegetation Science 16: 541-548.
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Projected Climate Change Impacts on Biodiversity in Mediterranean Ecosystems JOSEP PEÑUELAS, MARC ESTIARTE, PATRICIA PRIETO, JORDI SARDANS, ALISTAIR JUMP, JOSÉ M. MORENO, IVÁN TORRES, BLANCA CÉSPEDES, EDUARD PLA, SANTI SABATÉ & CARLOS GRACIA
,
Figure 1. The picture shows the regenerating stand in Anchuras (Central Spain) in the wet year (2004). It was burnt in 2002. Photo: I. Torres.
Madrid
SPAIN
Annual Poaceae
The Mediterranean region presents a large variety of terrestrial ecosystems, many of them quite unique. Climate change will bring a series of direct and indirect effects on them which will be accentuated by the interaction with other components of the global change such as land use changes or pollution. Many of these ecosystems that are already at their ecological or geographical limit will be especially sensitive. 2004 (691 mm, ∆ + 21 mm)
Annual Poaceae
Among them, the most vulnerable will be those located in islands in a broad sense (including edaphic islands and high mountain ecosystems) and in ecotones, the transition zones between ecosystems (Valladares et al. 2005). There is evidence showing that climate change affects the Mediterranean species phenology, their competitive ability, the interactions between them and finally the structure and composition of the communities (Peñuelas & Filella 2001) and generates species altitudinal and latitudinal shifts (Peñuelas et al. 2007a) along with extinction of local species. However, it is not known whether species will be capable of evolving and adapting to climate change in time (Jump & Peñuelas 2005). To gain knowledge on the impacts of climate change on Mediterranean biodiversity and ecosystems, there are at least three posible approaches: observations, experimentation and modeling.
and measurements were carried out in 2004 and 2005, that is, during the 2nd and 3rd year of regeneration after fire. Measurements were made in a 180 × 90 m plot, and spatial techniques (kriging) were used to interpolate results with a resolution of 2 m. The hydrologic year 2003-2004 was practically normal, while 2004-2005 was well below average (total rainfall was about 30 % of the long-term average). Figure 2 shows the changes in species richness that occurred during these two consecutive years for two groups of annuals: grasses and legumes. While legumes virtually disappeared from the plot, their presence being restricted to small patches, grasses were able to maintain a number of species throughout the plot, hence being much more resistant to changes in rainfall. This example indicates that changes in patterns of rainfall will very likely affect the future species composition and diversity of Mediterranean shrublands.
Observations The changes in the spatial patterns of species richness of two groups of annuals (grasses and legumes) during two consecutive years of very different precipitation rainfall in a burned Cistusdominated shrubland of Central Spain offer an example of the observational approach. The area was burned in 2002
Experimentation A number of experiments have studied the potential effects of climate change on diversity of plant species in different types of plant communities. This experimental approach can be addressed by temperature or rainfall manipulations at stand level where the interactions between species assemblies and climate occur. However, the effects of climate change on diversity have been more rarely considered in relation to the successional process given the necessity for such long field experiments involving climate manipulation. Plant community recovery (species richness, diversity and composition) of a post-fire Mediterranean shrubland was monitored over a seven year period (1998-2005) under experimental drought and warming that simulated the environmental conditions projected for this area in the coming decades. Species richness and Shannon’s Index were positively correlated with accumulated precipitation in the growing season and both variables were negatively affected by reduced water availability in drought plots. Species-specific responses to treatments were found. Drought and warming treatment reduced the competitive ability of the obligate seeder tree Pinus halepensis against native resprouter shrubs and consequently, the transformation from shrub to pine tree dominated vegetation was slowed down. Therefore, future drier and warmer conditions in
2005 (202 mm, ∆ - 468 mm)
Species richness (No./m2) 1 2 3 4
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50 m Figure 2. Species richness (Number m-2) of annual grasses (upper panels) and legumes (lower panels) in a 180 × 90 m plot burnt by a wildfire in 2002 at Anchuras (Central Spain). The figure depicts the data for two consecutive years (2004, 2005) that differed very markedly in their total precipitation (2004 was normal; 2005 was very dry). Kriging was used to interpolate field data with a resolution of 2 m.
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Figure 3. Number of species per 3-m transect in response to drought and warming treatments in 1998 (pre-treatment year) and in the years of the experiment (1999-2005) in a Mediterranean shrubland recovering from a fire in 1994. The pattern of relative abundances of life-forms (shrubs, trees) during the seven-year study period in control, drought and warming treatments is also shown. Bars indicate the standard errors of the mean (n=3 plots means). Modified from Peñuelas et al. (2007b) and Prieto et al. (2009).
Figure 4. The pictures show the shrubland experimental site and a detail of one warming experimental plot. Photos: M. Estiarte.
Mediterranean areas may severely affect plant community recovery after a disturbance, due to the existence of both abundance-dependent and species-specific responses that may change interspecific competitive relationships. Drier conditions may seriously affect species richness and diversity recovery after fire due to lower levels of plant establishment and reduced growth rates. However, continued study in later successional stages is needed to monitor the changing species competitive relationships and assemblies. Modeling In an example of the third possible approach, a modeling exercise conducted within the ALARM project explored the effects of different climate change scenarios on Mediteranean forest car-
bon and hydrology balances and ultimately on species performance. This approach was carried out using the process-based model GOTILWA+ (www.creaf.uab.cat/gotilwa+) for the whole European forest area. To supply the input data required for the model, an extensive database was built connecting diverse information sources at the European level. The database contains data related to forest functional types, forest cover, forest structure (tree density and size distribution), forest function (photosynthesis, respiration rates), soil hydrology, organic matter decomposition rates and management strategies. GOTILWA+ was thus run under different climate change scenarios. Mediterranean forests seem to be especially sensitive to the impacts of climate change. In some
areas and under certain climate scenarios, carbon balances would be affected by increasing respiration rates, thus reducing their carbon sequestration capacity. Moreover an increase of water stress is expected. As a consequence, the frequency of the forest mortality events would increase with likely consequences on forest diversity. References JUMP A, PEÑUELAS J (2005) Runnig to stand still: adaptation and the response of plants to rapid climate change. Ecology Letters 8: 1010-1020. PEÑUELAS J, FILELLA I (2001) Phenology: Responses to a warming world. Science 294: 793-795. PEÑUELAS J, OGAYA R, BOADA M, JUMP A (2007a) Migration, invasion and decline: changes in recruitment and forest structure in a warming-linked shift of European beech forest in Catalonia. Ecography 30: 830-838.
PEÑUELAS J, PRIETO P, BEIER C, CESARACCIO C, DE ANGELIS P, DE DATO G, EMMETT B, ESTIARTE M, GARADNAI J, GORISSEN A, LANG E, KRÖEL-DULAY G, LLORENS L, PELLIZZARO G, RIIS-NIELSEN T, SCHMIDT I, SIRCA C, SOWERBY A, SPANO D, TIETEMA A (2007b) Response of plant species richness and primary poructivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003. Global Change Biology 13: 2563-2581. PRIETO P, PEÑUELAS J, LLORET F, LLORENS L, ESTIARTE M (2009) Experimental drought and warming decrease diversity and slow down post-fire succession in a Mediterranean shrubland. Ecography 32: 1-14, doi 10.1111/j.1600-0587.2009.05738.x VALLADARES F, PEÑUELAS J, CALABUIG EL (2005) Ecosistemas terrestres. – In: Moreno JM (Ed.), Evaluación de los impactos del cambio climático en España. Ministerio de Medio Ambiente, Madrid. 65-112.
Mortality frequyency 1-4 5-7 8-11 12-14 15-17 18-21 22-24 25-28 29-31 32-34 35-38 39-41 42-45 Figure 5. Comparision of mortality events frequency for evergreen broadleaved forests in south Europe for the 1990 time slice (1961-1990) and 2080 time slice (2051-2080) under de GRAS (A1FI scenario) and HadCM3 GCM. Spatial resolution 1º × 1º.
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Climate Change Impacts on the Future Extent of the Alpine Climate Zone
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LASZLO NAGY, HARALD PAULI, MICHAEL GOTTFRIED & GEORG GRABHERR
Figure 1. Mountain areas of Europe that have alpine vegetation or open upper montane forest (orange) (Source: Bohn et al. 2004).
The upper limit to tree growth is an important bio-climatic boundary as it marks the lower edge of the alpine zone (e.g., Nagy & Grabherr 2009, Körner 2003, Figure 1). This boundary, called the potential climatic treeline, lies at about 500-700 m in the northern Scandes and ranges from 1900 to 2300 m in the Alps; however, it is diffi-
cult to determine its position in the Mediterranean mountains, where natural forests have largely been destroyed. Knowing the position of the potential treeline allows the calculation of the putative upward displacement of this climatic limit in response to forecast rises in temperature. The basis for such calculations is the existence of a tem-
perature gradient with altitude, which is about 0.6 °C per 100 m. It follows that an increase in temperature of 0.6 °C would displace the current lower limit of the alpine climate zone by c. 100 m upwards. The land area occupied by the alpine climate zone may be estimated from digital elevation models by summing the areas above the lower boundary of the alpine climate zone. The projected range of temperature increases for a region depends on the underlying scenario assumptions, such as whether there are going to be CO2 mitigating measures effected in the future or not. Forecast changes in temperature for each mountain region in Europe can be converted to increases in the lower limit of the alpine climate zone. The estimates in the reduction in the extent of the alpine climate zone for all of today’s alpine mountains appear similar. With the exception of the largest massifs, such as the Alps and the Scandes, current alpine climates would largely be eliminated by an increase of about 3 °C in mean annual temperature. For example, the mean lower limit of the alpine zone in the Pyrenees is found at ca. 2300 m (red areas in Figure 2) and it reaches its maximum elevation at the Pico de Aneto at 3404 m. The highest projected increase in mean annual temperature for the Pyrenees is 6.5 °C by 2100, according to a ‘business almost as usual’ scenario, using the Hadley-3 climate model. This suggests that the lower limit of an alpine temperature climate zone akin to today’s might be found only, if at all, on the very highest peaks by 2085. In other words, there might be hardly any alpine climate, as
we know it today, left. Even the mildest predictions put the increase in temperature at 3.1 °C higher than that averaged between 1960 and 1990; this would entail an upward shift of the alpine climate zone by just over 500 m, causing a 40-fold reduction in area and fragmentation of contiguous areas. The response to climate change by organisms that populate alpine habitats is likely to be varied and mostly determined by the extent to which the habitats themselves are affected. In addition to temperature, changes in spatial and temporal patterns of precipitation will be major contributors to shaping the physical environment that will be the battleground of future biotic interactions. References BOHN U, GOLLUB G, HETTWER C, NEUHAUSLOVA Z, RAUS T, SCHLUETER H, WEBER H (2004) Map of the natural vegetation of Europe. Scale 1 : 2 500 000. Part I. Explanatory text with CD-ROM. Bundesamt für Naturschutz, Bonn. KÖRNER C (2003) Functional plant ecology of high mountain ecosystems. Springer, Berlin. NAGY L, GRABHERR G (2009) The biology of alpine habitats. Oxford University Press, Oxford.
>1800 m
>650 m
Figure 2. Projected changes in the extent of the alpine climate zone in the Pyrenees as a result of an increase in mean annual temperature of 3.1°C. Red, current alpine zone (≥ 2300 m); black, projected extent of analogous alpine climate by year 2085; yellow over 650 m; orange over 1800 m (Source: A 30-arc-second (1-km) gridded, quality-controlled global Digital Elevation Model, http://www.ngdc.noaa.gov/mgg/topo/globe.html).
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Risk of Disappearing Sub-Arctic Palsa Mires in Europe
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MARGARETA JOHANSSON, STEFAN FRONZEK, TORBEN R. CHRISTENSEN, MISKA LUOTO & TIM R. CARTER
Geographically, the Arctic zone can be defined as the area north of the Arctic Circle (66.5ºN), while a political definition usually includes the northern areas of the eight arctic countries: Canada, Finland, Greenland, Iceland, Norway, Russia, Sweden and the USA. In the Arctic Climate Impact Assessment (ACIA 2005) the definition was broader and included a wide range of vegetation from continuous forest in the south, through forest with patches of tundra, tundra with patches of forest, treeless tundra, to polar desert. The Arctic is, in general, characterised by low species diversity (ACIA 2005). The Arctic has experienced a greater degree of climate change than any other place on Earth during the last few decades. The mean annual air temperatures have, on a pan-arctic scale, risen by about 2 to 3 ºC since the 1950s, and in winter the temperatures have increased by up to 4 ºC. Projections for the future climate suggest a continuation of the strong warming trend of recent decades. In addition to the warming experienced in the Arctic, precipitation has also increased during the last few decades. An increase in annual precipitation is projected to be between 7.5 and 18.1 % by 2080, mainly due to increasing atmospheric water vapour convergence, which results from the ability of a warmer atmosphere to transport more water vapour from lower to higher latitudes (ACIA 2005). The climatic changes that occur in the Arctic are likely to have profound impacts on Arctic ecosystems. One
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Figure 1. A palsa mire in northern Sweden where the active layer (upper layer on top of permafrost that thaws every summer) has become deeper and the permafrost has thawed rapidly during the last decade, mainly as a result of increasing air temperatures (modified from Åkerman & Johansson 2008).
example of an ecosystem that is already affected by ongoing climate change is the palsa mire (complexes with permanently frozen peat hummocks) in subarctic Fennoscandia. Palsa mires are known to be biologically heterogeneous environments with a rich diversity of bird species and unique geomorphological processes, which give them a high conservation status (Fronzek et al. 2006, Luoto et al. 2004). Monitoring of palsa mires in northern Sweden shows that the active layer (the soil above the permafrost that thaws and refreezes every year) is increasing as a result of the increasing air temperatures (Åkerman & Johansson 2008). In one of the palsa mires the active layer has become 2 cm deeper per year between 1996 and 2004 and permafrost has thawed and disappeared from 81 % of the mire (Figure 1). This has resulted in a change of vege-
tation from dry nutrient-poor conditions dominated by dwarf-shrubs to more nutrient-rich wet conditions dominated by grasses (Åkerman & Johansson 2008). Projections of the future distribution of the palsa mires show that they are expected to have more or less disappeared by the end of the twentyfirst century (Figure 2, Fronzek et al. 2006). This will have major implications for the biodiversity in these areas as, unlike other ecosystems whose distribution can follow that of a changing climate, palsa mires require unique conditions that cannot easily be found elsewhere. The observed and projected changes in palsa mires prompt feedbacks, for example changes in vegetation type influence the water and energy exchange, which can alter local climate. In addition, such changes are also likely to affect ecosystem-atmosphere
1961-1990
2010-2039
2040-2069
2070-2099
exchanges of greenhouse gases (Johansson et al. 2006), providing possibly significant feedback effects on climate warming (ACIA 2005). References ACIA (2005) Arctic Climate Impact Assessment. Scientific Report. Cambridge University Press, Cambridge. ÅKERMAN HJ, JOHANSSON M (2008) Thawing permafrost and deepening active layer in Sub-arctic Sweden. Permafrost and Periglacial Processes 19: 279-292. FRONZEK S, LUOTO M, CARTER TR (2006) Potential effect of climate change on the distribution of palsa mires in subarctic Fennoscandia. Climate Research 32: 1-12. JOHANSSON T, MALMER N, CRILL PM, FRIBORG T, ÅKERMAN JH, MASTEPANOV M, CHRISTENSEN TR (2006) Decadal vegetation changes in a northern peatland, greenhouse gas fluxes and net radiative forcing. Global Change Biology 12: 2352-2369. LUOTO M, HEIKKINEN RK, CARTER TR (2004) Loss of palsa mires in Europe and biological consequences. Environmental Conservation 31: 30-37.
! A
Figure 2. Simulated palsa mire distribution in northern Europe for the baseline (1961-1990) and three scenario periods 2010-2039, 2040-2069 and 2070-2099 for a single climate scenario using the HadCM3 climate model with a forcing according to the A2 emission scenario. The predicted occurrence of a palsa mire is shown with solid red grid cells (modified from Fronzek et al. 2006). Photo: M. Johansson.
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Climate Impacts on High Latitude Lakes
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MATS JANSSON, PER ASK, JENNY ASK, PÄR BYSTRÖM, JAN KARLSSON & LENNART PERSSON
-2 C
0C
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Figure 1. Deglaciated areas in northern Europe are rich in lakes. The map shows the distribution of the ca 70,000 lakes in northern Sweden. Lakes in these areas are located in different temperature regimes (annual mean air temperatures are shown) which to a large extent form their characteristics (see also Figure 2).
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Abiotic conditions The vast majority of high latitude lakes are small (< 1 km2) unproductive lakes which are ice-covered for 6-9 months. Air temperature has pronounced influences on lake productivity (Jansson et al. 2008) and biodiversity via its control of terrestrial primary production and related terrestrial export of organic carbon and inorganic nutrients, but also by its regulation of the length of the icefree period. Productivity Cold, nutrient poor and clear lakes have extremely low production in the water column (pelagic habitats) and up to 90 % of the total primary production takes place in benthic habitats, i.e., in the surface layer of soft, nutrient rich, sediments. A warmer climate means higher input of coloured organic compounds (Figure 2). Consequences are lower light penetration and, therefore, lower primary production in benthic habitats, and that pelagic habitats and food webs become dependent on bacterial production based on terrestrial organic carbon rather than on pelagic primary production. The net effect is probably an overall decrease in lake productivity.
4C
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Lakes are important landscape components in arctic, subarctic and boreal regions (Figure 1). Lakes here commonly cover up to 10 % of the landscape areas and are, among other things, used for food production (fish), recreational activities and as sources of drinking water. The characteristics of high latitude lakes and their future possibilities for use by man are dependent on climatic prerequisites. Of special interest are how lakes respond to climate changes in terms of productivity and biodiversity. Within ALARM these questions have been studied in climatic gradients in Northern Sweden which comprise a range of ca 6 °C in annual mean air temperature.
3
Biodiversity The biodiversity of high latitude lakes is tightly coupled to the prerequisites set by climatic influences on lake productivity. Cold and clear lakes are dominated by diverse benthic food chains, where benthic invertebrates form the highest trophic level. These food chains are exploited by lake top consumers like
Warming Figure 2. Warming at high latitudes changes the vegetation and productivity of terrestrial systems (Jansson et al. 2008). Catchments above the tree-line are invaded by birch and vegetation typical for birch forests, and coniferous forest vegetation successively become dominant in previous birch forests. This development is critical for lakes and most likely leads to a decrease of productivity and biodiversity which to a large extent is a consequence of increased export of organic carbon from terrestrial to aquatic systems. Photos: A. Jonsson.
Arctic char. In a warmer climate the abundance and diversity of benthic invertebrates is lower. In lakes with high input of organic carbon, species which tolerate low oxygen concentrations (e.g., chironomids), become dominant in benthic habitats. Fish in such lakes mainly exploit the pelagic food chain which to large extent is heterotrophic in the sense that it is based on bacterial exploitation of terrestrial organic carbon. Fish diversity is also controlled by temperature as certain species like Arctic char thrive better in very cold water then e.g. pike and perch which do well over a large temperature range.
Temperature controlled distribution of species Low temperature restricts the distribution of species, especially at higher trophic levels. Increased temperature, therefore, allows for temperature controlled invasion of species into systems which are not adjusted to their presence. A typical example is the upstream migration of pike into a lake containing balanced populations of Arctic char and stickleback after a period with increasing temperature (Byström et al. 2006). The invasion of pike had strong effects on the lake ecosystem changing both the food web configuration and the relative abundances of species at different trophic levels (Figure 3).
Summarizing important effects of warming Warming at high latitudes will affect lake ecosystems in several ways. Major effects follow upon higher input of organic carbon leached from more productive terrestrial lake surroundings which reduce lake productivity and biodiversity. In cold areas warming may permit invasions of fish species adapted to a warmer climate, with potentially dramatic effects on both lake ecosystem structure and lake biodiversity.
References BYSTRÖM P, KARLSSON J, NILSSON P, VAN KOOTEN T, ASK, J, OLOFSSON F (2007) Substitution of top predators. Effects of pike invasion in a subarctic lake. Freshwater Biology 52: 1271-1280. JANSSON M, HICKLER T, JONSSON A, KARLSSON J (2008) Links between terrestrial primary production and bacterial production and respiration in lakes in a climate gradient in northern Sweden. Ecosystems 11: 367-376. VEDIN H, WASTENSSON L, B RAAB (1995) National Atlas of Sweden. Climate, lakes and rivers. Almqvist and Wiksell International, Stockholm, Sweden. 175 pp.
Pike
Char
Charr Ch
Sticklebacks
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Sticklebacks
Macroinvertebrates Macroinvertebrates
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Figure 3. Warming at high latitudes means higher temperature in lakes which permit invasion of less cold tolerant species. Pike invasion into a subarctic lake following a ten year period of increasing water temperatures (Byström et al. 2006) had dramatic consequences for existing char and stickleback populations with top-down effects on lower trophic levels. Temperature isolines were derived from regional data of annual air temperature means for the period 1961-1990 (Source: Vedin et al. 1995).
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The Big Trek Northwards: Recent Changes in the European Dragonfly Fauna
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JÜRGEN OTT
Figure 1. Broad Scarlet or Scarlet Darter (Crocothemis erythraea, male) – maybe the best studied dragonfly species showing range expansion as a result of climatic changes. Photo: J. Ott.
Dragonflies are one of the best invertebrate groups to document the effects of climatic changes: they are mobile, depend on terrestrial and aquatic biotopes and so give a good picture of biotope and landscape changes, their biology and ecology is well known, they are attractive animals and easy to determine and finally their expansion is studied already for a long time (Ott 2001, submitted, Hickling et al. 2005). Since the first observation of climate change effects on the range expansion of Crocothemis erythraea and some other species (Ott 2001) mean-
Erythromma viridulum Aeshna affinis Aeshna mixta Anax parthenope Trithemis annulata Trithemis kirby
Figure 2. Range expansion of Mediterranean and African Odonata in Europe – some examples.
while many more examples could be added. Here some very obvious examples are shown, which were compiled and analysed for the ALARM-project. Crocothemis erythraea – the first tip of the (melting) ice berg The first species showing very well the effects of climate change induced range expansion was already in the mid eighties and thereafter the Mediterranean species Broad Scarlet (Crocothemis erythraea, Figure 1). After becoming autochthonous first in different parts of southern Germany in the late seventies and early eighties it meanwhile populated all German federal states from south to north and in 2008 it arrived at the northern border to Denmark (Ott 2001, 2007, submitted). The same process of northward expansion could be shown for all neighbouring countries, such as The Netherlands in the west or Poland in the east, it also arrived the United Kingdom crossing the Channel (Ott submitted). In addition, the species populated biotopes in higher elevations and also other biotope types, even mooreland waters are now settled. All these expansions are related with the increase of higher temperatures, no other reasons could be identified for these changes. Other dragonfly species of Mediterranean origin expanding their range to the north Beside Crocothemis erythraea a lot of other species with Mediterranean origin expanded their range in the recent years to the north which is shown in the following and in Table 1 and Figure 2. For this analysis the distribution maps of the species published in
Table 1. Damselfly and dragonfly species of Mediterranean origin showing clear northern range expansions in Europe compared to Askew (1988). Species name
Range expansion in
Coenagrion scitulum
France, Germany, Belgium, Luxemburg, also in the East, e.g. in the Czech Republic, new in the Netherlands
Erythromma lindeni
North-eastern France, parts of Belgium, northern and eastern Germany, new to Poland
Erythromma viridulum
North-eastern France and Netherlands, northern Germany, new to Sweden and the UK
Lestes barbarus
Central parts of Europe, becoming more abundant, new to the UK
Aeshna affinis
Northern France and Germany, Netherlands, new to the UK and Finland
Aeshna mixta
UK up to the central parts, new to Ireland, Sweden and Finland
Anax imperator
UK up to the central parts and new to Scotland, also new to Ireland, Denmark and Sweden
Anax parthenope
Northern France, Belgium, Netherlands, northern Germany and Poland, new to UK and Ireland
Boyeria irene
North-eastern parts of France, new to Germany
Gomphus pulchellus
Northern and eastern parts of Germany, also to Austria
Oxygastra curtisii
Rediscovered in Germany after more than 50 years
Crocothemis erythraea
All central Europe, new for the UK
Sympetrum meridionale
All central Europe, up to northern Germany and Poland
Species name
Increasing tendency of migrations/invasions, e.g. observed in
Anax ephippiger
Germany, also reproducing, and other central European countries
Sympetrum fonscolombii
UK, Ireland, northern France, Belgium, Netherlands, Germany, Poland, partly indigenous populations (second generation)
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Askew (1988) were compared with the actual situation (see Ott submitted) and maps published in Dijkstra & Lewington (2006), Boudot in Trockur et al. (in print), Boudot et al. (2009) and the websites of the French and Finish odonatological societies or organisations (www.libellules.org, www.sudenkorento.fi). In this group there are damselflies (Zygoptera, 4 species), as well as dragonflies (Anisoptera, 9 species, plus 2): in total about 15 species from nearly all taxonomic families show a clear range expansion in the last two decades; some more may be included, when more data will be available in the future as a consequence of different ongoing dragonfly atlas projects in Europe. The Africans are coming … Very recently a new process seems to have started: after the Mediterranean species populated more and more the central – and partly even the northern – parts of Europe, presently southern Europe is invaded by species formerly typical for Africa. The Violet Dropwing (Trithemis annulata), a typical species all over Africa and the Middle East, formerly occurred only up to southern Spain and central Italy. But now it can be found even in southern France, having crossed entire Spain and also the Pyrenees, in Italy it now arrived in the area of Ferrara (Boudot et al. 2009). Another Trithemis species – the afrotropical Orang-winged Dropwing (Trithemis kirbyi) (Figure 3) – was not known for Europe at all, but then was discovered for the first time in the isle of Sardinia in 2003 by O. Holusa. After being discovered in 2007 also in southern Spain near Malaga by D. Chelmick, in 2008 for the first time larvae of this species were found proving its first autochthony in Europe (Cano-Villegas & ConesaGarcia 2009). The third example of an African resp. Asian species expanding its range to the north is the Black Pennant (Selysiothemis nigra), which is actually found up to the area of Trieste/Venice (Boudot et al. 2009) in the eastern part and up to Parma (M. Salvarani pers. comm. 2009) in the western part of northern Italy (Figure 4). Conclusion: Risks for the indigenous dragonfly fauna? All these examples show a clear range expansion to the north: Mediterranean species to middle and northern
Figure 3. Orang-winged Dropwing (Trithemis kirbyi, male): new to the European fauna since 2003 and expanding in Andalucia, where it is indigenous. Photo: J. Ott.
Figure 4. Black Pennant (Selysiothemis nigra, male), now also found indigenous in northern Italy. Photo: J.-P. Boudot.
Europe during the last two decades, and very recently also African species to southern Europe. This phenomenon only could be explained by the increasing temperatures, even if it is still unclear which are the main factors responsible for these changes (mean annual or summer temperature, increased sunshine, less severe winters etc.) – probably it is a combination of all these factors. Other factors, e.g. like the installation of new waters like gravel pits, only play a minor role and interestingly barriers, like east-west orientated mountain chains (e.g., Alps, Pyrenees), do not show an effect. Beside these range expansions dragonflies also show clear reactions in their biology: trends to a faster development of the larvae, trends to more generations and longer flight periods, as well as general changes in the phenology are reported for a high number of species (Ott 2001, 2008, submitted, Hassel et al. 2007). On the contrary, no species until now was reported expanding its range into the south as a result of climatic changes. But there are some first indi-
dragonfly species, as their live cycle depends on the waters. Another – actually strongly increasing, but until present only poorly studied – risk for dragonflies are Alien Invasive Species (AIS). In particular invasive crayfish (e.g., Orconectes immunis, Pacifastacus leniusculus) do have without any doubt a negative affect on populations of autochthonous dragonflies, as most of the crayfish are omnivorous and/or carnivorous and also feed on dragonfly larvae.
cations that several so called Eurosiberian species show range contractions or local and regional populations are extinct (Coenagrion hastulatum, Aeshna juncea, Somatochlora arctica & Leucorhinia dubia – see Ott submitted). These species, being specialists of moorland biotopes, are negatively affected by altered abiotic conditions in their biotopes: changing water tables, drying out of the waters, increase of water temperature etc. are strong negative impacts on their biotopes (Figure 5). In addition, there are also changes in the coenosis. For example, new predators are now present in the waters – such as the aggressive larvae of the invading Anax imperator – and can hinder the resettlement of the former mooreland coenosis. In the Mediterranean until now no replacement of indigenous species by African species is reported, but here the lack of precipitation – in particular in the future, see the ALARMscenarios – will have severe effects on all kind of waters (Figure 6). This means a higher risk for more or less all
Figure 5. A water in the Palatinate in summer 2006, before drying out several mooreland species, like C. hastulatum and L. dubia, were present with big populations. Photo: J. Ott.
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References ASKEW RR (1988) The Dragonflies of Europe. Harley Books. Colchester, 291 pp. BOUDOT J-P, KALKMAN VJ, AZPILICUENTA AMORIN, MÓNICA, BOGDANOVIC, T., CORDERO RIVERA, A., DEGABRIELE, G., DOMMANGET, J.-L., FERREIRA, S., GARRIGÓS, JOVIC, M., KOTARAC M, LOPAU W., MARINOV M, MIHOKOVIC N, RISERVATO E, SAMRAOUI B, SCHNEIDER W (2009) Atlas of the Odonata of the Mediterranean and North Africa. Libellula-Supplement 9: 1-256. CANO-VILLEGAS FJ, CONESA-GARCIA MA (2009) Expansión de Trithemis kirbyi (Sélys, 1891 (Odonata: Libellulidae) en la provincial di Málaga (S. Penísula Ibérica). Boletín de la SEA 44: 569-572.
DIJKSTRA K-D, LEWINGTON R (2006) Field Guide to the Dragonflies of Britain and Europe. 320 pp. HASSEL C, THOMPSON DJ, FRENCH GC, HARVEY IF (2007) Historical changes in the phenology of British Odonata are related to climate. Global Change Biology 13: 933-941. HICKLING R, ROY DB, HILL JK, THOMAS CD (2005) A northward shift of range in British Odonata. Global Change Biology 11: 1-5. OTT J (2001) Expansion of mediterranean Odonata in Germany and Europe – consequences of climatic changes – Adapted behaviour and shifting species ranges. S. 89-111. – In: Walter G-R et al. (Eds), “Fingerprints” of Climate Change. Kluwer Academic Publishers, New York. OTT J (2007) The expansion of Crocothemis erythraea (Brullé, 1832) in Germany – an indicator of climatic changes. – In: Tyagi BK (Ed.), Odonata – Biology of Dragonflies. Scientific Publishers (India), 210-222. OTT J (2008) Libellen als Indikatoren der Klimaänderung – Ergebnisse aus Deutschland und Konsequenzen für den Naturschutz. Insecta – Zeitschrift für Entomologie und Naturschutz 11: 75-89. OTT J (Ed.) (submitted) Monitoring Climate Change with dragonflies. BioRisk. TROCKUR B, BOUDOT J-P, FICHEFET V, GOFFART PH, OTT J, PROESS R (in print) Atlas der Libellen – Atlas des Libellules. Fauna und Flora der Großregion/Faune e Flore dans la Grande Région, Saarbrücken.
Figure 6. A small river (Vezzola) in Abruzzi Mountains (Italy) in fall 2007, dried out for a long time. Photo: J. Ott.
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Effects of Climatic Changes on Odonata: Are the Impacts likely to be the Same in the Northern and Southern Hemispheres?
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JÜRGEN OTT & MICHAEL J. SAMWAYS
The effects of climatic changes are different in the northern and southern hemispheres – but to date, no formal comparison has been carried out to determine whether climate change has comparable effects on the distribution patterns and the ecology of dragonfly species in the two hemispheres. Here we present some first results using Odonata species as model organisms.
for the northern hemisphere, where studies are available e.g. for Europe, United States and Japan (Ott 2001, submitted). In Europe, some Mediterranean species have expanded their ranges within the last two to three decades over as much as several hundreds of kilometers, and have even been sighted or established on some islands (UK, Ireland), as well as Scandinavia. Recently, some African species have now also colonized southern Europe and are expanding their ranges northwards (Ott, this atlas, pp. 82f.). The situation in southern Africa appears to be very different. One of
Figure 1. Dried out water body near Kaiserslautern (Germany) in 2006: once a habitat of the endangered mooreland species Coenagrion hastulatum. Photo: J. Ott.
the reasons for this is that in this region, there have been many climatic bottlenecks as well as climatic cycling, including El Niño events. It seems that many of the savanna species at least are highly opportunistic and habitattolerant, moving readily in response to changing conditions, and tolerating very different winter conditions in comparison with those in summer (Van Huyssteen & Samways 2009). The greatly changing climatic conditions from one year to the next, and even one decade to the next, often means that certain species retract their geographical ranges in dry years, and then, opportunistically, expand again in wet years. One example, is the relatively rare Orthetrum robustum, which retreats to permanent lake refugia in wet years and then moves into recently-flooded pans in wet years. Like the also rare, Urothemis luciana, it is a strong flier and ready colonizer. Yet, of surprise has been the movement capabilities and ready establishment by seemingly weak-flying damselflies. Lestes virgatus has a very dynamic population spatial distribution, readily colonizing new and appropriate ponds as formerly suitable ones dry out. Aciagrion congoense showed even more extreme population spatial dynamics, moving south, over some 300 km, to St Lucia in South Africa from Mozambique during the huge floods in the year 2000. Yet it had never before been observed so far south. Then, by 2005, it had disappeared again from St Lucia, during the dry phase of the climatic cycle.
Figure 2. The parthenogenetic Crayfish Procambarus sp. – in Germany an alien species e.g. found in the Palatinate – preying upon a dragonfly larva (Libellula quadrimaculata). Photo: J. Ott.
Expansion of dragonfly species to higher elevations Besides geographical range expansion, movements to higher elevations have also been recorded, which has increased local Odonata diversity (Oertli et al. 2008). With continuing climate change, there may also be a reduction of sensitive, montane species loosing their preferred biotopes in higher elevations with a typical temperature regime and with perhaps increasing predominance of habitat generalists (Ott submitted). At least in the southern hemisphere, it is difficult to determine whether there has been an increase in elevational ranges of odonates, as the background ‘noise’ of prehistoric climate change and the current strong climatic cycles appear to mask local anthropogenic effects.
Expansion of geographical ranges of dragonfly species The expansion of geographical ranges of dragonfly species is well documented
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There is, however, very good evidence that certain species are confined to climatic refugia, when formerly they were likely to be more widespread. Today, for example, Chlorolestes fasciatus confined to the Mountain Zebra Park (as well as other mountain ranges) yet surrounded by the highly unsuitable and arid habitat of the Karoo (Samways 2008). There is no doubt, however, that certain narrow-range endemic species would be in a very precarious state should climate warming continue. One species is the recently-discovered Syncordulia serendipator, which only lives in the high reaches of the Cape mountains, with no elevational flexibility should conditions become too severe. Changes in the phenology of Odonata Changes of the dragonfly phenology are well documented in the northern hemisphere (Ott 2001, submitted, Hassel et al. 2007): earlier emergence takes place and there is a clear tendency to changes in voltism (e.g., a trend to bivoltism in some species in northern countries where these species formerly had only one cycle per year) and some species also show a prolonged flight season. If this process continues, more and more de-synchronisation of emergence may occur (e.g., emergence of spring species in late fall) which may have negative effects on the species survival. Again, as with issues of change in geographical range and in elevation, the situation in the southern hemisphere is masked by great differences in phenology, both in geographical area (Samways & Grant 2006) and from year to year, with even overwintering of some species from one year to the next at sometimes but not at others (Samways 2008). Species turnover of Odonata in water bodies Monitoring in the northern hemisphere has shown that there is an increasing number of Mediterranean species dominating certain water bodies (Ott submitted). Effects of drying out of water bodies on the Odonata assemblages Drying out of water bodies leads to a complete change, and even total elimination, of the Odonata assemblages. Short term drying out favours species with high colonising ability and a short
life cycle (r-strategists, such as Ischnura pumilio, Lestes barbarus, Libellula depressa) but when water bodies dry out for weeks or even months, the dragonfly species – in particular, the species of running waters with long-lasting larval stages – are eliminated (Ott submitted). As the southern hemisphere has been subject to great variations in droughting and flooding from one year to the next, the issue of odonate pond colonization is more about spatial population dynamics and suitability of habitat than about gradual accumulation or loss of certain species. Certainly, there are no assembly rules for dragonfly species establishing at new ponds, with species arriving as and when conditions are suitable for them, and then leaving when conditions become unsuitable. Possible synergistic effects of climatic changes with intensive land-use and emissions The eutrophication and acidification as a result of ongoing emissions lead to a continuous stress for aquatic biotopes in the northern hemisphere, even if in recent years in many areas water quality has improved. Possible synergistic effects of climatic changes with alien invasive species To date, little is known of synergistic effects in the northern hemisphere, but it seems that in particular the dragonfly larvae are negatively affected by invasive fish and crayfish (Figure 2), as many species prey upon larvae. In particular,
many alien crayfish species – a consequence of stocking by fishermen, for commercial use, or set free by aquarists – have now established as large populations in nearly all types of standing and running waters. Alien invasive species, mostly alien trees, play a major role in determining which dragonfly species can inhabit a particular water body, at least in South Africa (Samways & Taylor 2004, Samways 2007). When the alien riparian trees are removed, there is a rapid and significant recovery of the dragonfly fauna (Samways et al. 2005). While no doubt the alien trees change the water quality of the larval habitat, by far the most significant factor is the adverse effect of shade, which has been shown to be experimentally to be the critical factor. From circumstantial evidence, alien trout are considered also to have an impact on the southern hemisphere odonates in montane areas. The evidence is not strong, but there have been instances where certain endemic odonate species occur above waterfalls but not below them where trout are present. Which species / species groups are at risk? In both the northern and southern hemispheres, the species most at risk are those of sensitive habitats – such as moorland and montane species, as well as species requiring stable environmental conditions (e.g., water level). Species with small populations and a patchy distribution or isolated populations within these groups are even more threatened.
Figure 3. Alien invasive and planted pine trees caused a major decline in dragonfly species by shading out the habitat. Endemic species like Syncordulia venator were locally extirpated. Shown here is the removal of the pines and the regrowth of the local fynbos vegetation. Photo: M. J. Samways.
References HASSEL C, THOMPSON DJ, FRENCH GC, HARVEY IF (2007) Historical changes in the phenology of British Odonata are related to climate. Global Change Biology 13: 933-941. OERTLI B, INDERMUEHLE N, ANGÉLIBERT S, HINDEN H, STOLL A (2008) Macroinvertebrate assemblages in 25 high alpine ponds of the Swiss National Park (Cirque of Macun) and relation to environmental variables. Hydrobiologia 597(1): 29-41. OTT J (2001) Expansion of mediterranean Odonata in Germany and Europe – consequences of climatic changes – Adapted behaviour and shifting species ranges. S. – In: Walter G-R et al. (Eds), “Fingerprints” of Climate Change. Kluwer Academic Publishers, New York, 89-111. OTT J (submitted) Effects of climatic changes on dragonflies – results and recent observations in Europe. BioRisk. SAMWAYS MJ (2007) Threat levels to odonate assemblages from invasive alien tree cano-
Figure 4. Shown here is a fully restored stream after alien pines had been removed. The recovery of the local odonate fauna has been remarkable, indicating how their populations can be restored once a key threat has been addressed. Photo: M. J. Samways.
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pies. – In: Cordero Rivera A. (Ed.), Forests and Dragonflies. Pensoft Publishers, Sofia– Moscow, 209-224. SAMWAYS MJ (2008) Dragonflies and Damselflies of South Africa. Pensoft Publishers, Sofia–Moscow. SAMWAYS MJ, GRANT PBC (2006) Honing Red List assessments of lesser-known taxa in biodiversity hotspots. Biodiversity and Conservation 16: 2575-2586. SAMWAYS MJ, TAYLOR S (2004) Impacts of invasive alien plants on red-listed South African dragonflies (Odonata). South African Journal of Science 100: 78-80. SAMWAYS MJ, TAYLOR S, TARBOTON W (2005) Extinction reprieve following alien removal. Conservation Biology 19: 1329-1330. VAN HUYSSTEEN P, SAMWAYS MJ (2009) Overwintering dragonflies in an African savanna. Odonatologica 38: 167-172.
Figure 5. Syncordulia venator, a threatened endemic species which has benefited enormously from the removal of alien trees. Photo: M. J. Samways.
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Modelling the Range Expansion with Global Warming of an Urticating Moth: a Case Study from France
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CHRISTELLE ROBINET, JÉRÔME ROUSSELET, FRANCIS GOUSSARD, JACQUES GARCIA & ALAIN ROQUES
Figure 1. Larvae of pine processionnary moth. Photo: A. Devouard, Agence de Presse REA.
During the last decades, the pine processionary moth (PPM), Thaumetopoea pityocampa (Lepidoptera, Notodontidae; Figure 1), expanded northwards and upwards in Europe (Battisti et al. 2005, Figure 2). In north-central France (Paris Basin), moth range boundary has shifted by 87 km northwards between 1972 and 2004. The expansion coincided with a + 0.9-1.1 °C increase in minimum winter temperature according to the area (Robinet et al. 2007). Similar expansions in altitude were noted in the
Massif Central, in the French and Italian Alps and in Spain (Battisti et al. 2005). Because the moth is entering mountainous regions as well as semi- urban and urban areas, this spread is likely to result in important ecological consequences and sanitary threats Primarily pine forest defoliators, PPM larvae also impact health of humans, pets and cattle because mature larvae release severelyurticating hairs when disturbed. Originally, PPM is a Mediterranean organism but one of the few insects
whose larval development occurs during autumn and winter. Minor changes in weather conditions are thus likely to affect dramatically the survival of the larvae. Recent experimental studies have clearly related the geographic expansion with the increase in winter temperatures (Battisti et al. 2005). To maximize exposure to the sun, larvae build conspicuous, white winter silk nests on pine branches where they live in colonies of up to 300 larvae (Figure 3). Colony survival is dependant on minimal lethal temperatures (-16 °C), and nocturnal larval feeding during the cold period (i.e., the period during which the weekly mean of minimum daily T < 0 °C) requires two conditions.. The temperature inside the nest should reach +9 °C at least during the day, and then air temperature during the following night should be above 0 °C (Battisti et al. 2005). If one of these conditions is not fulfilled, the larvae do not go out of the nest to feed and may starve during consecutive days and weeks if the temperature remains under these thresholds. Using winter temperatures data recorded at local scale, we were thus able to reconstruct the past PPM feeding activity at different time periods in the expansion area of the Paris Basin. Downtown Paris appeared to have been
Paris
Paris
Figure 2. Range of PPM in France during: 1960-1980 (left), winter 2005-2006 (right) (Source: Robinet et al. 2007, modified).
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already favourable to the survival of PPM larvae in the 1990s, but an unfavourable area located ca 50-70 km south of Paris prevented the northern expansion at this time (Figure 4a). This barrier disappeared during the early 2000s in direct relationship with the increase in winter temperatures (Figure 4b, Robinet et al. 2007), the only remaining limit to the expansion being the low flight capacity of the females, ca. 3 km/year. Human-mediated changes in the habitat, especially the systematic planting of pines acting as relays for PPM progression along the road network, also favour significantly the expansion. We described explicitly the expansion dynamics in the Paris Basin using a reaction-diffusion model adjusted on the 1970-2005 expansion data. The model integrated the density of the host pines in the area, a female dispersal of 3 km/year and an indicator of the larval feeding activity. It allowed a partial reconstruction of the annual movement of the PPM front in the southern Paris Basin, including retraction phenomena with unusually cold winters, since the 1980s (Figure 5a). Under moderate hypotheses for the future climate (average increase of +3 °C; IPCC climate scenario B2), the model predicts a colonization of downtown Paris by 2025 (Figure 5b,
Robinet 2006). However, human-mediated long-distance dispersal of PPM, e.g. as pupae transported with soil of mature pines to be planted as ornamentals in northern areas, seems much more frequent than previously considered the more as winter warming nowadays allows larval survival in previously unfavourable areas. Several infestation spots have just been found across the Paris Basin and even in Alsace, up to 190 km from the present front. Genetic analysis suggested that one of the spots at least has to be attributed to a PPM translocation from southwestern France. The inclusion of these spots in the model led to predict a significant speed of the colonization process of Paris downtown which would be reached in 2013 (Figure 5c). In addition, climatic anomalies such as the heat wave which occurred in August 2003 in Western Europe are likely to modulate such expansions because of contrasting effects.
Extremely high temperatures resulted in PPM population collapse in the Paris Basin whereas range significantly expanded to higher elevations in the Italian Alps (Battisti et al. 2006). References BATTISTI A, STASTNY M, NETHERER S, ROBINET C, SCHOPF A, ROQUES A, LARSSON S (2005) Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecological Applications 15: 2084-2096. BATTISTI A, STASTNY M, BUFFO E, LARSSON S (2006) A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly. Global Change Biology 12: 662-671. ROBINET C (2006) Mathematical modelling of invasion processes in ecology: the pine processionary moth as a case study. PhD thesis, EHESS, Paris. ROBINET C, BAIER P, PENNERSTORFER J, SCHOPF A, ROQUES A (2007) Modelling the effects of climate change on the pine processionary moth (Thaumetopoea pityocampa L.) expansion in France. Global Ecology and Biogeography 16: 460-471.
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Figure 3. Winter nest of pine processionary moth. Photo: F. Goussard, INRA.
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Paris FRANCE
Melun
Melun 100 110 112 114 116
Orleans
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126 128 130 132 134 136 138
Figure 4. Reconstruction of the feeding potential of PPM larvae in the Paris basin during 1992-1996 (a) and 2000-2004 (b). The scale at right indicates the number of days with potential feeding (Source: Robinet et al. 2007, modified).
Paris
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Figure 5. Modelling PPM expansion in the Paris Basin: (a) predicted and observed (black line) in 2006, (b) predicted for 2025 without long-distance events; (c) predicted for 2013 with long-distance events. The scale at right shows the colour codes for the mean number of nests pine (Source: a, b – Robinet 2006, modified; c – yet unpublished data).
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Moorland Wildfires in the UK Peak District
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SARAH LINDLEY, JULIA MCMORROW & ALETTA BONN
The moorlands of the UK uplands are highly managed landscapes, shaped by land use practices such as grazing and prescribed burning. However, wildfire is increasingly threatening their biodiversity and their ability to deliver valuable ecosystem services, such as carbon storage, erosion prevention, water quality regulation and recreation opportunities. Wildfire has been identified as one of the 25 priority future risks to UK biodiversity (Sutherland et al. 2008). It is a major concern for blanket bogs since the underlying peat can ignite and, once alight, can burn for days. The Peak District National Park (PDNP) is located in the Pennines in the north of England (Figure 1). The 550 km² of heather moorland support internationally important plant and breeding bird assemblages (Figure 2) with designation as Special Area of Conservation (SAC) and Special Protected Area (SPA) under the
EU Habitats and Bird Directives (92/43/EEC, 79/409/EEC). The Peak District became the first UK National Park in 1951 and is one of the most visited National Parks in the world (PDNPA 2007). It is home to 38,000 people and surrounded by the densely populated cities of Manchester, Leeds and Sheffield. It is within one-hour’s drive of 16 million people. There are 500 km² of open access land and 600 km of public rights of way on the moorlands, notably part of the Pennine Way long distance footpath (Figure 3). Wildfire is one of the environmental pressures which comes from high accessibility and usage (McMorrow et al. 2008). Prescribed fires used for grouse moor and grazing management sometimes get out of control, but accidental fire and arson are much more common. A high density of access-related ignition sources combined with vulnerable habitats make the PDNP very susceptible to wildfire during prolonged dry periods (Figure 4). Indeed, the area is a useful analogue for currently wetter moorlands under climate change scenarios. One severe fire in April 2003 burnt over 7 km2 on the Bleaklow plateau. The National Park was the worst hit
area in England and Wales that April, with five of the 20 largest moorland wildfires covering a total of 19.3 km2. Whilst vegetation can recover from rapidly moving wildfire, and fire is an integral part of the ecosystem, deep hot burns into the peat create longlasting bare peat scars and erosion (Figures 4 and 5). The Moors for the Future Partnership (MFF) are now restoring 6 km2 of eroding peatland largely caused by severe wildfires at a cost of £ 1.8 million (www.moorsforthefuture.org.uk). To help to address the issue of wildfire, the Peak District Fire Operations Group (FOG) sought through MFF to better understand wildfire risk and its causality in the Park. Researchers at the University of Manchester worked with FOG and MFF to create a stakeholder informed moorland wildfire risk map. The ongoing aim of the work is to highlight areas of highest risk to assist with a strategic planning response. A Multi-Criteria Evaluation approach was used to represent the factors affecting the likelihood of ignition and to develop a model of the spatial distribution of wildfire risk. The work was based on an archive of 212 Holmfirth
National Park boundary Pennine way Major roads Reported wildfires (per km2) None reported 1-2 UNITED KINGDOM
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Figure 1. Location of the Peak District. Source: Ordnance Survey © Crown Copyright. All rights reserved. Licence number 100022765. Buxton
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Figure 2. Dunlin Calidris alpina. Photo: Alan Gladwin.
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Figure 3. Peak District National Park with density of wildfires 1976-2004 and moorland study area. The Pennine Way long-distance footpath crosses the Dark Peak in the north. Source: Ordnance Survey © Crown Copyright. All rights reserved. Licence number 100022765 and PDNPA ranger service.
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Figure 4. Wildfire on heather moorland. Photo: Peak District National Park Fire Operations Group.
Figure 5. Exposed peat created by wildfire. Photo: Amer Alroichdi and Moors for the Future Partnership.
historical fires for the period 1976 to 2004 held by the PDNP Authority. The model was developed with 60 % of the database, with the rest used for testing. Stakeholder input helped to develop an approved set of layers, scores for each layer and an appropriate weighting scheme. This process was assisted by empirical assessment of the frequency of fires on different types of habitat and the relationship between fire frequency and proximity to human ignition sources such as roads and footpaths. The maps opposite (Figures 6a-d) illustrate the factors contributing to the model and an average of the results from the best models is shown in the bottom frame (Figure 7). The ability to map areas of high risk is critically important for effective management of wildfire risk. The map is used to locate fire watches and fire ponds and thus protect the PDNP’s important biodiversity and carbon store for future generations. Protecting the highest risk areas is especially important given the increased pressures associated with a changing climate. It is already known that hot, dry summer spells are associated with high fire probability in the UK (McMorrow et al. 2008), but the expected increase in frequency of extreme conditions, like those associated with summer 2003, is just one part of the whole picture. Future wildfire risk is also affected by longer-term
a Minor Road Access
c Vulnerability of Habitat
Minor road access
d Foot Access
Vulnerability of habitat
Foot access
High : 10
High : 10
High : 10
Low : 0
Low : 0
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b Proximity to settlements
National Park boundary Open water Pennine way Major roads Wildfire risk-of-occurence High
Proximity to settlements
Low
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Low : 0
0
10 km
Figure 6 a-d. Factors included in the Multi-Criteria Evaluation wildfire model on a 0-10 scale. Photos: Moors for the Future Partnership.
warming. For example, a longer growing season increases the amount of biomass available to burn, and increasing evapo-transpiration, soil moisture deficit and plant stress then makes that biomass more susceptible to ignition (Running 2006). The future role of human factors, as the ultimate cause of wildfire igni-
tion, is one of the biggest uncertainties of all. What is certain, however, is that it is only with improved understanding, increased awareness and active management, that the Peak District, like many UK moorlands, will be able to retain its essential character and critical environmental roles.
0
20 km
Figure 7. Wildfire risk of occurrence scores for the Peak District moorland wildfire model on a 0-10 scale (grey lines denote major roads for cross reference to Figure 3). Source: Ordnance Survey © Crown Copyright. All rights reserved. Licence number 100022765.
References MCMORROW J, LINDLEY S, AYLEN J, CAVAN G, ALBERTSON K, BOYS D (2008) Moorland wildfire risk, visitors and climate change: patterns, prevention and policy. – In: Bonn A, Allott T, Hubacek K, Stewart J (Eds), Drivers of environmental change in uplands. Routledge, Abingdon, 404-431. PDNPA (2007) Peak District National Park Authority Factsheet No. 2. Available at
M O O R L A N D
W I L D FI R E S
http://www.peakdistrict-nationalpark.info/ studyArea/factsheets/ [Last accessed November 2008] RUNNING SW (2006) Is global warming causing more, larger wildfires? Science 313: 927-928. SUTHERLAND WJ and 38 others (2008) The identification of 100 ecological questions of high policy relevance in the UK. Journal of Applied Ecology 43: 617-627.
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South America: Climate Monitoring and Adaptation Integrated across Regions and Disciplines STEPHAN HALLOY, KARINA YAGER, CAROLINA GARCÍA, STEPHAN BECK, JULIETA CARILLA, ALFREDO TUPAYACHI, JORGE JÁCOME, ROSA ISELA MENESES, JIM FARFÁN, ANTON SEIMON, TRACIE SEIMON, PAMELA RODRIGUEZ, SOLEDAD CUELLO & ALFREDO GRAU
,
Introduction This chapter provides an overview and summary of ongoing ALARM research in the South American Andes. ALARM helped to set up the first network for monitoring the impact of climate change in the Andes of Argentina, Bolivia, Colombia and Peru, following GLORIA (Global Observation Research Initiative in Alpine Environments) methodology. The GLORIA network has developed a standardized methodology to establish and monitor permanent plots in alpine environments that are useful for comparing the impact of climate change on vegetation at a worldwide scale (www.gloria.ac.at). In addition to establishing GLORIA sites, we initiated interdisciplinary research on a variety of interdependent themes including: mammals, reptiles, amphibians, soil bacteria, glacial retreat, agriculture and ani-
mal husbandry (Seimon et al. 2007, Halloy et al. 2005). Here we show broad geographic, vegetation and climatic patterns for five research sites while highlighting some of the multidisciplinary branches being explored. From this data, current and future analyses of both human and climate impacts on high altitude ecosystems throughout the Andes are in progress. We identify some factors of variability among sites and introduce preliminary findings. Northern and Central Andes Study Sites (Figure 1) Altitudinal and latitudinal distribution of sites (Figure 2)
Mountains are an important reserve for biodiversity and the Tropical Andes have been identified as one of the world’s premiere hotspots. Study sites have been selected and implemented along the Andes to represent a broad spectrum of climatic and altitudinal conditions. This is achieved mainly by sampling at a range of altitudes from close to 4000 m to 5320 m, the highest GLORIA site in the world, as well as being close to the highest altitudinal limits of vascular plants. An additional data point is included for the Socompa volcano, a site sampled from 1983 and of particular interest for including the highest living autotrophic communities on earth. Overlaying the altitude dimension, the sites are also positioned along the east-west rainfall gradient across the Central Andes, providing a sensitive response to rainfall changes. Major drivers differentiating between sites and target regions The major drivers accounting for environmental and biotic differences across
Chingaza
! COLOMBIA A
the target regions include a consideration of the following factors: altitude (pressure, temperature, radiation), latitude (seasonal patterns, daily cycles), rainfall (decreasing from east to west and from north to south in general), high seasonal and multi-annual environmental variability that creates ‘noise’, geology (soil formation), hydrology and human activities (including pastoral production). Case studies in the Andes Here we present the current trends in plant species richness and additional interdisciplinary features observed in each target region of study. We will discuss five of the seven sites, from north to south: 1. Vilcanota, Peru 2. Apolobamba, Bolivia 3. Sajama, Bolivia 4. Socompa, Argentina 5. Cumbres Calchaquíes, Argentina 1. Vilcanota, Peru (5320 m, 13º46' S, 71º05' W)
ECUADOR
Rainfall gradient (schematic)
3 – Sajama 4 – Socompa 5 – Cumbres Calchaquíes
Huascarán
! A
Altitude (m)
1 – Vilcanota 2 – Apolobamba
Vilcanota
4
6,000
BRAZIL
6,000
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PERU
5,500 1 2
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3 5
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Distance from Eastern drop (km) Vilcanota
Sajama
Apolobamba
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! A
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A !
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GLORIA-ALARM sites GLORIA sites Comparative research site (not in GLORIA-ALARM) 500 km
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! A
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Sites Figure 1. Northern and Central Andes Study Sites.
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Figure 2. Altitudinal and Latitudinal Distribution of five southern Sites.
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4
Cumbres Calchaquíes
6,000
Cumbres Calchaquíes
3
Sites
Socompa
! ASocompa
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1
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The Cordillera Vilcanota is located in the Southern Andes of Peru (Figure 3). The mountain range descends nearly 5000 m to the Amazonian jungle to the NE. The Cordillera Vilcanota contains one of the highest large lakes in the world, Lake Sibinacocha, one of the sources of the Amazon River. The following text describes plant species richness, soil temperature, amphibian dynamics, glacial retreat and the human context in the Cordillera Vilcanota. Species richness The bar graphs represent the number of species of each taxon recorded on each GLORIA summit (sum of eight summit sections) (Figure 4). Soil temperature The temperature graph shows the daily maxima and minima recorded by a datalogger buried at 10 cm below the soil surface at GLORIA site Rititica (Figure 5). Periods where maxima join the minima indicate snow cover, which can occur at any time throughout the year. Human context at Vilcanota Rising temperatures, coupled with socio-economic drivers, have resulted in the altitudinal increase of potato cultivation, some 300 m, over the last fifty years in the Vilcanota.Traditional cultivation and genetic conservation by local communities have resulted in the development of several hundreds of varieties of potatoes thereby contributing to risk management in the face of climate variability (Halloy et al. 2005) Long term socio-economic trends affect the behavior of the social system, and can confound or interact with potential climate change effects. The graph in Figure 6 shows trends in social and environmental parameters in the Province of Cusco, which includes the Vilcanota region.
Increased grazing pressure BRAZIL
Combined ecological and social pressures push camelid grazing to higher altitudes (Figure 7). Through upward migration, camelids and other herbivores act as vectors transporting propagules to higher altitudes and facilitating the altitudinal increase of plant distributions. On the other hand, increased grazing pressure and rising snowfall line contribute to increased rates of erosion in some areas.
! A PERU
! A
BOLIVIA
Accelerated deglaciation Accelerated rates of deglaciation are manifest in the glacial isochrones following the end of the Little Ice Age (~1850) (Figure 8). Amphibians in the Vilcanota As glaciers retreated, amphibians (and other organisms) have quickly followed behind. Frogs of the genus Telmatobius had, by 2002, colonized some of the recently deglaciated areas. However, the invasion of chytridiomycosis disease into Telmatobius populations has decimated stocks in less than five years (Figure 9, Seimon et al. 2007). 2. Apolobamba, Bolivia (5200 m, 15o01' S, 69o08' W) Apolobamba is the region with the most recently established GLORIA site in the Andes. In addition to research
0
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CHILE
Figure 3. Cordillera Vilcanota.
on plant species, work is proceeding on amphibians, reptiles and the human context (Figure 10). 3. Sajama, Bolivia (4931 m, 18°12'26" S, 68°57'59" W) Sajama, of volcanic origin, is located in the western Cordillera of Bolivia. In this target region, plant species richness studies (Figure 11) are complemented by research on plant growth
dynamics, plant physiology (Hoch & Körner 2005), productive pasture management and local adaptation to climate change (Yager et al 2008). Plant growth dynamics Giant cushions of Azorella compacta may be over 2000 years old (Figure 12). Their compact and resinous structure may record valuable paleoclimatic and paleovegetation data, including pollen,
Rititica (5,250 m)
Orko Q'ocha (5,320 m)
Puma Chunta (4,960 m)
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Bryophytes
Bryophytes
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Vascular plants
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Figure 4. Plant species richness (above) and impressions (below) of Vilcanota, Perú. Photos: P. Sowell.
S O U T H
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rain, dust and ash falls, speed of growth and stomatal density.
indigenous people in Sajama and Apolobamba (Ulloa & Yager 2007). The workshops helped to increase the awareness and understanding of local knowledge and perceptions of climate change, generating a common understanding of the multiple interde-
Local adaptation Perceptions of change at a multiscale and multidisciplinary level were explored through workshops with
Max
Min
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10 8 6 4 2 0
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02 Au g2 00 2 Se p 20 02 O ct 20 02 N ov 20 02 D ec 20 02 Jan 20 03 Fe b 20 03 M ar 20 03 Ap r2 00 3 M ay 20 03 Ju n 20 03 Ju l2 00 3 Au g2 00 3
-2
Time Figure 5. Soil Temperatures (-10 cm) at Rititica commencing August 7, 2002.
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Infant mortality Arrable land, Cusco province
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4. Socompa, Argentina (6,060 m, 24°25' S, 68°15' W) The Socompa volcano, situated in NW Argentina on the border with Chile (Figure 14), harbours the highest known plant communities. Six sites sampled in 1983 could be revisited to observe changes at these record altitudes. At these sites, thick mats of bryophytes and lichens cover the ground between 5,750 and 6,060 m, with a recorded total of over 36 species (Figure 15, Halloy 1991) Soil microbial communities Biogeochemical and molecular-phylogenetic approaches were used to describe the bacterial and eukaryotic communities associated with fumarolic and non-fumarolic ground. The results provide remarkable insights into the richness of microbial life in conditions which approximate Martian environments more than anything on Earth. Fumarole-associated eukaryotes were particularly diverse, with an abundance of green algal lineages and a highly novel clade of microarthropods (Costello et al. 2009). The bryophyte mats and subjacent diversity is considered to be particularly fragile and vulnerable to human intervention. 5. Huaca Huasi, Argentina (4740 m, 26o40' S, 65o44' W) Huaca Huasi is situated on an isolated and ancient mountain range (Cumbres Calchaquíes), topped by a rolling plateau in NW Argentina (Figure 16).
Figure 7. Alpaca and llama graze at increasing altitudes near Lake Sibinacocha. Overgrazing combined with liquid precipitation leads to rill and gully erosion.
Liolaemus huacahuasicus, have declined considerably during the last 20 year drought, although there may be other causes. Pollinator interactions are important for a variety of flowers such as Tephrocactus, Calceolaria glacialis and Barneoudia balliana (Figure 17). Soil temperature At Huaca Huasi, soil temperature was recorded down to 190 cm, a depth where the ‘noise’ of daily and seasonal variability is strongly buffered, thus allowing a clearer long term trend signal (Figure 18). Temperatures closer to the surface reflect important differences between orientations around the summit at Piedra Blanca (Figure 19).
Irrigated land Utilised land Remaining forest
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1950
1960
1970
1980
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Year Figure 6. Rising potato cultivation, potato cultivar diversity, and trends in socio-economic and environmental parameters in Cusco province (Source: Halloy et al. 2005).
Biotic interactons and trends The species richness recorded at GLORIA sites in the Cumbres Calchaquíes is the greatest of the five study sites for South America. Both flora and fauna have reacted to a severe reduction in water during the last 20 years (lake level graph, Figure 20). Animals such as the endemic lizard,
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pendent driving forces of change and what can be done about them in terms of adaptation (Figure 13).
8
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Figure 8. Retreat of glaciers in the upper catchment of Sibinacocha lake as shown by repeat photography (1931-2005) and by glacial isochrones (Source: Seimon et al. 2007).
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Figure 9. Telmatobius frogs had advanced into recently deglaciated areas by 2003.
2008
Moraroni (5,200 m)
Pelechuco Mita (5,000 m)
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Figure 10. Plant species richness along a GLORIA altitudinal gradient in Apolobamba (red dot).
Jasasuni (4,930 m)
Sumac (4,760 m)
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Huincurata (4,570 m)
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Number of species 0
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250 km
Figure 11. Location of Sajama (red dot) and species richness of plants along an elevational gradient in Sajama.
Lake levels reflect variability High variability, superimposed on long term trends, is illustrated by the record of lake levels from Laguna Nostra, Huaca Huasi, 4,250 m (Figure 20). Plant growth dynamics Measurements over three decades have provided a wealth of information about the ages, population dynamics, growth forms, and growth variability in many species of high altitude plants (Figure 21).
Lognormal distribution of plant cover as indicator of change The distribution of the cover of different plant species in a community provides insights into biodiversity patterns and disturbance (Figure 22). The resulting curve varies from year to year, closely tracking climatic variations. Note that most of the curve in the figure is below 1 % cover. GLORIA methodology allows for a more accurate estimation of all covers below 1 %, providing the potential for this
Figure 12. Azorella compacta cushions in the Sajama region are the subject of growth dynamics and physiological studies.
Figure 13. Community participation was an essential part of workshops organized in Sajama to explore perceptions of climate change and adaptation.
S O U T H
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C L I M AT E
M O N I T O R I N G
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250 km Figure 14. The Socompa Volcano and its location in the high altitude desert of Atacama.
BOLIVIA
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PA R A G U AY CHILE
! A ARGENTINA BRAZIL
U R U G U AY 0
250 km Figure 16. Location of the Huaca Huasi plateau in the Cumbres Calchaquíes, NW Argentina.
type of statistical analysis. Classic vegetation sampling does not estimate covers below 1 %, potentially losing the most valuable information. Changes in distance to the lognormal (∆L) over time reflect changes in the environment, even in areas outside of major human influence (Halloy & Barratt 2007). Here data from the vegetation cover of Huaca Huasi reflect a severe disturbance due to drought and rapid recovery after rains return (Figure 23). Some Conclusions The highest vascular plant richness is found in the geologically oldest mountain range, Cumbres Calchaquíes. The highest lichen richness is found in the Cordillera Apolobamba. The highest bryophyte richness is found on the recent fumaroles of the Socompa volcano. Vascular plant species richness is strongly locally determined through geology, grazing, rainfall and other landscape heterogeneity. Thus numbers do not necessarily decline with altitude (e.g., Sajama), creating much richer and complex patterns than in temperate mountains (see figures). Trends related to climate change will consequently be more complex than is often assumed. The site with the lowest number of vascular species (10) is the Moraroni summit (Apolobamba) at 5200 m, with independent photographic records and local anecdotes suggesting lasting snow cover until relatively recently (1970s). Lichen species numbers have a tendency to increase with altitude at several sites, again contrary to general assumptions. In contrast to species numbers, cover (or total biomass) decreases dramatically at the highest sites (Orko
Socompa (5,750-6,060 m) Fungi Bryophytes Lichens Vascular plants 0
Bryophytes
Lichens
Lichens
Vascular plants
Vascular plants
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20
40
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Piedra Blanca (4,250 m)
Alazán (4,100 m) Fungi
Bryophytes
Bryophytes
Lichens
Lichens
Vascular plants
Vascular plants 20
40
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Figure 17. Examples of fauna (Liolaemus huacahuasicus, endemic) and flora of the Huaca Huasi area, together with graphs of plant species richness along its altitudinal gradient.
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Sinuosa (4,550 m)
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60
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Q’ocha (Vilcanota), Moraroni (Apolobamba) and Isabel (Huaca Huasi)). Thus, highest sites are characterized by relatively high species numbers, but represented by only a small number of colonizing individuals. Each of these sites has been covered in persistent ice or snow in relatively recent times (estimated at a few decades to a hundred years). There are clear indications of upward mobility in plants, vertebrates, cultivation and livestock (Seimon et al. 2007).
Fungi
40
60
Figure 15. Fumarole heated soils near the Socompa summit harbour compact mats of dozens of species of bryophytes and lichens.
Fungi
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Isabel (4,735 m)
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80
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Degrees (ºC) Figure 20. Lake level since 1968 for Laguna Nostra, Huaca Huasi.
Figure 18. Profiles of soil temperatures to -190 cm depth show the strongly buffered temperature range at increasing depths.
North
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90 Figure 21. Growth rates and growth form in Festuca nardifolia in Huaca Huasi.
Days from 27 March 2006
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Figure 19. Temperature variations over three months at 10 cm below the soil surface on four sides of GLORIA summit Piedra Blanca.
Acknowledgements We would like to extend our gratitude to the communities of the study areas and to the parkguards of Apolobamba and Sajama. Also a special thanks to all the people who assisted us in our fieldwork.
S O U T H
A M E R I C A :
References
C L I M AT E
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and Mountain Biodiversity, 2005. Boca Raton FL, USA: CRC Press LLC, 323-337. HOCH G, KÖRNER C (2005) Growth, demography and carbon relations of Polylepis trees at the world’s highest treeline. Functional Ecology 19: 941-951. SEIMON TA, SEIMON A, DASZAK P, HALLOY SRP, SCHLOEGEL LM, AGUILAR CA, SOWELL P, HYATT AD, KONECKY B, SIMMONS JE (2007) Upward range extension of Andean anurans and chytridiomycosis to extreme elevations in response to tropical deglaciation. Global Change Biology 12: 1-12. ULLOA D, YAGER K (2007) Memorias del Taller “Cambio Climático: Percepción local y adaptación en el Parque Nacional Sajama”. Comunidad de Sajama, Bolivia: Conservación Internacional – Bolivia, 42 pp. YAGER K, RESNIKOWSKI H, HALLOY SRP (2008) Grazing and climatic variability in Sajama National Park, Bolivia. Pirineo 163: 97-109.
A DA P TAT I O N
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AC RO S S
Figure 22. Lognormal distribution of cover values in cryptofruticetum type vegetation in Huaca Huasi. The blue and green areas show the difference to the fitted lognormal curve.
2.0
1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6
1.5 1.0 0.5 0.0 78
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Year Figure 23. The distance to the lognormal (∆L) has varied inversely to the degree of stress or disturbance related to drought in Huaca Huasi cryptofruticetum vegetation.
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COSTELLO EK, HALLOY SRP, REED SC, SOWELL P, SCHMIDT SK (2009) Fumarole-Supported Island of Biodiversity within a Hyperarid, High-Elevation Landscape on Socompa Volcano, Puna de Atacama, Andes. Applied and Environmental Microbiology 75: 735-747. HALLOY SRP (1991) Islands of Life at 6000 M Altitude – the Environment of the Highest Autotrophic Communities on Earth (Socompa Volcano, Andes). Arctic and Alpine Research 23: 247-262. Halloy SRP, Barratt BIP (2007) Patterns of abundance and morphology as indicators of ecosystem status: a meta-analysis. Ecological Complexity 4: 128-147. HALLOY SRP, SEIMON A, YAGER K, TUPAYACHI HERRERA A (2005)Multidimensional (climate, biodiversity, socio-economics, agriculture) context of changes in land use in the Vilcanota watershed, Peru. – In: Spehn EM, Liberman Cruz M, Körner C (Eds), Land Use Changes
Lake level (m)
Disease organisms are advancing as well, sometimes wiping out the gains in range expansion (amphibians followed by chytrids). Declining water availability has dried lakes and wetlands (Andean peat bogs) in some areas, with significant changes in species composition and livestock carrying capacity. Bibliography produced by this group and previous key papers are available from the authors.
Climate Change, Ecosystem Services and Biodiversity – Risks and Opportunities1
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KARIN ZAUNBERGER & MARTIN SYKES
„Recent observations confirm that, given high rates of observed emissions, the worst-case IPCC scenario trajectories (or even worse) are being realised. For many key parameters, the climate system is already moving beyond the patterns of natural variability within which our society and economy have developed and thrived. These parameters include global mean surface temperature, sea-level rise, ocean and ice sheet dynamics, ocean acidification, and extreme climatic events. There is a significant risk that many of the trends will accelerate, leading to an increasing risk of abrupt or irreversible climatic shifts...Inaction is inexcusable“ (http://climatecongress.ku.dk, see also Le Quere et al. 2009). Protecting and enhancing ecosystem resilience through biodiversity and ecosystem service conservation, are amongst the best and most cost effective ways of tackling both the causes and consequences of climate change. However changes in ecosystem structure, function and composition have important implications for the interactions between the biosphere and the climate system. Terrestrial and marine ecosystems currently absorb around half of anthropogenic CO2 emissions, therefore the carbon capture and storage capacity of oceans, forests, grasslands, wetlands and in particular peatlands is essential for mitigating climate change. On the other hand the degradation or destruction of these ecosystems can lead to the release of significant amounts of greenhouse gases. Globally, degraded peatlands contribute to 10% of human emissions; deforestation and degradation to 23%. There is growing evidence that the capacity of the Earth’s carbon sinks is weakening due to global warming itself, but also due to the degradation of ecosystems caused by other stress factors such as deforestation, soil erosion, inappropriate infrastructure development and poor management of fresh water and marine resources. However without healthy and resilient ecosystems it will not be possible to stabilise the climate system or to adapt to the unavoidable impacts of climate change. Therefore urgent action is needed to halt the further loss and degradation of biodiversity and ecosystem services, if we are to retain the ability to reduce the extent of climate change and manage its impacts. There are powerful economic and social arguments for taking action to protect biodiversity and ecosystems. Ecosystem-based approaches contribute to protecting and restoring natural ecosystems by conserving or enhancing carbon stocks, reducing emissions caused by ecosystem degradation and loss, and providing cost-effective protection against some of the threats that result from climate change. For example, coastal ecosystems such as salt marsh and barrier beaches provide natural shoreline protection from storms and flooding and urban green space cools cities (reducing the urban-heat island effect), minimises flooding and improves air quality. Nevertheless, true integration of climate and biodiversity policies still remains the exception. The role of biodiversity and ecosystem services in both climate change mitigation and adaptation is rarely appreciated or understood. Given the uncertainties surrounding future rates and impacts of climate change, as well as the gaps in knowledge and uncertainty of responses to policy initiatives, a precautionary approach is necessary. This would make use of a variety of policy options including regulation, market based instruments, insurance, soft options (e.g. awareness raising and education measures), research and development thereby combining top-down and bottom-up approaches and giving the potential for policy integration (see also Piper & Wilson, this atlas, pp. 250f.).
and ecosystem-based approaches. “We cannot halt biodiversity loss without addressing climate change, but it is equally impossible to tackle climate change without addressing biodiversity loss” 2. In addition even under current levels of climate change AR43 (IPCC 2007) suggests that the resilience of many ecosystems has already been exceeded. For example for coral reefs even the current levels of atmospheric CO2 are too high for their survival. The TEEB (The Economic of Ecosystems and Biodiversity) climate issues update of September 20094 states that accepting any CO2 stabilisation target above 350 ppm CO2 means that society has made a decision to do without coral reefs. Such loss has serious consequences on biodiversity, on sea fisheries around the world and on the half billion people who depend directly on coral reefs for their livelihoods. As biodiversity declines, so does the resilience of ecosystems when subject to shocks and disturbance. Ecosystems with low resilience, may reach thresholds at which abrupt change occurs. Biodiversity loss, ecosystem degradation and consequent changes in ecosystem services lead to a decline in human well-being and increase vulnerability to climate change. Ecosystem-based approaches provide an important route to sustainable action and represent a vital insurance policy against irreversible damage from climate change, whereas failure to acknowledge the relationship between climate change and biodiversity and failure to act swiftly and in an integrated manner could undermine efforts for improvements in both areas. Therefore the maintenance and restoration of diverse, functioning healthy ecosystems across the wider terrestrial, freshwater and marine environment is an important guiding principle as we move forward to “climate proof ” our policies and adapt to climate change. To preserve the ability of global ecosystems to continue to function as sinks for greenhouse gases and to avoid ecosystem feedbacks that accelerate global warming, climate change policy must address the wider ecosystem challenges of climate change and efforts to halting biodiversity loss must be stepped up. Biodiversity conservation issues should be incorporated into climate change adaptation and mitigation policies, sectoral policies and sustainable development strategies. The challenge is to move towards “win-win” or even “win-win-win” strategies including adaptation, mitigation and biodiversity (Berry 2009, Berry & Paterson, this atlas, pp. 194f.) or at least “win more and lose less” strategies. Ecosystem-based approaches, including green infrastructure planning, maintain ecological functions at the landscape scale in combination with multi-functional land uses and contribute to ecosystem resilience. These approaches can be applied to virtually all types of ecosystems, at all scales from local to continental and have the potential to reconcile short and long-term priorities. While contributing to halting the loss and degradation of biodiversity, as well as restoring water cycles, they also enable the functions and services provided by ecosystems to reach a more cost-effective and sometimes more feasible adaptation solution than can be achieved by relying solely on conventional engineered infrastructure or technology-led measures. In addition, these approaches reduce the vulnerability of people and their livelihoods in the face of climate change. They also help to maintain ecosystem services that are important for human well-being and vital to our ability to adapt to the effects of climate change. References
There are many positive and negative feedbacks in the climate system, including tipping points when fluxes in ecosystems become unpredictable and ecosystems lose resilience, and when carbon sinks turn into sources. Changes in land use that lead to loss of biodiversity can also lead to increased green-house gas emissions. Also, additional releases of CO2 and CH4 are possible from melting permafrost, peatlands, wetlands and large stores of marine hydrates at high latitudes. Losses of carbon from peat and other soils could easily outweigh savings made by any feasible reduction in fossil fuel use. These feedbacks are generally expected to increase with climate change. Therefore to tackle the climate crisis we need a portfolio of tools including technology, financing, building adaptive capacity, community engagement
BERRY PM (2009). Biodiversity in the balance – mitigation and adaptation conflicts and synergies. Sofia–Moscow, Pensoft Publishers. IPCC (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. LE QUERE C, RAUPACH MR, CANADELL JG, MARLAND G, BOPP L, CIAIS P, CONWAY TJ, DONEY SC, FEELY RA, FOSTER P, FRIEDLINGSTEIN P, GURNEY K, HOUGHTON RA, HOUSE JI, HUNTINGFORD C, LEVY PE, LOMAS MR, MAJKUT J, METZL N, OMETTO JP, PETERS GP, PRENTICE IC, RANDERSON JT, RUNNING SW, SARMIENTO JL, SCHUSTER U, SITCH S, TAKAHASHI T, VIOVY N, VAN DER WERF GR, WOODWARD FI (2009). Trends in the sources and sinks of carbon dioxide. Nature Geoscience 2: 831-836.
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This introduction is in part derived from the Discussion Paper – Towards a Strategy on Climate Change, Ecosystem Services and Biodiversity, which was developed by the EU Ad Hoc Expert Working Group on Biodiversity and Climate Change see http://ec.europa.eu/environment/ nature/pdf/discussion_paper_climate_change.pdf
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The Message from Athens. April 2009 see http://ec.europa.eu/environment/nature/biodiversity/ conference/index_en.htm AR4 stands for Fourth IPCC Assessment Report http://www.teebweb.org/LinkClick.aspx?fileticket=L6XLPaoaZv8%3d&tabid=1278&language=en-US
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LAND USE CHANGES AND THEIR IMPACTS
Land Use, Its Change and Effects on Biodiversity
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RICCARDO BOMMARCO
Land use and land use change are considered among the most important drivers of and pressures on biodiversity. Worldwide, humanity has converted more than half of the terrestrial land surface to crop and pasture land, managed forest or settlement areas. Of 14 major terrestrial biomes, 25% is partially disturbed and the remaining 25% is dominated by human land use (Gaston 2004, Gaston & Spicer 2004). The geographic distribution of natural and human-modified land depends on many factors such as climate, topography, demography, history and economy (Millennium Ecosystem Assessment 2005). Ecosystem types that have lost most include tropical dry forests, temperate broadleaf and mixed forests, temperate grasslands and savannas, Mediterranean woodlands and shrubs. It is important to note that habitat types for which most proportional area have been lost, such as temperate grasslands and Mediterranean habitats, are clearly underrepresented among current protected areas (Hoekstra et al. 2005). The EU 25 countries are generally characterised by a long history of agricultural land use and include some of the most industrialised and densely populated areas of the world (Millennium Ecosystem Assessment 2005). As a consequence the proportional area of undisturbed natural wilderness areas is less than 5 % in several European countries. Related to the long history of human land use, traditional management practices resulted in the creation of a wide variety of semi-natural habitats. Such habitats are today an essential part of the unique European cultural landscapes and important conservation areas (Tscharntke et al. 2005). However, agricultural intensification, urbanisation and infrastructure development has put a significant pressure, not only on natural habitats in the EU 25, but also on these semi-natural habitats (e.g. Vogiatzakis et al., this atlas, pp. 106f.). This has resulted in the loss of some of the most diverse ecosystems in temperate and Mediterranean regions such as dry and wet grasslands, heathlands, and Mediterranean forests, woodlands and scrubs (compare Nagy et al., this atlas, pp.110f.; Millenium Ecosystem Assessment 2005). In the EU member countries biodiversity faces a particularly high extinction risk due to the loss and increasing isolation between small remnant areas of natural habitats (see e.g. Bommarco et al. 2010). Global change exerts pressure in and around conservation areas that are of small average size (Vogiatzakis et al., this atlas, pp. 106f.). Land use change scenarios In view of growing demands for food, feed, fibre and biofuels, there is an increasing awareness that we need to acknowledge that there are strong economic and ecological interdependencies between natural resource use, ecosystem functioning and biodiversity, where shifts in ecosystem functioning due to biodiversity loss feed back to society and economy. To enable evaluation of alternative future directions of land use change under contrasting economic regimes and governance systems it becomes necessary to disentangle these complex links and understand the processes in human society that drive land use change. For this purpose, quantitative, spatially explicit and alternative scenarios of land use in Europe have been developed (Reginster et al., this atlas, pp. 100ff.), that form the baseline of many studies within and also beyond the present atlas (Chytrý et al. 2010 in press; Schweiger et al., this atlas, pp. 216f.), and will enable researchers to map effects on diversity from several combined drivers and pressures under future scenarios. Land use change effects on biodiversity To stop biodiversity declines and meet future challenges, a thorough understanding is needed on how biodiversity is affected by historic and current land use changes. Habitat loss and fragmentation leads to the reduction of proportional area of high quality habitat for biodiversity in a landscape. Large continuous natural and seminatural habitats are gradually converted leaving a landscape with many small habitat fragments that also become increasingly isolated. When this happens, small remnant habitat fragments are more impacted by border effects as their perimeter to area ratio increases resulting in a reduction of undisturbed core areas. A well known ecological pattern is that the number of species in a habitat increases with the area of that habitat (Rosenzweig 1995). The slope of this so called speciesarea relationship shows the degree of importance of habitat area as predictor of species richness, and it can vary for different organism groups and habitat types. 98
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The comparison of historical species lists with current occurrence reveals that seminatural grasslands are important for harbouring biodiversity. Contemporary grasslands have already lost a significant proportion of specialised plant species and that the loss is larger in small compared to large habitat remnants (Fischer & Stöcklin 1997). In addition, several recent European case studies indicate strong negative effects of habitat loss and fragmentation on flower visiting insects (bees and butterflies) in open semi-natural habitat types (Bommarco et al., this atlas, pp. 108f.; Bommarco et al. 2010 in press, Krauss et al. 2010, Öckinger et al. in press). Impacts of land use change on biodiversity may be direct, with extinctions taking place immediately as the land is converted. In many cases, however, extinctions occur with a time delay after key habitats are destroyed or deteriorated, and populations doomed to go extinct might survive for long time periods before they eventually disappear, This process can have implications for the conservation strategy we adopt (Kuussaari et al. 2009). Studies performed in grasslands in Northern Europe indicate that species diversity of plants in remnant semi-natural grasslands is explained by habitat area and connectivity of the investigated landscapes 50-100 years ago and suggest an estimated extinction debt of 40% of the current species number (Lindborg & Eriksson 2004, Helm et al. 2006). Such historic processes of habitat fragmentation are poorly understood, although they are widely recognized as important for predicting future survival of species for landscapes in which habitat of high value to biodiversity has been lost and fragmented. If the existence of such time lags for extinctions are prevalent it becomes spurious to secure a species long-term survival based only on current patterns of occurrence. However, a detected extinction debt in an area also provides an opportunity as species determined to go extinct have not yet been lost, and informed conservation measures has yet the chance to turn the trend (Kuussaari et al 2009). Impact generalisations through species traits Different types of environmental change may have profoundly different effects on individual species. It is a challenge to develop generally applicable mitigation strategies given the huge number of species and the multiple responses to a number of environmental factors. However, groups of species that share ecological characteristics such as dispersal and reproductive capacity, or diet and habitat preferences, may react similarly to a certain alternation in their environment. It is therefore of interest to explore how species groups with shared traits are impacted by environmental changes (Meyer & Steffan-Dewenter, this atlas, pp. 176f.) Theoretical models predict stronger effects of habitat fragmentation and reduced habitat areas on food specialists at higher trophic levels. Similarly, habitat specialists, species with limited dispersal ability, large area requirements, high population fluctuations, large body size, and low genetic diversity are expected to be particularly sensitive to reduced habitat area and increased habitat isolation (see Bommarco et al., this atlas, pp. 108f.). Existing case studies, which although they are limited to the temperate biosphere, partly confirm these expectations. For example butterflies with specialised larval food plant requirements show steeper species-area slopes and higher local extinction risks than generalistic butterfly and plant species (Krauss & Steffan-Dewenter 2003; Krauss et al. 2004). Bee species with specialised pollen use and parasitic bee species at a higher trophic level have steeper species-area relationships than bee species using a wide range of pollen sources acting on a lower trophic level (Steffan-Dewenter et al. 2006). In a recent pan-European study, large bodied bees with high dispersal capacity, as compared to more sedentary bee species, have been found to persist better in fragmented landscapes (Bommarco et al. 2010 in press). This has also been confirmed for butterflies and moths across Europe and North America (Öckinger et al. 2010 in prep). Such results can guide conservation to target particularly threatened groups of species. Land use related components of multiple pressure studies While land use change is a core element of global change and imposes serious threats to biodiversity (Millennium Ecosystem Assessment 2005), it hardly affects biodiversity in isolation. As explored in the present atlas, there is a multitude of
environmental pressures considered to impact biodiversity. Consequently, although there are only comparatively few studies included in the land use chapter, many studies elsewhere in this atlas deal with land use change and its interaction with climate change (Schweiger et al., this atlas, pp. 216f.), alien species invasions (Pysek et al., this atlas, pp. 146f.), and environmental pollution through exposure to chemicals (Sorensen et al., this atlas, p. 228). Also, studies on spatial connectivity have a strong link to land use issues (Vohland et al., this atlas, pp. 240f.). Land use pressure on biodiversity is of course a global phenomenon and results obtained from Europe, which is the main focus of this atlas, has the potential to be applied also for other continents are rare where impacts on biodiversity from land use and other environmental changes are pervasive (see Heong et al., this atlas, pp. 248f.). A great challenge for future research lies in the complexity of multiple pressures on communities of interacting species, and how anthropogenic impacts on biodiversity translates into ecosystem functioning and the provisioning of ecosystem services (Tylianakis et al. 2008, Schweiger et al. 2010). References BOMMARCO R, BIESMEIJER JC, MEYER B, POTTS SG, PÖYRY J, ROBERTS SPM, STEFFAN-DEWENTER I, ÖCKINGER E (2010) Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc. R. Soc. Lond. B (in press). CHYTRÝ M, WILD J, PYŠEK P, JAROŠÍK V, DENDONCKER N, REGINSTER I, PINO J, MASKELL LC, VILÀ M, KÜHN I, SPANGENBERG JH, SETTELE J (2010) Projecting trends in plant invasions in Europe under different scenarios of future land-use change: policy orientations will not reduce invasions. Global Ecology and Biogeography (in press). FISCHER, STÖCKLIN (1997) Local extinctions of plants in remnants of extensively used calcareous grasslands 1950 – 1985. Conservation Biology 11: 727-737 GASTON KJ (2004) Macroecology and people. Basic and Applied Ecology 5(4): 303-307. GASTON KJ, SPICER JI (2004) Biodiversity - An Introduction. 2nd Ed. Blackwell Publishing, Malden. HELM A, HANSKI I, PÄRTEL M (2006) Slow response of plant species richness to habitat loss and fragmentation. Ecology Letters 9: 72-77. HOEKSTRA JM, BOUCHER TM, RICKETTS TH, ROBERTS C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecology Letters 8: 23-29.
KRAUSS J, STEFFAN-DEWENTER I (2003) Local species immigration, extinction, and turnover of butterflies in relation to habitat area and habitat isolation. Oecologia 442: 591-602. KRAUSS J, KLEIN AM, STEFFAN-DEWENTER I, TSCHARNTKE T (2004) Effects of habitat area, isolation, and landscape diversity on plant richness of calcareous grasslands. Biodiversity and Conservation 13: 1427-1439. KRAUSS J, BOMMARCO R, GUARDIOLA M, HEIKKINEN RK, HELM A, KUUSSAARI M, LINDBORG R, ÖCKINGER E, PÄRTEL M, PINO J, PÖYRY J, RAATIKAINEN KM, SANG A, STEFANESCU C, TEDER T, ZOBEL M, STEFFAN-DEWENTER I (2010) Habitat fragmentation causes immediate and timedelayed biodiversity loss at different trophic levels. Ecology Letters 13: 597-605. ÖCKINGER E, SCHWEIGER O, CRIST TO, DEBINSKI DM, KRAUSS J, KUUSSAARI M, PETERSEN JD, PÖYRY J, SETTELE J, SUMMERVILLE KS, BOMMARCO R (in press). Life-history traits predict species responses to habitat area and isolation – A cross-continental synthesis. Ecology Letters (doi: 10.1111/j.1461-0248.2010.01487.x). KUUSSAARI M, BOMMARCO R, HEIKKINEN RK, HELM A, KRAUSS J, LINDBORG R, ÖCKINGER E, PÄRTEL M, PINO J, RODA F, STEFANESCU C, TEDER T, ZOBEL M, STEFFAN-DEWENTER I (2009) Extinction debt: a challenge for biodiversity conservation. Trends in Ecology & Evolution 24: 564-571. LINDBORG R, ERIKSSON O (2004) Historical landscape connectivity affects present plant species diversity. Ecology 85: 1840-1845. Millennium Ecosystem Assessment 2005 UNEP/GRID-Arendal, ‘Millennium Ecosystem Assessment - Sub-global assessments’, UNEP/GRID-Arendal Maps and Graphics Library, 2005,
[Accessed 14 March 2010] ROSENZWEIG ML (1995) Species diversity in space and time. University Press, Cambridge. SCHWEIGER O, BIESMEIJER JC, BOMMARCO R, HICKLER T, HULME PE, KLOTZ S, KÜHN I, MOORA M, NIELSEN A, OHLEMÜLLER R, PETANIDOU T, POTTS SG, PYŠEK P, STOUT JC, SYKES MT, TSCHEULIN T, VILÀ M, WALTHER G-R, WESTPHAL C, WINTER M, ZOBEL M, SETTELE J (2010). Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biological Reviews. doi: 10.1111/j.1469-185X.2010.00125.x STEFFAN-DEWENTER I, KLEIN AM, GAEBELE V, ALFERT T, TSCHARNTKE T (2006) Bee diversity and plant-pollinator interactions in fragmented landscapes. In: Waser NM, Ollerton J (eds) Plantpollinator interactions: from specialization to generalization, University of Chicago TSCHARNTKE T, KLEIN AM, KRUESS A, STEFFAN-DEWENTER I, THIES C (2005) Landscape perspectives on agricultural intensification and biodiversity - ecosystem service management. Ecology Letters 8: 857-874. TYLIANAKIS JM, DIDHAM RK, BASCOMPTE J, WARDLE DA (2008) Global change and species interactions in terrestrial ecosystems. Ecology Letters 11, 1351-1363.
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Land Use Change Scenarios for Europe
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ISABELLE REGINSTER, MARK ROUNSEVELL, ADAM BUTLER & NICOLAS DENDONCKER
Europe is characterised by a complex mosaic of urban and rural landscapes that has arisen from centuries of evolving historical, cultural and natural contexts. The past drivers of land use change have been demographic factors such as population size and density, technological development, economic growth, governance systems of ownership and exchange, attitudes, values and planning policies and these have overlain the heterogeneity of locations determined by climate and soil. The European territory is however currently at a crossroads. Influenced by new, often exogenous, drivers of change such as globalisation, rising energy prices, stronger immigration pressures and climate change, it also faces endogenous factors such as an ageing population and the struggle to promote competitiveness and improve the quality of life for regional and local communities (ESPON Report 2007). Evaluation of the potential, future direction of these drivers and their consequences for land use in Europe is, therefore, becoming an important and urgent need. For the ALARM project, quantitative, spatially explicit and alternative scenarios of land use in Europe have been developed for 27 countries (EU25+Switzerland and Norway) at a 10’ grid cell resolution. Seven land use types have been modelled annually from
a baseline year 2000 to 2080: urban land use, cropland, grassland, permanent crops, biofuels, forests and land in succession (abandoned agricultural land). The tool used for land use modelling is MOLUSC, an automated European land use change model (Reginster et al. in review). This regional model has been coupled with a global macro-economic model (GINFORS, Meyer et al. 2003) and a global ecosystem model (LPJmL, Bondeau et al. 2006) to assess the global socio-economic driving forces of land use changes in Europe and introduce the effects of global climate changes on potential yields in Europe. The three ALARM socio-economic storylines (Spangenberg 2007) have been interpreted to develop the land use scenarios in an integrated framework. The interpretations were based on future trends in current European policy that impact on land use, notably the European Spatial Development Perspective (ESDP) and its role in planning policy, the effects of the Common Agricultural Policy (CAP) on agricultural production and rural development and nature protection policies through the NATURA 2000 site network. Table 1 shows the guidelines of the storyline descriptions for the land use. More details can be found in Reginster et al. (in review). Changes in future land use were referenced to a baseline of the current
Socio-economic modelling in GINFORS
Land use modelling in MOLUSC
Three ALARM storylines
Climate scenario development
Potential vegetation modelling in LPJmL
Table 1. Summary of the storyline descriptions for the land use.
GRAS storyline (GRowth Applied Strategy) Deregulation and free market objectives lead to the reduction or abolition of zoning in regions. The guidelines of the ESDP are not applied leading to urban sprawl and diffuse peri-urbanisation. Liberalisation of food trade is a consequence of the free market policy with CAP subsidies being removed. Agricultural areas are only maintained in optimal locations with a comparative advantage, where profitability is positive. Current protected areas are maintained, but the NATURA 2000 site network is not enforced. BAMBU storyline (Business-As-Might-Be-Usual ) The guidelines of the ESDP are applied leading to compact city development and limited peri-urbanisation in rural areas. The CAP is maintained, but reformed to avoid overproduction. Agriculture is encouraged in optimal locations, but a minimum level of activity is maintained in traditional agricultural landscapes for rural development objectives. The current European aforestation policy is maintained. Current protected areas are preserved and the NATURA 2000 site network is enforced. SEDG storyline (Sustainable European Development Goal) Integrated social, environmental and economic policies lead to the extensification of agriculture and encourage organic farming. This helps to reduce unemployment in rural areas and reduce the effects of agricultural intensification on environmental quality. Planning policies are strict and favour compact settlement to reducethe need to travel, providing opportunities for efficient public transport and other energy savings. Current protected areas are preserved and the NATURA 2000 site network is enforced.
land use as defined by the PELCOM database (Mücher et al. 2000). PELCOM is a 1 km pan-European land-cover map derived from remotely sensed data. The classification methodology in PELCOM was based on a regional and integrated approach of the NOAA-AVHRR satellite data and ancillary information (Mücher et al. 2000). PELCOM covers the whole of Europe and is freely available (http:// www.geo-informatie.nl/projects/pelcom/public/index.htm). Synthesis of the ALARM land use scenario results The results of the land use scenario development show different quantities and spatial patterns of land use change for the three scenarios, although the basic land use change trends are the same for each of them (Figure 2). Some of the largest changes involve the abandonment of agricultural land
(cropland and grassland) with the greatest changes being observed for GRAS, then BAMBU, and finally SEDG. Some of the abandoned agricultural land is used for biofuels and forestry, but in spite of these transitions, important areas of surplus land are assumed for all three scenarios with again larger surplus areas occurring in GRAS than BAMBU and SEDG. Almost 12 % of the European land area is assumed abandoned in GRAS by 2050. A focus on the results for four land use types is presented in the following pages: grassland, cropland, forests and urban land. Agriculture Agriculture is the most important land use in Europe in geographic terms and because of this it plays a central role in the quality of the wider environment. European landscapes have experienced rapid changes in agricul-
Figure 1. Principal components of the linked modeling system
Figure 3. Agricultural areas: in geographic terms, the most important land use in Europe: ± 53 % of the total land use (30 % of cropland, 23 % of grassland). Goods and services include: production of food, forage and fibre; carbon and water storage; quality of rural landscapes; tourism; wildlife habitats.
Figure 2. Synthesis of the ALARM land use change scenarios for Europe.
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Figure 4. Percentages of food crops per cell for the baseline and absolute differences for the three ALARM scenarios.
tural land use throughout the second half of the twentieth century arising from developments in technology and management driven by socio-economic and political forces. These trends are anticipated to continue into the future through the effect of reforms
to the CAP, enlargement of the European Union, globalisation, technological change and climate change (Rounsevell et al. 2003). For the three ALARM scenarios, different spatial patterns are observed for cropland and grassland, which reflect
different allocation rules and the use of the agricultural rent map as a proxy for the optimal location of agricultural production in GRAS and BAMBU. For the GRAS scenario, the maps demonstrate large changes in land use with more regional disparities of
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these changes, especially in regions with lower agricultural rents, such as Eastern and Southern countries of Europe. For the BAMBU scenario, the maps demonstrate important land use changes, with some regional disparities.
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Figure 5. Percentages of grassland per cell for the baseline and absolute differences for the three ALARM scenarios.
Agricultural land areas decrease in Eastern and Southern countries, but regulatory mechanisms maintain a certain level of rural activity in traditional rural regions, even if these are not optimal compared with the intensive agricultural areas of western central Europe. 102
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For the SEDG scenario, the map shows slight changes in agricultural land use with fewer regional disparities. The scenario outcomes suggest that the assumptions about the alternative future directions of the CAP would have significant effects on
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agricultural land use in Europe, liberalisation of the CAP being associated with greater increases in agricultural land abandonment. Irrespective of changes in the CAP, however, all three scenarios anticipate some form of agricultural land abandonment,
which is consistent with the observed changes in European agriculture over the past 50 years. There are however important differences in the spatial patterns of this abandonment. The scenarios suggest that even with very different political strategies for
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Figure 6. Percentages of forests per cell for the baseline and absolute differences for the three ALARM scenarios.
Europe, future trends in land use change may be similar. This is consistent with the findings of other pan-European land use scenarios studies and suggests that the directions of change are a robust outcome of the scenario analysis.
Forests During the ten year period 1990–2000, the stock of forests is either stable or increasing across all the dominant landscapes types in Europe (EEA 2006). The European increase of forest areas is about 0.1 %.
It was assumed that the trends in forestry and forests of today would continue into the future until 2020. The changed circumstances described in the storylines were taken into consideration from 2020. Forests, however, have long rotation times in some
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regions, and trees planted today may only reach their harvesting age in 2080 or 2100. Even though the storylines describe rapid changes in societies, these changes may not be reflected in forests immediately, but may take decades to materialize. It was assumed,
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Figure 7. Percentages of urban land use per cell for the baseline and absolute differences for the three ALARM scenarios.
therefore, that the underlying driving forces that are relevant to changes in forest land today would also apply in the future (Rounsevell et al. 2006). Generally, for the BAMBU and SEDG scenarios, managed forest areas increase. There could be some 104
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decreases in GRAS, partly due to competition with other land uses (urban land use or biofuels). Urban areas Urban areas refers to land with buildings and other man-made structures,
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such as services, industries, and transport infrastructure. During the ten year period 1990–2000, the growth of urban areas and associated infrastructure throughout Europe consumed more than 8,000 km2 (a 5.4 % increase during the period) (EEA 2006). The
growth of cities in Europe has historically been driven by increasing urban populations. However, today, even where there is little or no population pressure, a variety of factors are still responsible for urban sprawl (EEA 2006). The potential environmental
The dispersion of new urban settlements, such as in the GRAS scenario, will affect agricultural areas, forests and semi-natural areas, except in protected areas. For the BAMBU scenario, compact city development and limited periurbanisation will reduce impacts on rural areas. Current protected areas are preserved and the NATURA 2000 site network is enforced. In the SEDG scenario, compact city development and limited peri-urbanisation also minimise impacts on agricultural areas, forests and semi-natural areas. Figure 8. The forests ≈ 28 % of the european land use. Goods and services: wood production, biodiversity, storage, tourism, landscape quality, leisure, water purification.
pressure of urban dispersion is a disturbance to, or reduction in, semi-natural habitats. The strategies and instruments to control sprawl are complex. The ESDP was adopted by the EU Ministers for Spatial Planning in 1999 (European Commission 1999) and designed as a means of guiding and shaping territorial policies in support of economic growth, cohesion and sustainable development. The ESDP endorses, for example, the concept of compact cities as a sustainable urban form. The ESDP also recognises, however, that different planning policies exist at the country or regional level within Europe (European Commission 1997, Compendium of European Spatial Planning Systems). Analysis of the urban maps Urban land use increases in all scenarios, but these changes are small in areal terms relative to the other land use classes. For the GRAS scenario, the map shows urban sprawl, peri-urban patterns and diffuse developments in rural areas. For the BAMBU and SEDG scenarios, the map shows more compact patterns. The local effect of urbanisation is, however, especially important for ecosystems and in this respect very different patterns of urbanisation are observed for the different scenarios.
Figure 9. Urban areas: a low percentage of land ± 5 % but about 80 % of all the citizens in Europe live in cities with more than 10,000 inhabitants.
References BONDEAU A, SMITH PC, ZAEHLE S, SCHAPHOFF S, LUCHT W, CRAMER W, GERTEN D, LOTZECAMPEN H, MÜLLER C, REICHSTEI M, SMITH B (2006) Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology 13: 679-706. DENDONCKER N, BOGAERT P, ROUNSEVELL MDA (2006) A statistical method to downscale aggregate land use data. Journal of Land Use Science 1: 63-82. DENDONCKER N, ROUNSEVELL MDA, BOGAERT P (2007) Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems 31: 188-205. DENDONCKER N, SCHMIT C, ROUNSEVELL M (2008) Exploring spatial data uncertainties in land use change scenarios. International Journal of Geographical Information Science 22: 1013-1030. EEA (2006 a) Land accounts for Euriope 19902000: towards integrated land and ecosystem accounting. European Environment Agency EA report 11: 107 p. EEA (2006 b) Urban sprawl in Europe: the ignored challenge. European Environment Agency EA report 10: 56 p. ESPON REPORT (2007) Scenarios on the territorial future of Europe. ESPON Project 3.2 Final report 60 p. EUROPEAN COMMISSION (1997) The EU Compendium of European Spatial Planning Systems and Policies. Regional Development Studies 191 p. EUROPEAN COMMISSION (1999) ESDP, European Spatial Development Perspective, adopted in Potsdam in May 1999 82p. http://europa. eu.int/comm/regional_policy/sources/ docoffic/official/reports/som_en.htm ROUNSEVELL M, ANNETTS JE, AUDSLEY E, MAYR T, REGINSTER I (2003) Modelling the spatial distribution of agricultural land use at the regional scale. Agriculture, Ecosystems and Environment 95: 465-479. MEYER B, LUTZ C, WOLTER MI (2003) Global Multisector/Multicountry 3E Modelling: From COMPASS to GINFORS. Paper presented at the 2003 Berlin Conference on the Human Dimensions of Global Environmental Change. Berlin, December, 5-6. REGINSTER I, ROUNSEVELL M, RIGUELLE F, CARTER T, FRONZEK S, OMANN I, SPANGENBERG JH, STOCKER S, BONDEAU A, HICKLER T (in review) The effect of alternative socio-economic and political strategies on European land use from 2006 to 2080. Land Use Policy. ROUNSEVELL MDA, REGINSTER I, ARAÚJO MB, CARTER TR, DENDONCKER N, EWERT F, HOUSE JI, KANKAANPÄÄ, S, LEEMANS R, METZGER MJ, SCHMIT C, SMITH P, TUCK G (2005) A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems and Environment 114: 57-68. SPANGENBERG JH (2007) Integrated Scenarios for Assessing Biodiversity Risks. Sustainable Development 15: 343-356.
DOWNSCALING Downscaling is the process of taking maps that have a low (coarse) spatial resolution, and using them to generate comparable maps that have a much higher (finer) spatial resolution. Corine: 250 m resolution
The output from downscaling is a detailed map, whereas the input is a fairly crude map. On the face of it, it involves gaining “something for nothing”, since we get out more information than we put in. Clearly, therefore, we cannot perform downscaling with certainty, and we cannot do it without making some assumptions about how the two maps will be related to each other. The key is to make a reasonable set of assumptions. Within ALARM, we went about downscaling the projections of future land use using an algorithm that was proposed by Dendoncker et al. (2006). As well as low resolution projections of future land use, this algorithm also relies on a high resolution map of present day land use. It then proceeds as follows: ◙ A statistical model – called a multinomial autologistic model – is used to describe the map of present day land use. This model encapsulates the spatial structure of the landscape, and allows us to estimate the present day probability that a particular location on the high resolution grid will have a particular land use (e.g., forestry), based on the land uses at neighboring locations. ◙ It is fairly straightforward to work out the overall proportions of each land use at a national level, using both the (high resolution) present day data and the (low resolution) projections of future land use. If we divide the future proportions by the present day proportions this tells us approximately how much the overall prevalence of each land use will change, nationally. ◙ We can then combine the output of the first two steps using Bayes’ theorem in order to estimate the future probability that a particular location on the high resolution grid will have a particular land use (e.g., forestry), based on the land uses at neighbouring locations. We then assume that that location will have the land use for which this probability is highest. There are some technical complications involved in each of these steps, but we do not dwell on those here (see Dendoncker et al. 2006, for more details). More importantly, the algorithm depends on a number of strong assumptions, and it is important to be aware of these when looking at the maps that it produces – if the assumptions were altered, the maps might look quite different! The results depend, first and foremost, on the socio-economic assumptions that went into producing the original, low resolution, projections of land use. They other key assumption, which is needed in order to move from the coarse resolution to the fine resolution, is that the structure of the landscape will remain the same in the future – certain land uses may become more or less prevalent, depending on which sets of policies are adopted, but the relative patterns of land use (e.g., whether areas of arable land are more or less likely to occur close to cities) will remain unchanged. The downscaling algorithm itself had already been developed prior to this project (Dendoncker et al. 2006), and been used to generate projections of future land use in Luxembourg and Belgium (Dendoncker et al. 2007, 2008). Our key challenge here was, therefore, to see whether the same approach could be used to produce projections of future land use for a much larger part of Europe – to be more precise, for the following 27 European countries: Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovenia, Slovakia, Spain, Sweden, Switzerland and the UK. This objective presented us with some immediate practical difficulties. The first issue was to find a suitable high resolution map of present day land use across Europe. The Corine land cover 2000 (CLC2000) database of the European Environment Agency provided an ideal solution, since it maps the occurrence of different land cover types at a resolution of 250-by-250 metres and covers all the countries that were of interest to us (apart from Norway and Switzerland, for which we had to use national land cover datasets). The 44 land cover types within CLC2000 do not, however, match up directly with those used in the ALARM projections, so some care (and subjectivity) were required in order to match the ALARM land use classes to the CLC2000 land cover classes. The second key issue was computational. We needed to produce maps that covered approximately 70 million grid cells, under 6 scenarios, and for 3 time steps (2020, 2050, and 2080) – 17 maps, containing a total of more than a billion individual pieces of information! Such an enormous task would require substantial computing resources – both processing power, to do the calculations, and memory, to store the results. How did we go about doing this? The downscaling algorithm was already coded up using a powerful and widely-used technical computing language, known as MATLAB, but would still have taken many months to run on a standard PC. We therefore made use of the high performance cluster at the Edinburgh Parallel Computing Centre (www.epcc.ed.ac.uk), and so were able – through the use of distributed computing – to run the algorithm simultaneously on hundreds of machines. This enabled us to complete all the computations in just a few hours.
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Evaluating Land Use Changes in and around Natura 2000 Sites: a Proposed Methodology
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IOANNIS N. VOGIATZAKIS, STUART P.M. ROBERTS, MARIA T. STIRPE & SIMON G. POTTS
Background Quantification and monitoring of landscape heterogeneity has become an important task in ecology and biogeography providing insights in the relationships between ecological processes and spatial patterns (Turner 2005). These activities are of extreme significance for the conservation and management of protected areas since spe-
has to take place in and around protected areas. Quantification and monitoring of landscape changes over time should be an integral part of management efforts in protected areas. Within the COCONUT project we are looking into historical land use changes for a selected set of Natura 2000 sites and adjacent landscapes and the effects of these changes on the main taxonomic
(i) A suite of landscape metrics were used to quantify changes in landscape pattern (composition and configuration); (ii) A simple “quality model” was developed to assess changes in habitat and overall landscape quality. Land cover data were derived from BIOPRESS project (Gerard et al. 2006).
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(Figure 1). The method is exemplified for Butser Hill, a UK Natura 2000 site. Butser Hill is a large area of chalk grassland in Hampshire, S. England. The site consists of improved and unimproved calcareous grassland with scattered scrub, chalk heath, yew woodland and semi-ancient broadleaved woodland. The site has a very rich bryophyte and lichen flora with over 200 lichen, moss
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Figure 1. Landcover Changes were based on BIOPRESS sites.
cies often depend on special habitats and are at greater risk of extinction when these habitats are degraded or lost (Fahrig 2003; Norris & Harper 2004). It is increasingly recognised that, in order to understand and mitigate the negative impacts of habitat fragmentation on biodiversity, impact assessment
BIOPRESS is an EU project that has produced a standardised measure of historical land cover changes established from time-series of aerial photographs (1950-1990-2000). The data used were 2 km x 15 km transects for seven countries across Europe, since that was the higher resolution data available
groups namely plants, birds, bees and butterflies. The method developed is explained herein as exemplified by a UK case study. Approach Two approaches were taken to analyse landscape changes:
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Figure 2. Landscape changes around 5 Natura 2000 sites including Butser Hill from 1950-1990-2000.
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and liverwort species being recorded in the chalk grassland. Over 30 butterfly species have been sighted here including the Duke of Burgundy fritillary, silverspotted skipper and chalkhill blue. Changes were evaluated within the site and outside at a range of 1 km buffer zone. A parsimonious set of landscape metrics (McGarigal et al. 2002) was employed to explore changes in composition and configuration of the site including Mean Number of Patch, Mean Patch Size, Mean Edge Density, Mean Nearest Neighbour and others. These metrics were calculated using with Patch Analyst1 within ArcGIS. For the area within and around the Natura 2000 site (1 km buffer) we calculated a Quality Index (QI) for every time slice (1950-1990-2000). This index was calculated as follows:
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experts for each taxonomic group. Survey was based on a questionnaire where land cover classes were rated on a scale from 0-5 (where 0 was the lowest value and 5 the highest). Quality is defined here as a broad measure of the overall value of the land cover type for general biodiversity for every taxonomic group; it is not related only to rare species. Judgment was made on the basis of which land cover types could potentially support the greatest diversity of plants, birds, bees and butterflies. Responses for every land cover category in and around Butser Hill were averaged, using the Mode, and mapped into the GIS in order to get a picture of changes in habitat quality over time. The last operation was to create the maps of land use changes and quality for the site and the buffer.
Figure 3. Butser Hill Natura 2000 site. Photo: Southdownsway.co.uk.
Findings and conclusion Although landscape composition has not changed significantly among the 3 time slices (1950, 1990, 2000) landscape configuration shows some notable changes particularly with respect to increased number of patches and decreased mean patch size and increase Mean Nearest Neighbour (Figure 2). This is the case particularly around the site and it more obvious for 1990-2000 which indicates fragmentation taking place. However, what is more profound is the loss of habitat quality for most of the taxonomic groups around the site during the time period examined (Figure 6). This is particularly alarming for conservation efforts in the area. As these preliminary results
Figure 4. Chalk grassland meadow, Ranscombe Farm, North Downs, UK. Photo: C. Rutter.
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Figure 5. Foraging bumblebee, Bombus terrestris. Photo: L. Hislop.
highlight there is a need to monitor activities beyond the N2K boundaries since understanding the spatial pattern of habitat patches and the character of the intervening matrix is of utmost importance for the ecological structure and function of protected areas. References FAHRIG L (2003) Effects of Habitat Fragmentation on Biodiversity. Annual Review of Ecology, Evolution, and Systematics 34: 487-515. GERARD F et al. (2006) BIOPRESS Final report EC-FPV Contract Ref: ENV-CT2002-00178 MCGARIGAL K, CUSHMAN SA, NEEL MC, ENE Е (2002) FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. NORRIS K, HARPER N (2004) Extinction processes in the hotspots of biodiversity and the targeting of pre-emptive conservation action. Proceedings of the Royal Society B: Biological Sciences 271: 123-130. TURNER M (2005) Landscape Ecology: What is the state of the Science. Annual Review of Ecology, Evolution, and Systematics 36: 319-344.
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Life History Traits in Insects and Habitat Fragmentation
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RICCARDO BOMMARCO, ERIK ÖCKINGER & AVELIINA HELM
Background Land use conversion and intensified land use have led to loss and fragmentation of natural and semi-natural habitat types that harbour a large share of the biodiversity in the landscape (Hoekstra et al. 2005). Species that inhabit small remnant habitat fragments are expected to have high extinction rates due to small population sizes and increased isolation from other populations of the same species (Fahrig 2003). The ability of a remaining habitat patch to support a certain number of species also depends on the habitat quality, which often deteriorates as a result of habitat loss and intensified use of the surrounding landscape. It is therefore important to consider size and isolation of remaining high value habitats for biodiversity, in order to understand how species communities respond to land use conversion, and also to develop efficient conservation schemes. Habitat loss and species richness Several studies have examined the effects of habitat fragmentation on species communities. These studies generally measure how species richness and densities of a taxonomic group of species, vary with habitat fragment size and degree of isolation
to other fragments. In particular, there are several such studies available for vertebrates and vascular plants. Butterflies is the most studied group. What is lacking are summarising quantitative analyses that assess large scale patterns across continents and that provide general estimates of species richness and habitat area relationships from multiple case studies. There is also a lack of studies for several species groups. For instance, wild bees are functionally important and species rich group which is considered to be threatened due to environmental changes (Biesmejer et al. 2006), but there are very few studies available on the effects of habitat loss on wild bee diversity. Taking species traits into consideration A problem with focusing purely on effects of habitat loss on overall species richness, is that we may underestimate the negative effects of habitat fragmentation on the species communities. Species richness may, for instance, be augmented by an influx of habitat generalists from the surrounding environment in small fragments. This could veil a true loss of habitat specialists. Importantly, species vary in their response to fragmentation and the extinction risk may be modified
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depending on species life histories and shared ecological characteristics (Ewers & Didham 2006). This is poorly examined empirically for large components of biodiversity and for functionally important species groups (SteffanDewenter et al. 2006). By understanding how species with contrasting dispersal ability, feeding preferences and other life history traits respond to habitat loss, we are able to predict which species are most sensitive and prone to extinction in changing landscapes and how ecological communities will be composed in highly fragmented landscapes. Research activities There are innumerable ways that species can be divided into trait groups when performing such analyses. Dispersal capacity, niche breadth, and habitat specialisation are categories that have been hypothesized as key determinants for species persistence in fragmented landscapes and for community organization in general (Ewers & Didham 2006). In our studies we consider the impact of fragmentation depending on the species characteristics that are linked to dispersal ability; such as wing span or body size. We further consider diet and habitat specialisation from field observation of dietary preferences
and occurrence in different habitat types, and, when information is available, reproductive capacity. Depending on the group of species studied, we also account for factors such as degree of sociality, trophic level, and searching behaviour. Within the COCONUT-project we collect available data from the literature and via personal contacts carry out synthesising analysis on distribution of species’ richness and density in fragmented landscapes. By merging information on species occurrence in semi-natural and natural habitat patches varying in size and isolation with information on species characteristics, we examine the general response of habitat loss and fragmentation for several insect groups and vascular plants. We mainly use information collected from grassland and forest fragments situated in agricultural landscapes in Europe and North America. Preliminary result Much of this work is in progress, but there are some general patterns that emerge in our preliminary analyses. First of all it is clear that overall species richness of wild bees, butterflies and moths is negatively affected by habitat loss and fragmentation. In addition, inclusion of species traits such dispersal capacity and degree of
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Figure 1. Change in extent of dry calcareous alvar grasslands on the Estonian islands of Saaremaa and Muhu from 1930's (a) to year 2000 (b). Calcareous grasslands harbour many unique plant and insect species, but these habitats suffer severe habitat loss and fragmentation. In Saaremaa and Muhu islands, 70 % of the area of alvar grasslands is lost in the last 70 years due to agricultural intensification and cessation of traditional management.
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Figure 2. The butterfly Euphydryas maturna has experienced a strong decline in many European countries and is listed as “Vulnerable” in the Red Data book of European butterflies. Photo: Erik Öckinger.
specialisation in the analysis improves our ability to predict changes in species richness and density due to fragmentation (Hambäck et al. 2007). In several cases, species with poor dispersal capacity emerge as particularly threatened by habitat loss. Habitat or diet specialists often, but not always, emerge as losers in changing landscapes. Large-bodied generalists with good dispersal capacity are not negatively affected by habitat loss at the spatial scales considered in the studies included. Final remarks This exercise shows that sharing data among scientific groups gives added
value and allows more general conclusions to be made. Our experience is also that carrying out analyses across studies using primary data is more efficient compared to metaanalysis, for instance, based on statistical estimates from the literature. We also found that considering species richness per se is only a crude first step and that inclusion of information on species traits improves our ability to predict the effects of habitat fragmentation on different species groups. As a result, community organisation in a habitat is altered by land use changes such as habitat loss, and this probably has implications for ecosystem functioning and provi-
Figure 4. Pyrgus armoricanus is a highly specialized and relatively sedentary butterfly, and therefore likely to be particularly sensitive to habitat loss and fragmentation. Photo: Erik Öckinger.
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Figure 3. The relationship between habitat patch area and butterfly species richness is stronger for sedentary (orange symbols and line) than for highly mobile (green symbols and line) butterflies. Redrawn from Öckinger et al. 2009.
sioning of ecosystem services. Information on how communities react to habitat loss will provide knowledge for developing targeted conservation schemes for biodiversity and ecosystem services. References BIESMEIJER JC, ROBERTS SPM, REEMER M, OHLEMULLER R, EDWARDS M, PEETERS T, SCHAFFERS AP, POTTS SG, KLEUKERS R, THOMAS CD, SETTELE J, KUNIN WE (2006) Parallel declines in pollinators and insectpollinated plants in Britain and the Netherlands. Science 313: 351-354. EWERS RM, DIDHAM RK (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews 81: 117-142.
FAHRIG L (2003) Effects of habitat fragmentation on biodiversity. Annual Review of Ecology and Systematics 34: 487-515. HAMBÄCK PA, SUMMERVILLE KS, STEFFANDEWENTER I, KRAUSS J, ENGLUND G, CRIST THO (2007) Habitat specialization, body size, and family identity explain lepidopteran density–area relationships in a cross-continental comparison. PNAS 104: 8368-8373. ÖCKINGER E, FRANZÉN M, RUNDLÖF M, SMITH HG (2009) Mobility-dependent effects on species richness in fragmented landscapes. Basic and Applied Ecology 10: 573-578. STEFFAN-DEWENTER I, KLEIN A-M, GAEBELE V, ALFERT TH, TSCHARNTKE T (2006) Bee diversity and plant-pollinator interactions in fragmented landscapes. – In N.M. Waser & J. Ollerton (Eds.). Plant-pollinator interactions from specialization to generalization The University of Chicago Press, Chicago, US, 387-407.
Figure 5. Maniola jurtina is common in most parts of Europe, and occurs across a wide range of grassland types. Photo: Erik Öckinger.
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Where Have All the Flowers Gone? From Natural Vegetation to Land Use/ Land Cover Types: Past Changes and Future Forecasts
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LASZLO NAGY, NICOLAS DENDONCKER, ADAM BUTLER, ISABELLE REGINSTER, MARK ROUNSEVELL, GEORG GRABHERR, MICHAEL GOTTFRIED & HARALD PAULI
Land use / land cover type
Climate models indicate regionally variable increases in temperature from little to as much as about 6 °C at high latitudes, largely in the arctic biome over the twenty-first century. There are a multitude of vegetation models whose output is largely dependent on the departure from the climatic conditions between 1960 and 1990. Most forecasts on vegetation change are made in relation to today’s potential or natural vegetation. A cartographic model of the natural vegetation of Europe, based on
expert consensus is available (Bohn et al. 2004). Predictive models of natural vegetation use largely regional climate, topography, parent material chemistry as variables to predict vegetation type. These models are useful, even though they have become largely hypothetical in many biomes because of the degree of conversion of natural ecosystems by human activities. Human impacts on biodiversity have been direct (e.g., land conversion) and indirect (e.g., modification of
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with regard to promoting bio fuels (Reginster et al., this atlas, pp. 100ff.). From an alpine ecosystem perspective it is of interest to see how policy and land use change decisions on productive land could impact areas above the treeline in Europe’s mountains. We illustrate here the historic degree of conversion from natural vegetation to managed land cover types in temperate forest and subalpine ecosystem types. In addition, we show the change indicated by a land use change model
Figure 1. Actual land cover of Europe (Global Land Cover map – GLC 2000, version 2)(http://www-gem.jrc. it/glc2000/) and the percent distribution of GLC 2000 land use / land cover types (graph panel). Cultivated and managed areas cover most of the total land area.
Irrigated agriculture Artificial surfaces and associated areas Snow and ice Bare areas Mosaic: Cropland / shrub / and/or grass cover Mosaic: Cropland / Tree cover / other natural vegetation Cultivated and managed areas Regularly flooded shrub and/or herbaceous cover Sparse herbaceous or sparse shrub cover Herbaceous cover, closed-open Shrub cover, closed-open, deciduous Shrub cover, closed-open, evergreen Tree cover, burnt Mosaic: tree cover / other natural vegetation Tree cover, regularly flooded, saline water Tree cover, regularly flooded, fresh water Tree cover, mixed leaf type Tree cover, needle-leaved, deciduous Tree cover, needle-leaved, evergreen Tree cover, broadleaved, deciduous, open Tree cover, broadleaved, deciduous, closed Tree cover, broadleaved, evergreen
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microclimate, or pollution). The degree of land conversion may be estimated by comparing land use – land cover maps (e.g., GLC 2000, CORINE) made by interpreting remotely sensed images against maps of natural vegetation. Such estimates of change are reasonably accurate in cases where the natural forest vegetation has been replaced by agricultural crops, or other non-forest vegetation that can readily be detected by remote sensing. It works less well in other cases, especial-
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ly where mosaics of land cover exist, and in naturally treeless environments. Present day land use decisions in Europe mostly concern the reassignment of existing land use types and affect semi-natural habitats to a lesser extent. The reason for this is the fact that most productive land has historically been converted to agricultural use. The ALARM project has been charged with exploring land use changes that might result from EU policy decisions
(MOLUSC, but with the output downscaled to a finer spatial resolution by using a statistical algorithm, Reginster et al., this atlas, pp. 100ff.) in the treeline ecotone and alpine zone (2000-2085). The main sources of information for this assessment were the Map of the Natural Vegetation of Europe (MNVE, Bohn et al. 2004), the Global Land Cover map (GLC 2000) version 2 for Europe (http://www-gem.jrc.it/ glc2000/), and the output maps from
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the MOLUSC land use change model, run under various ALARM scenarios (see Spangenberg 2007), in comparison with the baseline aggregated CORINE land use land cover map (http:// reports.eea.europa.eu/COR0-part1/en). The MNVE was used to delineate the subalpine, alpine and nival vegetation zones. The GLC 2000 in combination with MNVE was used to illustrate the degree of conversion of the various ecosystem types (see Figure 1 for an example). The CORINE system distinguishes 44 land cover categories. These were aggregated into six for MOLUSC: urban, agricultural crop, permanent crop, forest, grassland, and other. The ‘other’ category largely contained all unproductive land cover types. For exploring changes in the alpine zone alpine grasslands, moorlands, and open treeline ecotone scrub of the ‘other’ category were reassigned to grassland, and subalpine open forest to forest. Cultivated and managed areas stand out as being the single largest land use category in Europe (Figure 1). When various man-made, or derived land cover types are accounted for the proportion of conversion appears to be close to 60 %. Little of the remaining 40 % is unaffected by human activities and therefore is referred to as seminatural. Temperate forest (mesophytic deciduous broad-leaved and mixed coniferous – broad-leaved forests in MNVE) made up a high proportion of the naturally forest covered area of Europe (Figure 3). This biome has undergone the highest conversion to agricultural use (crops, permanent crops, grazing land). Oak forests, particularly the lowland and low hill (colline-submontane) types in continental Europe have borne the brunt of this conversion (Figure 2). In contrast, little obvious conversion can be discerned at the treeline (Figure 4), and especially above it, in the alpine and nival zones. The various scenarios indicated little change in land use above the treeline in the twenty-first century (up to 2080). There is an increase between 0.7-2.0 % in forest area (largely from secondary succession on abandoned grazing land) and a decrease of
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between 1.0 to 3.3 % in grazing land (grassland, moorland, open subalpine scrub). It is important to bear in mind that these changes are those modelled in relation to European policy decisions, primarily to the launching of a European bio fuel programme, whereby liquid or solid fuel is produced from crops. Since most bio fuel crops would be in competition with agricultural crops (food and fodder) or woodland on productive land, the low level of change that is forecast for the alpine zone appears to be reasonable. Forecasts of climate change impacts alone, based on modelled temperature increases and by using identical scenarios to those employed in MOLUSC have suggested a dramatic decrease in the extent of the alpine climate zone (Nagy et al., this atlas, p. 78), as did a dynamic vegetation model developed in ALARM by Lund University (Hickler et al., this atlas, pp. 238f.). The minimal change above the treeline suggested by the MOLUSC model can be most plausibly interpreted as implying that external (lowland) economic activities associated with bio fuel production are likely to have negligible impacts on the alpine vegetation. This is likely as, although climate change impacts were inherently taken into account in terms of land capability, (crop yield) changes were not large enough to allow crop growth in the alpine zone after climate change. As the MOLUSC model was not geared to evaluate forest expansion into the treeless alpine it only accounted for the changes that were likely to be caused by policy decisions and their translation into land use in the alpine zone. These policy-related changes in land use would be unlikely to dominate over the impacts of climate change. The low levels of future change suggested by MOLUSC are therefore unlikely to be realistic. In conclusion, we may say that the alpine zone appears to be marginal to European mainstream economic decisions that affect agriculture and forestry in the productive lowland and montane zones. Land use models tend to reflect these mainstream economic decisions, so this opens up avenues to explore high mountain land use impacts by using mountain-specific rule-based models. The radical changes in climate predicted for the alpine region makes the need for such modeling an urgent priority. References BOHN U, GOLLUB G, HETTWER C, NEUHAUSLOVA Z, RAUS T, SCHLUETER H, WEBER H (2004) Map of the natural vegetation of Europe. Scale 1 : 2 500 000. Part I. Explanatory text with CD-ROM Bonn: Bundesamt für Naturschutz. SPANGENBERG JH (2007) Integrated scenarios for assessing biodiversity risks. Sustainable Development 15: 343-356.
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Irrigated agriculture Artificial surfaces and associated areas Snow and ice Water bodies Bare areas Mosaic: Cropland / shrub / and/or grass cover Mosaic: Cropland / Tree cover / other natural vegetation Cultivated and managed areas Regularly flooded shrub and/or herbaceous cover Sparse herbaceous or sparse shrub cover Herbaceous cover, closed-open Shrub cover, closed-open, deciduous Shrub cover, closed-open, evergreen Tree cover, burnt Mosaic: tree cover / other natural vegetation Tree cover, regularly flooded, saline water Tree cover, regularly flooded, fresh water Tree cover, mixed leaf type Tree cover, needle-leaved, deciduous Tree cover, needle-leaved, evergreen Tree cover, broadleaved, deciduous, open Tree cover, broadleaved, deciduous, closed Tree cover, broadleaved, evergreen 0
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Percent Figure 3. Map of the areas of Europe whose natural vegetation would be temperate forest (mesophytic deciduous broad-leaved and mixed coniferous-broadleaved forests, from MNVE, green; top), and the percentage breakdown of these areas in terms of current land use classes (from GLC 2000; bottom).
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Irrigated agriculture Artificial surfaces and associated areas Snow and ice Water bodies Bare areas Mosaic: Cropland / shrub / and/or grass cover Mosaic: Cropland / Tree cover / other natural vegetation Cultivated and managed areas Regularly flooded shrub and/or herbaceous cover Sparse herbaceous or sparse shrub cover Herbaceous cover, closed-open Shrub cover, closed-open, deciduous Shrub cover, closed-open, evergreen Tree cover, burnt Mosaic: tree cover / other natural vegetation Tree cover, regularly flooded, saline water Tree cover, regularly flooded, fresh water Tree cover, mixed leaf type Tree cover, needle-leaved, deciduous Tree cover, needle-leaved, evergreen Tree cover, broadleaved, deciduous, open Tree cover, broadleaved, deciduous, closed Tree cover, broadleaved, evergreen 0
10
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Percent Figure 4. Map of the areas of Europe whose natural vegetation would be sub-alpine (open forest, from MNVE, violet; top), and the percentage breakdown of these areas in terms of current land use classes (from GLC 2000; bottom).
N AT U R A L
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Future Land Use Related Challenges for Biodiversity Research and Conservation
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RICCARDO BOMMARCO
As explained previously (Bommarco this atlas, pp. 98f.), confirmations on theoretical expectations on the effect of habitat loss and fragmentation and other pressures only cover limited regions and a few taxa, so that a consistent meta-analysis of species traits as predictors of fragmentation and other responses is still lacking. Furthermore, the impact of habitat fragmentation on mutualistic and antagonistic biotic interactions such as pollination, seed dispersal, decompositions, predation and parasitism are poorly investigated but might play an important role for the long-term survival of species communities in fragmented habitats (Lei & Hanski 1997, van Nouhuys & Hanski 2002, Tscharntke & Brandl 2004, Bommarco et al. 2010 in press). A rarely addressed aspect of habitat loss is the impact of the surrounding landscape matrix on biodiversity in the remnant habitat fragments (Vogiatzakis et al., this atlas, pp. 106f.). For instance, most researchers measure fragmentation at the patch scale, and not the landscape scale (Fahrig 2003), and extinction thresholds in a local habitat may depend on the proportion of remaining habitat in a landscape (Andrén 1994, Bascompte & Solé 1996). Intact landscapes which surround protected areas, may contribute additional resources required for species survival and corridors connecting fragmented habitats, whereas intensively managed resource-poor landscapes may increase the effective isolation of the landscape matrix (Vogiatzakis et al., this atlas, pp. 106f.). The landscape processes that modulate fragmentation effects are poorly understood, but are of high relevance for the implementation of efficient conservation schemes and the long-term survival of species in fragmented habitats. Biodiversity conservation in changing landscapes Recognising the threat to European biodiversity, the EU has set the objective of halting the loss of biodiversity, and also securing the restoration of habitats and natural systems. The EU has taken several actions for this. A major initiative has been the implementation of the Natura 2000 programme aiming to map the extent and distribution of areas with conservation status in the EU 25 countries and to provide the basis for landscape management and improved conservation strategies. While Natura 2000 data are increasingly becoming available, an evaluation of the Natura 2000 initiative remains notably unexplored (but see: Vogiatzakis et al., this atlas, pp. 106f.; Vohland et al., this atlas, pp. 234ff. and pp. 240f.). Combined effect of pressures on biodiversity While separate effects on biodiversity of the main global environmental changes, such as climate change, land use change and biological invasions are increasingly well documented, much less is known about the consequences when they act in combination. For instance, it is increasingly highlighted that habitat fragmentation and climate change pose significant individual threats to biodiversity and ecosystems worldwide, but the interaction between these two factors can lead to particularly severe consequences for biodiversity in a synergistic manner (Thomas et al. 2004). The climate of the earth warms at unprecedented rates (IPCC 2001). Because the distributions of many species are constrained by climatic factors, the predicted rapid climate change will lead to changing distribution areas of species. As the native areas become climatically unsuitable species are expected to migrate to new climatically suitable regions. This distributional range shift may be hampered in fragmented landscape where suitable habitat patches, that can act as stepping stones for the moving populations, are small and far apart (Hill et al 2001). A problem when mapping effects of multiple pressures is the poor quality of available land use coverage information across Europe. Current available information (e.g. CORINE) is too coarse for understanding the often finer scale ecological processes that are related to land use (Vogiatzakis et al., this atlas, pp. 106f.; Vohland et al., this atlas, pp. 234ff. and pp. 240f.). The information is of low resolution, consistency and ecological relevance, and allow only for making very crude biodiversity-land use links. There is an obvious risk that land use change in itself, but also in its interaction with other environmental changes, is 112
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underestimated when mapped with such data, and in combination with large scale changes such as climate change. Improved monitoring of biodiversity as well as enhanced quality and resolution of land use coverage at the European level would improve our understanding of current status and trends of biodiversity. Interestingly, ecologically useful land cover information does exist but is, in many countries, not available to researchers. A notable example is the detailed cropping patterns that are mapped each year across Europe by the national boards of agriculture. Access to such information and other detailed land cover maps produced at the national level, has the potential to greatly improve our understanding of land use change on biodiversity at a larger scale if made available to research and monitoring across Europe. Effects on services and functions The effects of global change are mainly investigated at an organism, population or community level, but knowledge about their effects on biotic interactions is scarce (Tylianakis et al. 2008). Yet, biotic interactions form an indispensable basis for the functioning of ecosystems and the provision of ecosystem services. Thus, the consideration of the effects of multiple interacting drivers of global change on biotic interactions (Elzinga et al. 2007) represents a significant challenge for predicting the future consequences of global change in general and land use change in particular (Schweiger et al. 2010 and this atlas, pp. 216f.). Although much still needs to be done, information is amassing in Europe how land use change and landscape context affect communities of organisms and the ecosystem services that they deliver. Less is known about these processes in other parts of the world, where the dependency on ecosystem services for high and sustainable food productivity probably are great, and where pervasive and large scale land use changes are taking place (e.g. Heong & Schoenly 1998, Tscharntke et al. 2008). References ANDRÉN H (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71: 355-366. BASCOMPTE J, SOLÉ RV (1996) Habitat fragmentation and extinction thresholds in spatially explicit models. Journal of animal ecology 65: 465-473. BOMMARCO R, BIESMEIJER JC, MEYER B, POTTS SG, PÖYRY J, ROBERTS SPM, STEFFAN-DEWENTER I, ÖCKINGER E (2010) Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc. R. Soc. Lond. B. (in press) ELZINGA JA, ATLAN A, BIERE A, GIGORD L, WEIS AE, BERNASCONI G (2007) Time after time: flowering phenology and biotic interactions. Trends in Ecology & Evolution 22: 432-439. FAHRIG L (2003) Effects of habitat fragmentation on biodiversity. Annual review of ecology and systematics 34: 487-515. HEONG KL, SCHOENLY KG (1998) Impact of insecticide on herbivore- natural enemy communities in tropical rice ecosystems. – In: Haskell, organisms, Chapman and Hall, London, pp 381-403. HILL JK, COLLINGHAM YC, THOMAS CD, BLAKELEY DS, FOX R, MOSS D, HUNTLEY B (2001) Impacts of landscape structure on butterfly range expansion. Ecology Letters 4: 313-321. IPCC 2001http://www.ipcc.ch/ LEI GC, HANSKI I (1997) Metapopulation structure of Cotesia melitaearum, a specialist parasitoid of the butterfly Melitaea cinxia. Oikos 78: 91-100. SCHWEIGER O, BIESMEIJER JC, BOMMARCO R, HICKLER T, HULME PE, KLOTZ S, KÜHN I, MOORA M, NIELSEN A, OHLEMÜLLER R, PETANIDOU T, POTTS SG, PYŠEK P, STOUT JC, SYKES MT, TSCHEULIN T, VILÀ M, WALTHER G-R, WESTPHAL C, WINTER M, ZOBEL M, SETTELE J (2010). Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biological Reviews. doi: 10.1111/j.1469-185X.2010.00125.x THOMAS CD, CAMERON A, GREEN RE, BAKKENES M, BEAUMONT LJ, COLLINGHAM YC, ERASMUS BFN, DE SIQUEIRA MF, GRAINGER A, HANNAH L, HUGHES L, HUNTLEY B, VAN JAARSVELD AS, MIDGLEY GF, MILES L, ORTEGA-HUERTA MA, PETERSON AT, PHILLIPS OL, WILLIAMS SE (2004) Extinction risk from climate change. Nature 427: 145-148 TSCHARNTKE T, BRANDL R (2004) Plant-insect interactions in fragmented landscapes. Annual Review of Entomology 49: 405-430. TSCHARNTKE T, SEKERCIOGLU CH, DIETSCH TV, SODHI NS, HOEHN P, TYLIANAKIS JM (2008) Landscape constraints on functional diversity of birds and insects in tropical agroecosystems Ecology 89: 944-951. TYLIANAKIS JM, DIDHAM RK, BASCOMPTE J, WARDLE DA (2008) Global change and species interactions in terrestrial ecosystems. Ecology Letters 11: 1351-1363. VAN NOUHUYS S, HANSKI I (2002) Multitrophic interactions in space: Metacommunity dynamics in fragmented landscapes in Multitrophic level interactions. Edition 1, pp. 124-147.
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ENVIRONMENTAL CHEMICALS AND BIODIVERSITY
Assessing the Impacts of Environmental Chemicals on Biodiversity and Ecosystems
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MARCO VIGHI & DAVID SPURGEON
The need to control the impact of chemical emissions on ecosystems has been recognised since the middle of the last century. A number of international regulations were developed for this purpose starting from the early 1970s that aimed to regulate chemical emissions and control the production of xenobiotic substances, in order to protect human health and the environment. Often the trigger for the implementation of these regulations came from effects on biodiversity rather than effects on human health. The scientific bases for supporting administrative and political choices are represented by the knowledge of the environmental behaviour (e.g. patterns of environmental distribution and fate) of potentially harmful chemicals, the knowledge of the effects that they can produce on living organisms and the development of procedures for assessing the risk for ecosystems.
For a site-specific assessment, as well for the assessment of the risk for community structure and biodiversity, the third component of the risk (biological systems) is needed. More precise knowledge on the characteristics of the endangered biological community (structure, vulnerability, naturalistic value, etc.) is necessary. Moreover, traditional approaches developed to implement European regulations, are usually applied for assessing risk for individual chemicals, with a complete lack of ecological realism. In the real environment, subject to anthropogenic pressure, ecosystems are exposed to complex mixtures of chemicals, as well as to a number of additional stress factors. The response of the community is the result of this complex interaction among potential stressors.
For a proper assessment of the environmental risks from chemicals, information is needed on three different factors: effects of the chemicals, extent of exposure and the characteristics of the biological systems potentially exposed (Figure 1). Each component of the assessment needs to be described with suitable indicators, such as a PEC (Predicted Environmental Concentration) and a PNEC (Predicted No Effect Concentration).
The objective of the Environmental Chemical Module of ALARM (which are mostly presented here) has been the development of tools that can be used to build on the different component of the environmental risk assessment to increase realism, as well to assess the responses in terms of structure and function of natural communities in real environments. The main activities are schematically shown in Figure 2. In particular, the final goal has been not only the development of tools capable of assessing the site-specific risk of chemicals for aquatic and terrestrial ecosystems and of mapping risk at different scales (local, regional, continental), in order to highlight the presence of site-specific hot
Many European Directives (Regulation EC 793/93, Directive EC 93/67, Biocide Directive EC 98/80) as well as the more recent REACH (Registration,
EXPOSURE ASSESSMENT
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Indicators Toolbox of Emission Processes (individual chemicals and mixtures)
Indicators Toolbox of Exposure for Large-Scale Systems
Emissions from
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Indicators Toolbox of Effects for Large-Scale Systems
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Degradation
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QSAR for Toxicity
CompartmentConnecting
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Figure 1. Relationships between the different components of Environmental Risk Assessment.
Geographically-Referenced Risk Indicator Integration System
Evaluation, Authorisation of Chemicals, Regulation EC 1907/2006) legislation require standardised procedures, suitable to be applied in a transparent and relatively easy way. Each use the PEC/PNEC ratio as a suitable indicator for risk characterisation, as suggested by the Technical Guidance Document (TGD) on Risk Assessment for new and existing chemicals of the European Commission (EC 2003). A comparable approach is those required by the Pesticide Directive 91/414, based on the TERs (toxicity/exposure ratio), calculated for indicator organisms in reference scenarios. It follows that these procedures are mainly based on the first two component of the risk (exposure and effects). They are non-site-specific, are referred as “generic” environmental scenarios (local, regional, continental) and are based on several default assumptions. Usually they are used for getting a “yes or no” answer. The objective is therefore to indicate the need for control measures at the European level, not to classify chemicals in terms of the risk to specific environments, to classify environmental quality, to assess the potential danger for the structure of specific biological communities. 114
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Combining GIS, Expert Systems and Probabilistic UncertaintyAnalysis
Indicators from Diversity Change in Natural Communities BelowGround
AboveGround
Freshwater Invertebrate
Methods for Coupling to Multiple Environmental Pressures Figure 2. Risk Indicator Diagram for the ALARM Environmental Chemical Module.
spots. The actual responses, in terms of changes in biodiversity and function have been also studied on natural communities exposed to complex stress factors in the real environment. In this chapter, just a few examples are given of approaches for assessing exposure to different kind of environmental chemicals, to characterising and mapping risk, to assessing effects as a function of habitat structure in real ecosystems. Moreover, as a result of the Modelkey project, an integrated approach capable to provide the information required by decision makers in order to assess ecological quality of ecosystems and for the management of the freshwater environment
A S S E S S I N G
T H E
I M PAC T S
O F
according to the requirements of the European Water Framework Directive (WFD; EC 2000) is shown as well. References EC (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official Journal of the European Communities, L327, 22/12/2000. EC (2003) Technical Guidance Document (TGD) on Risk Assessment of Chemical Substances (2nd edition). European Commission, European Chemical Bureau, Joint Research Centre, EUR 20418 EN/2.
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MODELKEY: European Rivers under Toxic Stress WERNER BRACK, JOOP F. BAKKER, ERIC DE DECKERE, DICK DE ZWART, TIMO HAMERS, MICHAELA HEIN, PIM LEONARDS, URTE LÜBCKE-VON VAREL, CLAUDIA SCHMITT, MECHTHILD SCHMITT-JANSEN & PETER C. VON DER OHE
,
Introduction Water is an inherited good that has to be protected and used in a sustainable way. Thus, the EU Water Framework Directive (WFD) demands a good ecological and chemical status of European rivers and lakes by 2015. However, there is increasing evidence that the majority of European water bodies will not achieve this goal. Since most of them face a multi-pressure situation, the identification of driving
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Ecological Quality Ratios. This is a promising approach integrating factors from the entirety of pressures and impacts. However, it does not often allow the assignment of observed effects to specific stressors and thus the identification of effective mitigation measures. The enhancement of the diagnostic power of impact assessment therefore appears to be a crucial prerequisite for the establishment of success-
S9
and other pollutants for the chemical status but toxicity is widely ignored as a potential driving force for insufficient ecological status. In three case studies in the river basins of the Elbe, Scheldt and Llobregat the Integrated MODELKEY Project provides new evidence that toxicants may actually be an important stressor in aquatic ecosystems. The WFD focuses on a holistic assessment of impact on ecological
Pardubice Přelouč Jirkow Most Jorba Martorell St. Joan Despí Rundvaartbrug Eenhoorn Hansweert Terneuzen Figure 1. In vitro effect matrix of selected MODELKEY sites of investigation in three river basins (Elbe, lines 1 to 4; Llobregat, lines 5 to 8; Scheldt, lines 8 to 11). Column 1 represents sites. Columns 2 to 5 represent mutagenicity towards the Salmonella typhimurium strains TA98 and TAMix without and with addition of S9 enzymes. Columns 6 to 10 represent different types of endocrine disruption, while the last column stands for antibiotic activity. All effects are transformed to the amount of sediment that needs to be extracted and dosed to give a characteristic effect. The green colour indicates no significant effects while effects increase from “yellow” to “red”.
forces for a sub-standard ecological status is an enormous challenge for water managers. Toxic pollution is assessed by monitoring 41 priority
status with respect to the Biological Quality Elements phytobenthos, phytoplankton, plants, benthic macroinvertebrates and fish on the basis of
complex mixture
biological biological analysis
chemical chemical analysis analysis
confirmation
biological biological analysis
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toxicant toxicant
Figure 2. General scheme of effect-directed analysis.
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ful Programs of Measures and thus the success of WFD implementation as a whole. Thus, it is the aim of MODELKEY to provide and apply a toolbox for the ◙ detection of toxic effects on different levels of biological complexity from the cellular to the community level, ◙ assessment of loss of biodiversity and sensitive species and its attribution to specific pressures including toxicants, ◙ identification of site-specific key toxicants with the potential to affect the biological quality elements fish, invertebrates, phytobenthos and phytoplankton, ◙ assessment of bioavailability and food web accumulation as important factors that determine the impact of toxicants in aquatic ecosystems, ◙ prediction of downstream ecological risks of toxicants emitted by point and diffuse sources in the upper reaches of a river and
◙
decision support for water managers for risk assessment and prioritization.
Findings Applying the MODELKEY toolbox the project provided new evidence that toxicants have a significant impact on biota and thus the ecological status in the three river basins under investigation. In addition to the river water itself sediments may play an important role for adverse effects. Site and endpoint specific in vitro effects could be detected in sediment extracts from all river basins under investigation. Effects include mutagenicity, antibiotic activity, endocrine estrogen and androgen disruption, dioxin-like activity and endocrine thyroid hormone disruption. The aggregated presentation of these results (examples in Figure 1) helps to discriminate between different degrees of impairment and the identification of hot spots and major effects. One of the major challenges of effect-based assessment of contaminated environments is the identification of those contaminants that are responsible for the measured effects. These effects in most cases cannot be attributed to regularly monitored priority pollutants. Thus, in MODELKEY there is a strong focus on toxicant identification beyond established priority lists using effectdirected analysis (EDA) (Brack 2003). This approach combines biological analysis using in vitro or in vivo tests together with effect-driven sample fractionation to reduce mixture complexity. Isolated toxic fractions or individual toxicants are subjected to structure elucidation and compound quantification and they are finally confirmed as the cause of the effect based on biotesting of neat standards or mixtures thereof (Figure 2). For a long time, the focus of environmental monitoring and risk assessment was on toxicologically well characterized non-polar toxicants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and other halogenated aromatic compounds, while recently so-called emerging toxicants have been of increasing concern. In addition, many unknown toxicants are expected to contribute to the effects of complex environmental mixtures including water and sediment extracts. Many of these compounds are rather polar, are
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Figure 3. Relative toxic potency of sediment extract fractions from different sites (P, M, B) in different in vitro bioassays increasing from 0 (green, no significant effect) to 4 (brown, highest relative potency).
12
Total number of embryos/snail after 56 days
concentrations of prometryne required to inhibit photosynthesis by 50 % (EC50) were about a factor of 5 higher in pre-exposed biofilms compared to the control (Figure 5). The contamination-dependent disappearance of sensitive species in situ was confirmed applying the SPecies At Risk index, a toxic impact specific metric developed for the evaluation of macroinvertebrate communities (Liess & von der Ohe 2005). SPEAR is based on the proportion of sensitive species in in situ communities. As shown for the Llobregat river basin (Spain) there is a clear correlation between the SPEAR index and contamination reflected by the sum of toxic units (TU) for Daphnia magna (Figure 6) (von der Ohe et al. 2009). The TU approach is based on chemical analytical data and normalizes measured concentrations to effect concentrations in a model organism. The two different correlations for sites with recovery potential, namely undisturbed river stretches upstream, and those without recovery potential highlight the importance of confounding factors for the risk of toxicants. In the case of macroinvertebrates the water flea Daphnia magna was selected as model organism since for this sensitive organism effect concentrations for many toxicants are available. The potential impact of toxicants on biodiversity on a basin scale was assessed by modelling multi-substance Potentially Affected Fractions (msPAF) based on bioavailability considerations, species sensitivity distributions and mixture toxicity calculations (Posthuma et al. 2002). The results for 3247 site/year combinations (site x in year y) in the River Scheldt are given in Figure 7. Based on yearly maximum concentrations, significant toxic risks to communities at about 30 % of the sites are expected (de Zwart et al. 2009). This means that more than 5 % of species are at risk. If average concentrations are considered, 10 % of all sites are still at risk. If we compare msPAF with the exceedence of Annual Average Environmental Quality Standards (AA-EQS) of WFD for priority pollutants it becomes obvious that many of the sites that comply with the good chemical status (Figure 7, blue crosses) are at toxic risk. Impact assessment within the frame of MODELKEY provided evidence that toxic chemicals are a relevant factor for the ecological status of European water bodies and need to be considered together with hydromorphology, eutrophication and other stressors. Priority pollutants represent only a portion of toxic pollution and are often poor predictors of eco-toxic stress.
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Figure 4. Total number of embryos per snail exposed to sediments from 11 different sites in three river basins (compare Fig. 1). While artificial sediment was used as a control (blue bar) the same river sediments were tested in vitro. For comparison with in vitro effects (Figure 1) the bars were coloured according to the respective response of the sample in vitro.
Mulde
Inhibition of photosynthesis of microphytobenthic communities (%)
not included in regular monitoring programs and suffer from a lack of toxicological characterization. MODELKEY supports this finding. Within the frame of EDA sediment extracts from different sites were fractionated according to a novel automated multi-step method (Lübcke-von Varel et al. 2008) providing non-polar fractions containing aliphatic and mono-aromatic compounds (Figure 3, fractions 1-3), PCBs and PCDD/Fs (fraction 4 and 5) and PAHs with increasing numbers of aromatic rings (fractions 6 to 12) as well as polar fractions (fraction 13 to 18) containing numerous aliphatic and aromatic compounds with nitro-, amino-, hydroxy-, keto-, and carboxylic acid groups. All fractions were tested with a battery of in vitro tests covering many effects related to reproduction, carcinogenesis and the endocrine system relevant to human and ecosystem health. The results strongly support the high relative potency of the polar fractions (Figure 3) for most toxicological endpoints. While in vitro assays are powerful diagnostic and early warning tools, the relevance of in vitro results for whole organisms under realistic exposure conditions needs to be proven. This can be done by biomarker responses in test organisms exposed to environmental samples under field conditions or in indigenous organisms sampled in situ. Testing sediments with the snail Potamopyrgus antipodarum for example confirmed estrogenic effects of sediment-associated contaminants in 3 of 6 sediments indicated as estrogenic in vitro (Figure 4). Pollution induced community tolerance (PICT) helps to confirm the impact of specific toxicants on the community level (SchmittJansen et al. 2008). It is the basic idea of PICT that communities that have been impacted by a toxicant are less sensitive to this compound than unaffected reference communities because sensitive species disappeared. This was shown for example for biofilm communities in a stream draining the industrial area of Bitterfeld (Germany) (Schmitt-Jansen et al. 2008). Effect-directed analysis based on cell multiplication of green algae in single species laboratory cultures identified the herbicide prometryne, which has been produced in Bitterfeld, as a key toxicant for algae at this site (Brack et al. 1999). The relevance of this contaminant in situ could be confirmed by harvesting biofilm communities from the contaminated stream and testing them for inhibition of photosynthesis by prometryne in comparison to biofilms from non-contaminated sites (Schmitt-Jansen et al. 2008). Effect
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Prometryne (mg/l) Figure 5. Dose-response-plots of inhibition of photosynthesis of microphytobenthic communities sampled from a polluted site (red circles), and a reference site (green squares) (modified from Schmitt-Jansen et al. 2008).
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Based on the tools provided by MODELKEY further strategic research is required to help water managers to assess the impact of toxic chemicals on ecological status. This involves the prediction of multi-stressor effects including chemicals, the identification of river basin specific toxicants affecting the ecological status, the development of stressor-specific metrics to assess the ecological status
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Proportion of Site/Year in Scheldt River (n = 3247) Figure 7. Average and maximum predicted loss of taxa expressed as multisubstance potentially affected fraction (msPAF) plotted against the proportion of site-year combinations in the Scheldt River. The blue and red crosses on top of the graph indicate compliance (blue) and exceedence of Annual Average Environmental Quality Standard (AA-EQS) values for the Water Framework Directive priority pollutants (modified after de Zwart et al. 2009).
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and to achieve a better understanding of the ecology of recovery. References BRACK W (2003) Effect-directed analysis: a promising tool for the identification of organic toxicants in complex mixtures. Anal Bioanal Chem 377: 397-407. BRACK W, ALTENBURGER R, ENSENBACH U, MÖDER M, SEGNER H, SCHÜÜRMANN G (1999) Bioassay-directed identification of organic toxicants in river sediment in the industrial region of Bitterfeld (Germany) – A contribution to hazard assessment. Arch Environ Contam Toxicol 37: 164-174. DE ZWART D, POSTHUMA L, GEVREY M, VON DER OHE PC, DE DECKERE E (2009) Diagnosis of ecosystem impairment in a multiple stress context – how to formulate effective river basin management plans. Integrated Environmental Assessment and Management 5: 38-49. LIESS M, VON DER OHE PC (2005) Analyzing effects of pesticides on invertebrate communities in streams. Environ Toxicol Chem 24: 954-965. LÜBCKE-VON VAREL U, STRECK G, BRACK W (2008) Automated fractionation procedure for polycyclic aromatic compounds in sediment extracts on three coupled normalphase high-performance liquid chromatography columns. J Chrom A 1185: 31-42. POSTHUMA L, SUTER GW, TRAAS TP (2002) Species sensitivity distributions in ecotoxicology. Lewis Publishers, Boca Raton, Florida, USA. SCHMITT-JANSEN M, VEIT U, DUDEL G, ALTENBURGER R (2008) An ecological perspective in aquatic ecotoxicology: approaches and challenges. Basic and Applied Ecology 9: 337-345. VON DER OHE PC, DE DECKERE E, PRÜSS A, MUNOZ I, WOLFRAM G, VILLAGRASA M, GINEBREDA A, HEIN M, BRACK W (2009) Towards an Integrated Assessment of the Ecological and Chemical Status of European River Basins. Integrated Environmental Assessment and Management 5: 50-61.
Sources and Fate of PAHs in an Urban Environment
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IAN T. COUSINS, KONSTANTINOS PREVEDOUROS, MARIA UNGER & ÖRJAN GUSTAFSSON
Background The objective was to develop novel methods for identifying sources, determining fate and monitoring chemicals in the urban environment. Polycyclic aromatic hydrocarbons (PAHs) were chosen as the test substance and Stockholm was the local field site (Figure 1). PAHs are formed during incomplete combustion of any organic material, including biomass burning (e.g., vegetation fires and woodfuel combustion) and fossil fuel combustion. Several PAHs are carcinogenic and/or mutagenic and being ubiquitous are a health concern. The effects of PAHs on both human and environmental health have recently led these pollutants to be regulated in the EU. Novel black carbon-inclusive urban fate model A computer model was developed to describe the fate and conduct a mass balance of PAHs in Stockholm (Prevedouros et al. 2008). While models of the urban environment have been developed previously, the novelty of this model was the inclusion of sorption to black carbon (BC) within the model structure. The presence of strongly sorbing pyrogenically-derived materials (i.e., BC), in sediment, soil and atmospheric particles has been shown to lead to enhanced sorption. We use the Stockholm model to determine the role BC has on chemical partitioning and fate of PAHs in the urban environment. Two versions of the Stockholm model were generated and run; one in which sorption to BC was included and one in which BC sorption was excluded. The inclusion of BC sorption did not cause any significant variations to air levels, but it did cause an average 20-30 % increase in sediment concentrations related to increased sediment solids partitioning. The model also predicted reduced advective losses out of the model domain, as well as chemical potential to diffuse from sediments, whilst total chemical inventory increased. In all cases, the lighter PAHs were more affected by BC inclusion than their heavier counterparts. The inclusion of sorption to BC in future chemical fate models is recommended, which as well as influencing fate will also alter (lower) chemical availability and, thus, wildlife exposure to hydrophobic chemicals. A quantification of the latter was derived with the help of the BC-inclusive model version, which estimated a lowering of dissolved water concentrations
between five and > 200 times for the different PAHs of this study. Novel methodology for sampling PAHs An alternative/complementary approach to using high volume air samplers for sampling airborne semivolatile contaminants was investigated, which uses the thin organic film that forms on all impervious surfaces, such as windows, by condensation of air contaminants (Unger et al. 2008). Since the film works as a passive sampler the vapour phase concentration of semivolatile contaminants such as PAH can be estimated if the partition coefficient between the surface film and the surrounding air can be determined. In this study, the spatial and seasonal variability in the PAH load on exterior windows in Stockholm was assessed. School windows were sampled in the centre of Stockholm (Figure 1) as well as suburban locations in both winter and summer. The window-area normalised concentrations of PAHs collected from school windows indicated more PAH contamination in the winter than in the summer in both the city centre and suburban locations, with highest concentrations in the city centre in the winter (450-470 ng m-2, sum of 43 PAHs). However, normalising the PAH load to the amount of fatty window film, as measured by extract-
able organic matter (EOM), gave a more homogeneous picture with the EOM-normalised PAH load being inseparable both between summer and winter and between city centre and suburban locations. To evaluate the possibility of quantitatively employing urban window films as a means to provide concentrations of PAHs in air, window film–air partition coefficients of PAHs were estimated using a set of coupled LFERs (linear free energy relationships) and physico-chemical properties of PAHs. Assuming dynamic equilibria between PAHs in air and those dissolved in the window film, the estimated PAH concentrations from the window films were shown to consistently overestimate the urban vapour-phase PAH concentrations by factors 4-135. This discrepancy is consistent with a strong and overwhelming association with BC aerosol particles accumulated in the window film. For compounds that have a lower tendency to associate with BC, bulk window film concentrations may work better than for PAHs to estimate their vapour phase concentrations in urban air.
Figure 2. Stockholm University scientists sampling school windows for organic contaminants. Photo: M. Unger.
References PREVEDOUROS K, PALM-COUSINS A, GUSTAFSSON Ö, COUSINS IT (2008) Development of a black carbon-inclusive multi-media model: Application for PAHs in Stockholm. Chemosphere 70: 607-615. UNGER M, GUSTAFSSON Ö (2008) PAHs in Stockholm window films: Evaluation of the utility of window film content as indicator of PAHs in urban air. Atmospheric Environment 42: 5550-5557.
C2 C1
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S5 Stockholm
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Figure 1. A map of Stockholm, Sweden, which was the conveniently located study site for modelling and field studies. Sites for sampling of school windows in central Stockholm and Stockholm surroundings. Sites C1, C2 are in central Stockholm, whereas sites S1-S5 are south of the city centre in a suburban region.
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Nitrogen Deposition – a Major Risk for Biodiversity
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FRANZ-W. BADECK & TILL STERZEL
Nitrogen deposition is regarded as one of the major threats for biodiversity because it changes the competitional balance between species in nitrogen-limited ecosystems. It is expected that nitrogen deposition will confer a competitive advantage to fast growing species that in turn will outcompete and exclude slower growing species. With an expert assessment of the major dangers for biodiversity throughout the 21st Century, Sala et
Nitrogen emissions and deposition Anthropogenic nitrogen deposition increased exponentially throughout the second half of the 20th Century. The increased deposition of oxidised (NO2, NO3-, ...) and reduced (NH3, NH4+, ...) nitrogen compounds is caused by human activities that alter the nitrogen cycle via the production of nitrogen fertilizers, combustion of
15.0
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transport, households and industry. Human creation of reactive nitrogen species was of minor importance in the 1860s, increased by about 10-fold by the 1990s and today is the dominant force in the transformation of N2 to reactive nitrogen species (about 58 % in 1990) on continents (Galloway et al. 2004). Further increased deposition has been projected for the mid 21st century in Europe. The scenarios of deposition of NOx (oxidised nitrogen species NO and NO2) available for use within the ALARM project corroborate this expectation. Emissions and deposition increase until the middle of the century within GRAS and BAMBU scenarios and subsequently decrease, reaching levels still above current (BAMBU) or close to current deposition rates (GRAS). Only with additional measures to reduce pollution as assumed within the SEDG scenario is a persistent decrease in deposi-
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Figure 1. Time trajectories of average NOx deposition rates in Europe from 1970-2100 for scenarios GRAS, BAMBU, and SEDG. Dotted lines represent averages ± standard deviations.
al. (2000) ranked nitrogen deposition as the third greatest threat (after land use change and climate change) at the global level. In northern temperate forests their scenario identifies N-deposition as the most important threat.
fossil fuels, cultivation of nitrogen-fixing species and other activities. Scenarios of future trends in nitrogen deposition suggest further increases because of projected increases in fertilizer use and use of fossil fuels in
tion projected. Deposition rates at the end of the 21st Century would then be below current levels (see Figure 1), however still substantially above pre-industrial background deposition rates.
ALARM NOx deposition scenarios The NOx nitrogen emission scenarios provided for use within ALARM were derived for the scenarios (GRAS, BAMBU and SEDG) in 5-year steps from 1970 to 2100. The basic assumptions underlying the scenarios are that all European nitrogen deposition stems from European emissions (estimated with the IMAGE model), and that its distribution across European countries does not change in the future (the 2000 shares are taken to distribute emissions). The main emission source is fossil fuel combustion due to road transport and power generation. Emissions from power generation are based on current point sources; all others are homogeneously distributed over the countries. Emissions per region, sector, energy carrier or activity (energy sectors, end-use energy, end-use transport, end-use services, end-use residential, end-use others, energy transformation, power generation, losses & leakages, bunkers.) were estimated. Then the EMEP/MSC-W (EMEP = Co-operative Programme for Monitoring and Evaluation of the LongRange Transmission of Air pollutants in Europe; MSC-W = Meteorological Synthesizing Centre-West) source-receptor calculations described regional transport in the atmosphere and local N-depositions (Lagrangian model). Finally each grid cell was assigned the value of the corresponding EMEP grid (0.5 × 1.0° resolution). A 10' grid is used. In consequence, the effective spatial resolution of the nitrogen deposition scenarios is lower than the 10-minute grid on which they are reported. The emission factors in the OECD regions have decreased due to improvements in technology or to increased diffusion of control technologies. For the near term (2010-2020) the assumptions on the emission factors are based on
NOx
Figure 2. During combustion of fossil fuels in transport, households, and industries NOx gases are released. Intensive agriculture leads to release of nitrogen compounds into the atmosphere through various modifications of mineral cycles. Photos: Franz-W. Badeck.
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1970-1995, observed deposition
full implementation of current national and international emission reduction policies (UN-ECE Gothenburg protocol 1999 for Europe). The resulting scenarios of NOx deposition for 2070-2100 (for a full
increase. The interactions between nitrogen deposition and other multiple stressors are certainly an important determinant for future trends in biodiversity especially in currently nutrientpoor ecosystems.
2070-2100, GRAS (SRES scenario A1FI)
2070-2100, BAMBU (SRES scenario A2)
2070-2100, SEDG (SRES scenario B1)
Acknowledgements The nitrogen emission scenarios provided for use within ALARM were produced within the research project Advanced Terrestrial Ecosystem Analysis and Modelling (ATEAM, contract No. EVK2-2000-00075). The emission scenarios were produced by Rik Leemans and collaborators of the IMAGE team. In collaboration with Maximilian Posch and Joseph Alcamo deposition scenarios were derived using the framework of the model RAINS (Regional Acidification Information and Simulation).
evaluation of the deposition load scenarios of reduced nitrogen deposition need to be added) show decreased deposition within the SEDG scenario, while they remain close to current levels within BAMBU and GRAS (Figure 3). Eutrophication – threats for biodiversity Nitrogen deposition affects biodiversity via eutrophication. Besides this effect it also acts as an acidifying agent and thus can potentially change species abundances via shifts in soil pH. Another important environmental issue related to nitrogen deposition is leaching of nitrate into groundwater bodies. We subsequently focus on effects on floristic biodiversity due to eutrophication. They include: gains for nitrophilous species, local losses of species, leading to reductions in species numbers and changes in floristic composition. Evidence for the operation of this mechanism has been obtained with analyses of repeated floristic surveys, studies of species richness across deposition gradients and experimental studies of biodiversity under manipulated nitrogen inputs. Strong indications of a shift in floristic composition consistent with a eutrophication effect were derived from an analysis of the British Countryside Survey data for 1998 vs. 1990. A shift towards higher abundances of species profiting from increased nutrient availability was deduced from the mean Ellenberg nitrogen indicator values of the plant communities and indicated significant responses
NOx deposition (kg ha-2y-1) 0-2.5 2.5-5.0 5.0-7.5 7.5-10.0 10.0-15.0
References
15.0-50.0 Figure 3. Average NOx deposition in observed (1970-1995) and future (2070-2100, for 3 different scenarios) periods.
in infertile grasslands, heaths/bogs, moorland grass/mosaics and upland woodlands (Smart et al. 2003). With a study on Agrostis-Festuca nutrient-poor grasslands performed at 68 sites across a deposition gradient (5 to 35 kg N ha-1 y-1) nitrogen deposition was the variable explaining the highest fraction of total variance (55 %) in species richness (Stevens et al. 2004). Nitrophilous species gained and N-sensitive vegetation has declined in European peatlands, heathlands, grasslands and forests since the mid 20th century (see references in Stevens et al. 2004 and Smart et al. 2003). The effects of nitrogen deposition on plant diversity can be reproduced
with experimentally manipulated deposition levels. Even at low chronic levels of nitrogen addition with fertilizer rates as low as 10 kg N ha-1 y-1 supplementing the background deposition of about 6 kg N ha-1 y-1 species numbers in the treatment plots decreased by on average 17 % relative to control plots after 21 to 23 years of experimentation (Clark & Tilman 2008). Thus, as effects depend on the amount of nitrogen accumulated within the ecosystems, they even occur at low chronic deposition rates. Consequently, even under reduced deposition loads that are still above pre-industrial levels, the driving force for changes in biodiversity may still
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CLARK CM, TILMAN D (2008) Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. Nature 451, 712-715. GALLOWAY JN, DENTENER FJ, CAPONE DG, BOYER EW, HOWARTH RW, SEITZINGER SP, ASNER GP, CLEVELAND CC, GREEN PA, HOLLAND EA, KARL DM, MICHAELS AF, PORTER JH, TOWNSEND AR, VÖRÖSMARTY CJ (2004) Nitrogen cycles: past, present, and future. Biogeochemistry 70: 153-226. SALA OE, CHAPIN FS, ARMESTO JJ, BERLOW E, BLOOMFIELD J, DIRZO R, HUBER-SANWALD E, HUENNEKE LF, JACKSON RB, KINZIG A, LEEMANS R, LODGE DM, MOONEY HA, OESTERHELD M, POFF NL, SYKES MT, WALKER BH, WALKER M, WALL DH (2000) Biodiversity – Global biodiversity scenarios for the year 2100. Science 287: 1770-1774. SMART SM, ROBERTSON JC, SHIELD EJ, VAN DE POLL HM (2003) Locating eutrophication effects across British vegetation between 1990 and 1998. Global Change Biology 9: 1763-1774. STEVENS CJ, DISE NB, MOUNTFORD JO, GOWING DJ (2004) Impact of nitrogen deposition on the species richness of grasslands. Science 303: 1876-1879.
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Is Atmospheric Nitrogen Deposition a Cause for Concern in Alpine Ecosystems?
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LASZLO NAGY, FRANZ-W. BADECK, SVEN POMPE, MICHAEL GOTTFRIED, HARALD PAULI & GEORG GRABHERR
Why nitrogen? Atmospheric nitrogen deposition (the input of reactive nitrogen species from the atmosphere to the biosphere) is listed as the third overall most important driver of biodiversity change globally, and a regionally important factor in arctic and alpine environments (Figure 1), after climate and land use (Badeck & Sterzel, this atlas, pp. 120f.). Its impact is through providing external input into an otherwise rather tight cycling of nitrogen. Microbial decomposition and the
on biodiversity is that, apart from some site-specific records, there are little measured data available (for the alpine zone see a brief overview in Nagy & Grabherr 2009, pp. 285-288) and most values are based on statistical models. One such model-projected data set pertaining to NOx is that reported by Badeck & Sterzel (this atlas, pp. 120f.) and used in ALARM. This shows that far northern Europe has negligible rates of NOx deposition as opposed to western and central Europe, and the British Isles, where values are high.
Figure 1. The Cairngorms, Scotland receive an annual total load of 7.1-7.5 kg N ha-1 year-1 from the atmosphere. Many other alpine and upper montane areas in the British Isles receive a nitrogen load that exceeds critical levels, and may cause appreciable changes in the structure and functioning of their ecosystems. Photo: L. Nagy.
release of mineralised nitrogen from leakage are fundamental to ecosystem functioning. For example, in temperate alpine ecosystems there appear to be two distinct successional phases over a year – one suite of microbes being active in the winter under snow and another rather different assemblage over several cycles in the growing season (Schmidt et al. 2007). External input can cause dramatic changes to ecosystem structure and function, as has been shown by numerous nutrient addition experiments in arctic and alpine ecosystems (Jonasson et al. 2001, Bowman et al. 2006). Nitrogen may be deposited with precipitation (rain, snow, fog), or settle out from the atmosphere in dry weather. The spatial distribution of atmospheric nitrogen deposition is rather heterogeneous. The difficulty in treating nitrogen deposition and its impacts 122
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Critical nitrogen load – ecosystem vs. species responses The impact of atmospheric nitrogen input is expected to have variable impacts on biomes and ecosystems. The sensitivity of an ecosystem may be expressed in terms of its critical load, i.e. the minimum annual quantity of nitrogen that causes appreciable perturbation to its structure and functioning (e.g., Rihm & Kurz 2001), and is reflected in the values proposed for different vegetation types in Switzerland: calcareous grasslands and mesotrophic fens have the highest values of critical load at (15) 20-35 kg N ha-1 year-1 and shallow noncalcareous water bodies and ombrotrophic bogs the lowest (5-10 kg N ha-1 year-1), with most alpine terrestrial habitat types having a value of 10 kg N ha-1 year-1. Williams & Tonnessen (2000) have suggested that critical levels in the Rocky Mountains, Colorado be set at
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4 kg ha-1 year-1 to protect sensitive ecosystems such as alpine lakes. This was also the level that Bowman et al. (2006) have found for some responsive plant species in their experimental plots in an alpine ecosystem at Niwot Ridge, Colorado Rocky Mountains, whilst for alpine communities dominated by graminoids they found a limit of 10 kg N ha-1 year-1 critical load. Long-term data from montane and alpine dwarf-shrub heath types in the Cairngorm Mountains, Scotland (Figure 1) indicates an annual total load of 7.1-7.5 kg N ha-1 year-1 (http://www.apis.ac.uk), a value that is conceivably high enough to have had some impact on sensitive plant species. However, increases in the cover of some grass and other indicator species reported in some other areas in the British Isles are more likely to have been caused by grazing and nutrient input by herbivores. There has been a large loss of Racomitrium lanuginosum heath (Figure 2) from some areas of the Pennines in England and in Snowdown in Wales, ascribed to nitrogen deposition from the atmosphere and input by livestock, and herbarium specimens have shown increasing concentrations of nitrogen over the last century. Forest ecosystems are more robust and their critical nitrogen load is about double that of alpine ecosystems. In Europe, large areas exceed the low thresholds proposed by Rihm & Kurz (2001) for the most sensitive ecosystems (alpine water bodies and alpine heath) at 5 to 10 kg N ha-1 year-1 (Figure 3) or that suggested for forests at 7 to 20 kg N ha-1 year-1. This certainly raises the possibility that nitrogen input from the atmosphere may be contributing to altering the functioning or structure of some ecosystems.
Secondary impacts of nitrogen deposition Nitrogen enrichment in an ecosystem may affect trophic interactions. In general, increased growth of plants is thought to enhance herbivory. Reviewing available literature Throop & Lerdau (2004) have concluded that nitrogen deposition had mostly positive effects on plant-feeding insects (probably by increased nitrogen and decreased carbon-based compounds in exposed plants). So far, it also appears that nitrogen deposition may have a positive effect on insect populations, too. The authors have warned that such impacts might have major ecological (and potential economic) implications in the future. However, such deposition-induced changes in plant– herbivore relationships will vary, depending on site (vegetation)-specific factors. Overall, it is certainly difficult to envisage how interactions between nitrogen deposition and other global change drivers such as climate and land use may amplify or cancel out nutrient impacts. As the results of experimental work in the Arctic have shown, a variety of synergistic interactions as well as no-responses may occur (Jonasson et al. 2001). For example, water alone had no impact; however when combined with nutrient addition or heating positive plant growth response was observed for some species. Interestingly, plant communities of dry habitats responded negatively. How much nitrogen has the future for alpine Europe? In general, in the alpine areas of Europe, the modelled emission of NOx under the various global change
Figure 2. Racomitrium lanuginosum moss heath (left) blankets many summits in the Scottish Highlands. Sustained grazing can turn it into heath dominated by graminoids (right). The impact is by nutrient input from faeces and urine, and clipping by browsing ungulates that encourages the growth of graminoids. In addition, trampling loosens up the Racomitrium felt, which then becomes exposed to erosion by heavy wind. It has recently been observed in the Pennines, England and Snowdon, Wales that the extent of Racomitrium heath reduced. This reduction has been linked to herbivores and also to nitrogen deposition impacts. Nitrogen deposition in England and Wales exceeds the critical load, however, the loss of Racomitrium cannot be unequivocally ascribed to this, as there is also heavy grazing in these areas. Photos: L. Nagy.
Figure 3. Potential critical loads of 10 and 20 kg ha-1 year-1 (grey) in Europe in 2100. Areas in grey show parts of Europe that have been modelled to have had NOx deposition in excess of 5 kg ha-1 year-1 (left) and 10 kg ha-1 year-1 (right) (under the BAMBU scenario; for data source see Badeck & Sterzel, this atlas, pp. 120f.). As total nitrogen received is approximately double that of NOx the above maps illustrate potential critical loads of 10 and 20 kg ha-1 year-1. The lower critical load that is thought to affect most alpine ecosystems while 20 kg ha-1 year-1 has been suggested as the upper limit of broad-leaved forests before they can have appreciable changes as a result of nitrogen input. Upper montane and alpine areas are indicated in brown.
I S
References BOWMAN WD, GARTNER JR, HOLLAND K, WIEDERMANN M (2006) Nitrogen critical loads for alpine vegetation and terrestrial
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ecosystem response: Are we there yet? Ecological Applications 16: 1183-1193. JONASSON S, CHAPIN III FS, SHAVER GR (2001) Biogeochemistry in the Arctic: patterns, processes and controls. – In: Schultze E-D, Heimann M, Harrison A, Holland E, Lloyd J, Prentice IC, Schimel D (Eds), Global Biogeochemical Cycles in the Climate System San Diego: Academic Press, 139-150. NAGY L, GRABHERR G (2009) The biology of alpine habitats. Oxford University Press, Oxford. PEARCE ISK, VAN DER WAL R (2008) Interpreting nitrogen pollution thresholds for sensitive habitats: The importance of concentration versus dose. Environmental Pollution 152: 253-256. POMPE S, HANSPACH J, BADECK F, KLOTZ S, THUILLER W, KÜHN I (2008) Climate and A
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land use change impacts on plant distributions in Germany. Biology Letters 4: 564-567. RIHM B, KURZ D (2001) Deposition and critical loads of nitrogen in Switzerland. Water, Air and Soil Pollution 130: 1223-1228. SCHMIDT SK, COSTELLO EK, NEMERGUT DR, CLEVELAND CC, REED SC, WEINTRAUB MN, MEYER AF, MARTIN AM (2007) Biogeochemical consequences of rapid microbial turnover and seasonal succession in soil. Ecology 88: 1379-1385. THROOP HL, LERDAU MT (2004) Effects of nitrogen deposition on insect herbivory: implications for community and ecosystem processes. Ecosystems 7: 109-133. WILLIAMS MW, TONNESSEN KA (2000) Critical loads for inorganic nitrogen deposition in the Colorado Front Range, USA. Ecological Applications 10: 1648-1665. A
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Considerations of potential combined impacts of nitrogen deposition and climate change Plant species might experience the combined impact of eutrophication and climate warming in some alpine regions in central-eastern Europe (Figure 3). To explore how such combined drivers might impact alpine and upper montane species distributions an analogue drawn from the current site indicator values of species with respect to nitrogen availability and temperature may be considered. When the modelled response of species ranges to climate and land use change scenarios (Pompe et al. 2008) is evaluated for species groups growing at medium to high (Ellenberg indicator values 4-9) vs. low (1-3) nitrogen availability and in cool (Ellenberg indicator values 1-3) vs. warm (4-9) environments it is seen that the potential distribution ranges of species predominantly found under low nitrogen avail-
ability may change more than those found growing at sites with medium to high nitrogen availability (Figure 4). Species whose ranges the climate and land use change models suggested that would shrink by over 75 % compared with their actual range are more numerous in the group with low Ellenberg nitrogen indicator values (21 % of the species) compared with the high Ellenberg nitrogen indicator values group (9 % of the species). In other words, cold-loving species, which are most likely to be impacted by climate change are largely species of low-nitrogen environments and therefore potentially are more susceptible to available nitrogen. This is not surprising, as low temperature, together with high soil humidity is one of the main constraints on mineralisation of soil organic matter in most alpine environments. In conclusion, whilst a transient local nitrogen impact is plausible in some central-eastern European alpine areas under current scenarios, any remarkable long-term change in nitrogen availability in alpine ecosystems is likely to result from a warmer and drier climate that will increase the rate of N mineralisation.
Loss (%)
scenarios appears to be varying from a decrease of 0-2 kg NOx ha-1 year-1 by 2100 from the value in 2010 to an increase of up to 2 kg NOx ha-1 year-1. In addition to annual dosages, it appears that for some organisms the concentration of active nitrogen compounds that they are exposed to may be at least as important as the dose they receive (Pearce & Van der Wal 2008).
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Figure 4. Range loss / gain by plant species in Germany for species with low Ellenberg nitrogen indicator values (1-3; light hue, n=248) in comparison with species with high Ellenberg nitrogen indicator values (4-9; deep hue, n=223), species with low Ellenberg temperature indicator values (blue) and species with high Ellenberg temperature indicator values (red). Percent change is evaluated for the ranges modelled for the ALARM BAMBU climate and land use change scenario (2051-2080) versus modelled present ranges (1961-1990) (see Pompe et al. 2008). Results for the ALARM scenarios SEDG and GRAS show similar differences between the two species groups. Different letters at the top of each column indicate statistically significant differences.
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Predicted Environmental Concentrations of Organic Pollutants on a European Scale as a Basis for Risk Assessment
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SANDRA MEIJER, ALEX PAUL & ANDY SWEETMAN
The risk assessment of chemicals on a large (e.g., European) scale can be divided into three stages: 1. Prediction of environmental concentrations; 2. Prediction of concentrations in target organisms; 3. Prediction of effects. Here, we discuss the prediction of environmental concentrations from emission estimates using a multimedia modelling approach. Environmental levels of persistent organic pollutants (POPs) are determined by a complex interplay of many different processes in – and exchange between – a variety of environmental compartments. For example, POPs undergo long-range trans-
port via the atmosphere, are degraded in the environment, and they partition between mobile (e.g., air or water) and stationary (e.g., soil, vegetation) environmental compartments at ambient temperatures. Multimedia fate and transport models are an important tool for improving our understanding of the source-receptor relationships of these compounds. For example they can help us answer questions such as: How are emissions linked to environmental concentrations and ultimately human exposure and effects? What is the predicted effect of emission reductions on environmental levels of pollutants? What is the effect of changing environmental conditions (e.g., climate
0-5 6-20 21-50 51-100 101-200 201-300 301-1,000 1,001-5,000 5,001-8,936
Figure 1. BaP – EMEP emission inventory on 50 × 50 km grid (kg/m2/yr). The map also shows the Evn-BETR model grid.
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Figure 3. BaP – PEC in soil (pg/g).
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ate predicted environmental concentrations (PECs) for the 50 model regions, for a range of POPs in various environmental compartments. Here we show the example of benzo[a]pyrene (BaP) in air and soil (Figures 2 and 3). These model outcomes can serve as input for exposure models assessing risk to humans and/or ecosystems on a regional scale. In cases where suitable surrogates are available that have a known correlation with the parameter in question, the model outcomes can be further resolved spatially, without increasing model complexity. The example shown here relates to BaP concentrations in air, which have been spatially resolved using population, since PAH levels have been shown to be correlated with population density (Hafner et al. 2005) (see Figure 4). The model has further been used to predict the effect of changing environmental conditions as predicted under various climate change scenarios, on the levels of POPs in the environment. The climate change scenarios used were generated within ALARM and comprise “BAMBU” (Business As Might Be Usual), “GRAS” (Growth Applied Strategy) and “SEDG” (Sustainable European Development Goal) in order of increased sustainability. A fourth scenario, “GRAS CUT” is a shock scenario where it is envisaged that extreme circumstances will cut off the Gulf Stream. For an in depth explanation of the different scenarios
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Figure 2. BaP – PEC in air (pg/m3).
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change) on environmental levels of pollutants. In order to predict environmental concentrations for a large geographical area such as Europe, a spatially explicit model is needed. A European scale multimedia model (Evn-BETR) has been developed at Lancaster (Prevedouros et al. 2004). The model divides Europe into 50 model regions, and describes contaminant distribution and transport between 7 environmental compartments (upper air, lower air, soil, vegetation, freshwater, freshwater sediment and coastal water) and between the 50 model regions. The environmental parameters of each model region were parameterized using georeferenced databases in a GIS (Geographical Information Systems) environment (Macleod et al. 2001). The size of the model regions was selected in order to provide a compromise between mathematical ease and transparency (but excessive simplicity and loss of fidelity to the system) on one hand, and high resolution (but high data requirements and complexity) on the other hand. Spatially resolved emission inventories (50 × 50 km grid) of a range of POPs are available for the European domain through EMEP, the organization which oversees the compilation of official emission inventories by the different countries (Figure 1). These emissions (after being geographically scaled to the Evn-BETR model grid) were used as input to the model to gener-
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0-0.005 0.005-0.1 0.1-0.25 0.25-1 1-2.5 2.5-10
Figure 4. BaP – PEC in air (pg/m3) spatially resolved by population.
and the rationale behind them, as well as the influence they might have on climate see Spangenberg et al. (this atlas, pp 10ff.). Some of the data extracted from the ALARM climate change and
land use scenarios had to be reformatted to suit the needs of the model. For example, Corrine Land Cover maps divide vegetation into 44 categories. This would be far too complex to incorporate into the
GRAS 1991-2020
model, therefore the categories were grouped into 4 classifications, i.e. grasslands, croplands, woodlands, and urban. An average Leaf Area Index (LAI) was then assigned to each category. Temperature and precipitation data from the ALARM climate change scenarios were averaged into 30-year periods. The Evn-BETR model was set up and run using four POP chemicals; PCB 28 & 153, HCBD and Endosulfan. Using 30-year time steps, the model was run four times for each chemical and each scenario (BAMBU, SEDG, GRAS and GRAS-CUT) to represent the years 1990-2100. Results were visualised with Arc View GIS software. Preliminarily results suggest a north-easterly chemical migration for “heavier” POPs such as PCB 153 (Figure 5); similar to predicted migration of species and habitats by other ALARM partners. Lighter (more volatile) POPs displayed a redistribution in Europe, concentrating in areas of higher rainfall (greater atmospheric stripping), but also
showing some net losses to exterior boxes (outside world). The less sustainable scenarios with greater changes (e.g., BAMBU) showed greater influence on chemical distribution, largely due to a dryer Southwestern Europe, and a wetter Northeastern Europe. References HAFNER WD, CARLSON DL, HITES RA (2005) Influence of local human population on atmospheric polycyclic aromatic hydrocarbon concentrations. Environmental Science & Technology 39: 7374-7379. MACLEOD M, WOODFINE DG, MACKAY D, MCKONE T, BENNETT D, MADDALENA R (2001) BETR North America: a regionally segmented multimedia contaminant fate model for North America. Environmental Science & Pollution Research 8: 156-163. PREVEDOUROS K, MACLEOD M, JONES KC, SWEETMAN AJ (2004) Modelling the fate of persistent organic pollutants in Europe: parameterisation of a gridded distribution model. Environmental Pollution 128: 251-261.
GRAS 2021-2050
GRAS 2051-2080
0.000000-0.000018
GRAS 2071-2100
GRAS CUT
0.000019-0.000027 0.000028-0.000037 0.000038-0.000050 0.000051-0.000068
Figure 5. PCB 153 in soil (pg/g) – influence of climate change and land-use changes as estimated by the GRAS scenario.
P R E D I C T E D
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Ecotoxicological Risk Assessment of Pesticides Considering Different Geographical Scales and Evolution through Time
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SERENELLA SALA & MARCO VIGHI
In agricultural landscapes, biodiversity is affected by several factors. The widespread use of pesticides is one of the most important and needs to be assessed in order to reduce the level of impact. The potential aquatic and terrestrial ecosystems at risk are related to pesticides' pathways of distribution and their environmental fate. Within ALARM, a GIS-based methodology to assess the potential risk for aquatic and
terrestrial (e.g., Barmaz et al., this atlas, pp. 218f.) ecosystems at several scales of assessment was developed. Performing a risk assessment for biodiversity is a difficult task because it covers several issues and most of them suffer from a lack of crucial information. Several indicators have been developed to assess some aspect of biodiversity on the national, international or global scale but they often do
ITALY
Rome
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not cover the complexity of the problem. This work represents an attempt to develop a tool for biodiversity risk assessment that integrates approaches and results coming from different disciplines (Ecotoxicology, Landscape Ecology, Hearth Sciences). The methodology is based on an integration of databases, algorithms for pesticide exposure evaluation, risks indices, landscape’s patch analysis using Geographical Information System for managing models' input data and results in a distribution over the area studied. Molecular properties, such as chemicalphysical and toxicological data of active
Figure 1. Meolo River basin. Photo: M. Vighi. 1
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Daphnia Fish
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Time Figure 2. Toxicity for Algae, Daphnia and Fish of mixture of all pesticide applied in all crops in the Meolo River basin.
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ingredients, and environmental characteristics, such as land use, crop distribution, landscape elements are managed for the elaboration and development of realistic application scenarios. The methodology allows the user to calculate exposure and ecotoxicological risk indices for the main organisms representative of aquatic and terrestrial ecosystems. The use of GIS allows the user to account for the spatial variability of input data and output results (Sala 2008). The steps towards an ecotoxicological site-specific risk assessment for biodiversity may be listed as follows ◙ identification of the problem and development of a conceptual model (emissions source, routes of exposure, potential targets) to achieve a scenario definition, possibly with a DPSIR scheme; ◙ landscape characterisation and selection of the scale of assessment; ◙ selection of suitable models to assess exposure; ◙ development of georeferenced and non-georeferenced databases of input parameter information for the selected model; ◙ site-specific exposure assessment according to different methodologies suitable for application in different environmental systems: aquatic, terrestrial hepigean (main targets: beneficial insects and birds), terrestrial hypogean; It requires the evaluation of both aquatic and terrestrial ecosystem, providing a specific assessment; ◙ effect assessment, performed using deterministic or probabilistic approaches depending on data availability; both approaches can be based on general (toxicity data on standard bioindicators) or site-specific (data on organisms representative of the community, function/structure of the ecosystem) information; ◙ potential risk characterisation, comparing exposure assessment with toxicological data; the assessment can be based on TERs (Toxicity Exposure Ratio), considering representative species, or on SSD (Species Sensitivity Distribution) where applicable; ◙ characterisation of exposed ecosystem: qualitative/quantitative ecological and landscape characterisation (faunistic vocation, distribution of living organisms, community structure, potential quality, actual quality) useful to determine vulnerability and sensitivity of the exposed ecosys-
tem. It will be based on approaches such as the Habitat Suitability Index, a numerical index that represents the capability of a given habitat to support a selected species. These models are based on hypothesised species-habitat relationships rather than statements of proven cause and effect relationships. The results of HSI model represent the interactions of the habitat characteristics and how each habitat relates to a given species. Indices of Landscape Ecology, useful to assess habitat fragmentation and shape of the landscape patch capable of affecting biodiversity are also applied; ◙ site-specific impact assessment, in terms of the risk posed by the stressors in the studied environment; ◙ experimental validation of results. Aquatic ecosystems In most recognised risk assessment procedures, the approach is based on the evaluation of chemical-physical and toxicological parameters, applied to more or less standardised scenarios where the territory, at different scale levels (local, regional, continental), is described without taking into account the spatial variability of data. This is the case for the European Technical Guidance Document (TGD) on risk assessment of chemicals and also the procedures required by European Directive 91/414EC on plant protection products. The EU Water Framework Directive (WFD) requires the development of site-specific tools and indicators for river basin management, promoting the ecological protection of surface water and assessing the deviation of the ecological status from reference conditions in terms of: ◙ quality of the biological community; ◙ hydromorphological characteristics; ◙ chemical characteristics. The result of a GIS-based procedure to assess ecotoxicological site-specific risk to aquatic ecosystems is presented (Verro et al. 2002). The Figures 3, 4 and 5 illustrates the step of the evaluation: from predicted environmental concentration (Figure 3) to risk index (Figure 5) related to a certain quality of the exposed environmental system (Figure 4), and a certain level of risk (Figure 5). The application of this methodology, and its further implementation (e.g., with meteo-climatic provisional scenarios, with temporal evolution of stressors, with socio-economic assessment), could represent a useful tool in order to combine and optimise provisional risk assessment for biodiversity supporting policy development. A case history is described, referred to the application of the methodology at different scales (from field to regional) in order to underline the flexibility of the site-specific approach. An example of pesticide risk assessment for biodiversity
E CO TOX ICO LO GICA L
PEC (μg/l)
is presented. The results allow comparison of active ingredients in order to draw a classification of the environmental sustainability of their use, to protect ecosystems and to evaluate vulnerability related to landscape elements.
0-0.5 0.5-1.5 1.5-3 3-5 5-10 10-15 No crop
Figure 3. Predicted Environmental Concentration.
Salmo trutta fario Salmo trutta marmoratus Reophilic Cyprinids Limnophilic Cyprinids
Figure 4. Potential environmental quality.
PRISW-1 value 0 0-3
Mixture assessment In natural ecosystems, biological communities are never exposed to individual stress factors or to individual potentially dangerous chemicals. In particular, in agricultural basins, surface waters contain complex mixtures composed of all the chemicals applied to the different crops grown in the basin and at different times of the growing season. The composition of the mixture, as well as its toxic potency, is very variable as a function of the application dates of pesticides, of their persistency and physical-chemical properties. At each rain event, all pesticides present in soil, as residues of all applications prior to the rain, may reach surface water through runoff. An example of time course of pesticide mixture risk over time in an intensive agricultural area (Meolo River basin, northern Italy) is shown in the Figure 2, where the effects on the aquatic community are calculated for all the 54 active ingredients applied to all the crops present in the basin. The response to the mixture is calculated by applying the Concentration Addition (CA) approach, that is based on the principle of additivity of the mixture components. The CA approach tends to overestimate mixture potency, but it has been demonstrated that it represents a realistic worst-case for estimating mixture potency. The toxic potency of the mixture is expressed as Toxic Units (TUs= Σ Ci/ EC50i) for the indicator organisms assumed to be representative of the aquatic community (algae, Daphnia and fish). The risk to the different components of the biological community changes with time. In spring the mixture is highly dangerous to algae, due to herbicide application, mainly to preemergence herbicides applied to maize. Insecticide applications start in July and this produces a sharp increase of the risk to crustaceans. Herbicide risk decreases due to degradation of chemicals applied in spring. (Verro et al. 2007)
3-6 6-12 12-16 16-24 No crop
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Figure 5. Risk index.
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Reference SALA S, VIGHI M (2008) GIS-based procedure for site-specific risk assessment of pesticides for aquatic ecosystems. Ecotoxicology & Environmental Safety 69:1-12. VERRO R, CALLIERA M, MAFFIOLI G, AUTERI D, SALA S, FINIZIO A, VIGHI M (2002) GIS based System for surface water risk assesment of Agricultural Chemical. 1 Methodological Approach. Environmental Science & Technology 36: 1532-1538. VERRO R, VIGHI M (2007) Ecotoxicological risk assessment in surface water for pesticide mixtures. SETAC Europe XVII Annual Meeting, Porto, May 2007.
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Chemical Effect Assessment within ALARM: Identifying Habitats in which Microbial Function may be Impacted by Metal Pollution
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DAVID SPURGEON, SARA LONG, RYSZARD LASKOWSKI & SANDRA MEIJER
Metal pollution The metal content of soil is derived from two main sources: either naturally from the geogenic weathering of rock or anthropogenically from sources such as mining, metal processing, fossil fuel burning, transport, fertilisers and waste processing. The increase in all these processes since the industrial revolution has meant that increased anthropogenic loads of metals have reached the soil. This raises concerns particularly because: ◙ a number of keystone soil taxa (microarthropod, nematodes, earthworms, microbes, plants) are sensitive to the toxic effects of metals; ◙ unlike organic contaminants, metals are not subject to degradation meaning that loss from soils can only be effected through the relatively slow process of erosion losses, leaching and crop removal. The natural presence and additional augmentation of the metal content of 0.7
Component effect on H’
exp(H’)=6.46-0.24*log(TI)+0.013*(C/N) 0.4 0.1 -0.2 -0.5
p<0.0001; R2=0.46 p(TI)<0.0001 p(C/N)=0.0025
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0.1
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Log (TI) Figure 2. Increasing metal pollution [Log(TI); see text for explanation of the toxicity index TI] significantly decreased the functional diversity of soil bacteria (Component effect on H’). The line shows the relative change in the predicted values of H’ which occurs when changing Log(TI) over its observed range. The full model together with significance levels for the regression and independent variables are also reported. Based on data from Stefanowicz et al. (2008).
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AT L A S
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Biod
ivers
Biodiversity
Aim Part of ALARM was devoted to gathering data, elaborating strategies and developing models that would allow us to improve the scientific basis for protecting ecosystems from the harmful effects of pollution before they appear. In this chapter we present an example of such a methodology that focusses on predicting the large scale effect of trace metal pollution on the functional characteristics of the soil microbial community.
ity =
PEC
Sum(TUs)
f (Po
llutio
n lev el)
Pollution level
Predicted biodiversity effects Figure 1. Scheme of the procedure used for conversion of measured environmental concentrations (MEC) or predicted concentrations (PEC) to predict pollution effects on biodiversity.
soils thus offers one of the most serious threats to above and below ground biodiversity (Hopkin 1989). Microbial functional diversity Microorganisms generally represent the largest part of soil biomass and carry out the majority of organic carbon mineralisation. In addition to their importance for soil functions, microbes may be useful biological markers since they are potentially sensitive to exposure to environmental contaminants, such as metals. Parameters relating to the size, structure and gross function (e.g., carbon mineralisation, nitrogen mineralisation, microbial enzyme activity) can be measured to assess the effects of environmental change on the microbial community. To complement size or gross function assessments, methods have also been developed to describe changes in the diversity or phenotypic characteristics of the microbial realm. One commonly used and easily implemented approach to measuring microbial functioning is “community level physiological profiling” using BIOLOG plates. This method is useful, because it is a relatively easy approach to undertake within large scale surveys. The main drawback is that it relies on traditional culturing methods. Since <10 % of soil microorganisms can be cultured, this means that only a small sub-set of the bacterial community is sampled.
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The example case: trace metals and soil microorganisms The example below describes a procedure that can be implemented for predicting the spatial and habitat distribution of multiple metal risks. The starting place is measured environmental concentrations (MEC) of toxicants (in this case metals) derived from a sampling campaign undertaken to quantify Cd, Cu, Cr, Ni, Pb, V, Zn concentrations in >1000 rural site soil samples from across the mainland UK (Spurgeon et al. 2008). By measuring metal concentrations in (predominantly) rural soils, an assessment of the distribution of the measured metals could be made across natural areas. Here the data is also used to investigate the potential risks of metals to soil-dwelling microorganisms (and in particular the functions that they perform) in different habitats. As a mixture of different potentially toxic metals are present in all the sampled soils, in the next step we need to turn measured concentrations into some (eco)toxicologically meaningful value. A simple but very useful approach is to recalculate the concentrations to toxic units (TU) by dividing measured levels by a relevant toxic effect for the taxa of interest (in this case microorganisms). After calculating TUs for all metals (where possible), these are summed up (SUM(TUs)) to a toxic index (TI) representing a toxic potential of the particular soil for the target group/spe-
cies. For predicting metal effects on biodiversity, empirical models that describe the relationship between TI and biodiversity of selected group of organisms are used. We will use an example of such a relationship here for the effects of metal pollution on functional diversity of soil microorganisms taken from work done by Stefanowicz et al. (2008) within ALARM. Having the necessary information about the environmental concentrations of metals, some crucial additional soil characteristics, and the empirical relationship shown on Figure 2, we can now predict potential decrease in the microbial functional diversity associated with particular metal levels in all surveyed soils. This can produce data for interpolation or for other types of analysis, such as in this case where we use the data to identify the habitats in which soil microbial functions may be at greatest risk from trace metal pollution. Assessment step 1: Measured concentrations and spatial distribution of metals at the UK scale A total of 1083 soil samples from across mainland UK were successfully analysed for metals. Median metal concentration of the measured samples decreased in the order Zn > Pb = V > Cr > Ni = Cu >> Cd. Mapping of the spatial trends for all measured metals indicated a trend for highest concentrations to be found in the Midlands or South (Figure 3). In some cases, the highest concentrations are associated with soils known to be derived from metal-rich base rock. These included Cumbria (North-West England), Midand South Wales and Devon/Cornwall (South West England). In other cases, however, high metal concentrations were not always found in naturally metalliferous areas. In the case of lead for example, the highest concentration tended to be clustered around urban centres in South-East, South-West and North-West England. This almost certainly reflects the major contribution of historic traffic pollution to current lead loads. For Cd, some high soil concentrations are associated with the metalliferous areas of Wales and Northern England. A further area of high Cd is, however, also found in soils in the Bristol/Somerset areas. This probably reflects inputs from the metal processing industries located in this area.
Cr
Cu
Cd (mg/kg)
Cr (mg/kg)
0.02-0.24 0.25-0.29 0.30-0.33 0.34-0.38 0.39-0.44 0.45-0.53 0.54-0.67 0.68-0.95 0.96-5.68
Figure 3. Maps of the concentration of Cd (mg/kg), Cr (mg/kg), Cu (mg/kg), Ni (mg/kg), Pb (mg/kg), V (mg/kg) and Zn (mg/kg) in mainland UK soils.
H'
1.8953-1.9149 1.9150-1.9206 1.9207-1.9235 1.9236-1.9262 1.9263-1.9291 1.9292-1.9323 1.9324-1.9365 1.9366-1.9409 1.9410-1.9453 1.9454-1.9578
Figure 4. Map showing the spatial distribution of predicted effects of the functional diversity of the soil microbial community resulting from the presence of trace metals in sampled soils.
CHE M ICA L
EF F ECT
Cu (mg/kg)
0.8-11.1 11.2-20.8 20.9-25.7 25.8-28.9 29.0-31.6 31.7-34.3 34.4-38.6 38.7-46.2 46.3-138.7
Pb
V (mg/kg)
8.5 8.6-25.5 25.6-42.5 42.6-59.5 59.6-76.5 76.6-93.5 93.6-110.4 110.5-144.4 144.5-178.4 178.5-246.3 246.4-382.2 382.3-4,339.4
tional diversity along pollution gradients formed around metal refining facilities located in the UK and Poland. Assessment step 3: Predicted effects on microbial function including habitat sensitivity Predicted effects for the sampled soils indicated at most only a small metal effect on microbial functional diversity (Figure 4). The maximum predicted value of H’ was 1.97, while the minimum value was 1.88. Thus, at most a 5 % reduction in functional diversity is predicted. The relatively small magnitude of the predicted effects is unsurprising for two main reasons. First, microbial function can be resilient to the loss of single “species” from the microbial consortium. This is because there is a large amount of functional redundancy between microbial taxa for many processes. The second reason for the low level of effect was that soils sampling focused on rural areas that are in the main not subject to high point source metal inputs. This means that there are likely to be areas of the UK (urban/ industrial sites) that are subject to increased human influence that are missed from our analysis. Indeed, results presented by Stefanowicz et al. (2008) indicated serious and significant decrease in microbial diversity in the surroundings of the studied smelter sites in the UK.
W I T HI N
A L A R M :
Zn (mg/kg)
2.7-19.9 20.0-30.1 30.2-37.0 37.1-41.2 41.3-44.9 45.0-48.2 48.3-52.4 52.5-63.9 64.0-121
The overall effects of human disturbance and activity on soil metal loads (and as a result effect) is illustrated by a comparative analysis risk in six habitats. Three of these habitats, bogs, coniferous woodland and dwarf shrub heathland are predominately located in remote areas that are less likely to be subject to direct human influence (beyond diffuse pollution inputs). The second group of habitats, however, namely built-up areas and gardens, arable and horticultural lands and improved grassland are likely to be subject to additional human influence (such as proximity to industry and organic and inorganic fertiliser addition) that could increase metal load. A comparison of values for predicted functional diversity in the six habitats indicates that the three habitats that are most likely to be subject to additional anthropogenically derived human metal input are indeed those at which the predicted effect of metals on microbial functional diversity would be greatest (Figure 5). This indicates that continued surveillance for and management of the effects of metals on soil ecosystems should be focussed on those areas that are currently subject to urbanisation and increased human management. This is illustrated by the severe functional effects that can be found in soils that are located in close proximity to a number of different metal processing facilities identified in ALARM work (Stefanowicz et al. 2008).
I D EN T I F YI N G
1.0-3.7 3.8-11.8 11.9-14.5 14.6-17.2 17.3-19.9 20.0-25.3 25.4-30.7 30.8-36.2 36.3-691.4
Zn
Pb (mg/kg)
A S S ES S MEN T
Ni (mg/kg)
2-6.3 6.4-11.4 11.5-15.0 15.1-17.2 17.3-19.4 19.5-21.5 21.6-25.1 25.2-31.7 31.8-186.4
V
Assessment step 2: Converting measured concentrations to predicted effects on microbial function The analysis conducted for UK soils indicated the presence of multiple metals at each site. Since each of the metals present can potentially contribute to toxic effects on soil microbes, it is important to assess the magnitude of this joint effect. As outlined, a simple modelling approach was used in which the number of “toxic units” present in each soil is summed to give a TI value. Toxic units values were calculated by dividing the concentration of a metal in the soil by the concentration of that metal causing a 50 % reduction in dehydrogenase activity (Welp 1999). The TI value for each site was then used in conjunction with a small number of other soil parameters (organic C, total N) to predict the likely effects of the metal present on microbial functional diversity. The relationship used for converting TI to a predicted functional effect on microbes was derived from the work of Stefanowicz et al. (2008) and is shown in Figure 2. The relationship was developed in work conducted in ALARM to measure bacterial func-
Ni
HA B I TAT S
I N
W H I C H
6.4-29.6 29.7-52.7 52.8-64.2 64.3-75.8 75.9-87.4 87.5-98.9 99.0-118.2 118.3-156.7 156.8-989.3
References HOPKIN SP (1989) Ecophysiology of Metals in Terrestrial Invertebrates. Elsevier Applied Science, London, UK. SPURGEON DJ, ROWLAND P, AINSWORTH G, ROTHERY P, LONG S, BLACK HIJ (2008) Geographical and pedological drivers of distribution and risks to soil fauna of seven metals (Cd, Cu, Cr, Ni, Pb, V and Zn) in British soils. Environmental Pollution 153: 273-283. STEFANOWICZ AM, NIKLINSKA M, LASKOWSKI R (2008) Metals affect soil bacterial and fungal functional diversity differently materials and methods. Environmental Toxicology and Chemistry 27: 591-598. WELP G (1999) Inhibitory effects of the total and water-soluble concentrations of nine different metals on the dehydrogenase activity of a loess soil. Biology and Fertility of Soils 30: 132-139. 1.97 1.96
a
a a
1.95
b
b
1.94
b
H’
Cd
1.93 1.92 1.91 1.90
Bog
CW
DSH
A&H
BuG
Broad habitats Figure 5. Effects of soil metal (Cd, Cr, Cu, Ni, Pb, V, Zn) load on the soil microbial functional diversity in three habitats usually remote from direct human influence (bog, coniferous woodland (CW), dwarf shrub heath (DSH) and three habitats subject to greater level of anthropogenic influence (Built-up areas and gardens (BuG), arable and horticultural (A&H), improved grassland (IG).
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Risk for Chemicals on Biodiversity: Which Future is to be Expected?
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MARCO VIGHI & DAVID SPURGEON
According to Van Straalen (2003), chemical control in the last few decades has substantially changed environmental and pollution problems in developed countries. This has led to the development of a new conceptual definition of environmental quality, as a consequence of the changed level of risk that is likely to occur in natural ecosystems (Vighi et al. 2006). Up to the 1970s, chemicals in ecosystems, in particular in surface water, were likely to produce effects at the acute or sub-acute level for natural populations. This creates a pressing need within the emerging field of ecotoxicology to develop tools capable to quantify the risk from introduced chemicals. These were mainly based on testing approaches for the assessment of dose/concentration-effects relationships for single chemicals in single species and were often restricted to laboratory studies. The increased level of control of chemicals within developed countries has undoubtedly led to a substantial reduction of acute effects due to toxic chemicals on ecosystems. Nonetheless, even in this more regulated worlds, a complete protection of ecosystems from the effects of pollutants does still remain to be attained. The effects that occur to day are more subtle in their nature (changes in structure of communities) and as a result are more difficult to attribute to single chemicals with other multiple factors also being responsible. As a consequence, there is an increasing need for more refined and sensitive approaches for assessing environmental risk. Therefore, the present (and future) objective of ecotoxicology is to answer these more complex questions. To do this, it is essential to improve the predictive power of ecology and ecotoxicology for describing effects at the hierarchical level of communities (Van Straalen 2003). To develop a more comprehensive suite of tools for assessing the impacts of chemical pollutants, we must be aware of key aspects of that the complexity of the problem is twofold: ◙ The complexity of biological communities: the characteristics of a community are not merely the sum of the characteristics of individual populations; structure and function of the community is regulated by emergent properties that are not easy to be described and predicted from lower hierarchical levels. ◙ The complexity of stress factors: toxic agents are only one component of the range of potential stress factors that can alter the dynamics of natural populations and the composition of ecological communities; therefore the combination of multiple stress factors (anthropogenic and natural) that can affect ecosystems need to be taken into account in studies seeking to attribute the basis of environmental changes. To combine the complexity of ecosystems and the complexity of external factors, the concept of a Normal Operating Range (NOR), defined as a multidimensional space, describing structure and functions of the community in the absence of stress (Kersting 1984), should be applied. The consequence of a multivariate stress on a community is a deviation from the Normal Operating Range (NOR) of the system.
The need for more complex approaches to define and characterise environmental quality, accounting for structure and functions of biological communities, is now recognised also by the political and regulatory community. In particular, the European Water Framework Directive (WFD) overcomes the concept of a Water Quality Criterion, traditionally focussed on agents (chemical or physical) with a potential for adverse effects, assuming ecological effects as a basis of control. Therefore, the assessment of water quality must be defined directly in terms of the ‘functioning and structure of ecological systems’ rather than be only based on chemical contamination. In this framework, water bodies represent environmental goods to be protected and not a resource to be exploited. As a metric, protection of biological-ecological quality of the water body assumes a key role. A similar approach is currently being developed within a potential future Soil Framework Directive. In the WFD, the definition of a “Good ecological status”, assumed as the objective to be attained in all European water bodies, implies the protection of all components of the aquatic ecosystem at a level that “deviate only slightly from those normally associated with the surface water body type under undisturbed conditions”. This kind of objective is substantially based on the protection of biodiversity. Moreover, it follows that the ‘ecological status’, according to the WFD, is never the consequence of the effect of individual factors, but it depends on the overall combination of potential ‘stressors’ capable to affect environmental quality. This new trend in environmental quality assessment represents a challenge for the scientific community. According to Lawton (1999) our lowest capability to predict ecological (and ecotoxicological) processes corresponds to the community level. The need for more ecology in risk assessment of chemicals has been recognised for a long time. Kareiva et al. (1996), for example, suggests demographic models, community theory and spatial analysis as three relevant approaches; while Van Straalen & Løkke (1997) call for the use of life-history theory and community analysis based on trophic networks as a means to improve the ecological basis of risk assessment. The approaches capture some of the key areas that need to be considered such as resilience, recovery, trophic interactions and secondary effects. These will very much shape the way that communities will respond to chemical exposure. The answer to the requirements for the protection of ecosystem structure and biodiversity can only derive from deeper studies on natural communities exposed to dangerous chemicals and from the development of sensitive tools capable to quantify the responses as biodiversity changes. We must be aware that the traditional approaches based on laboratory studies on the effects on a small number of indicator organisms can no longer be the only tools for ecosystem protection. Deeper understanding will come when population biology is combined with genetics, stress biology and community ecology to understand what drives population change in individual species and how effects on individual species interact to determine the nature of the community response. References
Another issue to be addressed is the recovery capability of the system. For a population, recovery capability from a single perturbation (e.g. pesticide application) is a function of the reproductive strategy and the potential growth rate (r) that allows, for ‘r’ strategist populations, a more rapid recolonisation if the stress pressure ends, or an easier genetic adaptation if the stress pressure continues. In cases where the stressor is persistent, then recovery potential may be dependent on the adaptive capacity of the species as mediated through changes in the frequency of adaptive alleles within the population. In a community, recovery should be intended as the restoring of NOR or as the change of NOR due to Pollution Induced Community Tolerance (PICT) that can be produced by changes in community structure, increasing the dominance of less sensitive populations (Boivin et al. 2002). This means that ecosystem level function may be retained even if the actual composition of species present is altered.
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BOIVIN MEY, BREURE AM, POSTHUMA L, RUTGERS M (2002) Determination of field effects of contaminants: significance of pollution-induced community tolerance. Human and Ecological Risk Assessment 8: 1035-1055. KAREIVA P, STARK J, WENNERGREEN U (1996) Using demographic theory, community ecology and spatial models to illuminate ecotoxicology. – In: Baird DJ, Maltby L, Greig-Smith PW, Douben PET (Eds), Ecotoxicology: Ecological Dimension. Chapman & Hall, N.Y, 13-24. KERSTING K (1984) Normalized ecosystem strain: a system parameter for the analysis of toxic stress in microecosystems. Ecological Bulletins 36: 150-153. LAWTON JH (1999) Are there general laws in ecology? Oikos 84: 177-192. VAN STRAALEN NM (2003) Ecotoxicology becomes stress ecology. Environmental Science & Technology 37: 325-330. VAN STRAALEN NM, LØKKE H (Eds) (1997) Ecological risk assessment of contaminants in soil. Chapman & Hall, N.Y. VIGHI M, FINIZIO A, VILLA S (2006) The evolution of the Environmental Quality Concept: from the US EPA Red Book to the European Water Framework Directive. Environmental Sciences & Pollution Research 13: 9-14.
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BIOLOGICAL INVASIONS
Are the Aliens Taking Over? Invasive Species and Their Increasing Impact on Biodiversity
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PHILIP E. HULME, MONTSERRAT VILÀ, WOLFGANG NENTWIG & PETR PYŠEK
The evidence to date is undeniable. Not only are the total numbers of alien species established in Europe increasing but, for many taxa, the rate at which they have become successfully introduced is higher now than at any time in the past (Hulme et al. 2009). Alien species may impact on the populations of specific native species through hybridisation, by facilitating the spread of pathogens or parasites, via grazing or predation or via competition for resources. As the examples in the forthcoming sections of this chapter illustrate, once established within Europe’s borders, the progressive spread across the continent of invasive species such as giant hogweed Heracleum mantegazzianum, the Asian ladybug Harmonia axyridis, and the horse chestnut leafminer Camararia ohridella appears inevitable and unhindered by current management response. But while we may have quantified numbers and distributions, what do we know of their impacts? Alien species may impact on specific native species through hybridisation, by facilitating the spread
presently occupied ecosystems. While examples of each of these threats are known from Europe (Table 1), a complete assessment is currently constrained by limited taxonomic knowledge, especially for invertebrates. The success of many alien species in new regions has been attributed to the escape from parasites and pathogens prevalent in their native ranges. Yet, there are many cases where an alien species arrives with its parasites/pathogens and the latter have detrimental impacts on native species. In some cases, the parasite or pathogen has a marked impact on native populations without unduly affecting the alien host. Dramatic examples in Europe include the transmission of parapox virus between alien grey and native red squirrels and plague fungus in NorthAmerican signal crayfish that has spread to native European crayfish. In these examples, the pathogen is believed to have facilitated the establishment and
Table 1. Selected examples for hybrids between alien and native species in Europe and the consequence of hybrid offspring (adapted from Hulme 2007).
Taxon Plants Birds Mammals
Organism Cordgrass Duck Mink
Alien species Spartina alterniflora Oxyura jamaicensis Neovison vison
Native species Spartina maritima Oxyura leucocephala Mustella lutreola
Consequence Allotetraploid hybrid is an aggressive invader of mudflats Hybridisation threatens genetic integrity of endemic native Infertile hybrid offspring reduce population growth rate of native
Table 2. Selected examples of pathogens and parasites transmitted to native hosts following the introduction of specific alien species into Europe (adapted from Hulme 2007).
Taxon Plants Crustacea Insects Fish Mammals
Alien host Rhododendron ponticum Pacifastacus leniusculus Apis cerana Anguilla japonica Sciurus carolinensis
Native host Quercus petraea Austropotamobius pallipes Apis mellifera Anguilla anguilla Sciurus vulgaris
Alien parasite/pathogen Sudden oak death fungus Crayfish plague fungus Varroa mite Swim-bladder nematode Parapox virus
Table 3. Selected examples of the impacts of alien vertebrates on native fauna of European island territories (adapted from Hulme 2007).
Island South Uist Madeira La Gomera Swedish Isles Bornholm Baltic Islands San Stephano Corsica Capraia
Territory UK Portugal Spain Sweden Denmark Finland Italy France Italy
Region North Atlantic North Atlantic North Atlantic Baltic Baltic Baltic Mediterranean Mediterranean Mediterranean
of pathogens or parasites, via grazing, predation or competition. Furthermore, when dominant, they can change nutrient and water cycling of ecosystems, and even disturbance regimes such as increasing soil disturbance, sedimentation, or fire risk. Existing knowledge of impacts in Europe is much less than on other continents. For example, from a total of 10,317 species alien in Europe the ecological and economic impacts are only documented for 1,094 and 1,347 species, respectively (Vilà et al. 2010). Thus the number and impact of harmful invasive alien species in Europe is chronically underestimated, especially for species that do not damage agriculture or human health. Hybridisation between alien and native species is a potentially serious threat to biodiversity. Hybridization may result in an infertile hybrid and this may lead to the decline of native species populations when hybrids represent the majority of offspring produced. Alternatively, the hybrids may be fertile and interbreed amongst themselves as well as the parental stock but generally perform less well than the native. Such “genetic pollution” threatens the integrity of native species and where this involves the spread of maladaptive genes, lower hybrid performance could lead to progressive native population declines. A further possibility is that the hybrid may exhibit new traits that enable it to occupy ecosystems from which either parent was previously absent or it may perform more vigorously in 132
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Alien predator American Mink Brown Rat Feral Cat American Mink Brown Rat American Mink Feral Cat Black Rat Feral Cat
Native prey Arctic Tern Trocaz Pigeon Giant Lizard Eider Duck Black-headed Gull Black Guillemot San Stephano Lizard Cory’s Shearwater Balearic Shearwater
spread of the alien host because the alien host is resistant to its pathogen whereas the European relatives are susceptible and die (Table 2). There are also examples where the introduction of an alien host has assisted the establishment of a parasite/pathogen but subsequently the latter has spread more widely via free-living stages (e.g., eel swim-bladder nematode Angullicola crassus) or several alternate native hosts (sudden oak death fungus Phytophthera spp.). Often the impact of parasites and pathogens is most marked in commercial populations of hosts where densities are high. The wider impact on wild populations is more difficult to assess but can occur over a large spatial scale and long time period as illustrated by the decline of elms (Ulmus procera) in the UK following the introduction and spread of Dutch elm disease (Ophiostoma ulmi). Where an alien predator has become successfully established it will more than likely subsist on a diet of native prey. The American mink (Mustela vison) is held partially responsible for the decline in water vole populations (Arvicola terrestris) in the UK. The muskrat (Ondatra zibethicus) preys, amongst other things, upon native freshwater mussels and can often lead to local population extinctions. The introduction of an alien amphipod (Gmelinoides fasciatus) from Lake Baikal into eastern European lakes resulted in the extinction of native amphipods. The predatory New Zealand flatworm (Arthurdendyus triangulatus) is suspected of causing declines
and local extinctions of earthworms in western Scotland. The most marked predatory impacts are often found on islands where small populations of relatively naïve prey such as small endemic bird populations are exposed to food-limited alien predators. In many cases the alien culprits are feral cats (Felis catus) and rats (Rattus spp.) and the victims are the flightless chicks of nesting seabirds or reptiles many of them endemic to specific islands (Table 3).
comb jelly (Mnemiopsis leidyi) to the Black and Caspian Seas. This predatory ctenophore has led to significant declines in zooplankton abundance that subsequently reduced pelagic fish populations. In Spain, the Argentine ant (Linepithema humile) displaces not only native invertebrates but also vertebrates and even impacts on plants through disruption of myrmecochorous seed dispersal mutualisms.
Evidence of alien herbivores impacts on specific native plant species populations is largely drawn from the agriculture and forestry sector where introduced pests cause significant damage to crops and plantations. Outside of managed ecosystems, it is generalist vertebrate herbivores that have a reputation of negative impacts on biodiversity, especially on islands. Feral goats (Capra hircus) and to a lesser extent sheep and cattle have established populations on many islands as a result of deliberate introductions or escapes from domestic livestock. Rabbits (Oryctolagus cuniculus) continue to pose problems in the Canary Islands and in the British Isles where by grazing they threaten unique plant communities and modify the landscape.
But what are the most widespread species causing impacts? Vertebrates and terrestrial invertebrates cause impacts across the widest number of regions in Europe (Vilà et al. 2010). For example, the muskrat (Ondatra zibethicus) and the raccoon dog (Nyctereutes procyonoides) are known to cause impacts in more than 50 European regions. Several insects such as the thrips Frankliniella occidentalis and Heliotrips haemorrhoidalis are also documented to cause impacts on crops in more than 30 regions. The most widespread aquatic organisms with impact are crustaceans such as the Chinese mitten crab (Eriocheir sinensis, 20 regions) and molluscs, for example the zebra mussel (Dreissena polymorpha, 20) and the Pacific oyster (Crassostrea gigas, 18). In contrast, alien terrestrial plants with known impacts are rarely widespread, often documented in only one region. Since many of these alien plants are widespread in Europe (Lambdon et al. 2008), this finding illustrates that the perception of impacts can be quite localised. Tree of heaven (Ailanthus altissima), black locust (Robinia pseudoacacia) and Japanese knotweed (Fallopia japonica) are the plant species with the most widespread impacts.
For plant communities there are many examples of alien plants outcompeting native plants by reducing seedling establishment, by shading or by decreasing plant growth by reducing soil nutrients and water availability. Anecdotal reports often suggest that alien animals can also compete and displace native animals. The larger, more aggressive Canadian beaver (Castor canadensis) is believed to outcompete and replace the European beaver (C. fiber) in northern Europe. Mandarin ducks (Aix galericulata) are assumed to compete with the native goldeneye (Bucephala clangula) since both species nest in tree holes close to rivers and such sites are in limited supply. In many cases, the impact of alien species is to replace or reduce the abundance of ecologically equivalent native species and there are rarely wider ecological implications. However, in selected cases alien species may act as ecosystem engineers or keystone species leading to significant alterations in invaded ecosystems. Alien species that act as ecosystem engineers have the potential to transform ecosystems by altering underlying biogeochemical, hydrological and/or geomorphological processes. Wholesale ecosystem changes occur following colonisation of coastal sand dunes by nitrogen fixing mimosas (Acacia spp.) that includes augmentation of soil nutrients, stabilisation of dunes and replacement of native plant species. Riparian habitats are prone to the impacts of alien burrowing animals such as the Chinese mitten crab (Eriocheir sinensis) and coypu (Myocastor coypu) that destabilise riverbanks and increase soil erosion as well as flood events. Dense populations of the freshwater Asiatic clam (Corbicula fluminea) may affect the structure of planktonic communities and thus shift primary production to benthic communities. Alien species may also have such a wide impact on the resident fauna and flora through competitive and trophic interactions that they are classed as keystone species. One of the most pronounced shifts in ecosystems has been as a result of the recent invasion of the American
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In summary, many invaders cause multiple impacts over a large area in Europe. The overall impact of invaders depends upon their area of distribution, local abundance and per capita effect, but these three components are difficult to quantify. Quantifying such impacts should be a priority in Europe and an essential component of risk assessment. References HULME PE (2007) Biological Invasions in Europe: Drivers, Pressures, States, Impacts and Responses. – In: Hester R, Harrison RM (Eds), Biodiversity Under Threat. Royal Society of Chemistry, Cambridge. Issues in Environmental Science and Technology 25: 56-80. HULME PE, PYŠEK P, NENTWIG W, VILÀ M (2009) Will threat of biological invasions unite the European Union? Science 324: 40-41. LAMBDON PW, PYŠEK P, BASNOU C, DELIPETROU P, ESSL F, HEJDA M, JAROŠÍK V, PERGL J, WINTER M, ANDRIOPOULOS P, ARIANOUTSOU M, BAZOS I, BRUNDU G, CELESTI-GRAPOW L, CHASSOT P, DIDŽIULIS V, JOGAN N, JOSEFSSON M, KARK S, KLOTZ S, KOKKORIS Y, KÜHN I, MARCHANTE H, PERGLOVÁ I, VILÀ M, ZIKOS A, HULME PE (2008) Alien flora of Europe: species diversity, geographical pattern and state of the art of research. Preslia 80: 101-149. MARCHANTE E, KJØLLER A, STRUWE S, FREITAS H (2008) Short- and long-term impacts of Acacia longifolia invasion on the belowground processes of a Mediterranean coastal dune ecosystem. Applied Soil Ecology 40: 210-217. VILÀ M, BASNOU C, PYŠEK P, JOSEFSSON M, GENOVESI P, GOLLASCH S, NENTWIG W, OLENIN S, ROQUES A, ROY D, HULME PE, DAISIE PARTNERS (2010) How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Frontiers in Ecology and the Environment 8: 135-144.
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DAISIE: Delivering Alien Invasive Species Inventories for Europe
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PHILIP E. HULME, WOLFGANG NENTWIG, PETR PYŠEK & MONTSERRAT VILÀ
The European Commission, under its Sixth Framework Programme, launched a call for an inventory of alien invasive species. The successful application was awarded to a consortium of leading researchers of biological invasions in Europe, drawn from 18 institutions across 15 countries. The resulting project, DAISIE (Delivering Alien Invasive Species Inventories for Europe), was launched in February 2005 and ran for three years. The general objectives of the project were:
Figure 1. Bay barnacle Balanus improvisus. Photo: S. Olenin.
◙ To create an inventory of all known alien species in the European terrestrial, freshwater and marine environments. ◙ To describe the most important key alien species known to be invasive in Europe and to assess their ecological, economic and health risks and impacts. ◙ To compile a directory of experts and of research on alien species. The information compiled by the DAISIE project has served at the same time as an important scientific basis for the research groups working in the invasion section of the ALARM project. The European Alien Species Database, an inventory of all alien species known to inhabit Europe, represented the major activity in DAISIE and involved compiling and peer-reviewing national lists of fungi, bryophytes, vascular plants, invertebrates, fish, amphibians, reptiles, birds and mammals. Data were collated for all 27 European Union member states, and where these states had significant island regions, data were collated
Number of alien species
70 60 50 y = 11.53Ln(x) + 5.46 R2 = 0.7197
40 30 20 10 0 0
10
20
30
40
50
60
70
Imports (billion $) Figure 2. The relationship between the number of recorded alien fungi and the level of imports of goods in 2005 for European countries (OECD) suggests trade as an important vector for the increasing number of alien species (from Desprez-Loustau in DAISIE 2009).
Figure 4. Japanese eel swim-bladder nematode Anguillicola crassus. Photo: D. Minchin.
Figure 5. Horse chestnut leaf-miner Cameraria ohridella. Photo: S. Augustin.
separately for these islands. In addition, data were collated for European states that are not in the European Union such as Andorra, Iceland, Liechtenstein, Moldova, Monaco, Norway, the European part of Russia, Switzerland, Ukraine as well as former Yugoslavian states in the Balkans. Finally, marine lists were referenced to the relevant maritime state and thus to have full coverage of the Mediterranean, marine data were included for North African and Near East countries. In total, the database contains documented introduction records of alien taxa for 71 terrestrial and nine marine regions. For each species, an attempt was made to gather information on native range, date of introduction, habitat, known impacts and population status. Considerable effort was required to ensure synonyms were accounted for accurately. By February 2008, records of about 11,000 alien species were included in the database, the majority of records are for vascular plants (5,789 species) with invertebrates (2,477 species) also a significant component. The European Invasive Alien Species Information System is a “onestop-shop” for information on biological invasions in Europe. It provides accounts
of 100 of the most invasive alien species in Europe and each includes information on biology, ecology, distribution, impact and management, with references, links and images. These accounts deliver to end users relevant details for species identification and management but also help raise public awareness of the issue of invasions. The accounts cover three fungi, 18 terrestrial plants, 16 terrestrial invertebrates, 15 vertebrates, 16 inland and 32 marine aquatic species invading natural and semi-natural habitats. Selection was based on ensuring a broad spectrum of life forms and functional types, a range of invaded ecosystems and clear examples of different impacts on European biodiversity, economy and health. A key requirement for the effective management of invasive alien species is the ability to identify, map, and monitor invasions in order to assess their extent and dynamics. The Common European Chorological Grid Reference System with the size of the mapping grid ca. 50 × 50 km was used to produce distribution maps. Data sources included European-wide and national atlases as well as regional checklists. For each species the known presence was plotted but areas where a species previously occurred
Alien to Europe
European origin 1,200
Unintentional introductions = 37.2 %
Number of species
1,000
Unaided 1.90 % Stowaway 9.92 %
Commodity contaminant 6.12 %
800
600
Mineral contaminant 1.82 %
400
Seed contaminant 9.08 %
Ornamental 39.95 %
Released 0.46 %
200
0 1500
1600
1700
1800
1900
2000
Date of introduction Figure 3. Increase in numbers of alien plants introduced to Europe over the last 500 years. Cumulative data are shown separately for species with native distribution area outside Europe and those with European origin, but occurring as alien in other parts of the continent (from Pyšek et al. in DAISIE 2009).
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Forestry 1.60 % Amenity 5.03 %
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Horticultural 17.53 %
Agricultural 6.59 %
Intentional introductions = 62.8 %
Figure 6. Relative contribution of pathways of introduction shown for alien plants with the area of origin outside Europe. Pathways of intentional introductions are in grey boxes, unintentional in pink (from Pyšek et al. in DAISIE 2009).
Figure 7. Himalayan balsam Impatiens glandulifera. Photo: P. Pyšek.
Figure 8. Muskrat Ondatra zibethicus. Photo: V.-M. Väänänen.
but was eradicated were also considered. Where precise information on distribution was missing but the species was known to occur in a country/region/district, the distribution in these administrative units was recorded and mapped by using hatching. A different format was adopted for mapping invaders in aquatic habitats where linear distributions or maritime areas were recorded. The European Expertise Registry represents a fundamental step towards providing the critical mass of expertise in alien species research to meet European-scale requirements. The European Expertise Registry has enabled the current breadth and scope of European knowledge on alien species to be assessed for the first time. The registry contains information on the field of expertise (distribution, conservation, ecology, economy, genetics, legislation, management, pathways, physiology, risk assessment and taxonomy) and on the taxonomic and geographic structure of the expertise. As a result, the Registry facilitates both clustering and information-sharing among different national programmes targeting the same alien species, and in the future will help establish teams of experts who can, once a
new alien incursion has been reported, assess the situation and prepare an action plan for the alien species at a particular site. By the end of 2008 the Registry already contained information on 1,700 experts from more than 90 countries for over 3,400 higher taxa and numbers steadily increase. Since February 2008, the DAISIE information system has been available at http://www.europe-aliens.org. Recently, the DAISIE information has been distilled in a Handbook of Alien Species in Europe (DAISIE 2009), which contains: ◙ Analytical chapters on alien fungi, bryophytes and lichens, vascular plants, terrestrial invertebrates, invertebrates and fish in inland waters, marine biota, birds, amphibians, reptiles, and mammals of Europe. ◙ A list of all species alien in Europe and to Europe. ◙ Species fact sheets of 100 of the most invasive alien species in Europe. ◙ A glossary of the main technical terms used in the inventory of alien species in Europe.
Number of alien invertebrates 1-150 151-300 301-450 451-600
References DAISIE (2009) Handbook of Alien Species in Europe. Springer, Dordrecht, 399 pp. Balanus improvisus
Figure 10. Numbers of alien invertebrates in European countries and regions. The Macaronesian islands (not shown) individually have from 163 to 203 alien species (from Roques et al. in DAISIE 2009).
Anguillicola crassus
Cameraria ohridella
Aquatic distribution
Aquatic distribution
&
Figure 11. The invaded area of the North American bay barnacle Balanus improvisus (Balanidae, Crustacea), a fouling organism on blue mussels and oysters, water intake pipes and heat exchangers, underwater constructions and ships’ hulls (DAISIE 2009).
Figure 12. European distribution of the Japanese eel swim-bladder nematode Anguillicola crassus (Anguillicolidae, Nematoda), a parasite of the European eel (DAISIE 2009).
Figure 13. Invaded area of Cameraria ohridella (Gracillariidae, Lepidoptera) a leaf-mining moth which infests horse chestnut trees. Its aesthetic impact by damaging trees planted in cities and villages is raising significant public concern (DAISIE 2009).
Impatiens glandulifera
1.0
Ondatra zibethicus
Dispersed Released
0.8
Transport Unknown 0.6
Grid distribution Regional distribution
&
Escaped
Introduced mammals
Grid distribution
& && & & & & & && & & & && & & & && & && &&& && & & && && & && & & & && & & && & & & & & && & && & && && & & & & && &&& && &&& & & & & & & && & && & & & &&& & && &&&&& & & && & & & & && & && & && & & & && & & & && & && &&&& && && & & && & & & & &&& & & & & && && & & & & & && &&&& &&& && & &&&&& & && && & && && & && && & & && & &&& & & & && && & && && && & & && &&& && & && & && && && & & && & & && & && & & & & & & & & && & & & && & && & && & && & && & && & && && & & & && & && & && & && && && & && && && & & & & & & & && & & & & && & & && & & & && && & & & & && & & & & & & && && && & & & & & & & & & & & & & & && & & & & & & & & && & && & && && & & & & & & & & & & && & & & & & & && & & & & & && & & && && && & & & & & && & && & & & && && && && & & & && & & & & && & & & & & && & && & & & & & & & & & & & &&& &&& && & & & & && & & & & & & && & && & & & && & & & & && & & & & & & & &&& & & & && & && && & & &&& &&& & && & & & & & && & & & & && & & & & & && & & & & & & & && & & & & && & & & & & & && & & & & & && & & & & & & & & & & && && & && && & & & & &&& & & & & & & &&& & & & & & & &&& & & &&& && & & &&& & && & & && & & & &&& & & & & & & && & & & & & & & & && & & & & & & & && & &&& & & && & & & & & && & & && & && & & & & & & & && & & & & & & &&& & & & & & & &&& & & & && & && & &&& & & &&& && & & & &&& & & && && && && & & & && & & & & & & & & & && & & & & & & && & && & & && & &&& && & & && & & & & & &&& & & && & & &&& & && & && && & && && & & && & && & & & &&& & && & && && & && & & && & & & && & & && & & & && & && & & & & && & & & && & & & & & & & & & & & & & & && & & & && & & && & && & & & & && & & & & && & & &&& && & & && & & & & & & & & & & & & && & & && & & & & && & & & & & & && & & & & && & & && & & & & & &&&& & & &&& & & & && & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & && & & & && & &&& && && & & & & & & & & & & & & & & & & & & & & & & && && & && & && & & && & && &&& & &&& &&& && & & & & & & & & & & & & & & && & & & && && & && & && & &&& & & & & & & & & & & && & & & & & & && & & && & && & && & && & && & & & & & & & & && &&& & && & & && & & & && && & && & &&& & & & & && & & && & & & & & & & & && & && & & & & & & & & & & && & & & & & & & && & & & &&& & & & & && & & & & && & & && &&& & & & &&& & & & & &&&& & & & & & & & & &&&& &&& & && & &&&& & &&&&& & & && & && & & &&& & && && && && & &&&& & & && & & & && & && & && & && && & & & &&& & & & & & & && && & && & && & & & && & & & & & & & & & && & & & & & && & & & & & & & & && & & & & && && & && & && & & & & & & & & &
!! ! ! ! ! !! ! ! ! !! ! ! ! !! ! !! !!! !! ! !! !! ! ! !! ! !! !! ! !! !! !!! !! !! ! !! !! !!!! ! !! !! ! !! ! !! !!!! !! !! ! !! ! !! ! ! ! !! !!!! !!! !! ! ! !!!!! ! ! ! !! !! !! ! !!! ! ! ! !! ! !! !! ! ! !! !!! !! ! !! ! !! !! !! ! ! !! ! ! ! !! ! ! ! ! !! ! ! !! ! !! ! !! ! !! ! ! !! ! !! !! ! ! ! !! ! ! !! !! !! ! ! !! ! !! !! ! ! ! ! ! !! ! ! ! ! !! !! ! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! !! ! ! ! !! !! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! !! ! !! ! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! !! !! ! ! ! ! ! ! !! !! ! ! ! ! ! ! !!! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !! ! !!! ! !! ! ! !! !! ! !!!! ! ! !! ! !!! ! !! ! ! ! !! ! ! ! !! ! ! !!! ! !! ! !! ! ! !! ! ! !! ! ! ! !! ! !! ! ! !! ! ! !! ! ! ! ! !! ! !! ! !! ! ! ! ! ! !! ! !! ! !! ! ! !! !! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !
#
# ##
!! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! !! ! ! ! ! ! ! !! ! !! ! !!! ! !!! ! ! ! !! ! ! !! ! !!! ! ! !! ! ! ! !! ! ! !! !! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! !! ! !! ! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! !! !! ! ! ! !! ! !! !! ! !! !!! !!!!! ! !! ! !! ! !! !! ! ! ! ! !! ! !! ! ! !! ! !! ! !! ! ! !!! ! !
! ! ! !!! !! ! !!! !!! !!! ! !!! !! !!! !! !
! ! ! ! ! ! !
!! ! ! !! !! ! !! ! !! ! !! !! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! !! ! ! !! ! ! !! !! ! ! ! ! ! ! !! !! !!!! !! ! !! !! ! !! !! ! ! !! ! !!! !! ! ! ! ! !! ! !! ! !! ! ! !! !! !!! !!! ! ! !! ! ! ! !! !! !! ! ! ! ! !! !! ! ! ! ! ! ! ! !! ! ! !! ! ! ! !! ! ! ! ! ! & ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! ! ! !! ! ! ! !!! ! ! ! !! !! ! ! !! ! ! !! !! ! ! ! ! ! !! !! ! ! ! !!! !!!! ! !!! ! !!! ! !! ! !!! ! ! ! !! ! !! ! ! !!!! !!!! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! !! ! ! ! !! !!! !!! ! ! ! ! ! ! !! !! ! !! ! ! !! !! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! !! ! ! ! ! ! !! ! !! ! !!! !! ! !! ! ! ! !!! ! !! ! ! ! ! ! !! ! !! ! ! ! !! ! ! !! ! ! !! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !!! ! ! !! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! !! ! ! !!!!!! ! ! ! !! !! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !
Eradicated Grid distribution
0.4
0.2
0.0
1500-1800
1800-1849
1850-1899
1900-1949
1950-2007
Figure 14. European distribution of the Himalayan balsam Impatiens glandulifera (Balsaminaceae, Magnoliophyta). It reduces the diversity of invaded communities, competes successfully for pollinators, and promotes erosion due to its modest root system (DAISIE 2009).
Figure 9. Changes over time in the role of major vectors responsible for the introduction of mammals in Europe (from Genovesi et al. in DAISIE 2009).
DA I SI E:
D EL I V ER I N G
A L I EN
I N VA S I V E
SP E C I E S
Figure 15. Distribution of the North American muskrat Ondatra zibethicus (Muridae, Mammalia) in Europe. It strongly affects vegetation dynamics through grazing, impacts endangered mussel populations, fish and ground nesting birds, damages riverbanks, railroads, dams and fences by burrowing, but also causes extensive damage to crops, irrigation structures and aquaculture industry (DAISIE 2009).
I N V E N TO R I E S
FO R
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135
Biological Pollution of Aquatic Ecosystems in Europe
,
SERGEJ OLENIN, DAN MINCHIN, DARIUS DAUNYS & ANASTASIJA ZAIKO
What is biological pollution? The term biological pollution has been used recently to define the impacts of alien invasive species sufficient to disturb ecological quality by effects on: ◙ an individual (internal biological pollution by parasites or pathogens); ◙ a population (by genetic change, i.e. hybridization); ◙ a community (by structural shift), ◙ a habitat (by modification of physical-chemical conditions); ◙ an ecosystem (by alteration of energy and organic material flow). The biological and ecological effects of biopollution may also cause adverse economic consequences (Elliot 2003, Olenin et al. 2007).
◙ Community – the changes caused in native species composition and abundance, including shifts in typespecific communities. C0
C1
C2 C3 C4
◙ Habitat – the character of habitat modification.
Biopollution assessment method To determine the biopollution level, the abundance and distribution range of alien species is assessed.
H0
Species occurs in low numbers in one or several localities Species occurs in low numbers in many localities, or in moderate numbers in one or several localities, or in high numbers in one locality Species occurs in low numbers in all localities, or in moderate numbers in many localities, or in high numbers in several localities Species occurs in moderate numbers in all localities, or in high numbers in many localities Species occurs in high numbers in all localities
H3
A
B
C D E
To assess the magnitude of impacts, the following categories are considered separately:
No displacement of native species, ranking of native species unchanged, type specific community present Local displacement of native species, dominant species remain the same, type-specific communities are present Large scale displacement of native species, changes in type-specific communities, shifts in community dominant species Population extinctions, alien species are dominant, loss of type-specific community Extinction of native keystone species, extinction of type-specific communities
H1 H2
H4
No habitat alteration Alteration of a habitat, but no reduction of spatial extent of a habitat Alteration and reduction of spatial extent of a habitat Alteration of a key habitat, severe reduction of spatial extent of habitat Loss of habitats in most or the entire assessment unit, loss of a key habitat
Application of the method for the monitoring of an invasion over time The presented biopollution assessment method may help to answer the question “How is biopollution caused by the same species changing over time?”. Example: invasion history of the ctenophore Mnemiopsis leidyi (Figure 1) in the Black Sea (Figure 2) in terms of biopollution. The invasion of the comb jelly Mnemiopsis leidyi in the Black Sea caused a 6-fold decrease in abundance of zooplankton, the only food source for the kilka (three species of Clupeonella). The white sturgeon (Huso huso) feeding on kilka, is considered to be highly endangered (Shiganova et al. 2001). Since 1982 (the first record of M. leidyi in Sudak Bay, coast of Crimea) up until 2000, the ADR of the species has changed from A to E. Conse-
quently the strength of its impact on the ecosystem and biopollution level has changed (BPL) (Figure 3). From the above example it may be concluded that: ◙ Abundance and distribution range of an alien species is generally proportional to its impacts on species and community structure. ◙ Impacts on habitats and ecosystem processes become evident at later stages of an invasion. ◙ Our ability to document impacts on habitats and ecosystem functions is also increasing with time as our knowledge progresses (BPL may be underestimated at early phases of invasions) (Olenin et al. 2007). Application of the method for different ecosystems The biopollution assessment method may be used for evaluation of alien
◙ Ecosystem – the impact on ecosystem processes and functioning. E0 E1 E2 E3 E4
No measurable impact Weak changes with no loss or addition of new ecosystem function Moderate modification of ecosystem performance, changes in functional group(s) Severe shifts in ecosystem functioning, reorganization of the food web Extreme, ecosystem-wide shift in the food web and/or loss of the role of a functional group(s)
Black Sea
The assessed biopollution level is the net result of a combination of abundance and distribution range (ADR) class and of impact of alien species on communities (C), invaded habitat (H) and ecosystem functioning (E). Figure 2. Initial recipient area of the invasive Mnemiopsis leidyi in Europe.
Levels of biopollution
Figure 1. American comb jelly in the Black Sea. Photo: O. Kovtun.
136
AT L A S
OF
No
1
Weak
2
Moderate
3
Strong
4
Massive
A+C0+H0+E0
Arrival
A+C1+H1+E1 B/C+C0+H0+E0 B+C1+H1+E1 B/C+C2+H2+E2 C+H1+E1 D/E+C1+H1+E1 B/C+H3 D+H2 D/E+C3+H3+E3 C+H4 D/E+C4+H4+E4
The overall biopollution level for a defined water body is determined according to the greatest impact level for at least one species which was noticed during the evaluation period (Olenin et al. 2007).
B I OD I V E RSITY
RISK
CHAPTER
6
Abundance and distribution range
0
1980
Establishment
Expansion
Adjustment
ADR=E C3 H4 E4 BPL=4
ADR=C C1-C2 H1-H2 E1-E2 0
ADR=C C2 H2 E? BPL=2
ADR=A C0 H? E? BPL=0
1985
1990
1995
Figure 3. A typical invasion pattern, in this case Mnemiopsis leidyi.
2000
2005
2010
16
14
14
14
12 10 8 6 4
12 10
2 0
Number of alien species
16
Number of alien species
Number of alien species
16
8 6 4 2
0
1
2
3
0
4
0
1
2
BPL
Application of the method to individual species The biopollution assessment method may also be applied to the comparison of the same alien species invasiveness in different ecosystems or the evaluation of biopollution dynamics caused by the same species over time. Example: invasion of the zebra mussel Dreissena polymorpha in three different ecosystems in terms of biopollution (Figure 8).
4
10 8 6 4 2 0
0
1
2
3
BPL
BPL
Figure 4. Lough Derg.
species invasiveness (potential to spread rapidly in a new region and to cause harmful environmental and economic impacts (IUCN 1999, Occhipinti-Ambrogi & Galil 2004) in a defined water body. This method may also be applied to the ranking of species according to their invasiveness, evaluation of biopollution levels in different ecosystems for different periods of time. To demonstrate the application of the biopollution assessment method, examples from comparatively well studied regions with different environments, composition of native biota and levels of anthropogenic impact are presented: ◙ a lake – Lough Derg, Ireland, 117 km2, 10 alien species observed (Minchin 2008) (Figure 4); ◙ a lagoon – Curonian Lagoon, Lithuania, 1584 km2, 22 alien species observed (Daunys 2008) (Figure 6); ◙ a river – Middle Danube, Serbia, 84 km, 23 alien species observed (Paunovic 2008) (Figure 5). As is shown in the upper diagrams, the invasibility of alien species in all three ecosystems varies. Consequently the biopollution level varies (3 – in Lough Derg and the Curonian Lagoon, 2 – in the Middle Danube). Since in all three assessment units many of the alien species occur in low numbers in few localities (ADR = A) and (or) have no measurable impact, they do not cause marked effects (BPL > 1) on the ecosystem. However, the total biopollution level of the ecosystem is determined by those several species causing the strongly pronounced impacts (e.g., Dreissena polymorpha, Pontogammarus robustoides and Obessogammarus crassus in the Curonian Lagoon and Dreissena polymorpha in Lough Derg).
3
12
Figure 5. Middle Danube.
Figure 6. Curonian Lagoon.
(BPL=3), is caused by the significant impact of the zebra mussel on habitats and communities.
Box 2. Curonian Lagoon Recorded in 1800s Adjustment phase ADR = D (moderate numbers in all localities) C3 H3 E2 BPL=3
References
" "
"
Box 1. Lough Derg Recorded in 1997 Adjustment phase ADR = D (moderate numbers in all localities) C2 H2 E2 BPL=3
"
Box 3. River Dnieper Recorded in ~ 1750 Adjustment phase ? ADR = A (low numbers in few localities) C1 H1 E1 BPL=1
Figure 7. Biopollution assessment for the zebra mussels.
The zebra mussel is a highly abundant invader all over the Europe. After its spread from the Ponto-Caspian region in the 1700s, this species has become established in many different ecosystems. However, its invasibility and induced biological pollution may vary depending on the ecosystem properties and change over time. The biopollution assessment for the zebra mussel is demonstrated in Figure 7 in three assessment units: ◙ a lake – Lough Derg, Ireland (Minchin 2008) (Box 1); ◙ lagoon – Curonian Lagoon, Lithuania (Daunys 2008) (Box 2); ◙ a river – Dnieper, Belorussia (Mastitsky 2008) (Box 3). The zebra mussel arrived in these three ecosystems at different times. Although its ADR differs in the assessment units (D in the Curonian Lagoon and Lough Derg and A in the River Dnieper), it has most probably reached the same introduction phase there (adjustment phase) by now.
The strength of the species impact on different parts of the ecosystems also varies, and consequently, the biopollution level induced by Dreissena polymorpha in the presented assessment units. The highest BPL reported for the Curonian Lagoon ecosystem
DAUNYS D (2008) Curonian Lagoon, assessment period 1980-2006. Biological Invasion Impact / Biopollution Assessment System (available at http://www.corpi.ku.lt/databases/index.php/binpas) ELLIOT M (2003) Biological pollutants and biological pollution – an increasing cause for concern. Marine Pollution Bulletin 46: 275-280. IUCN (1999) IUCN guidelines for the prevention of biodiversity loss due to biological invasions. Newsletter of the Species Survival Commission IUCN – The World Conservation Union 31: 28-42. MASTITSKY S (2008) Dniper, assessment period 1958-2006. Biological Invasion Impact / Biopollution Assessment System (available at http://www.corpi.ku.lt/databases/index. php/binpas) MINCHIN D (2008) Lough Derg, assessment period 1998-2006. Biological Invasion Impact / Biopollution Assessment System (available at http://www.corpi.ku.lt/databases/index.php/binpas) OCCHIPINTI-AMBROGI A, GALIL BS (2004) A uniform terminology on bioinvasions: a chimera or an operative tool? Marine Pollution Bulletin 49: 688-694. OLENIN S, MINCHIN D, DAUNYS D (2007) Assessment of biopollution in aquatic ecosystems. Marine Pollution Bulletin 55: 379-394. PAUNOVIC M (2008) Middle Danube, assessment period 1975-2005. Biological Invasion Impact / Biopollution Assessment System (available at http://www.corpi.ku.lt/databases/index.php/binpas) SHIGANOVA T, MIRZOYAN Z, STUDENIKINA E, VOLOVIK S, SIOKOU-FRANGOU I, ZERVOUDAKI S, CHRISTOU E, SKIRTA A, DUMONT H (2001) Population development of the invader ctenophore Mnemiopsis leidyi, in the Black Sea and in other seas of the Mediterranean basin. Marine Biology 139: 431-445.
Figure 8. Dense colony of zebra mussels. Photo: S. Olenin.
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4
Pathways of Aquatic Invasions in Europe
,
SERGEJ OLENIN, DAN MINCHIN, DARIUS DAUNYS & ANASTASIJA ZAIKO
How aquatic alien species get around Species become spread by a wide range of pathways either deliberately or inadvertently (Carlton 1988, Minchin 2001). Depending on the researcher and identification methods the number of possible pathways categories may vary from five to fifteen or even more. Although the number within each category also varies due to the way in which the collective pathways have been organised, all take into account the full suites of vectors. In this
been in constant use and carried elsewhere along with intertidal biota. Modern vessels have different ballast tank designs according to the ship class, size and position within the ship. The large volumes of water are of concern as different suites of species, that include marine, brackish and freshwater plankton and mico-organisms forming films on surfaces, are readily transmitted across oceans before discharge. Canals Canals enable species to enter previously uncolonized areas through corri-
IMPORT
UNINTENDED
DELIBERATE
CONFINED
ESCAPE
RELEASE
ARRIVAL
Culture Ornamental Research Biocontrol
Fisheries Culture Ornamental Research Biocontrol
Fisheries Culture Ornamental Research Biocontrol
Shipping Canals Leisure Altered flow
Figure 1. Pathways of Aquatic Invasions.
Figure 3. Artificial inland water way. Photo: S. Olenin.
account nine principle categories are defined for all aquatic environments.
dors linking separate seas and river basins either aided by the movement of vessels, by natural expansion or both. European canals for navigation, since the late 1700s, were built connecting Eastern Europe to the North Sea, The Mediterranean Sea and the Atlantic Ocean. Two canals are of particular importance for marine and brackish species. The Suez Canal, opened in 1869, has no locks to regulate water levels so allowing Red Sea species to enter the Mediterranean Sea. Some of them may contribute to local fisheries or act as pest species. The Kiel Canal, opened in 1895, connects the North and Baltic seas allowing movements of brackish tolerant species.
Shipping Increasing shipping activities resulted in the development of busy ports in many European countries connecting
Figure 2. Release of ballast water. Photo: S. Olenin.
Europe to ports all around the world and shipping is believed to be one, if not the most important pathway for species invasions (i.e., ballast waters, sediments in ballast tanks and hull fouling) (Leppäkoski et al. 2002). Hull fouling is one of the oldest operating vectors. Over time vessels have increased in size resulting in greater inoculation ability. As most modern vessels lack wooden hulls and use anti-fouling coatings the control of fouling has become more efficient, and boring organisms are seldom encountered. Control of fouling is needed as fouled vessels have higher fuel costs due to increased drag. Most vessels require ballast to provide stability, in earlier times stones were regularly used and would have 138
AT L A S
OF
Wild fisheries The development of fisheries ranges from marine, estuarine and freshwater
Figure 4. Cans of red king crab meat. Photo: S. Olenin.
B I OD I V E RSITY
RISK
CHAPTER
over most regions of Europe arising from deliberate introductions or from unintended releases and transmissions. The stocking of the migratory red king crab Paralithodes camtschaticus over decades eventually led to an established population in the Barents/White Sea and the manila clam Venerupis philippinarum is now a dominant member of the benthos in the northern Adriatic Sea. The accidental arrival of the predatory snail Rapana venosa to the Black Sea, which avidly consumed molluscs, became a pest but this status changed when it became fished. Culture activities A small number of alien species, mainly fishes, crustaceans and molluscs and stock cultures of phytoplankton, contribute to local economies world-wide and more are likely to be utilised in the future with changes in climate, ethnic movements and improved knowledge in culture. Some will include species used for sport and as ornamentals and biotechnology. Stock movements normally consist of consignments of thousands of individuals which will include associated biota. Rapid deliveries, now possible with modern transport, ensure that many of these survive where before this would not have happened. Unauthorised imports and releases to the wild occasionally take place and pose a risk in transmitting pests and diseases. Ornamental and life food trade With economic prosperity there has been a greater interest in making and creating garden ponds stocked with ornamental plants and fishes. Flooding events or direct releases can result in their appearance in the wild. Tropical freshwater and marine organisms can be displayed in centrally heated homes in northern climates, while many are unlikely to survive overwinter if released to the wild some may do so. With trends of warmer winters there are opportunities for some to become established,
Figure 5. Louisiana red swamp crayfish. Photo: S. Olenin.
6
perhaps in thermal plumes. In warmer regions of Europe direct releases to the wild are more likely to succeed. Species from different world regions are regularly flown to markets to be sold alive as food. With the global movement of people with different customs food species are likely to become spread. For example the American lobsters Homarus americanus may be found in restaurant aquaria from Europe to Asia. Leisure activities Bait organisms may be exported beyond their normal range and may
Figure 6. Leisure boat transportation. Photo: S. Olenin.
become discarded alive by anglers. Overland and ferried transmissions of small craft, carried on trailers, have been implicated in some species movements. Recreational craft, many of which are held for long periods at marina berths, have the ability to transmit their fouling biota elsewhere once re-engaged in use. Leisure craft may be important in the secondary spread from port regions to remote estuaries and bays. For example plants snagged in anchors may be inadvertently moved. Boats for sale pose special risks once transferred. Research and education Species may inadvertently become released or escape from different levels of confinement during or following experimental studies. On completion of experiments species may become disposed to the wild. Normally the numbers involved in such studies are small yet for some species low numbers released to the wild theoretically may evolve a new population. Biological control Biological control has been successful in controlling several terrestrial species but such an application is more difficult to manage within waterbodies. Some floating freshwater plants may be controlled using insects, such a weevils, and some fishes have been used to clear aquatic vegetation. While the
numbers of examples are small and have had varied successes there can also be some unwanted effects particularly should these escape from the target regions. In marine environments several species have been considered for control and some preliminary trials for some will have been undertaken but no elimination programme for a species in the sea is known. Alteration to natural waterflow With an increasing population follows a demand for more water for industry, energy generation, irrigation and consumption. Water may become distributed over different catchments, or to upstream holding areas, used for irrigation and any subsequent drainage may enter previously uncolonized areas. Local depletions of municipal water supplies are likely to result in such transmissions and sev-
The biggest variability of the “active” pathways of aquatic invasions is typical for the European inland waters. This is due to variety of the aquatic ecosystems as well as comprehensive utilization and management of water resources in the continental part of Europe. Such pathways as ornamental trade and leisure activities almost have no impact on distribution of alien species in the coastal regions but are rather well pronounced and substantial in the inland European waters. The importance of one or another pathway correlates with the trade intensity in the given region (Figure 13). However this is not a permanent index, it may considerably change in time. Such trends may be registered in a region even during the comparatively
Figure 8. Pathways of Aquatic Invasions in the Mediterranean region.
shipping
ornamentals
Figure 9. Pathways of Aquatic Invasions in the Atlantic coast.
canals
canals 300 250
canals
200
Figure 7. Artificial water reservoir. Photo: S. Olenin.
100
eral large-scale schemes have been considered within Europe.
What pathways are the most important? The role of a certain pathway in spreading of aquatic alien species depends on the specific ecological-economical features of the given region. For instance, the majority of alien species observed in the Mediterranean region from 1800 till now has arrived by canals (Figure 8). The number of introductions induced by shipping activities plays the secondary role. While in the Baltic Sea and Atlantic coast shipping is the main pathway for aquatic alien species invasion (Figures 9-11). Another very important pathway is culture activities. About 30 % of introduction events in the European inland waters happen due to the accidental or intentional release of cultured organisms in the region.
short period of time (1950-2008). For example, in the Baltic Sea, the increasing shipping activities and development of the new navigable waterways during the last 60 years has resulted in the increasing number of unintentional introduction of alien species, transported in ballast tanks or on ship hulls (Figure 12). Yet it is not a rule that aquatic alien species must have a unique pathway of introduction. There may be a number of recurring introductions of a non-indigenous organism into an ecosystem with a different pathway in the each case. Nearby activities are often implicated for their arrival even though other possibilities for the initial introduction may exist. Identification of the mode of arrival for species, not intentionally introduced, is seldom recognised until there is some impact. This could be some years following their establishment.
50
Figure 10. Pathways of Aquatic Invasions in the European inland waters.
Figure 11. Pathways of Aquatic Invasions in the Baltic Sea.
20
Number of introduction events
Other pathways Flotsam and jetsam appear to have increased during the last century and enabled transport over oceans and aid in the secondary spread of alien species. Restoration to stabilise mobile sediments could involve the plantings of alien species on shores or in shallow bays. Some introduced species may hybridize with native species, or become polyploid, to result in vigorous and more invasive plants.
Figure 13. Future predicted alien spread.
150
References
15
10
5
0
1950-1959
1960-1969
1970-1979
1980-1989
1990-1999
2000-2008
Time periods Figure 12. Growth of the shipping pathway importance in the Baltic Sea.
PAT HWAYS
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AQUATI C
CARLTON JT (1988) Changes in the sea: the mechanisms of dispersal of marine and aquatic organisms by human agency. Journal of Shellfish Research 7: 552. LEPPÄKOSKI E, GOLLASCH S, OLENIN S (Eds) (2002) Invasive aquatic species of Europe – distribution, impact and management. Dordrecht, Boston, London. Kluwer Academic Publishers. 583 pp. MINCHIN D (2001) Introduction of exotic species. In: Steele J, Thorpe S, Turekian K (Eds), Encyclopedia of Ocean Sciences. Academic Press, London, 877-889.
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Risk Assessment of Aquatic Invasive Species’ Introductions via European Inland Waterways VADIM E. PANOV, BORIS ALEXANDROV, KESTUTIS ARBACIAUSKAS, ROSA BINIMELIS, GORDON H. COPP, MICHAL GRABOWSKI, FRANCES LUCY, ROB S.E.W. LEUVEN, STEFAN NEHRING, MOMIR PAUNOVIĆ, VITALIY SEMENCHENKO & MIKHAIL O. SON
,
Introduction European inland waterways have provided opportunities for the spread of invasive alien aquatic (IAS) species for many centuries. Over the past century, the potential for species to expand their range has been enhanced both as a result of the construction of new canals and due to increased trade. At present, the complex European network of inland waterways is made up of > 28,000 km of navigable rivers and canals, connecting 37 countries in Europe and beyond (Figure 1). This aquatic network connects the previously isolated catchments
changes. The future developments of the European network of inland waterways will highly facilitate the transfer of IAS across European inland waters and coastal ecosystems. Appropriate risk assessment-based management options are required to address risks posed by human-mediated introductions of these species (Panov et al. 2007). Considering the current gap in addressing invasive alien species in European river basin management, our goal was to develop relevant risk assessment protocols and water quality indicators on IAS for possible consideration in
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Important shipping canals
Nort No rthe hern rn corrido corridor
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Baltic Sea North Sea
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Sein e
Rh ine Marne
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Figure 1. Important European waterways and invasion corridors for the spread of aquatic species (after Galil et al. 2007, modified). Main canal number: 1 – Volga-Don Canal, 2 – Volga-Baltic Canal, 3 – White Sea – Baltic Sea Canal, 4 – Bug-Pripyat Canal, 5 – Vistula-Oder Canal, 6 – Havel-Oder Canal, 7 – Mittelland Canal, 8 – Dortmund-Ems Canal, 9 – Rhine-Herne Canal, 10 – Ludwig Canal and Main-Danube Canal, 11 – Rhine-Rhône Canal, 12 – Canal du Centre, 13 – Canal de Briar, 14 – Rhine-Marne Canal, 15 – Kiel Canal. Solid red arrows indicate the Southern meridian invasion corridor and the Northern meridian invasion corridor.
of the southern European seas (Caspian, Azov, Black, Mediterranean) and the northern European seas (Baltic, North, Wadden, White), to provide corridors for IAS. In Europe, there are thirty main canals with >100 branch canals and > 350 ports (Galil et al. 2007). There are plans to deepen many of these canals to accommodate larger vessels and to prepare for the lower anticipated water levels arising from climate 140
Environmental indicators: 1. List of Extreme Risk pathways 2. List of High Risk pathways 3. List of High Risk donor areas
Impacts
Pressures Environmental indicators: 1. Biological Contamination Rate (BCR) 2. Pathway-specific Biological Contamination Rate (PBCR)
State
Environmental indicators: 1. Species-specific Biopollution Risk index (SBPR index) 2. Integrated Biopollution Risk index (IBPR index) 3. Grey, White and Black list of alien species
Environmental indicators: 1. Biological Contamination Level (BCL) 2. Site-specific Biological Contamination index (SBC index) 3. Integrated Biological Contamination index (IBC index)
Volga Neva
9
Driving forces
Figure 2. Environmental indicators and Risk Assessment Toolkit (RAT) for introductions of aquatic invasive species in the DPSIR framework (after Panov et al. 2009, modified). RBMP – River Basin Management Plans, DSS – Decision Support System on aquatic invasive species (for description of specific environmental indicators see text).
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Other main waterways
Aquatic RAT (risk-based DSS)
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Trunk waterways
Responses (measures within RBMP)
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the Common Implementation Strategy of the EC Water Framework Directive and as part of a holistic (cumulative) riskbased management of European river basins. The European Environmental Agency (EEA) ‘Typology of indicators’ and the Driving forces–Pressures–State– Impact–Response (DPSIR) framework was used to structure developed environmental indicators in the socio-economic context (Figure 2).
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Conceptual model of risk assessment of IAS introductions via European inland waterways Owing to the high degree of scientific uncertainty when dealing with such a global and complex ecological issue as large-scale intercontinental and intra-continental introductions of IAS, the qualitative model of risk assessment was selected for risk assessment of IAS introductions via European inland waterways (Panov et al. 2007, 2009). The present variant of this qualitative model of risk assessment of IAS introductions via navigable waterways includes six main components: ◙ Identification of main invasion gateways, routes and corridors in Europe, and selection of ecosystems as assessment and management units (AUs) within invasions corridors/invasion network. ◙ Identification and analysis of pathways of IAS introductions within the ecosystem – “Driving forces” according to the DPSIR framework. ◙ Assessment of inoculation rates (propagule pressure) within the ecosystem– DPSIR “Pressures”; ◙ Assessment of biological contamination level of the ecosystem – DPSIR “State”. ◙ Assessment of invasiveness of alien species, established in the ecosystem (potential biopollution risk) – DPSIR “Impacts”. ◙ Development of an online Risk Assessment Toolkit (RAT) with
early warning service for reporting of environmental indicators and recommendations for risk management to stakeholders – DPSIR “Responses”. For the purpose of testing this model, we selected a 10-year observation period (1997-2007) for analysis of pathways and assessment of propagule pressure within the selected ecosystems (Assessment Unit), and an observation period of time since 1900 for the assessment of biological contamination level of the ecosystem. Identification of main invasion gateways, routes and corridors in Europe There are four principal invasion corridors in Europe (Figure 1): ◙ The Northern corridor: linking the Black and Azov seas with the Caspian Sea via the Azov – Caspian waterway including the Volga-Don Canal, and with the Baltic and White seas via the Volga-Baltic waterway including the Volga-Baltic Canal, and the White Sea – Baltic Sea waterway, including the White Sea – Baltic Sea Canal. ◙ The Central corridor: connecting the Black Sea with the Baltic Sea region via Dnieper and Bug-Pripyat Canal, with Nemunas River branch connected to Pripyat and Bug by Oginsky and Augustov canals, correspondingly.
◙ The Southern corridor: linking the Black Sea basin with the North Sea basin via the Danube-MainRhine waterway including the MainDanube Canal. ◙ The Western corridor: linking the Mediterranean with the North Sea via the River Rhône and the RhineRhône Canal. These principal corridors are interlinked via two additional invasion corridors: the Southern meridian corridor linking the Northern, Central and Southern corridors on the south, and the Northern meridian corridor, linking the Northern, Central, Southern and Western on the north (Figure 1). This complex system of navigable waterways and invasion corridors can be considered as an European inland water invasion network (Figure 1), with estuaries of large European rivers (Don, Danube, Dnieper, Neva, Odra, Rhine) and lagoons (Curonian, Vistula) serving as entries to the main invasion corridors and considered as “invasion gateways” (Panov et al. 2009). In our study, we selected assessment units within three main invasion corridors (Northern, Central and Southern) in order to consider an ecosystem approach to the management of IAS using river basins as the main management units (Figure 3). Identification and analysis of pathways of IAS introductions within the ecosystem Pathways involved in the introductions of IAS can be considered as “Driving forces” according DPSIR framework (Figure 2). Principal pathways of aquatic IAS spread in Europe and qualitative descriptors of principal human activities involved in the spread of IAS have been identified (see in Panov et al. 2009). For the purpose of the present qualitative risk assessment of IAS introductions via inland waterways, these principal human activities were considered as potential pathways for any selected ecosystem (assessment unit – AU). Pathways are defined according to three classifications: ◙ A pathway with low certainty of the existence of a specific pathway for a specific AU, can be defined as “Low Risk (LR) pathway”. ◙ A pathway with a high level of certainty of its existence in the AU, but with no evidence existing of the introduction of alien species in AU by this pathway during the past 10 years, can be defined as “High Risk (HR) pathway”. ◙ Where the operating pathway can be defined as responsible for an introduction of specific alien species into a AU during the past 10 years (even if only one record of alien species within this period can
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NC6 BCR = 6 BCL = 13 IBPR = 3 NC4 BCR = 2 BCL = 4 IBC = 4 IBPR = 4 NC5 BCR = 1 BCL = 8 IBC = 4 IBPR = 4 CC12 BCR = 1 BCL = 8 IBC = 4 IBPR = 4
CC10 BCR = 2 BCL = 12 IBC = 2 IBPR = 4
CC16 BCR = 7 BCL = 27 IBC = 4 IBPR = 4
SC8 BCR = 12 BCL = 59 IBC = 4 IBPR = 4
CC9 BCR = 5 BCL = 16 IBC = 2 IBPR = 4
NC3 BCR = 2 BCL = 82 IBPR = 4
CC14 BCR = 6 BCL = 24 IBC = 2 IBPR = 4
SC4 BCR = 10 BCL = 73 IBC = 4 IBPR = 4
SC2 BCR = 11 BCL = 24 IBC = 4 IBPR = 4
SC3 BCR = 14 BCL = 38 IBC = 4 IBPR = 4
NC1 BCR = 7 BCL = 41 IBPR = 4
NC2 BCR = 12 BCL = 47 IBPR = 4
Figure 3. Assessment units selected within the Northern, Central and Southern inland water invasion corridors (NC, CC and SC, respectively): NC1 – River Don and Azov Sea, NC2 – lower part of River Volga and Caspian Sea, NC3 – upper and middle parts of River Volga, NC4 – Lake Ladoga, NC5 – River Neva estuary, NC6 – River Severnaya Dvina, CC9 – middle part of River Pripyat, CC10 – Dnieper-Bug canal, CC 12 – lower part of River Nemunas, CC14 – River Vistula, CC16 – River Oder, SC2 – lower part of River Danube, SC3 – middle part of River Danube, SC4 – upper part of River Danube, SC8 – lower part of River Rhine. The Integrated biological pollution risk (IBPR) is indicated both by numbers and colours of area boundaries (High biopollution risk and Very high biopollution risk are in orange and red, respectively).
be attributed with some level of certainty to the specific pathway), it can be defined as “Extreme Risk (ER) pathway”. Assessment of inoculation rates within the ecosystem In the present study we suggest assessing inoculation rate indirectly via the Biological Contamination Rate (BCR). “Biological contamination” of the ecosystem means the introduction of alien species regardless of their abilities to cause negative ecological and/or socio-economic impacts; in a case where impacts of introduced alien species are measurable, the “biological pollution” of the ecosystem should be evaluated (see in Panov et al. 2009). The Biological Contamination Rate (BCR) of the ecosystem or any assessment unit (AU) can be estimated as the number of recorded alien species in AU per observation/reporting period (e.g., total number of recorded alien species per year or per 10 years). BCR values for selected assessment
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units for last reporting period (19972007 in the present study) are provided in Figure 3. The Pathway-specific Biological Contamination Rate (PBCR) reflects the inoculation rate in AU by specific pathways and can be estimated by the number of recorded alien species in AU by specific pathway during the reporting period. PBCR can be used as a DPSIR Environmental indicator for “Pressures”. Where PBCR = 0, there is no biological contamination by existing pathway, whereas if PBCR > 0, then the Extreme Risk pathway (ER pathway) can be distinguished. Assessment of biological contamination level of the ecosystem Biological contamination level (BCL) of the AU (ecosystem) reflects the invasibility of the ecosystem (probability of establishment of alien species as a complex function of abiotic and biotic resistance of the ecosystem to biological invasions under a specific level of propagule pressure). This fea-
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ture of the ecosystem can be assessed via estimation of the number of established alien species and their relative roles in the structural organization of plant and animal communities. For the purposes of our study, BCL is estimated as the number of established alien species in AU since 1900 (BCL estimates for selected assessment units are provided in Figure 3). BCL can be used as a DPSIR Environmental indicator of “State”. The Site-specific Biological Contamination (SBC) index has been elaborated to assess biological contamination of the specific sampling site within AU with respect to “taxonomic” and “abundance” contamination (Arbačiauskas et al. 2008). For ranking of SBC index see Table 1; an example of assessment of SBC indices for macrozoobenthic communities and the corresponding ecological quality for 13 locations in three assessment units of River Pripyat are provided in Figure 5. The Integrated Biological Contamination (IBC) index for the
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Table 1. Scoring of Site-specific and Integrated Biological Contamination indices (SBC and IBC) with respect to abundance contamination index (ACI) and taxonomic contamination index (TCI). SBC or IBC ranks: 0 (high status, no biological contamination (BC), blue cell), 1 (good status, low BC, green cell), 2 (moderate status, moderate BC, yellow cells), 3 (low status, high BC, orange cells), 4 (bad status, very high BC, red cells) (after Arbaˇciauskas et al. 2008).
No
No
HPS
HPE
No
HPI
Grey list
ACI
TCI
none
none
0.01 – 0.10
0.11 – 0.20
0.21 – 0.50
>0.50
Yes
none
0.01 – 0.10
1
2
3
4
0.11 – 0.20
2
2
3
4
0.21 – 0.50
3
3
3
4
>0.50
4
4
4
4
HPE
No
White list
Yes
Yes
AU can be estimated by averaging “taxonomic” and “abundance” contamination of study sites (within AU), and can be ranked in the same way as SBC index (see Table 1 and example for macrozoobenthos of Pripyet River in Figure 5). The IBC index can be used both as DPSIR Environmental indicator of “State” (Figure 2) and for assessment of ecological status of the whole AU (aquatic ecosystem) (Figure 5). IBC indices for selected assessment units are provided in Figure 3. Assessment of invasiveness of alien species (potential biopollution risk) Estimations of actual impacts of alien species in specific aquatic ecosystems (e.g., AUs) are not always possible and usually require costly long-term research efforts in the specific water body. In this regard, a riskbased assessment of invasiveness of the established alien species can be considered the most cost-effective way for developing practicable indicators for “Impacts” in the DPSIR
framework. For this purpose we have developed a Species-specific Biopollution Risk (SBPR) index, which is based on the general assessment of the level of invasiveness of the specific alien species according to the estimates of three such descriptors of the species as High risk for dispersal (HRD), High risk for establishment in a new environment (HRE), and High risk to cause ecological and negative socio-economic impacts (HRI). The knowledge on HRD, HRE and HRI of the alien species is generally available from scientific reports and publications associated with a particular species introduction (Panov et al. 2009). This approach to the risk-based assessment of invasiveness of the alien species, established in the aquatic ecosystem (AU), was further used in the formal procedure of listing of alien species into the Grey, White and Black Lists (Figure 4). This ranking of alien species according their invasiveness along with information on relative abundance of invasive alien species in spe-
No Yes
HPI
Figure 4. Procedure for listing alien species according their potential invasiveness (after Panov et al. 2009, modified). “Yes” in this scheme means that information on potential invasiveness of the species is available, “No” means “Unknown”, or information is not available (HRD – High risk of dispersal, HRE – High risk for establishment in new environment, HRI – High risk to cause ecological and negative socio-economic impacts).
cific locations of the AU can be further used for estimation of the Integrated Biopollution Risk (IBPR) index. Where no alien species are present in the AU, IBPR = 0 (No biopollution risk: reference conditions, or “High” ecological status sensu the Common Implementation Strategy of the EC Water Framework Directive). If alien species from “Grey” or “White” lists are present in relatively low abundances (less than 20 % of total abundance of alien and native species in the community), then IBPR = 1 (Low biopollution risk: this may correspond to “Good” ecological status of a water body). Relatively high abundance of alien species (exceeding 20 %) from “Grey” or
SBC, IBC, IBPR scoring Belarus
CC10
CC9
Ecological status
0
High
1
Good
2
Moderate
3
Poor
4
Bad
CC8
IBPR
IBC
SBC
(2 ! (1 !
! (3
(5! ! (4 ! (6
(7 9 ( 11 10 ( ! !8 ! ( 12 ! ! ( ! (
13 ! (
Figure 5. Assessment of ecological status of three assessments units and specific locations in the River Pripyat basin based on estimations of Site-specific biological contamination (SBC), Integrated biological contamination (IBC) and Integrated biological pollution risk (IBPR) indices (after Panov et al. 2009, modified).
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Black list
“White” lists corresponds to IBPR = 2 (Moderate biopollution risk: “Moderate” ecological status). Where alien species from the “Black list” are present in the community, the IBPR can be estimated as 3 in a situation with relatively low abundance of these species (High biopollution risk: “Poor” ecological status), or 4 in a situation with relatively high abundance of “Black list” species (Very high biopollution risk: “Bad” ecological status) with the same 20 % threshold for “low” and “high” relative abundances (see Figures 3 and 5 for examples). Grey, White and Black Lists of IAS, SBPR and IBPR indices can be used as DPSIR Environmental indicators of “Impacts” (Figure 2). Also, the Black List can be used as the EEA SEBI 2010 indicator “Invasive alien species in Europe”, element ‘Worst invasive alien species threatening biodiversity in Europe’’ (European Environment Agency 2007). In addition, the IBPR index can be recommended for the riskbased estimation of ecological status of water bodies considering alien species introductions as a specific pressure (Panov et al. 2009). Development of an online risk assessment toolkit with an early warning service for reporting of environmental indicators and recommendations for risk management to stakeholders The aquatic component of the online Risk Assessment Toolkit (RAT) includes risk assessment protocols for IAS introductions via European inland waterways, supporting database and electronic journal “Aquatic Invasions” (Figure 6). The latter serves as an instrument to protect authors’ rights on IAS information stored in the database and as an early warning tool (Panov et al. 2008, see also Figures 6 and 7). The aquatic part of RAT will also serve as the decision-support sys-
tem (DSS), the online transmitter of essential information needed for decision-making (Figure 2, Panov et al. 2008), and will provide links to other IAS risk assessment protocols (http:// www.reabic.net and http://www.cefas. co.uk/4200.aspx). Conclusions The developed DPSIR environmental indicators for alien species (“Drivers” – List of Extreme Risk pathways for AUs, List of High Risk pathways for AUs, List of High Risk Donor Areas for AUs; “Pressures” – Biological Contamination Rate (BCR), Pathwayspecific Biological Contamination Rate (PBCR); “State” – Biological Contamination Level (BCL), Sitespecific Biological Contamination (SBC) index, Integrated Biological Contamination (IBC) index; “Impacts” – Species-specific Biopollution Risk (SBPR) index, Grey, White and Black lists of alien species and Integrated Biopollution Risk (IBPR) index, Figure 2) can be useful for risk management at the local, river basin, national and regional levels. Management measures for the DPSIR “Driving forces” and “Pressures” may include preventive actions toward management of Extreme Risk and High Risk pathways. Biological Contamination Rate (BCR) and Pathway-specific Biological Contamination Rate (PBCR) can be used as indicators of the effectiveness of preventive management. In contrast, the management actions for “State” and “Impacts” may involve the control and eradication of established species from Black List (according to CBD provisions), and Site-specific and Integrated Biological Contamination indices. Along with the Integrated Biopollution Risk index, these can be used as comparatively simple indicators of the effectiveness of these measures. Three environmental indicators from this list can be recommended as cost-effective “Quality Elements” (QEs) according to the Common Implementation Strategy of the Water Framework Directive for assessment of ecological status of aquatic ecosystems: Site-specific Biological Contamination (SBC) index, Integrated Biological Contamination (IBC) index and, specifically, based on precautionary approach, the Integrated Biopollution Risk (IBPR) index. References ARBAČIAUSKAS K, SEMENCHENKO V, GRABOWSKI M, LEUVEN RSEW, PAUNOVIĆ M, SON MO, CSÁNYI B, GUMULIAUSKAITĖ S, KONOPACKA A, VAN DER VELDE G, VEZHNOVETZ V, PANOV VE (2008) Assessment of biological contamination of benthic macroinvertebrate communities in European inland waterways. Aquatic Invasions 3: 206-224.
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GALIL BS, NEHRING S, PANOV VE (2007) Waterways as invasion highways – Impact of climate change and globalization. – In: Nentwig W, editor. Biological Invasions. Ecological Studies Nr. 193. Berlin, Germany: Springer, 59-74. PANOV V, DGEBUADZE Y, SHIGANOVA T, FILIPPOV A, MINCHIN D (2007) A risk assessment of biological invasions: inland waterways of Europe – the northern invasion corridor case study. – In: Gherardi F, editor. Biological Invaders in Inland Waters: Profiles, Distribution and Threats. Invading Nature – Springer Series in Invasion Ecology, Vol. 2. Heidelberg, Germany: Springer, 639-656. PANOV VE, GOLLASCH S, ALEXANDROV B, ARBACIAUSKAS K, GRABOWSKI M, LUCY F, MINCHIN D, OLENIN S, PAUNOVIĆ M, SON M (2008) New electronic journal “Aquatic Invasions”: an important part of the developing European early warning system on aquatic invasive species. Deliverable D 5.1.7 (The second volume of “Aquatic Invasions”) to the EC FP6 Integrated Project ALARM, 8 p. Available online at http://ec.europa.eu/environment/nature/ invasivealien/docs/alarm_deliverable.pdf PANOV VE, ALEXANDROV B, ARBACIAUSKAS K, BINIMELIS R, COPP GH, GRABOWSKI M, LUCY F, LEUVEN RSEW, NEHRING S, PAUNOVIĆ M, SEMENCHENKO V, SON MO (2009) Assessing the risks of aquatic species invasions via European inland waterways: from concepts to environmental indicators. Integrated Environmental Assessment and Management 5: 110-126.
1, 2
Decision-makers, managers, general public and other interested stakeholders (EC, EEA, OSPAR, ICES, CIESM, HELCOM)
RAT information transmitter system E-jornal Aquatic Invasions
Online risk assessment protocols and supporting information systems (Cefas and REABIC)
(protection of author's rights on primary data and early warning)
Information on records of alien species from monitoring and biological surveys, alien species checklists and biology research
Figure 6. Conceptual structure of the online Risk Assessment Toolkit (RAT) for aquatic alien species with early warning functions (after Panov et al. 2009, modified). EC – European Commission (http://ec.europa.eu/), EEA – European Environment Agency (http://www.eea.europa.eu/), CIESM – International Commission for the Scientific Exploration of the Mediterranean Sea (http://www.ciesm.org), OSPAR – OSPAR Commission for the Protection of the Marine Environment of the North-East Atlantic (http://www.ospar.org), HELCOM – Baltic Marine Environment Protection Commission (http://www.helcom.fi), Cefas – Cefas Risks and impacts of nonnative species Decision support tools (http://www.cefas.co.uk/4200.aspx), REABIC – Regional Euro-Asian Biological Invasions Centre information system (http://www.reabic.net).
3
5
4
11, 12
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7
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12
3
19
15
14
11
8
16 17 14
18 2
4
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9, 10
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15-21
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13 6 5
Figure 7. Selected new geo-referenced records of invasive alien species in European coastal and inland waters in 2007, published in the second volume of Aquatic Invasions (2007): 1 – the tubenose goby Proterorhinus marmoratus from the River Neva estuary, Russia (Antsulevich 2007), 2 – the tubenose goby Proterorhinus marmoratus from the Pripyat River, Belarus (Rizevsky et al. 2007), 3 – the Chinese mitten crab, Eriocheir sinensis from the River Volga, Russia (Shakirova et al. 2007), 4 – the Ponto-Caspian mysid Limnomysis benedeni from the River Pripyat, Belarus (Semenchenko et al. 2007), 5 – the Indo-Pacific humpnose big-eye bream, Monotaxis grandoculis in the Mediterranean Sea (Bilecenoglu 2007), 6 – the Red Sea mussel Brachidontes pharaonis from the Turkish coasts (Doˇgan et al. 2007), 7 – the Asian clam Sinanodonta woodiana from Eastern Romania (Popa et al. 2007), 8 – the Ponto-Caspian amphipod Dikerogammarus villosus (“killer shrimp”) in Lac du Bourget, France (Grabowski et al. 2007), 9 – the Quagga mussels Dreissena bugensis in Ukraine (Son 2007), 10 – the Quagga mussels Dreissena bugensis in the River Main, Germany (van der Velde and Platvoet 2007), 11-12 – the Asian amphipod Caprella mutica in coastal waters of UK and Norway (Cook et al. 2007), 13 – the American oyster drill, Urosalpinx cinerea in The Netherlands (Faasse and Ligthart 2007), 14 – the Asian tunicate Styela clava from the central German Bight (Krone et al. 2007), 15-21 – the North-American ctenophore Mnemiopsis leidyi in the Oslofjorden, Norway (Oliveira 2007), in Danish waters (Tendal et al. 2007), in south-western Baltic Sea (Kube et al. 2007), in the Gulf of Gda´nsk, southern Baltic Sea (Janas & Zgrundo 2007), in the central Baltic, Gulf of Bothnia and Gulf of Finland, respectively (Lehtiniemi et al. 2007).
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Distribution of Alien Bleak Alburnus alburnus (Linnaeus, 1758) in the Northeastern Iberian Mediterranean Watersheds: Past and Present ALBERTO MACEDA-VEIGA, ADOLFO DE SOSTOA, EDGAR SOLORIO-ORNELAS, MARIO MONROY, DOLORS VINYOLES, NUNO CAIOLA, FREDERIC CASALS, EMILI GARCIA-BERTHOU & ANTONI MUNNÉ
,
The native freshwater fish fauna in the Iberian Peninsula is characterized by high diversification at species level, with the largest percentage of endemic species in Europe, due to the geological history of each basin (Doadrio 2001). After habitat destruction, the introduction of exotic species is the second major threat to this region. Over the last few decades, exotic species have flourished in the Iberian Peninsula, mainly released for angling purposes or biological control, but also as a result of accidental introductions from aquaculture facilities. The bleak Alburnus alburnus (Linnaeus, 1758) is
Figure 1. Bleak (Alburnus alburnus).
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considered a forage species by anglers who have repeatedly introduced it into various reservoirs in the Iberian Peninsula, to improve the populations of exotic fish predators such as the northern pike Esox lucius (Linnaeus, 1758), the largemouth bass Micropterus salmoides (Lacepède, 1802), the zander Sander lucioperca (Linnaeus, 1758) and the wells catfish Silurus glanis (Linnaeus, 1758). The bleak is also frequently used as a bait fish (Elvira 1995). The bleak is a small cyprinid that feeds mainly on zooplankton. Its native distribution area ranges from the eastern slopes of the Pyrenees to the Urals
(Doadrio 2001) (Figure 1). Although its impact on the native fish fauna has not been studied in depth, there is concern that it may outcompete native fish due to its high reproductive output. Another potential source of danger is hybridization with native fish. Hybridization has already been reported with cyprinid species of genera Squalius, Blicca, Rutilus and Abramis. Besides its impact on native fish fauna, it also affects the trophic dynamic of reservoirs, which are the main source of water for human populations. It feeds on cladocerans and other small invertebrates which play an important
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Distribution of Alburnus alburnus 2007/08
200 km
# Distribution of Alburnus alburnus 2002/04
Figure 2. Distribution of bleak in NE of Iberian Peninsula during the 2002-2008 period.
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role in these ecosystems and whose activity directly affects the water quality. The bleak was collected for the first time in the Iberian Peninsula in the River Ribagorçana (Ebro basin) in June 1992 (Elvira 1995). Since then, it has spread to almost all the Iberian Peninsula (Vinyoles et al. 2007). The aim of this study is to examine the current distribution of bleak in the NE Mediterranean watersheds, which include the inland waterways of Catalonia, the Senia and Ebre basins, and part of Garonne basin (Figure 2). Fish data were collected from field surveys in these basins, carried out during 2006-2008 for the development of an Index of Biotic Integrity using fish as biological indicators. Additionally, the spread of bleak in Catalonian watersheds was measured over two periods: 2002-2003 and 2007-2008. The distribution of bleak in Catalonian basins was limited to the Llobregat, Ebre, Fluvià and Muga basins during 2002-2003. It had spread to the Ter and Foix basins by 20072008 (Table 1). However, its frequency of occurrence decreased in the Llobregat and the Catalonian part of the Ebre basin, in comparison to 20022003. Throughout the Ebre basin the bleak tends to occupy the reaches where deep water and more stable flows are more likely to be found (Figure 3). Attenuation of natural flow fluctuations in Mediterranean water bodies caused by dams and water extraction has favoured the establishment of this and other exotic species. Indeed, expansion of the bleak correlates strongly with the construction of dams (Vinyoles et al. 2007). This is corroborated by the results of our recent surveys (2007-2008) in Catalonian drainages: the highest density of bleak population was found at a sampling point located close to the Foix dam (Figure 4).
Figure 3. Lower River Matarranya.
According to 2007-2008 data, the localities where bleak was found were mainly inhabited by introduced species, which includes exotic and translocated fish. The fish community was dominated by native species only in the Fluvià, Muga and Ebre basins. The co-occurrence of bleak with exotic piscivorous species was found in almost all cases. The predator species collected were Ameiurus melas, S. lucioperca, S. glanis, Lepomis gibbosus, Oncorhynchus mykiss and M. salmoides. Apart from bleak, other typical foraging species were also detected such as Rutilus rutilus and Scardinius erythrophthalmus. Native predators were also present in some localities (Anguilla anguilla and Salmo trutta).
The rapid expansion of bleak argues the need for systematic monitoring of those communities where it has been introduced and the consideration of controlling plans if necessary. These measures should be applied in all water bodies inhabited by introduced species, but monitoring programs are also required in pristine communities to evaluate their conservation status over time and to detect early possibly releases of exotic species. Control and prevention of the introduction of alien species is a complex matter in which the social, economic and environmental aspects must be weighed before any policy or management strategy can be instituted. In Spain, almost all intro-
duced fish species are the result of recreational fishing, and so cooperation between anglers and the administration is necessary. References DOADRIO I (2001) Atlas y libro rojo de los peces continentales de España. Dirección General de Conservación de la Naturaleza. Ministerio de Medio Ambiente. 364 pp. Madrid. ELVIRA B (1995) Native and exotic freshwater fishes in Spanish river basins. Freshwater Biology 33: 103-108. VINYOLES D, ROBALO JI, DE SOSTOA A, ALMODOVAR A, ELVIRA B, NICOLA GG, FERNÁNDEZ-DELGADO C, SANTOS CS, DOADRIO I, SARDÀ-PALOMERA F, ALMADA VC (2007) Spread of the alien bleak Alburnus alburnus (Linnaeus, 1758). Graellsia 63: 101-110. Autochthonous
Table 1. Range of distribution of the bleak Alburnus alburnus in Catalonian watersheds in 2002-2003 and 2007-2008 and percentage change between two study periods. Basins marked with a dash were not surveyed in 2007-2008.
Translocated
Bleak
Exotic
160,000
Besòs
Occurrence in 2002-2003 (%)
Occurrence in 2007-2008 (%)
Changes in distribution area (%)
0.00
0.00
0.00
Daró
0.00
0.00
0.00
Ebre
10.58
7.83
-2.75
Fluvià
5.56
10.00
4.44
Foix
0.00
28.57
28.57
Francolí
0.00
0
0
Gaià
0.00
0.00
0.00
Llobregat
15.22
11.25
-3.97
Muga
11.54
30.00
18.46
Ridaura
0.00
-
-
Riudecanyes
0.00
0.00
0.00
Sènia
0.00
-
-
Accumulative fish densities (ind/ha)
140,000
Basin
120,000
100,000
80,000
60,000
40,000
20,000
Ter
0.00
2.38
2.38
Tordera
0.00
0.00
0.00
DIS T RIBUT ION
OF
A L I EN
B L EA K
A L BUR N US
0
Ebro
Fluvia
Foix
Llobregat
Muga
Ter
Basins Figure 4. Accumulative estimated fish densities (ind/ha) of native, exotic and translocated species in the localities of inland Catalonian boundaries and the entire Ebro basin, where the bleak A. alburnus was present in 2007-2008. The contribution of bleak density is also shown separately.
A L BUR N US
( L I N N A EUS,
1 7 5 8 )
I N
TH E
N O RTH E A S TE R N …
145
Mapping Invasion by Alien Plants in Europe
,
PETR PYŠEK, MILAN CHYTRÝ, JAN WILD, JOAN PINO, LINDSAY C. MASKELL & MONTSERRAT VILÀ
Invasions by alien plants differ among habitats, as some are more vulnerable to invasion than others (Chytrý et al. 2005, 2008b). Recent research shows that the role of habitat is crucial in determining how many alien species successfully invade; it is even more important than the role of other factors such as propagule pressure (i.e., how many alien species are in the surroundings of the target site) and climate (in temperate and boreal zone, areas with warmer climate are more prone to invasions) (Chytrý et al. 2008a). From this it follows that how much a region is invaded by alien plants depends to a considerable extent on its habitat composition; areas with large proportion of vulnerable habitats harbour more alien species, which are usually also more abundant, than areas consisting of resistant habitats. Within the ALARM project, the role of habitats was paid special attention, for both scientific and practical
reasons. Knowing which habitats are most endangered by alien plants is not only interesting from the scientific point of view but also of practical relevance, because it enables local authorities and landscape managers to spend resources efficiently by targeting habitats that impose the highest risk of further spread of invasive species. A comparative study of invasions in habitats was carried out in three regions representing distinct European climates along the north-south and west-east climatic gradients: United Kingdom as a representative of the oceanic climate, Czech Republic of the subcontinental climate and Catalonia of the Mediterranean climate. We used data from a large number of vegetation plots, collected by vegetation scientists for the purpose of vegetation classification and monitoring; for the three above regions, there were 16,362, 20,468 and 15,650 vegetation plots,
respectively, which made a very robust basis for the analyses. The plots were classified to habitats by using the standard EUNIS habitats classification system, which allowed to compare the three regions in terms of the level of invasion of each habitat present. The level of invasion is a measure of how much a habitat, or a plant community growing in it, is invaded by alien plants; in our study it was expressed as the average proportion of alien species from the total number of plant species recorded in plots assigned to a given habitat type (Chytrý et al. 2008a). We focus here on the proportion of neophytes, which are plant species introduced to the three regions studied in the last five hundred years since the discovery of America. This is because this group of alien plants is more relevant in terms of practical importance than archaeophytes, the second group of aliens distinguished in Europe on the basis of
a
b
c
d
the time of arrival, which was between the beginning of Neolithic agriculture and ca 1,500 AD (Pyšek et al. 2005). Neophytes are the group from which most noxious plant invaders recruit. The level of invasion is different from invasibility, which reflects the inherent vulnerability of a habitat (or a plant community, ecosystem, region) to invasion. A habitat can be resistant to invasion but if it is located in a site exposed to a high propagule pressure (meaning that there is a constant and intensive influx of propagules of alien species), its resistance may be overcome and the habitat may harbour more alien species than another, less resistant habitat located in area with a low propagule pressure (Chytrý et al. 2008a). The comparison of the three European regions has shown that the pattern of plant invasions is consistent across the continent, meaning that the same habitats that are highly invaded in
Figure 1. Examples of European habitats prone to invasion: (a) ruderal vegetation (Slovenia), (b) riverine scrub (Sicily, Italy) and resistant to invasion: (c) alpine vegetation (Belianske Tatry Mts, Slovakia), (d) Mediterranean heathland (Korsica, France). Photos: Milan Chytrý.
146
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the subcontinental climate of Central Europe, have high proportions of alien species also in the zones of oceanic and Mediterranean climate (Figure 1). The habitats with the lowest proportions of neophytes are on soils with constantly low nutrient availability, such as mires (bogs, poor fens, base-rich fens), some grasslands (alpine grasslands, woodland fringes), heathlands and scrub (subalpine scrub, temperate heaths) and evergreen Mediterranean vegetation (maquis, garrigue, Mediterranean heaths, evergreeen woodland). The habitats with the greatest proportion of neophytes in all regions are anthropogenic habitats (arable land, ruderal vegetation, trampled areas), coastal, littoral and riverine habitats (coastal sediments, sedge-reed beds, wet scrub) (Chytrý et al. 2008b) (Figure 2). The among-regional consistency of the pattern of habitat invasions, as well as results from various local studies of habitat invasions from different areas of Europe, suggest that the data from the three regions studied are probably also valid for those regions from which the data on habitat invasions are not available. It is highly probable that habitats with low nutrients are little invaded, while frequently disturbed habitats
with fluctuating resource availability are highly invaded in the whole Europe. This assumption allows to extrapolate the results from the three model regions to other parts of Europe with similar climates and upscale the available data to the continental level. Such extrapolation was done in another study conducted within the ALARM project (Chytrý et al. 2009), in which the European map of alien plant invasions was produced. Using habitats as mapping units is suitable because it allows the extrapolation of quantitative estimates of the level of invasion to other regions with similar climate. Since there is no spatially-explicit information on the distribution of the EUNIS habitat types across Europe, these types had to be transferred to the CORINE landcover classes to allow mapping. Since most of the CORINE classes correspond to more than one EUNIS habitat, proportional contribution of the relevant EUNIS habitats was estimated for each CORINE land-cover class and its level of invasion was calculated as an average value of the corresponding EUNIS habitats, weighted by their proportional contributions. Extrapolations were constrained by European biogeographical regions in order to account
Great Britain
Czech Republic
Level of invasion <1 % 1-5 % >5 %
Catalonia
F7 Mediterranean heaths F6 Garrigue F2 Subalpine scrub G2 Evergreen woodlands E4 Alpine grasslands
Figure 3. European map estimating the level of invasion by alien plants, based on the mean percentage of neophytes in vegetation plots corresponding to individual CORINE land-cover classes. Within the mapping limits, areas with non-available land-cover data or insufficient vegetation-plot data are blank. Taken from Chytrý et al. (2009), published with courtesy of Blackwell Scientific Publications.
F5 Maquis D1 Bogs E5.2 Woodland fringes F4 Temperate heaths D4 Base-rich fens D2 Poor fens E5.5 Subalpine tall forbs E5.3 Bracken C2 Running waters E1 Dry grasslands B3 Coastal rocks H2 Screes E2 Mesic grasslands FA Hedgerows G5 Disturbed woodlands G1 and 4 Deciduous woodlands A2.5, D6 and E6 Saline habitats F3 Temperate scrub C1 Standing waters E3 and E5.4 Wet grasslands F9 Wet scrub H3 Cliffs and walls H5.6 Trampled areas B1 and B2 Coastal sediments C3 and D5 Sedge-reed beds E5.1 Ruderal vegetation G3 Coniferous woodlands I1 Arable land 0
5
10
25
Level of invasion (% of neophytes) Figure 2. Level of invasion (mean percentage of neophytes among the total number of species recorded in vegetation plots) in EUNIS habitats in the three European regions considered. Habitats are ordered by increasing sum of mean values from the three regions. Based on data from Chytrý et al. (2008 b), see this source for complete values. Mean values are similar among regions for most habitats, except G3 (Coniferous woodlands; high values in Britain) and H3 (Cliffs and walls; high values in the Czech Republic). Habitats not present or from which data are not available are indicated: × Great Britain, + Czech Republic, O Catalonia.
for biogeographical and climatic effects on the patterns of plant invasion among different parts of Europe (Chytrý et al. 2009). The resulting European map of the level of invasion by neophytes (Figure 3) projects the highest levels of invasion in moderately dry and warm lowland areas of western Europe (e.g., southeastern England or northwestern France) and in agricultural regions of central and eastern Europe (e.g., northern Germany, Poland, Czech Republic, Hungary and the lower Danube valley). In contrast, low levels of invasion are projected for the Boreal biogeographical region, Scotland, montane zones throughout the continent, and the Mediterranean region (including the sub-Mediterranean zone) where higher levels of invasion are projected only along the coastline, in areas with irrigated agricultural land and along rivers (Chytrý et al. 2009). The map (Figure 3) reflects the current state of plant invasions in Europe, but also provides a solid background for the assessment of future risk and for modelling future changes under various scenarios of climate and land-use change (Pyšek et al. 2010).
M A P P I N G
I N VA S I O N
BY
References CHYTRÝ M, JAROŠÍK V, PYŠEK P, HÁJEK O, KNOLLOVÁ I, TICHÝ L, DANIHELKA J (2008 a) Separating habitat invasibility by alien plants from the actual level of invasion. Ecology 89: 1541-1553. CHYTRÝ M, MASKELL LC, PINO J, PYŠEK P, VILÀ M, FONT X, SMART SM (2008 b) Habitat invasions by alien plants: a quantitative comparison between Mediterranean, subcontinental and oceanic regions of Europe. Journal of Applied Ecology 45: 448-458. CHYTRÝ M, PYŠEK P, TICHÝ L, KNOLLOVÁ I, DANIHELKA J (2005) Invasions by alien plants in the Czech Republic: a quantitative assessment across habitats. Preslia 77: 339-354. CHYTRÝ M, PYŠEK P, WILD J, PINO J, MASKELL LC, VILÀ M (2009) European map of alien plant invasions based on the quantitative assessment across habitats. Diversity and Distributions 15: 98-107. PYŠEK P, CHYTRÝ M, JAROŠÍK V (2010) Habitats and land-use as determinants of plant invasions in the temperate zone of Europe. – In: Perrings C, Mooney HA, Williamson M (Eds), Bioinvasions and globalization: Ecology, economics, management and policy. Oxford University Press, Oxford, 66-79. PYŠEK P, JAROŠÍK V, CHYTRÝ M, KROPÁČ Z, TICHÝ L, WILD J (2005) Alien plants in temperate weed communities: Prehistoric and recent invaders occupy different habitats. Ecology 86: 772-785.
A L I E N
PL A N TS
I N
E U RO PE
147
European Plants in Southern South America – Unwanted Visitors?
,
EDUARDO UGARTE, NICOL FUENTES & STEFAN KLOTZ
The situation The colonization of Chile and Argentina by the Spanish not only changed dramatically aboriginal societies but also the physical landscape. Conditions were set then for changes in the original flora mainly by introduction of new species providing food, medicine, ornamentals or as agricultural weeds accompanying them. Questions emerge immediately: when, how, which factors are the main contributors to the alien introduction and spread in southern South America? Are similar processes operating as in Europe?
Chile and Argentina provide an attractive scenario for such an investigation as they are relatively isolated at the southern tip of South America and share a common history of social development. Between both countries the Andean Cordillera acts as a barrier impeding, or as connector facilitating species exchange.
the same time fluctuations in the economy as depicted by key products we can discover explanatory connections between both. Most of the economic activity and human population is concentrated in Central Chile around Santiago (the capital) and Valparaiso (the main port) where the Spanish settled first and the climate is similar to that of the Mediterranean coast of Spain. As seen in Figure 1, there is a parallel between the timing of the increment of alien species (most of them European; see paragraph below) and economic growth, as depicted by key indicators, both in Chile and Argentina.
Setting the pace, learning from dates of collection and locations If dates of collection from herbarium specimens are plotted cumulatively it is possible to have an image of how the invasion process developed. Even more if, at the same time, we plot at
35
Certainly not all the alien species in Chile and Argentina are European. Trading has introduced diversity but clearly Europe is the main source of alien flora, contributing more than fifty percent of the species (Figure 4). Specialized studies demonstrate that the composition at the level of families or genus also follows known patterns already seen in other countries. Three examples (see inserts) illustrated negative but also positive effects of alien plants: Rosa rubiginosa (wild rose) has expanded in Chile and at present is an interesting export product (more than five thousand tons exported in
9 8
30
0
1900
1910
1920
1930
1940
1970
3 2
1990
2000
Time (10-year period)
400 300 200 100
1 0
2003
500
0
18 99 19 06 19 13 19 20 19 26 19 33 19 40 19 46 19 53 19 57
2,000
1980
4
Production of livestock (thousands of heads by year)
4,000
5
1900
1910
1920
1930
1940
1950
Figure 2. Spread of alien specimens in Chile (modified from Fuentes et al. 2008). Locations (red dots) and dates from Herbarium at the Universidad de Concepción (CONC), Chile.
OF
B I OD I V E RSITY
3 2 1 0
1970
1980
1990
2000
Figure 1b. Proportion curve in Argentina (as in figure 1 a). Cumulative number of alien species divided by cumulative number of native species in Argentina in ten years intervals. Proportions plotted against time. Source: Zuloaga & Morrone (1996, 1999). Inset 1 Production of wheat (average tons every nine years) in Argentina from 1899 to 1957. Source: Cortés (1966). Inset 2 Production of livestock (thousands of heads by year). Source: Secretaria de Agricultura, Ganaderia, Pesca y Alimentos (SAGPYA). Republica Argentina.
Not surprisingly most of the human population is also heavily concentrated in the same area. It is clear from history that, in both Chile and Argentina, alien species spread from one main focal point, both ports, Valparaiso and Buenos Aires in Chile and Argentina respectively. Figure 2 illustrates how alien species expanded from the central part of Chile's long and narrow territory.
AT L A S
5 4
Time (10-year period)
Figure 1a. Proportion curve in Chile (modified from Fuentes et al. 2008). Cumulative number of alien species divided by cumulative number of native species in ten year intervals. Proportions plotted against time. Source: Records from Herbarium at Universidad de Concepción, Chile (CONC). Inset 1 Production of wheat and other cereals (in thousands of metric quintals) in Chile from 1878 to 1930 (Source: Cariola & Sunkel 1982). Inset 2 Volume of forestry products (Green metric tons) exported by Chile (lumber, wood pulp, timber, wood carving, furniture and wood chips). Source: Corporación Nacional Forestal and Instituto Forestal (Source: www.conaf.cl).
148
1960
6
19 0 19 8 1914 1919 1937 1947 1957 19 61 1968 1972 1976 1980 1984 1988 92
6,000
19 6 19 2 6 19 6 7 19 0 7 19 4 7 19 8 8 19 2 8 19 6 9 19 0 9 19 4 98
30
1960
8,000
0
19
3
1950
19
19 1
18
0
18
4,000
19 05
5
8,000
10,000
6
Production of wheat (average ton x 9 years)
Ratio alien vs native species (%)
12,000
78
10
16,000
18 85
15
Green tons (m 3 x 1,000)
20
Metric/quintal x 1,000
Ratio alien vs native species (%)
7 25
RISK
CHAPTER
6
The result Figure 3 summarizes the present situation in Chile. Statistics show a concentration of aliens in Central Chile and a connection between species number and density of roads, which is a good indicator of economic activity and human population.
2006), Ulex europaeus (gorse) however, is a pest causing significant losses in agrar soils, while Cytissus scoparius (Scottish broom) has an important centre of evolution in Spain. In Chile it is used for terrain stabilization but is also an invader. It is easily dispersed by roads and river banks where it modifies patterns of nitrogen fixation. What does the future hold? More European species moving to South America – or vice versa? Does Chile pose a threat to Argentina – or the other way round? Globalization and the expansion of trade mean new opportunities for species to expand into new territo-
ries. Better systems for prevention and combat are possible only after scientific knowledge of processes at different scales is achieved. The geographical situation of Chile and Argentina offers an advantageous “clean” scenario to study the “filtering” of species through a common border defined by a Cordillera. It is now possible to predict which species, in what specific biogeographical regions are potential invaders of the other country. Only international collaboration can provide the approach needed to design efficient protection systems.
Desert
Semi-Desert
Mediterranean
Temperate-rainy
Cold-rainy
Acknowledgements Research is funded by the EC within the FP 6 Integrated Project “ALARM” (Settele et al. 2005, and this atlas, pp. 38ff.).
Cold steppe
10,000
8,000
6,000
4,000
2,000
0
0
Roads (km)
20
40
60
80
100
120
Alien plant species
Population per administrative region
Figure 3. Density of roads, alien plant species and human population by biogeographical region in Chile.
Europe South America Eurasia America Central America Africa North America Asia Cosmopolitan Australia New Zealand
0.14 % 1.09 % 52.37 % 2.17 % 2.85 % 2.85 % 4.21 %
4.88 % 5.97 % 7.87 %
15.60 %
0.77 0.77 % % 10.51 10.51 % % 0.77 0.77 % % 4.79 4.79 % % 4.79 4.79 % % 57.19 57.19 % % 11.90 11.90 % %
7.42 7.42 % %
0.62 0.62 % % 0.93 0.93 % % 0.31 0.31 % %
Figure 4. Main geographical sources of alien plant species introduced to Chile and Argentina.
References CARIOLA C, SUNKEL O (1982) Un siglo de historia económica de Chile 1830–1930, dos ensayos y una bibliografía. Ediciones cultura hispánica del Instituto de Cooperación Iberoamericana, España. CORTÉS R (1966) Cambios históricos en la estructura de la producción agropecuaria en la Argentina utilización de los recursos. – Desarrollo Económico 5: 493-509. FUENTES N, UGARTE E, KÜHN I, KLOTZ S (2008) Alien plants in Chile. Inferring invasion periods from herbarium records. Biological Invasion 10: 649-657. SETTELE J, HAMMEN V, HULME P, KARLSON U, KLOTZ S, KOTARAC M, KUNIN WE, MARION G, O'CONNOR M, PETANIDOU T, PETERSEON K, POTTS S, PRITCHARD H, PYŠEK P, ROUNSEVELL M, SPANGENBERG J, STEFFAN–DEWENTER I, SYKES MT, VIGHI M, ZOBEL M, KÜHN I (2005) ALARM: Assesing LArge scale environmental Risks for biodiversity with tested Methods. GAIA – Ecological Perspectives in Science, Humanities, and Economics 14: 9-72. ZULOAGA F, MORRONE O (1996) Catálogo de las plantas vasculares de la República Argentina. I. Pteridophyta, Gymnospermae y Angiospermae (Monocotyledoneae). Monographs in Systematic Botany from the Missouri Botanical Garden 60: 1-323. ZULOAGA F, MORRONE O (1999) Catálogo de las plantas vasculares de la República Argentina (Dicotyledoneae). Monographs in Systematic Botany from the Missouri Botanical Garden 74: 1-1246.
Figure 5. Example of three invasive species in Chile Rosa rubiginosa, Cytisus scoparius, and Ulex europaeus. Photos by N. Fuentes.
E U RO P E A N
P L A N T S
I N
S O U T H E R N
S O U T H
A M E R I C A
–
U N WA N T E D
V I S I T O R S ?
149
The Hogweed Story: Invasion of Europe by Large Heracleum Species
,
PETR PYŠEK, JAN PERGL, ŠÁRKA JAHODOVÁ, LENKA MORAVCOVÁ, JANA MÜLLEROVÁ, IRENA PERGLOVÁ & JAN WILD
## ### ###
### ### #### ###### ####### ######## ########### ############# ## ##### ## ## # ##### ## ### ## ####### # ####### ####### # ## ###### ### # # # ### ## #### #### # # ### ## # # #### ### # # # # ### ### # ## ## ### ## #### ## # ### # ## # # # ## ### #### ### #### # # # # ## ### # # # # # # # # # # ## ## ## # ## # # ## # #### ## # # # # # # #### #### ## #### # ## # ## #### #### ### # # # # # # # # # # # # # # # ## # # ## # # ### # ## ## ## #### ## # # # #### ## # # # # # # # ### ## ## ## ### ## # ## ### ## ### ## # # # ## ### #### ## # # ## ## # # # ## #### #### # ## ## #### #### # # # # # #
Large hogweed species as invaders in Europe One of the most spectacular invasions of Europe by alien plants is that of species of the hogweed genus (Heracleum) from the family Apiaceae (Pyšek et al. 2007). Several of the large members of the genus were introduced as garden ornamentals or as fodder crops outside their native range. The most distinctive characteristic of these closely related species is their size; they can attain heights of up to 4-5 m, which ranks them among the tallest and largest herbs in Europe, thus they are called “large, tall or giant” hogweeds. Three “tall” hogweeds have become invasive in Europe: Heracleum mantegazzianum (native to Western Greater Caucasus), H. sosnowskyi (native to Central and Eastern Greater Caucasus and Transcaucasia) and H. persicum (native to Turkey, Iran and Iraq). For several reasons, historical data on the occurrence of these species in Europe are fairly detailed, especially in countries with a strong floristic tradition, and allow a good retrospective analysis of their spread. Large hogweed species are attractive enough to be recorded by botanists, because of their alien origin, tendency to spread and conspicuous appearance; this holds true especially for the most widely distributed species, giant hogweed, Heracleum mantegazzianum (Figure 1) (Jahodová et al. 2007a). In addition, the production of phototoxic sap, dangerous to human health (Figure 2) increases public awareness of this invasion (Nielsen et al. 2005).
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Figure 3. Distribution of three “tall hogweed” species invading in Europe in 50 × 50 km grid cell. Red – Heracleum mantegazzianum, blue – H. persicum, green – H. sosnowskyi. Regions displayed in respective colours are those from which the species are reported but exact distribution in grid cells is unknown. Note that H. sosnowskyi also occurs in Russia (Adapted from Jahodová et al. 2007a).
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History of introduction Heracleum sosnowskyi was introduced into Europe as an agricultural crop for silage, to provide fodder for livestock in north and north-west Russia since its introduction in 1947. From the 1940s onwards, it was introduced as a
this country it needed 15 years to appear in the wild. Ranked according to the date of introduction, the UK was followed by the Netherlands, Switzerland, Germany, Ireland, Denmark, the Czech Republic; other countries followed later on and cur-
Figure 1. Plants of giant hogweed (Heracleum mantegazzianum) are up to 4-5 m tall and a solitary plant can give rise to a population by means of self-fertilization. Photo: Jan Pergl.
Figure 2. Plants contain phototoxic juices which create blisters on human skin if it is exposed to the sun. Photo: Marion Seier.
crop to the Baltic countries (Latvia, Lithuania and Estonia), Belarus, Ukraine and the former German Democratic Republic. Although this practice was later abandoned, because the anise-scented plants affected the flavour of meat and milk of the animals fed this fodder and phototoxic saps imposed health risk to humans and cattle, it is still cultivated in northern Russia (Jahodová et al. 2007a). The other two hogweed species were introduced as garden ornamentals from their native ranges in the 19th century. The main mechanism of their introduction into Europe and further spread was ornamental curiosity. Seeds were planted in botanic gardens and the grounds of important estates; this continued for most of the 19th century and only declined and eventually ceased after warnings about the dangers of the plant appeared in western European literature towards the end of the 20th century (Nielsen et al. 2005). The first known record of Heracleum mantegazzianum is for England, when it appeared on the Kew Botanic Gardens, London seed list in the 1817. Eleven years later, in 1828, the first naturalized population was recorded in the wild in Cambridgeshire, England, and soon afterwards, the plant began to spread rapidly across Europe. Data from the Czech Republic confirm that the species was able to escape from cultivation after a very short period of time; in
rently giant hogweed is reported to occur in 19 European countries (Pyšek et al. 2008). The earliest record of Heracleum persicum comes from the seed list of the Kew Botanic Gardens in London, from 1819. Seeds from London populations of were taken by English horticulturalists and planted in northern Norway as early as 1836.
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Current distribution of large hogweed species in Europe In all three species, the introduction history determines their current distribution in the invaded European range. This is the main reason why H. sosnowskyi and H. persicum occur in the north-eastern part of Europe and Fennoscandia, respectively (Figure 3). In Heracleum mantegazzianum, the most widespread of the three species, the distribution is clearly biased towards the central and northern part of the continent (Figure 3; Jahodová et al. 2007a). That the species is virtually absent from southern Europe reflects its origin in the Caucasus mountains where the climate is cooler; plants are not adapted to the warm climate in the south of Europe which constrains their invasion (Moravcová et al. 2006). A likely reason for the widespread current distribution of Heracleum mantegazzianum in Europe is multiple introductions as suggested by genetic analysis of invading and native populations of this species (Jahodová et al. 2007b).
What makes giant hogweed so invasive? Unlike many other alien species, majority of which do not form large populations, Heracleum mantegazzianum usually occurs as a dominant species of invaded communities (Figure 4). It has been estimated that in the Slavkovský les region in the western part of the Czech Republic, where the species was first introduced, its invaded population cover about 7 % of nonforested landscape. This is made possible by a unique combination of traits (Table 1) and suitable environmental conditions. Although the species does not seem to possess any special characteristic/mechanism, extremely high fecundity, rapid growth, capability of self-pollination, extended germination period by means of short-term persistent seed bank, high germination and negligible impact of natural enemies are all characteristics associated with invasiveness in plants (Figure 5). Therefore, it is a combination of superior traits acting at different stages of the life cycle with remarkable invasion potential which resulted in the strategy a
b
c
Figure 4. Giant hogweed form large stands that dominate the landscape and are conspicuous at both flowering (a–b) and fruiting stage (c). Photo: Petr Pyšek (a, c), Jan Pergl (b).
Table 1. Life history traits of Heracleum mantegazzianum supporting its invasiveness. Based on data collected in invaded populations in the western part of the Czech Republic (see Pyšek et al. 2007 for summary). Germination
germinates in early spring high germination rate, 91 % seed germinate in laboratory Seedling competition seedling density 500-700/m2, with maxima up to 3700/m2 a high relative growth rate of seedlings low mortality of established seedlings Vegetative plants rapid growth of rosettes Flowering strategy no constraints to flowering (completed in ca 30-40 days), fruit released after 2 months ability to postpone flowering under unfavourable conditions Reproductive system ability to self-pollinate
advantage of developing populations well ahead of resident vegetation rapid and effective space pre-emption rapid and effective space pre-emption competitive advantage over resident vegetation competitive advantage, space pre-emption formation of dense cover, suppression of other species assurance of population reproduction population persistence over long period reproductive assurance, potential to found invasive population from a single individual population maintenance and spread good capacity for spread of seed to distant areas adjustment to between-year variation in environmental conditions
Regeneration ability
of Heracleum mantegazzianum being called a ‘master-of-all-traits’ of plant invasions (Pyšek et al. 2007). Spread of giant hogweed at local, regional and continental scales The data collated during the 5FP project GIANT HOGWEED (www.giantalien.dk) and analysed within the ALARM project make it possible to assess the spread of Heracleum mantegazzianum at the local scale of the Slavkovský les region (Figure 6, Müllerová et al. 2005), regional scale of the Czech Republic (Figure 7) and the European scale (Figure 8). Comparison of the rate of spread at the three scales indicates that there are two different mechanisms of spread acting together in this system, namely human influences and natural spread, and the relative influence of these mechanisms appears to change in an inverse proportion from the largest to the smallest scale: the invasion was slowest at the conti-
1991
1962
Figure 6. Invasion of giant hogweed at a local scale in the Slavkovský les Protected landscape area, Czech Republic. Individual plants are recognizable by large white flowering umbels, the grid indicates extent of hogweed population in 1962 and 1991 (Adapted from Müllerová et al. 2005).
nental scale and did not differ significantly between regional and local scales. At the local scale, under suitable habitat conditions, the process is driven by biological traits of the species related to dispersal. At the continental and regional scales, humans played a crucial role in the invasion of H. mantegazzianum by
planting it as a garden ornamental, and human-mediated dispersal seems to have been the major driver of spread, responsible for creating dispersal foci in the initial phases of invasion. Species traits played an important role in local spread, resulting in the colonization of new sites (Pyšek et al. 2008).
1970
!
!
^
1909
!
1899
!
!
1900
!
1907
!
1915
!
! !
!! !! ! !
!
Figure 5. High density of seedlings early in the spring (a) and massive production of fruits (b) are among traits supporting the invasion of giant hogweed. Photo: Jan Pergl (a), Petr Pyšek (b).
References
1920
1877
b
high fecundity, single plant produces ca 20,000 seeds effective dispersal by various means (human activities, water, wind) extensive and short-term persistent seed bank, > 2000 viable seeds/m2 present in soil in the spring seed longevity minumum 5 years long-term population persistence in the form of dormant seed if cut at ground level, regeneration in the same year produces 3-4 % of resistance to control measures seed of control plants
Dispersal Seed bank
!
a
! ! !!
!
!!
!
!
!
! !
!
!
!
!
!!!
!! ! ! !
!
!
!
!
!
! !
!
!
! !
! !
!
!! ! !
! !!
2000 !
! ! ! ! ! !!! ! !! !! ! !!! ! !! !! !!! !! !! ! !! !! ! !! ! !!! !! !! !! ! ! ! ! !!!!! !!!!! ! ! ! !!! !! !! !!!!! !!!!! ! ! ! !!!! ! !!!! !!!!!! ! ! ! ! !! !! !!!!! ! ! !! !!!!!!! ! ! ! ! ! ! ! ! ! ! !! !! !!!!!! !!!! ! ! ! ! ! ! ! ! ! !! ! !!! ! !!! !!! !!!!! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !
!! ! ! ! !!! ! ! ! ! !! ! ! ! !!! ! ! !
!
!
Figure 7. Invasion of giant hogweed at a regional scale of the Czech Republic. Distribution in grids of 11 × 12 km is shown for 1920, with years of first records indicated), 1970, before the start of the massive spread) and 2000 (Adapted from Pyšek et al. 2008).
JAHODOVÁ Š, FRÖBERG L, PYŠEK P, GELTMAN D, TRYBUSH S, KARP A (2007a) Taxonomy, identification, genetic relationships and distribution of large Heracleum species in Europe. – In: Pyšek P, Cock MJW, Nentwig W & Ravn HP (Eds) Ecology and management of giant hogweed (Heracleum mantegazzianum), CAB International, Wallingford, UK, 1-19. JAHODOVÁ Š, TRYBUSH S, PYŠEK P, WADE M, KARP A (2007b) Invasive species of Heracleum in Europe: an insight into genetic relationships and invasion history. Diversity and Distributions 13: 99-114. MORAVCOVÁ L, PYŠEK P, PERGL J, PERGLOVÁ I, JAROŠÍK V (2006) Seasonal pattern of germination and seed longevity in the invasive species Heracleum mantegazzianum. Preslia 78: 287-301. MÜLLEROVÁ J, PYŠEK P, JAROŠÍK V, PERGL J (2005) Aerial photographs as a tool for assessing the regional dynamics of the invasive plant species Heracleum mantegazzianum. Journal of Applied Ecology. 42: 1042-1053. NIELSEN C, RAVN HP, COCK M, NENTWIG W (Eds) (2005) The giant hogweed best practice manual. Guidelines for the management and control of an invasive alien weed in Europe. Forest and Landscape Denmark, Hoersholm, Denmark, 44 pp. PYŠEK P, COCK MJW, NENTWIG W, RAVN HP (Eds) (2007) Ecology and management of Giant Hogweed (Heracleum mantegazzianum). CAB International, Wallingford, UK. vii + 324 pp. PYŠEK P, JAROŠÍK V, MÜLLEROVÁ J, PERGL J, WILD J (2008) Comparing the rate of invasion by Heracleum mantegazzianum at the continental, regional and local scale. Diversity and Distributions 14: 355-363.
1964 1903 1900 1828 1869 1855
1840
_ ^
1850
_ ^
1900
_ ^
1844
_ ^
1940
1877
1965 1966
1950
1888 1904
Figure 8. Invasion of giant hogweed at the continental scale of Europe. Countries from which the species was reported are shown in 50-yr intervals, with the year of the first record in the country indicated. Countries with earlier introduction are indicated using a darker shade of red. Countries in blue are those that were studied but giant hogweed was not recorded there or the date of introduction is unknown (Adapted from Pyšek et al. 2008).
T H E
H O G W E E D
S T O RY:
I N VA S I O N
O F
E U RO P E
B Y
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151
Terrestrial Alien Vertebrates in Europe
,
WOJCIECH SOLARZ, WIESŁAW KRÓL, SVEN BACHER, WOLFGANG NENTWIG & DANIEL SOL
Biological invasions of alien terrestrial vertebrates are relatively well studied compared with invasions of other taxa. This is in part due to the fact that many alien herptiles, birds and mammals were introduced intentionally and their spread and impact upon native environment were often monitored from early stages. Additionally, 40 35
Number of species
30 25 20 15 10 5 0
<10
11-100
101-1,000
>1,000
Number of 50 x 50 km grid cells occupied by a species Figure 1. Area invaded by alien terrestrial vertebrates in Europe
terrestrial vertebrates are large enough to be easily detected and cannot usually be confused with other species. Thus, data on their current distribution is fairly accurate. The distribution of alien terrestrial vertebrates in different parts of Europe was mapped on a 50 × 50 km grid of 2089 cells based on information from atlases of amphibians and reptiles (Gasc et al. 1997), birds (Hagemeijer & Blair 1997) and mammals (MitchellJones et al. 1999). The grid covers most of Europe, although most of the eastern European countries (except for the Baltic States) were not considered since the alien terrestrial vertebrates from these regions were poorly sampled. We digitized the distribution of 29 alien herptiles occupying 294 grid cells, 21 alien birds occupying 2,266 grid cells, and 24 alien mammals in 6,414 grid cells. Altogether, we obtained distribution patterns of 74 alien terrestrial vertebrates occupying a total of 8,974 grid cells. The quality of the original atlas data for herptiles made it possible to identify for each grid cell the status of species that are native to parts of Europe and alien to other parts of the continent, and such species were included in the analyses. Bird and mammal data did not allow for such distinction, and we therefore only considered 152
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birds and mammals that are alien throughout their European range, i.e. of non-European origin. The most widespread alien species were the brown rat Rattus norvegicus, occupying 1,759 grid cells, pheasant Phasianus colchicus (1,456 cells), muskrat Ondatra zibethicus (944 cells), and American mink Neovison vison (891 cells). However, the range of the majority of alien terrestrial vertebrates is rather restricted, with nearly half (48.6 %) of all species occupying only 10 or fewer grid cells (Figure 1). While this bias may reflect generally low invasion potential of some species, it can certainly also be attributed to the fact that many species have been introduced only recently and expansion of their range still continues. The restricted range of some species can also indicate incomplete information for the areas where they occur, although the distribution of alien vertebrates in Europe is generally well-known. The average number of these species present in a single grid cell for which any data was available was 4.1 (S.D. = 2.2). Three grid cells had as many as 16 alien terrestrial vertebrates. Interestingly, these cells were clumped in south-eastern England (Figure 3). The United Kingdom generally appears to be severely invaded, with 14 cells occupied by as many as 12 or more alien terrestrial vertebrates. A large number of species (9 or more) per grid cell can also be found in parts of the
Benelux countries, Germany, Switzerland, Austria, the Czech Republic and Italy. Italy and the United Kingdom also appear as being severely struck by invasions if one takes into account the total number of alien terrestrial vertebrates for the whole country, with 28 and 23 species, respectively. However, it is France that takes first place with 31 species. Generally, a large total number of terrestrial alien vertebrates are found in western and southern European countries (Table 1). The greater number of alien species in some regions compared to others might in part be the result of a greater number of introduction attempts. To validate this possibility, we digitized data from the literature on 465 introduction places of alien terrestrial vertebrates, including 149 introductions of 37 herptile species, 219 introductions of 56 birds, and 97 introductions of 34 mammals (Figure 2). This dataset obviously does not cover all introduction events that have taken part in the past, and in each group there were a few species that accounted for a large proportion of the collected information, while for the majority of species we only managed to gather data on 1 or 2 introductions. Despite this, propagule pressure appears to contribute significantly to the regional variation in the richness of alien terrestrial vertebrates in Europe: the greatest numbers of introductions were recorded for the United Kingdom
Figure 2. Known introduction places of alien terrestrial vertebrates in Europe; one dot may represent more than one introduction.
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Table 1. Number of terrestrial alien vertebrates in European countries.
Country France
Number of terrestrial alien vertebrates 31
Italy
28
United Kingdom
23
Spain Germany
20 19
Netherlands
17
Belgium
15
Czech Republic
15
Austria
14
Former Yugoslavia
14
Switzerland
13
Soviet Union
11
Denmark
10
Finland
10
Ireland
10
Poland
10
Bulgaria
9
Greece
9
Sweden
9
Hungary
8
Lithuania
8
Portugal
8
Slovakia
8
Romania
7
Estonia
6
Norway
6
Turkey
6
Albania
5
Latvia
5
Isle of Man
4
Malta
4
Faroe Is.
3
(N = 155), Spain (N = 62), France (N = 54) and Italy (N = 35), that is, countries with very large total numbers of alien terrestrial vertebrates. Moreover, the total number of species in a country was positively correlated with the total number of introduction events for all species in that country (rs = 0.64; p < 0.001; N = 31). The analyses presented do not claim to be a comprehensive assessment of threat from invasions by alien terrestrial vertebrates. However, they confirm that no part of the continent is free from alien species and some areas are very severely affected by them. Thus, the problem of invaders, including terrestrial vertebrates, is a very serious one in Europe. The real picture of biological invasions is even worse, as the 74 species of terrestrial alien vertebrates that were used for the analyses constitute only about 2 % of all alien animals and less than 1 % of all alien species that were introduced to Europe (DAISIE 2008).
Infestation of Europe – all terrestrial vertebrates
>= 1 species (2230 cells)
>= 2 species (1984 cells)
>= 3 species (1635 cells)
>= 4 species (1261 cells)
>= 5 species (808 cells)
>= 6 species (445 cells)
>= 7 species (258 cells)
>= 8 species (158 cells)
>= 9 species (92 cells)
>= 10 species (46 cells)
>= 11 species (19 cells)
>= 12 species (14 cells)
>= 13 species (11 cells)
>= 14 species (6 cells)
>= 15 species (4 cells)
=16 species (3 cells)
Figure 3. Number of terrestrial alien vertebrates in 50 × 50 km grid cells.
References DAISIE European Invasive Alien Species Gateway (http://www.europe-aliens.org/) GASC JP, CABELA A, CRNOBRNJA-ISAILOVIC J, DOLMEN D, GROSSENBACHER K, HAFFNER
P, LESCURE J, MARTENS H, MARTÍNEZ RICA JP, MAURIN H, OLIVEIRA ME, SOFIANIDOU TS, VEITH M, ZUIDERWIJK A (Eds) (1997) Atlas of amphibians and reptiles of Europe. Collection Patrimoines Naturels, 29, Paris, SPN / IEGB / MNHN.
HAGEMEIJER WJM, BLAIR MJ (Eds) (1997) Atlas of European Breeding Birds: Their Distribution and Abundance. T & AD Poyser, London. MITCHELL-JONES AJ, AMORI G, BOGDANOWICZ W, KRYŠTUFEK B, REINJDERS PJH,
T ER R EST R I A L
A L I E N
SPITZENBERGER F, STUBBE M, THISSEN JBM, VOHRALIK V, ZIMA J (1999) The Atlas of European Mammals. Academic Press, London.
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153
The Exotic Mammals of Argentina
,
RICARDO A. OJEDA, AGUSTINA NOVILLO & FERNANDA CUEVAS
The exotic mammals in South America represent about 20 % of world mammal introductions. We recorded feral populations of 18 species of exotic mammals for Argentina (Novillo & Ojeda 2008). The majority of introductions occurred between the 18th and 20th centuries and their ports of entry were located in temperate ecosystems, between 34° and 55° S.
Alien mammals display good climatic matching (i.e., occupy ecoregions similar to their native ranges), and some species have experienced a range expansion to new habitat types (e.g., hare, rabbit and wild boar).
The majority of the species are from Eurasia, and most of their entry pathways were associated with human activities (e.g., sport hunting, food and fur industry).
Figure 2. Equs assinus. Photo: Ramiro Ovejero.
Figure 3. Lepus europaeus. Photo: Alec Earnshaw.
Figure 1. Temperate South America shares similar ecoregions with Eurasia. This is reflected in the good matching between native and invaded ecoregions and partially explains the successful establishment of introduced mammals. Table 1. Attributes of successful invaders
Several attributes were suggested for the success of invasive species. The table lists some of these traits as applied to the invader mammals of Argentina (Novillo & Ojeda 2008). These traits are not only intrinsic to the species (i.e., reproductive rate, body mass, abundance, size of native range, and so on) but also to the habitat they invade (i.e., vacant niches, climatic matching, diversity of resources, and so on). The species encompassing most of these traits are Sus scrofa, Cervus elaphus, Capra hircus, Lepus europaeus and the old world rats (Rattus and Mus). Six of the invader mammals occurring in Argentina are among the 100 worst invasive species in the world. The fauna of exotic mammals of Argentina represents a wide diversity of ecological groups which offer an enormous opportunity for longterm ecological research.
Figure 4. Callosciurus erythraeus. Photo: Fernando Milesi.
Table 2. NA: North America, EA: Eurasia, NH: North Hemisphere, PI: Pakistan and India.
“Good” traits
Native region
Date
Old world rats; L. europaeus; O. cuniculus; C. hircus; S. scrofa; E. assinus; E. caballus
Mustela vison
N.A
1930
Castor canadensis
N.A
1945
Larger than most relatives (advantage competition, dispersal)
C. elaphus; R. tarandus; S. scrofa; E. assinus; E. caballus
Ondatra zibethicus
N.A
1945
Callosciurus erythraeus
EA
1970
Associated with Homo sapiens (deliberate or no assistance)
Old world rats; L. europaeus; O. cuniculus; C. hircus; S. scrofa; E. assinus; E. caballus.
Rattus norvegicus
EA
1600-1800
Rattus rattus
EA
1600-1800
Generalist in habitat use – (Type of habitat is not a limiting factor)
L. europaeus; O. cuniculus; A. axis; C. elaphus; D. dama; R. tarandus; C. hircus; A cervicapra; S. scrofa; E. assinus; E. caballus.
Mus domesticus
EA
1600-1800
Lepus europaeus
EA
1888
Short generation time (high reproductive capacity)
M. vison; C. canadensis; O. zibethicus; Old world rats; L. europaeus; O. cuniculus; C. hircus; S. scrofa
Oryctolagus cuniculus
EA
1945
Large native range
M. vison; C. canadensis; O. zibethicus; R. norvegicus; L. europaeus; C. elaphus; R. tarandus; S. scrofa
Broad diet (generalist)
Examples
Species
Axis axis
Asia
1930
EA, NA
1904-1906
Dama dama
EA
1930
Rangifer tarandus
NH
1909
Capra hircus
EA
1856
PI
Unknown
Sus scrofa
EA
1904-1906
Cervus elaphus
No ecological counterpart (theory of vacant niche)
C. canadensis; O. zibethicus; C. elaphus; R. tarandus; C. hircus; A. cervicapra; S. scrofa; E. assinus; E. caballus
Antilope cervicapra Eqqus assinus
EA, N. Africa
Unknown
Climatic matching
L. europaeus; S. scrofa; O. cuniculus
Eqqus caballus
EA
1600
154
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The wild boar, Sus scrofa: a successful invader of the Monte Desert, Argentina The wild boar, Sus scrofa, is native to Eurasia and northern Africa. In the early 1900s it was introduced onto Argentina’s ranches as a game animal for hunting. During 1914 many individuals escaped, establishing feral populations and spreading their distribution over several provinces. In 1953, a national law declared the wild
boar a “plague” species because of the economic damage caused to agriculture and livestock rearing activities. Because of its impact on biodiversity and human activities, the wild boar has been classified as one of the 100 worst Invasive species in the world. Why is the wild boar successful as an invasive species? Among the possible causes are: its large body mass, wide natural geographic range (one of the largest geographic ranges among terrestrial mammals), adaptation to diverse ecoregions, high reproductive rate
(up to 2 litters per year), omnivorous diet, among others. Habitat It prefers damp areas in coniferous, deciduous or mixed forests, marshes and grasslands. In Argentina the wild boar expanded its geographic range from the grasslands of western Patagonia to the shrublands of central Argentina. In Mendoza province the wild boar has invaded the protected areas of the MaB Reserve of Ñacuñán, (site under study) and Llancanelo (a wetland RAMSAR site).
Figure 5. Wild boar. http://www.huntingchile.5u. com/about_1.htm
Figure 8. Soil rooting by wild boar. Photo: Fernanda Cuevas.
◙ It generates large areas devoid of vegetation, changing the soil properties, plant structure and composition (ongoing research by Fernanda Cuevas, GiB, IADIZA-CONICET). The establishment and expansion of plant species such as Pitraea cuneato-ovata (Verbenaceae), seem to be associated with the wild boar's soil rooting activity. Furthermore, this plant species is an important item in its diet. ◙ Frugivory and seed predation (i.e., predation on legume seed pods, Campos & Ojeda 1998). ◙ Predation on soil nesting birds and eggs (e.g., common rhea, Rhea americana, and tinamous Eudromia, Nothoprocta, Nothura; burrowing owl, Athene cunicularia; seed eating birds; eggs of tegu lizard, Tupinambis rufescens, desert turtle Chelonoidis chilensis, among others. ◙ Bark damage (scratching, gouging with tusks) to keystone species such as the legume trees algarrobo, Prosopis flexuosa and chañar, Geofroea decorticans. ◙ Damage to crops and transmission of diseases to humans as Trichinella and Cysticercosis. It is also common in departments of La Paz, General Alvear and Lavalle. Diet It is omnivorous. Its diet is based mainly on leaves (54.86 %), rhizomes of Pitraea cuneato-ovata (Verbenaceae; 20.76 %) and fruits (7.62 %). They may include animal matter as small rodents and birds, eggs, invertebrates and carrion.
Figure 6. A nest built by wild boar to give birth and to rest in. Photo: Agustina Novillo.
References
Figure 9. Tracks and faeces. Photos: Fernanda Cuevas. 60 50 40 30 20 10 0 Figure 7. Signs of rubbing: Once a boar leaves a mud bath, it rubs its body against trees, rocks or weeds. Photo: Fernanda Cuevas.
Potential impacts in the Monte Desert ecosystem
Leaves
Bulbs
Fruits
Seeds
Arthropods Glumes of grasses
Stems
Animal tissue
BAILEY RG (1989) Ecoregions. The ecosystem geography of the oceans and continents. Springer, USA. CAMPOS C, OJEDA RA (1997). Dispersal and germination of Prosopis flexuosa (Fabaceae) seeds by desert mammals in Argentina. Journal of Arid Environments 35: 707-714 CUEVAS MF, NOVILLO A, DACAR M, CAMPOS C, OJEDA RA (2006). Ecología del jabalí, Sus scrofa, en el desierto del Monte. XXII Reunión Argentina de Ecología. Agosto, Córdoba, Argentina. LONG JL (2003) Introduced mammals of the World their history, distribution and influence. CABI, UK. NOVILLO A, OJEDA RA (2008) The Exotic Mammals of Argentina. Biological Invasions, 10: 1333-1344.
Figure 10. Diet of wild boar in MaB Reserve of Ñacuñan, Mendoza, Argentina (Ongoing research).
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The ALARM Field Site Network, an Outstanding Tool for the Survey of Invasive Insects Infesting Seeds of Wild Roses in Europe MARIE-ANNE AUGER-ROZENBERG, EDUARDAS BUDRYS, THEODORA PETANIDOU, MILKA GLAVENDEKIĆ, RICCARDO BOMMARCO, SARA BONZINI, GYÖRGY KRÖEL-DULAY, JARA ANDREU URETA, MARI MOORA, SIMON G. POTTS, AGNÈS RORTAIS, JANE STOUT, IVÁN TORRES, CATRIN WESTPHAL, HAJNALKA SZENTGYÖRGYI, SÉBASTIEN DESBOIS, PHILIPPE LORME, JEAN-PAUL RAIMBAULT, PATRICK PINEAU & ALAIN ROQUES
,
Although worldwide exchange and trade of tree reproductive materials is rapidly increasing with the development of plantations and ornamentals parks using exotic tree species, little information is available about associ-
pests and their subsequent introduction in other continents. Thus, 8 species were recorded as aliens among the 21 species of Megastigmus seed chalcids (Hymenoptera: Torymidae) infesting
which is then entirely consumed by the developing larva (Figure 2).These insects have a high invasive potential because of specific biological patterns such as a parthenogenetic reproduction, an ability to extend larval dia-
Methods used in the survey Standardized collections of 50 mature hips, 5 per shrub on 10 shrubs whenever available, were carried out in both the disturbed and undisturbed areas of 15 ALARM sites between
Figure 3. M. aculeatus female: a – light form (left); b – black form (right). Photo: A. Roques, INRA.
tree seeds in Europe (Roques & Skrzypczyńska 2003). These introduced chalcids have yet colonised 29 species of native conifers, 28 species of Rosaceae and 9 species of Anacardiaceae. These introductions may negatively impact both natural regeneration of native forests and insect biodiversity with which they interact through competition for seed resources. Using its long ovipositor (Figure 1), the female chalcids lay eggs through the cone tissues directly into the seed
pause in seeds for several years, and a capability of developing within unfertilised seeds for some species (Roques & Skrzypczyńska 2003). Moreover, larvae cannot be detected by examining the outside of the seeds and only show up when X-rayed (Figure 2). This raises the question of whether the number of alien seed chalcid species recorded so far in Europe is not seriously underestimated. The distribution of ALARM field sites all over Europe (see Hammen et al., this atlas, pp. 42ff.) offered large facilities for a survey of alien seed insects and their impact on widespread species of trees and shrubs. Wild roses (Rosa spp.) were selected as model plant species because they are present throughout Europe. Only 2 native rose chalcid species were previously known in Europe, including the widespread M. aculeatus and the apparently central European M. rosae, whilst an alien species introduced from North America, M. nigrovariegatus, was scarcely observed in France (Roques & Skrzypczyńska 2003).
Figure 4. M. rosae (female). Photo: A. Roques, INRA.
Figure 5. M. alba (female). Photo: A. Roques, INRA.
Figure 1. Female of a native European seed chalcid, Megastigmus aculeatus, ovipositing in a rose hip. Photo: G. Rouault.
ated invaders and their possible impact on the native flora. International seed trade, which is often unregulated (e.g., via internet), is highly susceptible to facilitate the long-distance movements of seed
a
mid- February and mid- April in 2006 and in 2007. X-rays of seed lots allowed measurement of the insect impact on the potential of natural regeneration as the percentage of insect-infested seeds with regard to the total number of seeds available for insects (filled + insectinfested seeds). Each infested seed was reared individually until adult emergence. Molecular analysis using mtDNA (genes cytochrome b and cytochrome- oxydase I- COI) and nuclear markers (28S) was carried out on some specimens from each site to confirm the identifications (Auger-Rozenberg et al. 2006). Comparative range of native and alien species of rose seed chalcids Rose seeds hosted Megastigmus chalcids in all of the surveyed ALARM sites but the Irish one (Figure 7) As expected, the native species M. aculeatus was present all over Europe (Figure 8). However, unlike the usually yellowish form (Figure 3a) characterized by a thelitokous parthenogetic reproduction
c
b
d
Figure 2. Radiographic picture of rose seeds showing filled seeds (a), empty seeds (b), infested seeds including a larva (c) or a pupa (d). Photo: J.-P. Raimbault and P. Lorme, INRA
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Figure 6. M. nigrovariegatus (female). Photo: A. Roques, INRA.
(males represent 0 to 7 %, Roques & Skrzypczyńska 2003), a black form (Figure 3b) with a balanced sex-ratio was observed to dominate in Lithuania, Estonia and Sweden (Figure 8). The other native species, M. rosae (Figure 4), was observed to extend its central-European distribution to Sweden (Figure 9). In addition, a species new to science was found in both Lithuania and in the island of Lesvos. In both cases, it largely dominated the rose seed chalcid complex (Figure 9). Although related to M. rosae, this species tentatively named M. alba because of its pale colour (Figure 5), is morphologically and genetically distinct. Both its highly- scattered distribution and its occurrence mainly in the seeds of alien Rosa rugosa suggests an exotic origin. The alien M. nigrovariegatus (Figure 6) appeared more widespread than previously noticed since it was found in Lithuania (Figures 8 and 9). Interestingly, two different colour forms were observed. DNA analysis suggested they may correspond to separate introductions from distant areas of the western and eastern parts of the native North American range. These results provide a completely different picture of the entomofauna infesting rose seeds in Europe. Lithuania was revealed as a hotspot for seed chalcid species associated with
rose seeds with the presence of 3 species most probably related to the seed trade and rose plantations. Impact on the potential of natural regeneration of wild roses The decrease in seed yield resulting from chalcid predation did not differ significantly between disturbed and undisturbed areas (Figure 7). However, the impact on the potential for regeneration of wild roses linearly increased with the number of chalcid species present per site. This was confirmed by a larger sampling carried out in 2006 in Lithuania on sites where the number of chalcid species varied from 1 to 3. The additional presence of the two alien species, M. nigrovariegatus and M. alba, increased the percentage of infested seeds from up to 62 %.
30 % 20 %
References AUGER-ROZENBERG MA, KERDELHUÉ C, MAGNOUX E, TURGEON J, RASPLUS JY, ROQUES A (2006) Molecular phylogeny of conifer seed chalcids in the genus Megastigmus (Hymenoptera: Torymidae) and evolution of host-plant use. Systematic Entomology 31: 47-64. ROQUES A & SKRZYPCZYŃSKA M (2003) Seedinfesting chalcids of the genus Megastigmus Dalman (Hymenoptera: Torymidae) native and introduced to Europe: taxonomy, host specificity and distribution. Journal of Natural History 37: 127-238.
Figure 7. Mean percentage of rose seeds damaged by Megastigmus seed chalcids in disturbed (red) and undisturbed areas (green) of the ALARM field site network. Bar size is proportional to damage.
M. nigrovariegatus, western form M. nigrovariegatus, eastern form M. rosae M. alba M. aculeatus, light form M. aculeatus, black form
M. nigrovariegatus M. rosae M. aculeatus
Figure 8. Distribution and relative importance of the native and exotic species of Megastigmus rose seed chalcids in rose hips collected in the field sites of the ALARM network. The size of each slice is proportional to the relative importance of the considered species vs total Megastigmus rose seed chalcids in the sampled site; i.e., a pie showing only one color means that 100 % of the chalcids belonged to a single species at this site.
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Figure 9. Distribution range of the Megastigmus rose seed chalcids in Europe as it was known previously to the development of the ALARM project. M. aculeatus and M. rosae are native of Europe and M. nigrovariegatus is introduced from North America.
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The Rapid Colonization of the Introduced Black Locust Tree by an Invasive North-American Midge and Its Parasitoid
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MILKA GLAVENDEKIĆ, ALAIN ROQUES & LJUBODRAG MIHAJLOVIĆ
Black locust (Robinia pseudoacacia L.; Magnoliopsida, Fabaceae), a tree native of North America, was introduced about 400 years ago into Europe to reforest certain areas and for ornamental purposes. However, this plant species is nowadays considered as an invasive in several European countries. During the recent years, a number of non-native phytophagous insects were reported to feed on the leaves of black locust in Europe. Most of these insects were suspected to have been introduced through the development of global trade, the increase of traffic and the movement of people and goods. Among them, the black locust gall midge Obolodiplosis robiniae (Haldeman) (Diptera, Cecidomyiidae), a Robinia- specific species native of the South-Eastern United States, showed a very rapid expansion
throughout Europe. It also invaded China and Korea.
Life history and damage of black locust gall midge
Spatio-temporal expansion of the black locust gall midge in Europe Obolodiplosis robiniae was initially found in 2003 in the Veneto region of northeastern Italy (Duso & Skuhravá 2004). During the following year, it expanded over northern Italy (Friuli-Venezia Giulia, Trentino-Alto Adige, Lombardia and Emilia Romagna), Czech Republic (vicinity of Prague) and in Slovenia. Then, it was successively recorded in most of Central and Western Europe and in the Balkans in less than 5 years (Figure 5). Recent, unpublished records revealed its presence in 2008 in Macedonia and in the Corfu island where serious surveys noticed its absence in 2005. The latest record was from Bulgaria (Tomov et al. 2009).
Obolodiplosis robiniae induces galls rolling downwards the margins on the leaflets of R. pseudoacacia (Figure 1). The number of galls per leaf is variable (up to 8), depending on the level of infestation. Several larvae, usually 3 to 8, are feeding gregariously within the gall (Figure 2). The black locust gall midge is a multivoltine species with three to four generations per year in Europe. Detailed studies carried out in Serbia revealed the following succession of generations: April-May (1st), June to the beginning of July (2nd), July-August (3rd), September-October (4th). Pupation occurs at different places according to the generation, within the gall during the 1st, 2nd and 3rd generations but the larvae of the 4th generation leave the gall to hibernate and pupate in the soil beneath the tree.
Usually, the 2nd generation is the most abundant in Italy and Serbia. The level of infestation is highly variable per site, from 20 to 98 % in Italy and 25 to 55 % in Serbia. High infestation levels can result in a complete defoliation of black locust by the beginning of August. In order to compensate for the foliage loss, the defoliated tree initiates new leaves from adventitious buds, which impacts its physiological condition. In addition to black locust, midge damage have been observed on an other ornamental Robinia species, R. hispida L., and on the cultivar R. pseudoacacia “Umbracullifera”, which are largely used in the urban parks. Strong infestations are thus susceptible to result in significant aesthetic impacts. In China, this gall midge is affecting the survival of Robinia whereas it is considered to have a strong negative effect on honey production in Korea.
Figure 1. Gall of black locust gall midge, Obolodiplosis robiniae, on a leaf of black locust. Photo: Lj. Mihajlovi´c.
Figure 2. Gregarious larvae of black locust gall midge present in a gall. Photo: M. Glavendeki´c.
Figure 3. Adult of Platygaster robiniae, a larval parasitoid of black locust gall midge. Photo: M. Glavendeki´c.
Figure 4. Platygaster robiniae Buhl & Duso – pupal clusters and adults. Photo: M. Glavendeki´c.
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Pathways of invasion The black locust gall midge needed only five years to spread successfully several thousands of kilometres in Europe. Although the pathways of these accidental introductions could not be strictly ascertained, strong suscpicions exist on the trade of ornamentals and plants for planting. For example, the first midge symptoms in Serbia were found on an ornamental nursery stock. By another way, introduction in Corfu probably proceeded from transport of people and goods by ferry boats from either Italy or Albania. It is also likely that some of the tiny adults were naturally dispersed by wind. In addition, the invasiveness of black locust and its large, natural spread during the last decade in Europe constituted a favourable factor for the spread of its related gall midge. A quite synchronous expansion of the midge parasitoids Various studies on the biology and ecology of black locust gall midge revealed the presence of natural enemies, including a parasitoid wasp, Platygaster robiniae (Buhl & Duso) (Hymenoptera: Platygastridae – Figure 3). This species was first described from Italy, Czech Republic and Japan but it seems likely that both the parasitoid and its host were introduced from North America to Europe and Asia (Buhl & Duso 2008). P. robiniae is a polyembrionic wasp, its larvae being aggregated within the galls in clusters of approximately 3-14 individuals (Figure 4). Adult emergence occurs from July to late October. P. robiniae has quickly expanded following the movement of its host, and it is presently observed in a large part of the countries colonized by the midge in Europe (Figure 6). In Italy as well as in Serbia, P. robiniae was observed only one year after its host was found. In the Czech Republic, it was found two years after the first record of the midge but in Switzerland Montenegro, Macedonia and Bulgaria midges and parasitoids
appeared in the same year (Mihajlović et al. 2008, Tomov et al. 2009). The parasitism level depends on the midge generation, parasitized larvae being observed during the 2nd and 3rd host generation (Buhl & Duso 2008). In Serbia, although the percentage of midge- galled leaves exceeded 10 percent at almost all localities during 2007, P. robiniae usually parasitized less than 10 % of the larvae except in Western Serbia, where the level of parasitism varied between 11.3 and 24.2 %. Indeed, the parasitism was very low during the 2nd generation (<1 %) but increased to up to 24 % during the 3rd and 4th generation. The first results on research on parasitoid fauna of O. robiniae, suggest that pupae cold also be parasitized. Pupal parasitoid was observed in 2007 in Serbia (Figure 7 and 8). It is thus expected that this kind of “natural” biological control, with the accidental introduction of a specific, non-native parasitoid following its exotic host, may result in a control of the invader populations. However, the current status of the host tree, tending to be considered as an invasive itself, could counterbalance these beneficial effects of the parasitoid expansion.
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2003 2005 2006 2007 2008
Figure 5. Spatio-temporal expansion of the black locust gall midge, Obolodiplosis robiniae.
Annual distribution of Platygaster robiniae 2005 2006 2007
References
2008
BUHL PN, DUSO C (2008) Platygaster robiniae n. sp. (Hymenoptera: Platygastridae) Parasitoid of Obolodiplosis robiniae (Diptera: Cecidomyiidae) in Europe. Annals of the Entomological Society of America 101: 297-300. DUSO C, SKUHRAVÁ M (2004) First record of Obolodiplosis robiniae (Haldeman) (Diptera: Cecidomyiidae) galling leaves of Robinia pseudoacacia L. (Fabaceae) in Italy and Europe. Frustula entomologica XXV: 117-122. GLAVENDEKIĆ M, MIHAJLOVIĆ LJ, JAKOVLJEVIĆ I, MARJANOVIĆ S (2008) Obolodiplosis robiniae (Haldeman) (Diptera: Cecidomyiidae) – a new invasive insect pest in Serbia. Bulletin of the Faculty of Forestry 97: 215-226. TOMOV R, TRENCHEVA K, TRENCHEV G, COTA E, RAMADHI A, IVANOV B, NACESKI S, PAPAZOVA-ANAKIEVA I, KENIS M (2009) Non-indigenous insects and their threat to biodiversity and economy in Albania, Bulgaria and Republic of Macedonia. Pensoft Publishers, Sofia-Moscow, 112 pp.
Figure 7. Obolodiplosis robiniae – pupa. Photo: M. Glavendeki´c.
T H E
Annual distribution of Obolodiplosis robiniae
T H E
Figure 6. Spatio-temporal expansion of Platygaster robiniae, an hymenopteran parasitoid of the black locust gall midge.
Figure 8. Parasitoid emerging from pupa of Obolodiplosis robiniae. Photo: M. Glavendeki´c.
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A Stowaway Species from the Balkans – the Horse Chestnut Leafminer, Cameraria ohridella
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SYLVIE AUGUSTIN, MARC KENIS, ROMAIN VALADE, MARIUS GILBERT, JACQUES GARCIA, ALAIN ROQUES & CARLOS LOPEZ-VAAMONDE
was first observed in Macedonia, near the Ohrid Lake, in the early 1980s. Its larvae develop almost exclusively on the white-flowered horse chestnut, Aesculus hippocastanum L., which originates from the Balkans, but maples, Acer pseudoplatanus and A. platanoides can also be used occasionally as hosts. Larvae of C. ohridella make conspicuous mines on the leaves (Figure 2). Heavily infested trees can be prematurely defoliated before the end of summer resulting in an important aesthetic impact in urban areas where horse chestnut is commonly used as an ornamental.
Figure 1. Adult of horse chestnut leafminer Cameraria ohridella. Photo: Olivier Denux.
The horse chestnut leafminer, Cameraria ohridella Deschka & Dimić (Lepidoptera: Gracillariidae; Figure 1),
Dispersal Since its discovery, C. ohridella has subsequently invaded most of Europe over the last two decades at an approximate rate of 60 km per year (Šefrová & Laštůvka 2001, Figure 3).
1984 1985-1988 1989-1990 1991-1992 1993-1994 1995-1996 1997-1998 1999-2000 2001-2002 2003-2004 2005-2006 2007
Figure 3. Spatio-temporal spread of C. ohridella in Europe.
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Figure 2. Mines of Cameraria ohridella on leaf of horse chesnut. Photo: Sylvie Augustin.
Detected in Austria in 1989, it rapidly spread into most of Central and Western Europe, reaching France
in 2000. Since 2002 it has been reported in Spain, England, Denmark, Lithuania, Ukraine, Russia, Sweden, the south coast of Finland and Latvia. The moth is expected to spread up to the northern limit of its host tree distribution in the Scandinavian countries. The invasive success relates both to a high rate of population growth and a high rate of dispersal. Once established in a site, the populations reach outbreak densities within a few years because several generations develop per year whilst the impact of natural enemies is still limited. Local dispersal involves both adult moth flight and the dissemination of infested leaves blown by the wind. However, the rapid geographical expansion has mostly relied on longdistance passive transportation of infested leaf fragments and of adult moths as stowaway in/on cars, trucks and other vehicles, and transportation of infested seedlings. C. ohridella arrived at first in big cities where the higher population rate increases the risk of passive transportation by cars and the higher density of horse chestnut trees in cities also increases the probability of establishment. Studies of the invasion patterns in Germany and France using a stochastic simulation model showed that the best description of the observed spread was obtained using a stratified dispersal model (Gilbert et al. 2004, 2005, Figure 4). A model combining short- distance and long- distance dispersal events with the probability of long distance establishment varying according to
Figure 4. The observed distribution of C. ohridella infestation in France 2000-2004 compared with the predicted distribution of infestation probabilities provided by the stratified dispersal model.
human population density was applied in the United Kingdom. The distributions predicted for 2005-2007 corresponded well to the observed invasion (Augustin et al. 2009). But: where does it come from? Despite numerous studies, the moth's native area was subject of polemic debate. The moth was first suggested to be a glacial relict of the Balkans but contradictory ecological data tended to cast doubt on this hypothesis. In the natural stands of Aesculus, parasitism of Cameraria is low and due only to polyphagous parasitoids. In addition, continuous outbreaks have been observed in the natural stands of the Balkans since the discovery of Cameraria, both traits being characteristic of an introduced species rather than of a native one. Hellrigl (2001) suggested a switch from another host tree (e.g., Acer spp.) in the Balkans but no evidence has yet been provided. Surveys carried out in other potential areas of origin (Asia and North America) have also failed to find the species. Recent genetic studies have given a new insight into the moth's origin. Populations sampled all over the area newly occupied in Europe by C. ohridella during 1989-2006 revealed a significant decrease in both mtDNA (COI) and microsatellite genetic diversity compared to that of the populations present in the natural stands of horse chestnuts in the Balkan mountains (Figure 5). Populations of central, western and northern Europe mostly consist of a single haplotype (haplotype “red” in Figure 5). These results strongly suggest that the European populations of C. ohridella may actually derive from the Balkans. (Valade et al. 2009).
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References AUGUSTIN S, REYNAUD P (2000) Un nouveau ravageur pour le marronnier: Cameraria ohridella. PHM. Revue Horticole 418: 41-45. AUGUSTIN S, GUICHARD S, HEITLAND W, FREISE J, SVATOŠ A, GILBERT M (2009) Monitoring and dispersal of the invading Gracillariidae Cameraria ohridella. Journal of Applied Entomology 133: 58-66. GILBERT M, GRÉGOIRE J-C, FREISE J, HEITLAND W (2004) Long-distance dispersal and human population density allow the prediction of invasive patterns in the horse-chest-
nut leafminer Cameraria ohridella. Journal of Animal Ecology 73: 459-468. GILBERT M, GUICHARD S, FREISE J, GRÉGOIRE J-C, HEITLAND W, STRAW N, TILBURY C, AUGUSTIN S (2005) Forecasting Cameraria ohridella invasion dynamics in recently invaded countries: from validation to prediction. Journal of Animal Ecology 45: 805-813. HELLRIGL K (2001) Neue Erkenntnisse und Untersuchungen uber die RosskastanienMiniermotte Cameraria ohridella Deschka & Dimic, 1986 (Lepidoptera, Gracillariidae). Gredleriana 1: 9-81.
ŠEFROVÁ H, LAŠTŮVKA Z (2001) Dispersal of the horse-chestnut leafminer, Cameraria ohridella Deschka & Dimić, 1986, in Europe: its course, ways and causes (Lepidoptera: Gracillariidae). Entomologische Zeitschrift 111: 194-198. VALADE R, KENIS M, HERNANDEZ A, AUGUSTIN S, MARI MENA N, MAGNOUX E, ROUGERIE R, LAKATOS F, ROQUES A, LOPEZ-VAAMONDE C (2009) Mitochondrial and microsatellite DNA markers reveal a Balkanic origin for the highly invasive Horse-Chestnut leaf miner Cameraria ohridella (Lep. Gracillariidae). Molecular Ecology 18: 3458-3470.
Figure 5. Geographic distribution of C. ohridella mtDNA haplotypes in Europe.
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Invasion of the Harlequin ladybird, Harmonia axyridis, in Europe: When Beauty Becomes the Beast
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MARC KENIS, PETER M.J. BROWN, REMY L. WARE & DAVID B. ROY
Spread of Harmonia axyridis in Europe The spread of H. axyridis in Europe is being monitored through the collaboration of many European scientists and experienced naturalists (Figure 2). Releases made in the 1980s and early 1990s in Southern Europe (France,
Figure 1. The two main colour forms of Hamonia axyridis in Europe. Photo: M. Kenis.
Portugal, Italy, Greece and Spain) did not result in the establishment of invasive populations. In contrast, the ladybird seems to have spread from Belgium, the Netherlands or Germany soon after having been used as a biological control agent in the late 1990s (Brown et al. 2008). After the observation of the first feral populations, in 1999 in Germany, and in 2001 in Belgium, the ladybird started to spread rapidly, and is now regarded as established and invasive in at least 17 countries, from Wales in the west, to Hungary and Poland in the East, and from Norway in the North, to Southern France and Italy in the
South. Whilst H. axyridis has been recorded in further countries, such as Northern Ireland, Serbia, Sweden and Spain, its establishment there is uncertain. Considering its adaptability to a wide range of climatic conditions in other continents, it is predicted that the distribution will soon cover most of Europe. In most invaded countries, H. axyridis has quickly become one of the most abundant ladybird species, as observed in Belgium (Adriaens et al. 2008). In north-western Switzerland, regular surveys on broad-leaved trees showed that H. axyridis became more abundant than all other ladybirds counted together, only two years after it was first recorded (Figure 3).
Adverse effects of Harmonia axyridis Despite the benefits it offers as a biological control agent, H. axyridis causes several adverse effects, which can be divided into three general categories: effects as a household invader, effects on wine production and effects on non-target arthropods (Koch & Galvan 2008). Harmonia axyridis is considered a human nuisance because it aggregates in high numbers in buildings in the autumn and winter months when seeking overwintering sites (Figure 4), causing cosmetic damage and, occasionally, biting humans and causing allergic reactions. In North America, H. axyridis also commonly aggregates on, and occasionally causes damage to, fruit
Native Ladybird
Harmonia axyridis
700
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Number of individuals
Harmonia axyridis, the ‘Harlequin ladybird’ or ‘multicoloured Asian ladybeetle’, is a large, conspicuous coccinellid beetle, native to central and eastern Asia. The adults are very variable in appearance and several colour forms occur throughout its range (Figure 1). It is also a voracious predator of aphids and coccids and, as such, has been widely used as a biological control agent around the world, in greenhouses as well as in outdoor crops. In North America, since its establishment in the 1980s, it has spread and numbers have increased dramatically. Harmonia axyridis is now the dominant species of ladybird in much of the USA and Canada. Since 1982, it has been intentionally released in at least 12 western European countries and has been commercially available since 1995. Invasive populations have also recently been recorded in South America and South Africa.
400
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pre 2001 2001-2002 2003-2004 2005-2006 2007-2008
Figure 2. Recorded occurrence of Harmonia axyridis in 50 km squares in Europe, as at November 2008. Empty spaces in Germany reflect more a lack of national survey than and true absence of H. axyridis. The map was produced thanks to the help of the following collaborators who provided data from their regions: T. Adriaens, H. Bathon, G. Burgio, P. Ceryngier, J. Cuppen, P. de Clercq, A. Estoup, A. Goldarazena, T. Hägg, B. Klausnitzer, D. Kontodimas, I. Kovár, E. Lombaert, A. Loomans, P. Mabbott, S. Maini, M. Majerus, K. Martinou, O. Merkl, O. Nedved, J. Pedersen, M. Przewozny, W. Rabitsch, H. Roy, L. Sigsgaard, A. Soares, W. Solarz, A. Staverløkk, T. Steenberg, V. Ternois, R. Thalji, S. Toepfer, J. van Lenteren and I. Zakharov.
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0 2006
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Figure 3. Numbers of adult Harmonia axyridis and native ladybirds (19 species) found during regular sampling on broadleaved trees and shrubs at 15 permanent sites in north-western Switzerland, in 2006, 2007 and 2008. In 2005, no H. axyridis was found in the region, despite extensive surveys. Unpublished data collected in collaboration with R. Eschen, R. Zindel and J. Van Vlaenderen.
Figure 4. Aggregation of Harmonia axyridis on a building in autumn. Photo: N. Greatorex-Davies.
Figure 5. Harmonia axyridis on grapes. Photo: A. Katsanis.
Figure 6. Two Harmonia axyridis larvae preying on a Coccinella septempunctata larva. Photo: M. Majerus.
crops in late summer (Figure 5). The issue is most serious on wine grapes because, if the ladybirds are disturbed or crushed during harvest and processing of the grapes, they release a fluid that gives a bad taste to the wine. Finally, and most importantly, due to its high abundance and its ability to prey on, and compete with, other aphidophagous insects, H. axyridis may have strong negative effects on biodiversity. It is likely to affect many nontarget species, including native ladybirds, non-pest aphids and other herbivorous insects (Figure 6). In North America, the invasion of H. axyridis has been correlated with the decline of native ladybirds, particularly in agricultural ecosystems. In Europe, where the invasion is more recent, the decline of
broadleaved trees and shrubs. Laboratory tests between H. axyridis and native ladybirds showed that H. axyridis is a voracious, asymmetric predator of most species, at all life stages, but some species are protected by chemical, physical or behavioural defence mechanisms (Ware & Majerus 2008). Risk assessment studies are still on-going, but preliminary results suggest that, among native European species, Adalia bipunctata, Adalia decempunctata, Calvia decemguttata and Oenopia conglobata are particularly at risk (Figure 7).
indigenous species is presently being monitored by several research teams. Assessing the risk for native ladybirds Studies are currently being carried out to assess the risk posed by this invasive ladybird to each native European ladybird species. The risk for individual native species can be considered as the product of the likelihood that this species will encounter H. axyridis in the field and the consequence of this encounter, which itself is composed of the consequence of competition for food and direct predation between ladybird species. The native ladybirds most at risk are those that share the same ecological niche as H. axyiridis, i.e. the species that feed primarily on aphids on
a
What to do? Management methods to prevent or control aggregations in buildings and in vineyards are currently being implemented or are under develop-
b
ment (Kenis et al. 2008). New trapping methods based on semiochemicals and new cultural practices in vineyards are particularly promising. In contrast, no method is currently available to lower population densities in natural environments and to limit the impact of H. axyridis on native species. Only the adaptation of a parasitoid, parasite, predator or pathogen of native ladybirds, or the importation of a specific natural enemy from the area of origin of H. axyridis, may ultimately regulate populations. However, this latter option is not without risk and should be considered only if there is clear evidence that native ladybirds, or other aphidophagous insects, are seriously threatened over a significant part of their distribution. At present, the priority for research is to gather more reliable and quantitative data on the exact impact of H. axyridis on the native fauna in the invaded regions. References
c
d
Figure 7. Four European ladybirds particularly at risk due to the invasion of Harmonia axyridis: (a) Adalia bipunctata, (b) Adalia decempunctata, (c) Calvia decemguttata and (d) Oenopia conglobata. Photos: M. Majerus (a, b), N. Kirichenko (c), R. Zindel (d).
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W H E N
ADRIAENS T, SAN MARTIN Y GOMEZ G, MAES D (2008) Invasion history, habitat preferences and phenology of the invasive ladybird Harmonia axyridis in Belgium. BioControl 53: 69-88. BROWN PMJ, ADRIAENS T, BATHON H, CUPPEN J, GOLDARAZENA A, HÄGG T, KENIS M, KLAUSNITZER BE, KOVAR I, LOOMANS AJ, MAJERUS MEN, NEDVED O, PEDERSEN J, RABITSCH W, ROY HE, TERNOIS V, ZAKHAROV I, ROY DB (2008) Harmonia axyridis in Europe: spread and distribution of a non-native coccinellid. BioControl 53: 5-21. KENIS M, ROY HE, ZINDEL R, MAJERUS MEN (2008) Current and potential management strategies against Harmonia axyridis. BioControl 53: 235-252. KOCH RL, GALVAN TL (2008) Bad side of a good beetle: the North American experience with Harmonia axyridis. BioControl 53: 23-35. WARE RL, MAJERUS MEN (2008) Intraguild predation of immature stages of British and Japanese coccinellids by the invasive ladybird Harmonia axyridis. BioControl 53: 169-188.
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The Siberian Moth, Dendrolimus sibiricus – a Potential Invader in Europe?
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YURI BARANCHIKOV, NADEZHDA TCHEBAKOVA, NATALIYA KIRICHENKO, ELENA PARPHENOVA, MIKHAIL KORETS & MARC KENIS
The Siberian moth, Dendrolimus sibiricus Tschtv. (Lepidoptera: Lasiocampidae), is one of the most severe defoliators of coniferous forests in Northern Asia (Figure 1 and 2). In particular, the dramatic outbreaks over
Furthermore, our recent investigations on host tree suitability have shown that European and Asian congeneric conifer species are equally suited to the development of the pest, with larch, fir and five-needle pine species as preferred trees, and spruce and two-needle pine species as less suitable hosts (Kirichenko & Baranchikov 2007, Kirichenko et al. 2008). To map the moth’s range and potential outbreak distribution, a bioclimatic model was coupled with forest vegetation layers mapped across European Russia and Siberia (Figure 3). The map shows that a large part of Siberia is climatically suitable for the Siberian moth. In general, the potential distribution closely matches the existing distribution of the pest in Siberia. In European Russia, the potential for distribution is lower and limited by mild winter conditions. Indeed, for successful overwintering, larvae require continuous continental-type winters. These bioclimatic models of the Siberian moth range, when overlaid with the ranges of the host tree species, identify the potential distribution for the moth in Siberia and European Russia. In Siberia, the distribution of the moth is limited by the distribution of its preferred host trees, Abies sibirica, Pinus sibirica and Larix spp. In European Russia, the climatically most
Figure 1. Last instar larva of the Siberian moth feeding on larch (Larix sibirica Ledeb.). Photo: Yu. Baranchikov.
millions of hectares in Russian coniferous forests have enormous ecological and economic consequences (Baranchikov 1997). Recently published, albeit unconfirmed, records of the moth caught west of the Urals have suggested that the pest may soon become a threat to European forests. Since 1995, the species has been declared a quarantine pest by the European and Mediterranean Plant Protection Organisation (EPPO). !
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Figure 3. Bioclimatic model of the Siberian moth distribution showing the present distribution of the moth and forests situated in suitable or highly favorable conditions. It demonstrates that climatic conditions allow the pest to move westwards to North-Eastern Europe.
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favourable forests are dominated by Picea sibirica, which is not a favorable host. Thus, the potential for the Siberian moth becoming a pest in European Russia appears limited. Similar studies are currently being carried out to assess the potential distribution of the Siberian moth and other Siberian forest pests in Western and Central Europe, both in present and future climates.
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BARANCHIKOV YN (1997) Siberian forest insects: ready for export. Proceedings of the Exotic Pests of Eastern Forests Conference. Nashville: USDA Forest Service, 64-70. KIRICHENKO NI, BARANCHIKOV YN (2007) Appropriateness of needles of different conifer species for the feeding and growth of larvae from two populations of the Siberian moth. Russian Journal of Ecology 38: 198–203. KIRICHENKO NI, BARANCHIKOV YN, VIDAL S (2008) European conifers as host plants for neonate larvae of the Siberian moth – a potential invasive species to Europe. Proceedings of the IUFRO Workshop on Methodology of Forest Insect and Disease Survey in Central Europe, Austria, Gmunden, Sept. 11th-14th, 2006, 247-351.
How to Deal with Invasive Species? A Proposal for Europe
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PHILIP E. HULME, WOLFGANG NENTWIG, PETR PYŠEK & MONTSERRAT VILÀ
For Europe to address biological invasions at a continental scale there must be an end to the fragmented legislative and regulatory requirements addressing invasive species, an end to uncoordinated activities led by the different Directorates General of the European Union that do not appear to appreciate the cross-cutting nature of biological invasions, an end of the piecemeal approaches to tackling invasive species across Europe that fail to coordinate pre- and post-border actions and of course an end of underfunding of taxonomy, management efforts and basic research on invasive species. More than ever before, a single European coordinating centre with a specific remit to manage biological invasions is needed (Hulme et al. 2009a). A new agency, the European Centre for Invasive Species Management (ECISM), should be developed, perhaps along the lines of the European Centre for Disease Prevention and Control (ECDC), with a mission to identify, assess and communicate current and emerging threats to the economy and environment posed by invasive species. ECISM would integrate all invasion related activities across Europe and target six key areas: scientific advice; coordinating surveillance; identification of emerging invasion threats; initiating responses; supporting training; and communicating to the public and stakeholders (Hulme et al. 2009b). Scientific advice and research direction The study of biological invasions is still a young research field with rather fragmented knowledge: we do not have sufficient information on those characters which make a species invasive. This makes plausible prognoses extremely difficult, especially since biological invasions often show stochastic characteristics, influenced by a variety of events and driven by different factors. Today, we still have limited information on the spreading capabilities of species, their pathways into invaded habitats, and on the invasibility of ecosystems. Impact data are only available for 10 % of all alien species (Vilà et al. 2010) and this makes prioritisation among several alien species very difficult, if not impossible. Besides research at the species level we need more experimental investigations at the ecosystem level to understand how invasive species alter ecosystem structures and services. Our world is actually changing very fast and we would like to understand the interdependence of invasive species, global climate, land use changes and changes in biogeochemistry caused by changes in economic development and society trends (Nentwig 2007). In this regard, ECISM would establish a reputation for scientific excellence and leadership and be a major resource for scientific information and advice on biological invasions for the Commission, the Parliament, the Member States and their citizens. It would achieve this by: ◙ Being a catalyst of biological invasions research. ◙ Promoting, initiating and coordinating scientific studies. ◙ Producing guidance, risk assessments, scientific advice. This would involve improving research on biological invasions in the EU. ECISM would identify gaps in scientific knowledge and work with EU funders to steer research calls, as well as evaluate proposals. It would build links between scientists by maintaining an interactive directory of experts and running scientific symposia. The kind of research would cover both ecological understanding as well as technical solutions. This includes technologies to prevent invasive organisms from being transported via containers or by other introduction vectors, in or on other organisms, in wood or soil etc. Once an invasive organism has established, any countermeasure is much more costly than prevention, thus, control costs represent a good investment, able to prevent enormous ecological and economic damage. Also the methods which are presently applied may demand further improvements in efficacy, ease of application and costs. Of special concern is waterborne transport: its economic importance will continue to increase and the number of alien species spread by ships is expected to rise concurrently. For aquatic organisms, the most prominent invasion vectors are ballast water and hull fouling of ships, important pathways include the waterway networks in Europe or maritime canals, and technical solutions to minimise the spread of aliens are urgently needed. Surveillance and early warning An early warning system and the surveillance of key entry areas, based on warning lists of most dangerous alien species, and immediate removal of newly detect-
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ed invaders is the best strategy for management. This implies that expertise for the identification of relevant taxa is available, the responsible authorities established contingency plans for the eradication of specific taxa, and suitable methods are on-hand. Eradication of an alien species is always better than its control or management because the latter implies the persistence of the alien, and cannot prevent future ecological and economic impacts. ECISM would be responsible for the surveillance of invasive alien species in the EU and maintain the databases for such surveillance. It would: ◙ Develop integrated data collection systems covering all member states, maintain the databases for surveillance and establish EU-wide standard case reporting. ◙ Coordinate and ensure the integrated operation of the dedicated surveillance networks and support strengthening of national surveillance systems. ◙ Monitor trends of invasive species across Europe in order to provide a rationale for actions in member states and disseminate the results to stakeholders for timely actions at EU and country levels. This system will facilitate access to and exchange of information concerning invasive species, including, data on distribution and abundance of invasive species; their life histories and the economic, environmental, and human health impacts they might cause. A relevant step towards a comprehensive database of alien and invasive species in Europe and of experts has been achieved within the DAISIE project (www.europe-aliens.org, DAISIE 2009). Now, this database needs further maintenance and development to an early warning system. Horizon scanning and risk assessment Major challenges for the development of an integrated invasive species risk assessment scheme in the EU include the absence of data required to make accurate analyses of the risks throughout the region, risk assessment processes have insufficiently exploited important new scientific and technological developments, and the risk assessment procedures are complex, discouraging take-up among all EU member states (Baker et al. 2009). Many factors need to be considered to determine whether particular pathways can introduce pests; a particular pest can enter, establish and cause impacts in an area; and what measures would be appropriate to reduce the risk to an acceptable level. In cooperation with the member states, ECISM will establish procedures for systematically collecting, collating and analysing data across the globe with a view to identify emerging invasion threats which could affect the economy, environment and health of the Community. Activities would include: ◙ Preventing the intentional introduction and spread of invasive species, including the identification of emerging pathways. ◙ Minimise the risk of introductions via unintentional pathways. ◙ Provide for a science-based process to evaluate risks associated with introduction and spread of invasive species and a coordinated and systematic riskbased process to identify, monitor, and interdict pathways that may be involved in the introduction of invasive species. ECISM would forward to the European Parliament, the Council and the Commission an annual evaluation of the current and emerging threats from invasive species in the Community. ECISM will assess the potential economic and environmental impacts, capture and communicate uncertainty, map future endangered areas, summarise risk, link pathway analysis to the construction of systems approaches to prevent pest entry and create a decision support system for the management of pest outbreaks. Rapid and continuing response Much more effort has to be made to eradicate invasive species. Experience shows that it is possible to successfully eradicate alien species if careful planning, sufficient financial support and adequate political and social assistance are provided. In contrast, there are numerous cases of invasive species which were not removed because of limited awareness by decision makers, gaps in the legal framework or authorisation process, ignorance, or due to public opposition. ECISM would be
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the EU reference point to support the investigation and control of continuing and emerging invasion problems, including the: ◙ Appropriate and timely reaction in case of invasion threats. ◙ Coordinated approach in outbreak investigation and control between affected countries. ◙ Rapid mobilization of European experts in response to requests for assistance from countries. ◙ Efficient communication between all stakeholders during response activities. ECISM would aim to ensure the rapid mobilization of “outbreak assistance” teams, diagnostic capacity, and the immediate availability of the necessary material for priority eradications. For longer term management, guidelines and standard operating procedures would ensure that biological invasions are managed in an effective and coordinated manner.
awareness campaigns are necessary. This education from school level onwards should cover the whole society but especially specific sectors such as landowners, hunters, fishermen, foresters, gardeners, landscape architects, scientists, people involved in aquaculture and pet trade, and non-governmental organisations, especially animal rights organisations, are major target groups. ECISM would ensure that the public and any interested parties are rapidly given objective, reliable and easily accessible information with regard to the results of its work, act in close collaboration with the member states and the Commission to promote the necessary coherence in the risk communication process on invasion threats as well as with regard to public information campaigns. ◙ ◙ ◙
Training and capacity building Maintaining a relatively low exposure to pests and diseases is essential to the economic viability and environmental health in Europe. To maintain this level we must ensure the effective exclusion, eradication and management of invasive species. These goals can only be achieved if a sufficient number of people equipped with appropriate knowledge and skills in the management of invasive species are available in Europe. Presently, the education opportunities in Europe are limited to a small number of courses that build on generic provision in agricultural or environmental sciences, with minor specialisation in specific areas of invasions. While suitable for undergraduate training, these courses do not deliver the targeted training required by professionals working in this area. Therefore, current educational opportunities on offer in Europe may not adequately address current industry needs. The development of capacities in the EU to respond to biological invasion threats depends on the availability of training resources. ECISM would support and coordinate training programmes in order that the member states and the Commission have sufficient numbers of trained specialists, particularly in species identification, surveillance techniques, risk assessment, species distribution modelling, forecasting and population dynamics, and management techniques. Activities would include: ◙ Assessment of training provision across Europe relevant to management of invasions. ◙ Provision of short-courses targeted at professional development in key skill areas. ◙ The development of a network of training partners and sharing of training materials. ◙ Coordination and recognition of professional qualifications in invasive species management. ECISM would develop training curricula, promote use of a common language among European invasive species researchers, and produce field manuals for the management of alien species. Public awareness and stakeholder consultation Our society usually is not aware of its dependence on nature and neglects the threat of biological invasions, thus specific education programmes or public
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Efficiently communicate the scientific/technical output of the ECISM to professional audiences. Communicating key invasion messages to the media and to the European public. Support the development of member states communication capacities.
A key activity would be the hosting of an open access e-journal with short rapid communications and longer surveillance and research articles on invasive species including recent records, spatio-temporal trends, inventories and management methods. Public awareness has to be sharpened for two important principles. (1) The precautionary principle implies that future unwanted species introductions should be avoided wherever possible and aliens should be eradicated as soon as they are detected; (2) The polluter-pays principle applies economic costs of the damage caused by an alien species to be refunded by the responsible party. In contrast to the widespread laissez-faire policy, this visualises the connection between alien species and damage to ecosystem structure and function, goods and services, and their market valuation. Market-based instruments have to address invasion externalities and should offer incentives to avoid risks, e.g. licence fees (more risky products would be more expensive), insurance bonds or other costsharing instruments. Many exotic birds and fish, released or escaped from captivity into the wild where they cause problems, may serve as an example. These activities parallel those of ECDC and this agency currently runs on an annual budget of less than € 30 million. In the case of biological invasion, such a sum is less than 0.5 % of the annual cost of alien impacts to the European economy. The benefits gained by coordinated action across Europe will far outweigh these running costs. References BAKER R, BATTISTI A, BREMMER J, KENIS M, MUMFORD J, PETTER F, SCHRADER G, BACHER S, DE BARRO P, HULME PE, KARADJOVA O, OUDE LANSINK A, PRUVOST O, PYŠEK P, ROQUES A, BARANCHIKOV Y, SUN J-H (2009) PRATIQUE: A research project to enhance pest risk analysis techniques in the European Union. EPPO Bulletin 39: 87-93. DAISIE (2009) Handbook of Alien Species in Europe. Springer, Dordrecht. HULME PE, PYŠEK P, NENTWIG W, VILÀ M (2009a) Will threat of biological invasions unite the European Union? Science 324: 40-41. HULME PE, NENTWIG W, PYŠEK P, VILÀ M (2009b) Common market, shared problems: time for a coordinated response to biological invasions in Europe? Neobiota 8: 3-19. NENTWIG W (2007) Biological invasions. Springer, Berlin. VILÀ M, BASNOU C, PYŠEK P, JOSEFSSON M, GENOVESI P, GOLLASCH S, NENTWIG W, OLENIN S, ROQUES A, ROY D, HULME PE, DAISIE PARTNERS (2010) How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Frontiers in Ecology and the Environment 8: 135-144.
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DECLINE OF POLLINATORS AND ITS IMPACT
Pollination – a Key Service Regulating Ecosystems
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THOMAS TSCHEULIN, THEODORA PETANIDOU & SIMON G. POTTS
There has been much debate about whether Albert Einstein did or did not say that “if the bee disappeared off the surface of the globe then man would only have four years of life left. No more bees, no more pollination, no more plants, no more animals, no more man”. Most probably he never made this statement but if he did then he had insight way ahead of his time. It was not until much later that it was recognised that bees and other pollinators play a central role in the maintenance of ecosystem functioning (Williams et al. 1991, Millenium Ecosystem Assessment 2005) and not until 1997 that Costanza et al. many of made the first estimate of the value of their services. Europe has a wide variety of insect pollinators including honeybees (Apis mellifera), bumblebees, solitary bees, butterflies, and some beetles and flies. Honeybees, some bumblebees and solitary bees, are actively managed. However, the majority of vast majority of pollinating insects species are ‘wild’ and not managed. Without insect pollination entomophilous (insect-pollinated) plant populations all over the world would decline and eventually disappear and with it all the organisms depending directly or indirectly on them, most probably ourselves included. Whether this would take four years, as Einstein is supposed to have said, is unlikely but the collapse of terrestrial ecosystems would be inevitable in the long-term. It is well-known that the majority of higher plants, including many crop species, depend on insect pollinators for reproduction (e.g., Klein et al. 2007). These plants provide forage for pollinators, mostly in the form of nectar and pollen, in return for the dispersal of their male gametes, the pollen. Even many self-compatible plant species (e.g., tomatoes) benefit from out-crossing or the facilitation of self-pollination by insect pollinators. Pollination is therefore an important ecosystem service without which most wild plants and many crops would not be able to produce seeds. A recent study by Gallai et al. (2009) within the ALARM project estimated the global value of pollination to agriculture to be € 153 billion per year. It is estimated that more than 150 (84%) of European crops are directly dependent upon insects for their pollination (Williams 1994). European crops for which the number of fruits and seeds and their quality are dependent upon, or enhanced by, insect pollination (Corbet et al. 1991) include: • Fruits – apple, orange, pear, peach, melons, lemon, strawberry, raspberry, plum, apricot, cherry, kiwifruit, mango, currants • Vegetables – carrot, potato, onion, tomato, pepper, pumpkin, field bean, French bean, eggplant, squash, cucumber, and soy bean • Seeds and nuts – sunflower, almond, walnut and chestnut • Herbs – basil, sage, rosemary, thyme, coriander, cumin and dill • Industrial crops – cotton, oilseed rape, white mustard, and buckwheat • Fodder crops for animals – alfalfa, clover and sweetclover • Essential oils – chamomile, lavender, and evening primrose Many farmers obtain better pollination services by bringing large numbers of managed bees (e.g., honeybees or bumblebees) to crop fields or greenhouses to raise their yield. Unfortunately, beekeepers’ numbers are dropping and as can be seen from the article “Severe Declines of Managed Honeybees in Central Europe” by Potts et al. (this atlas, pp. 184f.) the supply of honeybees has been consistently declining in many parts of Europe in the past decades. Similar trends can be observed in North America (USDA National Agricultural Statistics Service 1977, 2006; National Research Council (U.S.) Committee on the Status of Pollinators in North America 2007). There are several possible causes for this including a variety of pests and diseases (e.g., the hive beetle, Varroa mites, and many viruses), inappropriate use of pesticides, lack of high quality forage, and colony collapse disorder. These multiple drivers have devastated the honeybee industry in the US and parts of Europe with loss of livelihoods for beekeepers and potential risks for crop and fruit producers. 168
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Many studies have shown that unmanaged, wild pollinators, are very valuable crop pollinators, but far less is known about their ecology and contribution to crop production (e.g., Greenleaf & Kremen 2006, Klein et al. 2007, 2008). In addition, unmanaged wild pollinators constitute the vast majority of the pollinator community of wild plants. As has been shown many of these pollinators are in decline in the United Kingdom and the Netherlands and so are the out-crossing plant species they visit (Biesmeijer et al. 2006). Although the United Kingdom and the Netherlands are the only countries for which this decline has so far been demonstrated, with the help of historical data the problem is most likely to be much more widespread (Banaszak 1995, Biesmeijer et al. 2006). It is still not fully clear what drives the decline of unmanaged pollinators but most likely it is a combination of causes. Important drivers include habitat loss, fragmentation and degradation which affect the availability of key foraging and nesting resources needed for bee populations. In addition, changes in agriculture practices such as the planting of monocultures of wind-pollinated crops, which provide no resources for bees, can also have negative impacts. The increased use of agrochemicals may also be a major pressure on wild pollinators; pesticides may have direct impacts through mortality or indirect, sub-lethal effects, by modifying flight and searching behaviour which makes foraging less efficient. Similarly, fertilizer applications can increase soil fertility and thereby modify wild plant community structure in agro-ecosystems by reducing diversity of floral food sources. Strong concerns about the impact a decline or even the loss of pollination services could have on dependent organisms have been reported (Diaz et al. 2005, Steffan-Dewenter et al. 2005, Biesmeijer et al. 2006, but see Ghazoul 2005) and have been formally recognised within the Convention on Biological Diversity as the International Initiative for the Conservation and Sustainable Use of Pollinators (IICSUP) in the ‘In-depth review of the programme of work on Agricultural Biodiversity’ at the 9th Conference of Parties (COP 9 Decision IX/1 2008) and the U.S. National Research Council’s Committee on the Status of Pollinators in North America (2007). Given the enormous potential environmental, social and economic impacts a decline in pollination services may have, it is surprising that so few studies have addressed this subject in any depth. This chapter aims to extend our knowledge and highlight some important aspects of wild and managed pollinators, their possible decline and the fate of the entomophilous plant species they visit. Until now, due to the lack of a standardised set of methods for assessing the decline of pollinators, it was often difficult to compare studies or to conduct them in the first place. The article by Westphal et al. (this atlas, pp. 170f.) provides us with a toolkit of field-tested methods and their evaluation designed for largescale and long-term monitoring schemes. If adopted by the scientific community it will greatly assist further research into pollinator decline. Adoption of standardised methods would also underpin the development of local, national, and regional monitoring programmes which would allow the long-term assessment of the status and trends of pollinators. The need to collect baseline data for such programmes is of paramount importance if we are to understand the fate of European pollinators and direct mitigation strategies towards halting losses and potentially reversing any declines. Focus on key guilds within the pollinator fauna is described by Budrys et al. (this atlas, pp. 172f.) who look at cavity-nesting Hymenoptera across Europe using small trap-nests on trees and buildings. They investigate the influence of landscape complexity and agriculture on the abundance and biodiversity of these important pollinators. Nielsen et al. (this atlas, pp. 174f.) assess the impact of pollinator shifts on wild plants and explore how plant reproduction and pollinator activity are controlled by plant population structure at different spatial scales. This aspect has important implications for the conservation of plants and their pollinators as wild plant populations must provide enough floral resources to attract and support sufficient pollinators to ensure plant recruitment and species survival.
Meyer & Steffan-Dewenter (this atlas, pp. 176f.) present a case study from Germany looking at possible drivers of pollinator loss. They examine the impact of landscape effects on pollinator abundance and biodiversity in relation to their specific life history traits. This study is especially timely as the intensification of agriculture in recent decades along with the abandonment of traditional land-use practices has caused a substantial decline in habitats that are commonly speciesrich such as calcareous grasslands. Szentgyörgyi et al. (this atlas, pp. 178f.) look at commercially available bumblebees, such as Bombus terrestris, which, as superior pollinators, are regularly used in greenhouses in Central Europe and increasingly elsewhere. The authors point out the risks such introductions may pose to the local biodiversity, also as vectors of various parasites, which can in turn infect native bumblebees. In the study presented in this chapter the authors test whether commercial bumblebees in greenhouses escape and introgress into the wild population. Furthermore, they test whether wild bumblebees near greenhouses show higher Nosema bombi infection levels than control bumblebees that have not been in contact with commercial bumblebees. Rortais et al. (this atlas, p. 180) investigate the status of the black honeybee (Apis mellifera mellifera), which is native to western Europe and is threatened in many places by commercial bee breeding. They point out the difficulties in distinguishing them from other subspecies of the European honeybee and present a geometric morphometric tool which aids identification and constitutes an important step towards effective conservation of the black honeybee in Europe. Rortais et al. (this atlas, p. 181) present another study on the distribution of the recently introduced alien, the Asian hornet (Vespa velutina), in south-western France. The Asian hornet is spreading rapidly and poses a serious threat to the provision of pollination services in the invaded areas as they attack honeybee colonies. Jaffe & Moritz (this atlas, pp. 182f.) raise the interesting question whether or not the honeybee should be considered a domesticated animal. They argue that where the honeybee is native, evidence for actual competition between the honeybee and “wild” bees is rare. They present data on hive densities throughout Europe and discuss socioeconomic reasons for regional differences. Furthermore, they suggest policy guidelines for the conservation of honeybee subspecies and sustainable beekeeping in Europe. Potts et al. (this atlas, pp. 184f.) present data on the relative changes in number of colonies, number of beekeepers and amount of honey produced in 20 European countries between 1965-2005 and 1985-2005. They discuss possible reasons for the decline in honeybee colonies and beekeepers taking into account
also socioeconomic aspects. The economic and biodiversity implications this decline of honeybees in combination with the decline of non-managed pollinators may have on the provision of pollination services are also pointed out. Since the days of Albert Einstein we have learned a lot about pollinators, their services, their decline and its assessment, not least due to the ALARM project and the pollinator related studies therein of which the following articles in this chapter provide an overview. References BANASZAK J (1995) Natural Resources of wild bees in Poland and an attempt at estimation of their changes. Pages 11-26 in J. Banaszak, editor. Changes in Fauna of Wild Bees in Europe. Pedagogical University, Bydgoszcz. BIESMEIJER JC, ROBERTS SPM, REEMER M, OHLEMÜLLER R, EDWARDS M, PEETERS T, SCHAFFERS AP, POTTS SG, KLEUKERS R, THOMAS CD, SETTELE J, KUNIN WE (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313: 351-354. CONVENTION ON BIOLOGICAL DIVERSITY (www.biodiv.org/default.shtml). COP 9 Decision IX/1. 2008. http://www.cbd.int/decisions/?m=COP-09&id=11644&lg=0. CORBET SA, WILLIAMS IH, OSBORNE JL (1991) Bees and the pollination of crops and wild flowers in the European Community. Bee World 72: 47-59. COSTANZA R, D’ARGE R, DE GROOT R, FARBER S, GRASSO M, HANNON B, LIMBURG K, NAEEM S, O’NEILL RV, PARUELO J, RASKIN RG, SUTTON P, VAN DEN BELT M (1997) The value of the world’s ecosystem services and natural capital. Nature 387: 253-260. DIAZ S, FARGIONE J, CHAPIN FS, TILMAN D (2005) Biodiversity Loss Threatens Human Well-Being. – In: Hassan R, Scholes R, Ash E (Eds), Ecosystems and Human Well-Being: Current State and Trends. Island Press, Washington, DC, 297-329. GALLAI N, SALLES JM, SETTELE J, VAISSIÈRE BE (2009) Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecological Economics 68: 810-821. Ghazoul J (2005) Buzziness as usual? Questioning the global pollination crisis. Trends in Ecology and Evolution 20: 367-373. GREENLEAF SS, KREMEN C (2006) Wild bee species increase tomato production and respond differently to surrounding land use in Northern California. Biological Conservation 133: 81-87. KLEIN A-M, VAISSIÈRE BE, CANE JH, STEFFAN-DEWENTER I, CUNNINGHAM SA, KREMEN C, TSCHARNTKE T (2007) Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society B: Biological Sciences 274: 303-313. KLEIN AM, CUNNINGHAM SA, BOS M, STEFFAN-DEWENTER I (2008) Advances in pollination ecology from tropical plantation crops. Ecology 89: 935-943. MILLENIUM ECOSYSTEM ASSESSMENT (2005) Ecosystems and Human Well-Being, Synthesis. Island Press, Washington, D.C. NATIONAL RESEARCH COUNCIL (U.S.) Committee on the Status of Pollinators in North America (2007) Status of pollinators in North America. National Academies Press, Washington, D.C. STEFFAN-DEWENTER I, POTTS SG, PACKER L (2005) Pollinator diversity and crop pollination services are at risk. Trends in Ecology and Evolution 20: 651-653. USDA NATIONAL AGRICULTURAL STATISTICS SERVICE (1977) 1976 Honey production report. United States Department of Agriculture, Washington, DC. USDA NATIONAL AGRICULTURAL STATISTICS SERVICE (2006) 2005 Honey production report. United States Department of Agriculture, Washington, DC. WILLIAMS IH, CORBET SA, OSBORNE JL (1991) Beekeeping, wild bees and pollination in the European Community. Bee World 72: 170-180. WILLIAMS IH (1994) The dependence of crop production within the European Union on pollination by honeybees. Agricultural Zoology Reviews 6: 229-257.
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Methods for Quantifying Pollinator Loss CATRIN WESTPHAL, RICCARDO BOMMARCO, GABRIEL CARRÉ, ELLEN LAMBORN, NICOLAS MORISON, THEODORA PETANIDOU, SIMON G. POTTS, STUART P.M. ROBERTS, HAJNALKA SZENTGYÖRGYI, THOMAS TSCHEULIN, BERNARD E. VAISSIÈRE, MICHAŁ WOYCIECHOWSKI, JACOBUS C. BIESMEIJER, WILLIAM E. KUNIN, JOSEF SETTELE & INGOLF STEFFAN-DEWENTER
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To counteract the ongoing decline of pollinators, systematic and long-term pollinator monitoring schemes need to be established. Currently, several international initiatives aim at the documentation of shifts in pollinator diversity and abundance and the putative causes of these changes (São Paulo Declaration on Pollinators 1999). However, pollinator loss can Biographical Regions Alpine North Alpine South Boreal Atlantic Continental
Study Regions Pannonian 1 France Mediterranean 2 Great Britain 3 Germany no data 4 Poland 5 Sweden
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Figure 1. Overview of the study regions.
only be identified if pollinator abundance and diversity are recorded with standardized sampling protocols, allowing the direct comparison of records across space and time. Within the ALARM Project, we evaluated the performance of six commonly used sampling methods with respect to their efficiency in assessing bee diversity to develop standardized sampling protocols for monitoring schemes. We focused on bee pollina-
tors (Hymenoptera: Apiformes) since they represent the most important pollinator groups worldwide (Michener 2000). Methods tested Using a highly standardized experimental set-up (Figure 3), the methods were tested in five European countries (Figure 1) and in two different habitat types: intensively managed agricultural habitats with mass-flowering annual crops and semi-natural habitats with low-level agricultural management (Figure 2). We analyzed the performance of three census methods: observation plots, standardized transect walks, and variable transect walks (Dafni et al. 2005, Figures 3 and 4). In each study site, ten rectangular quadrats of 1 × 2 m were established as observation plots, in which all flower visiting bees were recorded during 6 min observational periods. For the standardized transect walks, a permanently marked 250 m long and 4 m wide corridor (transect) was established and all bees within the corridor were collected during 50 minutes recording time. For the variable transect walks, a 1 ha plot was established. Within this plot bees were recorded at the most attractive resource patches during an observational period of 30 min. The variable transect walks were only performed in the semi-natural habitats in which the floral resources are heterogeneously distributed. Moreover, three passive sampling methods were tested: pan traps, trap nests with reed internodes and paper tubes (Dafni et al. 2005, Figures 3 and 4). We set up UV-bright yellow, white, and blue pan traps, which represented the prevailing floral colours of the study sites. A commonly used method to sample cavity-nesting bees is the introduction of artificial nesting
substrates known as trap nests. Ten poles with two different types of trap nests were established in the semi-natural habitats from early spring to autumn. We used trap nests with reed internodes and trap nests with paper tubes (www.birdfood.co.uk). The trap nests were not established in agricultural habitats due to the frequent disturbances in crops (i.e., applications of fertilizers or pesticides and harvesting activities). Performance of the methods The three methods that were tested in both habitat types differed greatly in their sample coverage, which was defined as the number of species that were detected per individual method divided by the total number of species per study site. The most efficient method was the pan trap method, followed by the standardized transect walks, while the observation plots performed poorly (Figure 5a). We found a higher sample coverage in the homogeneous and species-poor agricultural habitats than in the more heterogeneous and species-rich semi-natural habitats (Figure 5b). Comparing the efficiency of the methods that were tested in the seminatural habitats, the pan traps were again the most efficient method. The second most efficient were the transect methods. In comparison with the observation plots, the trap nests with reed internodes performed relatively well despite being restricted to sampling cavity-nesting bees only. The lowest sample coverage of all methods tested was recorded for the trap nests with paper tubes (Figure 5c). Recommendations We recommend UV-bright pan traps as the most suitable method for longterm and large-scale pollinator monitoring schemes, because it proved to be
highly efficient at sampling the overall bee fauna and was not biased by surveyor experience. Hence, it is likely to provide reliable results when operated by many surveyors in different habitats, regions, and years. If the aim of monitoring schemes is to maximize the number of bee species recorded within a site, then trap nests with reed internodes represent a complementary, unbiased method for detecting additional species. Because of their collector bias, the transect methods can only be recommended for large-scale and long-term monitoring schemes after prior taxonomic training and standardization of surveyor experience. Generally, the recommended methods operate efficiently in a wide range of entomophilous crops and extensively used European grassland habitats, and they may also be efficient in other habitat types and geographical regions. For all methods, the preparation and identification of the collected specimens are substantial parts of the work and thus should not be underestimated. References DAFNI A, KEVAN PG & HUSBAND BC (2005) Practical Pollination Ecology. Enviroquest, Cambridge, Ontario, Canada. MICHENER CD (2007) The bees of the world. 2nd ed. The John Hopkins University Press, Baltimore, Maryland, USA. SĂO PAULO DECLARATION ON POLLINATORS (1999) Report on the recommendations of the workshop on the conservation and sustainable use of pollinators in agriculture with emphasis on bees. Brazilian Ministry of the Environment, Brasilia, Brazil. WESTPHAL C, BOMMARCO R, CARRÉ G, LAMBORN E, MORISON N, PETANIDOU T, POTTS SG, ROBERTS SPM, SZENTGYÖRGYI H, TSCHEULIN T, VAISSIÉRE BE, WOYCIECHOWSKI M, BIESMEIJER JC, KUNIN, WE, SETTELE J., STEFFAN-DEWENTER I (2008) Measuring bee biodiversity in different European habitats and biogeographical regions. Ecological Monographs 78: 653-671.
Figure 2. Examples of two study sites: an oilseed rape field and a calcareous grassland in Germany and a buckwheat field in Poland. Photos: C. Westphal and H. Szentgyörgyi.
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Observation plot
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Observation plot
Pan traps
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Standardized transect
Figure 3. Standardized experimental set-up (a) in semi-natural habitats and (b) in agricultural habitats. The 1 ha plot for the variable transects is not shown. Modified from Westphal et al. (2008).
a
c
d
b
e
Figure 4. The tested methods: (a) UV bright pan traps, (b) observation plots, (c) transect walks, (d) trap nests and observation plot, (e) trap nests with reed internodes and paper tubes. Photos: R. Bommarco and C. Westphal.
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30
45
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Figure 5. (a) Differences in sample coverage between the methods that were tested in both habitat types and (b) between the agricultural and semi-natural habitats. (c) Differences between the sample coverage among the methods that were tested in the semi-natural habitats. Trap nests with paper tubes (TNP) and reed internodes (TNR), observation plots (OP), standardized (ST) and variable (VT) transect walks, and pan traps (PT). Modified from Westphal et al. (2008).
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Cavity-Nesting Hymenoptera across Europe: a Study in ALARM Project Field Site Network Sites Using Small Trap-Nests on Trees and Buildings EDUARDAS BUDRYS, JARA ANDREU URETA, AUŠRA BRILIŪTĖ, ALEKSANDAR ĆETKOVIĆ, SILKE HEINRICH, GYÖRGY KRÖEL-DULAY, MARI MOORA, SIMON G. POTTS, AGNÈS RORTAIS, ERIK SJÖDIN, HAJNALKA SZENTGYÖRGYI, IVÁN TORRES, MARCO VIGHI, CATRIN WESTPHAL & ANNA BUDRIENĖ
,
Introduction The order Hymenoptera includes insect groups with the most sophisticated reproduction biology: ants, wasps and bees. These insects build nests, containing brood chambers, or cells, which
Figure 1. Small Hymenoptera trap-nest on tree trunk. Photo: E. Budrys.
Figure 2. Small Hymenoptera trap-nest on building. Photo: E. Budrys.
Figure 3. Nests of solitary Hymenoptera in trap-nests. Photos: E. Budrys and L. Somay.
they provision with food for their larvae. Some of them, such as ants, honeybees and hornets, are social and live in larger or smaller colonies, containing infertile females, the workers. However, most wasp and bee species are solitary: each female builds her own nest and cares for her progeny. Some of the solitary wasps and bees build their nests in pre-existing natural cavities, such as the exit holes of xylophagous beetles in deadwood, hollow stems of plants, or burrows of various insects in loess or loam cliffs. Females of cavity-nesting species usually occupy tubular burrows and construct series of brood cells, separated by diaphragms and provisioned with paralyzed insects 172
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(wasps) or pollen (bees) for consumption by the offspring. Afterwards, the nest cavity may be closed by an external plug. The nest diaphragms and plug are built of debris, mud, wood dust, macerated leaf mass or entire pieces of leaves, or excreta of the wasp or bee female, such as silk or similar materials. Many wasp species are important mesofaunal predators, controlling populations of some phytophagous insects, for instance, leaf-rolling caterpillars, chemically protected larvae of some leaf beetles and weevils, not consumed by the other predators. Bees are one of the main pollinators of the entomophilous plants, considered a keystone insect group in most ecosystems. Due to the complicated brood care and, as a consequence, relatively low reproduction rate, as well as specialised trophic links, wasps and bees are considered to be sensitive to ecosystem changes. Evaluation of the abundance and species diversity of these insects may provide information on the state of the assessed ecosystem (Tscharntke et al. 1998, Gayubo et al. 2005). The cavity-nesting wasps and bees readily occupy various simple artificial trap-nests, made of drilled wooden blocks, paper or plastic tubes, or bundles of internodes of reed or bamboo stems. Trap-nests of standardised construction are used as simple field method to assess the abundance and diversity of the cavity-nesting wasp and bee community. In the study sites, the trap-nests are usually exposed for the whole spring and summer period and collected in the autumn. To trigger the further development of diapausing prepupae or, for some species, imagoes, the nests have to be kept outside during the winter or cooled in a refrigerator for few months. There are two basic methods used in order to learn which species have been nesting and how many nest cells have been built in a trap-nest. One is to place 3
it in a container and wait until hatching of its inhabitants. The other includes dismantling of the trap-nests and opening each cavity or tube containing a wasp or bee nest to count living and dead larvae, their parasites and inquilines. In the latter case, the nests or the contents of their cells have to be placed into individual containers for their further development. This method requires more effort but provides much more information about the trophic structure and population ecology of the community. In the present study, performed in twelve countries in 2007, we used the network of the ALARM project field sites to answer questions such as: Is the composition of the cavity-nesting Hymenoptera, settling in simple standardised trap-nests, comparable in different parts and biogeographic regions of Europe? Does the composition of these communities differ in semi-natural and agricultural landscapes? How does it differ in trap-nests on tree trunks and those on buildings? Methods To construct trap-nests, we used reed stem internodes of random diameter, wrapped into the Tetrapack carton and fixed using sticky tape (Figures 1 and 2). In each landscape type, there were two sets of trap-nests applied: five smaller ones, of 20 reed internodes, attached to tree trunks (Figure 1), and four larger ones, of 25 internodes, attached to parts of old buildings (Figure 2). The trapnests were exposed by field site managers of the ALARM FSN sites in midApril (southern Europe) or the start of May (northern Europe); they were collected in September and mailed to the laboratory for study. The trap-nests were dismantled, the internodes with nests dissected and, using the construction of the nest, the remnants of prey or the morphology of larvae (Figure 3), the hosts and 30
Dipogon (Pompilidae) Rhopalum (Crabronidae) Discoelius (Vespidae)
2
inquilines were identified as to genus. The number of brood cells was counted, and the living larvae placed into small containers for hibernation in a climatic chamber at +4 °C. After the reactivation, portion by portion, the containers with larvae were taken out and kept at +25 °C until the adults hatched. In total, we obtained and studied more than 1,500 Hymenoptera nests, built by bees and wasps of 20 genera from 4 families: Pompilidae, Vespidae, Crabronidae and Apidae (Figure 7). Do the communities of the cavity-nesting Hymenoptera differ in various parts of Europe? The study revealed that local fauna typically contains from 8 to 12 genera of trap-nesting wasps and bees. The similarity analysis using the presence/absence of genera as characters of the community demonstrated that the Hungarian field site was characterised by the most distinctive local fauna (Figure 8). A further distinction was between the Mediterranean sites (Italy and Spain), forming a separate cluster, and the remaining field sites. In the latter group, fauna of the Boreal and Continental regions did not essentially differ from the Atlantic and Alpine regions, forming altogether a single cluster. In general, most of the communities of the trap-nesting Hymenoptera in Europe consist of a limited number of common and widely distributed wasp and bee genera; the regional faunae of these insects are relatively similar and highly comparable across Europe. Are predatory wasps indicators of naturalness? The pan-European comparison of the composition of trap-nesting Hymenoptera communities in the seminatural and the agricultural landscape revealed considerable differences in abundance of some predatory wasps. 7
Semi-natural landscape Agricultural landscape
6 5
20
Nesting in trees only Nesting in trees and buildings Nesting in buildings only
4 3 1
10
0
0
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Semi-natural landscape
Figure 4. Number of cells of selected wasp genera per trap-nest.
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Eastern Europe
Figure 5. Number of bee cells per trap-nest.
0
Southern Europe
Northern Europe
Figure 6. Number of genera per FSN site.
The average number of cells of Dipogon spider wasps per trap-nest was significantly higher in the semi-natural landscape than in the disturbed environments (Figure 4). The crabronid wasps Rhopalum were much more abundant in the undisturbed landscape as well; the Discoelius predatory wasps were not found at all in agricultural landscapes. What are the reasons for these differences? Predatory spider wasps hunt spiders, that is, other predators, and are thus considered as top predators in the mesofaunal trophic chains. Their lower abundance in agricultural ecosystems confirms the fact that anthropogenous disturbance first decreases the diversity at the highest levels of the trophic pyramids. Thus, relative abundance of spider wasps in the trap-nesting Hymenoptera communities may be considered a potential indicator of naturalness of a landscape or a habitat. Rhopalum clavipes, the commonest representative of the genus in tubular trap-nests, hunts Psocoptera barklice. Many species of these prey insects depend on lichens, which in turn are sensitive to air pollution. This sensitivity may be the primary reason for a relatively lower abundance of Rhopalum in the disturbed environments. Discoelius is a scarce wasp inhabiting natural forests. Its absence in agricultural landscapes suggests that the genus is susceptible to the anthropogenous disturbance and survives only in natural landscape. Does the intensity of agriculture affect the abundance of cavitynesting pollinators? The abundance of bees negatively depends on the intensity of application of agrochemicals; the abundance of bees should therefore be lower in the agricultural environment than in the natural one. On the other hand, the diversity of bees depends on the pollen resources of flowering herbaceous plants, including annuals, which are most abundant in disturbed areas. Low intensity agriculture must therefore stipulate prosperity of bee populations. Taking both factors into account, we might expect a lower as well as a higher abundance of bees in agricultural landscapes, in comparison with semi-natural ones. The general comparison of the abundance of bees (the number of brood cells per trap-nest) across the ALARM FSN field sites did not demonstrate significant differences between agricultural and semi-natural landscapes. However, a separate comparison of data from the sites in Western Europe (Austria, France, Germany, Italy, Spain, Sweden, UK) and the sites in Eastern Europe (Estonia, Hungary, Lithuania, Poland, Serbia) revealed surprising differences (Figure 5). In the Western Europe, the number of bee cells per trap-nest was remarkably (although not
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significantly at the current number of observations) lower in the agricultural sites (6 cells on average) than in the semi-natural ones (10 cells per trapnest). However, it was significantly higher in the agricultural sites of Eastern Europe (24 cells on average). A possible explanation of this contrast may be the transitional farming land structure in the post-soviet countries with a higher share of extensive pastures, abandoned and set-aside fields, as well as a lower average level of pesticide application, facilitating abundance and diversity of bees. This positive effect on bee populations in the Eastern Europe may be temporary if further development of the agricultural land structure does not follow the principles of sustainability. Did the cavity-nesting bees and wasps spread northwards together with humans? Part of the cavity-nesting Hymenoptera species exploit human constructions with small cavities of various origins as nesting substrate. The species forming these synanthropic communities naturally nest in dead tree trunks. Using the trap-nests, we compared the composition of the cavity-nesting Hymenoptera communities inhabiting tree trunks and those inhabiting human buildings across Europe. We tried to estimate, which part of the synanthropic species comes from forest habitats. Interestingly, the overlap between the tree and the building communities differed considerably in the northern and the southern parts of Europe (Figure 6). In the southern field sites (Austria, France, Hungary, Italy, Serbia and Spain), the number of genera of the synanthropic cavity-nesting Hymenoptera, nesting on buildings only, did not significantly exceed the number of genera nesting in tree trunks only. The situation was different in the northern field sites (Estonia, Germany, Lithuania, Poland, Sweden and UK): here the number of strictly synanthropic genera was significantly higher than the number of genera nesting on trees only. Among the wasps and bees, whose ratio of abundance in tree and building trap-nests was different, 6 genera (Auplopus, Psenulus, Rhopalum, Trypoxylon, Hylaeus and Osmia) were relatively more abundant on buildings in the North, and only 3 genera (Ancistrocerus, Symmorphus and Megachile) were relatively more abundant on buildings in the South. A possible explanation of this phenomenon is that some cavity-nesting Hymenoptera, naturally distributed in woodlands of Southern Europe, have spread to the North together with humans, nesting in their buildings and adapting to urban environments. Outlook The main result of this pilot study using small trap-nests are reference
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Auplopus
Nitela
Dipogon
Pison
Discoelius
Trypoxylon
Ancistrocerus
Rhopalum
Symmorphus
Hylaeus
Euodynerus
Chelostoma
Leptochilus
Heriades
Passaloecus
Hoplitis
Psenulus
Osmia
Solierella
Megachile
Figure 7. Generic composition of trap-nesting Hymenoptera communities in the ALARM FSN sites. Area of sectors is proportional to the abundance of brood cells in the trap-nests.
Atlantic
UK FR
Alpine Continental
AT DE LT
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EE SE SJ
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Mediterranean
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Figure 8. Similarity of the ALARM FSN sites in various biogeographic regions by presence/absence of trapnesting bee and wasp genera (complete linkage, Euclidean distance).
values of trap-nesting wasp and bee community parameters in semi-natural and agricultural landscapes, allowing future comparisons with the other landscapes and habitats, as well as monitoring of changes caused by such pressures as climate and land use change. Like some earlier studies (Tscharntke et al. 1998, Gathmann & Tscharntke 1999, Gayubo et al. 2005, etc.), our results support the opinion that the trap-nesting Hymenoptera community parameters may be applied, in addition to the others, as indicators reflecting state and trends in ecosystems, habitats or landscapes.
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References GATHMANN A, TSCHARNTKE T (1999) Landschafts-Bewertung mit Bienen und Wespen in Nisthilfen: Artenspektrum, Interaktionen und Bestimmungsschlüssel. Naturschutz und Landschaftspflege Baden-Württemberg 73: 277-305. GAYUBO SF, GONZÁLEZ JA, ASÍS JD, TORMOS J (2005) Conservation of European environments: the Spheciformes wasps as biodiversity indicators (Hymenoptera: Apoidea: Ampulicidae, Sphecidae and Crabronidae). Journal of Natural History 39: 2705-2714. TSCHARNTKE T, GATHMANN A, STEFFANDEWENTER I (1998) Bioindication using trapnesting bees and wasps and their natural enemies: community structure and interactions. Journal of Applied Ecology 35: 708-719.
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Assessing the Impact of Pollinator Shifts on Wild Plants ANDERS NIELSEN, JENS DAUBER, WILLIAM E. KUNIN, ELLEN LAMBORN, BIRGIT MEYER, MARI MOORA, SIMON G. POTTS, JOSEF SETTELE, VIRVE SOBER, INGOLF STEFFAN-DEWENTER, THOMAS TSCHEULIN, DANIELE VIVARELLI, JACOBUS C. BIESMEIJER & THEODORA PETANIDOU
,
Many plant and animal species are mutually dependent on each other through pollination interactions. The animals receive vital resources, such as pollen or nectar displayed as floral rewards by the plants, while the plants get their pollen dispersed among their flowers, a process that is crucial for their sexual reproduction. Several studies have shown that pollinators respond to the spatial structure of plant populations, visiting selectively areas with high amounts of floral resources (Rathcke 1988, Westphal et al. 2003). However, pollinators perceive the landscape in contrasting ways. For instance, a landscape can be perceived as homogeneous and resource-depleted by honeybees searching for areas with high amounts of floral rewards needed to sustain their colony (Visscher & Seeley 1982). On the other hand, solitary bees may perceive the same landscape as very heterogeneous, with small but highly rewarding flower patches scattered throughout the landscape. This variation in how pollinators visit flowers, in relation to the plants’ spatial population structure might ultimately affect plant reproduction. Thus, rare plants, i.e. species with small and sparse aggregations of flowers, might experience reduced seed set due to pollen limitation, as they do not attract enough pollinators to fertilize all their ovules (Oostermeijer et al. 2000, Leimu et al. 2006). To better understand how the spatial structure of plant populations affects plant reproduction and pollinator visitation, we studied ten plant species located throughout Europe using the same standardized protocol (Table 1, Figure 1, 2 and 4). For each species we selected large and small, as well as dense and sparse populations separated by at least 800 m. Within each population
a
we selected large and small, as well as dense and sparse patches of plants separated by at least 2 metres. In each patch we measured both pollinator visitation and seed set. Pollinators were grouped into five guilds, namely: honeybees, bumblebees, solitary bees, hoverflies, and remaining insects. This approach enabled us to estimate at what spatial scales (patch and/or population) pollinator visitation and plant fitness might be affected by plant population structure, if at all. Thus, by including ten different species and by using the same sampling protocol, it became possible to pinpoint the effects of spatial structures regardless of any peculiarities related to the particular species. To model plant fitness seen as pollinator visitation and seed set we employed the following variables: plant population size (taken as small and large) and density (dense and sparse); plant patch size (m2) and density (number of flowers/m2); visitation frequency per insect guild (number of insect visits per flower and time unit); seed set (number of seeds produced per flower); air temperature during observation of pollinator visits (°C). Plant fitness was measured as pollinator visitation by all flower visitors and as seed set. We found general effects on plant fitness by the area covered by the plants and by their flower density both at the population and the patch scales. Larger and denser patches received more pollinator visits per flower and set higher number of seeds than smaller and sparser patches. The effects were more pronounced at the local (patch) scale than at larger (population) scale. This implies that local rarity (i.e., at the patch scale) may affect more negatively plant fitness than rarity at the larger (population) scale.
GÖTTINGEN: Hippocrepis comosa Primula veris LEEDS: Primula farinosa Origanum vulgare
TARTU: Verbascum nigrum
READING: Clinopodium vulgare LESVOS: Echium plantagineum Ballota acetabulosa Thymus capitatus
BOLOGNA: Ononis masquillieri
Figure 1. Location of the study sites throughout Europe in which fieldwork was carried out (green circles). In each location the names of the species studied are listed.
Not surprisingly, each pollinator guild responded differently to the structure of plant populations and at different spatial scales (Figure 3). Honeybee visits were more frequent in larger patches and populations, but they appeared relatively unrelated to flower density at any of the spatial scales. High visitation frequencies of solitary bees were associated with dense populations while those of hoverflies with dense patches. Bumblebees preferred small populations but patches of high density, maybe as a consequence of competition with honeybees, with the latter excluding the former from the larger populations. The results of this European scale study show that both plant reproduction and pollinator activity are controlled by plant population structure at different spatial scales. For the conservation of plants and their pollinators it is, therefore, important to sustain viable local populations of plants. These
populations must provide enough floral resources to attract sufficient pollinators which may ensure, in their turn, plant recruitment and species survival. References LEIMU R, MUTIKAINEN P, KORICHEVA J, FISCHER M (2006) How general are positive relationships between plant population size, fitness and genetic variation? Journal of Ecology 94: 942-952. OOSTERMEIJER JGB, LUIJTEN SH, PETANIDOU T, KOS M, ELLIS-ADAM AC, DEN NIJS HC (2000) Pollination in rare plants: is population size important? Det Norske Vitenskaps akademi. I. Matematisk Naturvitenskaplig Klasse, Skrifter, Ny Serie 39: 201-213. RATHCKE B (1988) Interactions for pollination among coflowering shrubs. Ecology 69: 446-457. VISSCHER PK, SEELEY TD (1982) Foraging strategy of honeybee colonies in a temperate deciduous forest. Ecology 63: 1790-1801. WESTPHAL C, STEFFAN-DEWENTER I, TSCHARNTKE T (2003) Mass flowering crops enhance pollinator densities at a landscape scale. Ecology Letters 6: 961-965.
b
Figure 2. Examples of two study sites within this project: a – calcareous grassland (Göttingen, Germany) and b – Mediterranean phrygana (Lesvos, Greece). Photos: B. Meyer (a), H. Dahm (b).
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Figure 3. An ordination biplot (RDA) showing the relationship between the spatial structure of the plant populations (blue arrows and orange circles for continuous and categorical variables, respectively) and the visitation frequencies observed by the different pollinator guilds (red arrows). The latter are specified as honeybees, bumblebees, syrphids, solitary bees and remaining insects. Green circles show the position of the different plant species in the ordination space. Plant species are abbreviated as in Table 1.
0.8 Tc
Honeybees 0.6
0.4 Ep Temperature
Large Solitary bees -0.6
Small
Syrphids Sparse
0.4
0.6
0.8
Table 1. The plant species studied throughout Europe.
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Remaining insects
Ba Om
Ov
Dense
Hc
-0.4
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Patch size
Pv Bumblebees Pf Cv -0.4 Vn
-0.6
a
Abbr.
Plant species
Family
Location (country)
Pf
Primula farinosa
Primulaceae
Leeds (UK)
Ov
Origanum vulgare
Lamiaceae
Leeds (UK)
Cv
Clinopodium vulgare
Lamiaceae
Reading (UK)
Pv
Primula veris
Primulaceae
Göttingen (Germany)
Hc
Hippocrepis comosa
Fabaceae
Göttingen (Germany)
Vn
Verbascum nigrum
Scrophulariaceae
Tartu (Estonia)
Tc
Thymus capitatus
Lamiaceae
Lesvos (Greece)
Ep
Echium plantagineum
Boraginaceae
Lesvos (Greece)
Ba
Ballota acetabulosa
Lamiaceae
Lesvos (Greece)
Om
Ononis masquillierii
Fabaceae
Bologna (Italy)
b
c
d
f e
Figure 4. Some of the plant species studied within this study: a – Echium plantagineum; b, c – Ballota acetabulosa; d, h – Thymus capitatus; e – Hippocrepis comosa; f – Verbascum nigrum; g – Primula veris. Photos: Th. Petanidou (a, d, h), H. Dahm (b, c), B. Meyer (e), M. Moora (f), F. Jauker (g).
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Drivers of Pollinator Loss – a Case Study from Germany
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BIRGIT MEYER & INGOLF STEFFAN-DEWENTER
Agricultural landscapes in Central Europe are becoming increasingly devoid of semi-natural habitats. The intensification of agriculture in recent decades along with the abandonment of traditional land-use practices such as sheep- or goatherding has caused a substantial decline of e.g., once common calcar-
1
large (5.14 ha, Figure 2b) grasslands. In 2004, between April and September, more than 8000 bees (Hymenoptera: Apoidea, Figure 3) and hoverflies (Diptera: Syrphidae, Figure 4), the two most important pollinator groups in temperate regions, were recorded. Strong species-area relationships in both bees and hoverflies were
depends heavily on the various life history traits of these insects. Body size is a life history trait which is a good indicator of the dispersal ability of bees. Small calcareous grasslands support only small populations of bees that consequently suffer from a greater risk of extinction. These grasslands therefore depend on
! (
Study sites from largest (1) to smallest (32) calcareous grassland
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Land use types Arable land Forest Grassland Hedgerows Heath Wetland Plantation Built-up area Garden land Calcareous grassland Orchard meadow Other
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Figure 1. Location of the 32 calcareous grassland study sites around the city of Göttingen in Southern Lower Saxony, Germany.
eous grasslands (Poschlod & WallisDeVries 2002). Calcareous grasslands are one of the most species-rich habitats in Central Europe, harbouring many rare plants and animals and being home to species that provide crucial ecosystem services, such as pollination, within the agricultural landscape. The abandonment of extensive grazing in these grasslands leads to succession; shrubs such as hawthorn, blackthorn, or dogwood quickly displace the original herbaceous plant community. The small calcareous grassland fragments that remain might then fail to offer sufficient foraging and nesting resources for pollinator communities. Trait-dependent species-area relationships Pollinator communities were assessed in 32 calcareous grasslands around the city of Göttingen in Southern Lower Saxony, Germany, (Figure 1). The size of the examined habitats ranged from very small (0.03 ha, Figure 2a) to very 176
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observed (Figure 5a). Larger habitat fragments that offer more feeding and nesting resources can support larger populations of pollinator species that are not as prone to extinction as small populations. Therefore, as the area of calcareous grasslands increases, pollinator species richness also increases. The species-area relationship for bees
immigration and large, mobile bee species can more easily colonise these empty patches than small, less mobile ones. Because small bees with small foraging ranges need a high density of available resources per unit area and large calcareous grasslands provide foraging and nesting sites in one place, they are very suitable for small bees. a
Hence, species richness of small bees increases more strongly with increasing area than species richness of large bees (Figure 5b). Another life history trait in bees is their social behaviour. Social bees such as bumblebees and small bees of the genera Halictus and Lasioglossum form colonies that persist throughout the entire vegetative period. These individuals need to exploit various resources over the year and are hence rather generalist bees. Females of solitary bee species construct their nests individually and usually collect pollen or nectar for their offspring over a short period of time and are therefore more specialised than social bees. Because specialists generally suffer more from the effects of fragmentation than generalists (Ewers & Didham 2006), species richness of solitary bees increases more strongly with increasing area of calcareous grassland than species richness of social bees (Figure 5c). Functional-group-dependent landscape effects Even though pollinators are mobile, pollinator movements between scarce suitable habitats might be impeded, since the grassland remnants are often situated in a vast, structurally poor landscape. The study region of Southern Lower Saxony is mostly dominated by arable land and forest; calcareous grasslands are patchily distributed fragments of semi-natural habitats in this agricultural landscape and cover only about 0.3 % of the area (Figure 1). The 32 examined calcareous grasslands had a surrounding landscape matrix of differing complexity that was calculated using the Shannon Diversity Index. The landscape structure ranged from very homogeneous, where calcareous grass-
b
Figure 2. A small, unmanaged calcareous grassland (a) and a large calcareous grassland regularly grazed by goats (b). Photos: B. Meyer.
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Figure 3. Halictus tumulorum (Hymenoptera: Apoidea) on Centaurea jacea. Photo: F. Jauker.
Figure 4. Helophilus pendulus (Diptera: Syrphidae) on Knautia arvensis. Photo: F. Jauker.
lands were surrounded by arable land only (Figure 6a), to very heterogeneous consisting of extensively managed grasslands, orchard meadows, gardens, hedgerows etc. (Figure 6b). A complex landscape not only facilitates movement through the landscape but it also provides additional resources
for bees and hoverflies. Hence, pollinator richness increases with increasing landscape diversity (Figure 7a). Whereas species richness and density of bees were both positively correlated with landscape complexity, contrasting responses were observed in hoverflies (Figure 7a, b): There were
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more hoverflies in structurally poor landscapes. This can be attributed to the high number of hoverflies with aphidophagous larvae. Hoverflies such as Episyrphus balteatus, whose larvae predate crop aphids, occur in large numbers in homogeneous landscapes because of the large amount of available annual crop fields (Figure 7c). Increased abundance, however, is gained at the cost of species number, as aphidophagous species show little niche differentiation in the larval habitat and species with non-aphidophagous larvae are reduced (Meyer et al. 2009). Similar effects were observed in obligate forest hoverflies as more individuals were found in calcareous grasslands with a high percentage cover of forest in the surrounding landscape. Small bees
35
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Large bees
The case studies presented show that both the loss of semi-natural habitats and the reduced landscape complexity are important drivers of pollinator loss in agricultural landscapes. References EWERS RM, DIDHAM RK (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews 81: 117-142. MEYER B, JAUKER F, STEFFAN-DEWENTER I (2009) Contrasting resource-dependent responses of hoverfly richness and density to landscape structure. Basic and Applied Ecology 10: 178-186. POSCHLOD P, WALLIS DEVRIES MF (2002) The historical and socioeconomic perspective of calcareous grasslands – lessons from the distant and recent past. Biological Conservation 104: 361-376.
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Figure 5. Habitat loss: Species-area relationships in calcareous grasslands for (a) bee and hoverfly pollinators, (b) small versus large bees and (c) solitary versus social bees.
a
b
Figure 6. A structurally simple landscape with a high percentage of arable land (a) and a complex landscape with a high percentage of non-crop area (b) (grasslands, hedges, woodlands), both north-west of Göttingen. Photos: B. Meyer.
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Figure 7. Landscape complexity: Effects of landscape diversity on (a) species richness and (b) density of bee and hoverfly pollinators and the relationship between percent cover of arable land and (c) the density of hoverflies with aphidophagous larvae.
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Domesticated Bumblebees
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HAJNALKA SZENTGYÖRGYI, DAWID MOROŃ, MANDY ROHDE, ELŻBIETA ROŻEJ, MARTA WANTUCH, JOSEF SETTELE, ROBIN F.A. MORITZ & MICHAŁ WOYCIECHOWSKI
Domestication of bumblebees Bumblebees visit a wide variety of flowers, performing so called “sonication” or “buzz-pollination”. In particular flowers with pollen firmly attached to the anthem are pollinated more efficiently by bumblebees. Pollinating bumblebees typically grasp the flower, wrap their body under the anthem and vibrate their flying muscles. Thanks to this method bumblebees can be 400 times more efficient pollinators than honeybees, particularly in a greenhouse setting but also in open crops (Figure 1).
Figure 1. Foraging wild bumblebees. Photo: M. Woyciechowski.
The first attempts to domesticate bumblebees date back to almost a century ago. But as recently as the mid-1980’s Dr. R. de Jonghe, a Belgian veterinarian took a serious interest in bumblebees and founded a company commercially producing bumblebee colonies for pollination. Other companies followed swiftly, all initially focusing on the large earth bumblebee (Bombus terrestris), which was found to be easily bred, thanks to simple mating control, had high reproductive success and large colony size comparing to other bumblebee species (Velthuis 2002). Last but not least, this species is a generalist, thus readily visiting a wide variety of plants offered. Today the bumblebee buzz-pollination system has been spread all over the world, as both an economically and ecologically preferred method in greenhouses Concerns about biodiversity conservation B. terrestris is a common species in Europe, but absent on other continents. Even in Europe this species has several clearly distinguished subspecies in different bio-geographic regions. For commercial breeding the most popular subspecies turned out to be the most southern ones such as B. t. dalmatinus or B. t. sassaricus, which also produce colonies during 178
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the winter. If greenhouses were closed environments, the use of nonendemic subspecies should not be a source of concern for the conservation of biodiversity. However, it may be overly optimistic to assume that greenhouses, no matter how well built, form tight and secure barriers to keep bees inside. It has been shown that bumblebees can forage significant amount of time outside the greenhouse and such escaped bumblebee can have contact with local populations. Clearly, the introduction of a new generalist and highly adaptive species such as B. terrestris is in principle a biodiversity risk and can endanger local pollinator faunae. During the last century, this species was successfully introduced into New Zealand and the island of Hokkaido in Japan, and there are alarming reports of pollination disturbances in native plants. In answer to this problem, governmental restrictions on using B. terrestris, as a managed pollinator have been implemented in numerous countries. Today several species are used in crop pollination. Besides biogeographic problems, bumblebees can be hosts to various parasites. Naturally, not all cause serious diseases, but some, such as the Microsporidia, Nosema bombi (Figure 2), can cause severe infections: reducing the mating rate of virgin queens, colony founding, male production and even leading to the decay of entire colonies. N. bombi is relatively easily spread. In addition, it is not species-specific and can infect most, if not all, existing bumblebee species (Schmid-Hempel et al. 1998).
Sarnów Kwasniów
Katowice
Krzeszowice
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Pszczyna Sulkowice Andrychów Bielsko-Biala
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Greenhouse complexes Sites near the greenhouse complex Control sites
Warsaw POLAND
Figure 3. Tested sites in Southern Poland and an example of a greenhouse interior. Photo: M. Woyciechowski.
The Polish example Commercially reared bumblebees for pollination purposes are also used in Poland, especially for tomato produc-
Figure 2. Nosema bombi spores under light microscope. Photo: H. Szentgyörgyi.
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tion. Within the ALARM network, we tested whether commercial bumblebees in greenhouses escape and introgress into the wild population. Three greenhouse complexes were chosen for testing. Additionally near each greenhouse, a population of wild bumblebees was tested and also three control populations at least 30 km from each of the tested greenhouses were sampled (Figure 3). Wild bumblebee workers from commercially reared colonies and those caught outside the greenhouse were distinguished by using microsatellite DNA genotyping using four highly polymorphic loci (B11, B96, B124, B126) following standard PCR protocols (Walsh et al. 1991, Estoup et al. 1993, 1995). Colony assignment was done using the COLONY 1.3 software based on all nine sampled populations. Introgression was quantified using the software packages BAPS vs. 5.1 and STRUCTURE vs. 2.2.
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Figure 5. Assingment of workers to populations.
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colonies purchased straight from two international producers, colonies used in the greenhouse and wild-caught individuals were sampled, dissected and checked for the presence of N. bombi spores in the intestines and other internal organs using a light microscope. Bumblebees purchased straight from two international producers showed significant levels of infection in all colonies. Bumblebees already collected from the greenhouses had very high levels of infection, showing that the Nosema infection multiplied readily in the conditions prevailing in these facilities. Neighbouring populations to the greenhouses sampled showed higher levels of infection than
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Results We first assigned all sampled workers to the corresponding mother colonies and only used the genotypes of the sexual reproductives provided by COLONY for our further calculations. We found 8 distinct populations using the BAPS software tool (Figure 4). The two greenhouses in Krzeszowice and Pszczyna were not separated into two different populations, which fits well with the population history since the same breeder supplied the colonies for both greenhouses. All the other populations were clearly separated from each other. STRUCTURE shown in Figure 5. Every single individual is represented as a vertical line. The number of the
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Figure 4. Population assignment of the sampled bumblebees using BAPS.
Figure 6. Nosema bombi infection levels in populations near greenhouses and controls.
colours reflects the number of populations and the amount of the colour represents the probability of an individual to belong to the according population Comparing the three clusters: greenhouses, near greenhouses and control sites, we found that the potential for misclassifications was lowest at the control sites There are 34 % misclassified individuals in the areas around greenhouses but only 18 % misclassified individuals in the control sites (p < 0.001) which might be an indication for admixture of wild and commercial bumblebee populations. We also studied if managed bumblebees are carrying enhanced levels of N. bombi parasites and mass rearing conditions of greenhouse bumblebees can cause higher level of N. bombi infestation. To check this, fresh
should we take to minimize potential associated risks? Some countries have already taken steps to solve this problem, by using endemic bumblebee species or other native solitary bee species (when possible) for pollination purposes. Rearing condition should also be better controlled to exclude infected queens or colonies from further breeding and thus minimize the spread of unwanted parasites. Decentralizing pollinator production using locally available breeds including wild populations might help to avoid both the spread of parasites and the mixing of various subspecies by using native populations for rearing. Appropriate control of commercial colonies will be vital to avoid disturbance of the environment and ensure that bumblebee pollination can be a sustainable agricultural practice.
control populations, which theoretically had no contact with commercially reared individuals (Figure 6). To summarize, commercial bumblebees might be pollinating not only in the greenhouse, but also paying visits to the surrounding environment and spreading Nosema spores, thus might raise infection levels in native populations in these areas. Possible solutions The use of commercially produced bumblebee colonies is clearly a great success story for pollination in greenhouses (Figure 7). It has reduced the application of insecticides and has made crop production more environmentally friendly. On the downside there is a clear and measurable biodiversity risk: do the pros outweigh the cons of the bumblebee trade? If so, then what steps
References ESTOUP A, SOLIGNAC M, HARRY M, CORNUET J (1993) Characterization of (GT)(n) and (CT)(n) microsatellites in two insect species: Apis mellifera and Bombus terrestris. Nucleic Acids Research 21: 1427-1431. ESTOUP A, TAILLIEZ C, CORNUET, J-M, SOLIGNAC M (1995) Size homoplasy and mutational processes of interrupted microsatellites in two bee species, Apis mellifera and Bombus terrestris (Apidae). Molecular Biology and Evolution 12: 1074-1084. SCHMID-HEMPEL P, LOOSLI R (1998) A contribution to the knowledge of Nosema infections in bumblebees, Bombus spp. Apidologie 29: 525-535. VELTHUIS HHW (2002) The Historical Background of the Domestication of the Bumble-Bee, Bombus terrestris, and its Introduction in Agriculture – In: Kevan P, Imperatriz Fonseca VL (Eds), Pollinating Bees – The Conservation Link Between Agriculture and Nature. Ministry of Environment/Brasília, 177-184. WALSH PS, METZGER DA, HIGUCHI R (1991) Chelex® 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. BioTechniques 10: 506-513.
Figure 7. Tomato-growing greenhouse complex in Southern Poland. Photo: M. Woyciechowski.
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A Geometric Morphometric Tool for the Conservation of the Black Honeybee in Europe
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AGNÈS RORTAIS, MICHEL BAYLAC, GÉRARD ARNOLD & LIONEL GARNERY
The honeybee Apis mellifera is naturally distributed over three continents (Europe, Asia, Middle-East, and Africa) and includes 26 subspecies grouped into four geographically well structured evolutionary lineages: the M and C lineages in Western and Northern Europe, respectively, the
Figure 1. Home range of the black honeybee Apis mellifera mellifera.
O lineage in the Caucasia and Turkey and the A lineage in Africa and Middle-East (Ruttner 1988). The four lineages are distinguished by classical morphometrics and molecular markers. However these approaches present a number of challenges such as, for example, operator-bias in character designation (morphometrics). And whilst genetic assays can resolve some of these issues, they are both costly and time-consuming to implement. In Western Europe, (North of Spain – Finland) (Figure 1), the native black honeybee (A. m. mellifera) is threatened in many places by commercial bee breeding (Jensen et al. 2005). Persistence of the black honeybee in its natural range requires surveys that identify the management units and appropriate management strategies (SICAMM 2006). France holds ~60 % of all black honeybee hives in Western Europe (i.e., in UK, Denmark, Netherland, Belgium, Ireland, Finland, and Sweden) (http:// www.beekeeping.com/databases/ europe_92_96.htm). More important-
Nord
1992 A !
A A ! ! A! A A A! A! A! ! A ! !! A A ! A ! ! A ! A! ! A! A! A A A A! ! A! ! AA ! A! A ! ! A A ! A A ! A ! A! ! A ! A ! A ! A! A ! A! ! A A ! A ! A ! A ! A! A ! ! A ! A A ! A !
Orne 2005 A ! A A A! A! ! A ! A! ! !! A A! A! A A ! A A! ! A! ! A! A ! A A! ! A A A ! A A ! A! ! A ! A A! A! A A A ! !! A A A! ! A! ! A! ! A ! A! A! ! A ! A ! A ! A ! A A! ! A !! A A A ! ! A ! A! ! A ! A! ! A ! A AA ! A ! A !
Evolution of the maternal origin of the colonies, between 1992 and 2005 in the “Nord” region
1998 A ! A A! ! A! ! A ! !! A A A ! A A ! A ! A ! A ! A! A A! A A A! ! A! ! A A A ! A A ! A! ! A ! ! A ! A! ! A A! ! A! ! A A! ! A! ! A A ! A! A! ! A! A A! ! A A! ! A A! ! A! ! A! A A A! ! AA ! A A! A! ! A! A! ! A A ! A A! ! A! A A! ! A! A ! A! ! A A! ! A A ! A ! A! ! A A ! A A! ! A ! A ! ! A ! A ! A ! A! ! A A ! A! ! A A ! ! A ! A! ! A ! ! A A ! A A A ! A ! A! A! ! A A ! A A A! ! A! ! A! ! A ! A ! A ! A A ! A A ! A ! A! ! A ! A ! A ! A ! A ! AA ! A ! A ! A A ! A A ! A! A! ! A ! A A! ! A ! A A ! A ! A! A! ! A! A ! A! ! A ! A ! A ! A ! A ! A ! A! ! A ! A ! A A ! A ! A ! A ! A ! A ! A ! AA ! ! A ! A! ! A! A ! A A ! ! A ! A ! A ! A ! A ! A! A ! A A ! A! ! A! ! A A A! A! A ! A ! A ! A ! A ! A ! A! A ! A ! A ! A A ! A! A ! A ! A ! A ! A ! ! A A ! A! A! ! A ! A ! A! A ! A ! A ! A ! A ! A A! A A! ! A! ! A ! A ! A! A! ! A ! A A! A! A ! ! A ! A ! A A ! A A! ! A ! A ! A ! A ! A! ! A ! A ! A A! ! A A! ! A A! ! A ! A A! ! A ! A
2004
Figure 2. Maternal origin of honeybees in France, in 2006 (lineages: M in red, C in blue, and A in yellow).
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the Nord and Orne locations (Figure 2) which suggests that beekeepers’ practices in this area are sustainable for the conservation of the black honeybee and the development of reserves on the subspecies’ natural range of distribution. In an attempt to identify optimal diagnostic tools that facilitate straightforward traceability of honeybees, geometric morphometric analyses were conducted on honeybees’ wings (Figure 3) and related to molecular data to design an expert system. The system platform is located at the Muséum National d’Histoire
Figure 3. A honeybee wing used in geometric morphometrics. A total of 19 nervure intersections are used to determine the origin of honeybees.
Naturelle) in Paris, and connected to local stations equipped with microscopes and computers from where professionals can trace back the origin of their bees and manage their livestock. This is the largest survey achieved in Europe on honeybees’ diversity and conservation, and the most complete database on the subspecies A. m. mellifera. The expert tool that follows is an important step towards effective conservation of the black honeybee in France and Europe and should be regarded as an interesting case study for the conservation of other threatened and endangered species. References
Evolution of the maternal origin of the colonies, between 1998 and 2004 in the “Orne” region
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ly, France’s black honeybee populations are reported to have faced recurrent losses over the last 10 years, forcing beekeepers to either restock or abandon their livestock. Accordingly, France’s black honeybee populations can be considered one such important management unit to study. In a recent comprehensive study coordinated by the CNRS (Centre National de la Recherche Scientific) under the European framework programme EC No 1221/97, the genetic diversity of 3300 honeybee colonies from France and Belgium was determined with mitochondrial markers (COI-COII intergenic region). Data show that A. m. mellifera is well represented in some areas, and occurs as a minority in the South-Western and North-Eastern regions of France (Figure 2). Comparisons with surveys conducted in previous years at the same locations may bring useful information for conservation issues. For example, between 1992 and 1998, the black honeybee (M lineage) remained predominant and stable in
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EUROPEAN REGULATION EC No 1221/97 for the application of measures to improve the production and marketing of honey. JENSEN A, PALMER K, BOOMSMA J, PEDERSEN B (2005) Varying degrees of Apis mellifera ligustica introgression in protected populations of the black honeybee, Apis mellifera mellifera, in northwest Europe. Molecular Ecology 14: 93-106. RUTTNER F (1988) Biogeography and taxonomy of honeybees. Spinger Verlag, Berlin. SICAMM International Society for the Conservation of Apis Mellifera Mellifera (2006) 7th International Conference. 18-21/09/06. Université de Versailles, Versailles, France.
A New Enemy of Honeybees in Europe: the Asian Hornet, Vespa velutina
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AGNÈS RORTAIS, CLAIRE VILLEMANT, OLIVIER GARGOMINY, QUENTIN ROME, JEAN HAXAIRE, ALEXANDROS PAPACHRISTOFOROU & GÉRARD ARNOLD
Honeybees are essential pollinators of crops. In Europe, important pollinator declines have been reported, which jeopardise pollination services in agricultural ecosystems and have great economic impacts. The recent accidental introduction of the Asian hornet Vespa velutina into Europe (south-west of France) (Villemant et al. 2006) represents a new threat to pollinators, mainly honeybees. The Asian hornet V. velutina is naturally distributed in mountainous regions from northern India to the Indochinese Peninsula, Taiwan and Indonesia. In 2004, the form nigrithorax, was recorded, for the first time, in France (Lot-etGaronne district), and has spread in 4 years across 20 districts, over approximately 120,000 km² (Figure 1). As demonstrated by the rapid growth of colonies (Figure 2), the species has adapted perfectly well to its new environment so that eradication is no longer possible. If no rapid and efficient control management is conducted, further expansion is expected to occur, in the near future, into other European countries where the hornet can find suitable environmental conditions (climate and resources) to develop. At present, and with this new species record, a total of three hornet species are found in Europe: the European hornet (V. crabro) widely distributed all over Europe, the Oriental hornet (V. orientalis) in south-east Europe, and the invasive yellow-legged hornet
1 cm
20 cm Figure 2. Nests of Vespa velutina: embryo (top) and mature (bottom) from 5 cm in diameter up to 1 m high and 80 cm wide within 6-7 months. Photos: J. Haxaire.
a
c e ! e! ! e ! e ! e e ! e!! e ! e ! e ! ee ! e! ! e! ! ! e ! e ! e! e! e e! ! e! e ! e e ! ! e! e e ! e e! ! e e ! e ! e ! e! ! e ! e e e! ! e! e! e ! e ! e ! e ! e ! e e! ! e ! e e ! e! e ! ! e e! ! e e! ! ! e ! ! e! ! ! ee e ! e ! e ! ! ! e! e e ! e ! e! e! ee e ! e ! e e! ! e ! ee ! ! e! e e! ! ! e! e e e ! e! ! ! e! e e! e e! e! ! ! ! e! e e e e ! ! e e ! ! e e ! ! e! e ! ! e e e ! e! e ! ! e e ! e ! ! ! e e! e!! ! e! ! e ! e! ! e ! ! e e e e e ! e ! e ! e ! ! ! ! ! e e e e e ! ! e e e e e! ! ! e e ! e ! ! ! e e e ! ! e ! ! e! e! ! e! e ! e e ! ! e! e! e! e ! ! e! e! e! ee! e ! ! e e e ! e! e ! ! ! e e e! e! e ! ! ! e e ! e! e e! e! ! ee !! ! ! e e ! e e e! e ! e ee! ! e! ee e! e! ! e! e ! e ! ! e! e e ! e! e ee e! e ! ee! e ! ! e ! e! e! e e! ! ! e e! e! ! e e ! e e ! ! e! ! ! e ! e e ! e! e ! ! ! ! e ! e e! !! ! ee ! ! e e e! ! e ! e e ! e ! e e ! ! e e ! ! e e ! ! ! e e ! ! e e ! e e! e! ! e!! ! ! e! e! e e ! e ! e! ! e! ! ! e! e ! e ! e e e! e ! e! ! e! e! e ! e ! e! e! ! e ! e e! e! e ! ! e! e! e e ! ! ! e! e e e e e e ! ! e e e! ! ! e e ! ! ! e e e e! ! ! e e! ! ! e e e e! ! ! e ! e e e! ! e ! e e ! ! ! ! e e e e ! ! !! e! e e ! ee ! e ! e! e e! ! e! ! e ! e e! e! e ! ! e ! e ! e e! ! ! ! e e! e! ! ! e! e ! ! e e! e e ! e e e ! e e e ! e e ! ! e e ! ! ! e ! e e ! e e ! ! e e ! e ! ! e ! e ! ! e e e ! e ! ! ! ! e e ! e e e ! e ! e !! e ! e! e e! ! e! ! e! ee ! ! e e e ! ! ! e e e e ! e ! e ! e e ! ! e e ! ! ! e e! e ! ! ! e e ! e ! e ! e ! e! ! e e e! e e! ! ! ! e! e e e! e! e ! e! e ! e! ! e! e! e! ! ! e e! e e e ! ! e! ! e ! e! ! e ! e e ! e ! e ! e ! ! e! e e e ! e ! e e e ! e e ! e! e ! e! e! e e ! ! e! e ! e! ! e! ! ! e! e! ! e! e! e! ! e e! e e! e ! ee ! e! ! ! ! e e e ! ! e e ! ! e! e ! e! e e e! e! e! ! e ! e ! e ! e ! ! e e ! e ! e ! e ! ! e e ! e ! e e e ! ! e ! e ! ! e !! e ! e ! e e ! e ! e ! e ! e ! e ! e ! e ! e e !! e
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Figure 1. (A–B) Records of the presence of V. velutina in France from 2004 to 2007 (area limits represent districts). (C) Detailed map of the distribution of nests in the south-west of France.
(V. velutina) in the south-west of France (Figure 3). The three species are morphologically distinct (colour and size), and while they are strongly attracted to honeybees, their impact on them may not be equal. Vespa velutina may be more competitive than V. crabro and V. orientalis because of the larger size of its nests and colonies. Hornets attack honeybee colonies (Figure 4) to feed their brood with proteins, and to get carbohydrates (honey). In Asia, native honeybees have developed efficient strategies to defend their colonies. For example, when attacked by the giant hornet (V. mandarinia) or the yellow hornet (V. simillima), the eastern (A. cerana) and dwarf (A. florea) honeybees form a ball of workers around the intruder and kill it by heatstroke (Ono et al. 1995). In Europe, while native honeybees display the same balling behaviour, the underlying mechanism by which they kill their predator is different. For example, in Cyprus, honeybees (A. m. cypria) kill the hornet (V. orientalis) by asphyxia (Papachristoforou et al. 2007). However, native European honeybees (e.g., mellifera, ligustica, carnica ssp.) may not be able to compete with the new invader (V. velutina), as confirmed by observations made in the field on A. mellifera and V. velutina: in France, V. velutina feeds predominantly on honeybees (Perrard et al. 2009), and in Asia, introduced A. m. mellifera colonies exhibit ineffective defence strategy against attacks of V. velutina (Tan et al. 2007). In a context of globalization and global warming, the threat of invasive species that may impact on honeybees
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The European hornet Vespa crabro L., ♀ 30-40 mm long
The Asian hornet Vespa velutina Lepeletier, ♀ 25-30 mm long
The Oriental hornet Vespa orientalis L., ♀ 25-35 mm long
Figure 3. Hornets’ distribution in Europe (dimensions refer to queens' size). Photos: J. Haxaire (two top photos) and Alexandros Papachristoforou (bottom photo).
in the same way as hornets is expected to increase, as demonstrated by the recent accidental introduction of V. orientalis in Mexico. To limit the introduction and spread of such invasive species, a joined action is required at both European and global levels, such as the setting-up of an alert network.
References
Figure 4. Vespa velutina attacking a honeybee colony. Photo: J. Haxaire.
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ONO M, IGARASHI T, OHNO E, SASAKI M (1995) Unusual thermal defence by a honeybee against mass attack by hornets. Nature 377: 334-336. PAPACHRISTOFOROU A, RORTAIS A, ZAFEIRIDOU G, THEOPHILIDIS G, ARNOLD G (2007) Smothered to death: hornets asphyxiated by honeybees. Current Biology 17: R795-R796. PERRARD A, HAXAIRE J, RORTAIS A, VILLEMANT C (2009). Observations on the colony activity of the Asian hornet Vespa velutina Lepeletier 1836 (Hymenoptera: Vespidae: Vespinae) in France Annales de la Société Entomologique de France 45: 119-127. TAN K, RADLOFF S, LI J, HEPBURN H, YANG M, ZHANG L, NEUMANN P (2007) Bee hawking by the wasp V. velutina on the honeybees Apis cerana and A. mellifera. Naturwissenschaften 94: 469-472. VILLEMANT C, HAXAIRE J, STREITO J-C (2006) Premier bilan de l’invasion de Vespa velutina Lepeletier en France (Hymenoptera, Vespidae). Bulletin de la Société Entomologique de France 111: 535-538.
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Beekeeping and the Conservation of Native Honeybees in Europe
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RODOLFO JAFFÉ & ROBIN F.A. MORITZ
The honeybee as a natural pollinator The western honeybee, Apis mellifera, (Figure 1) is arguably the world’s most important beneficial insect, being of great value to man and nature. They produce honey, pollinate crops worth billions of Euros per year, and provide full and part time employment to beekeepers throughout the globe. The honeybee is also a vital member of many terrestrial ecosystems, pollinating a broad spectrum of wild flora. Moreover, because they hibernate as colonies with large numbers of workers, they are essential for ensuring early spring pollination, when other pollinators are absent. European honeybees fall into two major postglacial lineages. The Western European “M” lineage, including two subspecies (A. m. iberiensis and A. m. mellifera) and a suite of local ecotypes, ranges from Gibraltar to Scandinavia and from Ireland to Poland (Cánovas et al. 2008). The Eastern European “C” lineage includes more than ten subspecies from central Europe and the northern Mediterranean (De la Rúa at al. 2009). Within its native range, Apis mellifera is a vital part of ecosystems, essential for their functioning. In many regions of Europe, however,
Figure 2. Apiary in southern Tyrolia, Italy. Photo: R. Jaffé.
honeybees are often regarded as pests, displacing other endemic but rare species. Competition with other pollinators is certainly possible, as some cases have been reported where other bees shifted their floral forage spectra when honeybees where introduced. However, it is not clear to what extent these effects matter at the ecosystem level. To date there are no rigorous reports of any extinctions or critical pollinator declines as a response to honeybee introductions (Paini 2004, Moritz et al. 2005). Even in the most dramatic example of a honeybee invasion, the introduction of African honeybees in the Americas, which has been scrutinized for many decades, there are no 182
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Figure 1. A honeybee worker (Apis mellifera) on a pollen collection flight. Photo: R.F. A. Moritz.
reports of extinctions of native pollinators. Indeed, the potential for conflict with other pollinators may be less dramatic than it seems at first glance, given that honeybees have different nesting requirements than other bees, they do not forage aggressively (as do several stingless bee species), and can rapidly shift the foraging force to noncompetitive nectar sources. For instance, there is also evidence pointing in the opposite direction: The removal of feral honeybee colonies from Santa Cruz Island in California, caused an increase in the abundance of other flower visiting insects, showing that honeybees did not cause the extinction of the native pollinators of this island. Honeybees and beekeeping As diverse as the biology of the endemic honeybee subspecies are the beekeeping traditions in Europe, with deep cultural roots dating back to prehistoric times. Honeybees are therefore often mistakenly considered as “domesticated” animals. Nevertheless, honeybees can (and more often then desired by the beekeeper do) leave their hive at any time to successfully reproduce in the wild. Beekeeping should thus be regarded more like aquaculture, where wild fish are bred in artificial ponds or estuaries. The human-mediated destruction of natural habitats has caused a reduction in the suitable nesting sites for honeybees and in the floral resources upon they feed. Hence, local mass assemblies of honeybee colonies in beekeepers apiaries (Figure 2), do not
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mean that the surrounding habitats can naturally support a high number of colonies. Any decline in beekeeping will therefore cause a honeybee decline. Managed honeybees have suffered a progressive decline throughout the world over recent decades. The recent occurrence of so called Colony Collapse Disorder (CCD) in the USA and similar phenomena in Europe resulted in the death of hundreds of thousands of colonies. The magnitude of these losses may jeopardize the pollination services honeybees provide. This is alarming, considering the total economic value of pollination worldwide, amounted to € 153 billion in 2005 (or 9.5 % of the value of the world agricultural production used for human food that year; Gallai et al. 2008). Beekeeping in Europe De la Rúa et al. (2009) surveyed several research institutions, beekeepers’ organizations, and open access data bases, gathering beekeeping statistics of 33 European countries. Based on their data we constructed two maps, showing the density of managed hives and the mean number of hives per beekeeper in Europe. The density of managed hives (calculated as the total number of hives divided by the area of each country) is an informative indicator of the national status of honeybees, given wild honeybee populations have all but disappeared in many European countries (Jaffé et al. in press). On the other hand, the number of hives regularly kept by beekeepers provides an idea of the size of bee-
keeping operations in each country. Looking at the density of managed hives through Europe, is evident that beehives become more abundant from North to South and from West to East (Figure 3). The North to South gradient may be explained by climatic factors. While beekeeping operations in the Mediterranean region are likely to benefit from milder climates, allowing the maintenance of a large number of hives, countries such as Ireland and Britain, famous for their rainy summers, only allow a limited number of bee foraging days. The West-East increase in the density of managed hives is less readily explained by climatic factors. Countries such as Germany and Hungary share a similar continental climate and yet beehive densities in Hungary are nearly six times higher than in Germany (Figure 3). On the contrary, the number of beekeepers is five times higher in Germany than in Hungary, and hence Hungarian beekeepers handle on average many more hives than Germans do (Figure 4). Differences in beekeeping practices thus seem likely to explain the abundance of managed hives. For instance, the density of managed hives across all 33 European countries was positively correlated with the mean number of hives kept by local beekeepers (Figure 5), suggesting the size of beekeeping operations influences the local abundance of beehives. Another interesting pattern observed in Figures 3 and 4 is the political, rather than geographical distribution of beehives in some areas. Major differences in hive densities and number of hives per beekeeper occur between neighbouring countries such as Germany and the Czech Republic. This shows that socioeconomic aspects, the societal framework and the different national cultural histories also come into play in determining the density of honeybees across Europe. Beekeeping and the conservation of native honeybees Beekeeping policies can profoundly influence the genetic diversity of honeybee populations (De la Rúa et al. 2009). Whilst the importation of foreign queens can result in the introgressive hybridization of native subspecies, large scale queen breeding and the widespread propagation of selected stock will ultimately reduce effective population size, making populations more susceptible to the deleterious
Hives/km2
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Figure 3. Density of managed honeybee colonies in 33 European countries.
Figure 4. Average number of hives per beekeeper in 33 European countries.
effects associated with inbreeding (De la Rúa et al. 2009). Two examples may illustrate how the introduction of imported honeybees had disastrous consequences in the past. Honeybee queens introduced from Africa to Brazil in the 1950’s spread as “Africanized” or “killer” honeybee swarms throughout the American continent, reaching the southern states of the US within a few decades. Likewise, parasitic Cape honeybees (A. m. capensis) from the Western Cape in South Africa were transported in large numbers to the north by migratory beekeepers. This caused the so called “capensis calamity”, where thousands of the endemic A. m. scutellata colonies were destroyed due to the invasion of parasitic A. m. capensis workers, which can kill the queen, establish themselves as pseudoqueens and eventually destroy the colony (Moritz et al. 2005). Although the impact of bee breeding practices on population structure may appear less spectacular, its consequences are also clearly detectable. For instance, differences in the honeybee population structure of the Iberian Peninsula (with little or no breeding) and Italy (with strong breeding and migration) are remarkable. No population substructuring was detectable between north and south Italy (Dall’Olio et al. 2007) whereas various discrete subpopulations can be found in Spain (De la Rúa et al. 2009). The conservation of native honeybee subspecies should be a pressing priority given that they are important reservoirs of local adaptations, ultimately determining the survival and pollination success of hon-
Figure 5 shows that the size of apicultural operations does matter when it comes to honeybee densities. Many countries only maintain high colony densities because beekeepers have large operations, suggesting that the economic benefits of apiculture in these countries play a more important role than in countries with small scale apiaries (where apiculture serves more as a rewarding pastime activity). In consequence, the attitudes of beekeepers towards beekeeping will be very different. The price bee products can achieve in the local markets will ultimately drive their decision to expand, maintain or close their businesses. This fact should be considered by policy makers when offering financial or technical incentives to promote beekeeping. Extension officers must also explain why working
eybees in the wild. Beekeepers should thus take into account the need for conserving endemic wild populations (De la Rúa et al. 2009). Conservation policy tools Sustainable strategies to conserve the European honeybee diversity could be implemented by establishing honeybee reserves by and promoting the use of endemic honeybees in beekeeping. Some European nations already have regulations in place to control honeybee transport and protect endemic populations. The island of Læsø, for example, is a reserve to conserve Danish honeybees. On mainland, the size of these areas must exceed the mating rage of queens and drones (10 km²) and the number of participating beekeepers needs to be sufficient to sustain viable population sizes. 2.5
Hives per beekeeper (logaritmic scale)
2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7 0.5
0
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with local bees is better than with imported stock, in spite of potential short-term shortcomings (e.g., marketing honey of regional bees, reduce spread of diseases, improve mating control). Policies promoting apiculture with endemic honeybee subspecies must therefore take into account not only the biological aspects of the honeybees, but also the sociocultulral diversity of Europe. Moreover, regional regulations rather than a single paneuropean directive are more likely to allow a sustainable conservation of the vast variety of local honeybee subspecies in Europe. References CÁNOVAS F, DE LA RÚA P, SERRANO J, GALIÁN J (2008) Geographic patterns of mitochondrial DNA variation in Apis mellifera iberiensis (Hymenoptera: Apidae). Journal of Zoological Systematics and Evolutionary Research 46: 24-30. DALL’OLIO R, MARINO A, LODESANI M, MORITZ RFA (2007) Genetic characterization of Italian honeybees, Apis mellifera ligustica, based on microsatellite DNA polymorphisms. Apidologie 38: 207-217. DE LA RÚA P, JAFFÉ R, DALL ’OLIO R, MUÑOZ I AND SERRANO J (2009) Biodiversity, conservation and current threats to European honeybees. Apidologie 40: 263-284. GALLAI N, SALLES JM, SETTELE J, VAISSIÈRE BE (2008) Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecological Economics 68: 810-821. MORITZ RFA, HÄRTEL S, NEUMANN P (2005) Global invasions of the western honey bee (Apis mellifera) and the consequences for biodiversity. Ecoscience 12: 289-301. PAINI DR (2004) Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Austral Ecology 29: 399-407.
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Severe Declines of Managed Honeybees in Central Europe
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SIMON G. POTTS, STUART P.M. ROBERTS, ROBIN DEAN, GAY MARRIS, MIKE BROWN, RICHARD JONES & JOSEF SETTELE
Background In Europe there is an increasing number of reports that honeybees (Apis mellifera, Figure 1) are declining, but the evidence is patchy and often poorly documented. While there have been clear honeybee losses in some regions, to date there has been no large scale assessment of the extent of the prob-
the importance of the honeybee industry in Europe, and the reliance of many crops on pollination, coupled with the growing evidence for declines in managed pollinators, it is therefore prudent to bring together and critically assess the available information. We therefore aim to quantify the extent of historical changes in honeybee colony
Figure 1. Honeybees (Apis mellifera) are the most important managed pollinators in Europe. Photo: International Bee Research Association.
lem. Other important European pollinators, such as some solitary bees, bumblebees and hoverflies, have been shown to have severely declined since 1980 in the UK and Netherlands (Biesmeijer et al. 2006). In North America, the U.S. Department of Agriculture statistics show declines in honeybee colonies in 1947-1972, 19891996 and a recent drop in 2005 (National Research Council 2006). It is estimated that 83 % of Europe’s crops depend, at least in part, on insect pollination, and honeybees are the most commonly managed pollinators for pollination services (Williams 1991). The total value of crop production dependent on insect pollination in Europe is approximately € 14.2 billion (Gallai et al. 2009). Given
numbers, beekeeper numbers and honey production across Europe. Approach Using national beekeeping journals, national beekeeping organisations and government reports we collated data on the total number of colonies, total number of beekeepers and total honey production for 20 European countries: Austria (AT), Belgium (BE), Czech republic (CZ), England (EN), Finland (SF), France (FR), Germany (DE), Greece (GR), Ireland (IE), Italy (IT), Luxemburg (LU), Netherlands (NL), Norway (NO), Portugal (PT), Poland (PL), Scotland (SC), Slovakia (SK), Spain (ES), Sweden (SE) and Wales (WA). Two comparison periods were selected based on good data coverage
Table 1. Mean (± SE) changes in the numbers of honeybee colonies and beekeepers in European regions between 1965 and 2005, and between 1985 and 2005. Comparison
Region
1965-2005
All Europe Central Europe Peripheral Europe All Europe Central Europe Peripheral Europe
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% change in Colony numbers +4.5 ± 11.8 -26.6 ± 5.4 +51.2 ± 13.1 -11.2 ± 5.9 -23.3 ± 4.9 +5.9 ± 9.7
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% change in Beekeeper numbers -30.2 ± 13.1 -57.8 ± 7.8 +11.1 ± 13.2 -30.2 ± 4.6 -36.6 ± 5.1 -18.5 ± 6.6
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and historical relevance: net percentage change between 1965 and 2005 and net percentage change between 1985 and 2005. To examine regional differences, countries were assigned as central European (AT, BE, CZ, DE, EN, FR, LU, NL, PL, SC, SK and WA), Scandinavian (NO, SF and SE), Mediterranean (GR, IT, PT, ES) or Atlantic (IE), with the last three categories pooled as peripheral Europe. Findings There were mixed changes in honeybee colony numbers among countries for both time period comparisons, with countries showing a net increase in colonies and others showing a net decrease (Figures 2a and 2b). There were consistent declines in central Europe (1965-2005 mean decline = 27 %, 1985-2005 mean decline = 23 %; Table 1) and increases in peripheral regions (except Sweden). Net changes in the central and peripheral regions of Europe were significantly different for both the 1965-2005 and 1985-2005 comparisons. In the 1965 and 1985 comparisons with 2005, there was an overall European trend for decreases in beekeeper numbers of 30 % (Table 1). For both comparisons there were severe declines in central Europe (Figures 2c and 2d; 1965-2005 mean = 58 %, 1985-2005 mean = 37 %; Table 1). In 1965 there were mixed trends in the peripheral regions with some increases (Finland and Italy) and a decrease (Norway). In contrast, in 1985, all peripheral regions showed significant declines in beekeeper numbers as did central Europe. Honey production substantially increased in all countries (Figures 2e and 2f; except Austria in 1985-2005) with a mean increase across Europe of 114 % since 1965, and 45 % since 1985. Discussion Our study suggests that there has been a serious decline in honeybee colonies and beekeepers in central Europe since 1965. Several drivers of honeybee loss have been proposed for Europe but to date there is little consensus on which driver, or combinations of drivers, are responsible for observed declines. A number of diseases are know to destroy hives or severely injure colonies such as Varroa mites, tracheal mites, chalk brood and Nosema parasites. Decreases in beekeepers are also attributable to many possible causes
including: consolidation of bee colonies managed by larger businesses to service pollination needs of mass-flowering crops; cheap alternatives to local honey making small scale beekeeping less profitable; and increased prices for treating bee diseases. Despite decreases in colonies and hives, honey production has consistently increased across Europe and reflects improvements in beekeeping practices and technology leading to larger and more productive hives. Conclusions In addition to honeybees, other pollinators such as some unmanaged bees, are also in decline and this presents a major potential threat to pollination services for crops and wild flowers. The economic and biodiversity implications of a widespread reduction in pollination services may be severe and geographically extensive effecting many sectors of society including farmers, conservationists and consumers. It is therefore critical to understand the causes of the observed declines in order to identify appropriate mitigation strategies. Glossary Honeybee (Apis mellifera) – a species of social bee (Hymenoptera: Apidae) building large perennial colonies and producing and storing honey. They are the most commonly managed pollinator species in Europe and worldwide. Pollination – is an important step in the reproduction of flowering plants and involves the transfer of pollen grains (containing the male gametes) to the ovule (which houses the female gametes). Biotic pollination is mediated by an animal, most usually insects and especially bees; abiotic pollination is mediated by wind or water. References BIESMEIJER JC, ROBERTS SPM, REEMER M, OHLEMÜLLER R, EDWARDS M, PEETERS T, SCHAFFERS AP, POTTS SG, KLEUKERS R, THOMAS CD, SETTELE J, KUNIN WE (2006) Parallel declines in pollinators and insectpollinated plants in Britain and the Netherlands. Science 313: 351-354. GALLAI N, SALLES JM, SETTELE J, VAISSIÈRE BE (2009) Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecological Economics 68: 810-821. NATIONAL RESEARCH COUNCIL (2006) Status of pollinators in North America. National Academic Press, Washington D.C. WILLIAMS, IH (1994) The dependence of crop production within the European Union on pollination by honey bees. Agricultural Zoology Reviews 6: 229-257.
a Colonies 1965-2005
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Figure 2. Summary of proportional changes (%) in: total numbers of honeybee colonies between 1965 and 2005 (a) and 1985 and 2005 (b); total numbers of honeybee keepers between 1965 and 2005 (c) and 1985 and 2005 (d); total honey production (tonnes) between 1965 and 2005 (e) and 1985 and 2005 (f). Red arrows indicate decreases, green arrows indicate increases and the height of the arrow is proportional to the percentage change with reference arrows provided in legends.
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The Future of Pollinators?
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SIMON G. POTTS, THEODORA PETANIDOU & THOMAS TSCHEULIN
The current status of pollinators in Europe is just now beginning to be understood at a continental level, as revealed by ALARM. Severe declines in managed honeybees are reported throughout Europe (Chapter 7, Potts et al.) and the indications are that this trend is set to continue unless concerted efforts are made to improve honeybee health and reduce the risks of new pests and diseases. While Colony Collapse Disorder (CCD), as documented in the USA, is not formally recognised in Europe, the loss of managed honeybees is so widespread that the likelihood of a catastrophic collapse of honeybee populations is no longer a highly improbable event. Lessons can, and should, be learnt from North America, where recent chronic losses of honeybees through CCD have had major negative impacts on the availability of managed pollination services for the multi-billion dollar almond industry. Indeed, this underlines the well established ecological principle that reliance on a single species to deliver an ecosystem services is a high risk strategy. In contrast, the availability of a diverse community of pollinators provides ‘insurance’ should environmental stressors remove one or more species, the chance is that the remaining species can still deliver pollination as a service. The wild pollinators of Europe, however, are also under increasing threat, and many taxa such as bumblebees, solitary bees and hoverflies are also declining at a worrying rate (Biesmeijer et al. 2006). ALARM, for the first time, has quantified the extent of these losses at the national (UK and Holland) and local level, but the next step is to extend this assessment to other countries and regions. Further, we need to know specifically which pollinators, and in which regions, declines are occurring, and what are the life history traits which make these species more sensitive to drivers of loss. ALARM has established a standardised set of tools to assist in further assessing the status of pollinator communities throughout Europe and beyond (Chapter 7, Westphal et al.), but to understand more fully the condition of Europe’s pollinators we need to develop a monitoring framework which can detect changes and set a baseline for future comparisons to be made. The sound platform built by ALARM will underpin the STEP project (Status and Trends of European Pollinators, www.step-project.net) which tackles these, and many other, key challenges. STEP will produce the first ever continental Red List for bees which will act as a guide to direct policy to support conservation activities targeted at particular species; STEP will also develop a framework to monitor both pollinators and pollination services throughout Europe. The drivers responsible for the observed declines in pollinators are diverse, and ALARM has provided clear indications that climate change (Chapter 3), environmental chemicals such as pesticides (Chapter 9, Barmaz et al.), invasive plants (Chapter 9, Vilà et al.) and pathogens (Chapter 9, Szentgyörgyi et al.) may all be important risks for pollinators. There are many remaining questions to address about the drivers of loss, and ALARM has set out some of the major issues for future research, such as quantifying the relative importance of the drivers of change and predicting future changes so that policy can respond appropriately. From this jumping off point, STEP will investigate the interaction of drivers to better understand how the sub-lethal effect of one driver makes pollinators much more vulnerable to a second (or even third) driver. STEP will also help us understand how drivers operate at different spatial and temporal scales from short-term effects at the individual species and habitat scale (e.g. pesticides) through to longterm drivers at the continental scale (e.g. climate change). Given the documented declines in pollinators, ALARM has also highlighted the potential consequences of pollinator losses for the delivery of pollination services for both crops (Chapter 8, Gallai et al.) and wild flowers (Chapter 7, Nielsen et al.). While pollination services to crop production in Europe is estimated to be € 22 billion per year this valuation does not take into account many other additional benefits such as: (i) those fruits and vegetables grown in small scale gardens
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and allotments that are directly consumed without passing through a market; (ii) the contribution of pollination to forage crops which are an important part of the diet of cattle and therefore help support the meat and dairy industry; (iii) the role of seeds and fruits from wildflowers which support birds, mammals, insects and other wildlife; (iv) the critical need for healthy wild plant communities which support other ecosystems services such as soil fertility, flood protection, water purification and contribute to the cultural and aesthetic value of flower meadows and amenity parks. ALARM has set the stage and identified these additional values and STEP will build on this to quantify them. Pollinator losses have occurred, and will continue to occur, without concerted interventions. Europe has the best established network of protected areas, the Natura 2000 network, in the world and these many thousands of interlinked sites can provide good quality habitats for pollinators and also connectivity to potentially allow pollinators to move through the landscape under global change. However, this is not enough on its own, as Natura 2000 presents only a small fraction of the total terrestrial area of Europe, and the quality of sites and degree of connectivity can still be significantly improved for pollinators. Much of the remaining landscape in Europe is under various forms and agriculture and this is where many pollinators (and other components of biodiversity) are found. Agrienvironment schemes are a widespread and potentially effective instrument to introduce pollinator friendly habitats into the farmed environment. Several countries already have scheme options targeted towards pollinators but there is still considerable scope to expand this, and coordinate the spatial allocation of options to improve the connectivity of the wider landscape. Other major land cover types, such as forest and urban areas, can also be managed to safeguard pollinators, but currently there are few policies and mechanisms specifically aiming to do this. ALARM has outlined this need and STEP will pick up the baton and run to identify policies and management practices which cover protected and managed areas of Europe to better adapt existing polices to help pollinators and also suggest new policies which will be effective under global change. As national governments and the European Commission begin to more widely adopt the ‘Ecosystems Approach’, they will need to increasingly consider the role of ecosystem services derived from biodiversity in their policies. Pollination is one such service with wide ranging benefits to society. Core to the adoption and implementation of the Ecosystems Approach will be a more holistic move towards to policy-making and delivery, with the focus on maintaining healthy ecosystems and ecosystem services such as pollination. Further, the value of pollination and other services will need to be better reflected in decision-making at the appropriate spatial scale and include the application of adaptive management of the natural environment to respond to changing pressures, including climate change. Demands on European landscapes are increasing with the need to meet food security, biodiversity conservation and ecosystem management goals, and pollination will also be a key part of these considerations. The next few decades will see many manifestations of environmental change for much of Europe’s biodiversity (Chapter 9). Only now we are starting to understand how loss of biodiversity impacts on ecosystem function, and ultimately on the livelihoods dependent upon these ecosystem services. Pollinators play a key role in the maintenance of ecosystem integrity and the protection of pollinator communities and sustainable management of pollination services requires a clear understanding of the causes of pollinator loss and identification of appropriate interventions to mitigate against losses. ALARM has helped set the agenda for pollinators by identifying key risks, and the new challenge is to develop future research programmes, such as STEP, to provide evidence for how best to manage these risks in the future – in Europe and beyond!
Chapter
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SOCIO-ECONOMICS AND ITS ROLE IN BIODIVERSITY LOSS
Mankind as the Driver behind Global Change and Socio-Economics as a Research Discipline to Find Solutions?
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JOACHIM H. SPANGENBERG, JOAN MARTÍNEZ-ALIER & MARTIN O’CONNOR
Anthropogenic climate change, human alterations of the landscape – for infrastructures such as roads, business parks and housing, and for diverse forms of agriculture, with agrofuel production a current example – or the introduction of invasive species along trade routes are effects of a socioeconomic system almost certain to affect biodiversity and the stability of ecosystems. This is simply because certain species will be favoured by changes, while others will not. The simplicity, however, stops here: What can be done to halt the loss of biodiversity is a more complex question ? Traditionally, the answer has been curative measures (see Figure 1) i.e., species protection programs and regulations such as the Washington Convention (restrictEcosystem service and biodiversity impacts on society and economy
Society & Economy
}
Tertiary drivers: Structures of society incl. ideologies Secondary drivers: Precesses in policies, governance, economy Primary drivers: Interventions, biophysical disturbance
Curing symptoms
{
}
Ecosystems, economies and societies are all complex co-evolving systems (Bossel 1998). As with all evolving systems, their future behaviour is unpredictable due to inherent uncertainties (van der Sluijs 2002), and not less so is the result of their co-evolution. This calls for integrative methods of problem analysis and strategy development: neither socio-economics nor biosciences can be called upon to save the day once other disciplines have failed. Scientifically qualified and politically effective biodiversity research requires their integration from the very outset. For Europe, the main anthropogenic disturbance factors or pressures have been identified for genetic, species and ecosystem diversity by EuroStat, UNEP, and in particular by the EEA: ◙ Overuse and transformation (exploiting biological resources beyond their regeneration capacity, transforming e.g., wetlands into agricultural land). ◙ Fragmentation (reduction of biotope size and thus of population numbers). ◙ Biological pollution (deliberately/unconsciously introduced foreign or modified species). ◙ Climate impacts (including hydrological changes). ◙ Chemicals (pesticides, other persistent organic chemicals, petroleum products, endogenous disruptors, etc).
Changing course
Pressures: anthropogenic, social and biophysical impacts on biodiversity and environment
research has to improve our understanding of damage-causing mechanisms, of how social and economic driving forces create a challenge which none of the bioscience-based disciplinary approaches is capable of dealing with in isolation.
Pressure relief
Ecosystem functions, structural impacts on the macro level Bioprocesses, process impacts on the micro level
Nature Human impact on ecosystem functions and biodiversity Figure 1. The hierarchies of drivers, pressures and impacts, and two complementary perspectives on them.
ing the trade in endangered species) or the EU Birds Directive. More recently the approaches have been scaled up, now addressing the ecosystem level, such as in the Ramsar Convention on wetland protection and the EU Habitat Directive. This cannot be the end of the story, however: as long as the factors causing the problems addressed by such regulations prevail, any curative measure will have only limited effect. Thus biodiversity conservation efforts have to go further, focussing on the causes and not only on the symptoms of biodiversity loss. This is where socio-economic research takes effect. Biodiversity protection policies sometimes have technically predefined objectives that do not take into account the various social perspectives. For these to be effective, socio-economic
These pressures on biodiversity have to be relieved for successful biodiversity conservation. Pressure analysis is, however, only a first step, not yet the solution: the drivers behind the pressures must be identified and addressed for biodiversity politics to be effective. The distinction between drivers and pressures and the terminology used has been adapted from earlier work of the European Environment Agency EEA which popularised the Driving Force – Pressure – State – Impact – Response model to characterise the caused of environmental degradation. The model has proved to be quite successful as a tool for communicating with decision makers; for a description of how the DPSIR scheme can be applied to biodiversity see Maxim et al. (this atlas, pp. 16f.); for more details see the special section of Binimelis et al. (2009). If, to achieve a lasting pressure relief, the drivers have to be changed, then they deserve a closer look. Figure 1 illustrates the different levels of drivers that have
AN ALTERNATIVE VIEW: THE FOCUS ON ECOSYSTEM SERVICES There is also another way of perceiving the dynamics which plays a key role in the current biodiversity politics debate, the focus on ecosystem services and their preservation. In Figure 1 it is represented by the right hand arrow. In this view, the chain of argumentation starts from biodiversity loss impinging on ecosystem functions and services, then pressures on the anthropogenic system emerge, leading to economic impacts such as reduced agricultural yield due to invasive species or pollinator loss, which trigger policy reactions and – hopefully – on a higher level a rethinking of development trajectories. The benefit of this description is that it engages conceptually those decision makers who are used to economic arguments as the prime motivation of politics. However, there is a price to pay for this advantage: the argument provides good reasons for conserving those parts of biodiversity which are essential for providing economically valuable ecosystem functions, but the risk is that all other elements of biodiversity and their ecosystem functions are considered superfluous. Thus the special focus chosen by the ecosystem service (ESS) approach to biodiversity conservation is good for policy resonance, but can be bad for comprehensive biodiversity conservation (Spangenberg & Settele in press). It furthermore runs the risk of focussing on a selected set of services, often those which count in the market place: the RUBICODE project found that 80 % of current ESS studies either focus on a single service, or sell old research using the ESS terminology for
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promotion. Most tend to ignore or at least underestimate the relevance of other value categories such as cultural, ethical and aesthetic values which are, however, of key importance to the local populations (see Maxim et al., this atlas, pp. 16f.). Furthermore, focussing on exchange values in markets, other value systems based on intrinsic and inherent values of objects, used or not used, symbolic or material, are not taken into account: the monetary language tends to dominate (Martinez-Alier 2009). Thus different agents describe the same system from different perspectives. The challenge is to accommodate both views in a joint narrative – otherwise, the analysis might be interesting from an economic, social or agricultural point of view, but not from the respective other disciplinary point of view. In the end, this may render the effort irrelevant for decision makers since it does not permit relevant conclusions, i.e. not suitable as the basis for “fact based decision making”. Figure 1 illustrates that both descriptions are complementary ways of describing the same object, different ways to slice the cake. As both have different strengths (the DPSIR analysis being broad but complex, the ecosystem service analysis being straightforward but selective) only together do they provide a truly comprehensive picture of the nature-society interaction and its impacts on biodiversity, understandable to decision makers.
Figure 2a. heavily disturbed landscape with overuse (sealed soil), fragmentation, climate and biodiversity impacts. Photo: J.H. Spangenberg.
Figure 2b. Anthropogenic landscape with biodiversity conserving elements: less sealed soil, smaller fields, hedges, but still significant fragmentation. Photo: J.H. Spangenberg.
to be addressed for changing course: not only the direct disturbances, but also the politics triggering them and the orientations underlying such politics. Here another problem emerges: political and administrative decisions, including those on biodiversity pressure management, are taken on the local, regional, national or supranational level, and they apply within political borders, not within ecological boundaries. According to the UNEP–FI biodiversity working group, industry contributes a decisive share to all these pressures, making it a key driver (including banks and insurance companies which could make loans and polices conditional upon social and environmental standards). This is why in a number of case studies we analysed the interaction of environmental/biodiversity trends, social motivations and economic interests of stakeholders on different but interacting levels of governance (see Rodríguez-Labajos et al. 2009 and this atlas, pp. 198ff., Monterroso et al., this atlas, pp. 202f.). The challenge in each case was to find strategies on the institutionally adequate scale, informed by bioscience analysis, helping to steer decision making with a sufficient degree of reliability towards effective biodiversity conservation. Other information is helpful to contextualise the message, but the essence must refer to what the decision makers can influence. In a nutshell: if the intention is to mainstream biodiversity protection within the political processes by transforming biological insights regarding pressure sources into criteria applicable in decision making, the research results must be formulated in the language of decision makers at the appropriate level. For the EU, this level is obviously joint EU policies. These have been analysed regarding their impact on biodiversity, and their different future developments under the three world views represented by the ALARM scenarios (see Spangenberg et al., this atlas, pp. 10ff.). Socio-economic analysis permits the drawing of conclusions regarding the changes in EU policies deemed necessary, or at least supportive for the conservation of biodiversity. (They have been elaborated in the socio-economic case studies and in the scenarios in more detail):
Figure 3. The diversity of ecosystem services of is illustrated by slide shows for each of the different categories of services. Photo taken in Tabasco, Mexico 2009.
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◙ In the Common Agricultural Policy CAP (here including fisheries policies and forest policy), reducing overuse and overproduction would be beneficial for biodiversity, but also cost saving and a contribution to global sustainable development. ◙ Structural Funds are the main tool for the EU level to influence regional planning (Funding criteria explicitly mention sustainability as a key objective.), and could address fragmentation and transformation, if properly enforced and monitored. ◙ Trade policy: as conducted by the EU, it emphasises the unhindered flow of goods within the Union, and (with some exceptions) between the Union and the rest of the world. Controls that would be
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◙ ◙ ◙
helpful in reducing biological invasions are usually considered to be an impediment to free trade and not pursued. Transport policy, internal market regulations and TEN (Trans European Networks): Transport is the most important growing source of greenhouse gas emissions in the EU, with air transport showing the fastest growth. It also contributes to fragmentation. Energy policy: the emphasis on deregulated markets and reduced prices is not necessarily helpful to combatting climate change. Chemical Policy: Important influence on the chemicals risk. The recent process of watering down the REACH initiative illustrates this point. Biotechnology policy forces even unwilling countries and regions to accept the deliberate release of genetically modified organisms, in the name of the Common Market. While most industrial uses of GMOs seem to be without unacceptable risk, deliberate release is highly disputed.
These examples illustrate the ways in which socio-economic driving force analysis combined with bioscience analysis and scenario techniques can provide decision support for biodiversity conservation. References BOSSEL H (1998) Earth at a crossroads – Paths to a sustainable future. Cambridge University Press, Cambridge. BINIMELIS R, SPANGENBERG J, MARTINEZ-ALIER J (Eds) (2009) The DPSIR framework for Biodiversity Assessment. Ecological Economics 69: 9-75. MARTINEZ-ALIER J (2007) Keep Oil in the Ground: Yasuni in Ecuador. Economic and Political Weekly (October 20, 2007): 4227-4228. RODRIGUEZ-LABAJOS B, SPANGENBERG JH, MAXIM L, MARTINEZ-ALIER J, BINIMELIS R, GALLAI N, KULDNA P, MONTERROSO I, PETERSON K, UUSTAL M (Eds) (2009) Assessing biodiversity risks with socio-economic methods: THE ALARM experience. Pensoft Publishers, Sofia-Moscow. SPANGENBERG JH, SETTELE J (in press) Precisely incorrect? Monetising the value of ecosystem services. Ecological Complexity. VAN DER SLUIJS J (Ed) (2002). Management of Uncertainty in Science for Sustainability. Utrecht, Copernicus Institute, Utrecht University, Utrecht/NL.
Figure 3. Transport is a major factor causing terrestric fragmentation, but shifting all transport to boats could have an impact on rivers, water and air quality and biodiversity.
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Monetary Valuation of the Pollination Service Provided to European Agriculture by Insects
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NICOLA GALLAI, JEAN-MICHEL SALLES, GABRIEL CARRÉ, NICOLAS MORISON & BERNARD E. VAISSIÈRE
Figure 1. Bumblebee in a sunflower. Photo: N. Morison.
Introduction The service provided by insect pollinators is important economically for agriculture worldwide (Gallai et al. 2009). Indeed, 70 % of the crop species directly consumed by humans throughout the world are dependent upon or benefit from pollinator activity (Klein et al. 2007), and in Europe insect pollinators contribute to the production of 84 % of the crop species (Williams 1994). But the abundance and diversity of wild bees as well as the abundance of honeybees are now declining in Europe and some species are clearly at risk (see Tscheulin et al., this atlas, pp. 168f.). More attention is thus needed to (1) better assess the potential loss in terms of economic value that may result from this decline and ultimate total loss of pollinators, and (2) to put
this impact in perspective by considering the vulnerability of European agriculture in the face of pollinator decline. Following White (1974), a precise definition of vulnerability was given by Turner et al. (2003). Vulnerability is a function of three elements: exposure, sensitivity and adaptive capacity. Given this framework, the vulnerability represents that part of the agricultural production value which would be lost following total pollinator loss. Exposure is the decline of pollinators, sensitivity is the speed and magnitude of pollinator loss, and the adaptive capacity is the farmers’ response when confronted with the loss of production resulting from pollinator decline. Our first objective was to assess the potential loss in terms of economic value that would result from total pollinator loss in Europe. To do so, we evaluated the economic loss in 2005 based upon the pollinator dependence ratio of the various European crops produced following Klein et al. (2007). Study context The geographical scale of our study was 27 countries in Europe (Figures 3 and 8), that is the 25 members of the European Union in 2005 plus Switzerland and Norway. We limited the scope of our study to direct crops and commodity crops used for human food as reported for these 27 countries. Direct crops are
Table 1. Total 2005 economic value of crops in 106 € based on their dependency on insect pollination: essential, great, modest, little, no increase and mixed response (Source: Klein et al. 2007).
Impact of animal pollination Following Klein et al. (2007)
List of crops (listed by common name)
Total Economic Value (TEV)
Following FAO
106 €
Essential
Kiwifruit; Pumpkins, Squash and Gourds; Watermelons; other melons (including cantaloupes)
1,524
Great
Almond; Apple; Apricot; Avocado; Cherries (included sour cherries); Cranberries, Blueberries; Cucumbers and Gherkins; Peaches, Nectarines; Pears; Quinces; Plums and Sloes; Raspberries and other Berries
14,773
Modest
Broad bean, dry (Broad bean, Faba bean, Field bean, Horse bean); Chestnut; Cottonseed; Currants; Gooseberries; Eggplants (aubergines); Figs; Mustard seed; Rapeseed; Sesame seed; Soybean; Strawberries; Sunflower seed
8,293
Little
Bean, dry (Kidney bean, Haricot bean, Lima bean, Aduki bean, Mungo bean, String bean); Bean, green; Chillies and Peppers, dry; Chillies and Peppers, green; Citrus fruit*; Grapefruit and Pomelo; Peanuts; Lemons and Limes; Linseed; Oranges; Tangerines, Mandarins, Clementines; Tomatoes
25,267
No increase
Artichoke; Asparagus; Banana; Barley; Broccoli; Cabbages and other brassicas; Carrots and Turnips; Cauliflowers; Chick pea; Bengal gram; Garbanzo bean; Dates; Garlic; Grapes; Hazelnuts; Leeks and other alliaceous vegetables; Lettuce and Chicory; Lentils; Maize; Millet; Mixed grain; Mushrooms; Oats; Olives; Onion, Shallots, Welsh onion (green); Peas, dry; Peas, green; Pepper (Piper spp.); Pistachios; Rice, Paddy; Rye; Sorghum; Spinach; Sugar Beet; Sugar Cane and Sugar crops*; Tea and Maté; Triticale; Walnuts; Wheat; Pineapples; Potatoes; Sweet potatoes;Yam
92,904
Commodities (mixed response)
Anise, Badian, Fennel, Coriander; Cereals*: Buckwheat, Canary seed; Quinoa, Fonio,Triticale, Mixed grain, others Cereals; Fruit*: Persimmons, Pome fruit*, Stone fruit*, Cashewapple, Carobs, Fruit* (included tropical fruit); Leguminous vegetables* (included Broad beans), green; Nuts*: Kolanuts, Brazil nuts, with shell, Arecanuts and others nuts; Oilseed*: Safflower seed, Poppy seed, Melonseed, Oilseeds*; Pulses*: Pigeon peas, Lupins, Vetches, Bambara beans and others Pulses; Vegetables*: Maize green, Okra, Cassava leaves, Chicory roots and other vegetables (may include aromatic herbs)
7,238
* not elsewhere specified
those listed individually with their production, while commodity crops are an aggregation of different crops for which the production figures are pooled together. Most of these commodity crops are reported as “Not Elsewhere Classified” and commodity production figures are based on a questionnaire that countries fill out to include important crops for the world market which are not listed individually by the FAO. We included commodity crops in the study because they represent a significant part of the agricultural output in Europe. We found 80 direct crops and 9 commodities used directly for human food in
Figure 2. Apples. Photo: N. Morison.
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the 27 surveyed countries in 2005. Among these, 41 direct crops are dependent or benefit from insect pollination and pollinators are essential for 4 of these crops including watermelon (Figure 4a, Table 1). The contribution of insect pollinators is also reported as great for 12 direct crops including almond (Figure 4b, Table 1), modest for 13 including sunflower seed (Figure 4c, Table 1) and little for 12 including tomato (Figure 4d, Table 1). The total 2005 economic value of these 41 direct crops was € 50 billion (Table 2), which is about 33 % of the 2005 total European agriculture production value (TEV, Table 2). The other
Edible oil crops
a
Linseed Olive Rapeseed Sunflower seed a
b
Fruits
b
Apples Cherrie Figs Oranges Peaches and Nectarines c
Strawberries
d
Nuts
c
Almonds Hazelnuts Walnuts
e
f Figure 3. Europe-wide distribution of the crops with the largest area in each country and within each of three categories: edible oil crops (a), fruits (b) and nuts (c).
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Figure 4. Variability of the dependence of crops on insect pollinators. Insect pollination is essential for the production of watermelon (a), great for almond (b), modest for sunflower seed (c), little for tomato (d) and nill for banana (e) and grape (f). Photos: N. Gallai, N. Morison and J. Vaissière.
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Table 2. Economic value of pollinators and vulnerability of the European agriculture confronted with pollinator decline.
Crop category (following FAO)
Total economic value (TEV)
Economic value of Vulnerability ratio pollinator (EPV) (VR)
106 €
106 €
%
Nuts
1,542
736
47.75
Fruits
31,377
9,269
29.64
Edible oil crops
15,373
1,298
8.45
Pulse
2,190
116
5.31
Vegetables
38,776
2,813
7.26
Spices
304
4,215
1.39
Cereals
28,572
0
0.00
Roots & tubers
7,414
0
0.00
Stimulant crops
0.023
0
0.00
Sugar crops
6,289
0
0.00
150,001
14,237
9.49
Total
direct crops are not directly dependent on insect pollinators as, for example, banana or grape (Figures 4e and 4f, respectively), both of which are among the most consumed fruits in Europe. Fruits are the most represented crops in the two highest level of pollinator dependence in Table 1 since 12 out of 16 crops are fruits. We sorted the fruit crops by decreasing land use area for each country, apple being the crop with the largest land use surface among 19 countries (Austria, Belgium, Croatia, Czech Republic, Estonia, France, Germany, Hungary, Ireland, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Slovakia, Slovenia, Switzerland, United Kingdom; Figure 3b). The land use for orange was the second most important in Europe and it was the most important in Cyprus, Italy and Spain. The land use for strawberry was most important for fruit crops in Europe as strawberry was the most important fruit crop in Finland and Sweden.
The economic value of pollinators We defined the economic value of pollinators (EVP) as the value of the pollinator contribution to the total economic value of crop production (Gallai et al. 2009). This contribution was calculated with a dependence ratio of crops towards pollinators, that is the proportion of the yield attributable to insect pollinators. The economic value of pollinators was thus calculated as follows: I
EVP=
X
Σ ΣP
i =1 x =1
ix
× Qix × Di ,
where P is the producer price per production unit, Q is the quantity produced for each crop i ∈ [1; I] and for each country x ∈ [1; X], and D is the dependence ratio for each crop i ∈ [1; I]. For Qix and Pix, we used the 2005 FAO production and price data, respectively. The production data are expressed in metric tons. The price data are expressed in US dollars on the FAO database and expressed in euros
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in the text using the mean of the 2005 exchange rate (http://fxtop.com). We calculated the dependence ratios Di based upon the five levels of the extensive recent review of Klein et al. (2007). Starting with the complete set of direct and commodity crops used for human food, we selected the ones which are pollinated by insects and for which we had production and price data. For the individual crops among the 9 commodities, neither the production nor the producer price was available and the crops that made each of these commodities were not all dependent on insect pollination at a similar level. Consequently we could not calculate the economic value of pollinators for these commodity crops and they were not considered further. For the remaining direct crops, we focused on those reviewed in Appendix 2 of Klein et al. (2007) for which we calculated the average dependence ratio based on the reported range of dependence to insect-mediated pollination.
The 2005 total production value (TEV) for crops used for human food in Europe was almost € 150 billion, and the total economic value of pollinators (EVP) slightly exceeded € 14 billion (Table 2). This overall figure covers a wide range of values among the different crop categories. The most pollinator-dependent categories ranked by decreasing economic value of pollinators were vegetables, fruits, edible oil crops, nuts, pulses and spices (Table 2). Fruits alone represented nearly 65 % of the EVP (Table 2). European agriculture vulnerability to pollinator decline The second objective of our study was to quantify national and European vulnerability confronted with pollinator decline. The agricultural vulnerability to pollinator decline depends upon crop dependence to pollinators, and farmers’ capacity to adapt to pollinator decline.
Figure 7. Honeybee hives by a field of onion seed production. Though not taken into account in our calculations here for lack of economic data, insect pollinators are essential for the seed production of most vegetable and flower crops. Photo: N. Morison.
Figure 5. Honeybee pollinating an onion flower. Photo: N. Morison.
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Figure 6. Honeybee hive in a pear orchard. Most fruit trees are self-incompatible and need insect pollinators to transfer pollen among compatible varieties and insure fruit set. Photo: N. Morison.
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We calculated the ratio of vulnerability for European agriculture as defined by Gallai et al. (2009): I
∑∑( P
ix
VR=
Vulnerability ratio 1.4-5.7 %
X
i =1 x =1 I
× Qix × Di )
X
∑∑( P
ix
i =1 x =1
5.7-8 %
(%).
8-9.4 %
× Qix )
9.4-12.1 % 12.1-19.3 %
So defined, the vulnerability of European agriculture faced with pollinator decline was about 10 % in 2005 (Table 2) and the three most vulnerable crop categories were nuts, fruits and pulse (Table 2). Looking at the geographical distribution of the vulnerability ratios, we find that northern-most countries had a lower vulnerability than southern ones. Indeed, if we consider the 16 countries that are crossed by or north of the 50° north parallel (Denmark, Estonia, Finland, Ireland, Latvia, Lithuania, Netherlands, Norway, Sweden, Belgium, Czech Republic, France, Germany, Luxembourg, Poland, United Kingdom), their vulnerability ratio averaged 6.2 % (SE = 0.7 %) and this was significantly less than that of the 11 countries located south of this parallel (Austria, Cyprus, Greece, Hungary, Italy, Malta, Portugal, Slovakia, Slovenia, Spain, Switzerland; mean = 12.6 %, SE = 1.1 %; t = 4.705, df = 25 and P < 0.0001). This clearly demonstrates that, at present, the agriculture of southern countries is more dependent on insect pollinators than that of the northern countries (Figure 8). This north-south difference likely results from the climatic pressure that restricts the growing of many insect-pollinated fruits, nuts and edible oilseed crops to southern countries. Indeed the most vulnerable crop categories are nuts, fruits and edible oil crops (Table 2). We observed that three nut crops are dominant in Europe, but only almond is an insectpollinated crop (Table 1; Figure 3c) and it is the dominant fruit-crop nationwide only in southern Europe (Portugal, Spain, Italy, Cyprus and Greece). The land use for the production of edible oil crop is different from country to country. The northern countries used most of the edible oil crop land for the production of rapeseed, while the southern countries planted mainly olive groves (Figure 3a). This is a counter example of the north-south vulnerability since olives are not dependent on insect pollination while rapeseed is dependent on insect pollination (Table 1). Nevertheless, our results suggest that, in the case of a total pollinator loss in southern Europe, it would probably not be feasible to compensate this loss in production in other European countries and it would therefore be necessary to import this produce from other parts of the world.
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Figure 8. Distribution of the vulnerability ratio across Europe.
Conclusion The aim of our work was to assess the vulnerability of the food production in Europe confronted with the decline of insect pollinators. Agricultural production not used directly for human food (e.g., fodder crops), seed production and natural vegetation will also be impacted by pollinator decline, but were not considered here. Using a bioeconomic approach, we calculated that the 2005 value for the contribution of pollinators to the crop production used directly for human food in Europe exceeded € 14.2 billion, which is about 10 % of the total value of the 2005
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crop production used for human food Europe-wide. We also found that this ratio was not homogeneous across Europe as the economic vulnerability of southern countries was nearly twice as high as that of the northern countries (12.0 vs 6.3, respectively). References GALLAI N, SALLES JM, SETTELE J, VAISSIÈRE, BE (2009) Economic valuation of the vulnerability of world agriculture confronted to pollinator decline. Ecological Economics 68: 810-821. KLEIN A-M, VAISSIÈRE BE, CANE JH, STEFFAN-DEWENTER I, CUNNIGHAM SA,
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KREMEN C, TSCHARNTKE T (2007) Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society 274: 303-313. TURNER BL, KASPERSON RE, MATSON P, MCCARTHY JJ, CORELL RW, CHRISTENSEN L, ECKLEY N, KASPERSON JX, LUERS A, MARTELLO ML, MATHIESEN S, POLSKY C, PULSIPHER A, SCHILLER A, TYLER N (2003) A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences USA 100: 8074-8079. WHITE GF (1974) Natural Hazards: Local, National and Global. Oxford University Press, New York. 304 pp. WILLIAMS IH (1994) The dependence of crop production within the European Union on pollination by honey bees. Agricultural Zoology Review 6: 229-257.
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Climate Change Mitigation and Adaptation Measures and Biodiversity
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PAM BERRY & JAMES PATERSON
Climate change is already having an observable impact on biodiversity (Sykes & Hickler, this atlas, pp. 64f.) and several chapters have shown how it could impact in the future on vegetation and species distributions (Hickler et al., this atlas, pp. 238f.) and ecosystem services, including pollination and invasive species (Harrison et al., this atlas, pp. 8f., Baranchikov et
Figure 1. Climate space of the Scarce Swallowtail in 2080 under the GRAS (A1FI) scenario; Grey: losses, orange: constant; dark brown: gains (from Settele et al. 2008, p. 117).
Positive
al., this atlas, p. 164). There are two possible ways of decreasing these impacts: mitigation and adaptation and both are thought to be necessary in order to avoid the undesirable effects of climate change. Climate change mitigation Mitigation involves humans in the reduction in net greenhouse gas emissions (sources), and/or increasing stores (sequestration) or avoiding loss of storage for greenhouse gases. This should help to decrease the amount and rate of climate change and the vulnerability of ecosystems and species. Greenhouse gas emissions could be reduced, for example, through switching to renewable energy technologies or by decreasing travel. Some of the stores of greenhouse gases are in living plants, especially in forests and wetlands. Mitigation, therefore, could occur through preventing the loss of forest, especially tropical rain forests, planting more trees or by conserving or restoring wetlands (e.g., palsa mires – Johansson et al., this atlas, p. 79). Mitigation will only slowly become effective, and without mitigation it is likely that some natural systems will be unable to adapt and in some human systems high economic and social costs would be incurred. So, mitigation alone will not be enough and adaptation is also needed.
Effect on biodiversity
Negative
Expanding protected areas
Mitigation
Win-Lose-Lose
Win-Lose-Win
Adaptable protected areas
Alien animal species management Buffers/matrix management
Win-Win-Win
Win-Win-Lose
Corridor/stepping stones Alien plant species management Gene/seed Banking Ex-situ conservation Translocation/assisted migration
Lose-Win-Lose
Lose-Win-Win
Adaptation
Fire Management
Figure 2. Known and potential relationships between mitigation and adaptation measures and their impacts on biodiversity. The position of the boxes on the biodiversity axis is based on the literature review of the biodiversity impacts of various mitigation and adaptation schemes and represents the typical outcome; the whiskers demonstrate the potential range of impacts. (from Berry 2009, Paterson et al. 2008).
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Climate change adaptation Adaptation is the adjustment of natural or human systems to the changing climate. It may occur spontaneously (autonomously) in natural systems as they respond to changed temperature and precipitation or both systems can be affected by planned human responses. An example of the first is species dispersing into their projected new suitable climate space. Already many warmth-loving species, such as palms (Walther & Berger, this atlas, pp. 212f.) and most butterflies are expanding their range northwards as an adaptation to increased temperatures (see Scarce Swallowtail in Figure 1). The dispersal of species is thought to be important for the future of many species (see dark brown areas in Figure 1, which cannot be reached without dispersal). Nagy et al. (this atlas, p. 78) have shown that the alpine climate zone could move 500m upwards in response to a 3.1 °C rise by 2100, but question the extent to which species will be able to adapt to this. The amount of autonomous adaptation will be important when priority setting for conservation (Vohland et al., this atlas, pp. 234ff.). Humans have always adapted to changes in weather and climate and nowadays are also responding, for example by planting better adapted crops or varieties of crops (e.g., grapes and nowadays Miscanthus grasses for bioenergy, but which also contribute to climate change mitigation). Many adaptation actions are often part of wider measures to deal with unfavourable situations such as coastal flooding, food and water security and increasingly sustainable development and thus an integrated approach to their identification and implementation is required. In the ALARM project consideration has been given to biodiversity under a range of climate and socioeconomic scenarios and it has been shown that planned adaptation is necessary in order to try to safeguard the future of many species. This adaptation could take the form of increasing habitat area or landscape connectivity and other measures aimed at facilitating the movement of species. Some of these issues, with particular regard to the Natura 2000 network, have been considered by Vohland et al. (this atlas, pp. 240f.). Mitigation and adaptation interactions with biodiversity Mitigation and adaptation, however, have consequences for each other
Figure 3. A reduction in air travel can help to mitigate climate change through the reduction in greenhouse gas emissions. Photo: James Berry.
and for biodiversity. The MACIS project reviewed the ways in which mitigation and adaptation measures in eight sectors (agriculture, forestry, energy, built environment, river and coastal flooding, health, tourism and leisure and conservation) could interact with biodiversity (Berry 2009, Paterson et al. 2008). These interactions can have a number of different outcomes for biodiversity (Figure 2): 1. neutral – e.g., many of the animal husbandry and breeding measures, such as certain slurry management options and artificial insemination, and some management of flood losses, such as insurance have no recorded effect on biodiversity; 2. negative for biodiversity: a) positive for mitigation, neutral or negative for adaptation – e.g., dams and tidal barrages, which can, for example, lead to changes in habitats and water flow, and interruption of fish migration patterns; some renewable woody biomass which can lead to the loss of habitat and species richness; b) neutral or negative for mitigation, positive for adaptation – e.g., the control of mosquitoes by draining of wetland, resulting in the loss of habitat and carbon storage; snow-making in ski resorts uses energy and water and is largely negative for biodiversity by delaying plant development and changing vegetation types. 3. positive for biodiversity: a) positive for mitigation, neutral or negative for adaptation – e.g., avoided deforestation, which ensures that carbon stores and habitats and species are maintained, and some slurry management options which reduce NH3 and CH4 emissions and improve water quality;
Table 1. The interactions between health mitigation and adaptation measures for climate change and other sectors (based on Berry 2009).
Health
Agriculture
Forestry
Energy
Some mosquito control measures e.g. the use of insecticides may prevent the spread of vector species which transmit human and livestock pathogens, vegetation management for tick control may lead to a loss of agriculturally used land
Afforestation may reduce the urban heat island effect, vegetation management for tick control may lead to a loss of forest area
The use of nuclear power bears the risk of radioactive contamination of humans, biofuels may be produced at the expense of food production increasing the risk of malnutrition
b) neutral or negative for mitigation, positive for adaptation and for biodiversity – e.g., conservation measures, such as ex situ conservation, restoring connectivity; reopening drains or re-vegetation of ditches for reduction of flood runoff rates can create aquatic and riparian habitat, improve conditions for aquatic organisms and provide corridors for wildlife movement; c) positive for mitigation and adaptation to varying degrees – e.g., urban green spaces can increase carbon storage, moderate climate change and provide habitats for species and their movement. The restoration of wetlands can have a similar effect on carbon, while increasing flood water storage and wetland habitats. The impact of each measure on biodiversity varies depending on the manner of implementation and as indicated by the whiskers on Figure 2. Biofuels, for example can lead to the loss of habitats of conservation interest, but on degraded land they can enhance biodiversity. These mitigation and adaptation measures may interact positively as in the case of no-till cultivation and the (re)creation of wetlands or negatively,
Built environment
Flood management
Urban landscape planning and building design may reduce the urban heat island effect
Dikes and embankments protect settlements from flooding and may prevent casualties, the creation of green spaces within urban areas may enhance flood protection
with the adaptation measure of snowmaking in ski resorts requiring the use of energy and water resources. In the first case, they may be seen as complementary actions, in the second as conflicting, and often there will need to be trade-offs between them. For example, in agriculture one mitigation measure is the breeding of cattle for improved feed and reproductive efficiency as well as improved growth rate, but it has been noted that cattle bred for improved productivity are more susceptible to heat stress. This could lead to the adaptation of breeding for improved heat tolerance, but this might reduce growth efficiency. In some cases (3c above) it has been possible to identify measures which are positive for each of the three components of mitigation, adaptation and biodiversity (win-winwin) and, from a climate change point of view these would represent the best actions (Figure 2). Creating these synergies between mitigation and adaptation could make them more cost-effective, although often the sectors and actors primarily involved in mitigation (e.g., industry, agriculture and transport) are not the same as those directly concerned with adaptation which takes place in a wide variety of sectors and at more regional to local levels.
Figure 6. Green roofs can results in small-scale carbon storage, while also insulating houses against extremes of heat and providing a habitat for a number of plants and animal (see text for more details). Photo: Pam Berry.
C L I M AT E
C H A N G E
Tourism & leisure Mosquito control e. g. by Bt toxins may increase the attractiveness of wetlands for leisure activities
Conservation Increase of open space or afforestation to reduce the urban heat island effects on health could be beneficial. Introduction of vector control agents – benefits questionable
Cross-sectoral interactions Identifying cross-sectoral interactions is also important, in order to identify synergies and Table 1 shows some interactions between health measures and the other sectors considered. Key
urban areas and to mimic endangered habitats. Peck et al. (1999) have also identified other biodiversity benefits, including providing: increased (island) habitat availability; stepping stones for species which are aerially dispersed; homes to sensitive plants that are easily damaged by trampling and to ground-nesting birds and undisturbed soil, which can increase insect diversity. Thus there are multiple climate change and biodiversity benefits related to the use of green roofs and it presents a clear win-winwin measure. Conclusions Climate change mitigation and adaptation measures are important responses to climate change and they can have significant impacts on biodiversity and each other. In order to achieve coherence there needs to be cross-sectoral
Figure 4. A small-scale adaptation option for flood management is (re)vegetation of ditches to slow runoff (left hand side of picture, compared with right hand side which has been cleared). Photo: Simon Berry.
interactions occur through pest control and the use of insecticides, as while they may prevent the spread of disease vectors and enhance the use of wetlands for recreation, they can have a negative impact on aquatic biodiversity. The same measure may also be proposed by more than one sector e.g., green roofs, which can contribute to both mitigation and adaptation. From a built environment perspective, the plants will change albedo, reducing the urban heat island effect and decreasing energy demand for temperature control in houses. This effect could be enhanced by green walls or vertical gardens. The use of green roofs is also a proposed adaptation strategy for the management of stormwater runoff and flooding. In temperate regions, retention rates in summer can vary between 70-100 % and in winter between 40-50 %, depending on the rooftop garden design and the weather conditions. They may also have a side benefit of reducing pollutants. In Europe, they have been used as a part of wildlife corridors in
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Figure 5. Avoided deforestation can maintain carbon stores (mitigation) and be positive for biodiversity as habitats and species are retained. Photo: Simon Berry.
policy integration and win-win-win measures should be promoted in order to ensure that cost-effective and environmentally sound actions are implemented. References BERRY P (Ed) (2009) Climate change Impacts of mitigation and adaptation measures on biodiversity. Pensoft Publishers, Sofia– Moscow. PATERSON JS, ARÁUJO MB, BERRY PM, PIPER JM, ROUNSEVELL MDA (2008) Mitigation, Adaptation and the Threat to Biodiversity. Conservation Biology 22: 1352-1355. PECK SW, CALLAGHAN C, BASS B, KUHN ME (1999) Greenbacks from Green Roofs: Forging a New Industry in Canada Toronto, Canada Canadian Mortgage and Housing Corporation (CMHC). [http://www.greenroofs.org/pdf/Greenbacks.pdf] SETTELE J, KUDRNA O, HARPKE A, KÜHN I, VAN SWAAY C, VEROVNIK R, WARREN M, WIEMERS M, HANSPACH J, HICKLER T, KÜHN E, VAN HALDER I, VELING K, VLIEGENTHART A, WYNHOFF I, SCHWEIGER O (2008) Climatic Risk Atlas of European Butterflies. BioRisk 1: 1-710.
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Socio-Economic Modelling of the ALARM Scenarios. Results for Europe
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INES OMANN, ANDREA STOCKER & JILL JÄGER
As described in Spangenberg et al. (this atlas, pp. 10ff.) the ALARM storylines the three ALARM storylines, GRAS, BAMBU and SEDG, show possible future pathways for Europe, where different driving forces such as economic growth or energy consumption patterns are leading to different impacts on natural and socio-economic systems. A set of policy measures such as, for instance, increased transport costs, material extraction tax, reduction of agricultural subsidies, which describe the intention of the scenario in an adequate way, has been identified for each storyline (see Table 1). These measures have been simulated with an integrated environ-
BAMBU
mental-economics model, GINFORS (Global INterindustry FORecasting System; Meyer et al. 2005, 2007). This multi-country, multi-sector, macro-economic framework includes trade flows within Europe, as well as between Europe and all other economically relevant parts of the world. The GINFORS model directly integrates comprehensive bio-physical data on material and energy flows in European and global simulations up to the year 2020 and shows their relation to structural indicators of social and economic development. The analysis of biodiversity changes requires the application of simulation
GRAS
Growth rate of GDP
models that permit the quantification and evaluation of socio-economic trends (e.g., demographic and lifestyle changes) and their effects on the economy and the environment, as well as evaluation of economic impacts of disruptive environmental changes (e.g., climate change or flooding). Such models also allow the formulation and evaluation of scenarios of the economic and social/ distributional impacts of key environmental policy measures, as well as of the impacts of economic measures on the environment. Based on these scenario evaluations, policy recommendations can be formulated for the best way to achieve the intended objectives.
SEDG
Growth rate of GDP
0.01-2.00 2.01-4.00 >4
Growth rate of GDP
0.01-2.00 2.01-4.00 >4
0.01-2.00 2.01-4.00 >4
Figure 1. GDP growth rate 2005-2020 in Europe.
Growth rate of total extraction
BAMBU
Growth rate of total extraction
GRAS
<0 0.01-2.00 2.01-4.00 >4
Growth rate of total extraction
SEDG
<0 0.01-2.00 2.01-4.00 >4
<0 0.01-2.00 2.01-4.00 >4
Figure 2. Total material extraction growth rate 2005-2020 in Europe.
Material input per capita
90 80
2005 Mineral ores
2020 Mineral ores
70
Fossil fuels Biomass
Fossil fuels Biomass
SEDG
60 50 40 30 20 10 0
EU25
at
be
dk
fi
fr
de
gr
ie
it
nl
pt
es
se
gb
cz
hu
Figure 3. Material extraction and its composition in SEDG.
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In the following, results for selected economic (GDP growth) and environmental (material extraction, energy efficiency, material input per capita according to categories biomass, fossil fuels and minerals) variables are presented for the EU plus Switzerland, Norway and Iceland. Economic variables The most widely used indicator for economic development is the gross domestic product (GDP) of a country. It shows in monetary terms the amount of products and services produced within one economy in a given period (usually one year). Figure 1 gives the average GDP growth rate between 2005 and 2020 in the 29 simulated European countries. As expected, GDP growth is strongest in GRAS, the growth-oriented scenario. For Western and Southern Europe the results are very similar for BAMBU and SEDG. In some countries in Northern and Eastern Europe economic growth is stronger for BAMBU than for SEDG, showing the sustainability orientation of the latter. Increasing economic growth goes along with increased use of material, energy and land, which is in general a non-sustainable path. This effect can be seen in Figure 2 below. What is remarkable, however, is GDP growth remains positive throughout in most of the countries. Thus these measures towards sustainable development do not lead to negative economic growth in the long term. This is true for the macro perspective, which does not automatically mean that all economic sectors will grow. Most probably there will be losers, such as the 'coal-mining' sector. Environmental variables Economic activities require the input of material, energy and land. In general, the less of these are used, the better for natural systems and for sustainable development, since an overuse of resources leaves future generations with less and the use of energy or material produces “bads”, such as GHG emissions, air pollution or other forms of waste. Thus, the total material extraction used to produce the economic output of a country is a good indicator; showing how much a country is using of its natural environment as resource base and as sink. Figure 2 shows the average growth rates of material extraction between 2005 and 2020 for the European countries (there are no environmental data for Luxembourg). The highest reduction of material input is derived in SEDG, the lowest in GRAS. Here only very few countries show a reduction; in most
countries the material input is growing. The opposite is true for SEDG, which is the expected and also desired result. However, the positive GDP growth rate (Figure 1) in all countries in SEDG leads over the long term to a so-called “rebound effect”, which overcompensates for the reduction of material input, leading to a re-increase of material use. This is the case because using fewer materials as a result of higher resource efficiency reduces material costs. A reduction in material costs leads to lower prices and thus to a higher demand for goods and services. This higher demand is connected with a higher resource use, so that in the end no resource savings are achieved. In the following we show how the material extraction is composed. We differentiate between fossil fuels, biomass and minerals. As the composition does not differ too much between the scenarios, SEDG is chosen in Figure 3. Material extraction is in general the lowest in SEDG. It increases between 2005 and 2020 in two thirds of the countries modelled. The composition differs across the countries. The share of the different material forms does not change markedly between 2005 and 2020. The share of fossil fuels shows the biggest change – it decreases. See for instance Greece, Poland, or Estonia. It increases only in Lithuania. Another important environmental indicator is energy efficiency, measured in US Dollars used for a unit of energy input, oil equivalent in this case. The higher this number is, the more efficient is the economy in its use of energy sources and in principle the lower its CO2 emissions given per unit of economic activity – depending on the energy sources. In Figure 4 it can be seen that in all scenarios the energy efficiency increases within the 15 years of simulation; this increase is strongest in SEDG, where most policy measures supporting energy and resource efficiency are taken. The differences are, however, quite small. In general the energy efficiency is higher in Western European countries than in the Eastern ones. Generally, the approach of incorporating environmental components into models that are primarily designed for
economic purposes helps to identify measures that reduce the driving forces and pressures on biodiversity. However, we have to keep in mind that an approach strongly rooted in economics is not able to reach a complete integration of the ecological and economic systems with their various linkages and feedbacks. One reason is the different time scale of reactions and cycles. 20 years present a long-term scale in macro-econometric models, whereas for climate change models this is very short-term oriented view. Another reason is the difficult task of combining data of different units; again another one is the different spatial scale (national level versus 1 minute grids). Such an evaluation of sustainability scenarios for Europe can significantly enhance the understanding of the interactions between environmental changes and socio-economic trends that are often the driving forces of undesirable environmental impacts. The results presented above indicate that strong efforts are needed to change the ongoing trends in most policy areas, which are causing pressures on biodiversity. A growth-oriented policy design, such as that presented in the GRAS scenario is not compatible with sustainable development in general and the conservation of biodiversity in particular. Based on the results of the BAMBU scenario one can conclude that additional policy measures are needed to deal more “sustainably” with the development of unemployment, resource use, energy use and CO2 emissions and other indicators. Only in the SEDG scenario do the policy measures support the idea of a sustainable development, but they are still not ambitious enough in some cases. The total primary energy supply, for instance is still increasing, mainly as a result of growing demand for electricity due to air conditioning or a higher number of electrical devices in households. Here, other instruments such as information campaigns supporting energy saving are called for. The rather weak decrease of material input (Figure 2) reflects a lifestyle, which is not adequately based on sufficiency1 and efficiency, both requirements for a sustainable develop2005
no change
(3) Enforcement in resource productivity
no change
no change
(4) Transport costs
BAMBU_1: rising transport costs for EU25 countries (with IO model) plus 5 % until 2020 no change
no change
(5) Enforcement in labour productivity
(6) Competitiveness
BAMBU BAMBU_0: adapted IEA scenario
(7) Change of labour no change working hours without adoption of salaries (8) Recycling no change
(9) Reform of subsidies BAMBU_2: on the EU level (agrislower reduction of restrictions culture) on trade: -export prices rise by 5 % -imports rise by 5 %
(10) EU budget
no change
(11) Decoupling of economic growth and use of materials
no change
(12) Carbon tax
BAMBU_3 and BAMBU_4: carbon tax: stepwise increase until 40 Euro per ton CO2 in 2020 (corresponds to the EU Kyoto Target). Taxes of firms are decreased by the same amount
8 6 4 2
at be fi fr de gr ie it nl pt es lu dk se gb cz hu pl sk cy lv lt ee si mt
References
2005 SEDG
10 8 6 4 2
at be fi fr de gr ie it nl pt es lu dk se gb cz hu pl sk cy lv lt ee si mt
12 10 8 6 4 2 0
at be fi fr de gr ie it nl pt es lu dk se gb cz hu pl sk cy lv lt ee si mt
Figure 4. Energy efficiency 2005-2020 in the three scenarios. 1
2020
14
12
0
SEDG_4: employment increases during the years 2006 – 2010 by 5 % SEDG_5: recycling quotas for materials are rising by 1 % per year (and not by 0.7 % as in BAMBU) GRAS_4: SEDG_6: restrictions on trade are reduced - the same as BAMBU 2 -export prices of agricultural - but additional increase of export products rise by 10 % prices for processed food and -import of agricultural products beverages by 10 % and increase by 10 % - increasing imports of those products by 10 % GRAS_5: SEDG_7: -EU budget for 10 new countries is EU budget for new countries rises reduced by 10 % until 2020 by 40 % until 2020 -total EU budget does not shrink, only the share of 10 new countries no change SEDG_8: for other mining and quarrying a more dynamic decoupling is assumed (0.994 per year) no change SEDG_9 and SEDG_10: carbon tax: stepwise increase until 120 Euro per ton CO2 in 2020. Taxes on firms are decreased by the same amount
2020
Energy Eff. (Mill. $/1000 t Oil Equiv.)
Energy Eff. (Mill. $/1000 t Oil Equiv.)
10
no change
JÄGER J (2008) Our planet. How much more can Earth take? London, House Publishing. MEYER B, LUTZ C, WOLTER MI (2005) Global Multisector/Multicountry 3E Modelling: From COMPASS to GINFORS. Revista de Economia Mundial 13: 77-97. MEYER B, LUTZ C, SCHNUR P, ZIKA G (2007) National Economic Policy Simulations with Global Interdependencies. A Sensivity Analysis for Germany. Economic Systems Research. Journal of the International InputOutput- Association 19(1): 37-57.
GRAS
12
no change
have been more positive with respect to sustainable development.
ment (Jäger 2008). However, the simulated measures have their strongest effects in the first few years of the simulation period, indicating that some improvements would still be possible if additional measures were taken in later years. It should also be noted here that the storylines (Spangenberg et al., this atlas, pp. 10ff.) contain many measures and activities enhancing a sustainable lifestyle, which are not reflected in the socio-economic modelling results, as they could not be simulated. If these had been taken into account, the results for SEDG would most likely 2005
SEDG SEDG_0: energy prices 50 % higher than BAMBU in 2020 SEDG_1: minus 15 % in agriculture and minus 10 % in sectors 2-27 until 2020 starting in 2006 SEDG_2: in EU25 countries with IO models: - intermediate inputs of production sectors shrinking by 10 % until 2020 - in the first year the saving of inputs is given to other business activities (36) SEDG_3: the same as BAMBU but transport costs rise by 10 % until 2020
GRAS_1: employment is reduced due to rising labour productivity by 7.5 % until 2020 GRAS_2: shrinking wage rates due to international competition (- 5 % in 2020) GRAS_3: employment decreases by 4 % until 2020 no change
no change
14
BAMBU
Energy Eff. (Mill. $/1000 t Oil Equiv.)
(2) Changes in consumption structure
GRAS GRAS_0: energy prices l33 % lower than BAMBU in 2020 no change
(1) World energy prices
2020
14
0
Table 1. Design of the quantifiable policy measures in the three scenarios.
Sufficiency aims at reducing negative environmental consequences through a reduction of the demand for consumer goods. It requires changes in infrastructures and choices as well as a questioning of the levels and drivers of consumption.
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Chronicle of a Bioinvasion Foretold: Distribution and Management of the Zebra Mussel (Dreissena polymorpha) Invasion in Spain
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BEATRIZ RODRÍGUEZ-LABAJOS, ROSA BINIMELIS, CARLES CARDONA, KRISTOFER DITTMER, JOAN MARTÍNEZ-ALIER, ILIANA MONTERROSO & ANTONI MUNNÉ
This paper summarizes the current knowledge of zebra mussel distribution in the Iberian watersheds. Additionally it presents some results of the participatory socio-economic research carried out by the ALARM project (Binimelis et al. 2007, Rodriguez-Labajos 2006) and how these were taken into account in the management response by the Catalan authorities. Zebra mussel, basic profile of a global invader The zebra mussel (Dreissena polymporpha) is a bivalve mollusc native to the
Distribution in the Iberian Peninsula The sporadic appearance of shells indicates the presence of zebra mussels in the Douro River, near Porto (Portugal), in the late 19th century. Zebra mussel juveniles were also detected in the middle section of the Llobregat River (Catalonia) but they disappeared with the floods of 1982. The presence of this species in the Iberian watersheds was not therefore considered as an invasion until some individuals were found in 2001 by an environmental group in the Flix meander of the lower Ebro River. The first monitoring showed that the affected area extended downriver from the Riba-roja reservoir. The most plausible cause of the introduction is the recreational angling of alien fish predators such as Wels catfish (Silurus glanis), pike-perch (Sander lucioperca) or black-bass (Micropterus salmoides). This practice developed in the 1970s in the large new hydroelectric reservoirs in the Ebro that nowadays are the destination of many central European anglers. The introduction of alien fishes such as bleak (Alburnus alburnus) to be used as living bait has very likely caused the accidental release of zebra mussel larvae into the Ribaroja reservoir since the mid-1990s. From there, boat traffic, and the deliberate transport and release of living fish presumably transferred larvae and adult specimens to new water bodies. The news of the invasion did not provoke concern apart from within scientific and environmentalist circles. However, in 2002 the massive damage
Figure 2. Zebra mussel colonizing the Riba-roja reservoir. Photo: Grup de Natura Freixe.
caused to several infrastructures triggered public alarm. The response from the watershed authority – Confederación Hidrográfica del Ebro, CHE, www.chebro. es – was focussed on boat traffic restrictions. Nevertheless the main risk area, the large Mequinensa reservoir, was rapidly colonized. In September 2005, both larvae and adults of the species were detected in the Sitjar reservoir, in the Jucar Basin. Adult individuals have also been found in the Santa Quiteria reservoir and the Forata reservoir, both in the Jucar Basin. The severity of the invasion became public in September 2006. A larvae sampling by the CHE revealed affected points along the main course of the Ebro. Further inspections exposed the presence of adults in the
Sobron reservoir, the dam of Puenlarrá and the Lodosa Canal, all in the upper reaches of the river. During October 2006 the presence of larvae was confirmed in the main tributaries of the Ebro, in different reservoirs and fish farms of the Segre River. Adults were found in the Imperial Canal of Aragon, where a population of Margaritifera auricularia, a highly endangered native unionoid, still survives. The resulting alarm led to the monitoring for zebra mussels in other Iberian basins. Then a serious spread along of the Segura River was made public. The Confederación Hidrográfica del Segura (www. chsegura.es) reportedly found larvae from the Tagus-Segura aqueduct at the head of the Mundo River, down to the mouth of the basin. This result was not
Figure 1. Cross-polarized light is useful for detecting zebra mussel veligers. Photo: Catalan Water Agency.
Ponto-Caspian region. Due to its high fecundity, its capacity for survival for a time outside water, and larval transport with water currents, the species is an effective colonizer and thus has spread worldwide. By filtering nutrients, it alters water quality. Adult individuals establish in dense colonies, firmly attached to any solid surface. Thus, the zebra mussel modifies substrates and collapses infrastructures, causing a variety of ecological and socioeconomic impacts. Hence it is listed as one of the world’s 100 worst invaders by the Global Invasive Species Program (www.issg.org/database/) 198
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Figure 3. Native Anodonta cygnea affected by D. polymporha. Photo: Grup de Natura Freixe.
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Figure 4. Boats can disperse D. polymorpha between water bodies. Photo: Grup de Natura Freixe.
GALLEGO at Anzáñigo Sobrón R. Ullívarri R.
CANTABRIC BASINS
Cillaperla ta R.
" )
" )
! (
" )
Sabiñánigo R.
E. de Lareo
Lanuza R.
" )
Lodosa I C. Lodosa II C. Lodosa III C.
ARGA Búbal R. Barasona R.
" ) " )
GALLEGO Bardenas C.
! ( ! (! (" )
Lodosa IV C. Lodosa V C.
Talarn R. Oliana R.
" )
" )
" )
ALHAMA at Alfaro
ESERA
Fish farm (T. Segre)
" ) ) "" )
" )
Tauste C.
CINCA
Ranillas Meander
Imperial Canal of Aragón
! ( " )
" ) " ) " )
" )
EBRO BASIN
Fish farm (Palau)
" ) " )
Rialb R. St. Llorenç de Mongai R.
HUERVA
" ) " )
INTERNAL BASINS OF CATALONIA
" )
" )
MESA
SEGRE
! (
EBRO
! ( ! (
" ) " )
Tranquera R.
Flix R.
" )
Riba-Roja R.
MARTÍN Calanda R.
Mequinensa R. GUAQALOPE Santolea R.
! (
JUCAR BASIN
! (
Sitjar R.
Santa Quiteria R.
! ( MUNDO
Forata R.
Talave R. Camarillas R.
La Mulata W.
" ) " )" )
De Ojos W.
SEGURA
Almadenes W.
" )
SEGURA at Orihuela
" ) " )" )
" ) " ) )" " )
Guardamar of the SEGURA
De la Contraparada W.
Rojales W.
SEGURA BASIN Pedrera R.
SEGURA at Murcia
! ( 2001
Adult individuals
! ( 2004
Adult individuals
! ( 2005
Adult individuals
! ( 2006
Adult individuals
" ) 2006
Larvae
" ) 2007
Larvae
" ) 2008
Larvae
Affected river section
0
100
Affected Reservoir (R), Canal (C) or Weir (W)
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Figure 5. Records of the zebra mussel in the Iberian watersheds according to official data, 2001-2008.
C H RO N I C L E
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199
Administrative / normative framework
Environmental
Water use management
Leisure / business
Status quo
Integrated intervention
Every man for himself!
Politically correct business
Utilitarian focus Spread of bioinvasions ‘Easy solutions (e.g. chemicals)’ Local ecosystems strongly impaired
Management subordinated to political will (social agreement) Certain control of the species, shared costs
BAU
Administrative chaos with good intensions
Shangri-la
Passive lack of cooperation Reduced Spreads of the species, high costs
Real control from shared guardianship
Figure 6. Local scenarios on bioinvasion in the Ebro. Note: BAU stands for business-as-usual scenario. The names of the scenarios were suggested by the participants.
confirmed in subsequent reports. In 2007 the presence of larvae in very low density was announced in the Lareo reservoir in the Cantabrian Basin flowing to the Bay of Biscay, but the invasion was not confirmed. Recent results point to the spread of the zebra mussel. In the Ebro, new affected reservoirs were reported in the Segre River in 2007 and in the Gállego and Zadorra Rivers in 2008, as well as the Cillaperlata reservoir at the head of
the basin. In 2009 the presence of the species was confirmed in the Los Bermejales reservoir, in the Guadalquivir Basin, the second longer river in Spain. Impacts of the invasion The ecological effects of zebra mussel in the Ebro come through trophic alteration and change in substrates. It is a likely threat to native bivalves and the vector of an alien parasite; it also triggers processes of cyanobacteria proliferation. It
Figure 7. Irrigation infrastructures affected by D. polymorpha in the Ebro. Photo: Grup de Natura Freixe.
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may contribute to water transparency and to the increase of macrophytes. Public concern about zebra mussel has arisen since 2002 not so much from the ecological effects as from the damage to infrastructures such as power stations and irrigation and municipal water supply facilities. The Riba-roja dam and the Ascó nuclear plants, and later the Garoña nuclear plant, have suffered production losses, and replacement and treatment costs.
The main electrical company developed a research program on the invasion and has become a scientific advisor to the other sectors. The invaded reservoirs supply large irrigation infrastructures. When interviewed, their representatives considered the presence of the species as a nuisance rather than as a serious concern. However, there is information on growing damage to grills and pipes, and replacement costs. The invasion has caused expenses in research and control costs. Control measures in closed systems were thermal treatment (nuclear power plant), and chemical treatment with chlorine and hydrogen peroxide (irrigation and municipal supply). None of these measures was applied in natural systems. The navigation restrictions to prevent the spread altered the leisure value of the river. Conflicts arose between the authorities and the recreational users. Water transfers from areas at risk are a main concern. A water transfer from a non-colonized reservoir at the head of the basin to the Cantabric basins was temporarily stopped in 2006 as a precautionary measure. Since 2007 a filtering system can process up to 17,000 m3/h of water. Similar preventive schemes are being considered in 2008 for the reservoirs supplying the metropolitan area of Barcelona. A monetary valuation of zebra mussel costs has been published by the CHE, estimating a mix of damage and control costs around € 2.6 million between 2001 and 2005. Clearly, this has been increasing over the last few years. Bionvasions and participatory scenarios During 2005 local and regional stakeholders participated in a structured exercise of scenario development on
Figure 8. Stakeholders coordinating preventive measures for navigation activities, May 2008. Photo: Universitat Autònoma de Barcelona.
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Figure 9. Sports Federations are actively engaged in preventing the spread of zebra mussel to new water bodies. Photo: Catalan Canoe Federation.
aquatic bioinvasions in the Ebro organized by ALARM. Scenarios depict alternative futures built up from plausible assumptions about the evolution of the problem at hand. The procedure entailed a facilitated process of identification and clustering of driving forces and recognition of key uncertainties. The alternative evolution of such key uncertainties configured four main scenarios, shown in Figure 6. According to the participants the most relevant drivers compelling the aquatic bionvasions in the Ebro are inadequate knowledge of the territory on the part of the population, lacking means for any institutional response, and the kinds of uses of the river, especially those carried out by external actors. Water use management and institutional coordination emerged as key uncertainties of the case. By developing scenarios, stakeholders could exchange views in the problem definition, and frame the business as usual trends (BAU) and their local concerns in the dynamics at larger scales. The Catalan experience with the management of the zebra mussel invasion The Catalan Water Agency (Agencia Catalana de l’Aigua, ACA, http://mediambient.gencat.cat/aca) is the watershed authority in Catalonia, sharing this task with the CHE in the Catalan section of the Ebro. In charge of leading the regional control of the zebra mussel since 2006, ACA decided to integrate the ALARM results within its management scheme and also in its contribution to the Spanish Strategy for the Zebra Mussel Control. The aim was to develop prevention and mitigation measures based on shared responsibility and active
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public participation, a guiding principle of the EU Water Framework Directive. In order to prevent the spread of the invasion to the non-affected areas, there was a focus on the dispersal pathways. Control of fish repopulations, restrictions to boat traffic and the development of a boat disinfection system, in line with the ALARM recommendations, have been agreed with regional sports associations (anglers, boat owners, water skiers and canoeists) and local stakeholders. Institutional coordination has prompted management measures involving several governmental agencies at the regional and national scale. An example of such coordination is the monitoring of the invasion based on larvae sampling and tracking of the population’s distribution and density. A pilot scheme for detection through genetic techniques is currently tested. In Spain, the invasion of Dreissena polymorpha has produced a better public understanding of invasive species as an environmental problem. Although this awareness is rather related to the economic impacts of the species, public actors and scientific advisors highlight the opportunity to initiate a more significant discussion about water quality. The presence of the species entails a serious menace for fulfilling the quality targets of the Water Framework Directive.
Figure 10. Different units of the regional government develop prevention and control measures against zebra mussel. Photo: Generalitat de Catalunya.
Figure 11. The case of the zebra mussel has increased public awareness about the relevance of invasive species as an environmental problem. Photo: Centre d’Estudis dels Rius Mediteranis.
References BINIMELIS R, MONTERROSO I, RODRÍGUEZLABAJOS, B (2007) A Social Analysis of the Bioinvasions of Dreissena polymorpha in Spain and Hydrilla verticillata in Guatemala. Environ Management 40: 555-566. RODRIGUEZ-LABAJOS B (2006) Interlinked biological invasions in the Ebro River. A multi-scale scenario approach. Master thesis, Autonomous University of Barcelona, Bellaterra, 91 p. (http://selene.uab.es/brodriguezl)
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Figure 12. Preventive measures are being addressed in the reservoirs of the internal basins of Catalonia Photo: Catalan Water Agency.
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“The Farmer’s Terror”: Glyphosate Resistant Johnsongrass in Argentina
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ILIANA MONTERROSO, ROSA BINIMELIS & WALTER PENGUE
Figure 1. Possible field Infested by Johnsongrass. Photo: Agricultural field-trial station in Tucuman, Argentina, 1921.
This chapter presents the case of johnsongrass. This weed, considered a problem in more than 50 countries, is listed as one of the 10 most damaging exotic species that have been deliberately or accidentally introduced into a new ecosystem with detrimental effects (http://www.issg.org/database/). Specifically, the process of emergence of a new johnsongrass biotype resistant to glyphosate in Argentina is
Figure 2. Johnsongrass in Argentina. Photo: W. Pengue.
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described. The broad-spectrum herbicide glyphosate is nowadays the largestselling crop-protection product worldwide. This case highlights the role that humans have in the mechanisms of herbicide-resistant weeds’ appearance and spread and analyses the management responses. “The perfect weed” Johnsongrass (Sorghum halepense (L.)) is a cosmopolitan plant, growing almost everywhere around the globe (Figure 2). It is a permanent grass native to the Mediterranean region. It has a high rate of seed reproduction and it is able to spread through a number of ways including by wind and contaminated farm machinery. Seeds may also move long distances transported by water or in the excreta of bird or livestock. It has extensive rhizomes. These characteristics make it extremely invasible. The list of crops affected by this weed includes maize, sugarcane, grain sorghum, soybean, sunflowers, wheat, citrus crops and cotton. This has gained johnsongrass a terrible reputation among farmers. Johnsongrass in Argentina Historical data show that johnsongrass was introduced deliberately in Argentina at the beginning of the 1900s. It was promoted by the Argentinean government to be used for animal feed. Sales were banned in 1936 but it continued being used, facilitating its spread throughout the country (Figure 1). In 1980, studies estimated that about 15 million ha of the Argentinean Pampa were infested, affecting about 100,000 farmers (Ladelfa et al. 1983). The Pampas is a vast flat region of Argentina that covers more than 55 million ha, the largest portion of arable land in the region. In 1970s, a governmental program encouraged the recuperation of johnsongrass infested fields by means of integrated management: response actions that combined the use of control methods, the application of herbicides, mechanical and manual removal, rotation between crops and pasture lands. It also included a communication strategy that facilitated monitoring activities between farmers and government officials. Herbicides: The “stepping stone” to overcome weeds’ problems? However, after an ample range of herbicides were introduced to Argentina at the end of the 1970s, farmers
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adopted herbicides as the major strategy to combat weeds. Afterwards, glyphosate, a non-selective systemic herbicide became the stepping stone for managing weeds. It allowed controlling annoying plants in agricultural fields at very low costs with a single product. This was particularly important because farmers could manage more hectares and increase overall productivity. Glyphosate was initially very successful for controlling johnsongrass, giving the idea of invincibility to manage it. Despite known risk factors associated with recurrent applications of herbicides, any provisions were taken for preventing the emergence of herbicide-resistant weeds. Resistance to herbicide occurs when there is strong pressure, associated with extensive
use, in an intensive cropping system such as a cultivation of a single crop, in this case soybean. Current trends in agricultural production in Argentina Argentina is one of the most important cereal producers in Latin America, the existence of large extensions of arable lands has promoted changes in agrarian structures and technologies to allow expansion of monocultures. Soybeans alone represent fifty percent of agricultural production, becoming the most important crop at the national level (Figure 4). Argentina is the third world soy producer. The area cultivated has grown three times while the yield has increased five-fold during the last ten years (Figure 5). This represents over US$ 4,000 million/year in
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Figure 4. Soybean production area in Argenina. Source: Dirección de Coordinación de delegaciones SAGPyA (1997-2002).
The return of johnsongrass Ten years after the assumed success over this weed, in 2002 farmers detected the presence of a glyphosate-resistant johnsongrass in the Northern region of Argentina (Figure 7). The existence of a strand of johnsongrass resistant to glyphosate was officially recognized three years later. Time lag between appearance reports and confirmation made difficult its control. There is still little information on how resistance mechanisms operate in GR johnsongrass. Affected areas are only partially reported by the affected farmers, mainly due to the fear to the land depreciation and lack of reliable information on how the government will manage and proceed with affected areas. In the north of Argentina where the problem started it is estimated that the potential area can reach up to 100,000 ha (Figures 6a and 6b). Until now, most of the responses to the problem deal with technological responses without taking into account the perception of the different actors involved in the invasion process: farmers, agricultural extensionists and scientists. It is important to understand how the costs and benefits associated with the impacts derived from the existence of GR johnsongrass are distributed, but also to understand the implications
Impacts There are some effects associated with johnsongrass including not only economic costs derived from losses in production but also damages associated with disruptions inflicted on ecosystem processes. For instance, in some countries johnsongrass is associated with fire risk during summer, competitive exclusion of other plants and reduction of soil fertility. More specific disturbances in Argentina are associated with implica-
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Figure 6. No-tillage system of glyphosate-resistant soybean cultivation in Argentina, 2000 (a); Johnsongrass expanded on abandoned fields in north east of Argentina, Villa Angela, Chaco, 2007 (b). Photos: W. Pengue (a), W. Pengue & R. Binimelis (b).
tions for farmers, mainly medium and small ones, who have little access to technological packages, neither have the economic solvency to cover for unexpected effects or the increment of control costs. Inability to respond to negative impacts associated with johnsongrass can result in land abandonment and depreciation and therefore decrease of land prices. Other indirect effects associated with the spread of this new biotype are related to the advance of the agricultural frontier to sustain actual benefits at expenses of an increase in deforestation rates (Morello & Pengue 2007).
erate the evolution of multiple resistances if they fail to meet basic criteria for resistance management or are applied repeatedly. This means that unforeseen events may occur.
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CERDEIRA AL, DUKE SO (2006) The current status and environmental impacts of GR crops: A review. Journal of Environmental Quality 35: 1633-1658. LADELFA A, LAVEZZARI D, MORIS N, SILLA R, NÚÑEZ A (1983) Control de sorgo de alepo en soja. INTA, Pergamino. MORELLO J, PENGUE WA (2007) Manifiesto contra la deforestación. Noticias. Septiembre. 2007. Buenos Aires. PENGUE WA (2000) El futuro de la agricultura argentina. Sustentables, hasta cuándo? Le Monde Diplomatique. Edición Cono Sur. 1: 25-27. Buenos Aires.
References BINIMELIS R, PENGUE WA, MONTERROSO I (2009) “Transgenic treadmill”: Responses to the emergence and spread of the Glyphosate-Resistant Johnsongrass in Argentina. Geoforum 40: 623-633.
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“New” old methods for managing infestation levels Despite Argentina has dealt with johnsongrass for a century there is still the expectation that an alternative strategy based on technological developments will combat GR johnsongrass without questioning/compromising the agricultural model. Right now, most response actions rely on chemical options as the keystone strategy. This includes combining a series of already commercialized, but abandoned herbicides with glyphosate directly or using them indirectly through the development of new GM crops with new herbicide-resistant mechanisms or varieties resistant to even higher doses of glyphosate. Both options imply returning to more severe toxic, old herbicidal ingredients such as MSMA or 2.4D or combinations of these with glyphosate. However, herbicide mixtures can accel-
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Figure 7. Map showing area saffected by johnsongrass invasion in North east, Argentina. Source: Own elaboration based on Binimelis et al. 2009.
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Genetically modified soybean in Argentina Another important factor in the production system is the adoption of genetically modified (GM) crops. In Argentina there are over 16 million hectares (almost 100 % of the Argentinean soy) growing GM glyphosate-resistant soybeans, genetically manipulated to resist the application of this broad-spectrum herbicide. In fact, the use of glyphosate has increased from approximately 20 million tons in 1996 to more than 140 million tons in 2006 (Figure 3). The increase in the use of glyphosate due to the adoption of GM crops has been associated with the emergence of glyphosate resistant weeds, as an additional risk factor. Three cases of resistant weeds worldwide are linked specifically with the use of herbicide resistant crops (Cerdeira & Duke 2006). The land devoted to the production of GM soy is far from being reduced, due to the increasing demands of the sharply growing agrofuel industry. New energy policies such as the EU goal of 5.75 % biofuel blending by 2010 will require a fivefold increase in EU production, posing a great demand for raw materials import. International demand could press further for the expansion of the agricultural frontier and the intensification of the production system.
associated with the implementation of management responses. Area cultivated (million of ha)
export taxes, in 2007, 35 % of the revenues coming from soy production accounts for such retentions.
Future Contributions of Socio-Economic Research to the Conservation of Biodiversity JOACHIM H. SPANGENBERG, LEWIS AKENJI, ALAIN AYONG LE KAMA, TOM BAULER, ROSA BINIMELIS, JEAN-MARC DOUGUET, BIRGIT BEDNAR-FRIEDL, JILL JÄGER, KRZYSZTOF KAMIENIECKI, PIRET KULDNA, JYRKI LUUKANEN, JOAN MARTÍNEZ-ALIER, LAURA MAXIM, MARTIN O’CONNOR, KAJA PETERSON, BEATRIZ RODRIGUEZLABAJOS, LARS RYDEN, KARLHEINZ STEINMÜLLER, UNO SVEDIN, SERGIO ULGIATI, MEELIS UUSTAL, JEROEN VAN DER SLUIJS & JOSEF SETTELE
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Using the results of the Alarm Project Even at the end of the project, the full potential of socio-economics for ALARM, as well as the full potential of ALARM for the socio-economics of biodiversity has not been exhausted, and necessarily so. On the one hand, the wealth of results from (bio-)science research is overwhelming, and on the other, integration is a time- consuming process. Even though it was part of the conception phase, integrating the research results, at least partly, is only possible after, not in parallel to, the research process. So whereas a first series of thematic briefing sheets was produced early in the project, a set of policy conclusions suggesting improvements in the EU biodiversity action plan could be compiled only after the formal end of the project. The ALARM Consultative Forum supported the work of the research teams throughout the project. It discussed the project results and their potential future usefulness for biodiversity conservation in its final meeting, suggesting a number of steps that could be taken to enhance the impact of the research conducted (Figure 1). Besides some general guidance, it emphasised the necessity to present the project’s message in ways adapted to the respective institutional settings and the actors involved.
◙ To raise awareness of the importance of biodiversity, it is necessary to identify the motivations of the different actors and the benefits of biodiversity accruing to them, as well as the priority obstacles inhibiting a biodiversityconscious performance. ◙ Different measures will be appropriate for each actor group. For consumers, education could be most appropriate, while for business information on the value of biodiversity and ecosystem services could support appropriate strategies. In all cases, it is important to emphasise monetary and non-monetary values of biodiversity. ◙ Policy insufficiencies due to a lack of knowledge, instruments or vision have to be addressed specifically, e.g. by research, policy analysis and best practice examples, and by convincing scenarios, respectively. ◙ Ethical issues have to be dealt with specifically as well: whereas for some people, biodiversity conservation is a self-explanatory ethical imperative, for others the gains from biodiversity, and the losses from not having it are decisive and should be addressed by the science – policy interface.
The way forward The socio-economic analyses in ALARM have shown that many policies have biodiversity implications, for instance water policy, regional and urban development, Linking knowledge to action energy policy with special focus on agrofuels, trade, agriculture and the general When establishing a science – policy interface to support general and sectoral pol- commitment to economic growth (Rodriguez-Labajos et al. 2009). These policies should re-define their objectives taking into account that they are or will eventualicies, not only scientific information counts: stakeholder participation and comly become a driving force for biodiversity change and loss. To emphasise the munication are essential. importance of this insight, the riches biodiversity provides (economic, cultural, spiritual, etc.) should be emphasised more strongly, and the risks associated with ◙ In order to link “knowledge” to “action”, it is important to differentiate the losing them highlighted. This should result in a clear expression of the political drivers of biodiversity changes according to competent actors and determine where consumers, business and politics are decisive, respectively. will to protect biodiversity, in each of these policies, by integrating biodiversity concerns into decisions and norms, by providing access to information and by Links to be emphasised making sufficient human and financial Key • Climate: biodiversity as adaptation strategy • Threats to services, unknown impacts, increasing resources available. • • • • •
• Policy frameworks: CBD, EU Strategy • Indicator frameworks: CSD, SEBI, GRI (corporate level: biodiversity-subset?) • Industrial ecology/ metabolism, link to biodiversity e.g. via (embodied) HANPP & LJJ models
pressures (e.g. agrofuels) Ethics/Coexistence postulate Relevance of politics (or not) Policy insufficiencies: Knowledge, instruments, vision Landscape management, organic agriculture Education, knowledge provision
Tools • Scenarios • Case Studies • Indicators, DPSIR • Post normal science • KerALARM
Policy • Address the different classes of reasons people have to conserve biodiversity, including showing short term gains and win-win opportunities (positive message: biodiversity provides riches to society) • Cross cutting: biodiversity emphasis is needed in all policy fields, and for that, a systemic approach, a strategic process instead of disjunct policies and measures. This requires revising overall priorities, including life styles and consumption habits, scenarios as illustration. Coalitions across policy fields are possible • In this context, name old concerns confirmed, new ones identified • Complexity means there is no silver bullet, no mere technical solutions • Show uncertainties, use shock scenarios for that • Make cost-benefit analysis of scenarios (non-monetary political costs) • Assess economic costs of scenarios (compare GDP trends)
To be offered / prepared / considered • Update messages after economic melt-down • Summary for policy makers/ for lay people • Briefing sheets on the role of biodiversity for robustness and resilience of ecosystems and their services • Training workshops for decision makers, even a “Biodiversity Situation Room”, plus red list: what can go wrong in politics • Biodiversity research and advisory capacity for the Commission, e.g. a transdisciplinary biodiversity research institute in the JRC? • Distance learning tools for biodiv management • Seminar with ICLEI on local biodiversity management
Internal insights: • With the right setting, diverse disciplines can co-operate, speak/develop a similar language, provide joint and thus innovative results. • People from outside the scientific community can contribute to this. • With policy relevant insights, scientists should act as concerned citizens.
Figure 1. The suggestions of the ALARM Consultative Forum, and their structural links (Source: ALARM Consultative Forum).
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Preserving those riches incurs the cost of conservation, but what is the balance? Economic cost-benefit analyses of different policy options are necessary (despite all the known weaknesses of the method), but they are not sufficient: political, social and environmental costs and benefits cannot be measured in monetary units. In order to include all the relevant assessment methods, strengthening interdisciplinarity in biodiversity research is crucial, including social sciences beyond economics, such as political science, sociology, humanities, but also new disciplines such as industrial ecology and the bio-energetic research on the Human Appropriation of Natural Primary Production (HANPP, which may be a prime candidate for an overall biodiversity pressure indicator, see e.g. Haberl et al. 2004). In all these efforts, it must remain clear that no single measure will rescue biodiversity – there is no silver bullet. Instead, a systematic review of all poli-
BS 12: Impacts on environmental services by invasive species IS – when to act, what to do?
perceived by humans, while impacts on supporting or regulating services are indirect and the perception of alteration depends to greater extent on the state of knowledge.
ALARM LOGO
Iliana Monterroso, Rosa Binimelis, Beatriz Rodríguez-Labajos, Mariana Walter
Introduction of exotic species? What actions can be chosen in order to limit pressures from introduced species? Who should participate in the decision? What kind of information is available and what kind is missing?
When to act? what to do? Defining invasion processes Invasion processes are those where exotic species are introduced into new environments threatening ecosystems, habitats and species. Lacking native predators or facilitated by high vulnerability in recipient environments, invasive (IS) species have an aggressive ability to reproduce offspring causing serious socioeconomic and environmental impacts. They are considered the second cause of biodiversity loss by the Convention of Biological Diversity; therefore, addressing their management has become a key target in conservation policy.
A human well-being approach to assess invasive species impacts Ecosystems services, defined as the conditions and processes through which natural ecosystems and the species that make them up, sustain and fulfil human life, are foundations of human well-being. By affecting the ecological processes at the genetic, species and ecosystem level, IS modify the provision of resources and processes supplied by natural ecosystems, which can be of high socioeconomic and environmental relevance. Biological invasions develop in contexts where there is high degree of human influence. Understanding how human participate in invasion processes requires highlighting human dimensions of the problem. This involves analyzing socio- economic driving forces associated with their introduction, the impacts associated with their establishment and the management responses. Identifying impacts of invasive species is required in order to evaluate the consequences of invasion processes and to implement response actions. From the socioeconomic point of view impacts of biological invasions are changes of recipient ecosystems which are perceived by humans. The assessment of these effects will determine different responses to invasive species. Perception of impacts is heterogeneous,
context-dependent and dynamic (BS 13). To facilitate decision-making, assessment of impacts should be able to provide information in situations where values, interests, and institutions are multiple and sometimes conflicting.
Altered ecosystem services by IS in Europe A general overview of the altered ecosystems in Europe by invasive species shows, following categories developed by the Millennium Ecosystem Assessment, that invasive species alter all ecosystem service categories at the European level. Some differences can be noted. While provisioning and cultural services are affected by species pertaining to all taxonomic groups analyzed, other categories are perceived as only being affected by a determined group of IS. However both provisioning and cultural services are ecosystem benefits that can be directly
ALARM SE Briefing Sheets BS 0: Socio-economics in biodiversity research – the role of the SE team in ALARM BS 1: Assessment of Socio-Economic Methods to evaluate Biodiversity BS 2: Risk Definitions and the Role of Uncertainty in Assessments BS 3: Ecological Economics for biodiversity policy. The ALARM example BS 4: Multi-factor Biodiversity Scenarios: Methodology and Results BS 5: Deliberative Procedures BS 6: Deriving and Spreading Policy Recommendations BS 7: Socioeconomic Modelling for biodiversity preservation: lessons from ALARM BS 8: Climate change and socio-economics in biodiversity research (ALARM) BS 9: Socio-economic modelling and the future of biodiversity- results from ALARM BS 10: Risks of systemic insecticides on honeybees: early warnings? BS 11: Agrofuels in Europe - recommendations based on a multicriteria assessment BS 12: Impacts on environmental services by invasive species – when to act, what to do BS 13: Perception of invasive species and strategies against invasive species – from analysis to action (including Cameraria and Zebra Mussel BS 14: Chemical risks for biodiversity – the future of REACH BS 15: Weed resistance in GMO soybean cultivation in Argentina/EU relevance for European Policy on imports of feed and agro-fuels BS 16: The doubtful coexistence between GMO and organic agriculture. What needs to be done? BS 17: Agricultural land use changes in EU new member states and impacts on pollinators on micro level
Policy prescriptions towards IS should take into account irreversibility effects and equity implications of any action taken during management. This suggests that a preventive strategy is advised, one that identifies major drivers of change and adresses the likelihood of an invasion processes to happen. Nonetheless, actions towards IS are taken usually after they have established. The use of an ecosystem service approach helps to understand the role of invasive species from an ecological and human point of view. Additionally, it allows for displaying and structuring information on impacts. This framework evidences that one single species could impact ecosystems in diverse ways. It also highlights the context of uncertainty in which these phenomena develop and the conflict associated with deciding what is the desirable environmental performance of ecosystems: Which ecosystems services are at risk of IS events? How do we measure positive and negative impacts linked to the
PROVISIONING SERVICES SUPPORTING SERVICES Nutrient cycling Habitat stability Soil formation
Freshwater aquatic organisms: Eg. Alteration of food and oxygen availability by zebra mussel (Dreissena polymorpha) Terrestrial plants: Eg. Alteration of local soil nitrogen cycel by the silver and blue wattle (Acacia dealbata) Terrestrial vertebrates: Eg. Changes in community assemblages and structure produced by the coypu (Myocastor coypus)
Food, fuel, wood, fur, fibber and non-timber products Genetic resources All analysed taxonomic groups Freshwater aquatic organisms: Eg. Loss or gain in commercial production and harvest (agriculture, forestry, fisheries, aquaculture) Comb jelly Mnemiopsis leidy Terrestrial plants: Eg. Threat to the viability of endangered species sour sorrel Oxalis pescaprae Terrestrial vertebrates: Eg. Genetic hybridization, canada goose Branta canadensis
REGULATING SERVICES Water regulation and purification Biological control Disease regulation Erosion regulation Pollination Natural hazard protection Freshwater aquatic organisms: Eg.chocking waterways, swamp stonecrop Crassula helmsii Terrestrial plants: Eg. Reduction of native species through displacement, predation and resource competition, japanese knotweed Fallopia sp. Vertebrates: Eg. Injuries produced by the grey squirrel Sciurus carolinensis Invertebrates: Eg. Vectors f human, livestock, wildlife or plant diseases produced by the Tiger mosquito Aedes albopictus
CULTURAL SERVICES Recreation
Aesthetic values
Education
All analysed taxonomic groups Freshwater aquatic organisms: Eg. Changes in recreational use of natural sites produced by the sea trout Salvelinus fontinalis Terrestrial plants: Eg. Reduction of recreational use of natural sites produced by the giant howeed Heracleum sp. Vertebrates: Eg. Affectation to eco-tourism activities produced by the coypu (Myocastor coypus) Invertebrates: Eg. Changes in the perception of natural landscapes produced by the horse chestnut leaf=miner Cameraria ohridella
Costs to human well-being Damage to infrastructures and utilities
Freshwater aquatic organisms, terrestrial plants and vertebrates
For additional information see: - Binimelis, R.; Born, W.; Monterroso, I.; Rodríguez, B. “Socio-economic impacts of biological invasions” in ‘Ecological studies: biological invasions’ (Wolfang Nentwig, ed.), 2007. Springer Verlag. 331-347 pp.
ALARM ALARM is an Integrated Project under the EU’s 6th Framework Programme. 68 institutes from all over Europe and the world work together for five years to assess large-scale risks for biodiversity resulting from climate change, environmental chemicals, invasive species and pollinator loss.
Socio-economic Team: ALARM includes one socio-economic module and a socio-economic team of six institutes. It analyses the driving forces behind and the pressures on biodiversity, derives indicators and suggests policy strategies for biodiversity protection. Coordinator of the socio-economic team: Dr. Joachim H. Spangenberg, UFZ
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Sustainable Europe Research Institute (SERI), Vienna
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Estonian Institute for Sustainable Development (SEI), Tallinn
www.seit.ee
[email protected]
Universitat Autònoma de Barcelona (UAB)
www.uab.es
[email protected] [email protected]
Centre d’Economie et d’Ethique pour l’Environnement et le Développement (C3ED)
www.c3ed.uvsq.fr
[email protected] [email protected] [email protected]
Helmholtz Zentrum für Umweltforschung - UFZ
Figure 2. Example of ALARM Briefing Sheet.
cy fields is necessary to incorporate biodiversity (in the EU, such processes are known as policy integration or policy coherence): mainstreaming biodiversity requires “hunting for policy issues” where biodiversity concerns should, but do not yet, play an adequate role. This includes also biodiversity policy: research results should be used to continuously update programs and indicator sets (like the EEA set of European biodiversity indicators SEBI [http://reports.eea.europa. eu/technical_report_2007_11/en/] or the EU environmental and structural indicators [http://epp.eurostat.ec.europa.eu/portal/page/portal/ structural_indicators/introduction]). For the corporate level, such yardsticks (for example incorporated into the Global Reporting Initiative’s (GRI) set of corporate social responsibility (CSR) indicators [http://www.globalreporting.org/Home]), and more general biodiversity-supportive criteria are largely missing so far; they would provide a robust underpinning of the EU Business and Biodiversity initiative. Such tools are urgently needed, but just as urgent is the need to make their existence known, by offering easy-to-understand but scientifically robust information to the public and to decision makers. Examples of such ready-made, easily digestible but solidly scientifically backed information tools are the ALARM briefing sheets and policy recommendations. Both the briefing sheets (see Figure 2 for an example) and the policy recommendations (Figures 3) are still useful after the project end and could in future be complemented with additional information). An additional option is the propagation of decision support and distance learning tools such as KerALARM which was developed within the project. However, better than offering decision support tools to be applied in an otherwise unchanged mental and institutional setting is decision training, e.g. in workshops for policy makers, a “Biodiversity Situation Room”. On the local level this could include training by scientists and practitioners for biodiversity managers and local executives, e.g. in coop-
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eration with the International Council for Local Environmental Initiatives (ICLEI). Such workshops could actively contribute to an improved uptake of the available scientific knowledge in the decision-making process. Beyond that also more formal recurrent processes for better managing the relationships between knowledge and action should be imagined. A recommendation to policy makers is institutional, sectoral, and regional coordination, an open dialogue about the implications of management activities at the regional level (a two-way communication) and the strengthening of local agency (access to information and the reinforcement of social networks). We have evidence that people are willing to be actively involved in different aspects of biodiversity protection (decision making, monitoring, surveillance) but there are not always links between them and the formal processes. The members of the ALARM Consultative Forum would not only welcome, but are ready – in their respective walks of life – to actively support the implementation of such measures. The ALARM project has been completed now, but the work for biodiversity conservation must go on, making use of a strengthened socio-economic component. References HABERL H, SCHULZ NB, PLUTZAR C, ERB KH, KRAUSMANN F, LOIBL W, MOSER D, SAUBERER N, WEISZ H, ZECHMEISTER HG, ZULKA P (2004) Human Appropriation of Net Primary Production and Species Diversity in Agricultural Landscapes. Agriculture, Ecosystems & Environment 102 (2): 213-218. RODRIGUEZ-LABAJOS B, SPANGENBERG JH, MAXIM L, MONTERROSO I, BINIMELIS R, MARTINEZ ALIER J, KULDNA P, PETERSON K, UUSTAL M, GALLAI N (eds)(2009). Assessing biodiversity risks with socio-economic methods: The ALARM experience. Pensoft Publishers, Sofia–Мoscow.
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Policy Recommendation 19
Policy Recommendation 6
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Protecting the diversity and abundance of pollinators in agro-ecosystems.
Efficient management of invasions of alien plants
Recommendations
Recommendations
Reversing the declining trend of pollinator, in particular of bumblebees, requires measures to ◙ reduce the limits permitted for agro-chemicals input and field size, ◙ to maintain uncultivated field margins and set-a-side lands in intensive agricultural areas, ◙ to preserve and restore the flower-rich natural grasslands and ◙ to increase the area of leguminous plants, which are important forage areas for bumblebees. Implementing these measures needs policy changes, including requirements in the EU Common Agricultural Policy and Rural Development.
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Reasoning: why is this recommendable The natural grasslands and leguminous plants are especially vital for long-tongued bumblebees that depend on these habitats. The decline of leguminous plants has taken place due to several reasons such as: ◙ Switch from hay to silage for cattle fodder since cutting for silage is done before the legumes flower; ◙ Improvement of grasslands to increase their productivity by adding fertilisers which leads to a decline in floristic diversity; ◙ Replacement of crop rotation involving legume crops by fertilisers. Reducing the use of pesticides can decrease the loss of pollinators; the increased pollination efficiency compensates the possibly higher number of pests. The scientific base: data and how they were derived Based on documentary evidence and expert judgement, land use practices and the use of agrochemicals were regarded in our research as the most significant pressures that affect pollinators adversely. The habitats required by pollinators are reported as being lost through agricultural intensification as well as loss or fragmentation of natural and semi-natural habitats. Pesticides pose a major threat on pollinators through direct toxic effects, the use of herbicides and fertilisers can influence indirectly through the loss of food sources on field margins and roadsides. A historical analysis (1875-2005) of the relationship of bumblebee diversity, status of their vulnerability and the land use in two pilot areas (Tartu, Estonia and Kent, UK) revealed that the decline of bumblebee diversity and increase of vulnerability follow the declining trend of open habitats, especially of grasslands, such as clover fields. The long-tongued and medium-tongued species that depend on plants with long corollas (e.g., legumes and labiates) have become the most rare. The vulnerability of open habitat bumblebee species is likely to further increase due to habitat loss and intensive farming practises. The study covered 25 species of bumblebees (Bombus sp.) and cuckoo bumblebees (Psithyrus sp.).Vulnerability status of each species studied was attributed by authors by using the IUCN Red List categories of vulnerability, that is the extinction risk faced by species. References Uustal M, Peterson K, Luig J, Roberts S, Dendoncker N (2009) Historical land use and pollinator diversity: a comparative study of bumblebees in Kent County (UK) and Tartu County (Estonia). In: Rodriguez-Labajos B et al. (eds)(2009). Assessing biodiversity risks with socio-economic methods: The ALARM experience. Pensoft Publishers, Sofia–Moscow. Kuldna P, Peterson K, Poltimäe H, Luig J (2009) An application of DPSIR framework to identify issues of pollinator loss. Ecological Economics, in press. Contact: Kaja Peterson ([email protected])
Figure 3. Examples of ALARM Policy Recommendations.
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Enhance efficiency of detecting and controlling invasive plants by focussing on habitats with a high risk of invasion For that behalf, incorporate habitat type within control measures against invasions Use our susceptibility predictions for different landscape management scenarios to reduce risk of invasions and for cost-effective early detection of invasions Further improve susceptibility prediction capabilities by supporting the collection of robust data on invasion in habitats across Europe
Reasoning: why this recommendation Anthropogenically disturbed habitats (urban, intensive agriculture) are most at risk of invasions, a. major threat to biodiversity and in many cases with considerable impact on economies and human health. Within Europe, the levels of invasion dramatically differ depending on the habitat type and region invaded. Thus, for effective management of biological invasions it is important to understand which areas and ecosystems are at the highest risk of invasion. This knowledge enables local authorities and landscape managers to spend resources efficiently by targeting habitats that have the highest risk of further spread of invasive species. The scientific base The method The level of invasion by alien plants differs among habitats, some of which are more vulnerable to invasion than others. Recent research within the ALARM project showed that the role of habitat is crucial in determining how many alien species successfully invade and is even more important than the role of other factors such as propagule pressure (i.e., how many alien species are in the surroundings of the target site) and climate (in temperate and boreal zone, areas with warmer climate are more prone to invasions). Furthermore, a comparative study based upon more than 52 000 vegetation plots across three climatic regions of Europe (United Kingdom: oceanic climate; Czech Republic: subcontinental climate; Catalonia: Mediterranean climate), revealed that disturbed habitats, such as urban and agricultural habitats, are most invaded, while natural and semi-natural grasslands, woodlands, and Mediterranean evergreen vegetation, heathlands and peatlands are less invaded. This pattern is consistent among the three European regions despite their contrasting climates, biogeography, history and socio-economic background. These findings made it possible to extrapolate data from the regions studied to the continental scale and create the first continental map of risk from plant invasions in Europe. The level of plant invasions basically depends on the distribution of different ecosystem types across Europe. High levels of invasion are typical of lowland areas of western and central Europe while low levels are found in northern Europe and mountain regions across the continent. References (for further reading) Chytrý M, Maskell L, Pino J, Pyšek P, Vila M, Font X, Smart S (2008) Habitat invasions by alien plants: a quantitative comparison between Mediterranean, subcontinental and oceanic regions of Europe. Journal of Applied Ecology 45: 448-458. Chytrý M, Jarošík V, Pyšek P, Hájek O, Knollová I, Tichý L, Danihelka J (2008) Separating habitat invasibility by alien plants from the actual level of invasion. Ecology 89: 15411553. Chytrý M, Pyšek P, Wild J, Maskell LC, Pino J, Vilà M (2009) European map of alien plant invasions, based on the quantitative assessment across habitats. Diversity and Distributions 15: 98-107. Contact: Milan Chytrý ([email protected]), Petr Pyšek ([email protected]), Montserrat Vilà ([email protected])
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THE COMBINED EFFECTS OF MAJOR DRIVERS AND PRESSURES ON BIODIVERSITY
Designing Projects for Integrated Research – the ALARM Experience
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JOSEF SETTELE, MARTIN ZOBEL, JOACHIM H. SPANGENBERG, STEFAN KLOTZ, VOLKER HAMMEN & INGOLF KÜHN
It is well established that ongoing global change seriously affects biodiversity and ecosystem functioning. While separate effects of the main drivers of global change, such as climate change, habitat loss, chemical pollution and biological invasions, are increasingly well documented (World Resources Institute, 2005; this atlas, previous chapters), much less is known about their consequences when acting in combination. This may result in flawed conclusions when seeking for sustainable management of ecosystems and resources, as multiple pressures can act in a nonadditive manner on biodiversity. Since different environmental drivers rarely act in isolation, it is important to consider interactive effects. In addition, global change seriously affects biodiversity and ecosystem functioning but little is known about the effects on essential biotic interactions. Until now the effects of global change are mainly investigated at organismic, population or community level, but knowledge about their effects on biotic interactions is scarce. There is increasing evidence that biotic interactions form an indispensable basis for the functioning of ecosystems and the provision of important ecosystem services. Thus, considering the effects of multiple interacting drivers of global change on biotic interactions represents a significant challenge for predicting the future consequences of global change. The threats and challenges mentioned above are all not less important when upscaled to European level. The landscapes of Europe have altered substantially in the last 60 years, under the pressures of the agricultural intensification, industrialization and urbanization. Many traditional land use systems have been lost or diminished, as land uses have polarized either towards extensification and even re-wilding or intensification. Some of these pressures have been simply economic, but there also was a large policy element, especially in agriculture, where agricultural subsidies have driven farm practices. There has been a general lack of coherence of policies; despite its obvious potential to contribute there. Knowledge of the combined effect of drivers of global change, as well as response of biotic interactions to drivers, is of primary importance when designing the appropriate strategy and tactics of biodiversity conservation and sustainable management of ecosystems and landscapes, requiring integrated assessments. Integrated research therefore aims at inter- and transdisciplinarity. The ALARM project (“Assessing LArge-scale environmental Risks for biodiversity with tested Methods”; see Settele et al., this atlas, pp. 38ff.), providing the majority of the contributions in this atlas, is one example of how this may be achieved. The studies of the present chapter are all examples of successful integration in the project. Without any exception they show results of collaborations among researchers from very different disciplines. In the present contribution we want to briefly elaborate, from the subjective view of the ALARM project coordination, how integrated research developed or better evolved within the project and what we experienced as preconditions and limitations of success – without claiming that the experience presented here is the only way to a successful integration. Promoting integrated research in ALARM Straight from the phase of drafting the project proposal, it was clear that ALARM would have to provide an integrated assessment of the state of and pressures on biodiversity, plus delivering policy recommendations to minimise the negative effects of human impacts. Given this broad task, it was an inevitable to involve a broad range of scientific (natural and social science) disciplines, and to combine their insights with trans-disciplinary and practical expertise to come up with meaningful recommendations. Thus a key challenge was to establish an iterative and participative process integrating the knowledge from such diverse sources in a comprehensive manner. Additionally, non-scientific knowledge was no simple addendum to the scientific process, but had to be integrated into the research process, however without compromising the scientific quality of the research. In a first step, ALARM followed the traditional rationalist model of integrated assessment, framing it as a research task to explore the impact of (mostly) disci208
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plinarily generated results for agents from other backgrounds in a broader, interdisciplinary context. The means to achieve this were scientific discourses across disciplinary boundaries, but strictly adherent to the scientific logic and attitude. By the number and diversity of the researchers involved in ALARM they constituted a kind of “scientific public” to be addressed. This way, different levels of analysis and different disciplinary approaches could be integrated, addressing (although not exhaustively covering) the entire complex cause-effect-relation-web, including the interaction of anthropogenic and bio-/geogenic factors. In doing so, ALARM was able to provide significant added value as compared to any smaller or mono-disciplinary project. In a second methodological step, following the discourse based model of assessment, the scientific results were evaluated regarding their suitability for delivering information to decision makers relevant to the current, ongoing decision processes as well as for the long term policy orientations. The method employed consisted of stakeholder discourses within a so-called Consultative Forum and with the advisory board of ALARM. The results of these discourses were fed back to the scientific process, constituting an iterative process for integrated assessment, and delivering results relevant to decision making on both the scientific and the policy level. Conceptual development of the ALARM project To quantify the impacts of the pressures on the state of biodiversity, ALARM used combined risk likelihood and risk consequences scores to identify low, medium or high risks consequent on the respective pressure(s). This approach was used for single as well as multiple pressures. Scenarios have been applied to simulate and explore future environmental threats, to quantify risks subsequent on these, and to analyse the potential results of different plausible responses to the pressures and impacts identified (see Spangenberg et al., this atlas, pp. 204ff.). Results of these different risk assessment approaches have been and shall be communicated to stakeholders as tested methods for broader application – e.g. through the ALARM Risk Assessment Toolkit (see Marion et al., this atlas, pp. 252f.), the scenarios (see Spangenberg et al., this atlas, pp. 10ff.), the development of decision support software (called KerALARM, http://keralarm.c3ed.uvsq.fr) and the integration of driver-specific risk assessments. These methods and instruments are to be used to communicate risks to biodiversity to end users, and indicate policy options to mitigate such risks. Putting the concept into practise: the work package structure of ALARM To achieve the objectives, ALARM consisted of 3 major, methodologically defined work blocks (compare Figure 1) which were defined already in the initial work plan; precise enough to see what would be done, but open enough to be further specified as a result of the integrated assessments amongst participants in the course of the project, to accommodate emerging research questions: ◙ Application of scientific methods for basic research ◙ Method tests and protocol development for applications ◙ Dissemination of results and toolkits The clear but open formulation opened the opportunity for and established the necessity of regular reflection phases and interdisciplinary scientific assessment of mutual impacts, resulting in an iterative development of the research targets over the five years duration of the project. The three working blocks were arranged in relation to seven modules, which also formed the basis of the governance structure shown in Figure 2. The dissemination elements, linked to outreach from the research process, had been clustered in module seven. As participatory and integrative processes are basic for any successful outreach, the feedback into the research process and the training activities were continuous tasks throughout the project duration.
II. RTD / Innovation: Method tests
I. RTD: Scientific Methods Module 4: Pollinator Loss (PL)
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Figure 1. Research activities within ALARM and their components, broken down into Work Packages (= WPs; after Settele et al. 2005)
Governance and coordination structures as precondition of integration As a direct result of the concept development, we had to seek for an adequate formal structure to implement integrated assessment; the one we decided for ALARM is shown in Figure 2. Particularly modules 5) Socioeconomics module; 6) Methods for multiple pressures and geographical information systems; and 7) Methods tests, training and dissemination were foreseen for integrated assessment activities. More details on the modules are described in Settele et al. (2005). The co-ordination structure of ALARM was based on experience gained from managing other major research projects with similarly rigorous requirements. Like integrated assessment the coordination in practise had to be a reflective and iterative participatory process that linked knowledge and action regarding complex science and technology issues.
European Commission
Approval ALARM Co-ordinator (UFZ)
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Figure 2. Governance structure of ALARM (dark brown area: Project Coordination Committee as central management and decision unit within ALARM; SC: Steering Committee; UFZ: Helmholtz Centre for Environmental Research – UFZ; after Settele et al. 2005)
D E S I G N I N G
Regular meetings of the socio-economic research partners and with the Consultative Forum were planned to assure an integrated view of the scenarios, and a comparable structure of socio-economy case studies (respecting fully the bio-geographical and political-institutional differences). As the users of the socio-economic component results are obviously different from those of the natural science research, a parallel information dissemination process was foreseen, under supervision and in close collaboration with the project coordinator. Beyond the usual mechanisms of scientific outreach like publications and conference presentations this included the preparation of background papers and (once relevant results had been obtained) policy briefing sheets for media and decision makers (see Spangenberg et al., this atlas, pp. 204ff.), and the presentation of both results and recommendations in Brussels, in collaboration with the members of the Consultative Forum.
WP 3.2: Multiple pressures across landscapes
WP 7.1: Scientific Coordination (RTD)
For the socio-economic component, a matrix coordination structure was foreseen: on the one hand, one institute was specifically allocated to each module (vertical integration: climate change: SERI Vienna; environmental chemicals: UVSQ Versailles; biological invasions: UAB Barcelona; pollinator loss: SEIT Tallinn). However, given the overlaps of the pressure mechanisms in particular in the socioeconomic sphere, besides focussing on specific pressure mechanisms the four institutes were meant to coordinate their work horizontally to derive comprehensive policy scenarios bringing together the scenarios used in the different modules.
P RO J E C T S
Some major scientific achievements of ALARM The creation of integrative work packages (WPs 3.1, 3.2 and 4.2.; see Figure 1) and modules (modules 5, 6, and 7; see Figure 2) was an appropriate tool to make researchers aware that there is a clear obligation to work across disciplines. Although this process of integration took some time, it was the ultimate goal of ALARM and now (especially after the official funding stage of ALARM is finished) is about to lead to a high number of multi-disciplinary publications often written by very international teams of authors (e.g., Biesmeijer et al. 2006; Dauber et al. 2010; Gallai et al. 2009; Kenis et al. 2009; Moron et al. 2009; Schweiger et al. 2010; Tscheulin et al. 2009; Walther et al., 2009; Westphal et al., 2008; see also examples on the following pages and references therein). References BIESMEIJER JC, ROBERTS SPM, REEMER M, OHLEMÜLLER R, EDWARDS M, PEETERS T, SCHAFFERS AP, POTTS SG, KLEUKERS R, THOMAS CD, SETTELE J, KUNIN WE (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313: 351-354. DAUBER J, BIESMEIJER JC, GABRIEL D, KUNIN WE, LAMBORN E, MEYER B, NIELSEN A, POTTS SG, ROBERTS SPM, SÕBER V, SETTELE J, STEFFAN-DEWENTER I, STOUT JC, TEDER T, TSCHEULIN T, VIVARELLI D, PETANIDOU T (2010) Effects of patch size and density on flower visitation and seed set of wild plants: a pan-European approach. Journal of Ecology 98: 188-196. GALLAI N, SALLES J-M., SETTELE J, VAISSIÈRE BE (2009) Economic valuation of the vulnerability of world agriculture confronted to pollinator decline. Ecological Economics 68: 810-821. KENIS M, AUGER-ROZENBERG MA, ROQUES A, TIMMS L, PÉRÉ C, COCK MJW, SETTELE J, AUGUSTIN S, LOPEZ-VAAMONDE C (2009) Ecological impact of invasive alien insects – a world review. Biological Invasions 11: 21-45. WORLD RESOURCES INSTITUTE (2005) Millenium Ecosystem Assessment. Washington, DC. MOROŃ D, LENDA M, SKORKA P, SZENTGYÖRGYI H, SETTELE J, WOYCIECHOWSKI M (2009) Wild pollinator communities are negatively affected by invasion of alien goldenrods in grassland landscapes. Biological Conservation 142: 1322-1332. SCHWEIGER O, BIESMEIJER JC, BOMMARCO R, HICKLER T, HULME PE, KLOTZ S, KÜHN I, MOORA M, NIELSEN A, OHLEMÜLLER R, PETANIDOU T, POTTS SG, PYŠEK P, STOUT JC, SYKES MT, TSCHEULIN T, VILÀ M, WALTHER G-R, WESTPHAL C, WINTER M, ZOBEL M, SETTELE J (2010) Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biological Reviews. doi: 10.1111/j.1469-185X.2010.00125.x SETTELE J, HAMMEN V, HULME P, KARLSON U, KLOTZ S, KOTARAC M, KUNIN W, MARION G, O’CONNOR M, PETANIDOU T, PETERSON K, POTTS S, PRITCHARD H, PYSEK P, ROUNSEVELL M, SPANGENBERG J, STEFFAN-DEWENTER I, SYKES M, VIGHI M, ZOBEL M, KÜHN I (2005) ALARM – Assessing LArge-scale environmental Risks for biodiversity with tested Methods. Gaia-Ecological Perspectives for Science and Society 14(1): 69-72. TSCHEULIN T, PETANIDOU T, POTTS SG, SETTELE J (2009) The impact of Solanum elaeagnifolium, an invasive plant in the Mediterranean, on the flower visitation and seed set of the native co-flowering species Glaucium flavum. Plant Ecology 205: 77-85. WALTHER GR, ROQUES A, HULME PE, SYKES MT, PYŠEK P, KÜHN I, ZOBEL M, BACHER S, BOTTADUKÁT Z, BUGMANN H, CZÚCZ B, DAUBER J, HICKLER T, JAROSIK V, KENIS M, KLOTZ S, MINCHIN D, MOORA M, NENTWIG W, OTT J, PANOV V, REINEKING B, ROBINET C, SEMENCHENKO V, SOLARZ W, THUILLER W, VILÀ M, VOHLAND K, SETTELE J (2009) Alien species in a warmer world – risks and opportunities. TREE – Trends in Ecology and Evolution 24: 686-693. WESTPHAL C, BOMMARCO R, CARRÉ G, LAMBORN E, MORISON N, PETANIDOU T, POTTS SG, ROBERTS SPM, SZENTGYÖRGYI H, TSCHEULIN T, VAISSIÈRE BE, WOYCIECHOWSKI M, BIESMEIJER JC, KUNIN WE, SETTELE J, STEFFAN-DEWENTER I (2008) Measuring pollinator biodiversity in different habitats and biogeographic regions. Ecological Monographs 78: 653-671.
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Mapping Plant-Invader Integration into Plant-Pollinator Networks
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MONTSERRAT VILÀ, IGNASI BARTOMEUS, ANKE DIETZSCH, THEODORA PETANIDOU, INGOLF STEFFAN-DEWENTER, JANE STOUT & THOMAS TSCHEULIN
Invasions by alien species are causing major problems to biodiversity worldwide and many of the impacts result from disruptions of interspecific interactions. The success of an introduced species often depends largely on how it interacts with the species in the recipient community; including whether it competes with native species, whether there are new natural enemies to cope with and whether they form new mutualistic relationships to fulfil basic life history needs. If an introduced plant species is entomophilous and self-incompatible it will require the service of resident pollinators to transfer pollen among flowers and plants and to ultimately set seed. Pollinators are often observed visiting invasive plants but little is known about the impact of these new interactions on the whole plant-pollinator network (Memmott & Waser 2002, Traveset & Richardson 2006, Bjerknes et al. 2007). Many introduced flowering plants are pollen- and nectar-rich species attracting a wide range of native pollinators. Within ALARM we surveyed areas invaded by 5 different alien plant species (Carpobrotus affine acinaciformis, a
0
Opuntia stricta, Rhododendron ponticum, Impatiens glandulifera and Solanum elaeagnifolium) across Europe to investigate their impact on the plant-pollinator networks. In general, invaders receive a high proportion of visits even at an early stage of invasion, suggesting that they play a central role in plant-pollinator networks. While some invasive plants have a generalist pollination syndrome, attracting a wide variety of pollinator guilds (e.g., Carpobrotus), other invasive plants have more specialized flowers, attracting few guilds such as bumblebees, but in large quantities (e.g., Impatiens). Does the high attraction of pollinators to invasive plants interfere with the pollination of native plant species? Few plant–pollinator interactions are exclusive to the invader, i.e many pollinators visit both the invasive and native plant species. Some alien plants, such as Carpobrotus and Rhododendron can be considered “magnet species” in some sites because their presence increases the visit of pollinators to some native species (Bartomeus et al. 2008). However, in some cases, pollinators might prefer to visit the invader and reduce the number and duration of visits to native plant species. This is the c
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case in sites invaded by Opuntia and Solanum where the total number of visits to the community does not decrease, but most of the visits are to the invader. In this case, the invasive plants lure pollinators away from native plants. Little is known about the impact of alien flowers on native pollinators. What are the consequences of adding new nectar and pollen resources to the diet of the pollinators apart from changes in foraging behaviour? The consequences for pollinator population of foraging on a new species are very difficult to study because in general we know little about pollinator life history cycles. In invaded areas the diversity of pollinator species may not decrease, but relative abundances of pollinators might change. For example, the presence of bumblebees increases in sites invaded by Impatiens. Similarly, sites invaded by Rhododendron support a higher number of bumblebee colonies. Overall, invasive plants are very well integrated in the recipient communities and attract a wide range of pollinators, some in large numbers, but the consequences for the native community is very much dependent on the cond
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text. Some invaders attract pollinators to the entire plant community, but others compete for them, reducing visitation to native plants and probably reducing their fitness. References BARTOMEUS I, VILÀ M, SANTAMARÍA L (2008) Contrasting effects of invasive plants in plantpollinator networks. Oecologia 155: 761-770. BJERKNES AL, TOTLAND Ø, HEGLAND SJ, NIELSEN A (2007) Do alien plantinvasions really affect pollination success in native plant species? Biological Conservation 138: 1-12. BORATYNSKI A, BROWICZ K, ZIELINSKI J (1992) Chorology of Trees and Shrubs in Greece. Polish Academy of Sciences, Sorus, Poznan/ Kornik. MEMMOTT J, WASER NM (2002) Integration of alien plants into a native flower-pollinator visitation web. Proceedings of the Royal Society of London Series B-Biological Sciences 269: 2395-2399. PRESTON CD, PEARMAN DA, DINES TD (2002) New atlas of the British flora. Oxford University Press. SANZ-ELORZA M, DANA ED, SOBRINO E (2004) Atlas de las plantas alóctonas invasoras de España. Dirección General para la Biodiversidad, Madrid. TRAVESET A, RICHARDSON DM (2006) Biological invasions as disruptors of plant reproductive mutualisms. Trends in Ecology and Evolution 21: 208-216. 0
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!! !! ! !!! ! !!!! ! !!! !!!! !!!!! !!! !! !! !! ! ! ! ! ! ! !! !! !! ! !! ! ! ! !! ! ! ! !!! !!!!! ! !!! ! ! ! ! !! !! ! ! ! ! !!! ! !! ! !! !! !!!! ! !! !! !!! !!! !! !! !! !! ! !!! ! ! ! !!! !! !!!! !!! ! ! ! ! ! !! ! ! !! ! ! ! !! !! ! !! !!! ! !!! !!! !! !!! !! !!! !!! !! ! !! !! !! ! ! ! ! !! !! !! !! !!! !! !!! !! !! !!! !! !! ! !! ! ! ! !! !! ! !! !! ! ! !!!!! !! !! !! !! !!! ! ! !! ! ! !! !! !!!! !!!! !! !! !! ! ! ! ! ! ! ! !! !! ! !! !! ! ! ! !! !!!!! !! !! ! ! !! ! ! ! !! ! !! ! ! ! ! ! !! ! ! !! !! !! !!! !! !! ! ! ! !! !! !! ! !! ! ! ! ! ! !! !!!!!!! ! ! !! !! ! !! ! ! ! ! ! ! ! !!! !! !! ! !! ! !! !! ! !! ! !! ! ! ! !! ! !! ! !! ! !! ! !! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! !!!! ! ! !!! ! ! ! !! ! ! !! ! !! ! ! ! !! !! ! !! ! ! !! ! !! ! ! !! ! !! ! ! !! ! ! !! ! ! ! ! !! ! !! !! ! ! !! ! ! ! !! ! ! !! ! ! ! !! ! ! ! !! !! ! ! !! ! !!! ! ! ! ! ! !! !! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! !!! ! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! !!! ! ! ! ! ! ! !! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! !!!!! !! !!!! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !!!! !!!! !!!! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! !!!! !! ! !! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! !!!!!! ! !!! !!! ! ! ! ! ! ! !! !!! !! ! ! ! !! ! ! ! ! ! ! ! ! ! !!!!!!! !!! !!!! !!!! !!! !! !!!!! !!! !!!!!! !!!!!! ! !!!! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!!! !! !! !!!!! ! !!! !!!!! !!! !!!! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!!!! ! ! ! ! ! !! !!!!! !!!! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !!!! !!! !!! !!!! !!!! ! !! ! ! !! !!! !!! !!!! !!!! ! !!! ! !!! !!!! !! !!! !!! !!!! !! !!! !!! !!!! !!! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !!! !!! ! !! !!! !!! !!! !!! ! !!! !! !!! ! !!! !!! !! !! ! ! ! !! !! !!! !!! !! !! ! !! !!! ! !!! !! !! !! ! !! !! !! !!! !! !! !! !!! !! ! ! ! !! !! ! !! !! !!! !! !! ! !! !! !! ! !! !! ! ! !! !!! !! !! !! !! ! ! !! ! ! ! !! !! !! !! !! !! !! !! !! ! ! !! ! !! ! ! ! ! !! !! !! !! !! ! ! !! ! ! ! !! ! ! !! !! ! !! ! !! !! !! ! !! ! ! ! ! ! ! !! !! ! ! ! !! ! !! !! ! !! ! ! ! !! !! ! ! ! ! ! !! ! !! !! ! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! !! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !!! !!! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !!!! !!! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! !!! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! !! ! !! !!!!!!!!!!!!!!!!!!!!! ! !!! !!! !!!!!!!!!!!! !!!!!!!! ! ! ! !! ! ! !!!!!! !! !!!!!!! !!! !!! ! !! !! ! !!!! ! ! !! !! !!!!!!!!!! !! !! ! ! ! ! ! ! ! ! ! !!!!! !!! !!! !!! !!! !! !!! !!! !! !!!! !!!!! !! !!!! !!! !! ! !! !!!!! ! !! !!!! ! ! ! ! ! ! ! ! ! ! !! !!! !!!!! ! !!! !!! !!!!! !!! !! !! !! !!!! !!! !!!! !!!! !!!! !!!! !!!! ! !!! ! ! !! ! !!! ! ! !!! !!! !! !!!! ! ! !! !! !!! !! ! !! !!! !!! !!! !!! !! !! !! !!! ! !! !!! !!! !!! !! !!! !! !! !!! !! !!! ! !!! !! !! !! !!! !!! !! ! !! ! !! !!! !! ! ! ! ! ! !
Rhododendron ponticum
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Solanum elaeagnifolium
Carpobrotus aff. acinaciformis
Figure 1. The location of the 5 cases studied is shown on the map of Europe. Small country maps show the degree of invasion for each invasive plant in the European country where the study was carried out. a) and b) distribution map of Spain according to Sanz-Elorza et al. (2004); c) distribution map of Ireland according to Preston et al. 2002; d) distribution map of Germany according to www.floraweb.de; e) distribution map of Greece per prefecture based on authors’ research and historical data adapted from Boratynski et al. (1992).
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Photo: T. Petanidou.
Photo: M. Vilà.
Photo: I. Bartomeus.
Photo: A. Dietzsch.
Photo: I. Bartomeus.
Solanum elaeagnifolium Cav. (Solanaceae)
Opuntia stricta (Haw.) Haw. (Cactaceae)
Carpobrotus edulis (L.) N.E.Br. (Aizoaceae)
Rhododendron ponticum L. (Ericaceae)
Impatiens glandulifera Royle (Balsaminaceae)
Common name. Silverleaf Nightshade
Common name. Prickly-pear cactus
Common name. Iceplant
Common name. Rhododendron
Common name. Himalayan balsam
Plant with woody lower stems and extensive root system. Deeply lobed, star-shaped bright blue to purple (and rarely white) corolla with long yellow anthers. Flowering from May to September. Fruits are berries containing up to 150 seeds dispersed by water, wind, machinery, agricultural produce and livestock.
Tall cactus with succulent flat, oval and segmented stems. Plants produce large regular yellow flowers and purple fig shaped fruits. Flowering from June to July. Seeds are dispersed by birds, feral pigs and lizards that feed upon fruits.
Succulent plant forming large mats. In the Mediterranean basin, C. edulis hybridizes with C. acinaciformis forming a hybrid complex known as C. affine acinaciformis. Flowering from March to May. The fleshy, indehiscent fig-like fruits are eaten by wild mammals.
Evergreen large multistemmed shrub with pink-purple flowers held in dense inflorescences. Main flowering season from May to July. Each flower produces several hundred tiny, wind-dispersed seeds in woody capsules.
Tall annual plant with pale pink-purple zygomorphic flowers and green fruits. Flowering from June to October. The seeds are ejected from the fruits via ballochory.
Native range. South and Central America. Introduced range in Europe. Mediterranean countries. Invaded habitats (EUNIS code). Arable land and market gardens (I1), Trampled areas (H5.6), Dry grasslands (E1), Mesic grasslands (E2), Anthropogenic forb-rich habitats (E5.6). Introduction pathway. Imported fodder, seeds, soil and fertilizer. Impacts. Competes with native plant species, interferes with crop production, toxic to livestock. Description summarized from DAISIE (http://www.europe-aliens.org).
Native range. tropical America from Mexico to Colombia Introduced range in Europe. Mediterranean countries and Macaronesian islands. Invaded habitats (EUNIS code). Coastal dune and sand habitats (B1), Rock cliffs, ledges and shores, including the supralittoral (B3), Spiny Mediterranean heaths (F7) , ThermoAtlantic xerophytic habitats (F8), Coniferous woodland (G3), Waste deposits (J6). Introduction pathway. Ornamental and as fencing. Impacts. The spines can cause injuries; interferes with livestock grazing. Invaded woodlands are misperceived as typical Mediterranean landscapes. Description summarized from DAISIE (http://www.europe-aliens.org).
Native range. Cape region of South Africa Introduced range in Europe. Mediterranean countries and Macaronesian islands. Invaded habitats (EUNIS code). Coastal dune and sand habitats (B1), Coastal shingle habitats (B2), Rock cliffs, ledges and shores, including the supralittoral (B3), Inland cliffs, rock pavements and outcrops (H3), Miscellaneous inland habitats with very sparse or no vegetation (H5), Littoral zone of inland surface waterbodies (C3), Garrigue (F6), Constructed, industrial and other artificial habitats (J). Introduction pathway. Ornamental and landscaping Impacts. Competes with native plant species. Increases soil N and organic C and reduces soil pH. In dune habitats it hinders the disturbance regime.
Native range. Disjunct with R. ponticum ssp. baeticum in SW Spain and S Portugal; ssp. ponticum is found in Turkey, Lebanon, Bulgaria and the Caucasus. Introduced range in Europe. UK, Ireland, Belgium, France, Netherlands, Germany and Austria. Invaded habitats (EUNIS code). Mixed deciduous forest (G1), temperate heaths (F4), raised and blanket bogs (D1). Introduction pathway. Ornamental Impacts. Competes with native plant species, constributes to litter accumulation, interferes with refuges for gamebirds; tissues contain grayanotoxins, toxic to humans and animals.
Native range. Himalayas Introduced range in Europe. Temperate countries Invaded habitats (EUNIS code). Riverine and fen scrubs (F9), transport networks and other constructed hardsurfaced areas (J4), highly artificial man-made waters and associated structures (J5). Introduction pathway. Ornamental and planted by bee keepers for nectar production. Impacts. Competes with native plant species, can promote riverbank erosion when dominant. Description summarized from DAISIE (http://www.europe-aliens.org).
Description summarized from DAISIE (http://www.europe-aliens.org).
Description summarized from DAISIE (http://www.europe-aliens.org).
Solanum elaeagnifolium invading cultivated fields west of the city of Thessaloniki, Northern Greece. Photo: T. Petanidou.
Opuntia stricta invading abandoned vineyards in Catalonia-Spain. Photo: M. Vilà.
Carpobrotus affine acinaciformis invading a coastal shrubland in Catalonia-Spain. Photo: I. Bartomeus.
Rhododendron ponticum invading a mixed decidous open forest in Ireland. Photo: A. Dietzsch.
Impatiens glandulifera invading riparian areas in Central Germany. Photo: I. Bartomeus.
Plant-pollinator networks in invaded communities. Species names are omitted and represented by circles (left: insects, right: plants). Circles in red: alien species, green: native plants, yellow: Hymenoptera, blue: Diptera, violet: Coleoptera, and orange: Lepidoptera. The amplitudes of the lines are proportional to the number of visits.
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Palms (and other Evergreen Broad-Leaved Species) Conquer the North
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GIAN-RETO WALTHER & SILJE BERGER
There is hardly any better plant species than palms to symbolise warm climatic conditions. Palms reach their greatest proliferation in the tropics and are much less prominent and diverse in regions with a temperate climate. In mainland Europe, there is only one native palm species, which reaches its northernmost outpost at the Gulf of Genoa (Italy). However, in recent years, there has been increased evidence that the hardiest (to cold) palm species are occurring beyond the usual latitudinal range limit of palms (Walther et al. 2007, Stähler & Spanner 2008, see also Francko 2003). In Europe, the introduced palm species Trachycarpus fortunei is the most widely cultivated species at and beyond the latitudinal palm range margin (Figure 1). The mild winters of the last few decades have not only allowed this species to survive outdoors as ornamental plants in gardens and parks in Europe, but it has also started to spread from cultivation into surrounding forests at the southern foot of the Alps in southern Switzerland and northern Italy (Walther et al. 2007). A new northerly palm population Adult Trachycarpus fortunei palms resist temperatures as low as -14 °C (Sakai & Larcher 1987). The saplings demand even more favourable environmental conditions to grow up, facing the competition of the native vegetation. Findings from the native range in China suggest the combination of the average temperature of the coldest
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Figure 1. Top: Latitudinal limits of natural palm distribution with the native range of Trachycarpus fortunei in China, and a compilation of sites where T. fortunei occurs outside its native range (from Walther et al. 2007, modified). Bottom: left) a compilation of sites where T. fortunei occurs in Europe (for status see legend); middle) the simulated range for T. fortunei in Europe based on 1991-2000 climate data using the STASH bioclimatic model (dark green colours denote better conditions for establishment and growth than lighter shades, for details see Walther et al. 2007); and right) the results of a germination experiment using the ALARM field site network (see Hammen et al., this atlas, pp. 42ff.; Biesmeijer et al., this atlas, pp. 46f.).
January mean temperature (ºC)
4 3 2 1 0 -1 -2 1900
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Figure 2. Annual values for mean monthly temperature in January from 1864 to 2008 (Begert et al. 2005; Meteorological Station: Lugano; 46 00 N / 08 58 E; 273 m a.s.l.; data updated from SMA MeteoSwiss: www. meteoschweiz.ch/web/de/klima/klimaentwicklung/homogene_reihen.html). The smoothed values for 10-year averages (black line) highlight periods with unfavourable (red) and favourable (green) periods.
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Figure 3. Wild palm population in a lowland forest near Locarno/Switzerland. Photo: G.-R. Walther.
Prunus laurocerasus
Laurus nobilis
Trachycarpus fortunei
Overlap of the potenctial ranges of all four evergreen broad-leaved species
being even hardier to cold than the palm. Considering the substantial overlap of potentially suitable areas for these evergreen broad-leaved species in Europe, one might assume an increase in the proportion of evergreen broad-leaved species in deciduous broad-leaved forests in the near future as has happened in the past at the southern foot of the Alps (Figure 5 and 6). Thus, climate change provides opportunities for new species to survive in areas of Europe, where this was not possible in the past. Evergreen broad-leaved species are particularly favoured by milder winter temperatures, as they profit from positive net photosynthesis in periods with favourable climatic conditions even during the winter (Berger et al. 2007).
month and the cumulative summer warmth as important climatic factors limiting its native distribution towards colder areas (Walther et al. 2007). In the introduced range, the average temperature of the coldest month suggests a critical threshold for the species to establish (Figure 2). According to measured local meteorological data, periods with temperatures above the threshold value of +2.2 °C average January temperature have obviously increased in length and frequency in the course of the 20th century. The smoothed 10-years averages reveal that at the beginning of the measurements unfavourable periods dominated despite some isolated occasional shortterm events with favourable conditions. But since the 1950s, the former dominance of unfavourable conditions has developed into frequent short-term favourable events and has finally changed completely to continuous
favourable conditions since the mid 1970s (Figure 2). In agreement with this climatic amelioration, the palms have been able to establish fertile populations in lowland forests south of the Alps in the course of the last few decades (Figure 3), which occur ca. 300 km north-northeast beyond the northernmost wild palm limit as recognized up to now. Based on climatic threshold parameters from the native range, the potential range of Trachycarpus fortunei may be derived for Europe (Figures 1 and 4). A considerable area of Europe is supposed to become increasingly suitable for Trachycarpus fortunei and thus, suggests new regions where this palm species might be able to survive in the wild in the near future. A germination experiment with palm seeds in Europe using the ALARM field site network (Hammen et al., this atlas, pp. 42ff.) revealed
many areas where the seeds were able to germinate (see bottom right chart in Figure 1). However, the climatic conditions in the areas where the palm was able to germinate did not always allow this species to achieve a more mature stage and to establish. This is reflected in the decline of the seedlings after germination. From species to communities The palm Trachycarpus fortunei is just one representative of evergreen broadleaved woody species present in Europe. Northward expansion of the native Ilex aquifolium has already been noted in Central Europe and southern Scandinavia (see Walther et al., this atlas, pp. 74f.). There are more and larger potentially suitable areas in Europe (Figure 4), for other evergreen broad-leaved species of southern European origin or introduced nonEuropean species, especially those
Number of days with frost
Figure 4. Potential single species’ ranges and overlap of all four evergreen broad-leaved species to derive the potential spatial extent and diversity of an assemblage of evergreen broad-leaved species in Europe (from Berger et al. 2007, modified).
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Figure 6. Decrease in the number of days with frost per year in the 20th century and increase in the number of exotic evergreen broad-leaved (evbl) species in forests in southern Switzerland (from Walther et al. 2002, modified).
References
Figure 5. A community of native and introduced evergreen broad-leaved species in the understorey of a deciduous forest at the southern foot of the Alps. Photo: G.-R.Walther.
PA L M S
( A N D
O T HER
EV ERG R EEN
B R OA D - L E AV E D
BEGERT M, SCHLEGEL T, KIRCHHOFER W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. International Journal of Climatology 25: 65-80. BERGER S, SÖHLKE G, WALTHER G-R, POTT R (2007) Bioclimatic limits and range shifts of cold-hardy evergreen broad-leaved species at their northern distributional limit in Europe. Phytocoenologia 37: 523-539. FRANCKO DA (2003) Palms won’t grow here and other myths. Timber Press, Portland, 308 pp. SAKAI A, LARCHER W (1987) Frost survival of Plants. Ecological Studies 62. Springer, Berlin, 321 pp. STÄHLER M, SPANNER TW (2008) Winterharte Palmen. Medemia, Berlin, 320 pp. WALTHER G-R, POST E, CONVEY P, MENZEL A, PARMESAN C, BEEBEE TJC, FROMENTIN J-M, HOEGH-GULDBERG O, BAIRLEIN F (2002) Ecological responses to recent climate change. Nature 416: 389-395. WALTHER G-R, GRITTI ES, BERGER S, HICKLER T, TANG Z, SYKES MT (2007) Palms tracking climate change. Global Ecology and Biogeography 16: 801-809.
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Number of exotic evbl spp
Ilex aquifolium
Modelling the Potential Expansion as a Result of Global Warming of the Invasive Pinewood Nematode in China
,
LILIN ZHAO, JIANGHUA SUN, ALAIN ROQUES & CHRISTELLE ROBINET
The pinewood nematode (PWN, Figure 1), Bursaphelenchus xylophilus (Steiner and Buhrer) Nickle (Tylenchida: Aphelenchoididae), which causes pine wilt disease, is presumed to originate from North America. The disease was identified for the first time in the United States in Columbia,
massive control efforts, the nematode then spread throughout most of Japan (Togashi & Jikumaru 2007). During the 1980s, PWN invaded China, Taiwan and South Korea, where it has subsequently caused severe damage to pine forests. In 1999, PWN was accidentally introduced into Europe, in the area of
the most important vector of PWN in Japan and China (Yang et al. 2003). PWN passes through two different modes during its lifecycle, a propagative mode and a dispersal mode. The propagative stage (called Jn) causes mortality of pine trees, thus providing oviposition resources for Monochamus (Linit
(“dauer” or JIV) which enters the tracheae of the beetle to be carried to other pine trees (Yang et al. 2003). Host association In its native range, PWN was observed on 27 species of Pinus but also on a
Figure 1. Adult of Bursaphelenchus xylophilus. Photo: L. Zhao.
Figure 2. Adults of the vector long-horned beetle, Monochamus alternatus Hope. Photo: L. Zhao.
Missouri in 1979. Since then, PWN has been found in 36 US states, including all the Great Plains states except North Dakota. This widespread distribution suggests that PWN is native to the United States. Around the beginning of the 20th century, PWN was transported with timber from America to the southern Japanese island of Kyushu. Despite
1988). As an infested pine dies, the availability and quality of both food and moisture decline, and PWN 3rd-stage juveniles (called JIII), adapted for dispersal, are produced. These JIII aggregate in pupal chambers formed in the outer xylem by mature larvae of Monochamus (Zhao et al. 2007). In synchrony with the emergence of adult beetles, PWN (JIII) moult to another larval stage
Setubal, Portugal where it is confined until recently (Mota et al. 1999). Since 2008, it has spread all over Portugal and entered Spain. PWN biology The nematodes are dispersed to uninfested host pines by long-horned beetles of the genus Monochamus (Linit 1988). M. alternatus Hope (Figure 2) is
! A 1982-1989
few other conifers in the genera Abies, Pseudotsuga, Cedrus, Larix and Picea (Yang et al. 2003). Apparently, the dead wood of all pine species can act as a substrate for PWN development. However, only a few pine species seem susceptible to be killed as mature pines by pine wilt disease. Such susceptible trees include the Far Eastern species Pinus bungeana, P. densiflora, P. luchuensis, P. massoniana
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Figure 3. Development of PWN invasion in China between 1982 and 2005.
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Figure 4. Dying pines due to pinewood nematode. Photo: L. Zhao.
9
and P. thunbergii in their native habitats, and the European species P. nigra and P. sylvestris planted in North America, and P. pinaster planted in China. In Europe, P. pinaster and P. nigra would be the species most at risk in southern and central areas whilst P. sylvestris would be threatened in northern areas. Impact No cure exists for pine wilt disease once a susceptible tree becomes infested. Upon PWN infection, the transmission of water within the plant is hindered, the leaves turn yellow and soon the whole tree wilts and dies (Figure 4). It takes only 2-3 months from the infection to the death of the plant. PWN infection has already caused the death of over 16 million of pines in China, resulting in direct economic losses of approximately US$ 300 million, and indirect economic losses exceeding US$ 3 billion (Yang et al. 2003). Dispersal The timber trade is the most probable pathway for international transport of PWN. B. xylophilus has been intercepted on a number of occasions on sawn wood, logs and wood chips imported from North America. PWN introduction is facilitated when imported together with long-horned beetle vectors which may carry the nematodes to coniferous trees. At present B. xylophilus has not arrived in the main forest area of China, but due to the existence of Monochamus alternatus, the highly efficient vector of B. xylophilus, the suitable climate and lack of natural enemies, B. xylophilus could very easily spread in China and cause disastrous damage to the ecological system in China. Modelling future expansion At a local scale, PWN dispersal is likely composed of short-distance dispersal related to insect vectors and long-distance events mediated by man. In China, two distinct primary introductions seem to have occurred during the 1980s (Figure 3), in the areas of Nanjing (1982) and Hong-Kong (1988) respectively. In the Nanjing area, PWN first spread naturally at a constant speed of 7.5 km per year from 1982 to 1987. Then, human-mediated dispersal (log transportation, wooden crates, railway construction) disseminated it all over the region (Robinet et al. 2009). Using the climate thresholds defining suitable areas for PWN survival (mean of July temperatures > 21.3 °C and mean of January temperatures > -10 °C; Ma et al. 2006), a model was developed to forecast the suitable area for PWN under the current climatic conditions and with different hypothesis for further global warming (Figure 5). The major part of China, except the Himalayan range, appears suitable
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Figure 5. Modelling of suitable locations for PWN (red) compared to areas already infested (green dots; compare Figure 3) under different temperature conditions.
for PWN establishment with an increase of +3 °C in the mean temperature (Figure 5). References LINIT MJ (1988) Nematode-vector relationships in the pine wilt disease system. Journal of Nematology 20: 227-235. MA, R-Y, HAO, S-G, KONG, W-N, SUN, J-H, KANG, L (2006) Cold hardiness as a factor for assessing the potential distribution of the Japanese Pine Sawyer Monochamus
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alternatus (Coleoptera: Cerambycidae). Annals of Forest Science 63: 1-8. MOTA MM, BRAASCH H, BRAVO MA, PENAS AC, BURGERMEISTER W, METGE K, SOUSA E (1999) First report of Bursaphelenchus xylophilus in Portugal and in Europe. Nematology 1: 727-734. ROBINET C, ROQUES A, PAN HY, FANG GF, YE JR, ZHANG YZ, SUN JH (2009) Role of Human- Mediated Dispersal in the Spread of the Pinewood Nematode in China. PLoS One 4(2): e4646. doi: 10.1371/journal. pone.0004646
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TOGASHI K, JIKUMARU S (2007) Evolutionary change in a pine wilt system following the invasion of Japan by the pinewood nematode, Bursaphelenchus xylophilus. Ecology Research 22: 862-868. YANG BJ, PAN HY, TANG J, WANG YY, WANG LF, WANG Q (2003) In: Bursaphelenchus xylophilus. Chinese Forestry Press, Beijing. ZHAO LL, WEI W, KANG L, SUN JH (2007) Chemotaxis of the pinewood nematode Bursaphelenchus xylophilus, to volatiles associated with host pine, Pinus massoniana, and its vector Monochamus alternatus. Journal of Chemical Ecology 33: 1207-1216.
I N VA S I V E
P I N E WO O D …
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Will Interacting Species Still Co-Occur in the Future?
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OLIVER SCHWEIGER, INGOLF KÜHN, OTAKAR KUDRNA, STEFAN KLOTZ & JOSEF SETTELE
Introduction
Interactions such as pollination of crops and wild plants, predation in general and on pest organisms in particular, parasitism or herbivory significantly determine the structure and functioning of local communities and whole ecosystems. Effective interac-
Species interactions are one of the fundamental characteristics of nature and are crucial for the functioning of ecosystems and the provision of ecosystem services to the benefit of humanity.
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Figure 1. Current (1971-2000) distribution of Polygonum bistorta (black circles) and downscaled projection of current distribution (green).
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Figure 2. Current (1971-2000) distribution of Boloria titania (black circles) and modelled niche spaces of B. titania and Polygonum bistorta. Green, niche space of P. bistorta; yellow, niche space of B. titania; brown, overlap of both (projected realised niche space of B. titania).
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tion depends on the synchronised occurrence of the relevant species both in space and in time. This synchronised occurrence is questionable in the course of climate change (e.g., Kudo et al. 2008). Recent climate change has already affected the performance of single species (e.g., Hickling et al. 2006) and future changes are projected to have even more severe impacts (e.g., Thuiller et al. 2005). Besides its direct effects on single species, which are increasingly well documented, climate change can be expected to affect biotic interactions since individual species will react differently depending upon their species-specific ecology, physiology and behaviour and their relation to different sets of climatic variables (Schweiger et al. 2008). These species specific responses to various components of climate change have the potential of causing temporal and/or spatial changes in the composition of species assemblages and therewith changes in, or even disruptions of, species interactions (Traveset & Richardson 2006). A number of studies already reported effects of temporal mismatching of interacting species at single points in space (e.g., Martin 2007), but there still is a substantial lack of investigation whether the ranges of interacting species may become spatially mismatched. Ecological niche modelling To explore potential effects of climate change on biotic interactions we exemplarily analyzed trophic interactions between the monophagous butterfly Boloria titania and its larval host plant Polygonum bistorta. P. bistorta distributed across Europe except the northern parts of Scandinavia and central parts of Greece and the Iberian Peninsula (Figure 1) and it occurs in humid or wet meadows. B. titania is a mountain species of Central Europe, the Baltic states and southern Finland (Figure 2) occurring in mires and humid flowery meadows preferably in mountain valleys. Based on their current European distribution, separate ecological niche models were developed for the butterfly and its host plant. Therefore, we related the distributional data to relevant environmental variables including climate parameters and their variations, land cover and – for the host plant – soil conditions. We calculated separate models for both species which were
then projected to three different global change scenarios for Europe in 2080, based on storylines developed within the EU funded project ALARM. These scenarios cover a broad range of potential developments in demography, socio-economics and technology during the 21st century and range from moderate change (SEDG; mean expected temperature increase in Europe until 2080 is 2.4 °C) to intermediate change (BAMBU; 3.1 °C) to maximum change (GRAS; 4.1 °C; see Spangenberg et al., this atlas, pp. 10ff.; Fronzek et al., this atlas, pp. 68ff.). Since the ability to colonise new potentially suitable areas in the course of climate change highly depends on a species’ dispersal ability but detailed dispersal distances are not available for both species, we made two extreme assumptions: unlimited dispersal such that the entire projected niche space denotes the actual future distribution; and no dispersal in which the future distribution results from the overlap between current and future niche space. A potential spatial mismatch of both species’ ranges was assessed by the overlap of both future niche spaces. Current spatial mismatch When both the modelled range of the host plant and the potentially suitable areas of the butterfly where overlaid, it was evident that currently there is substantial mismatch and that the butterfly is limited by both host plant availability and climate or land use (Figure 3). There are large suitable areas mainly in Scandinavia but also in southern Europe where the butterfly potentially could live but is limited by its host plant. On the other hand, there are large parts in temperate Europe where the host plant is present but the butterfly is not. Here, limitation by climate appears as a likely explanation but other biotic interactions, such as predation, competition or parasitism might contribute, too. Increased future spatial mismatch Overlaying the projected future niche spaces of the butterfly and its host plant under the assumption of unlimited dispersal of both reveals that the observed mismatch between both niche spaces under current conditions will be more pronounced in the future, especially in the areas of current co-
occurrence along the Alps and the Baltic states (Figure 4a, c). It was also evident that larger areas in Scandinavia will provide suitable conditions for cooccurrence leading to a general increase in the niche space of the butterfly. However, these new suitable areas are distant and have to be reached by both butterfly and host plant. In case of absolute dispersal limitation of P. bistorta, the mismatch between both niche spaces was extremely high leading to a substantial decrease of future suitable area for the butterfly (Figure 4b, d). General consequences The current study shows that trophic interactions and dispersal limitation matter at continental scales and that they can play a significant role in shaping the dynamic responses of species to climate change. The analysed trophic interaction is rather basic, but the principle mechanism of changes in spatial matching of interacting species in the course of climate change can be expanded to other biotic interactions. For instance, the distribution of the butterfly might be limited, in addition to climate and host plant availability, by competitors, predators, parasites or diseases. Our study shows that co-occurring, interacting species do not necessarily react in a similar manner to global change. As long as the ranges of interacting species do not perfectly match, they will depend on different sets of climatic and other biotic conditions and thus respond individually. These individual responses might result in more severe or even converse consequences of climate change than simple climate envelope models would suggest. Pronounced negative effects can be a consequence of increased mismatching of beneficial interactions, such as trophic dependencies as was the case in our study, or better matching of adverse interactions (e.g., limiting competitors or parasitoids). Nevertheless, also positive effects could be a consequence of future better matching of beneficial interactions (e.g., pollination) or more mismatching of adverse interactions (e.g., release of a plant from its herbivore). The current study indicates that climate change bears the potential that currently interacting species can be spatially disconnected and consequently the structure of local biotic interactions may change severely.
thesis, and reproduction in deciduous forest understory plants. Ecology 89: 321-331. MARTIN TE (2007) Climate correlates of 20 years of trophic changes in a high-elevation riparian system. Ecology 88: 367-380. SCHWEIGER O, SETTELE J, KUDRNA O, KLOTZ S, KÜHN I (2008) Climate change can cause spatial mismatch of trophically interacting species. Ecology 89: 3472-3479. THUILLER W, LAVOREL S, ARAUJO MB, SYKES MT, PRENTICE IC (2005) Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences of the United States of America 102: 8245-8250. TRAVESET A, RICHARDSON DM (2006) Biological invasions as disruptors of plant reproductive mutualisms. Trends in Ecology and Evolution 21: 208-216.
Sources All maps were taken from Schweiger O, Settele J, Kudrna O, Klotz S, Kühn I (2008) Climate change can cause spatial mismatch of trophically interacting species. Ecology. 89: 3472-3479. Copyright 2008 by Ecological Society of America. Reproduced with permission of Ecological Society of America in the format Tradebook via Copyright Clearance center.
Figure 3. Map of current (1971-2000) niche spaces of the butterfly Boloria titania and its host plant. Green: niche space of P. bistorta; yellow: niche space of B. titania; brown: overlap of both (realised niche space of B. titania)
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References HICKLING R, ROY DB, HILL JK, FOX R, THOMAS CD (2006) The distributions of a wide range of taxonomic groups are expanding polewards. Global Change Biology 12: 450-455. KUDO G, IDA TY, TANI T (2008) Linkages between phenology, pollination, photosyn-
Figure 4. Match and mismatch of projected niche spaces of Polygonum bistorta and Boloria titania for (a and b) moderate and (c and d) maximum global-change scenarios for 2080 under the assumption of unlimited (a, c) and no (b, d) dispersal of P. bistorta. Green: niche space of P. bistorta; yellow: niche space of B. titania; brown: overlap of both (potentially realised niche space of B. titania).
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217
How to Evaluate Effects of Pesticides in Terrestrial Ecosystems
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STEFANIA BARMAZ, CLAIRE BRITTAIN, SERENELLA SALA, SIMON G. POTTS & MARCO VIGHI
The Millennium Ecosystem Assessment highlights the fact that the assessment of impacts on biodiversity is often rather descriptive and based on examples or snapshot information. A further decline in biodiversity of 63-70 % is projected by 2050 (CBN 2007), and agroecosystems are considered to be under substantial pressure from pesticides. Although the decline in biodiversity is difficult to attribute to individual pesticides, there is sufficient evidence to indicate that their use has negative impacts on biodiversity. Therefore, an integrated ecotoxicological risk methodology is essential for assessing risks to biodiversity due to pesticide use, and must consider the complexity of agricultural scenarios in order to deliver a meaningful site-specific assessment. The research activity presented here has the aim of developing an integrated terrestrial risk assessment for biodiversity by mapping potential risk at the
a
local scale, as a basis for a larger scale assessment. The methodology takes into account both hypogean (below-ground) and epigean (ground surface) elements of the terrestrial ecosystem, focusing on three different targets of impact: earthworms, pollinators and birds. The Meolo watershed, in the North-East of Italy, was chosen as a pilot area of study, being representative of intensive agricultural areas found in southern Europe. Meolo as a model system The study was conducted in the basin of the River Meolo (North-Eastern Italy, Figure 1), a small resurgence river, 17 km in length, and originating from the confluence of single streams deriving from the same groundwater outcrop. The river has a flow rate of approximately 3 m3s-1. The catchment covers an area of 2,878 hectares and the altitude ranges between 3 and 23 m a.s.l. The basin is hydraulically isolated, there-
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proportion of wheat, barley, soyabean, sugar beet, and vineyards has increased over recent years (Figure 3). The land use is highly heterogeneous and includes small fields, hedgerows, urban, and industrial developments (Bonzini 2006).
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Figure 1. Position of the pilot area (Meolo watershed).
fore, all surface water runoff rapidly flows into the river. The basin is characterized by 86 % arable land cover, of which nearly all is cultivated (95 %). The main crop is maize; however, the
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Figure 2. Important pollinator groups in the Meolo area: (a) A bumblebee (Bombus sp.) foraging, (b) Honeybee Apis mellifera, (c) Swallowtail Papilio machaon. Photos: C. Brittain.
PEC/Fenithrothion (mg/kg) 0-0.20 0.20-0.23 0.23-0.26 0.26-0.29 0.29-0.30
Land use urbanised area not cultivated wood hedge corn soy wheat beetroot Meolo River street vineyards
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Figure 4. Example of PEC related to fenitrothion application on vineyards, in mg/kg soil.
RISK
Exposure assessment Exposure patterns to pesticides are different in aquatic and terrestrial ecosystems. In most official European procedures, developed to fulfil the requirements of chemical regulations (e.g., EC 2003, EC 2002, EPPO 2003), risk assessment is performed on more or less standardised scenarios, where the territory, at different scales (local, regional, continental), is described without taking into account the spatial variability of parameters. The complexity of the terrestrial environment requires that differences in the behaviour and biology of target organisms, as well as different emission routes and environmental behaviour of pesticides, are taken into account to assess exposure. In this methodology three groups of organisms are selected as being representative of relevant terrestrial ecosystems: hypogean (earthworms) and epigean (birds and pollinators). These organisms may be exposed to pesticides in different ways (dietary and contact) strictly linked to their physiology and biology, and also to the formulation (liquid, granular) and the emission route of plant protection products. Furthermore these organisms and particularly pollinators are well known to provide key ecosystems services to both natural and agro-ecosystems (e.g.,
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Figure 5. Non crop vegetation in Meolo basin.
Potts et al. 2006). In the map (Figure 4), the level of exposure on soil is presented: exposure is higher in areas where treatments are direct, exposure decreases from the point of application to the external areas, due to drift. Pollinator biodiversity assessments Pollinators were surveyed in the Meolo Basin using standardised methodologies developed within ALARM (Westphal et al. 2008). As pollinators represent a diverse set of taxa, three complementary methods were used: transect walks for bumblebees and butterflies, water-filled pantraps for wild bees and hoverflies, and trap nests for aerial nesting bees. The sample points were stratified across the landscape to effectively cover land use types and explore the full extent of the existing pesticide pressure gradients. Surveys were conducted four times through the main pollinator activity period (May to August 2006-2007) and were timed to be just before and after the two
The most suitable habitat for pollinators in agroecosystems is non-crop vegetation (Figure 5). For this reason, an evaluation of exposure on hedgerows is required in order to assess the potential risk for these organisms. The main mechanism by which pesticides may reach non-target ecosystems is droplet drift, i.e. the fraction of sprayed plant protection products carried off-target by wind during application. Recently, a model for assessing exposure on hedgerows has been proposed (see below, Lazzaro et al. 2007). Starting from the amount of pesticide that impacts on the hedgerow, some meteorological data (wind speed), information on the hedgerow's structure (hedgerow width and optical porosity) and spray cloud kinetic energy (shape factor), this model predicts the amount of pesticide that will cross the hedgerow (mg/m2). The pesticide that accumulates on vegetal parts is calculated by subtracting the amount that crosses the hedge from the amount that impacts on it. Pollinators are exposed to
be included as explanatory factors for the number of species. In addition, other explanatory factors important to pollinators were included, such as the amount of surrounding uncultivated land, flower abundance and diversity, so that the effects of pesticide pressure were discernible as an additional factor structuring pollinator communities. At the small scale no effect of distance from the field of application was found on bees. The number of applications of the pesticide was an important explanatory factor of the species richness of solitary bees. The number of wild bee species did not decrease after one application of pesticide but dropped significantly after two applications (Brittain et al. 2010). It could be that bees are more susceptible to the toxic effects of a second spray when already in a stressed state from a prior application. At the wider landscape scale pollinator diversity was compared between vineyards where the pesticide was applied, maize fields as the other major
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References BONZINI, S, VERRO R, OTTO S, LAZZARO L, FINIZIO A, ZANIN G, VIGHI M (2006) Experimental validation of a geographical information systems-based procedure for predicting pesticide exposure in surface water. Environmental Science and Technology 40: 7561-7569. BRITTAIN C, VIGHI M, BOMMARCO R, SETTELE J, POTTS SG (2010) Impacts of a pesticide on pollinator species richness at different spatial scales. Basic and Applied Ecology 11: 106-115. CBN – SECRETARIAT OF THE CONVENTION ON BIOLOGICAL DIVERSITY AND NETHERLANDS ENVIRONMENTAL ASSESSMENT AGENCY (2007) Cross-roads of Life on Earth – Exploring means to meet the 2010 Biodiversity Target. Solution oriented scenarios for Global Biodiversity Outlook 2. Secretariat of the Convention on Biological Diversity, Montreal, Technical Series No. 31. EC (2002) Guidance document on risk assessment for birds and mammals under council directive 91/414/EEC, SANCO/4145/2000 European Commission, 2002.
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tion of a pesticide in a system are key factors behind the risk of negative impacts on pollinator diversity.
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Figure 6. The mean (± SE) species richness of wild bees (excluding Bombus) in vine (purple), maize (orange) and uncultivated (green) fields, in four sampling rounds. In the Meolo basin (a) there was an interaction between the sampling round and field type, but in the basin where no pesticides were applied (b) only sampling round was an important explanatory factor.
main pesticide spraying events in mid June and mid July (Brittain et al. 2010). Predicted risk Official European procedure for agrochemicals risk assessment is based on the Technical Guidance Document (EC 2003). In this procedure characterisation of potential risk is based on the ratio between a Predicted Environmental Concentration (PEC) and the Predicted No-Effect Concentration (PNEC); if this ratio is higher than 1 further testing is required. Another procedure to characterize risk is Toxicity Exposure Ratio (TER): the ratio between an ecotoxicological endpoint and the predicted environmental concentration. A methodology to assess risk in agroecosystems for pollinators had been proposed within the ALARM project.
active ingredients by contact or orally: comparing concentration on hedgerows as the amount of active ingredient per square metre of foliage or as the amount of active ingredient per weight of foliage respectively with contact and oral toxicity endpoints, it is possible to quantify the theoretical risk for pollinators. The predicted risk could be compared with information on the real risk. Real risk The impacts of pesticide application on pollinator biodiversity in the Meolo basin were analysed for wild bees (excluding bumblebees), butterflies and bumblebees. The applied model, predicting the drift and distribution of the dominant insecticide applied in the basin, was used to allow the predicted point concentration of the pesticide and summed concentration over a 200 m radius buffer to
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crop and uncultivated fields (Brittain et al. 2010). In the Meolo basin the number of solitary bee species generally increased through the season in both maize and uncultivated fields (Figure 6a). In vineyards where the pesticide was applied, the number of bee species dropped significantly towards the end of the season. In the parallel system where no pesticides were applied, bee species generally increased in all three field types (Figure 6b). The decline in the number of bee species in the system where pesticides were used, specifically in the fields where pesticides were applied, points to a reduction in bee diversity resulting from pesticide application. These results indicate that it is solitary bees rather than bumblebees or butterflies that are at greatest risk from the impact of pesticides and that the number of applications and accumula-
E F F E C T S
O F
P E S T I C I D E S
I N
EC (2003) Technical Guidance Document (TGD) on Risk Assessment of Chemical Substances, second ed. European Chemical Bureau, Joint Research Centre, EUR 20418 EN/2. EPPO (2003) Environmental risk assessment scheme for plant protection products. EPPO Bull. 33(2): 211238. LAZZARO L, OTTO S, ZANIN G (2008) Role of hedgerows in intercepting spray drift: evaluation and modelling of the effects. Agriculture, Ecosystems and Environment 123: 317-327. POTTS SG, PETANIDOU T, ROBERTS S, O’TOOLE C, HULBERT A, WILLMER P (2006) Plantpollinator biodiversity and pollination services in a complex Mediterranean landscape. Biological Conservation 129: 519-529. WESTPHAL C, BOMMARCO R, CARRÉ G, LAMBORN E, MORISON N, PETANIDOU T, POTTS SG, ROBERTS SPM, SZENTGYÖRGYI H, TSCHEULIN T, VAISSIÈRE BE, WOYCIECHOWSKI M, BIESMEIJER JC, KUNIN WE, SETTELE J, STEFFAN-DEWENTER I (2008) Measuring bee biodiversity in different habitats and biogeographic regions. Ecological Monographs 78: 653-671.
T E R R E S T R I A L
E C O S Y S T E M S
219
Do Declines in the Use of the Organotin (TBT), Used as an Antifoulant, Result in an Increase in Aquatic Alien Species Establishment?
,
DAN MINCHIN
Figure 1. Dry-docking of a ship where antifouling wastes can enter the environment. Photo: D. Minchin.
Figure 2. High-pressure removal of paint while in dry dock. Photo: D. Minchin.
Baie de Arcachon in the late 1970s and reduced oyster settlements. Several Pacific oyster culture operations discontinued as a result of lower meat yields and highly distorted shells in different regions of Europe. The principal source of TBT was from the antifouling paints that leached from the hulls of leisure craft, ships and wastes from dry docks (Figures 1 and 2). Applications from the early 1970s to the early 1980s of TBT antifouling paints on small craft resulted in leaching of the organotin from the paint to result in pulse releases to the environment, mainly in the spring, a normal period for immersion. Larger vessels and ships had similar paints. Subsequently a self-polishing paint with TBT bonded to the paint matrix 220
AT L A S
OF
was used, from about 1987, and resulted in a reduced rate of release. The first account of distorted oysters in Ireland was from Kinsale Harbour in the late 1970s, although no link with the usage of antifouling paints was made at that time. It was not until 1985 that grossly distorted oysters were examined in Baltimore and Cork Harbour when a link emerged. Elsewhere the usage of TBT flexible paints, applied to the nets of farmed salmon cages, was associated with failed recruitment of the scallop Pecten maximus during 1982 to 1985 due to concentrations of ~220 ng/litre of TBT. On account of the effects of the organotin TBT on a wide range of biota, and the high levels once found in port regions, its presence may have suppressed the ability of alien propagules to form founder populations due to impacts on larval and later stages. The current concern is that declines in the usage of TBT, and subsequent improvement in water quality, would result in increased invasion rates within port regions, areas where many alien species first colonize. Here we examine whether there are any trends in invasion-rate since the declines of TBT in the environment. Biological indicators for TBT contamination In north-western Europe snails and oysters have been used as indicators of the concentration of TBT in tissues and in water. The Pacific oyster Crassostrea gigas develops shell thickening of the upper (flat) shell and in severe conditions these oysters expire because it affects soft tissue metabolism and the cupped shell growth can curve to press upon the flat shell to reduce shell gape (Figure 3). The shell height-to-length ratio can provide an index that relates to concentrations of TBT in water. TBT alters the sexual condition in the predatory dogwhelk, Nucella lapillus, (Figure 4) resulting in different morphological effects, due to endocrine disruption. The resulting effects involve male reproductive tissues, including the occurrence of a penis, becoming superimposed on the female, and enlargement of the penis in females from levels as low as 1ng TBT per litre. In severe cases the extension of the vas-deferens seals the vagina, preventing release of egg cap-
B I OD I V E RSITY
RISK
CHAPTER
9
Figure 3. Shell distortion in the Pacific oyster as a result of high levels of TBT in water. Photo: D. Minchin.
Figure 4. Dogwhelks on a shore. Photo: D. Minchin.
sules and rendering the female sterile. This endocrine disruption effect has been termed imposex. Severe cases usually result in mortality from a build-up of necrotic egg masses. The index is known as the Vas Deferens Sequence Index (VDSI). Dogwhelk hatchlings do not have a planktonic
stage but crawl directly after leaving the egg case. It is for this reason that, where females in a population expire, recruitment from elsewhere is unlikely to take place readily and the population can die-out. Imposex stages range from 0 (unaffected snails) to 6 (necrosis) and have been used to map the
5.0 4.8 4.6 4.4 4.2
VDSI
Tri-butyl-tin (TBT) is an organotin that has an impact on a wide range of organisms causing endocrine disruption and metabolic disorders. At high levels of contamination it can cause harmful effects to most biota including plants, crustaceans, tunicates, fishes and mammals. Molluscs are particularly susceptible, first recognised following the shell distortions of cultivated Pacific oyster Crassostrea gigas in the
4.0 3.8 3.6 3.4 3.2 3.0
0
500
1,000
1,500
2,000
2,500
3,000
Figure 5. The effect of a point source of TBT contamination within a sheltered tidal harbour showing the linear decline in TBT-induced vas deference development (VDSI) with distance (in meters) from the source (Minchin and Duggan unpublished).
Irish ports
25
20
15
ng/l TBT
Introduction
10
5
0
1985
1990
1995
2000
2005
2010
Figure 6. Estimated vales of TBT in water expressed as ng of TBT in water of Irish ports as derived from the condition of dogwhelk and oyster bioindicators (Minchin unpublished).
distribution of TBT in Irish port regions and elsewhere.
Level of contaminatione Ireland
Results from bioindicators All measurements of imposex have declined in port and other regions over time indicating reduced TBT contamination. However, in one inlet in 1999, unauthorized usage of TBT-based paints resulted in oyster shell distortion at a nearby oyster farm, making the oysters unmarketable. Imposex values indicated the position of the source of contamination to a boatyard, using unapproved TBT paint applications, to within ~200 m. Releases produced a local plume that decreased linearly with distance from the source (Figure 5). Results from Irish ports show declines of TBT contamination since 1994 (Figure 6). Dogwhelk populations that had died out in some regions showed some recovery from this time onwards. TBT plumes and distribution of contamination within port regions In Ireland, sources of TBT contamination can be identified from plumes coming from port regions (Figure 7). The main sources are from boatyard, shipyard or berthing areas. Declines have taken place since the 1987 legislation that resulted in a marked reduction in usage at that time by leisure craft. A 1997 study on the south coast of Ireland shows a westward residual drift from port regions as well as a decline in contamination with distance from source. Thus it is clear that the main areas of contamination are within port areas. Temporal changes of TBT varied, with a rapid build-up from its first use in the early 1970s. This may have resulted in a sudden loss of scallop recruitment in Cork Harbour by 1976 and the cause of shell distortion in oysters in Kinsale by 1978. In Mulroy Bay, scallop and other bivalve recruitment was affected most probably from flexible TBT antifouling applications used on fish cage netting. Early antifouling paints freely leached TBT to the environment, which probably resulted in higher levels of contamination. Later paints had TBT chemically bonded to the paint matrix, resulting in a reduced rate of loss. After the 1987 legislation, there were significant improvements, and dogwhelk populations began to recolonize areas where formerly they had become extinct. Ports as a source of alien species The release of the wide diversity of biota from ballast water and “dropoff ”, or spawning events, from ship hulls following entry to a port may be the basis of most inoculations of alien species. The westwardly skewed plumes shown in Figure 7 also provide an indication as to where alien propagules may also become dispersed. In some
D O
D E C L I N E S
I N
T H E
Dublin
1 - trace
Ports
2 - low
Sampling stations
3 - moderate to high Study area
Ports CORK WATERFORD 12 11 13 10 14 9 15 17 16 8
KINSALE 1 2
6
4
5 7
3
23
WEXFORD
22
28
24 19
20
21
25 26
18
27 37
30
29
DUNMORE EAST
36
31
32
35 34
KILMORE QUAY 33
VDSI Nucellalapillus
2 1 2
3
3
1
Figure 7. Theoretical plumes off the south coast of Ireland based on shore sampling data and residual flow. Port regions have the highest levels of contamination. Above, samplings stations and below VDSI levels eminatings from port regions (Minchin unpublished).
ports, such as Cork Harbour with a wide range of habitats, the contamination varies according to the plumes of dispersal guided by tidal exchanges. As a general rule the most contaminated ports will have the highest volumes of shipping traffic and so a greater opportunity for propagules to target a ‘window’ for colonization. This is particularly the case in sheltered harbours having a wide range of conditions, as in the case of Cork Harbour. The principal volumes of ballast water releases as observed in a study during the period 1994-1995 were in Cork Harbour, Rosslare Harbour and the Shannon Estuary, followed by lower volumes from Foynes, Dublin and Drogheda (Figure 8), while the number of ships at this time was greatest for Dublin Bay, Rosslaire Harbour and Cork Harbour (Figure 9).
!
< 50,000 mt
!
50,000-100,000 mt
!
100,000-200,000 mt
!
!
!
O F
T H E
O RG A N O T I N
!
Drogheda !
!
!
( T B T ) ,
!
!
Rate of alien arrivals
U S E
300,000-400,000 mt
!
Assessment of alien species
Records of the first known dates of appearance of aquatic aliens arriving to Irish and British ports were used taken from a dataset on alien species collected during the EU 6th Framework Project DAISIE. Records of alien species first discovered in different time periods were counted: a) 1947 to 1972 – a fifteen-year period with no known usage of TBT; b) 1973-1987 – a fifteen-year period when TBT was widely used until its usage on small craft (< 25 m) and other floating structures
200,000-300,000 mt
Dublin !
!
!
! !
Shannon ! ! !
!
!
!
! Rosslare
!
Cork
Figure 8. Estimated levels of ballast water discharges in Irish ports (1994-1995) (Minchin unpublished).
U S E D
A S
A N
A N T I F O U L A N T,
R E S U LT
I N
A N
I N C R E A S E …
221
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
>30,000 20,000-30,000
rd
10,000-20,000 1,000-10,000
C or k
Ro ss lai re
Tonnes DWT
lin
<1,000
D
ub
Port
W ate rfo
N ew
Ro ss
D ro gh ed a
Li
m
er
ick
Fo yn es
Ar klo w
G
alw
ay
D un
G
da
lk
re en o
re
0
Figure 9. The size and number of ships arriving in Irish ports in a year period (1994-1995). DWT = deadweight tonnage.
21
!
!
23
!
22
!
22
25 20
! !26 27 !
!
! 28 ! 29
1
! 19
!
2 4
! 3 !! !
5
6
18
!
8
17
!16
!
!
!
!15 10
12 14
!
!! ! 13
11
!
Floating pontoons
Figure 10. Sampling sites in Ireland used for the survey of alien species. On all sampling sites there were floating pontoons that included floating landing stages beside fish farms as well as marina pontoons.
222
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Rate of spread A rapid assessment survey took place in 2005/2006 to determine the occurrence and distribution of ten alien species known to occur in Ireland that might have spread to new localities. The species chosen were known from pontoons at marina sites and shipping ports and from some fish farms (Figure 10). In addition, a further ten species known in Europe and likely to appear in Ireland were looked for. The rate of spread is deduced from the species distributions known and recorded prior to 2000 and compared with those found since 2000 to 2006. For several species an arrival time could not be estimated. All sampling was from firm surfaces using a scraper (Figure 11).
7
!
9
! !
(i.e., fish cage netting) was discontinued following legislation in Ireland and Britain in 1987; c) 1988-2002 – a fifteen-year period following the 1987 legislation to the time of the European general controls on TBT in 2003; d) 2003-2007 – a five-year period following the 2003 EU legislation.
were previously unknown, based on a survey in 2005/6, ranges from 3.6 to 5.8 when the uncertain records are included (Table 2). These records will only be for sessile fouling species and their associates per year and is almost certainly greater than this rate as those occurring in the plankton and in sediments have not been considered. Estimates of spread before 2000 were < 0.6 per year based on the starting point of 1947. Species selected for analysis are presumed to have been introduced with ships or small craft. Unfortunately there are few cases with direct information of an introduction associated with vessels arriving to Ireland or Britain. Most records are based on a likely means of arrival with hull fouling or ballast water discharges, where vessels are the only known vector operating in the region where an alien is first found. The main Irish shipping ports are in Dublin Bay, Rosslaire and Cork Harbour. Cork Harbour is the largest and most sheltered with a tidal range of ~3 m and retains some water at each tidal cycle. This may explain the preponderance of alien species in Cork Harbour over other Irish regions. Before the usage of TBT, four species are known to have arrived in Cork Harbour, but the exact dates of arrival are unknown (Figure 12). Similarly, other arrivals of aliens are likely to be earlier than when first discovered. Dublin Bay is less sheltered. Here ships berth within an estuary (Dublin Port) or in a man-made harbour (Dun Laoghaire). Rosslaire is a man-made harbour on an exposed coastline with almost complete exchange with each tidal cycle, so purging TBT and any released biota. Unfortunately, monitoring aliens in the marine environment has often been casual, irregular and dependent on available experts and limited to areas where appropriate investigations took
CHAPTER
9
Alien species arrivals and spread The numbers of alien species recorded in Irish and British ports appears to have increased over the last thirty years, the greatest rate per year appearing since 2000 or 2003 (Table 1). The estimated rate of spread of aliens to areas in Ireland where they
Figure 11. The scraper used for removing biota from the sides of pontoons. Photo: D. Minchin.
Table 1. Summary of first alien records for Irish and British ports and mean number of first records per year.
Total
Ireland Alien
Total
Britain Alien
all aliens
N/yr
<1947 1947-1972 1973-1987 1988-2002 2003-2007
10 7 7 7 6
6 6 0 2 5
25 13 12 15 5
15 6 4 11 5
21 12 4 13 10
0.34 0.27 0.86 2.00
Total <2000 2000-2007 Total
37 30 7 37
19 13 6 19
70 62 8 70
41 33 8 41
60 46 14 60
Years
1.75
Table 2. Records of aliens species to Irish Ports providing an indication of rate of spread. Uncertain records 2000+ 16
place. The development of rapid assessment monitoring provides a better basis for measuring future changes in alien distributions. The mean rate of alien arrivals to Britain and Ireland since 2000 at ~2.0 per year is greater than for all previous time periods (Table 1). For the spread of sessile species (and their associates) to new localities within Ireland, the value ranges from 3.6 to 5.8 per year. Such rates must be underestimates because plank-
8
tonic and infaunal aliens have not been fully investigated. Some species likely to have arrived very recently include the tunicates Corella eumyota from the southern hemisphere, the North Pacific Botrylloides violaceus, and the invasive Didemnum vexillum, now appearing in several world regions (Figures 13 to 15). These are almost certainly moved by vessels and are likely to spread further. The use of rapid assessment survey methods for
8 NRT (million tonnes)
8
Mytilicola intestinalis
Conclusion Organotin concentrations are unevenly distributed about coastal areas. In Ireland, they are highest in port regions, especially in areas with low levels of tidal ventilation and that have ship/ boat repair facilities. Legislation in 1987 resulted in a decline of organotin contamination of many regions in Ireland,
8
TBT
8
Elminius modestus
8
4
the rate of spread for such species can be cost effective and easily achieved.
8
Ficopomatus enigmaticus
8
5
New records 2000-2006 25
8
Reliable records 1947-2000 16
References
8
Styela clava
3
Europe & World 2
1
Ireland & Britain
liners 0 28
33
38
43
48
53
even within some ports. These declines have continued until the most recent 2005 survey. For Britain and Ireland, the mean annual alien invasion rate of 0.34 was slightly higher prior to the usage of TBT before 1973 than the rate (0.27) during the severe contamination period from the late 1970s to1987. From 1987 onwards, following some water quality improvements, there was a notable increase in the invasion rate estimated at 0.84 for the period 19882002, with a further increase to 2.0 during the period 2003-2008. Since 1947, the starting period of this study, there has been an increase in shipping traffic and alien species movements. For this reason, the estimated numbers of alien records reported per year may not be comparable over all historical time periods chosen, and may not be entirely attributed to changes in port toxicity. Nevertheless, the observation that the rate of arrival of alien species did not increase during the period of heavy TBT contamination combined with the strong increase after the ban on TBT suggests that port contamination may have played an important role in reducing the colonization and spread of alien species. With improvements in port water quality, more records of aliens might thus be expected.
58
63
68
73
78
83
88
93
Year Figure 12. Annual shipping tonnage entering Cork Harbour and showing the windows when arrivals for four species are likely to have taken place. Also shown are the changes in the origin of ships with more arriving from continental Europe and other world regions, than from elsewhere in Ireland and Britain, during the 1980s.
ICES 1986 Report on the results of the seventh intercalibration exercise on trace metals in biota. ICES Co-operative Report 138. MINCHIN D (2007a) A checklist of alien and cryptogenic aquatic species in Ireland. Aquatic Invasions 2: 341-366. MINCHIN D (2007b) Rapid coastal survey for targeted alien species associated with floating pontoons in Ireland. Aquatic Invasions 2: 63-70. OEHLMANN J, BAUER B, MINCHIN D, SCHULTEOEHLMANN U, FIORONI P, MARKERT B (1998) Imposex in Nucella lapillus and intersex in Littorina littorea: interspecific comparison of two TBT-induced effects and their geographical uniformity. Hydrobiologia 378: 199-213. WALDOCK MJ, THAIN JE, WAITE ME (1995) An assessment of the value of shell thickening in Crassostrea gigas as an indicator of exposure to tributyltin. – In: Champ M, Siegmann PF (Eds), Organotin. Chapman and Hall, London, chapter 11: 219-237.
Figures 13 to 15 (left to right). Some tunicates on the move: Corella eumyota, Botrylloides violaceus and Didemnum vexillum. Photos: D. Minchin.
D O
D E C L I N E S
I N
T H E
U S E
O F
T H E
O RG A N O T I N
( T B T ) ,
U S E D
A S
A N
A N T I F O U L A N T,
R E S U LT
I N
A N
I N C R E A S E …
223
The Effect of Heavy Metal Pollution on the Development of Wild Bees DAWID MOROŃ, HAJNALKA SZENTGYÖRGYI, IRENA GRZEŚ, MARTA WANTUCH, ELŻBIETA ROŻEJ, JOSEF SETTELE, SIMON G. POTTS, RYSZARD LASKOWSKI & MICHAŁ WOYCIECHOWSKI
,
Heavy metal contamination Contamination of the natural environment with various products of human activity is a long lasting problem. Pollutants accumulate efficiently in litter and soil due to their high affinity to organic substances and clay particles. Although many metals are indispensable to organisms (for example, iron, cobalt, copper, manganese, molybdenum, zinc), they all become toxic when the required levels are exceeded. Others, such as mercury, plutonium, and lead, often called xenobiotic metals, are not used by organisms in any biochemical process and become toxic in concentrations exceeding their natural, background levels. Both groups are emitted, due to human activity, often in such high amounts that they reach toxic levels at least in some compartments, disturbing the functioning of a whole ecosystem. Such negative effects of metal pollution have been shown for a number of organisms, including
Figure 1. The red mason bee (Osmia rufa L.). Photo: H. Szentgyörgyi.
many species of invertebrates. However, most of these studies were restricted to soil dwellers or other organisms somehow connected with the soil environment. Much less is known about the effects of metal pollution on organisms not connected directly with soil but otherwise living in polluted areas, such as pollinators. Pollination One of the keystones of a well-functioning terrestrial ecosystem is pollinators. Pollinators, and especially bees
Warsaw ! A
POLAND
A ! A ! A ! A ! A !
! A Zinc Smelter “Bolesław” ! A Test sites
Figure 3. Gradient of heavy metal pollution in Poland.
(Figure 2), are gaining more and more interest due to the “pollination crisis” or “pollinator decline” recognized all over the World (Kearns et al. 1998). Economic analysis of the value of pollinators for humans is adding up to tens of billions of dollars every year for only agricultural crops and only taking into account the services provided by honey bees (Morse & Calderone 2000). If we imagine that besides honey bees there are approximately 20,000 species of bees, then we are starting to get an idea of the scale of the value of pollinators. One of them is the red mason bee (Osmia rufa L.) often used for pollinating orchards. Red mason bee The red mason bee (Figure 1) is a relatively common species in Europe. It has an annual life-cycle similar to other solitary bees. Adults emerge in spring, then mate and prepare nests for their progeny. As they do not excavate their nests, as most other bees do, red mason bees search for natural cavities and holes but are satis-
Figure 2. Honey bee, solitary bees and bumblebee at work. Photos: H. Szentgyörgyi & M. Woyciechowski.
224
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9
fied even with broken reeds or artificial paper tubes provided by man. Females start collecting pollen and laying eggs; each egg, provided with pollen, is separated by a mud wall built by the female and that is why they are called mason bees. The eggs develop into larvae which, after completing this stage, spin cocoons and enter the pupal stage. Imago hibernate through the winter and emerge next spring as adult bees. Heavy metals and the red mason bee No studies have hitherto been conducted on wild solitary bees and our understanding of the effects of metal exposure for this group was scare and rather hypothetical. Extrapolation of data from other invertebrates is not a solution since laboratory experiments and field observations have shown very different and sometimes even contradictory effects for closely related species or populations of the same species, differing in their tolerance to particular chemicals. On the basis of the abovementioned difficulties it seems
next to impossible to predict, without proper experimental or at least observational studies, how bees may react to metal pollution. Bees on a metal pollution gradient A few-year-long studies were carried out to learn how metal pollution influences red mason bees. The freshly emerged bees were released along a metal pollution gradient near zinc smelters in Poland (Figure 3) and England. At a number of sites selected, following the earlier studies on contamination levels, they were provided with reed tubes where females could found their nests. The sites represented a broad range of pollution (Figure 4), from background levels some 20-30 km from the pollution sources to extremely polluted areas next to the smelters (Figure 5). At the end of the season the bees’ nests were collected and their progeny development was observed and measured (Figure 8). The results were worrying. Females on contaminated sites provisioned their larvae with contaminated pollen and,
500 a 450
400
Mean ZN (mg/kg) level
350
300
250 b 200
150
100
50
0
Bukowno
Olkusz
Dąbrówka
Klucze
Złożeniec
Figure 5. Mean levels of Zn [mg/kg] in pollen collected by bees on the tested pollution gradient.
Figure 4. The most (a) and the least (b) polluted sites on the heavy metal pollution gradient in Poland. Photos: H. Szentgyörgyi.
lation numbers of the forthcoming generation. There is a possibility, of course, that either some pollinator species will adapt to these circumstances or will be replaced by other, more tolerant species. Indeed, some invertebrates have been shown to be able to function well in polluted environments, but it is unknown so far whether and how bees
therefore, they produced fewer adult offspring. Developing bees were smaller at emergence and died more frequently during development (Figure 7). Higher mortality rates resulted in lower numbers of offspring and, in consequence, smaller populations of reproducing adults in the next season. This, combined with the disturbed sex ratio (Figure 6), will inevitably reduce popu-
2005
1.0
will manage to do so. The pollinator decline observed all over the world may suggest, however, that bees belong to species rather sensitive to environmental changes. Pollution may not be the main reason for this worldwide decline, but we have shown that it is yet another negative effect added to the long line of anthropogenic environmental problems. 2006
References KEARNS CA, INOUYE DW, WASER NM (1998) Endangered mutualisms: The conservation of plant-pollinator interactions. Annual Review of Ecology and Systematics 29: 83-112. MORSE RA, CALDERONE NW (2000) The value of honey bees as pollinators of U.S. crops in 2000. Gleanings Bee Culture Supplement, 1–15.
2005
0.6
2006
0.5
0.8 0.7
Ratio of dead offspring
Ratio of females in emerging bees
0.9
0.6 sex ratio 1:1
0.5 0.4 0.3 0.2
0.4
0.3
0.2
0.1
0.1 0.0
0
50
100
150
200
250
300
350
0.0
0
50
100
Zn (mg/kg) level in pollen
150
200
250
300
Zn (mg/kg) level in pollen
Figure 6. Sex ratio of bees on heavy metal gradient.
Figure 7. Ratio of dead offspring of bees on heavy metal gradient.
Figure 8. Installation of trap nests and an example of a reed tube with empty cells left after adult bees have emerged. Photos: H. Szentgyörgyi.
T H E
E F F E C T
O F
H E AV Y
M E TA L
P O L LU T I O N
O N
T H E
D E V E L O P M E N T
O F
W I L D
B E E S
225
350
Agricultural Land Use Shapes Biodiversity Patterns in Ponds
,
TOM DE BIE, ROBBY STOKS, STEVEN DECLERCK, LUC DE MEESTER, FRANK VAN DE MEUTTER, KOEN MARTENS & LUC BRENDONCK
Why ponds? Much attention in conservation biology and in community ecology is directed towards large-scale ecosystems, such as forests, rivers, deep lakes and marine systems. Small-scale landscape elements, such as freshwater ponds and pools, are often neglected although they are functionally different from larger lakes and important for several reasons: ponds are very abundant in an average landscape and they can contribute disproportion-
eated ‘islands’ in a terrestrial habitat matrix. The spatial structure, the total number and the quality of neighbouring aquatic habitats play a significant role in understanding population persistence and recovery from disturbance. Recent evidence also points to the significant role of small freshwater ecosystems in important key processes such as nutrient interception or organic carbon storage in sediments. Because ponds are abundant, well delineated and relatively easy to sample, they provide ideal model systems for research in conservation biology and community ecology (De Meester et al. 2005).
outlet) and their low water turnover rates, ponds are assumed to be very sensitive to external inputs and tend to accumulate nutrients, organic matter or pollutants. Pond integrity and land use To address the question of how the surrounding land use matrix determines the ecological integrity of ponds, we investigated 72 ponds in a gradient of agricultural land use intensity in seven different countries (Belgium, Denmark, Hungary, Germany, Poland, Slovenia and the United Kingdom). Land use classes were indicated as “rather pristine (P)”, “extensive use (E)” and “intensive use (I)”. To filter out the geographic effect, ponds were sampled each time in clusters of three ponds (one pond each in P, E and I areas; Figure 2) across the regions considered. To study land use effects in more detail, a similar core study of 42 clusters (or 126 ponds) was carried out in Belgium. Habitat characteristics, macro-invertebrates and zooplankton (cladocerans) of these ponds were examined in relation to the surrounding land use at different spatial scales (Figure 3). The results of both our core study and our assessment at the European scale demonstrated consistent and signif-
Potential threats Ponds are often located in agriculturedominated landscapes (Figure 1) and are therefore exposed to a wide array of anthropogenic stress factors, such as physical destruction, chemical pollution, eutrophication and the introduction of exotic species. Human actions at the landscape scale are assumed to be a principal threat to the ecological integrity of pond systems, impacting habitat, water quality and the biota via numerous complex pathways. Due to the isolated nature of ponds (the majority of ponds lack an
Figure 3. Land use cover variables were assessed at seven different spatial scales. The percentage cover of land use types was estimated for circular areas with centre at the location of the ponds and a radius ranging from 50 m over 100, 200, 400, 800, and 1600 m to a maximum of 3200 m.
icant associations of land use variables with several measures of ecological pool integrity. Ponds in close proximity to arable land tended to be characterized by high values of water turbidity, total phosphorus, chlorophyll a concentrations (a measure of phytoplankton density), sludge on the sediments, and by sparser aquatic vegetation. Conversely, ponds at locations with high forest cover showed the opposite pattern. Effects of
I
2.5 km
I
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Figure 1. Some examples of farmland ponds across Europe. Photos: T. De Bie.
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ately to regional diversity (Davies et al. 2008). This high contribution to regional diversity is due both to the fact that ponds often differ strongly in species composition from each other, and to the occurrence of species that are specific to small ponds and pools. Ponds are also vital for many rare and endangered species and support meta-populations of many aquatic species, including amphibians, invertebrates and wetland plants by providing stepping-stones and increasing the connectivity between other freshwater habitats. For obligatory aquatic organisms, ponds are well delin226
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Figure 2. Location of sampled pond clusters in Europe. Each cluster contained three small farmland ponds that were located within a circular area of approximately 20 km². The ponds within clusters were selected along a gradient of land use intensity, ranging from relatively natural areas to areas with intensive agricultural activities (Satellite photos provided by © 2008 Google – Map Data © 2008 Tele Atlas).
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Invertebrate diversity and emission patterns of POP’s On the European scale, we did not find a clear relation between invertebrate diversity measures in ponds and estimated emission patterns of persistent organic pollutants (POP’s). POP’s (for example: hexachlorobenzene, hexachlorocyclohexane, polychlorinated biphenyls, polycyclic aromatic hydrocarbons and the candidate POP endosulfan) are organic substances that possess toxic characteristics, are persistent, bioaccumulate, are prone to long-range transboundary atmospheric transport and deposition and are likely to cause adverse human health or environmental effects both near to and distant from their source. Additionally, when these concentrations were translated into freshwater concentrations, based on the contaminant distribution and transport between the aerial and aquatic compartment, no relation with invertebrate richness was found (for example endosulfan; Figure 6). Our finding of a lack of significant correlations between biodiversity and estimated pesticide emissions in Europe is probably due to the result of the coarse spatial scale by which the pollutant concentrations are estimated and to the low variability in some pollutants between the selected study sites. Our earlier results suggested that the most important land use effects on ponds operate at relatively small spatial scales, which means that local inputs of either nutrients or pesticides may override effects of average pesticide emission on a large-scale. Moreover, diversity patterns are not only determined by general gradients in pollutants or by dominant land use characteristics, but also, for example, by historical biogeographical factors, such as past climate and post-glacial recolonization processes, and by the degree of isolation. Together with management history and past land use, these factors might influence the observed patterns and create
AG R I C ULT UR A L
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considerable variance that acts as noise when we want to extract the impact of general pollution-related patterns.
Acknowledgements We would like to thank Dirk Ercken and Hendrik Trekels for their assistance in the field and Dr. Andy Sweetman for providing us with the predicted contaminant concentrations.
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Log distance to cropland (m) Figure 5. Relationship between invertebrate taxon richness and the proximity of arable land in each of the selected countries (POL: Poland, HUN: Hungary, DEN: Denmark, UK: United Kingdom, SLO: Slovenia, BEL: Belgium and GER: Germany). Species richness of cladocerans (a) and family richness of macroinvertebrates (b).
References DAVIES BR, BIGGS J, WILLIAMS PJ, LEE JT, THOMPSON S (2008) A comparison of the catchment sizes of rivers, streams, ponds, ditches and lakes: implications for protecting aquatic biodiversity in an agricultural landscape. Hydrobiologia 597: 7-17. DECLERCK S, DE BIE T, ERCKEN D, HAMPEL H, SCHRIJVERS S, VAN WICHELEN J, GILLARDE V, MANDIKI R, LOSSON B, BAUWENS D, KEIJERS S, VYVERMAN W, GODDEERIS B, DE MEESTER L, BRENDONCK L, MARTENS K (2006) Ecological characteristics of small farmland ponds: associations with land use practices at multiple spatial scales. Biological Conservation 131: 523-532. DE MEESTER L, DECLERCK S, STOKS R, LOUETTE G, VAN DE MEUTTER F, DE BIE T, MICHELS E, BRENDONCK L (2005) Ponds and pools as model systems in conservation biology, ecology and evolutionary biology. Aquatic Conservation: Marine and Freshwater Ecosystems 15: 715-725. ROUNSEVELL MDA, REGINSTER I, ARAUJO MB, CARTER TR, DENDONCKER N, EWERT F., HOUSE JI, KANKAANPÄÄ S, LEEMANS R, METZGER MJ, SCHMIT C, SMITH P, TUCK G (2006) A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems and Environment 114: 57-68.
Endosulfan in freshwater (pg/L) 0-11 12-33 34-78 79-175 176-279 ! ! A ! AA ! A
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Figure 6. Predicted concentration of endosulfan in freshwater (pg/L) as estimated by a model based on emission data. Sample locations are indicated with dots.
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Figure 4. Relationship of average atrazine concentration in function of the proximity of arable land. The number of ponds is indicated above each category. Error bars represent standard errors.
Conclusion Referring to the current situation, the lowest pond diversity can be found in vast and intensively used agricultural areas. Pond biodiversity will change most in those regions which will strongly increase in agricultural area or in which the remaining agricultural areas tend to be used more intensively. Many scenarios of European agriculture land use, however, predict a substantial decline in the area of agricultural land, but an increase in their intensity. This is especially the case if technology continues to progress at current rates, unless political decisions stimulate extensification or accept widespread overproduction (Rounsevell et al. 2005). The finding that farmland ponds are mainly governed by land use in the close vicinity of the ponds makes it possible to mitigate adverse external influences at a small scale. This means that conservation of the ecological quality, in both existing and newly created ponds, can be successful, even in landscapes dominated by arable land.
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0.8
Distance to cropland Macroinvertebrate family richness (log)
the ecological quality and invertebrate diversity of ponds. The small catchment size of ponds can be a benefit as well as a disadvantage for their protection. Because they are shallow and have small volumes, ponds are highly vulnerable to degradation caused by incoming water pollutants from their surroundings. Unlike lakes and rivers, there is little possibility of dilution or buffering. Consequently, strongly affected ponds are often degraded to an extreme state rarely seen in larger waters. On the other hand, because of their small catchment area, ponds can also have an exceptional high ecological quality as they can be completely protected from land-derived pollutants. Successfully conserving and creating highly diverse clear water ponds thus requires relatively little effort compared to lakes and rivers, and may be feasible even in landscapes dominated by arable land.
Atrazine (ng/l)
the land use variables arable land and forest were most pronounced at the local scale (< 200 m; Declerck et al. 2006). The negative association between the proportion of arable land and the ecological quality of ponds is in line with the results of former studies on rivers, lakes and man-made reservoirs. Arable farming, especially row-crop farming with frequent tillage and the intense application of fertilizers, generates high soil erosion and high nutrient and sediment export rates. This will lead to increased nutrient loads in the ponds adversely affecting water plant cover and richness in favour of phytoplankton. The distance to cropland estimated in situ, irrespective of the crop type and intensity, appeared to be a better predictor of the ecological state of the pond than the percentage cover of crop land obtained by GIS application. Based on our core study in Belgium, pesticide concentrations (diuron, simazine, atrazine, endosulfan and lindane) in pond water were relatively low, but extremely high levels sometimes occurred. However, we should take into account that pesticide concentrations often occur as short pulses that remain undetected. As expected, the average concentration of atrazine, which was present in the majority of the ponds, was highest in ponds that were located in the immediate neighbourhood of crop fields (Figure 4). Both cladoceran and macro-invertebrate community composition and taxon richness were affected by the surrounding land use matrix. The distance to crop land was positively correlated with invertebrate taxon richness (Figure 5). The effects of land use on invertebrates are mainly caused indirectly via their effect on a number of local environmental factors such as the presence of aquatic vegetation (structural diversity) and the concentration of nutrients (productivity), which are known drivers of invertebrate richness and composition. Our results indicate that land use, in addition to the effect of local variables, also determines a prominent part of invertebrate composition. This could mean either that some important local variables for invertebrates were not taken into account or that there are direct effects of land use (e.g., mortality caused directly by pollutants such as pesticides, short-lived disturbances,…) that do not affect the measured local variables but significantly affect invertebrate communities. Care should be taken, however, because landscapes that are dominated by intensive cropland can also be characterised by low pond densities. The effect of increased isolation of ponds in agricultural landscapes compared to ponds in a more natural environment may provide an alternative explanation for some of the observed effects. Overall, these results show that the intensity of land use, especially in close proximity to ponds (< 200 m), can affect
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4
Mapping Relative Risk to Biodiversity from the Application of Pesticides, Focussing on Pollinators
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PETER BORGEN SØRENSEN, STEEN GYLDENKÆRNE, SIMON G. POTTS, CLAIRE BRITTAIN & MARIANNE THOMSEN
Background Risk assessment in relation to the impacts of pesticides on biodiversity is highly complex. Assessment the largescale poses many difficulties, but identification of risk hotspots based on relative risk mapping can provide important information on where to focus smaller scale investigation. Hot spot mapping can help to focus the more detailed assessment and/or monitoring of harmful effects. This principle is illustrated using an example covering the UK. The mapping combines estimated pesticide usage for a given area with data for toxicity of each active ingredient as a Hazard Quotient. This neglects many factors governing the fate and toxic action of the substance and is thus a robust but rough indicator for the highest risk level. The crop area distribution is determined using the CORINE Land Cover classification at 250 × 250 m scale which is aggregated to 10 × 10 km grids in this study. The maps produced reflect the agricultural structure of the landscape. The pesticide dosage level can either be estimated using surveys as done in the UK based on questionnaires returned from a representative group of farmers, or as sales numbers where expert judgements are applied to assess the usage on separate crop types. The dose level for every active ingredient is assumed to be controlled only by crop type. A series of different data on pesticide active ingredients, such as the PAN database (Kegley et al. 2008), are used for gathering information about bee toxicity and to provide two standard toxicity measures: the oral intake and contact toxicity LC50.
Method The estimated crop distribution in each grid cell is combined with the statistical information about the pesticide usage on each crop type. This yields an estimate of the amount of each active ingredient used in each grid cell. A Hazard Quotient for each grid cell is calculated based on the toxicity of each active ingredient together with the estimated annual amount used in every grid cell: HQ =
Σ
For all active ingredients
Used amounti LD50i
Some substances are used in a way that limits their contamination of bees. This is especially true for substances in soil treatments compared to treatments in the folio, where the bees are active. Thus, the example excludes substances for soil treatments and non-systemic seed treatments. Results As indicated in Figure 1, there is a large difference in the contribution to the HQ between the active ingredients, a few substances dominate the total figures. The HQ per area for each crop type is used to calculate the sum of toxic units within 10 × 10 grids, (Figure 2). This indicates an area in the central/eastern part of the UK has the highest risk potential for bees and insects. Figure 2. A map of the risk for bees from pesticides (HQ) for the UK.
Conclusion This method is also useful for ecological endpoints other than bees if toxicological data are available and if the agricultural praxis, in terms of the
Cypermethrin Dimethoate Lambda-cyhalothrin Imidacloprid Deltamethrin Others
Figure 1. The contribution to the total sum of toxic units for the dominant active ingredients.
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crop distribution, governs the impact on biodiversity. A more detailed study is possible as a follow up to the country scale analysis, where the focus is to find “local super hotspots” inside the hotspot identified in the national scale analysis. In this way a more detailed analysis of the central/eastern part of the UK could be made in order to identify more precisely the conditions that result in a high level of risk to the bees?. The conclusions from this can be used to design a risk assessment scenario specific to the UK. It will also yield information of value for monitoring activities to assess the real impact on biodiversity related to application of pesticides.
The focus in this example is the UK, but estimates of crop distribution are held for the entire European area. However, there exists only limited information about pesticide application in several European countries. More information is needed on pesticide applicatons in southern and eastern parts of Europe. Activities by EUROSTAT will try to fill this gap in the coming years. For information about this please contact the first author (e-mail: [email protected]). Reference KEGLEY SE, HILL BR, ORME S, CHOI AH (2008) PAN Pesticide Database, Pesticide Action Network, North America, San Francisco, CA, http:www.pesticideinfo.org
Integration in Large-Scale Research: on the Art and Science of Coordination JOSEF SETTELE, JOACHIM H. SPANGENBERG, VOLKER HAMMEN, ALEXANDER HARPKE, STEFAN KLOTZ, SILKE RATTEI, ANNETTE SCHMIDT, OLIVER SCHWEIGER, SUSANNE STOLL-KLEEMANN, KARIN ZAUNBERGER & INGOLF KÜHN
,
The integration of scientific, disciplinary work, especially in large consortia, can only be implemented through guidance, promotion and stimulation by the project coordination. Therefore, though often underestimated, scientific management is decisive for the success of integrated, trans-disciplinary research projects, even more so if they are as large as ALARM (see Settele et al., this atlas, pp. 38ff.). Based on the experiences in the coordination of ALARM we want to summarize some core conclusions for future projects, their size, organisation and the quality demands to the scientific management. Promoting open-mindedness and pragmatism Already when setting up the structure of ALARM we were aware, that successfully managing such a large integrated project (see this atlas, pp. 38ff.) would most probably mean a huge challenge for scientists, as it – first of all - requires open mindedness in leaving one’s own topical track to quite some extend. In consequence this means interdisciplinary learning of methodologies and new ways of cooperation, and reinterpreting one’s own results in the broader context provided by other disciplines. This also meant to be prepared to reconsider own insights, perspectives and experiences for the sake of the larger picture. In other words, we expected intercultural learning as one of the preconditions to make an integrated project happen – and all this in the course of an ongoing and ambitious research process. Creating space for reflection and initiating interdisciplinary and transdisciplinary discourses, enriching without disturbing the research process requires a particular type of project coordination, including a broad appreciation of different methodologies and scientific cultures. It is the combination of formal structures and applied (and often pragmatic) management which makes the difference for integrated projects. This approach was what we - to a very large extend - regard as the basis for the success of ALARM. With our concept it was indeed possible to implement coordination as a reflective and iterative participatory process that linked knowledge and action regarding complex science and technology issues. Consequently, while many of the detailed management aspects have been foreseen, the vast majority of them did not have to be activated. Inter- and transdisciplinarity, bringing together different scientific worlds was explicitly foreseen, although the detailed means for implementation evolved throughout the project. Examples for such components are the CF (Consultative Forum; see Spangenberg et al., this atlas, pp. 204ff.), the advisory board, the field site network (FSN; see Hammen et al. 2010 and this atlas, pp. 42ff.; Biesmeijer et al., this atlas, pp. 46f.), the Risk Assessment Toolkit (RAT; Marion et al., this atlas, pp. 252f.), and last but not least the scenarios (see Spangenberg et al., this atlas, pp. 10ff.) which initially only existed as an element of Figure 2 (as shown on page 39 of this atlas). The RAT served as one major tool towards integration, strongly linked to scenarios and both based on the DPSIR philosophy (modified to suit biodiversity issues and overcome some conceptual weaknesses in the course of ALARM, Binimelis et al. 2009) as a common terminological and conceptual approach. Specific risk matrices used in the RAT incorporated uncertainty as core element, and the online KerALARM element (a discourse support tool) offers a specific section on Knowledge Quality Assessment (KQA; see Marion et al., this atlas, pp. 252f.; http://keralarm.c3ed.uvsq.fr). The scenarios themselves offered the option to integrate the vast majority of ALARM activities, many of which otherwise would at best only have been loosely connected; and the application of scenarios also sharply increased the policy relevance of ALARM outcomes. Thinking in scenarios still seems to be “terra incognita” for many natural science disciplines and scientists, in particular if they do not work in applied fields. For social scientists it was still quite an experience to confront socio-economic thinking with natural sciences perceptions of society and economy in the development of the scenarios. Breaking up mental barriers on both sides was a precondition for success.
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To facilitate the project’s progress, the coordination team had to invest certain (variable) amounts of energy to help in the initiation of inter- and transdisciplinary processes. The challenge was not to make this a constant energy input but to make it a catalytic process which then was self-maintained and sometimes even had to be slowed down and just occasionally be stimulated. If, however, the project structures, the disciplines, or the characters involved prevent catalytic initiation and self-maintained progress within the interdisciplinary working groups, then the energy flow from the coordination could only have reached the top level of the respective working units, would have run out of steam quickly and would not have been be able to keep the process on track. Case studies offered ideal opportunities for transdisciplinary integration and for experiencing multiple scales and their interactions through the linkage of local/ regional scenarios with those on the continental scale (Özkaynak & RodriguezLabajos 2010; Rodriguez-Labajos 2009). An ALARM specific example of case studies is the ALARM-FSN. This joint infrastructure, made people think of further opportunities of integrated analysis. Particularly due to the fact, that each partner had to “sacrifice” 2 % of their own budget made them try to get some of this money back. As a result, some of the finest collaborative studies in ALARM have been conducted on the FSN, and some of the most practical, down-to-earth recommendations were derived from it. For instance two of the socio-economic studies contributed to the setting up of a new regional authority unit on biological invasions in Northern Spain, and strengthened a body working as a science-policy interface in the Ile de France region – in both cases, ALARM staff members were recruited. Training activities within ALARM encompassed the development of taxonomic/ systematic expertise, molecular methods and statistical/quantitative approaches needed to solve contemporary problems in biodiversity research, model integration and discursive local scenario development. This was achieved through funding of graduate and postgraduate level research, conducting formal training sessions, and through the synthesis activities of the many scientific gatherings. Through the Consultative Forum, participation of EC policy officers in all major meetings, presentations at the Commission and the EEA a high degree of stakeholder involvement has been achieved within ALARM, making the project results a valuable input to ongoing policy development and political decision making processes. During the 60 months of project duration, ALARM was successful in accomplishing the key goals and milestones. In addition, the project provided a large number of benefits that were not initially planned. This can be seen as a result of the willingness of partners to take advantage of unplanned opportunities. Good indicators for success were the sense of community that developed among the various partners, the many collaborations initiated, and the solutions developed for problems arising along the way. Impact of ALARM Not least as a result of the good experience with the integrated assessment, which most probably is related to the working atmosphere which developed in the course of ALARM, many new initiatives emerged, built upon established interand trans-disciplinary contacts from within and around ALARM (while at the same time taking new partners on board). Due to the positive experience these new initiatives adopted the coordination and management structure (see Figure 2 in Settele et al., this atlas, pp. 208f.) of ALARM (e.g. MACIS: Kühn et al. 2008; STEP: Status and Trends of European Pollinators; SCALES: Henle et al. 2010; COCONUT: Understanding effects of land use changes on ecosystems to halt loss of biodiversity due to habitat destruction, fragmentation and degradation). Also the establishment of the field site network (FSN; Hammen et al., this atlas, pp. 42ff.) might have a potentially important long-term impact derived directly from ALARM, as due to its pragmatic structure it should have a high chance to prevail as integrated assessment platforms for long time after the end of the ALARM project, e.g. within the LT(S)ER-Europe network (Mirtl 2010, Mirtl et al., this atlas, pp. 52f.).
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On the policy level, the ALARM results address all stages of the decision making process: Scenarios are no impact assessment of future policies, but being archetypical for relevant policy orientations, they help to clarify the kind of impacts associated with them and thus support the choice of policy directions. In the decision preparation phase, KerALARM offers innovative tools for multi-stakeholder participation processes, and in the decision making phase the information provided by RAT supports evidence based decision making. The ALARM recommendations serve to inform specific decisions in biodiversity politics. Size also matters: a subjective outlook Some key statements were previously published on this topic (Settele et al. 2007, 2008). But here, we elaborate on these issues in more depth as we consider a clear and experience-based statement important. Projects of the size of ALARM offer opportunities for productive partnerships, with a comprehensive information base provided. The sheer size of the project and the balanced composition of the partners made sure that all relevant natural and social science information was on board before any new steps were planned. This “vacuum cleaner effect” made sure that at no time the project was at risk of reinventing the wheel, but focussed on innovative research questions. Our experience in ALARM is that, if scientists are given the opportunity (in particular a highly diverse scientific environment and sufficient resources in terms of time and funding) and the freedom of choice, new and productive partnerships emerge, and the possibilities for fruitful new collaboration options and hence their success increases with project size up to the level of ALARM. Our large consortium also included many leading scientists, who increasingly appreciate the opportunities offered through a project of such size and scope e.g., by forming new teams conducting inter- and trans-disciplinary research. This is exactly what is urgently needed in science, as expressed by Carpenter et al. (2006): “Meeting the research needs described will require new coalitions among disciplines that traditionally have been isolated….The [Millennium Ecosystem Assessment] has provided a road map; now, we need to start the journey.” We think that large integrated projects have the clear potential to fulfil these requirements. By initiating the IP (Integrated Project) instrument, the European Commission created considerable support to get the journey started. Considering the success story of ALARM and other “flag-ship projects” we very much encourage to maintain the opportunity of having consortia of such a size also for the future, complemented of course with a diversity of smaller research projects exploring specific questions. Innovative combinations of both would also be welcome. Thus integrating different experiences particularly with EU funded research, we favour more projects of variable sizes, organized through work plans and accompanied by model agreements – including a reasonable proportion of large integrated projects to create opportunities for interdisciplinary and productive partnerships. In such a combined approach we see the best and urgently needed opportunity to make inter- and transdisciplinary integration a permanent quality standard in European research. The science and art of coordination Surely one key issue in larger undertakings is the science and art of coordination. Often derogatively considered by fellow scientists as “only” being managers and administrators (an attitude unsurprisingly not shared by the management community), we think that coordinators at first are scientists with the challenging task not only to identify new promising roads into so far unknown, fertile territory, but also to conceptualise approaches how to successfully walk these roads. It is the coordinators who must have a vision for a successful future enterprise, setting the scenes, developing the proposals and putting together elements which potentially have completely new value to the scientific community. Admittedly, this is not what is usually perceived as scientific work. Still it is crucial for research relevant to decision makers. And in times and on topics where “stakes are high, data are uncertain, decisions are urgent and values are disputed” (Funtowicz & Ravetz 1994) it is an indispensible, basic contribution to the scientific process. It requires the combined skills of creative scientists and efficient business managers rolled into one (not by chance the German 2009 “Manager of the Year”, following predecessors from the banking and the automobile industry, is a scientist running a major research centre). A further challenge is to keep the diversity of characters and the disciplines they represent together in order to maintain the opportunities for productive interactions. Besides sensible leadership, administrative means such as the consortium agreements 230
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required for integrated projects may play a key role in the process. For ALARM, they were signed by all partners before the project started. Laying down rules may seem unnecessary as members cooperate to avoid adverse consequences. However, clear rules avoid disputes (transaction costs), help structuring the search for solutions and support a joint outcome of the cooperation. In the end, however, it is the direct contact and open-mindedness one has to maintain in order to keep a tanker manoeuvrable – and that is rather an element of art or psychology than of science. Coordination as a core element of scientific work is the key to successful integration and transdisciplinary research! Acknowledgements The authors are indebted to the members of the ALARM consortium for active and especially passive contributions to the analysis presented here, namely through their very research work providing the proof for the hypothesis that multidisciplinary integration to tackle cross-cutting issues is possible if there is enough freedom to nurture an atmosphere which makes scientists keen to achieve integration. We are particularly thankful to all colleagues who through their advice helped us to make ALARM a success story: ◙ from the advisory board: Jane Feehan (EEA), Barbara Herren (FAO), Jeff McNeely (IUCN), Bernd Wilke (SwissRe); ◙ from the CF: Lewis Akenji, Alain Ayong Le Kama, Tom Bauler, Gina Ebner, Birgit Friedl, Silvio Funtowicz, Mario Giampietro, Krzysztof Kamieniecki, Gottfried Kirchengast, Tony Long, Jyrki Luukkanen, Bernd Meyer, Ivica Radovic, Jan Rotmans, Lars Rydén, Paredis, Jeroen van den Sluis, Leo Seserko, Karlheinz Steinmüller, Uno Svedin, Sergio Ulgiati; ◙ from the European Commission and the review panel: Astrid Kaemena, Dov Sax, Martin Sharman. This contribution is largely based on Settele et al. (in review). References BINIMELIS R, SPANGENBERG JH, MARTINEZ-ALIER J (Guest eds)(2009) Special section: The DPSIR Framework for Biodiversity Assessment. Ecological Economics 69: 9-75. CARPENTER SR, DEFRIES R, DIETZ T, MOONEY HA, POLASKY S, REID WV, SCHOLES RJ (2006) Millennium Ecosystem Assessment: research needs. Science 314: 257-258. FUNTOWICZ SO, RAVETZ JR (1994) The worth of a songbird: ecological economics as a post-normal science. Ecological Economics 10: 197-207. HAMMEN VC, BIESMEIJER JC, BOMMARCO R, BUDRYS E, CHRISTENSEN TR, FRONZEK S, GRABAUM R, JAKSIC P, KLOTZ S, KRAMARZ P, KROEL-DULAY G, KÜHN I, MIRTL M, MOORA M, PETANIDOU T, POTTS SG, RORTAIS A, SCHULZE CH, STEFFAN-DEWENTER I, STOUT J, SZENTGYÖRGYI H, VIGHI M, VILÀ M, VUJIC A, WOLF T, ZAVALA G, SETTELE J, KUNIN WE (2009) Establishment of a cross-European field site network in the ALARM project for assessing large-scale changes in biodiversity. Environmental Monitoring and Assessment 164: 337-348. HENLE K, KUNIN W, SCHWEIGER O, SCHMELLER DS, GROBELNIK V, MATSINOS Y, PANTIS J, PENEV L, POTTS SG, RING I, SIMILÄ J, TZANOPOULOS J, VAN DEN HOVE S, BAGUETTE M, CLOBERT J, EXCOFFIER L, LENGYEL S, MARTY P, MOILANEN A, PORCHER E, STORCH D, STEFFAN-DEWENTER I, SYKES MT, ZOBEL M, SETTELE J (2010). Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal, and Ecological Scales – approach of the SCALES project. Gaia-Ecological Perspectives for Science and Society (subm.). KÜHN I, SYKES MT, BERRY PM, THUILLER W, PIPER JM, NIGMANN U, ARAÚJO MB, BALLETTO E, BONELLI S, CABEZA M, GUISAN A, HICKLER T, KLOTZ S, METZGER M, MIDGLEY G, MUSCHE M, OLOFSSON J, PATERSON JS, PENEV L, RICKEBUSCH S, ROUNSEVELL MDAR, SCHWEIGER O, WILSON E, SETTELE J (2008) MACIS: Minimisation of and Adaptation to Climate Change Impacts on BiodiverSity. Gaia-Ecological Perspectives for Science and Society 17(4): 393-395. MIRTL M (2010) Introducing the next generation of ecosystem research in Europe: LTER-Europe´s multifunctional and multiscale approach. – In: Müller F, Baessler C, Schubert H, Klotz S (Eds), LongTerm Ecological Research – Between Theory and Application. Heidelberg, Berlin. Springer. ÖZKAYNAK B, RODRIGUEZ-LABAJOS B (2010) Issues of Scale and Participation in Local Scenario Development: Two Case Studies from Turkey and Spain. – In: Spangenberg J, Ghosh N (Eds), Indicators and Scenarios for Sustainable Development, Oxford University Press India, New Delhi. RODRIGUEZ-LABAJOS B, SPANGENBERG JH, MAXIM L, MONTERROSO I, BINIMELIS R, MARTINEZ ALIER J, KULDNA P, PETERSON K, UUSTAL M, GALLAI N (Eds)(2009) Assessing biodiversity risks with socio-economic methods: The ALARM experience. Pensoft Publishers, Sofia–Moscow. SETTELE J, HAMMEN V, HULME P, KARLSON U, KLOTZ S, KOTARAC M, KUNIN W, MARION G, O’CONNOR M, PETANIDOU T, PETERSON K, POTTS S, PRITCHARD H, PYSEK P, ROUNSEVELL M, SPANGENBERG J, STEFFAN-DEWENTER I, SYKES M, VIGHI M, ZOBEL M, KÜHN I (2005) ALARM – Assessing LArge-scale environmental Risks for biodiversity with tested Methods. Gaia-Ecological Perspectives for Science and Society 14: 69-72. SETTELE J, KÜHN I, KLOTZ S, HAMMEN V, SPANGENBERG J (2007) Is the EC afraid of its own visions? Science 315: 1220. SETTELE J, SPANGENBERG J, KÜHN I (2008) Large projects can create useful partnerships. Nature 453: 850. SETTELE J, SPANGENBERG JH, HAMMEN V, HARPKE A, KLOTZ S, RATTEI S, SCHMIDT A, SCHWEIGER O, STOLL-KLEEMANN S, ZAUNBERGER K, KÜHN I (in review). Integrated Analysis of Biodiversity and the ALARM experience: the science and art of project coordination as a condition for the assessment of biodiversity and environmental stressors. Global Environmental Change.
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THE FUTURE OF BIODIVERSITY AND BIODIVERSITY RESEARCH
Aspects of the Future of Biodiversity and Biodiversity Research
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MARTIN T. SYKES, THOMAS HICKLER & JOSEF SETTELE
The vast majority of examples presented in this atlas document what has already happened and explores the relationships between biodiversity and factors that influence it. In addition many of them are also oriented towards the future as can be seen in the many scenario based contributions. Throughout the underlying message is concerned with the future of biodiversity and its research. The tools Basic biodiversity data and monitoring If we look at the state of knowledge on biodiversity which was dealt with in chapter 1, it becomes obvious that we often lack even the most basic data about the living organisms that surround us. This starts with the knowledge of the species’ distributions, continues with their environmental requirements and derived from this the threats they face. Studies like the ones by Schweiger et al. (this atlas, pp. 216f.) depend on comprehensive compilations of distribution data. These data are in most cases compiled by volunteers and professionals (who get paid for other activities than these). In addition practically all the large-scale knowledge we have on population dynamics relies on volunteer monitoring networks. Thus, if there is little public support for such schemes it is necessary to employ pragmatic solutions at least to get them initiated. The German Butterfly Monitoring Scheme as described by Kühn et al. (this atlas, pp. 242f.) was initiated in response to activities by the press at the start of the ALARM project and its coordination was part of the ALARM work (and budget) at the Helmholtz-Centre for Environmental Research. As it is a very promising approach, the initiative is now supported within other research projects, but also is about to receive little long-term funding for the coordination. However, additional funding is required to guarantee continuity of a quality product. Such requirements are not only necessary for this particular group of species in one country, but are also needed for the vast majority of groups in the vast majority of countries. Priority setting for Conservation Different approaches to the setting of conservation priorities, given the availability of only limited resources, are explored by Vohland et al. (this atlas, pp. 234ff.) based on the European-wide Natura 2000 sites. The network is the biggest conservation network in the world and the network of sites ranges from the Mediterranean in the south to the Arctic in the north. They concluded that priority setting depends on both the selected approach and the scale. At the European scale mountains in the Mediterranean are a particular area of concern for conservation being species rich with many endemics and are also important watersheds subject to severe droughts. At regional and local scales the concern is with integration and development of different ecosystem services, e.g. agriculture, forestry to allow for space for species to adapt to a changing climate in an anthropogenic landscape. Conservation strategies Hickler et al. (this atlas, pp. 238f.) using a vegetation model explored what might happen to the regional-scale vegetation zones that are currently found in Natura 2000 sites. They use the dynamic vegetation model LPJ-GUESS and two scenarios of changing climate to the end of the 21st century to explore changes in the dominant tree species and associated vegetation types or habitats that would naturally occur in the Natura 2000 sites. Their results suggest that many of these sites show the potential for radical shifts in vegetation driven by the changing climate. In some cases this change may be delayed for example by mature trees remaining in the site even though regeneration is no longer possible under the different climate. They conclude that dynamic and flexible strategies for conservation in these sites need to be developed that take into account rapid changes in climate. Ecological Networks Changing landscapes caused by changing land use influences biodiversity in part by fragmenting the landscape and isolating populations of species. Ecological networks, where core areas are defined, buffer zones, corridors and stepping stones within the landscape, are designed to aid species dispersal especially under rapid climate change. Vohland et al. (this atlas, pp. 240f.) introduce networks in different (rivers, forest, grasslands) ecosystems in Germany, they conclude that further 232
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research is needed to identify species that need specific landscape structures and elements and to study the effects of reduced precipitation and increased fire risk. Managing Aliens The role of alien or invasive species in European landscapes is controversial, not all alien species are detrimental to the native ecosystems and some may even contribute to necessary ecosystem services. However some alien species can be severely detrimental to native biodiversity and to ecosystem functioning. Minchin (this atlas, pp. 244ff.) describes the management process involved in managing alien aquatic species. This involves risk assessment, targeting of species, monitoring, response, public education and controls. Management involves a wide option of possible actions from banning of importation to confinement and mitigation. Case studies include the zebra mussel and exotic aquatic plants. Biological control systems The biological control of species is discussed by Heong et al (this atlas, pp. 248f.) in an example from outside Europe, using tropical rice, where the control consists of pest invasion resistance and pest population size regulation. There at least 200 species of parasitoids and 150 predators that live in tropical rice fields. Maintaining their diversity is key to maintaining biological control systems. Factors such as landscape structure, habitat diversity, cropping and management practices can influence the success of these control systems. Overview and conclusions Policy options Piper & Wilson (this atlas, pp. 250f.) describe policy options and their influence on biodiversity under climate change as they concluded from the MACIS project. They advocate urgent reviews and upgrades with regard to current policies and new policy approaches should be evaluated, including the integration of policy across sectoral boundaries as well as developing cross-cutting options for example with regard to sustainable development. This may help in avoiding adverse interactions affecting biodiversity, and at the same time should increase the relevance of policy at local level, making effective implementation easier. Designated biodiversity sites will continue to be of vital importance to protect species and habitats so, where possible, they should be given further protection and enhancement, such as by buffer zones. New biodiversity areas are also needed (land, wetland and water bodies), both to reduce pressures on the most valuable sites and to enable species to disperse through landscapes over time, within their available climate space. They state that there is a clear need for measures at many scales: institutional level for policy; operational level for plans and technical level for implementation measures. This will help ensure consistency and avoid conflicts. Other necessary measures will be the better assessment of cumulative impacts, the recognition of ecosystem services as cost-effective means of supporting sustainable lifestyles, and the provision of adequate resources for the measures introduced. Risk assessment Finally Marion et al. (this atlas, pp. 252f.) summarize the whole ALARM project with regard to risk assessment, from scenario development, to impacts on biodiversity and the development of the risk assessment toolkit. While the term risk assessment can be interpreted in many different ways, they adopt a broad definition. In a formal sense risk is understood as a combined measure of the likelihood of a particular event and the consequence of that event. Biodiversity risk assessment therefore involves defining the event of interest and then assessing both the likelihood and its impact, or consequence, on biodiversity. Events which are relatively unlikely to occur but have a very serious consequence will be scored as high risk, as will events that are quite likely to occur but only have minor to moderate consequences. However, optimal management strategies are likely to be very different for each of these, even though the risk may be equally high. Therefore it is often more useful to report the likelihood and conse-
quence assessments separately. The process of risk assessment is plagued by uncertainties at every level. Thus when reporting risk assessments it is critical to provide information on both the assessment itself and on the quantitative and qualitative uncertainties involved. This knowledge is very important for end users by allowing them to gauge the quality of the information provided. When they are assessed impacts can be scored on both quantitative and qualitative scales. It is common to use a rather limited number of possible scores in a qualitative scale with some definition of what each point on the scale represents. Quantitative scales depend on the details of the aspects of biodiversity to be assessed (e.g., species richness or abundance/probability of presence in a given location). As the authors summarize, within this atlas – particularly in the ALARM contributions the term risk assessment is interpreted broadly to mean one or all of the following types of assessments: a) specification of the event of interest; b) the likelihood of this outcome; c) the consequence or impact; and d) the formal combination of these to produce a risk score (see box on page 253 for further details on risk assessment and its definition and application). The authors also emphasise that understanding the responses of particular species, species groups and entire ecosystems to actual and potential environmental change is an extremely complex and difficult task, as many of the case-studies in this atlas testify. For example, at present there is only limited understanding of how to predict the single or combined effects of different pressures on specific aspects of biodiversity (e.g., the separate and joint impacts of climate change and invasive species on pollinators, as dealt with by Schweiger et al. 2010, or how climate change and the future trade patterns will influence invasions). Furthermore they highlight that combining such assessments in order to arrive at a complete quantification of risks to biodiversity under historic, present day, or potential future scenarios is beyond current scientific understanding, and will depend on an individual’s values. Risk assessment there was based on the collective expertise of the ALARM consortium, which was distilled into a qualitative assessment of relative impacts on biodiversity. This involved developing a consensus about what pressures and aspects of biodiversity should be assessed, and the subsequent adoption of common qualitative impact scales to be used by experts within ALARM when completing a questionnaire. In order to enhance knowledge transfer and exploitation of the complex array of quantitative and qualitative risk assessments, the ALARM project has developed the Risk Assessment Toolkit (RAT) which provides a web-based interface to a database that allows the user to access: a) the outputs of risk assessments created by the scientific teams within ALARM; b) metadata about the quality, scale and scope of these assessments; and c) tools & methods for the creation of future risk assessments. The RAT will provide key summaries of particular subjects and access to the ‘most relevant’ risk assessments related to these. In addition a general search facility will enable users to search and organise multiple risk assessments according to a range of criteria including pressures, aspects of biodiversity and
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scenario, in order to compile a comprehensive collection of assessments relevant to their interests e.g. to compare assessments across scenarios. For future research activities and projects it is planned to further develop the Risk Assessment Toolkit (RAT) in order to make it applicable to a broader range of ecosystems and pressures (in isolation as well as in concert). In particular examples from outside Europe, for example the systems shown in Heong et al. (this atlas, pp. 248f.) will be more emphasised. Integrated research As Settele et al. (this atlas, pp. 208f.) state, it is well established that ongoing global change seriously affects biodiversity and ecosystem functioning. While separate effects of the main drivers of global change, such as climate change, habitat loss, chemical pollution and biological invasions, are increasingly well documented (see many contributions in this atlas), much less is known about their consequences when acting in combination. This may result in flawed conclusions when exploring sustainable management options for ecosystems and resources, as multiple pressures can act in a non-additive manner on biodiversity. Different environmental drivers rarely act in isolation, therefore it is important to consider interactive effects. In addition, global change seriously impacts on biodiversity and ecosystem functioning but little is known about the effects on essential biotic interactions. Until now the effects of global change are mainly investigated at organismic, population or community level, but knowledge about their effects on biotic interactions is scarce. There is increasing evidence that biotic interactions form an indispensable basis for the functioning of ecosystems and the provision of important ecosystem services. Thus, considering the effects of multiple interacting drivers of global change on biotic interactions represents a significant challenge for predicting the future consequences of global change. Integrative approaches within large projects is an appropriate tool to make researchers aware that there is a clear obligation to work across disciplines. Although this process of integration may take some time, it clearly can lead to answers for the most pressing questions in biodiversity risk research, as could be shown by the results of larger research initiatives like the ones included in the present atlas (see also Los, this atlas p. 62; Mirtl et al., this atlas, pp. 52f.; Pfeiffer et al., this atlas, pp. 26ff.; Settele et al., this atlas, pp. 38ff., 208f. & 229f.; Spangenberg et al., this atlas, pp. 204ff.). References SCHWEIGER O, BIESMEIJER JC, BOMMARCO R, HICKLER T, HULME PE, KLOTZ S, KÜHN I, MOORA M, NIELSEN A, OHLEMÜLLER R, PETANIDOU T, POTTS SG, PYŠEK P, STOUT JC, SYKES MT, TSCHEULIN T, VILÀ M, WALTHER G-R, WESTPHAL C, WINTER M, ZOBEL M, SETTELE J (2010) Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination. Biological Reviews. doi: 10.1111/j.1469-185X.2010.00125.x
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KATRIN VOHLAND, THOMAS HICKLER, JANE FEEHAN, MARLIES GUMPENBERGER, MIGUEL B. ARAÚJO & WOLFGANG CRAMER
Introduction The loss of habitat is one of the major threats to biodiversity. The main drivers of this loss are the intensification of agriculture, urbanisation and infrastructure development. To conserve biodiversity and the associated ecosystem services, effective nature conservation policies are urgently required. Only limited resources are available for conservation in terms of money and land. Priority setting in conservation has been dominated by concepts such as biodiversity hotspots, which are regions with high numbers of highly vulnerable (endemic) species (Myers et al. 2000). But there are other useful concepts for conservation such as representativeness of ecoregions, the provision of ecosystem services including carbon sequestration, pollination or landscape aesthetics, and approaches to maintain adaptive capacity of ecosystem function under climate change. In this chapter, the different approaches for priority setting are compared on the basis of the European network Natura 2000 (Figure 1). We show that priorities are highly dependent on the approach chosen and the spatial scale.
Natura 2000 The EU’s Natura 2000 network is the biggest conservation network in the world (Figure 1). As a whole, about 20 % of the European Union’s area is included in the Natura 2000 network. Here, we explore the consequences of pursuing different priorities for biodiversity conservation, and analyse the differences in spatial performance, using the most recent spatial information on the Natura 2000 network received from the European Environmental Agency (EEA) in 2007. Risks for biodiversity The Mediterranean region is the European region with the highest total number of species and the highest number of restricted range (endemic) species. With climate change the region is threatened with increased drought (Schröter et al. 2005). According to the world classification of hotspots (Myers et al. 2000) the Mediterranean region is the only EU region that is classified as a global hotspot (Figure 2). Representativeness Protected areas should represent the most typical habitats and species in all regions. There are different approaches used to classify landscapes and ecosystems at different scales, and therefore
different measures of representativeness. Being on a medium scale of complexity, the ecoregion classification of the WWF (2008) includes all regions of Europe. A comparison of the representativeness of different WWF Ecoregions within the Natura 2000 network reveals that the mountain areas are best represented while lowland beech forest is less represented according to this kind of classification (Figure 3). Representativeness of Ecoregions is not the only criterion that matters; representation of species is also important since they are the carriers of evolution and ecosystem services. The numbers of bird and mammal species per grid cell are usually a useful indicator of biodiversity since they are relatively easy to measure (Figures 5 and 6, Araujo et al. 2005). Ecosystem services Nature conservation areas are increasingly recognized for the ecosystem services they provide, such as flood control, climate regulation, carbon sequestration, and recreation. For example, the conservation of old forest and bogs prevents emissions of the greenhouse gas CO2, and sustainable forest management might even contribute to an uptake of greenhouse gases (Figure 4).
Natura 2000 sites also differ in their potential natural carbon content. In the north and at high altitudes, carbon in living vegetation is limited by short and cold growing seasons, while south of the Alps water scarcity limits the amount. The carbon content in the soil shows a different pattern, assigning the northern Swedish soils the highest content because soil decomposition is limited by cold temperatures (Figure 7). Climate change laboratories Changing climates are intrinsically related to biosphere evolution. Species interact with their environment which has never been stable. But the response possibilities differ between the species. For example, some are restricted by narrow temperature ranges, others by their diet. Changing climates, and especially rapid changing climates, require a large repertoire of different species that are adapted or can adapt to new environments. Physically large parts of this information are in the genome of the different species. Recently radiating species are more similar to each other than for example fossil relicts. The loss of these relict species might reduce genetic information more than the loss of a more recently radiating species. Therefore, phylogenetic age combined
Natura 2000 sites
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Figure 1. Natura 2000 Network in Europe.
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Figure 2. Parts of the Natura 2000 network belonging to a global hotspot identified by Myers et al. (2000), marked in red.
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50 % 40 % 30 % 20 % 10 % 0 % Pyrenees Borealic uplands Dinaric western Balkan The Carpathiens Ibero-Macaronesian region Alps Eastern Balkan Hellenic western Balkan Hungarian lowlands Italy and Corsica Western highlands Eastern plains Baltic province Central highlands Pontic province Western plains Central plains Fenno-scandian shield England Ireland and Northern Ireland
Figure 3. Representativeness of Ecoregions as classified by WWF (2008) within the Natura 2000 network.
with threat can be a good indicator for the urgency of action. In a study for mammals, mainly large mammals belong to the most endangered ones (Isaac et al. 2007). On the other hand, radiating species might have a higher capacity to adapt to rapid changing environments. However, for successful adaptation a minimum population size is required in a comparatively large and wild area. Wild areas Large wild areas might be required to allow stable populations of plants, animals and other organisms, and the food webs through which they interact, to persist sustainably. The wildest areas in Europe are national parks and are usually represented by the IUCN categories I-II. However, only minor parts of Europe, mainly in Scandinavia, belong to this category (Figure 8). The human impact in protected areas is expected to increase with the IUCN category number as are other ecosystem services such as recreation and sustainable agricultural produc-
Figure 4. Old trees store large amounts of carbon, host many other plant and animal species, and supplementary support recreation by their inspiring forms. Photo: K.Vohland.
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Number of bird species per 10 km2
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Figure 5. Mean breeding bird diversity in Natura 2000 sites as derived from data from Araújo et al. (2005).
tion. Especially in Germany, large parts of the landscape are assigned to category V, although mostly lying outside the Natura 2000 network. As Europe is dominated by cultural landscapes, biodiversity of man-made ecosystems, e.g. farmlands and forests, can be substantially increased by changes in management, such as organic farming.
Figure 6. Mean mammal diversity in Natura 2000 sites as derived from data from Araújo et al. (2005).
Retreat areas with high gradients Climate change induces movements of plants and animals, mainly towards the poles as well as to higher altitudes. Knowledge of previous climate changes, namely glacial periods, indicates that species can also move to lower altitudes. Mountainous areas, which present strong climatic gradients on a small spatial scale, are therefore important
Carbon content of the soil
refugia to allow migration of species in response to climate change and therefore act as an insurance to maintaining the regional species pool in the long term (Figure 9). Therefore, mountainous areas are assigned a high conservation value (Hannah 2001). Natura 2000 network areas occur across different altitudinal ranges in Europe, with large parts of the areas
with the highest ranges lying within the mountainous regions of the Mediterranean (Figure 10). Biodiversity Governance Nature conservation strategies are, of necessity, spatially explicit to some extent. Therefore success depends strongly on competing land uses, mainly agriculture. The share of cropland in
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Figure 7. Carbon content of the vegetation calculated by an ecosystem model [LPJ (http://www.pikpotsdam.de/members/erbrecht/lpjweb/), provided by Marlies Gumpenberger]. The warmer the temperatures the higher the turnover rates: in boreal zones in particular, large amounts of litter accumulate and contribute to soil carbon.
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Figure 8. Natura 2000 sites and IUCN categories (IUCN 2008). While in Scandinavia some sites of the Natura 2000 network belong to the highest IUCN-category, in Germany category V prevails. Further, also in other EU countries many areas outside the Natura 2000 network are categorized under the IUCN framework, also mainly in the categories V and VI.
different regions of Europe is one indicator of the strength of competition for land (Figure 11). Biodiversity Governance aims to find sustainable compromises between competing ecosystem services such as food and fibre production, housing, and biodiversity protection. Conclusion Priority setting depends very much on the chosen approach and the scale of decision. At the European scale, the mountainous areas of the Mediterranean demand priority for several reasons: they are species-rich, with many endemic species, they are crucial watershed areas, and they are particularly vulnerable, especially to drought. They also provide a variety of niches in a small area, which implies an evolutionary and adaptation insurance. From the perspective of representativeness, the larger plain areas of Europe are underrepresented, e.g. with regard to the ecoregion classification, although, additionally, they do not belong to IUCN I or II sites. However, breeding birds and mammals find a variety of niches outside mountainous areas, and from a carbon perspective, the north European plain areas provide sequestration and storage capacities. On regional and local decision scales, the integration of different ecosystem services including agriculture, forestry and as cultural services prevail, leading to very tiny-scaled patterns as, for instance, in Germany. Here, conserving large-scale processes
is less important than providing space for adaptation in a highly anthropogenic landscape. For sustainable biodiversity conservation facing climate change we need a spatial mixture of both strategies: large and wild retreat areas as natural laboratories for future development, and the integration of biodiversity conservation in cultural landscape management. References ARAÚJO MB, THUILLER W, WILLIAMS PH, REGINSTER L (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography 14: 17-30. BUTLER A, DENDONCKER N, ROUNSEVELL M, MARION G (2008) Downscaled projections of future European land use for the ECOCHANGE and ALARM projects: Guidance notes for Version 1.4. Technical report, Biomathematics and Statistics Scotland, Edinburgh (UK). URL: http:// www.bioss.ac.uk/alarm/downloads/downscaled_guidance_v1.4.doc HANNAH L (2001) The role of a global protected areas system for conserving biodiversity in the face of climate change. – In: Visconti G, Beniston M, Iannorelli ED, Barba D (Eds), Global Change and Protected Areas. Kluwer Academic Publisher, 413-422. ISAAC NJB, TURVEY ST, COLLEN B, WATERMAN C, BAILLIE JEM (2007) Mammals on the EDGE: Conservation Priorities Based on Threat and Phylogeny. PLoS ONE 2: e296 IUCN (2008) World database on protected areas. http://www.unep-wcmc.org/wdpa (downloaded 17.2.2008) MYERS N, MITTERMEIER RA, MITTERMEIER CG, FONSECA GAB, KENT J (2000) Nature 403: 853-858. PELCOM (http://www.geo-informatie.nl/projects/pelcom/public/index.htm)
Figure 9. Mountainous areas offer large gradients in ecological conditions and provide many niches for plants and animals. Besides they store natural history and represent natural monuments, too. Photo: K.Vohland.
SCHRÖTER D, CRAMER W, LEEMANS R, PRENTICE C, ARAÚJO MB, ARNELL NW, BONDEAU A, BUGMANN H, CARTER TR, GRACIA CA, DE LA VEGA-LEINERT, AC, ERHARD M, EWERT F, GENDINING M, HOUSE JI, KANKAANPÄÄ S, KLEIN RJT, LAVOREL S, LINDNER M, METZGER MJ, MEYER J, MITCHELL TD, REGINSTER I, ROUNSEVELL M, SABATÉ S,
SITCH S, SMITH B, SMITH J, SMITH P, SYKES MT, THONICKE K, THUILLER W, TUCK G, ZAEHLE S, ZIERL B (2005) Ecosystem service supply and vulnerability to global change in Europe. Science 310: 1333-1337. WWF (2008) Ecoregions of the world. http:// www.worldwildlife.org/science/ecoregions/ biomes.cfm (downloaded 17.4.2008).
Elevation Range Percentage of agricultural land use
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Figure 10. Range of elevation covered by Natura 2000 in different provinces in Europe, in red are marked the top 100. The higher the differences, the more niches exist that allow species movements along altitudinal gradients as an adaptation to climate change. Despite the large extension of the provinces in Scandinavian countries, the highest height ranges are found in the alpine areas of the Pyrenees, Alps and Dalmatians, mostly within the Mediterranean area.
Figure 11. Percentage of agricultural land use per grid cell at Natura 2000 sites in Europe. Red indicates a high percentage of land used for agriculture, while in blue parts forestry predominates. Extensive agricultural land use enhances species diversity while intensive agricultural land use reduces species diversity. An increasing population, an increasing demand for food and the use of crops for biofuels is intensifying agriculture more than ever (Crop data for 2000 from Butler et al. 2008 on PELCOM basis, ALARM).
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Vegetation on the Move – Where Do Conservation Strategies Have to be Redefined?
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THOMAS HICKLER, KATRIN VOHLAND, LUIS COSTA, WOLFGANG CRAMER, PAUL A. MILLER, BENJAMIN SMITH, JANE FEEHAN, INGOLF KÜHN & MARTIN T. SYKES
By the end of the century, climate zones in many areas across Europe might shift by about 500 km to the north and east (see Fronzek et al., this atlas, pp. 68ff.; Ohlemüller et al. 2006), corresponding to nearly 14 m per day. Rapid climate change will affect the potential distribution of major vegetation types across Europe. For effective planning of nature conservation and landscape management, it is necessary to know where major current vegetation types might be replaced in the future. In this study, we projected just this kind of potential change with a vegetation model. Based on the model results, we identified Natura 2000 areas in which the current potential natural vegetation is likely to change, implying that current conservation strategies should be re-considered. Natura 2000 is the EU’s system of protected areas established under the 1992 Habitats Directive1. It covers more than 20 % of EU territory2. Although species composition will change, Natura 2000 will continue to provide essential space for nature and continuing flow of ecosystem services.
Vegetation modelling Most existing regional projections of changes in species distributions and vegetation are based on one of two different approaches: bioclimatic envelope models have been used for projecting changes in suitable climate space for large numbers of species, but interactions between species, such as competition, are neglected; dynamic global vegetation models (DGVMs) have been used to model transient changes in vegetation and ecosystem properties, such as uptake or release of CO2, accounting for competition between different types of plants. DGVMs, however, commonly represent ecosystems in terms of a small number of plant functional types (PFTs), and vegetation dynamics are highly simplified. Thus, both approaches have obvious shortcomings. Here, we simulated future changes in potential natural vegetation (PNV) across Europe up until the year 2100, combining features of both approaches. We used a model that integrates the generalized ecophysiological model
b HadCM3
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representations of the Lund-PotsdamJena (LPJ) DGVM with detailed representations of vegetation dynamics, as are commonly used by tree-speciesbased forest gap models (“LPJGUESS”; Smith et al. 2001). In contrast to earlier PFT-based simulations on this scale, we ran the model with 16 major tree species and four shrub and herbaceous PFTs (Hickler et al. 2009, supplement). The output of the model was translated into general vegetation classes similar to those in an independently derived map of European potential natural vegetation (Bohn et al. 2003). The modelled present-day vegetation (Figure 1a) corresponded well with the independent map. The climate data used as input to the vegetation model consisted of the output from two Global Climate Models (GCMs) run with one scenario for the future development of global greenhouse gas emissions (ALARM scenario BAMBU (see Fronzek et al., this atlas, pp. 68ff.) corresponding with the A2 scenario of the Intergovernmental Panel on Climate Change [IPCC]3). The overall climate projected for Europe by the HadCM3 GCM is relatively hot and dry, while that predicted by the PCM GCM is rather cool and wet.
Figure 1. Modelled current (averaged for 1961-1990) and future (2071-2100) potential natural vegetation (PNV) in Europe under the BAMBU (IPCC A2) emission scenario, with two climate models (HadCM3, PCM).
Future Vegetation According to the model, the potential vegetation type covering between 31 and 42 % of Europe (as defined by the geographical window used here) and between 30 and 44 % of the current Natura 2000 reserve network will change by 2085 (averaged for 2071-2100), depending on the climate model (Figures 1 and 3a). In both models, the northern boundaries of temperate forests and hemiboreal forests in southern Scandinavia and north-eastern Europe (Figure 2) move northwards (about 300 to 500 km), and most of the arctic/ alpine tundra (Figure 4) is replaced by forests. Thermophilous mixed forests also expand to the north. These shifts are primarily caused by the northward shift of temperature zones. In large parts of the Mediterranean, sclerophyllous forests (primarily Quercus ilex and Pinus spp.) are replaced by shrubland. In this case, the changes are a result of increased drought. Different countries are affected very differently by the vegetation changes. There is a tendency towards more
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Arctic/alpine desert Arctic/alpine tundra Boreal/alpine forest/woodland Boreal/alpine conifer forest Hemiboreal mixed forest Temperate beech and mixed beech forest Temperate mixed broad-leaved forest Thermophilous mixed broad-leaved forest Mediterranean sclerophyllous forest/woodland Mediterranean sclerophyllous scrub Steppe woodland Steppe
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http://www.ipcc.ch/ipccreports/sres/ emission/index.htm
marked effects in northern and southern countries, but France is strongly affected as a country at intermediate latitudes (Figures 3a and 6). However, some vegetation shifts might have stronger impacts on conservation planning than others. The replacement of some hemiboreal mixed forests by temperate mixed broadleaved forests in the Baltic countries, for example, might have less severe impacts than the replacement of arctic alpine tundra by forest, or the forest dieback in parts of the Mediterranean (Figure 1). In the presented simulations, it was assumed that establishment of a new tree species would readily occur once the climate became favourable towards it. In reality, however, limited seed dispersal and other factors, such as soil conditions and presence of mycorrhizae might limit the true response of vegetation to climate change, even in the absence of human management decisions (see below). Thus, the model may be expected to overestimate the potential future changes in vegetation. The model does, however, account for lags in the vegetation response due to the generation times and interactions of species through competition. In the case of forests, such lags operate on time scales that are long relative to the projected changes in climate, and the long-term equilibrium vegetation will not be achieved by the end of the simulations. The projected vegetation thus represents a “transient” phase in the transition to a future state in equilibrium with the climate (Figure 5). Land use was not accounted for in the simulations. Browsing by managed livestock populations, such as reindeer
Figure 2. In many mixed forests composed of temperate broad-leaved trees and boreal conifers, climate change will favour temperate trees in competition with the conifers, which may ultimately disappear. The picture shows a mixed forest with Smallleaved Lime (Tilia cordata) and Norway Spruce (Picea abies) in Bialowieza National Park (eastern Poland). Photo: B. Smith.
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Natura 2000 areas with no change in vegetation type according to either of the GCMs
Natura 2000 forests with no change in vegetation type according to either of the GCMs
Natura 2000 areas with change in vegetation type according to either of the GCMs
Natura 2000 forests with change in vegetation type according to either of the GCMs
Natura 2000 areas with change in vegetation type according to both of the GCMs
Natura 2000 forests with change in vegetation type according to both of the GCMs
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Figure 3. Natura 2000 areas that are projected to undergo a shift in vegetation: a) all Natura 2000 areas, b) forested Natura 2000 areas.
Figure 4. Treeless alpine vegetation and the species living there are under pressure because of shifts of the climatic treeline to higher altitudes and latitudes. Photo: T. Lennartsson.
in Scandinavia, could be used to counteract the projected shift of the climatic treeline to higher altitudes. In many heavily managed areas of the Alps, the current treeline is only weakly correlated with climatic variables (Dullinger et al. 2004). Many Natura 2000 sites are designed to protect a cultural landscape of open vegetation types, such as pastures and meadows. Our results apply
mostly to the tree component in these areas. Many protected forests are closer to their natural state, and our results are therefore of particular significance for these. We identified the forested Natura 2000 areas covered with forests by overlaying the Corine 2000 land cover data (CLC20004) with the protected area database. The general pattern of vegetation changes across Europe for forested Natura 2000 sites, however, was very similar to the pattern for all sites (Figures 3 and 6). It is important to note that the two climate scenarios used here do not represent the full spectrum of possible future climates (see Fronzek et al., this atlas, pp. 68ff.). Uncertainties also arise from the vegetation model that we applied. Thus, further research is necessary to provide more reliable guidance to conservation planners, and adaptation strategies should be flexible in order to account for uncertainties in our projections.
Conclusions Our results suggest that conservation measures have to account for climate change as a driver of potentially rapid vegetation changes across Europe. Consequences for conservation include the need for objectives to be more dynamic, focusing on ecosystem health and not only on species composition. We identify those areas where substantial changes are most likely to occur. Long-lived trees are an important component of most reserves, and many young forests today are likely to face a climate to which the adult trees are not optimally adapted, which might lead to competitive replacement and increased susceptibility to environmental stresses, such as drought, pests and pathogens. As trees also influence habitat availability and quality for many other species, because of their large size and dominant role in many ecosystems, the results have implications for most organism groups.
References BOHN U, GOLLUP G, HETTWER C, NEUHÄUSLOVA Z, RAUS T, SCHLUETER H, WEBER H (2003) Map of the natural vegetation of Europe. Landwirtschaftsverlag, Münster, Germany. DULLINGER S, DIRNBOCK T, GRABHERR G (2004) Modelling climate change-driven treeline shifts: relative effects of temperature increase, dispersal and invisibility. Journal of Ecology 92: 241-252. HICKLER T, FRONZEK S, ARAÚJO MB, SCHWEIGER O, THUILLER W, SYKES MT (2009) An ecosystem model-based estimate of changes in water availability differs from water proxies that are commonly used in species distribution models. Global Ecology and Biogeography 18: 304-313. OHLEMÜLLER R, GRITTI ES, SYKES MT, THOMAS CD (2006) Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931-2100. Global Ecology and Biogeography 15: 395-405. SMITH B, PRENTICE IC, SYKES MT (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography 10: 621-637.
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Percentage of area in Natura 2000 forests undergoing vegetation type change according to both and either of the GCM's
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Year Figure 5. Forest succession takes time: modelled species composition in terms of the above-ground biomass for a forest in central southern Sweden (15º08' E; 58º9' N) under favourable soil conditions, starting after disturbance in the year 1,500 AD. Early 20th century climate (1901-1930) was assumed between 1500 and 1900, actual historical climate change during the 20th century, 100 years changing future climate (HadCM3-A2), and 500 years of future climate (2091-2100, de-trended and repeated for 2100 to 2600). 4
Figure 6. Fraction of the Natura 2000 area and forested Natura 2000 area projected to undergo changing vegetation type by country and climate model.
http://dataservice.eea.europa.eu/dataservice/metadetails.asp?id=1007
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Ecological Networks as One Answer to Climate Change
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KATRIN VOHLAND, STEFAN KLOTZ & SANDRA BALZER
Introduction Biodiversity as represented at different scales, genes, species and ecosystems is reduced by a variety of human activities. Ecosystem functions are modified, and with regard to human requirements ecosystem services are reduced (Reid 2005). Land use changes are the most important factor affecting biodiversity, interacting with climate change, pollution and other threats (Watt et al. 2007). Main mechanisms relevant are that animals are disrupted from their diet, nesting habitats and mating partners, and also populations and subpopulations of plant species become smaller and more fragmented. As a result, the risk of local extinction increases. Global extinction might increase, if there is no pathway to recolonize or colonize the former or other suitable habitats, respectively. Additionally, it is expected and observed that climate change induces shifts of distribution areas, which generally means polewards and upslope. To ensure migration pathways landscape resistance has to be low enough to allow crossing. Ecological networks therefore are seen as one of the main solutions to provide landscape permeability for plants and animals. However, the suitability of the suggested elements of the ecological network differs for different organisms (Kettunen et al. 2007).
Ecological Networks as an answer to landscape fragmentation Ecological networks are generally defined as networks of core areas, buffer zones, corridors and stepping stones interacting with the landscape matrix in which they are embedded. Ecological networks are becoming more and more important in public consciousness. They are commonly related to linear structures such as hedges or rivers (cf.
Figure 1) but functions are also assigned to smaller patches such as stepping stones for the dispersal and migration of target species. The proposed selection of target species for the Pan European Ecological Network (PEEN), an initiative for a European ecological network of the Council of Europe, follows the criteria of legal protection, threat and degree of endemism (Ozinga & Schaminée 2005).
Figure 2. The thick shelled river mussel (Unio crassus) suffers by high sedimentation rates and reduced oxygen as caused by an increasing number of low water days. Furthermore, rivers are artificially fragmented, overloaded with nutrients (nitrogen) and inhabited by non-host fishes for the glochidiae (larvae). Photo: courtesy of "Haus der Natur”, Cismar, Vollrath Wiese.
Climatic water balance difference (mm) (1961-90 to 2046-55)
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Figure 1. Proposed core areas of German ecological network of rivers (Fuchs et al. 2007) with underlying projected changes in the Climatic Water Balance in the respective ecoregions, 2046-2055 compared to 1961-1990. Left: Wet scenario, right: dry scenario, both based on the same ECHAM5 temperature trend but different precipitation projections of the regional climate model STAR (Orlowsky et al. 2008); spatial data on ecoregions and the proposed German ecological network of rivers are from the Federal Agency of Nature Conservation, Germany.
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Ecological network for forest species Many species rely on forests to fulfill dietary requirements or to find shelter. Forest might be connected via forest fragments or hedges as stepping stones. For example, in Germany large areas are proposed to be part of the ecological network. There is a large discrepancy between the potential natural vegetation, and the high percentage of conifer forest that actually covers the landscape (Figure 3). Furthermore, with climate change advancing especially confers such as spruce are at risk, but also proneness of pine to fire increases.
BOX: Transition zone between forest and grassland
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Ecological network for river species For rivers it is known for long time that one of the major threats to their species is the high fragmentation. For example, some fishes are no longer able to reach their spawning grounds. Another threat is the high degree of river regulation which reduces the natural dynamic of the system. As a new and increasing threat caused by climate change, the increasing risk of low water periods is added, especially in regions with decreasing precipitation (Figure 1). Low water induces higher temperatures, higher nutrient loads and less oxygen of the remaining water body, with negative effects for most species under the Habitats’ Directive Natura 2000 or the indicators of the EU Water Framework Directive to judge (Figure 2).
Ecological network for species of open grasslands A variety of endangered species needs open habitats and landscapes. Some of them react on climate change by colo-
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For some tropical and Mediterranean regions open grassy landscapes with scattered trees, resembling at parks, are typical. In contrast, in most industrialized landscapes the transition between grassland or open habitats and forests is sharp. Many species rely on both types of habitats, for example trees are needed for breeding or nesting, and open habitats are needed for foraging. As hegdes and grass stripes compete for space to connect habitats, transition zones might be a mean to combine approaches of different species, and also support spatial and functional connectivity between forest as well as open habitats.
nization new regions in the North. Many species of open habitats such as grasslands fulfill important ecosystem functions such as pollination and therefore open habitats as well as their connectivity will remain an important goal for nature conservation. Spatial connectivity and functional coherence The effectiveness of ecological networks is determined by the total network area, the habitat quality of the components, network density and permeability of the matrix (Opdam et al. 2003). Together, these features constitute the spatial cohesion of the landscape that allows functional relations. Ecological networks can be delineated at different spatial scale. Small species with low dispersal ability need often networks on local to regional spatial scale; larger species with higher dispersal ability need them on larger spatial scales. Although it is theoretically approved that species need space for dispersal, the practical evaluation of the effectiveness of the ecological networks is rare and difficult. There are different methods to analyse dispersal patterns. Radar tracking or the use of markers are some common field practices but cost and time intensive. Furthermore, dispersal not only relates to the transition of the landscape but also to the successful establishment of species at a new habitat which is even more time consuming to investigate. Another facet concerns the differentiated dispersal pathways. Many species are dispersed by animal or human vectors, and linear structures in landscapes might be used by invasive species, too.
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Figure 3. Forest areas in Germany according to data derived from Corine Land Cover 2000. Right: Forest type as indicated by CLC 2000, left: Potential natural vegetation according to Bohn et al. 2000/2003 in current forest areas. The percentage of conifers is much higher than naturally expected.
Functional coherence at different spatial scales As species have different requirements with regard to dispersal pathways, spatial planning should follow a nested approach. A low scale, for example within the range of a flightless cricket, a complete gradient of ecological conditions should be covered so that the crickets can adapt their habitat to changing conditions “on feet”. At medium scale, for species such as red deer or lynx, an analyses of their habitat, dispersal ability and migration
BOX: Microhabitat scale A study dealing with projected climate change impacts on the snail (Gastropoda) community of the Bavarian Forest national park revealed that a) some snails except for example (Semilimax kotulae) are expected to increase their distribution area, and b) that the occurrence of old trees indicating old habitats is a main pre-
Broad leaved forest
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dictor of abundance of those species (Müller et al. 2009). As snails have a low dispersal ability, the occurrence of suitable habitat in form of old forests generating cool conditions is also at smaller scale a prerequisite to allow species the adaption of their distribution range to climate change.
requirements is necessary to effectively place mitigating infrastructure, e.g. green bridges (Figure 4). At large scale, enough space providing habitats throughout Europe is necessary to give the possibility for species to adapt their distribution area to changing environmental conditions. Conclusion If ecological networks are perceived as linear landscape structures only, they however might have an important function in providing supplementary space, but their full potential is not realized. Ecological networks work at different scales, in very different shapes, for different plant and animal species. A whole bunch of habitats buffers against local extinction. To adapt the ecological network to climate change, research is still needed a) to identify on one hand species relying on specific landscape structures and elements, b) to optimize landscape elements for the different requirements, and c) to reduce negative impacts deriving from climate change such as low water periods or forest fire, and d) for suitable future habitats and the establishment of species. Future planning should also integrate “normal” agricultural landscape in order to reduce the barrier function and enhance landscape permeability. Different scales for the different target organisms and systems should be considered and combined in a nested approach. References
Figure 4. A “Green Bridge” allowing larger mammals such as lynx or deer to cross large roads. Photo: courtesy of Uwe Riecken, BfN.
BOHN U, NEUHAUSL R, GOLLUB G, HETTWER C, NEUHAUSLOVA Z, SCHLUTER H, WEBER H (2000/2003) Karte der naturlichen
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Vegetation Europas. Masstab 1 : 2 500 000. Teil 1: Erlauterungstext mit CD-ROM; Teil 2: Legende; Teil 3: Karten. Landwirtschaftsverlag, Münster. FUCHS D, HÄNEL K, JESSBERGER A, RECK H, REICH M, SACHTELEBEN J, FINCK P, RIECKEN U (2007) National bedeutsame Flächen für den Biotopverbund. Natur und Landschaft 82: 345-352. KETTUNEN M, TERRY A, TUCKER G, JONES A (2007) Guidance on the maintenance of landscape connectivity features of major importance for wild flora and fauna. Guidance on the implementation of Article 3 of the Birds Directive (79/409/EEC) and Article 10 of the Habitats Directive (92/43/ EEC). Institute for European Environmental Policy (IEEP), Brussels. MÜLLER J, BÄSSLER C, STRÄTZ C, KLÖCKING B, BRANDL R (2009) Molluscs and climate warming in a low mountain range national park. Malacologia 51: 133-153. OPDAM P, WASCHER D (2004) Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biological Conservation 117: 285-297. ORLOWSKY B, GERSTENGARBE F-W, WERNER PC (2008) A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM. Theoretical and Applied Climatology 92: 209-223. OZINGA WA, SCHAMINÉE JHJ (2005) Target species – Species of European concern. A database driven selection of plant and animal species for the implementation of the Pan European Ecological Network, 1-193, Alterra-report 1119, Wageningen, Alterra. REID WV (2005) Ecosystems and Human Wellbeing. Synthesis. A Report of the Millennium Ecosystem Assessment. WATT AD, BRADSHAW RHW, YOUNG J, ALARD D, BOLGER T, CHAMBERLAIN C, FERNANDEZGONZALEZ F, FULLER R, GURREA P, HENLE K, JOHNSON R, KORSOS Z, LAVELLE P, NIEMELÄ J, NOWICKI P, REBANE M, SCHNEIDEGGER C, SOUSA JP, VAN SWAAY C, VANDBERGEN A (2007) Trends in Biodiversity in Europe and the impact of land use change, 135-160. – In: Hester RE, Harrison RM (Eds), Issues in Environmental Science and Technology, No. 25, Biodiversity Under Threat. The Royal Society of Chemistry.
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Establishing a Volunteer-Based Butterfly Monitoring Scheme – the German Experience
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ELISABETH KÜHN, ALEXANDER HARPKE, NORBERT HIRNEISEN, REINART FELDMANN, PATRICK LEOPOLD & JOSEF SETTELE
In spring 2005 the first nationwide monitoring scheme for butterflies (and diurnal moths) was launched in Germany (TMD = TagfalterMonitoring Deutschland). It is coordinated by the Helmholtz Centre for Environmental Research – UFZ. In order to attract public attention and to find volunteers for the project, it was
servation in gardens, schools and public green spaces as well as a one-day campaign for recording a selection of six common species in people’s neighbourhoods. As a side effect of the TV show and a press conference, TV program guides and newspapers reported on “Abenteuer Schmetterling” and the new monitoring throughout the year.
projected schemes active schemes
heartedness and joy of life and for an intact nature in general. The decline of butterflies makes the loss of biodiversity obvious to everyone. To join a nationwide long-term monitoring scheme gives every volunteer the chance, to contribute to the knowledge of butterflies in his/her country which leads to a better protection of butterflies. Since the focal point of the TMD is to count butterflies (and not moths), even beginners can learn to determine the most common species in a relatively short time. This way, important data for most of the German butterfly species are collected, which contributes to the analyses of population trends, distribution shifts and (together with weather data) the impact of climate change on insects. Monitoring methods The TMD network now consists of transect walkers, regional coordinators and the UFZ and GfS (Gesellschaft für Schmetterlingsschutz = Society for the Conservation of Butterflies and Moths) as principal coordinators of the project. The success of the scheme mainly depends on the number and efforts of these volunteers, who spend their free time collecting butterfly data by walking along fixed routes. The regional coordinators are experts on butterflies (and moths) who have agreed to be contact persons for the recorders in their region or state. Like the recorders, they volunteer their time. Results from a 2008 questionnaire have shown that in Germany each of them takes care of six transect walkers. On average they invest
Figure 2. Monitoring data will expose how species like Coenonympha arcania are effected by changes in climate and land use. Photo: Knud Schulz.
slightly more than 2 hours per week on this activity. 40 % are able to link their TMD work with their jobs and 1/3 can profit from TMD results for their own scientific and/or professional interests. Besides helping with identification and field methodology, 2/3 of these regional coordinators organize workshops and field trips and 46 % additionally interact with the media. Butterflies in Germany are counted according to the method of Pollard & Yates (1993) in order to guarantee the comparability of results with those from other European monitoring schemes. Habitat type and location of the transects are chosen by the walkers themselves. That means they show a biased spread across the country area and they are not selected by any random choice mode. A transect
Figure 1. European Butterfly Monitoring Schemes (according to Van Swaay et al. 2008).
integrated in a public relations campaign named “Abenteuer Schmetterling” (Adventure Butterfly). In March 2005, a popular science show on public German TV (“Abenteuer Wissen”) reported on the decline of butterflies in Europe and the research at UFZ. Here and in other broadcasts throughout the summer of 2005 the public was encouraged to register as volunteer transect walkers in the new monitoring scheme (“take a walk in service of science”). Simultaneously the BUND, Germany’s representative of “Friends of the Earth” and second largest environmental NGO, appealed to its members to become butterfly recorders. Other components of “Abenteuer Schmetterling” were a contest for the best ideas for butterfly con242
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A census of butterflies? Although butterflies are already very well studied, there are still large gaps in the knowledge of their ecology, especially on a national scale. Little is known about the nationwide development of populations and the behaviour of species at their edges of distribution. Where do new species immigrate and where do other species retreat or become extinct? While for some German states good compilations are available, a nationwide distribution atlas is still lacking. Remarkably, the largest gaps of knowledge on distribution, and particular dynamics and trends, exist for the more common species. Butterflies are very easy to detect in the field and have a highly positive image. They are a symbol of light-
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Figure 3. Day flying moths like Euclidia glyphica may be recorded optional. Photo: Knud Schulz.
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Figure 4. Monitoring data provide evidence for a more frequent third generation of Aglais io in Germany. Photo: Walter Mueller.
consists of a variable number of sections, each 50 m in length. The habitat type within a section should be homogenous. All butterflies and burnet moths seen within a virtual 5 × 5 × 5 m cube are recorded (see Van Swaay et al. 2008). Registration of diurnal moths is optional. The TMD aims, with its standardized method, to generate reliable and comparable recording data, which are of great value for reporting and interpretation (also on the European scale). In cooperation with science4you, a publisher of interactive media, the UFZ and GfS have built an internet platform for recorders to enter the data collected in their transects. This internet platform also provides technical and scientific information and includes a forum to exchange news and ask questions (www.tagfalter-monitoring.de; in the German language). Monitoring objectives More and more European countries are joining the monitoring network – cooperating under the umbrella of Butterfly Conservation Europe (BCE; www.europeanbutterflies.net). Their common objectives are to provide a central component for the analysis of biodiversity and to investigate and develop the role of butterflies as indicators for the state of biodiversity (Kühn et al. 2008, Van Swaay et al. 2008).
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First results Currently 700 transect walkers are registered for TMD. In 2005, the first year of the project, data for 172 transects have been entered into the database. Up until 2007 the number increased to 345 transects. Another 150 transects were managed separately but according to the same method in the state of Northrhine-Westfalia. Meanwhile the schemes are linked and add up to 680 transect routes. 101
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Figure 5. Transect walkers in Germany range from age 10+ to 90+. Photo: Ilse Opitz.
E S TA B L I S H I N G
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◙ Can we detect significant trends for the increase or decline of butterfly populations? ◙ Does a species expand its area or reduce it? ◙ If trends for the population changes can be detected, do they correlate with other factors such as landscape changes or climate? ◙ Which species have to be protected and how can this be achieved? ◙ How can monitoring data contribute to international obligations such as the Convention on Biodiversity (2010) and the Habitats Directive?
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◙ Where in a country does a specific butterfly species occur and how frequent is it in particular areas? ◙ Which habitat types and structures do the different butterfly species use? ◙ What impacts do changes in landscape structure (different types of landscape management, change or loss of habitats) have on population sizes and butterfly communities’ compositions in a particular region? ◙ What are the flight periods of specific species in different regions (finding the right time for landscape management)?
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Figure 6. Distribution of transects over Germany (Map UFZ State: spring 2009) .
transects have been walked continuously from 2005 to 2007. The spread of transects (Figure 6) still shows considerable gaps, particularly in the northern parts of Germany. Lower densities of human population make it difficult to find a sufficient number of transect walkers. The transect map also shows the importance of regional coordinators. Wherever people could be found willing to operate as regional coordinators, a clump of transects soon gathered around his/her location. People enjoy meeting other transect walkers during field trips or workshops and appreciate having a local expert who organizes these meetings and who can answer questions. It will take many years before the data recorded for TMD can be used for scientific analysis. Insect populations naturally show high fluctuations in size between different years and it almost takes a decade until a trend can be analysed. So the start of this kind of project forms a bottleneck where it is difficult to raise funds and to maintain the motivation of the people involved. Subsequently, the data collected will be of extremely high value since ecological datasets for long timelines are not manageable by traditional project funding and thus are very rare. Acknowledgements The project is financed by Helmholtz Centre for Environmental Research – UFZ and the European Union within
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Figure 7. Species like Plebeius argus depend on the management of heathland and dry grassland. Photo: Steffen Caspari.
FP6-project ALARM (GOCE-CT2003-506675). We owe special thanks to all volunteer transect walkers and regional coordinators who support the project with their effort and dedication. References KÜHN E, FELDMANN R, HARPKE A, HIRNEISEN N, MUSCHE M, LEOPOLD P, SETTELE J (2008) Getting the public involved into butterfly conservation – Lessons learned from a new monitoring scheme in Germany. Israel Journal of Ecology and Evolution 54: 89-104. POLLARD E, YATES TJ (1993) Monitoring butterflies for ecology and conservation. Chapman and Hall, London: 274 S. VAN SWAAY CAM, NOWICKI P, SETTELE J, STRIEN AJ VAN (2008) Butterfly Monitoring in Europe – methods, applications and perspectives. Biodiversity and Conservation 17: 3455-3469.
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Managing Alien Aquatic Species
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DAN MINCHIN
Introduction Not all aliens result in recognisable harm economically, or to the environment, but the small number that do may need to be managed (Figure 1). While management options do exist prior to an alien’s arrival, they are seldom carried out in time. This is because their presence often remains unknown until some impact, often years later. This can lead to more costly management measures. Aliens are distributed by different pathways (see
1. Gobius melanostomus
Olenin et al., this atlas, pp. 138f.) over which managers have different powers of control. In some cases any control measure may not prevent a spread. The management process Management depends on recognition, known distribution, modes of spread and the practical measures that may be effective in undertaking a reduction or elimination of the caused impact. Management involves assessment of the risk to implement a practical measure.
2. Colpomenia peregrina
3. Corella eumyota
Assessing risk Predicting the impact from a species in advance of an arrival involves an evaluation of the possible consequences for communities, habitats and ecosystems (see Panov et al., this atlas, pp. 140ff.), human health and activities. In this way risk can be ranked according to severity, probability of establishment and likely mode of arrival. However, some species not known to cause harm, may do so on arrival to a different region. Advantages for aliens can be provided with environmental alteration (i.e., construction of reservoirs, marinas, port re-configuration) as well as from access and egress of varying transport modes via trading networks (Figure 2). Climate warming can also result in incremental poleward changes to the ranges of both native and alien biota and thereby alter community structure.
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14. Elodea canadensis Figure 2. There are many opportunities for alien species spread, for example the small port of Limerick in south-west Ireland has links to many European ports (red spots).
Figure 1. Aquatic alien species across Europe. Photos: D. Minchin (1-10, 12-14), S. Olenin (11).
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ly determine how it might be managed. Monitoring should be undertaken where an arrival is likely. For example, ornamental, living food and aquaculture species inspections should take place at import hubs before the products become distributed. Whereas in the case of inadvertent arrivals with small craft and ships the sampling of nearby port structures, such as floating pontoons, provide for efficient sampling during any tidal state. Unless monitoring (Figures 3 and 4) is regularly undertaken and rapidly reported there can be little opportunity for effective control. Management response Effective management depends on an agreed deployable plan according to the known distribution, impact and likely future consequences following a finding. A contingency budget is required, as arrivals will often be unexpected, and appropriate responsible agencies carrying out such management need to have advanced decisions on an arrival. While several options exist to manage an alien species, few may be practical. Public education and controls Managing the environment is a shared responsibility; involved stakeholders need to be kept aware of an arrival and spread of impacting species. Approaches involve media interviews, flyers and posters. New approaches are always needed to sustain interest, for example, the tag-rugby team “the
invaders” advertises different impacting aliens to promote a knowledge among those active in outdoor pursuits, otherwise often difficult to reach (Figure 5). In cases where there are unauthorised releases it is still prudent to develop legislative powers. Review Reviews, should be based on local and other experience, and are necessary to improve risk assessment and the efficacy of management methods. New information may include likely transmissions involving different pathways, trading routes, new found distributions and taxonomic knowledge.
Figure 7. The mysid Hemimysis anomala, originally used to stock lakes and reservoirs in eastern Europe, has spread rapidly westwards through Europe. Photo: D. Minchin.
4. Enhancement To provide additional food sources for fish Ponto-Caspian amphipods and mysids (Figure 7) were released
5. No action Natural spread may render any management effort ineffective. For example, the nematode worm Anguillicola crassus (Figure 8), a parasite of the freshwater eel, is spread by a wide range of carriers from copepods to insects that would inevitably result in spread between river systems. 6. Elimination Where an alien species occurs within confined areas elimination may be possible. For example, the small freshwater flatworm Gyrodactylus salaris was transferred with salmon from Sweden to the Norwegian watershed causing high mortalities of Norwegian salmon. Infested rivers were chemically treated to purge this flatworm with some successes. 7. Mitigation Ornamental plants can become abundant once in the wild. The South African pondweed Lagarosiphon major (Figures 9 and 10), widely available in garden centres, now chokes European lakes and ponds. Its abundance can be controlled, in part, by removal; but re-growth is likely, and fragments easily spread the species, and so herbicides may also be needed. Dredging and removal of slipper limpets in France are thought to have reduced the impact on oyster cultivation.
Figure 5. New ways to develop public interest: Tag rugby team “the invaders” sponsorship (back) Undaria, Sargassum, Didemnum, Corella, Crepidula, Alexandrium, Elminius, (front row) Crangonyx, Karenia, Styela, Elodea, Azolla. Photo: D. Minchin.
Management choices Much depends on the accuracy of the information on the impact and distribution of an arriving species. However, once identified a series of options arise that depend on the level of confinement, biology and activities that surround an area where an arrival has taken place (Figure 6). 1. Importation banned Some organisms in trade carry highrisk diseases or parasites. Such imports must be cleared by inspections or otherwise prevented in arriving or destroyed on entry.
Figures 3 and 4. Simple methods can be used to sample and quantify invasive aquatic plants using a grapnel to extract plants and scales to weigh the relative biomass. Photos: D. Minchin.
3. Confinement Isolating imports within fully controlled environments can reduce the risk of disease to the wild. For example, imports of the American lobster Homarus americanus for direct human consumption has resulted in outbreaks of a bacterial disease “gaffkaemia” to native lobsters when held in coastal storage ponds. “New” aquaculture species need to be fully assessed and brought through a secure quarantine procedure.
2. Importation While there have been unapproved introductions of alien species, a deliberate introduction of an alien species may be deemed necessary, on account of the socio-economic effects. For example, following the demise of the flat oyster Ostrea edulis in the 1970s consignments of half-grown Pacific oysters Crassostrea gigas were imported by plane from Japan to France. Although some pests and diseases were introduced, the oyster culture industry was able to maintain and improve production.
to Lithuanian and Russian lakes and reservoirs. Successful stocking of the red king crab Paralithodes camtshaticus in the Barents Sea resulted in establishment and spread to the Norwegian north coast, whereas Pacific salmon stocking experiments in Europe were unsuccessful.
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Figure 6. Scheme for management options in aquatic invasive species. Numbers in the paragraphs of this section refer to different management approaches.
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8. Reduced spread Some species inevitably increase their range; However, by understanding the likely ways that humans distribute species it may be possible to reduce this spread (Figures 11 and 12). Such action delays environmental impacts and provides an opportunity to develop mitigation measures in advance of an impending problem. 9. Develop a product Removal of an organism can be costly; but if developed to produce a commercial product can result in control without management costs. For example, the Asian predatory whelk, Rapana venosa, (Figure 13) in the Black Sea is presently fished and exported to Asia. This fishery also reduces the impact of its predation on fished molluscs. Case studies Some impacting species continue to expand their range in Europe:
Figure 8. An invasive nematode damages the internal organs and the swimbladder of the freshwater eel. Photos: D. Minchin.
Figures 9 and 10. The South African pondweed grows rapidly and in the wild is costly to manage. Photos: D. Minchin.
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1. The zebra mussel The zebra mussel Dreissena polymorpha, native to the Black Sea region, spread in the late 1700s via canal constructions in Eastern Europe and arrived in the Baltic Sea in the early 1800s. By the mid-1800s it had reached Britain and The Netherlands with timber exports. It later penetrated European river and canal networks in central and western Europe. In the last twenty years it spread to Italy, the Gulf of Finland, Ireland and more recently to Spain. It fouls hard surfaces and, despite its small size, forms dense encrustations that smother unionid bivalves, result in clearances to water, foul leisure craft, impact municipal water supplies and irrigation systems, foul sluices and thrash racks of dams and cooling systems of power plants (Figure 14). Their sharp shells can also result in foot lacerations enabling infection of wounds. It attains its greatest densities in lakes and reservoirs.
Figures 11 and 12. Reducing the spread of zebra mussels is a practical option based on the setting up of cleaning stations and managing boat movements. Photos: D. Daunys (11), D. Minchin (12).
The zebra mussel is easily spread with overland transport of leisure craft on trailers. In Spain controls involve boat cleaning stations using high-pressure water; and launched craft must be accompanied by a dated certificate indi-
Figure 13. The rapa whelk also occurs in the Adriatic Sea, Brittany and the southern North Sea. Photo: D. Minchin.
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cating a hull cleaning. For municipal water supplies filtration is a practical control method. Other control options have been considered on the Ebro catchment that includes draining down of reservoirs; but this is likely to cause social disruption and algal blooms, once re-flooded. Here it is unlikely all individuals would expire as residual populations are likely to survive within the damp and flooded spaces of drowned villages, railway tunnels, watertowers and municipal piping and re-colonised by larvae carried downstream. 2. The expansion of ornamental aquatic plants in Europe Ornamental plants are readily distributed and rapidly dispersed via supermarket chains, garden centres and pet stores. Although attractive, they can on release to the wild cause impacts to fisheries, water supplies and bathing beaches (Figure 15). Plant fragments are easily distributed to form new populations either downstream or with attachment to boat trailers or engines carried over-
Figure 14. Zebra mussels can suffocate native animals, foul boats and mooring chains, municipal water supplies, power stations and have implications for human health. Photos: D. Minchin.
land or distributed along navigations. Some plants, such as Elodea nuttallii when carried elsewhere, may also enhance the spread of zebra mussels. Plants that naturally occur in Europe have been spread outside of their natural range and have become locally invasive, i.e. Stratiotes aloides and Hottonia palustris. Some non-European plants, such as the floating fern Azolla filiculoides and the American duckweed Lemna minuta, could also be released from aquaria and transmitted by wildfowl and mammals. Controls on the sales of ornamental aquatic plants with known detrimental environmental impacts are necessary and many new expansions are expected over the coming decades, in particular, to Eastern Europe. Warmer winters may provide opportunities for the expansion of some of these.
References HAYES KR, MCENNULTY FR, SLIWA C (2002) Identifying potential marine pests: an inductive approach. Centre for Research on Introduced Marine Pests CSIRO, 40. ICES (2005) ICES Code of Practice on the Introductions and Transfers of Marine Organisms (www.ices.dk). MINCHIN D (2007a) Aquaculture and transport in a changing environment: overlap and links in the spread of alien biota. Marine Pollution Bulletin 55: 302-313. MINCHIN D (2007b) Rapid coastal survey for targeted alien species associated with floating pontoons in Ireland. Aquatic Invasions 2: 63-70. OLENIN S, DAUNYS D (2005) Invaders in suspension-feeder systems along the regional environmental gradient and similarities between large basins. – In: Dame R, Olenin S (Eds), The comparative role of suspension feeders. NATO Science Series, Earth and Environment Series 47: 211-237.
Figure 15. Invasive ornamental aquatic plants distributed in trade can foul beaches and canals, hamper swimming, result in deoxygenation and aid in the distribution of other alien species. Photos: D. Minchin.
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Biological Control Ecosystem Services in Tropical Rice
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KONG LUEN HEONG, ROBERT J. HIJMANS, SYLVIA VILLAREAL & JOSIE LYNN CATINDIG
Rice is grown on over 100 million hectares of the world’s arable lands and is the staple food of more than 3 billion people. Rice is produced in landscapes ranging from extreme monocultures to areas that are highly
pest population size regulation. These services are mainly performed by two arthropod groups, the predators and the parasitoids (see pictures). There are at least 200 species of parasitoids and 150 species of predaa
tors that live in tropical rice fields. Their diversity and abundance are key indicators of the degree of biological control services they provide. Since rice is an ephemeral habitat most pest species are exogenous invaders. Many
of their natural enemies are less mobile, particularly the generalist predators such as spiders and crickets. These species tend to take refuge in habitats surrounding the rice fields when rice is harvested and enter the
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Figure 1. A tropical rice landscape. Photo: K.L. Heong.
diverse. In tropical rice fields, naturally occurring biological control ecosystem services are vital in keeping pest populations below economic threshold levels. Biological control consists of two services: pest invasion resistance and
Figure 2. a) Ophionea nigrofasciata – a predator of leaffolder larvae; b) Sepedon sp. – a parasitoid of snails; c) Trichomma cnaphalocrosis – parasitoid of leaffolder larvae. Photos: S. Villareal.
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rice fields again when a new crop is established. Factors such as landscape structure, habitat diversity, cropping patterns and farmers’ crop management practices can influence these groups and the services they provide. High pesticide use is a well known reason for the breakdown of biological control services, and has led to uncontrollable outbreaks of the brown planthopper in some areas. Only by reducing pesticide use, and hence strengthening the biological control ecosystem service, was it possible to reverse this situation (Way & Heong 1994). There is less information available about the effect of landscape patterns, but reduction in landscape heterogeneity and increase in agricultural land was associated with loss of diversity in Vietnam (Wilby et al. 2006). Predators and parasitoids need to survive seasons when there is no rice crop in the fields. We hypothesized that the more diverse the landscape is, the better the opportunities for local predator and parasitoids populations to survive. Pest species would benefit less from this as they are more mobile. Hence, we expect stronger biological control services in diverse landscapes than in areas that are dominated by rice.
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OKSANEN J, KINDT R, LEGENDRE P, O’HARA B, SIMPSON GL, SOLYMOS P, HENRY M, STEVENS H, WAGNER H (2008) vegan: Community Ecology Package. R package version 1.15-0. http://cran.r-project.org/, http://vegan.r-forge.r-project.org/ WAY MJ, HEONG KL (1994) The role of biodiversity in the dynamics and management of insect pests of tropical irrigated rice – a review. Bulletin of Entomological Research 84: 567-587. WILBY A, LAN LP, HEONG KL, HUYEN NPD, QUANG NH, MINH NV, THOMAS MB (2006) Arthropod diversity and community structure in relation to land use in the Mekong Delta, Vietnam. Ecosystems 9: 538-549.
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very weak, and in most cases statistically insignificant. Figure 7 shows the relation between land use intensity (the percentage agricultural land use, Figure 3), and parasitoid diversity. The relative strength of spatial autocorrelation between sites, and the weakness of more association with local variables suggest that either larger scale processes shape the biological control ecosystem services; or that our results are strongly influenced by sampling artifacts (sampling time, agronomic practices and regions are confounded). While is it clear that parasitoid and predator biodiversity are closely related to land use patterns, there is a need to establish direct linkages between diversity indicators and biological control services. Of particular interest is the effect of agronomic practices on biological control. Further research will address these questions.
10
http://ionia1.esrin.esa.int/) at ~300 m spatial resolution. These indices included the percentage of agricultural land (Figure 3) and a number of diversity indices for an area of 1,500 by 1,500 m surrounding each pixel on the map. We caught 11,041 predators representing 109 species (81 genera), and 6,682 parasitoids from 156 species (87 genera). Not all individuals could be determined to the species level. For both groups combined, the number of individuals caught at different sites was between 37 and 2,518, with a median of 209. The number of species was between 11 and 79 (median 36) (Figures 4-6). There is spatial clustering of the results (Figures 4 and 5). Spatial autocorrelation (Moran’s I) was highly significant for Sobs and α (P < 0.001), and also significant for n (P = 0.03). Associations between species diversity and habitat and crop management were
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To investigate these hypotheses we assessed the distribution of predators and parasitoids in rice fields on the island of Luzon in the Philippines (Figure 3). We obtained 3,050 net sweep samples from 61 sites, using a standard procedure. Sites were selected using remote sensing-derived land cover data to assure that sampling covered a gradient of landscapes with the respect to the fraction of land being used for agriculture (Figure 3). Predator and parasitoid species sampled were sorted and counted in the laboratory. We computed the total individuals sampled, Sobs, the number of species observed, Fisher’s α and other diversity indices using the vegan package in R (Oksanen et al. 2008). We estimated landscape diversity indicators during the field work and also computed them from recent satelitederived land cover data (GlobCover;
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Figure 8. a) Mirid planthopper egg predator, Cyrtorhinus lividipennis. b) Cricket leaffolder egg predator, Metioche vittaticollis. Photos: S. Villareal.
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Policy Options to Protect Biodiversity under Climate Change
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JAKE PIPER & ELIZABETH WILSON
Introduction and approach Climate change and sectoral policies affecting biodiversity were investigated as part of the MACIS project (Kühn 2008). MACIS examined interactions between mitigation and adaptation policies as they affect biodiversity, and also the impacts of other policy areas – such as agriculture, transport, water, energy generation, etc. – upon biodiversity. The review identified direct impacts (e.g., from policies which promote or reduce
are learning from each other and collaborating on research and policy development, such as through co-operative transboundary activities in the Baltic and the Alps. Awareness is growing of the important role biodiversity plays in providing ecosystem services which contribute to moderating climate change and its impacts, but there is still some evidence that biodiversity is treated separately from other policy areas (Wilson & Piper 2008). Other topics –
Governance
Autonomy
LOCAL STEWARDSHIP
Conventional development
Community
Consumerism
NATIONAL ENTERPRISE
Values
WORLD MARKETS
restore biodiversity, to redress past losses and to ensure resilient and robust adaptation to future climate change.
tionships and negotiating responses. Socio-economic scenarios, by representing possible futures, also have a role in policy assessment (Figure 1 – UKCIP scenarios)1.
Policy options Using an options framework defined by other EC policy work, potential policy instruments were reviewed for their current and future use with respect to biodiversity. Examples of these are: Regulation. Site and species designation and protection; introducing buffer zones and connectivity requirements; requiring assessment work (Figure 2) and safeguarding of sites with potential importance for biodiversity in the future. Market based instruments. Fiscal measures and off-setting; habitat banking (under strict conditions); water rights trading and emissions trading2; incentives for green roofs (Figure 3 – green roof at Canary Wharf, London; and see Oberndorfer et al. 2007). Insurance. Requiring insurance against risk, as well as promoting physical “insurance” measures which offer
Strategic planning Clear strategic planning towards an achievable goal is needed at EU level, to include funding, targets, implementation measures and monitoring; in all these areas responsibilities must be allocated and a schedule set for expected progress towards aims. Vital to this process will be the raising of policy ambition, aiming for “net gain” in biodiversity (rather than merely “halting decline”; see EC 2008), as well as incorporating greater resilience of biodiversity as an aim. For this to be achieved strategic plans and action plans will be needed, doing more than just the minimum in terms of sites, forms of protection and enhancement measures. All sectors must take responsibility for biodiversity – to mitigate the impacts of their policies or projects, to enhance and
Determine within each priority sector (e.g., transport)
GLOBAL SUSTAINABILITY
• Step A. What priority risks and opportunities? • Step B. Define adaptation objectives, targets, indicators • Step C. What are the interactions between policy sectors? • Step D. What are the best options? Across priority sectors (e.g., transport + housing)
Interdependence
• Step E. What are the overlaps/conflicts between policy sectors? • Step F. Adjust and harmonize proposed policy changes
Figure 1. Early example of conceptualizing socio-economic scenarios, using axes of governance (from interdependence to autonomy) and values (from consumerism to community) to give four broad scenarios (UKCIP, 2001 Socio-economic scenarios for climate change assessment, see: www.ukcip.org.uk).
greenhouse gas emissions) as well as indirect impacts which work through complex pathways. Policy options and instruments which should be considered by policy makers in order to improve the resilience of species and habitats to climate change were also analysed. The MACIS policy work was concerned chiefly with EU and Member State policy, but information and policy options from other regions and countries were also reviewed. The principal methods used were document-based policy review and consultation with interested EU stakeholders and experts invited from NGOs, the Commission and EU agencies, as well as others involved in relevant research. Findings Policy development Climate change policy development across the EU is uneven, but nations 250
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Aim: “Adapt to the unmanageable”
such as health and agriculture – generally take precedence in climate change policy development, whilst their impacts upon biodiversity (which interact with those of climate change) are not sufficiently recognised. Some countries are further advanced than others in planning for biodiversity under a changing climate – late starters may make rapid progress based on material, practices and models now available. Assessment framework Policy assessment and policy integration are needed take into account potential impacts associated with climate change, extreme weather and sea level rise and to enhance resilience to climate change (Kabat & Vellinga 2005). An assessment framework and some analytical tools were developed to help these processes become systematic and transparent; these are proposed as a basis for uncovering rela-
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Figure 2. Stages in a policy assessment process (Piper & Wilson 2008, MACIS Del. 4.1; see www.macis-project.net).
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See also: PRELUDE scenarios: www.eea.europa.eu/multimedia/interactive/prelude-scenarios/prelude
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See Australian guidance on market-based instruments: http://marketbasedinstruments.gov.au/
scope for biodiversity, such as wetland development within flood storage schemes on floodplains. Also, facilitating insurance for uptake of new approaches, such as sustainable drainage systems (see Figure 4, sustainable drainage system, Scotland). Research and development. Filling in biodiversity knowledge gaps across the EU and seeking better understanding of the impacts of policies and measures; scenario development; cumulative sectoral impacts on biodiversity. “Soft” options. Guidance, codes (e.g., on development/construction and biodiversity), awareness raising, supporting collective action and community engagement (Figure 5 – launch of Low Carbon Wolvercote); environmental performance targets. These instruments will need to be assessed in specific cases for their effectiveness and efficiency and how they conform to legislation, for example, on policy integration and the precautionary principle. Conclusions and recommendations Action is needed more urgently to review and upgrade existing EU and Member State policy – and to assess new policy. This will help in avoiding adverse interactions affecting biodiversity, and at the same time should increase the relevance of policy at local level, making it easier to implement effectively. Designated biodiversity sites will continue to be of vital importance to protect species and habitats so, where possible, they should be given further protection and enhancement, such as by buffer zones. New biodiversity areas are also needed (land, wetland and water bodies), both to reduce pressures on the most valuable sites and to enable species to disperse through landscapes
Figure 3. Green roof at Canary Wharf, London: providing multiple benefits including opportunity for biodiversity. Photo: D Gedge.
over time, within their available climate space. The ideal of policy integration is still difficult to achieve. Integrating policy, i.e. working towards the achievement of both common objectives and shared ambitious targets for biodiversity, will mean working across sectoral programmes as well as working with cross-cutting policies such as sustainable development (Swart & Raes 2007). There must also be strategic support for biodiversity at EU level within major sectors such as transport. Such integration is important for the achievement of the EU’s biodiversity policies under conditions of climate change. There is therefore a clear need for measures at many scales: institutional level for policy; operational level for plans and techni-
cal level for implementation measures – this will help ensure consistency and avoid conflicts. Other necessary measures will be the better assessment of cumulative impacts, the recognition of ecosystem services as cost-effective means of supporting sustainable lifestyles, and the provision of adequate resources for the measures introduced. References EC (EUROPEAN COMMISSION) (2008) Progress towards halting the loss of biodiversity by 2010 A first assessment of implementing the EC Biodiversity Action Plan. Brussels: EC. Available at: http://ec.europa.eu/environment/nature/knowledge/rep_biodiv_ap/ pdf/2007_report.pdf KABAT P, VELLINGA P (2005) Climate proofing the Netherlands. Nature 438: 282-284.
Figure 4. Pond installed as part of sustainable drainage system (Scotland) for sustainability benefits including biodiversity. Photo: Urban Water Technology Centre, University of Abertay Dundee.
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KÜHN I, SYKES MT, BERRY PM, THUILLER W, PIPER JM, NIGMANN U, ARAÚJO MB, BALLETTO E, BONELLI S, CABEZA M, GUISAN A, HICKLER T, KLOTZ S, METZGER M, MIDGLEY G, MUSCHE M, OLOFSSON J, PATERSON JS, PENEV L, RICKEBUSCH S, ROUNSEVELL MDAR, SCHWEIGER O, WILSON E, SETTELE J (2008) MACIS: Minimisation of and Adaptation to Climate change Impacts on biodiverSity. GAIA – Ecological Perspectives for Science and Society 17(4): 393-395. OBERNDORFER E, LUNDHOLM J, BASS B, COFFMAN RR, DOSHI H, DUNNETT N, GAFFIN S, KOHLER M, LIU KKY, ROWE B (2007) Green roofs as urban ecosystems: ecological structures, functions and services. Bioscience 57: 823-833. SWART R, RAES F (2007) Making integration of adaptation and mitigation work: mainstreaming into sustainable development policies? Climate Policy 7: 288-303. WILSON E, PIPER J (2008), Spatial planning for biodiversity in Europe’s changing climate. European Environment 18:135-151.
Figure 5. Collective action in a village: launch of Low Carbon Wolvercote. Photo: I Curtis. Oxfordshire ClimateXchange.
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Biodiversity Risk Assessment for Europe – Putting It All Together GLENN MARION, RALF GRABAUM, VOLKER GRESCHO, ADAM BUTLER, STIJN BIERMAN, JEAN-MARC DOUGUET, VOLKER HAMMEN, THOMAS HICKLER, PHILIP E. HULME, LAURA MAXIM, INES OMANN, KAJA PETERSON, SIMON G. POTTS, ISABELLE REGINSTER, JOSEF SETTELE, JOACHIM H. SPANGENBERG & INGOLF KÜHN
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ALARM was an integrated project developing large-scale risk assessments for biodiversity in terrestrial and freshwater ecosystems with a focus on five key environmental pressures – climate, landuse, environmental chemical pollution, biological invasions, and pollinator loss – and their impact on biodiversity (see Settele et al., this atlas, pp. 38ff.). The term risk assessment can be interpreted in many different ways and here we adopt a broad definition (see box on opposite page). To minimise the possibility of misinterpretation or over confidence in the use of such assessments it is important to communicate quantitative and qualitative uncertainties associated with each result. As illustrated throughout the pages of this atlas, a wide variety of methods, with distinctive strengths and
weaknesses, have been developed and applied to provide risk assessments at different scales and for different aspects of biodiversity. In order to make such assessments more relevant to European level policy making the ALARM project developed contrasting integrated scenarios for climate, land-use and socio-economics under which projections of risks for biodiversity can be made. The three principle scenarios represent broad policy choices (see Spangenberg et al., this atlas, pp. 10ff.) as follows: BAMBU – Business-As-Might-Be-Usual – Policy decisions already made in the EU are implemented and enforced; GRAS – Growth Applied Strategy – Deregulation, free trade, growth and globalisation will be policy objectives actively pursued by governments; and SEDG –
Qualitative assessments by biographical regions Alpine North
55.3 %
Alpine South
42.1 %
Boreal
76.3 %
Atlantic
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Continental
98.7 %
Pannonian
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Mediterranean
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Sustainable European Development Goal – Integrated social, environmental and economic policy aimed at enhancing the sustainability of societal development. Understanding the responses of particular species, species groups and entire ecosystems to actual and potential environmental change is an extremely complex and difficult task, as many of the case-studies in this atlas testify. For example, at present there is only limited understanding of how to predict the single or combined effects of different pressures on specific aspects of biodiversity (e.g., the separate and joint impacts of climate change and invasive species on pollinators, or how climate change and the future trade patterns will influence invasions). Furthermore combining such assessments in order to arrive at a complete quantification of risks to biodiversity under historic, present day, or potential future scenarios is beyond current scientific understanding, and will depend on an individual’s values. For example, how should the loss of one species be weighed against the drastic reduction in abundance of several others? Answers to such questions will depend on one’s goals e.g. maintaining ecosystem resilience, promoting species diversity, or enhancing economic or cultural value. Furthermore this will remain the case even if future scientific developments are able to GRAS compared with SEDG
quantify the contribution made by each element of biodiversity to particular aspects of ecosystem functioning. Therefore what emerges from the ALARM project, and in general from the study of risks to biodiversity, is a highly complex and uncertain picture with a huge range of qualitative and quantitative assessments of the threats posed by different (single and multiple) pressures on a wide range of different aspects of biodiversity from particular species to components at the ecosystem-level. Moreover these assessments often only account for the impact of a sub-set of relevant pressures, and can be carried out at different scales and locations, be based on contrasting methodologies and data, and relate to historic or present-day conditions, or to specified scenarios in the future. Qualitative assessment of biodiversity impacts under contrasting scenarios A complete quantitative comparison of the three scenarios described above is not possible since, notwithstanding the discussion above, the scenarios are based on storylines including qualitative elements. Therefore the collective expertise of the ALARM consortium was distilled into a qualitative assessment of relative impacts on biodiversity. This involved developing a consensus about what pressures and aspects of biodiversity should be BAMBU compared with SEDG 62 %
49 % 38 %
12 %
1% 0%
36 %
2%
0%
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GRAS compared with BAMBU 41 %
Large increase Small increase No difference Small decrease Large Decrease 51 % 4%
Figure 1. The seven biomes used in the qualitative risk assessments (see text). These are based on the classification of biogeographic zones described by Metzger et al. (2005). The charts on the map show the percentage of possible ecosystem-level assessments included in the questionnaire that were answered. Presented are the percentage of all possible ecosystem questions answered by at least one person in the qualitative risk assessment questionnaire for each biogeographical region.
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Figure 2. Pair-wise comparison of scenarios in terms of the difference in the ecosystem-level impacts scored for each combination of biome, pressure and aspect of biodiversity. The figure shows the relative proportion of such differences on a 5-point scale, where e.g. the “large increase” under GRAS compared with SEDG shows the proportion of assessments where GRAS was scored at least 2 levels higher than SEDG on the ecosystem-level impact scale: minimal, minor, moderate, major, or massive. This impact scale combines assessments of the impact of pressures on several aspects of ecosystem biodiversity of relevance to conservation, population and community ecology, and ecosystem services (see Hickson et al. 2000).
assessed, and the subsequent adoption of common qualitative impact scales to be used by experts within ALARM when completing a questionnaire. Biodiversity impacts were assessed in the year 2050 for each of seven biomes (Figure 1) at ecosystem and sub-ecosystem (species-group) levels for each of five key pressures and three scenarios discussed above. Experts in the ALARM project were asked to make such assessments only where they had expert knowledge, but wherever such an assessment was made they were asked to score all three scenarios. Figure 1 shows the proportion of possible assessments that were made at the ecosystem level for each biome. Comparing the scores of impacts on biodiversity at the ecosystem level (see caption of Figure 2 for a description of the scoring system) assigned to each scenario for each combination of pressure, ecosystem type and biome reveals a picture of the differences between the scenarios that is relatively robust to differences in the way individuals qualitatively score risk. Note that the scoring system measures impacts on aspects of biodiversity and does not attempt to assess the important issue of resultant socio-economic impacts (although these are discussed in several articles in this atlas). Using this approach shows that impacts on biodiversity under SEDG are less severe than the impacts under BAMBU which in turn are less than the impacts under GRAS (Figure 2) This result holds at the European level and when broken down into individual biomes. However, there is considerable variability in this overall response, for example, when comparing any two scenarios the majority of assessments showed no differences, and a small number go
against the overall trend e.g. 2 % of assessments have a higher impact under SEDG than under BAMBU. These results provide only a qualitative overview of differences between these scenarios, specific details of which can found in many articles in this atlas. Risk assessment toolkit In order to enhance knowledge transfer and exploitation of the complex array of quantitative and qualitative risk assessments described above, the ALARM project has developed the Risk Assessment Toolkit (RAT) which provides a web-based interface (Figure 3) to a database that allows the
user to access: the outputs of risk assessments created by the scientific teams within ALARM; metadata about the quality, scale and scope of these assessments; and tools & methods for the creation of future risk assessments. The RAT will provide key summaries of particular subjects (e.g., the risks associated with climate change) and access to the ‘most relevant’ risk assessments related to these. In addition a general search facility will enable users to search and organise multiple risk assessments according to a range of criteria including pressures, aspects of biodiversity and scenario, in order to compile a comprehensive col-
lection of assessments relevant to their interests e.g. to compare assessments across scenarios (Figure 3). References HICKSON R, MOEED A, HANNAH D (2000) HSNO, ERMA and risk management. New Zealand Science. Review 57: 72-77. METZGER MJ, BUNCE RGH, JONGMAN RHG, MÜCHER CA, WATKINS JW (2005) A climatic stratification of the environment of Europe. Global Ecology and Biogeography 14: 549-563. WALKER WE, HARREMOES P, ROTMANS J, VAN DER SLUIJS JP, VAN ASSELT MBA, JANSSEN MP, KRAYER VON KRAUSS MP (2003) Defining Uncertainty: A Conceptual Basis for Uncertainty Management in ModelBased Decision Support. Integrated Assessment 4: 5-17.
Figure 3. The Risk Assessment Toolkit web-portal allows users to search the database for stored biodiversity risk assessments e.g. maps of water deficit calculated under the three scenarios described in the text (see Spangenberg et al., this atlas, pp. 10ff.)
Biodiversity risk assessment In a formal sense risk is understood as a combined measure of the likelihood of a particular event and the consequence of that event. Biodiversity risk assessment therefore involves defining the event of interest and then assessing both the likelihood and its impact, or consequence, on biodiversity. Events which are relatively unlikely to occur but have a very serious consequence will be scored as high risk, as will events that are quite likely to occur but only have minor to moderate consequences. However, optimal management strategies are likely to be very different for each of these, even though the risk score may be equally high. Therefore it is often more useful to report the likelihood and consequence assessments separately; in any case
reporting the likelihood assessment enables calculation of additional risk scores based on future impact assessments. Successful management of risk will also depend on the nature of the threat and will be influenced by the details of the risk assessment process, since this will determine the level of confidence placed on the assessment. As can be seen in the examples discussed in Marion et al. (this atlas, pp. 58ff.) the process of risk assessment is plagued by uncertainties at every level. Therefore when reporting risk assessments it is critical to provide information on both the assessment itself and on the quantitative and qualitative uncertainties involved (Walker et al. 2003). This knowledge is critical in allowing end users to gauge the quality of the information provided.
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In some cases only the event is defined and for computational reasons no assessment of its likelihood is provided. Similarly often only an impact assessment is reported. Impacts are often multifaceted (e.g., there are many different aspects of biodiversity), and appropriate measures of impact (e.g., do we measure changes in biodiversity directly, or assess consequential socio-economic impacts) will vary depending on who is asking the question (e.g., government, conservation groups, commercial organizations). When they are assessed impacts can be scored on both quantitative and qualitative scales. It is common to use a rather limited number of possible scores in a qualitative scale (e.g., see Figure 2) with some definition of what each point on the scale represents.
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Quantitative scales depend on the details of the aspects of biodiversity to be assessed e.g., species richness or abundance/probability of presence in a given location. In this atlas the term risk assessment is interpreted broadly to mean one or all of the following, assessment of: (i) specification of the event of interest; (ii) the likelihood of this outcome; (iii) the consequence or impact; and (iv) the formal combination of these to produce a risk score. The array of such risk assessments presented is extensive and diverse in terms of application area (i.e., aspects of biodiversity assessed) and the methodologies used, reflecting the nature of biodiversity and the broad range of disciplines of the contributing scientists.
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Future Biodiversity Research – Targets, the Human Factor and Lessons Learned
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JOSEF SETTELE, INGOLF KÜHN, MARTIN SHARMAN, ALLAN WATT & JOACHIM H. SPANGENBERG
Biodiversity and targets Biodiversity research in recent years has often been based on politically-set biodiversity targets: the final objective of finding policy and management options for improving the state of biodiversity has been used as a justification of this research and its funding. But have targets and the monitoring and indicators used to measure progress towards these targets been the right approaches? Have they been operational from a policy point of view, and effective (let alone efficient) from a biodiversity perspective? Measuring biodiversity, deriving targets Biodiversity has many dimensions, some of which might be grouped into bundles labeled “composition”, “structure”, “dynamics” and “function”. We can measure and set targets related to some elements of composition, including – at least in theory – the identity and variety of genes, populations or species, and we can establish various measures of genetic and species diversity. Structural measures and targets are generally more problematic. For example, while we can perhaps measure characteristics of isolation and connectivity of habitat patches, the complexity needed in creating habitats makes target-setting difficult. It is not at all easy to imagine targets that would relate to the dynamics between the elements that create that habitat, the changes within gene assemblages, populations and communities, and the ecological and evolutionary processes that emerge. We understand rather little about complex systems with non-linear feedbacks – surely one reason why the concept of identifying thresholds might not be useful although theoretically it sounds interesting. Since it is so difficult to predict the behaviour of complex systems under pressure, and since such systems are affected by many aspects of global change, this whole domain is one that needs considerable research. Functional measures of biodiversity, including energy and nutrient cycling, may in some circumstances prove easier to measure and to set targets for. But many people see biodiversity as extending to ecosystems and the services they provide to living things, including humans, making functional measures more difficult to derive. Target setting is based on societal values. But people are diverse: we don’t necessarily share values, goals, or world views. This makes it considerably more difficult to set targets, even for relatively simple compositional measures. This is easily illustrated. More biodiversity in some measures is not necessarily good: for example, having more troublesome non-native species in an area at the end of a period than at the start does not necessarily mean that biodiversity in that area is somehow better. But even that depends on the point of view of the person assessing it. To a gardener who likes bamboo, a garden-full of various invasive bamboo species might be a delight, while to an ecologist it might be a nightmare. It becomes obvious that the concept of “invasive species”, despite their scientific definition and their potential harm to ecosystems, is a societal construct, based on utility aspects, perspectives and preferences. By extension, it is easy to see that it is pointless to set a single target for “biodiversity”: a single measure could be meaningless, even misleading. One might imagine a case in which various compositional aspects were changing in one direction while functional aspects were unaffected and at least some structural aspects were changing in another direction. Could we then agree at any point that we had “halted the loss of biodiversity”? As for “managing” biodiversity, most probably we can only manage in any target oriented way those ecosystems that are hugely simplified. There are an endless number of options to manage ecosystems, which, however, give no guarantee of achieving pre-defined targets. This even holds true if we include no management as a management option. Historically at least, management (e.g. by land use) generally starts by simplifying the ecosystem, or even by sweeping it away and replacing it (as is the case with any agriculturally dominated system, landscape or even continent; see e.g. Heong et al., this atlas, pp. 248f.; Reginster et al., this atlas, pp. 100ff.). 254
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Consequently, it surely is questionable whether we can achieve whatever it is we are trying to achieve using targets for biodiversity and treating and managing biodiversity, in particular when referring to biodiversity as an entity independent from and largely untouched by all the other human activities which modified and keep on modifying this planet. Under these circumstances a much deeper and more thorough re-thinking of our place on this planet and more thorough research on how to make it sustainable seems necessary. In order to make the transition to a human endeavor that does not undermine structure and function of ecosystems, however, can we do without targets? The human factor As just stated above, the human factor is a core element of our future and consequently for the future of biodiversity, however defined. The human factor is the central target of socio-economic research, and it is crucial in all walks of life, but in particular in the field of policy and politics, where target setting and strategy implementation for biodiversity conservation take place. Socio-economic analyses have shown that many policies have biodiversity implications - positive and (more often) negative ones (see Spangenberg et al., this atlas, pp. 204ff.). Political decisions are amongst the key driving forces of most kinds of biodiversity loss, and they have influence on the other pressures generating socio-economic processes. Consequently, policies need to be designed to relieve the pressures on biodiversity, and this is where the recommendations gained from research could and should play a role. These recommendations illustrate the need for policies to take into account their role as a driving force for biodiversity change and loss. To highlight the importance of this insight, the riches biodiversity provides (economic, cultural, spiritual, etc.) should be emphasized more strongly. Preserving those riches incurs the cost of conservation, but what is the balance? Economic cost-benefit analyses of different policy options are necessary, but they are not sufficient: political, social and environmental costs and benefits cannot be measured in monetary units. In order to include all the relevant assessment methods, strengthening interdisciplinarity in biodiversity research is crucial, including social sciences beyond economics, such as political science, sociology, humanities, but also new disciplines such as industrial ecology and the bio-energetic research on the Human Appropriation of Natural Primary Production (HANPP, which may be a prime candidate for an overall biodiversity pressure indicator, see e.g. Haberl et al. 2004). What does that imply for target setting? On the one hand, policy formulation requires goals, i.e. targets to be achieved. To be operational and thus potentially implemented, however, such targets must be formulated in the appropriate language, must be achievable by political means and must refer to a level of action (from local to European) which the decision makers – to whom the advice is addressed to - can indeed influence, and which may be quite different from any biome or ecosystem level geographical unit. Such tools are urgently needed, but just as urgent is the need to make their existence known, by offering easy-to-understand but scientifically robust information to the public and to decision makers, and propagating decision support and distance learning tools. Decision training, however, is even more important than decision support tools. This could actively contribute to an improved uptake of the available scientific knowledge in the decision-making process. Beyond that more formal recurrent processes for better managing the relationships between knowledge and action should also be imagined (see Spangenberg et al., this atlas, pp. 204ff., for further details). In all these efforts, it must remain clear that no single policy measure will rescue biodiversity – there is no silver bullet. Instead, a systematic review of all policy fields is necessary to incorporate biodiversity. Mainstreaming biodiversity requires “hunting for policy issues” where biodiversity concerns should, but do not yet, play an adequate role. This includes biodiversity policy: research results should be used to continuously update programs and indicator sets. The most meaningful
indicators, however, are progress indicators measuring the distance to target – which brings us back to the issue of target setting. Lessons learned Scientifically formulated targets fall short of effectively redirecting politics, while politically formulated targets can easily lack a solid scientific underpinning. What is needed are robust scientific insights, the analysis of socio-economic pressures and their driving forces, policy recommendations to reduce and redirect such drivers, and a joint effort of political monitoring (assessing whether the measures have indeed been implemented) and scientific monitoring (evaluating whether they have been effective in reducing negative effects on the aspects of biodiversity which caused concern in the first place, without unacceptable side effects). This is a challenging task that no single scientist and no single discipline can successfully take on. It requires even more than intensive cooperation across disciplinary borderlines, it calls for real integration in the formulation of research questions, the choice of methods and parameters and in the interpretation and communication of results. In concert, multiple disciplines can contribute significantly to repositioning humankind inside the biosphere and its diversity.
References
First steps towards such a much deeper and more thorough re-thinking of our place on this planet and more thorough research on how to make it sustainable have been undertaken within a new generation of projects such as ALARM (Settele et al., this atlas, pp. 38ff. & 229f.). With a highly diverse grouping of col-
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laborators from very different disciplines not only the limitations of and to disciplinary research became very obvious, but also the opportunities emerging from integrated transdisciplinary research. The experience clearly demonstrated that attempts to work across disciplines, while not being easy and straightforward, are worth the effort if our aim is to contribute to sustainable solutions for the most pressing problems on our planet. Only within such a much broader picture one might also find realistic ways to contribute to the long-term survival of much of our biodiversity, although (or possibly exactly because) there have not been scientifically formulated biodiversity conservation targets (see further above) at the outset of the research activities, but rather the aim to come nearer to an understanding of the complexities of the ecosystems which provide the services that guarantee our survival as humans, and the factors undermining the delivery of these services. Via this human centered sustainability approach the conservation of biodiversity might “simply” be a by-product of a broader movement towards sustainable societies and economies, but as the policy scenarios developed have shown (Omann et al., this atlas, pp. 196f.; Spangenberg et al., this atlas, pp. 10ff.), biodiversity conservation will not be achieved in an unsustainable society, while sustainability cannot be achieved without safeguarding biodiversity.
HABERL H, SCHULZ NB, PLUTZAR C, ERB K H, KRAUSMANN F, LOIBL W, MOSER D, SAUBERER N, WEISZ H, ZECHMEISTER HG, ZULKA P (2004) Human Appropriation of Net Primary Production and Species Diversity in Agricultural Landscapes. Agriculture, Ecosystems & Environment 102 (2): 213-218.
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A AKENJI Lewis – IGES Institute for Global Environmental Strategies, 2108-11, Kamiyamaguchi, Hayama, Kanagawa, 240-0115 Japan, [email protected] AKTAÇ Nihat – Trakya University (TU), 22050 Edirne, Turkey ALEXANDROV Boris – Institute of Biology of the Southern Seas, 37 Pushkinska Str., Odessa Branch, 65125 Odessa, Ukraine, [email protected] ANDREU URETA Jara – Centre de Recerca Ecològica i Aplicacions Forestals, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, [email protected] APPELTANS Ward – Vlaams Instituut voor de Zee (VLIZ), Fortstraat 128, 8400 Oostende, Belgium, [email protected] ARAÚJO Miguel B. – Museo Nacional de Ciencias Naturales, CSIC, C/ José Gutiérrez Abascal 2, Madrid 28006, España, [email protected] ARBACIAUSKAS Kestutis – Vilnius University, Institute of Ecology, Akademijos g. 2, LT–08412 Vilnius–21, Lithuania, [email protected] ARIZPE Nancy – Institut de Ciencia i Tecnología Ambientals, Edifici Ciencies, Torre Area 9, 4a planta C5-438, Universitat Autònoma de Barcelona 08193 Bellaterra, Spain, [email protected] ARNOLD Gérard – Laboratoire Evolution, Génomes, Spéciation, CNRS UPR9034, 91198 Gif-sur-Yvette, France, [email protected] ASK Jenny – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden, [email protected] ASK Per – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden, [email protected] AUGER-ROZENBERG Marie-Anne – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] AUGUSTIN Sylvie – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans CEDEX 2 France, [email protected] B BACHER Sven – Department of Biology, Ecology & Evolution Unit, University of Fribourg, Ch. du Musée 10, 1700 Fribourg, Switzerland, [email protected] BADECK Franz-W. – Potsdam Institute for Climate Impact Research (PIK), 14412 Potsdam, Germany, [email protected] BAKKER Joop F. – National Institute for Coastal and Marine Management / RIKZ, Kortenaerkade 1, 2500 EX 's-Gravenhage, The Netherlands BALZER Sandra – Bundesamt für Naturschutz, Konstantinstr. 110, D-54179 Bonn, Germany, [email protected] BARANCHIKOV Yuri – Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia, [email protected] BARMAZ Stefania – University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy, [email protected] BARTHLOTT Wilhelm – Nees Institute for Biodiversity of Plants, Rheinische Friedrich-Wilhelms-Universität, Meckenheimer Allee 170, 53115 Bonn, Germany, [email protected] BARTOMEUS Ignasi – Center for Ecological Research and Forestry Applications (CREAF), Edifici C, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Catalonia, Spain BAUCH Bianca – Helmholtz Centre for Environmental Research – UFZ, Department of Conservation Biology, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] BAULER Tom – Chaire Environnement & Economie, Institut de Gestion de l’Environnement et d’Aménagement du Territoire, Université Libre de Bruxelles (CP130-02), Av. FD Roosevelt 50, B-1050 Brussels, Belgium, [email protected] BAYLAC Michel – UMR CNRS 5202 – USM 601, Origine, Structure et Evolution de la Biodiversité, Département Systématique et Evolution, Muséum National d’Histoire Naturelle, CP 51, 55 rue Buffon, 75005 Paris, France, [email protected] BECK Stephan – Herbario Nacional de Bolivia, Casilla 10077, La Paz, Bolivia, [email protected] BEDNAR-FRIEDL Birgit – Wegener Center for Climate and Global Change, University of Graz, Leechgasse 25, A-8010 Graz, Austria, [email protected] BELL Sandra – Durham University, Durham DH1 3LE, UK, [email protected] BERENDSOHN Walter – Free University of Berlin, Botanic Garden and Botanical Museum Berlin-Dahlem (BGBM), Berlin, Germany, [email protected]
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BERGER Silje – Institute of Geobotany, Leibniz University of Hannover, D-30167 Hannover, Germany, [email protected] BERRY Pam – Environmental Change Institute, School of Geography and the Environment, South Parks Road, Oxford OX1 3QY, UK, [email protected] BIERMAN Stijn – IMARES, Institute for Marine Resources and Ecosystem Studies, PO Box 68, NL-1970 AB IJmuiden, The Netherlands, [email protected] BIESMEIJER Jacobus C. – Institute of Integrative and Comparative Biology and Earth and Biosphere Institute, University of Leeds, Leeds LS2 9JT, UK, [email protected] BINIMELIS Rosa – Institut de Ciencia i Tecnología Ambientals & Instituto de Economía Ecológica y Ecología Política, Edifici Ciencies, Torre Area 9, 4a planta C5-438, Universitat Autònoma de Barcelona 08193 Bellaterra, Spain, [email protected] BOEGH Phillip – Zoological Museum (ZMUC), University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen, Denmark BOMMARCO Riccardo – Swedish University of Agricultural Sciences, Ulls v. 16, 75007 Uppsala, Sweden, [email protected] BONN Aletta – Peak District National Park, Moors for the Future Partnership, Moorland Centre, Edale, S33 7ZA, UK; IUCN UK Peatland Programme, 1 St George‘s Place, York YO24 1GN, UK, a.bonn@sheffield.ac.uk, [email protected], [email protected] BONZINI Sara – Università degli Studi di Milano, Bicocca Dipartimento di Scienze, dell‘Ambiente e del Territorio, Piazza della Scienza 1, 20126 Milano, Italy, [email protected] BORGEN SØRENSEN Peter – Aarhus University, Department of Policy Analysis, National Environmental Research Institute, Box 358, Frederiksborgvej 399, DK 4000 Roskilde, Denmark, [email protected] BOUMANS Louis – University of Amsterdam (UvA), Zoological Museum, Mauritskade 61, 1092 AD Amsterdam, The Netherlands, [email protected] BOURGOIN Thierry – National Natural History Museum (NMHN), 5 Rue Cuvier 75005 Paris, France, [email protected] BRACK Werner – Helmholtz Centre for Environmental Research – UFZ, Department of Effect-Directed Analysis, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] BRENDONCK Luc – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium, [email protected] BRILIŪTĖ Aušra – Nature Research Centre, Akademijos 2, 08412 Vilnius, Lithuania BRITTAIN Claire – Centre for Agri-Environmental Research, University of Reading, Reading RG6 6AR, UK, [email protected] BROWN Mike – National Bee Unit, Food and Environment Research Agency, Sand Hutton, York YO41 1LZ, UK BROWN Peter M.J. – Department of Life Sciences, Anglia Ruskin University, East Road, Cambridge CB1 1PT, UK, [email protected] BUDRIENĖ Anna – Nature Research Centre, Akademijos 2, 08412 Vilnius, Lithuania BUDRYS Eduardas – Institute of Ecology of Vilnius University, Akademijos 2, 08412 Vilnius, Lithuania, [email protected] BUSCOT François – Helmholtz Centre For Environmental Research – UFZ, Department of Soil Ecology, Theodor-Lieser Str. 4, 06120 Halle, Germany, [email protected] BUTLER Adam – Biomathematics and Statistics Scotland (BioSS), JCMB, The King’s Buildings, Edinburgh, EH9 3JZ, Scotland, United Kingdom, [email protected] BYSTRÖM Pär – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden, [email protected] C CAIOLA Nuno – Aquatic Ecosystems, IRTA, Ctra. de Poble Nou, Km 5.5, E-43540 Sant Carles de la Rápita, Tarragona, Spain CARDONA Carles – Agència Catalana de l’Aigua, Provença 204-208, 08036 Barcelona, Spain CARILLA Julieta – Instituto de Ecologi’a Regional (IER), Universidad Nacional de Tucumán (UNT), cc 34 (4107) Yerba Buena, Tucumán, Argentina CARRÉ Gabriel – INRA, UMR 406 Abeilles & Environnement INRA-UAPV, site Agroparc, 84914 Avignon cedex 9, France, [email protected] CARTER Timothy R. – Finnish Environment Institute (SYKE), Climate Change Programme, P.O. Box 140, FI-00251 Helsinki, Finland, tim.carter@ymparisto.fi
CASALS Frederic – Secció de Fauna Silvestre, Departament de Producció Animal, E.T.S.E.A. Universitat de Lleida, Av. Rovira Roure 191, 25198 Lleida, Spain CATINDIG Josie Lynn – International Rice Research Institute, Los Banos, DAPO 7777 Metro Manila, Philippines, [email protected] CATTERALL Stephen – Biomathematics & Statistics Scotland, James Clerk Maxwell Building, The King’s Buildings, Edinburgh EH9 3JZ, UK, [email protected] CÉSPEDES Blanca – Department of Environmental Sciences, University of CastillaLa Mancha, Av. Carlos III s.n., 45071 Toledo, Spain ĆETKOVIĆ Aleksandar – Institute of Zoology, Faculty of Biology, University of Belgrade, Studentski trg 16, 11000 Belgrade, Serbia CHRISTENSEN Torben R. – Department of Earth and Ecosystem Sciences, Division of Physical Geography and Ecosystem Analyses, Lund University, Sölvegatan 12, 223 62 Lund, Sweden, [email protected] CHYTRÝ Milan – Masaryk University Brno, Joštova 10, 602 00 Brno-Město, Czech Republic, [email protected] COOK Alex – Department of Statistics and Applied Probability, Block S16, Level 7, 6 Science Drive 2, Faculty of Science, National University of Singapore, Singapore 117546, [email protected] COPP Gordon H. – Cefas, Salmon & Freshwater Fisheries Team, Lowestoft, UK, [email protected] COSTA Luis – Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, D-14473 Potsdam, Germany, [email protected] COSTELLO Mark – Ecological Consultancy Services Ltd (EcoSERVE), B19 K.C.R. Industrial Estate 12, Dublin, Ireland COUSINS Ian T. – Department of Applied Environmental Science – ITM, Stockholm University, SE-10691 Stockholm, Sweden, [email protected] CRAMER Wolfgang – Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, D-14473 Potsdam, Germany, [email protected] CUELLO Soledad – INQUINOA (CONICET), Ayacucho 471, 4000 San Miguel de Tucumán, Argentina CUEVAS Fernanda – Institute for Aridlands Research of Argentina (IADIZACONICET), Biodiversity Research Group, Cricyt Mendoza, Argentina, [email protected] D DAUBER Jens – Laboratory of Biogeography and Ecology, Department of Geography, University of the Aegean, University Hill, GR-81100 Mytilene, Greece; Earth & Biosphere Institute, IICB, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK; Department of Botany, School of Natural Sciences, Trinity College Dublin, Dublin 2, Republic of Ireland, [email protected] DAUNYS Darius – Klaipeda University Coastal Research and Planning Institute, H. Manto 84, Klaipeda, LT-92294, Lithuania, [email protected] DE BIE Tom – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium, [email protected] DE DECKERE Eric – University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium DE JONG Yde – University of Amsterdam (UvA), Zoological Museum, Mauritskade 61, 1092 AD Amsterdam, The Netherlands, [email protected] DE MEESTER Luc – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium, [email protected] DE SOSTOA Adolfo – Departamento de Biología Animal, Vertebrados, 1ª Planta, Despacho 151, Facultad de Biología, Universidad de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain DE ZWART Dick – Laboratory for Ecological Risk Assessment, National Institute for Public Health and the Environment, A. van Leeuwenhoeklaan 9, 3720 HA Bilthoven, The Netherlands DEAN Robin – Red Beehive Company, 51 Elm Road, Bishops Waltham, Southampton SO32 1JR, UK DECLERCK Steven – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium; Department of Aquatic Ecology, Netherlands Institute of Ecology, Rijksstraatweg 6, 3631 AC Nieuwersluis, The Netherlands, [email protected] DENDONCKER Nicolas – Formerly: Centre for the Study of Environmental Change and Sustainability, University of Edinburgh, Drummond Library, Room 1, Drummond Street, Edinburgh EH8 9X, UK, current address: Department of Geography, FUNDP UCLouvain 61 Rue de Bruxelles B 5000 Namur Belgium, [email protected]
DESBOIS Sébastien – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] DIETZSCH Anke – School of Natural Sciences, Trinity College Dublin, Dublin 2, Republic of Ireland, [email protected] DITTMER Kristofer – Universitat Autònoma de Barcelona (UAB), Institut de Ciència i Tecnologia Ambientals, 08193 Bellaterra (Cerdanyola del Vallès), Barcelona, Spain, [email protected] DOHERTY Ruth – School of GeoSciences, Crew Building, University of Edinburgh, EH9 3JN UK, [email protected] DOUGUET Jean-Marc – Université de Versailles Saint-Quentin-en-Yvelines, REEDS (EA_4456), UVSQ, Quartier des Garennes, 11 Boulevard d’Alembert, 78280 Guyancourt, France, [email protected] DVOŘÁK Libor – Municipal Museum Mariánské Lázně, Goethovo náměstí 11, CZ-35301 Mariánské Lázně, Czech Republic, [email protected] E ENGHOFF Henrik –Zoological Museum (ZMUC), University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen, Denmark ESTIARTE Marc – CREAF-CEAB-CSIC Global Ecology Unit, Center for Ecological Research and Forestry Applications, Edifici C, Universitat Autònoma Barcelona, 08193 Bellaterra, Spain F FALKENBERG Karl – European Commission, DG Environment, B-1049 Brussels, Belgium FEEHAN Jane – European Environment Agency; current address: Sustainable Development Unit, European Investment Bank, 100 boulevard Konrad Adenauer, L-2950 Luxembourg, [email protected] FELD Christian K. – Department of Applied Zoology/Hydrobiology, University of Duisburg-Essen, D-45117 Essen, Germany, [email protected] FELDMANN Reinart – Helmholtz Center for Environmental Research – UFZ, Environmental Education and Events, Permoserstr. 14, 04318 Leipzig, Germany, [email protected] FERGUS Alexander J.F. – Institute of Evolutionary Biology and Environmental Studies, University of Zürich, Winterthurerstr. 190, CH-8057 Zürich, Switzerland, [email protected] FISCHER Markus – University of Bern, Institute of Plant Sciences, Altenbergrain 21, 3013 Bern, Switzerland, markus.fi[email protected] FRAMSTAD Erik – Norwegian Institute for Nature Research, Gaustadalleen 21, Oslo 0349, Norway, [email protected] FRENZEL Mark – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] FRONZEK Stefan – Finnish Environment Institute (SYKE), Climate Change Programme, P.O. Box 140, FI-00251 Helsinki, Finland, stefan.fronzek@ymparisto.fi FUENTES Nicol – Institute of Geobotany and Botanical Garden, Martin-LutherUniversity Halle-Wittenberg, Am Kirchtor 1, D-06108 Halle, Germany G GALLAI Nicola – INRA, UMR 406 Abeilles & Environnement INRA-UAPV, site Agroparc, 84914 Avignon Cedex 9, France, [email protected] GARCIA Jacques – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] GARCÍA Carolina – Herbario Nacional de Bolivia, La Paz, Bolivia, and Laboratorio de Ecobiosis, Departamento de Botánica, Universidad de Concepción, Chile GARCIA-BERTHOU Emili – Institute of Aquatic Ecology, University of Girona, Campus de Montilivi, 17071 Girona, Spain GARGOMINY Olivier – Muséum National d’Histoire Naturelle, Département d’écologie et gestion de la biodiversité, USM 0308, Service du patrimoine naturel, 57 rue Cuvier, 75005, Paris, France, [email protected] GARNERY Lionel – Université de Versailles Saint Quentin en Yvelines & Laboratoire Evolution Génomes et Spéciation, CNRS UPR9034, 90198 Gif-sur-Yvette, France, [email protected]
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GEORGIEV Teodor – Pensoft Publishers, Geo Milev Str. 13a, 1111 Sofia, Bulgaria, [email protected] GILBERT Marius – Biological Control and Spatial Ecology Lab, CP 160/12, Universite Libre de Bruxelles, 50 av. FD Roosevelt, B-1050 Bruxelles, Belgium, [email protected] GLAVENDEKIĆ Milka – Faculty of Forestry University of Belgrade, Kneza Viseslava 1, 11030 Belgrade, Serbia, [email protected] GOCKEL Sonja – Friedrich-Schiller-University of Jena, Institute of Ecology, Dornburger Str. 159, 07743 Jena, [email protected] GOTTFRIED Michael – EcoScience Scotland, 2/1, 27 Glencairn Drive, Glasgow G41 4QP, Scotland, Department of Conservation Biology, Vegetation and Landscape Ecology, University of Vienna, Rennweg 14, A-1030 Vienna, Austria, [email protected] GOUSSARD Francis – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] GRABAUM Ralf – OLANIS Expert Systems GmbH, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] GRABHERR Georg – EcoScience Scotland, 2/1, 27 Glencairn Drive, Glasgow G41 4QP, Scotland; Department of Conservation Biology, Vegetation and Landscape Ecology, University of Vienna, Rennweg 14, A-1030 Vienna, Austria, [email protected] GRABOWSKI Michal – University of Łódz, Department of Invertebrate Zoology & Hydrobiology, ul. Banacha 12/16, 90-237 Łódz, Poland, [email protected] GRACIA Carlos – CREAF (Center for Ecological Research and Forestry Applications) and Universitat Barcelona, Campus Universitat Autònoma Barcelona, 08193 Bellaterra, Spain, [email protected] GRAU Alfredo – Instituto de Ecologi’a Regional, Universidad Nacional de Tucumán, Centro Universitario Horco Molle, 4107 Yerba Buena, Argentina GRESCHO Volker – OLANIS GmbH, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] GROBELNIK Vesna – Centre for Cartography of Fauna and Flora, Antoličičeva 1, SI-2204 Miklavž na Dravskem polju, Slovenia, [email protected] GRZEŚ Irena – Institute of Environmental Sciences, Jagiellonian University, Ul. Gronostajowa 7, Kraków 30-387, Poland GUMPENBERGER Marlies – Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, D-14473 Potsdam, Germany, [email protected] GÜNTSCH Anton – Free University of Berlin, Botanic Garden and Botanical Museum Berlin-Dahlem (BGBM), Königin-Luise-Straße 6-8, 14195 Berlin, Germany GUSTAFSSON Örjan – ITM, Stockholm University, 10691 Stockholm, Sweden, [email protected] GYLDENKÆRNE Steen – Aarhus University, Department of Policy Analysis, National Environmental Research Institute, Box 358, Frederiksborgvej 399, DK 4000 Roskilde, Denmark, [email protected] H HALLOY Stephan – The Nature Conservancy, Santiago, Chile, [email protected] HAMERS Timo – Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands, [email protected] HAMMEN Volker – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] HANSPACH Jan – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] HARPKE Alexander – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] HARRISON Paula A. – Environmental Change Institute, Oxford University Centre for the Environment, South Parks Road, Oxford OX1 3QY, UK, [email protected] HAXAIRE Jean – Muséum National d’Histoire Naturelle, Département Systématique & Evolution, CP50 Entomologie, 45 rue Buffon, 75005, Paris, France, [email protected] HEIKKINEN Risto K. – Finnish Environment Institute, Natural Environment Centre, P.O. Box 140, FI-00251 Helsinki, Finland, risto.heikkinen@ymparisto.fi HEIN Michaela – Helmholtz Centre for Environmental Research – UFZ, Department of Effect-Directed Analysis, Permoserstr. 15, 04318 Leipzig, Germany, [email protected]
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HEINRICH Silke – HBLFA Raumberg-Gumpenstein, Altirdning 11, 8952 Irdning, Austria HELM Aveliina – Institute of Ecology and Earth Sciences, University of Tartu, Tartu 511005, Tartu, Estonia, [email protected] HEMP Andreas – University of Potsdam, Institute for Biochemistry and Biology, Biodiversity research/Spec Botany, Maulbeerallee 1, 14469 Potsdam, Germany, [email protected] HENLE Klaus – Helmholtz Centre for Environmental Research – UFZ, Department of Conservation Biology, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] HENRY Pierre-Yves – UMR 5137 & UMR 7179, Départment Écologie et Gestion de la Biodiversité, Muséum National d’Histoire Naturelle, 55 Rue Buffon, Paris 75005, France, [email protected] HEONG Kong Luen – International Rice Research Institute, Los Banos, DAPO 7777 Metro Manila, Philippines, [email protected], [email protected] HICKLER Thomas – Geobiosphere Science Centre, Department of Physical Geography & Ecosystems Analysis, University of Lund, Sölvegatan 13, 223 62 Lund, Sweden, [email protected] HIJMANS Robert J. – International Rice Research Institute, Los Banos, Philippines, DAPO 7777 Metro Manila, Philippines, [email protected] HIRNEISEN Norbert – Science & Communication, von Müllenark Str. 19, 53179 Bonn, Germany, [email protected] HULME Philip E. – The Bio-Protection Research Centre, PO Box 84, Lincoln University, Lincoln 7647, Christchurch, New Zealand, [email protected] HUSSEY Charles – Natural History Museum (NHM), Cromwell Road, London SW7 5BD, UK HYAM Roger – Natural History Museum (NHM), Cromwell Road, London SW7 5BD, UK J JÁCOME Jorge – Universidad Javeriana, 40-62 Carrera 7, Bogotá, Colombia, [email protected] JAFFÉ Rodolfo – Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia, [email protected] JÄGER Jill – SERI Nachhaltigkeitsforschungs und -kommunikations GmbH, Garnisongasse 7/21, A-1090 Wien, Austria, [email protected] JAHODOVÁ Šárka – Institute of Botany AS CR, Průhonice, Czech Republic, [email protected] JAKSIC Predrag – Institute for Biological Research “Sinisa Stankovic”, University of Belgrade, ”, Despot Stefan Boulevard 142, Belgrade, Serbia, [email protected] JANSSON Mats – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden, [email protected] JOHANSSON Margareta – Dept. of Earth and Ecosystem Sciences, Division of Physical Geography and Ecosystem Analyses, Lund University, Sölvegatan 12, 223 62 Lund, Sweden, [email protected] JONES Richard – Bee Research Association, 16 North Road, Cardiff CF10 3DY, UK JUMP Alistair – Global Ecology Unit CREAF-CEAB-CSIC, Center for Ecological Research and Forestry Applications, Campus Universitat Autònoma Barcelona, 08193 Bellaterra, Spain JYLHÄ Kirsti – Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland, kirsti.jylha@fmi.fi K KALKO Elisabeth K.V. – University of Ulm, Institute of Experimental Ecology, Albert-Einstein Allee 11, 89069 Ulm, Germany, [email protected] KAMIENIECKI Krzysztof – Institute for Sustainable Development, Nabielaka 15/1, 00-743 Warszawa, Poland, [email protected] KARLSSON Jan – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden KENIS Marc – CABI Europe-Switzerland, Rue des Grillons 1, 2800 Delémont, Switzerland, [email protected] KIER Gerold – Nees Institute for Biodiversity of Plants, Rheinische FriedrichWilhelms-Universität, Meckenheimer Allee 170, 53115 Bonn, Germany, [email protected] KIRICHENKO Nataliya – Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia
KLOTZ Stefan – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] KORETS Mikhail – Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia KOTARAC Mladen – Centre for Cartography of Fauna and Flora, Antoličičeva 1, SI-2204 Miklavž na Dravskem polju, Slovenia, [email protected] KOUWENBERG Juliana – University of Amsterdam (UvA), Zoological Museum, Mauritskade 61, 1092 AD, Amsterdam, The Netherlands KRAMARZ Paulina – Jagiellonian University, Gronostajowa Str. 7, 30-387 Krakow, Poland KRAUZE Kinga – European Regional Centre For Ecohydrology u/a UNESCO, International Institute of Polish Academy of Sciences, 3 Tylna Str., 90-364 Lodz, Poland, [email protected] KREFT Holger – Biodiversity, Macroecology & Conservation Biogeography Group, Georg-August University of Göttingen, Büsgenweg 2, 37077 Göttingen, Germany, [email protected] KRÖEL-DULAY György – Institute of Ecology and Botany, Hungarian Academy of Sciences, Alkotmány u. 2-4., 2163, Vácrátót, Hungary, [email protected] KRÓL Wiesław – Institute of Nature Conservation, Polish Academy of Sciences, Al. Mickiewicza 33, 31-120 Kraków, Poland, [email protected] KUDRNA Otakar – Geldersheimer Strasse 64, 97424 Schweinfurt, Germany, [email protected] KÜHN Elisabeth – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] KÜHN Ingolf – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] KULDNA Piret – Stockholm Environment Institute Tallinn Centre, Lai Str. 34, 10133 Tallinn, Estonia, [email protected] KUNIN William E. – Institute of Integrative and Comparative Biology and Earth and Biosphere Institute, University of Leeds, Leeds LS2 9JT, UK, [email protected] L LAMBORN Ellen – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK, [email protected] LASKOWSKI Ryszard – Institute of Environmental Sciences, Jagiellonian University, Gronostajowa Str. 7, 30-387 Kraków, Poland, [email protected] LE KAMA Alain A. – EQUIPPE, Universities of Lille, Université de Lille 1, Faculté des sciences économiques et sociales, 59655 Villeneuve d’Ascq Cedex, France, [email protected] LENGYEL Szabolcs – Department of Ecology, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary, [email protected] LEONARDS Pim – Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands, [email protected] LEOPOLD Patrick – Pastoratsweg 4, 53343 Wachtberg, Germany, [email protected] LEUVEN Rob S.E.W. – Department of Environmental Science, Institute for Wetland and Water Research, Radboud University Nijmegen, The Netherlands, [email protected] LIIRA Jaan – Institute of Ecology and Earth Sciences, University of Tartu, Lai Str. 40, Tartu, EE51005, Estonia, [email protected] LINDLEY Sarah – Geography, School of Environment and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK, [email protected] LINSENMAIR K. Eduard – University of Wuerzburg, Theodor-Boveri-Institute of Biosciences, Animal Ecology and Tropic Biology, Am Hubland, 97074 Würzburg, Germany, [email protected] LONG Sara – Department of Zoology, Faculty of Science, The University of Melbourne, Victoria 3010, Australia, [email protected] LOPEZ-VAAMONDE Carlos – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans CEDEX 2 France, [email protected] LORME Philippe – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, philippe.lorme @orleans.inra.fr LOS Wouter – Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94216, 1090 GE Amsterdam, The Netherlands, [email protected]
LÜBCKE-VON VAREL Urte – Helmholtz Centre for Environmental Research – UFZ, Department of Effect-Directed Analysis, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] LUCK Gary W. – Institute for Land, Water and Society, Charles Sturt University, Albury NSW 2640, Australia, [email protected] LUCY Frances – Environmental Services Ireland, Institute of Technology, Sligo, Ireland, [email protected] LUOTO Miska – Department of Geosciences and Geography, P.O. Box 64, 00014 University of Helsinki, Finland, miska.luoto@helsinki.fi LUUKANEN Jyrki – Finland Futures Research Centre, Turku School of Economics, Tampere Office, Pinninkatu 47, FI-33100 Tampere, Finland, jyrki.luukkanen@tse.fi M MACEDA-VEIGA Alberto – Departamento de Biología Animal, Vertebrados, 1ª Planta, Despacho 151, Facultad de Biología, Universidad de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain MARION Glenn – Biomathematics & Statistics Scotland, James Clerk Maxwell Building, The King’s Buildings, Edinburgh EH9 3JZ, UK, [email protected] MARRIS Gay – National Bee Unit, Food and Environment Research Agency, Sand Hutton, York YO41 1LZ, UK MARTENS Koen – Freshwater Biology, Royal Belgian Institute of Natural Sciences, Vautierstraat 29, 1000 Brussel, Belgium, [email protected] MARTÍN María – Department of Environmental Sciences, University of Castilla-La Mancha, Av. Carlos III s.n., 45071 Toledo, Spain MARTÍNEZ-ALIER Joan – Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona (UAB), Mailbox 317, 08193 Bellaterra, Barcelona, Spain, [email protected] MASKELL Lindsay C. – Centre of Ecology and Hydrology, Lancaster, UK, [email protected] MAXIM Laura – Institut des Sciences de la Communication du CNRS, UPS 3088, 20 Rue Berbier du Mets, 75013 Paris, [email protected] MCMORROW Julia – Geography, School of Environment and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK, Julia. [email protected] MEIJER Sandra – Environmental Science Department, Lancaster University, Lancaster, LA1 4YQ, UK, [email protected] MENESES Rosa Isela – MNHN-LPB, Casilla 10077, La Paz, Bolivia, [email protected] MEYER Birgit – Department of Natural History, Hessisches Landesmuseum Darmstadt, Friedensplatz 1, 64283 Darmstadt, Germany, [email protected] MIHAJLOVIĆ Ljubodrag – University of Belgrade, Faculty of Forestry, 1 Kneza Višeslava Str., 11000 Belgrade, Serbia, [email protected] MILLÁN Amparo – Department of Environmental Sciences, University of CastillaLa Mancha, Av. Carlos III s.n., 45071 Toledo, Spain MILLER Paul A. – Geobiosphere Science Centre, Department of Physical Geography & Ecosystems Analysis, Lund University, Sölvegatan 12, 223 62 Lund, Sweden, [email protected] MINCHIN Dan – Marine Organism Investigations, 3 Marina Village, Ballina, Killaloe, Co Clare, Ireland, [email protected] MIRTL Michael – Federal Environment Agency, Spittelauer Lände 5, 1090 Wien, Österreich/Austria, [email protected] MONROY Mario – Departamento de Biología Animal, Vertebrados, 1ª Planta, Despacho 151, Facultad de Biología, Universidad de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain MONTERROSO Iliana – Area de Medio Ambiente, Población y Desarrollo Rural, Facultad Latinoamericana de Ciencias Sociales, Instituto de Economía Ecológica y Ecología Política, Guatemala, imonterroso@flacso.edu.gt MOORA Mari – Institute of Ecology and Earth Sciences, University of Tartu, Lai Str. 40, Tartu 51005, Estonia, [email protected] MORAVCOVÁ Lenka – Institute of Botany AS CR, Průhonice, Czech Republic, [email protected] MORENO José M. – Department of Environmental Sciences, University of CastillaLa Mancha, Av. Carlos III s.n., 45071 Toledo, Spain, [email protected] MORISON Nicolas – INRA, UMR 406 Abeilles & Environnement INRA-UAPV, site Agroparc, 84914 Avignon Cedex 9, France, [email protected] MORITZ Robin F.A. – Molecular Ecology Research Group, Martin Luther University, Halle-Wittenberg, Hoher Weg 4, 06099, Halle, Germany, [email protected] MOROŃ Dawid – Institute of Environmental Sciences, Jagiellonian University, Ul. Gronostajowa 7, Kraków 30-387, Poland, [email protected]
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MÜLLEROVÁ Jana – Institute of Botany AS CR, Průhonice, Czech Republic, [email protected] MUNNÉ Antoni – Agència Catalana de l’Aigua, Provença 204-208, 08036 Barcelona, Spain, [email protected] MUTKE Jens – Nees Institute for Biodiversity of Plants, Rheinische FriedrichWilhelms-Universität, Meckenheimer Allee 170, 53115 Bonn, Germany, [email protected] N NAGY Laszlo – EcoScience Scotland 2/1, 27 Glencairn Drive, Glasgow G41 4QP, Scotland; Department of Conservation Biology, Vegetation and Landscape Ecology, University of Vienna, Rennweg 14, A-1030 Vienna, austria, [email protected] NEHRING Stefan – AeT Umweltplanung, Koblenz, Germany, [email protected] NENTWIG Wolfgang – Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland, [email protected] NIELSEN Anders – Laboratory of Biogeography and Ecology, Department of Geography, University of the Aegean, GR-81100 Mytilene, Lesvos, Greece, [email protected] NIESCHULZE Jens – Max-Planck Institute for Biogeochemistry, Hans-Knoell Str. 10, 07745 Jena, Germany, [email protected] NOVILLO Agustina – Institute for Aridlands Research of Argentina (IADIZACONICET), Biodiversity Research Group, Cricyt Mendoza, Argentina, [email protected] O O’CONNOR Martin – Université de Versailles Saint Quentin en Yvelines; UVSQ, Quartier des Garennes, 11 Boulevard d’Alembert, 78280 Guyancourt, France, [email protected] ÖCKINGER Erik – Department of Ecology, Swedish University of Agricultural Sciences, Box 7044, 75007 Uppsala, Sweden, [email protected] OHLEMÜLLER Ralf – Institute of Hazard and Risk Research, University of Durham, Durham DH1 3LE, UK, [email protected] OJEDA Ricardo A. – Institute for Aridlands Research of Argentina (IADIZACONICET), Biodiversity Research Group, Cricyt Mendoza, Argentina, [email protected] OLENIN Sergej – Klaipeda University Coastal Research and Planning Institute, H. Manto 84, Klaipeda, LT-92294, Lithuania, [email protected] OMANN Ines – SERI Sustainable Europe Research Institute, Garnisongasse 7/21, A-1090 Vienna, Austria, [email protected] OTT Jürgen – L.U.P.O. GmbH, Friedhofstr. 28, D-67705 Trippstadt, Germany, [email protected] P PANOV Vadim E. – St. Petersburg State University, Universitetskaya nab. 7-9, 199 034 St. Petersburg, Russia, [email protected] PAPACHRISTOFOROU Alexandros – Laboratoire Evolution, Génomes, Spéciation, CNRS UPR9034, 91198 Gif-sur-Yvette, France, [email protected] PARPHENOVA Elena – Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia PATERSON James – Environmental Change Institute, School of Geography and the Environment, South Parks Road, Oxford OX1 3QY, UK, [email protected] PAUL Alex – Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ UK, [email protected] PAULI Harald – Austrian Academy of Sciences, Institute of Mountain Research (IGF), c/o University of Vienna, Faculty Centre of Biodiversity, Rennweg 14, A-1030 Vienna, Austrianna, Austria, [email protected] PAUNOVIĆ Momir – Institute for Biological Research “Sinisa Stankovic”, Despot Stefan Boulevard 142, Belgrade, Serbia, [email protected] PENEV Lyubomir – Central Laboratory for General Ecology, Bulgarian Academy of Sciences, Yuri Gagarin Str. 2, 1113 Sofia, Bulgaria; Pensoft Publishers, Geo Milev Str. 13a, 1111 Sofia, Bulgaria, [email protected] PENGUE Walter – Area de Ecologia, ICO, Universidad Nacional de General Sarmiento, Argentina, [email protected]
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PEÑUELAS Josep – CREAF-CEAB-CSIC Global Ecology Unit, Center for Ecological Research and Forestry Applications, Edifici C, Universitat Autònoma Barcelona, 08193 Bellaterra, Spain, [email protected] PERGL Jan – Institute of Botany AS CR, Průhonice, Czech Republic, [email protected] PERGLOVÁ Irena – Institute of Botany AS CR, Průhonice, Czech Republic, [email protected] PERSSON Lennart – Department of Ecology and Environmental Science, University of Umeå, SE-90187 Umeå, Sweden PETANIDOU Theodora – Laboratory of Biogeography and Ecology, Department of Geography, University of the Aegean, GR-81100 Mytilene, Lesvos, Greece, [email protected] PETERSON Kaja – Stockholm Environment Institute Tallinn Centre (SEI Tallinn), Lai 34, Tallinn 10133, Estonia, [email protected] PFEIFFER Simone – University of Potsdam, Institute for Biochemistry and Biology, Biodiversity research/Spec Botany, Maulbeerallee 1, 14469 Potsdam, [email protected] PINEAU Patrick – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, patrick.pineau @orleans.inra.fr PINO Joan – Centre for Ecological Research and Forestry Applications, Edifici C, 08193 Bellaterra, Barcelona, Spain, [email protected] PIPER Jake – Oxford Brookes University, Headington, Oxford OX3 0BP, UK, [email protected] PLA Eduard – CREAF (Center for Ecological Research and Forestry Applications) and Universitat Autònoma Barcelona, Edifici C, 08193 Bellaterra, Barcelona, Spain, [email protected] POLCE Chiara – Formerly: Institute of Integrative and Comparative Biology and Earth and Biosphere Institute, University of Leeds, Leeds LS2 9JT, UK; current address: Net Gain: The North Sea Marine Conservation Zone Project, The Deep Business Centre, Hull HU1 4BG, UK, [email protected] POMPE Sven – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] POTTS Simon G. – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, RG6 6AR, UK, [email protected] PÖYRY Juha – Finnish Environment Institute, Natural Environment Centre, P.O. Box 140, FI-00251 Helsinki, Finland, juha.poyry@ymparisto.fi PRATI Daniel – University of Bern, Institute of Plant Sciences, Altenbergrain 21, 3013 Bern, Switzerland, [email protected] PREVEDOUROS Konstantinos – European Chemicals Agency – ECHA, Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Finland PRIETO Patricia – CREAF-CEAB-CSIC Global Ecology Unit, Center for Ecological Research and Forestry Applications, Edifici C, Universitat Autònoma Barcelona, 08193 Bellaterra, Spain PYŠEK Petr – Institute of Botany, Academy of Sciences of the Czech Republic, Průhonice & Faculty of Science, Charles University in Prague, Czech Republic, [email protected] R RAIMBAULT Jean-Paul – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] RATTEI Silke – Helmholtz Centre for Environmental Research – UFZ, EU – Liaison Office, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] REGINSTER Isabelle – Formerly: UCL – Department of Geography, Place Pasteur 3, B- 1348 Louvain-la-Neuve, Belgium; current address: IWEPS – Institut Wallon de l‘Evaluation, de la Prospective et de la Statistique, rue Fort de Suarlée 1, B-5001 Namur, Belgium, [email protected] REINEKING Björn – University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany, [email protected] ROBERTS Stuart P. M. – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK, [email protected] ROBINET Christelle – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] RODRIGUEZ Pamela – Herbario Nacional de Bolivia, Casilla 10077, La Paz, Bolivia, [email protected]
RODRÍGUEZ-LABAJOS Beatriz – Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona (UAB): postal address: Escola Tècnica Superior d‘Enginyeria (ETSE), Mailbox 317, 08193 Bellaterra (Cerdanyola del Vallès), Barcelona, Spain, [email protected] ROHDE Mandy – Institute of Zoology, Martin-Luther-University Halle-Wittenberg, Hoher Weg 4, 06099 Halle, Germany, [email protected] ROME Quentin – Muséum National d’Histoire Naturelle, Département Systématique & Evolution, UMR 7205, OSEB, CP50 Entomologie, 45 rue Buffon, 75005, Paris, France, [email protected] ROQUES Alain – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] RORTAIS Agnès – Emerging Risks Unit, European Food Safety Authority, Largo N. Palli 5/A, 43121 Parma, Italy, [email protected] ROUNSEVELL Mark – Centre for the Study of Environmental Change and Sustainability, University of Edinburgh, Drummond Library, Room 1, Drummond Street, Edinburgh EH8 9X, UK, [email protected], [email protected] ROUSSELET Jérôme – INRA UR0633, Zoologie Forestière Orléans, 2163 Avenue de la Pomme de Pin – CS 40001 – Ardon, 45075 Orléans Cedex 2, France, [email protected] ROY David B. – Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK, [email protected] ROŻEJ Elżbieta – Institute of Environmental Sciences, Jagiellonian University, Ul. Gronostajowa 7, Kraków 30-387, Poland, [email protected] RYDÉN Lars – The Baltic University Programme, Uppsala Centre for Sustainable Development Uppsala University Villavägen 16, S-752 36 Uppsala, Sweden, [email protected] S SABATÉ Santi – CREAF-CEAB-CSIC Global Ecology Unit, Center for Ecological Research and Forestry Applications, Edifici C, Universitat Autònoma Barcelona, 08193 Bellaterra, Spain, [email protected] SALA Serenella – Dipartimento di Scienze dell’Ambiente e del Territorio, Universitа degli Studi Milano Bicocca, Piazza Scienza 1, 20126 Milano, [email protected] SALLES Jean-Michel – CNRS, UMR LAMETA, 2 place Viala, 34060 Montpellier Cedex 1, France SAMWAYS Michael J. – Department of Conservation Ecology & Entomology, Stellenbosch University, South Africa, [email protected] SARDANS Jordi – CREAF-CEAB-CSIC Global Ecology Unit, Center for Ecological Research and Forestry Applications, Edifici C, Universitat Autònoma Barcelona, 08193 Bellaterra, Spain, [email protected] SCHMELLER Dirk S. – Station d’Ecologie Expérimentale du CNRS a Moulis USR 2936, Moulis, 09200 Saint-Girons, France, [email protected] SCHMID Bernhard – Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland, [email protected] SCHMIDT Annette – Helmholtz Centre for Environmental Research – UFZ, EU – Liaison Office, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] SCHMITT Claudia – University of Antwerp, Department of Biology, Ecosystem Management Research Group, Universiteitsplein 1, 2610 Wilrijk, Belgium, [email protected] SCHMITT-JANSEN Mechthild – Helmholtz Centre for Environmental Research – UFZ, Department of Bioanalytical Ecotoxicology, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] SCHULZE Christian H. – Department of Population Ecology, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090 Vienna, Austria, [email protected] SCHULZE Ernst-Detlef – Max-Planck Institute for Biogeochemistry, Hans-Knoell Str. 10, 07745 Jena, Germany, [email protected] SCHWEIGER Oliver – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] SEIMON Anton – Assistant Director for Latin America and Caribbean Programs, Wildlife Conservation Society (WCS), Global Conservation Programs, 2300 Southern Boulevard, Bronx, NY 10460-1099, USA, [email protected] SEIMON Tracie – Wildlife Conservation Society (WCS), 2300 Southern Boulevard, Bronx, NY 10460-1099, USA
SEMENCHENKO Vitaliy – Scientific and Practical Center for Bioresources, 27 Akademicheskaya Street, Minsk BY-220072, Belarus, [email protected] SETTELE Josef – Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, Germany, [email protected] SHARMAN Martin – European Commission, DG Research, B-1049 Brussels, Belgium, [email protected] SJÖDIN Erik – Division of Landscape Ecology, Swedish University of Agricultural Science, Box 7044, 75007 Uppsala, Sweden, [email protected] SMITH Benjamin – Geobiosphere Science Centre, Department of Physical Geography & Ecosystems Analysis, Lund University, Sölvegatan 12, 223 62 Lund, Sweden, [email protected] SOBER Virve – Institute of Ecology and Earth Sciences, University of Tartu, Lai Str. 40, Tartu 51005, Estonia SOL Daniel – CREAF (Centre for Ecological Research and Applied Forestries), CEAB-CSIC (Centre for Advanced Studies of Blanes-Spanish National Research Council), Autonomous University of Barcelona, Bellaterra, Catalonia E-08193, Spain, [email protected] SOLARZ Wojciech – Institute of Nature Conservation, Polish Academy of Sciences, Al. Mickiewicza 33, 31-120 Kraków, Poland, [email protected] SOLORIO-ORNELAS Edgar – Departamento de Biología Animal, Vertebrados, 1ª Planta, Despacho 151, Facultad de Biología, Universidad de Barcelona, Av. Diagonal, 645, 08028 Barcelona, Spain SON Mikhail O. – Institute of Biology of the Southern Seas, 37 Pushkinska Str., Odessa Branch, 65125 Odessa, Ukraine, [email protected] SPANGENBERG Joachim H. – Sustainable Europe Research Institute SERI Germany e.V., Vorsterstr. 97-99, 51103 Cologne, Germany, [email protected] SPRINGATE Simon – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK SPURGEON David – Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxon OX10 8BB, UK, [email protected] STEFFAN-DEWENTER Ingolf – Lehrstuhl für Zoologie III, Am Hubland, 97074 Würzburg, Germany, ingolf.steffan-dewenter(at)uni-wuerzburg.de STEINMÜLLER Karlheinz – Z_punkt GmbH The Foresight Company, Manfredvon-Richthofen-Straße 9, D-12101 Berlin, Germany, [email protected] STERZEL Till – Potsdam Institute for Climate Impact Research (PIK), 14412 Potsdam, Germany, [email protected] STIRPE Maria T. – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK STOCKER Andrea – SERI Nachhaltigkeitsforschungs und -kommunikations GmbH, Garnisongasse 7/21, A-1090 Wien, Austria, [email protected] STOKS Robby – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium, [email protected] STOLL-KLEEMANN Susanne – University of Greifswald, Institute of Geography and Geology, Jahnstr. 16, 17487 Greifswald, Germany, [email protected] STOUT Jane – School of Natural Sciences, Trinity College Dublin, Dublin 2, Republic of Ireland, [email protected] SUN Jianghua – Institute of Zoology, Chinese Academy of Sciences, DaTun Road, Chaoyang District, Beijing 100101, China, [email protected] SVEDIN Uno – Director of International Affairs, The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas), Box 1206, 111 82 Stockholm, Sweden, [email protected] SWEETMAN Andy – Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ UK, [email protected] SYKES Martin T. – Department of Earth and Ecosystem Sciences, Lund University, Sölvegatan 12, 223 62 Lund, Sweden, [email protected] SZENTGYÖRGYI Hajnalka – Institute of Environmental Sciences, Jagiellonian University, Gronostajowa Str. 7, 30-387 Kraków, Poland, [email protected] T TCHEBAKOVA Nadezhda – Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia THOMSEN Marianne – Aarhus University, Department of Policy Analysis, National Environmental Research Institute, Box. 358, Frederiksborgvej 399, DK 4000 Roskilde, Denmark TORRES Iván – Universidad de Castilla-La Mancha, Fabrica de Armas, Edificio Sabatini, Lab. 03. Av. Carlos III s.n., 45071 Toledo, Spain
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TSCHEULIN Thomas – Laboratory of Biogeography and Ecology, Department of Geography, University of the Aegean, GR-81100 Mytilene, Lesvos, Greece, [email protected] TUPAYACHI Alfredo – Proyecto Gloria Cordillera de Vilcanota, Urb. Lucrepata B-8, Apartado Postal 468, Cusco, Peru U UGARTE Eduardo – Departamento de Botánica, Universidad de Concepción, Casilla 160-C, Concepción, Chile, [email protected] ULGIATI Sergio – Department of Sciences for the Environment, Parthenope University, Centro Direzionale, Isola C4, 80143 Napoli, Italy, [email protected] UNGER Maria – Department of Applied Environmental Science (ITM), Stockholm University, SE-10691 Stockholm, Sweden UUSTAL Meelis – Stockholm Environment Institute Tallinn Centre, Lai Str. 34, 10133 Tallinn, Estonia, [email protected] V VAISSIÈRE Bernard E. – INRA, UMR 406 Abeilles & Environnement INRAUAPV, site Agroparc, 84914 Avignon Cedex 9, France, [email protected] VALADE Romain – UMR 1290 BIOGER-CPP, INRA-AgroParisTech, Avenue Lucien Brétignières, BP 01, 78850 Thiverval-Grignon, France, [email protected] VAN DE MEUTTER Frank – Laboratory of Aquatic Ecology and Evolutionary Biology, K.U.Leuven, Charles Deberiotstraat 32, 3000 Leuven, Belgium, [email protected] VAN DER SLUIJS Jeroen – Copernicus Institute for Sustainable Development and Innovation, Utrecht University, Heidelberglaan 2, 3584 CS UTRECHT, The Netherlands, [email protected] VANHOORNE Bart – Vlaams Instituut voor de Zee (VLIZ), Oostende, Belgium VARGAS Jim Farfán – Curcurpata, 729 San Blas, Cusco, Perú, [email protected] VIGHI Marco – University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy, [email protected] VILÀ Montserrat – Estación Biológica de Doñana, Centro Superior de Investigaciones Científicas (EDB-CSIC), Avda. María Luisa s/n, Pabellón del Perú, 41013 Sevilla, Spain, [email protected] VILLAREAL Sylvia – International Rice Research Institute, Los Banos, Philippines, DAPO 7777 Metro Manila, Philippines, [email protected] VILLEMANT Claire – Muséum National d’Histoire Naturelle, Département Systématique & Evolution, UMR 7205, OSEB, CP50 Entomologie, 45 rue Buffon, 75005 Paris, France, [email protected] VINYOLES Dolors – Departamento de Biología Animal, Vertebrados, 1ª Planta, Despacho 151, Facultad de Biología, Universidad de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain VIRKKALA Raimo – Finnish Environment Institute, P.O. Box 140, FI-00251 Helsinki, Finland VIVARELLI Daniele – Laboratory of Biogeography and Ecology, Department of Geography, University of the Aegean, University Hill, GR-81100 Mytilene, Greece; University of Bologna, Department of Evolutionary Experimental Biology (BES), Via Irnerio 42, I-40126, Bologna, Italy VOGIATZAKIS Ioannis N. – Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK, [email protected] VOHLAND Katrin – Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, D-14473 Potsdam, Germany; current affiliation:
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Museum for Natural History, Invalidenstrasse 43, D-10115 Berlin, Germany, [email protected] VON DER OHE Peter C. – Helmholtz Centre for Environmental Research – UFZ, Department of Effect-Directed Analysis, Permoserstr. 15, 04318 Leipzig, Germany, [email protected] VON RAAB-STRAUBE Eckhard – Free University of Berlin, Botanic Garden and Botanical Museum Berlin-Dahlem (BGBM), Berlin, Germany VUJIC Ante – Centre for the Balkan Biodiversity Conservation, PMF, Department of Biology and Ecology, Trg Dositeja Obradovi´ Str. 6, Novi Sad, Serbia, [email protected] W WALTHER Gian-Reto – Department of Plant Ecology, University of Bayreuth, D-95440, Bayreuth, Germany, [email protected] WANTUCH Marta – Institute of Environmental Sciences, Jagiellonian University, Ul. Gronostajowa 7, Kraków 30-387, Poland, [email protected] WARE Remy L. – Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, [email protected] WATT Allan – Centre for Ecology & Hydrology (CEH) Edinburgh, Bush Estate, Penicuik, Midlothian, EH26 0QB, Scotland, UK, [email protected] WEISSER Wolfgang W. – Friedrich-Schiller-University of Jena, Institute of Ecology, Dornburger Str. 159, 07743 Jena, Germany, [email protected] WELLS Konstans – University of Ulm, Institute of Experimental Ecology, Albert-Einstein Allee 11, 89069 Ulm, Germany, [email protected] WESTPHAL Catrin – Department of Animal Ecology, University of Bayreuth, D-95440, Bayreuth, Germany; Department of Crop Sciences, Agroecology, Georg-August-University, Waldweg 26, 37083 Göttingen, Germany, [email protected] WILD Jan – Institute of Botany, Academy of Sciences of the Czech Republic, Průhonice, Czech Republic, [email protected] WILSON Elizabeth – Oxford Brookes University, Headington, Oxford OX3 0BP, UK WOLF Torsten – Institute of Geography, University of Leipzig, Johannisallee Str. 1l, 04103 Leipzig, Germany WOYCIECHOWSKI Michał – Institute of Environmental Sciences, Jagiellonian University, Ul. Gronostajowa 7, Kraków 30-387 Poland, [email protected] Y YAGER Karina – Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA, [email protected] YURTSEVER Selçuk – Trakya University (TU), 22050 Edirne, Turkey Z ZAIKO Anastasija – Klaipeda University Coastal Research and Planning Institute, H.Manto 84, Klaipeda, LT-92294, Lithuania, [email protected] ZAUNBERGER Karin – European Commission, DG Environment, B-1049 Brussels, Belgium, [email protected] ZAVALA Gonzalo – University of Castilla-La Mancha, Plaza de la Universidad 2, 02071 Albacete, Spain, [email protected] ZHAO Lilin – Institute of Zoology, Chinese Academy of Sciences. DaTun Road, Chaoyang District, Beijing 100101, China, [email protected] ZOBEL Martin – Institute of Ecology and Earth Sciences, University of Tartu, Lai Str. 40, Tartu 51005, Estonia, [email protected]
J. Settele L. Penev T. Georgiev R. Grabaum V. Grobelnik V. Hammen S. Klotz M. Kotarac I. Kühn
The Atlas combines the main outcomes of the large European project ALARM (performed by 68 partner organisations from 35 countries from Europe as well as other continents) with some core outputs of numerous further research networks. A total number of 366 authors from more than 180 institutions in 43 countries provided information and contributed to the Atlas. The Atlas is addressed to a wide spectrum of users. Scientists will find summaries of well-described methods, approaches and case studies. Conservationists and policy makers will use the conclusions and recommendations based on academic research output and presented in a comprehensive and easy-to-read way. Lecturers and teachers will find good examples to illustrate the main challenges in our century of global environmental changes. The Atlas is an indispensible tool to any library or institution in biodiversity and environmental sciences. Finally, all people concerned with environmental issues will find the Atlas a powerful weapon in their fight for saving the life on our Planet!
IVE ER RIISK SK A T L A S O F B I O D IV RSSIT ITY R
The present Atlas of Biodiversity Risk is the first of its kind to describe and summarise in a comprehensive, easy-to-read and richly illustrated form the major pressures, impacts and risks of biodiversity loss at a global level. The main risks identified are caused by global climate and land use change, environmental pollution, loss of pollinators and biological invasions. The impacts and consequences of biodiversity loss are analyzed with a strong focus on socio-economic drivers and their effects on society. Three scenarios of potential futures are the baseline for predicting impacts and explore options for mitigating adverse effects at several spatio-temporal scales. Elements of these futures are modeled, tested and illustrated. The Atlas is divided into chapters which mostly deal with particular pressures. It furthermore is based on case studies from a large set of countries, which are completed by introductory and concluding texts for each chapter.
ATLAS of Biodiversity Risk Edited by Josef Settele, Lyubomir Penev, Teodor Georgiev, Ralf Grabaum, Vesna Grobelnik, Volker Hammen, Stefan Klotz, Mladen Kotarac & Ingolf Kühn
