Atlas of Biodiversity Risk

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

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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

50

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

78

Risk of Disappearing Sub-Arctic Palsa Mires in Europe MARGARETA JOHANSSON, STEFAN FRONZEK, TORBEN R. CHRISTENSEN, MISKA LUOTO & TIM R. CARTER

79

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

82

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

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European Plants in Southern South America – Unwanted Visitors? EDUARDO UGARTE, NICOL FUENTES & STEFAN KLOTZ

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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

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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 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

162

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

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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

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The Future of Pollinators? SIMON G. POTTS, THEODORA PETANIDOU & THOMAS TSCHEULIN

186

Chapter 8. Socio-Economics and Its Role in Biodiversity Loss

187

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

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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

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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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C O N TE N TS

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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

List of Contributors

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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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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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BIODIVERSITY BASELINE INFORMATION

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

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F O R

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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.

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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

CHAPTER

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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Economy

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Missing Links

Biodiversity

Climate

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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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

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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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15

Drivers, Pressures, Impacts: DPSIR for Biodiversity

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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

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17

European Biodiversity and Its Drivers – an “Inter-national” Analysis

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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 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

R I SK S

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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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BAMBU (A2)

SEDG (B1)

a

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.

56

GRAS (A1F1)

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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2080

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.

I

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.

II

III Change [%] in 1945-analogous area

1945-analogous area [103 km2]

a

no analogous grid cells -99.9 to -80 -79.9 to -60 -59.9 to -40 -39.9 to -20 -19.9 to 0 0.1 to 20 20.1 to 40 40.1 to 60 60.1 to 80 80.1 to 100 >100

no analogous grid cells 0.1-20 20.1-40 40.1-60 60.1-80 80.1-100 100.1-120 120.1-140 140.1-160 160.1-180 180.1-200 >200

Mean distance to 1945-analogous cells [km]

Change in mean distance to 1945-analogous cells [km] no analogous grid cells 1,000

no analogous grid cells 6-200 201-400 401-600 601-800 801-1,000 1,001-1,200 1,201-1,400 1,401-1,600 1,601-1,800 1,801-2,000 >2,000

b

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).

ASSESSI N G

R I SK S

F O R

B I O D I V ER SI T Y

W I T H

B I OC L I M ATI C

E N V E L O PE

M O D E L L I N G

57

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

(

(

( (

( ( ( ( ( ( ( ( (

!

(

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).

58

AT L A S

OF

B I OD I V ERSITY

RISK

CH A PTE R

2

Minimum

Small (33%)

Large (66%)

Maximum

0.2

0.6

1.0 0.2

0.6

1.0 0.2

0.6

1.0 0.2

0.6

1.0

( ( ( ( (( ( (

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( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!( (! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! !( ! ! ! ! (! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ( ( ! ! ( ! ! ! !! (! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ( ! ! ! (! ! ! !( ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! !! ! ! ! ! ! !! ! ! ! ! ! ! (! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ( ! ! ! !! ! !! ! ! ! ! !! ! !! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ( ! ! ! ! ( ! ! ! ! ! ( ! ! ! ! ! ! ! (! ( ( ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ( ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !( ( ( ( (! ! ! ! ! ! ! ! ! ( ! (! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ( ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ( !! ! ! ! ! ! ! ! ! ! ( ( ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ( ! !! ! ! ! ! ! ! ! !! ( ( ( ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! !( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ( ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! (! (! ! ! ! ! ! ( ! ! ! ! ! ! ! ( (! (! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! !( ( ( ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! (! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !(! ( (! (! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!!(! ! ! ! ! ! ! ! ! ! ! ! ! ! (! ! !! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ! ! ! ! ! ! ! ! ! ! ! ! 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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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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 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

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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)

2

-4

European annual mean temperature in 1500-2004 2

2

3

1

0

-1

-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)

20

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

Northern Europe, JJA

40

40

30

30

20

10

0

-10

SEDG (HadCM3-B1) BAMBU (HadCM3-A2)

Precipitation change (%/century)

Precipitation change (%/century)

Northern Europe, DJF

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-

BAMBU (CSIRO2-A2)

20

BAMBU (NCAR-PCM-A2) 10

BAMBU (RCA3 RCM) GRAS (HadCM3-A1FI)

0

AR4 AOGCMs-B1 AR4 AOGCMs-A1B

-10

AR4 AOGCMs-A2 -20

0

1

2

3

4

5

6

7

-20

8

0

1

Temperature change (ºC/century)

2

3

Southern Europe, DJF

5

6

7

8

Southern Europe, JJA

20

20

10

10

SEDG (HadCM3-B1)

0

0

BAMBU (HadCM3-A2)

Precipitation change (%/century)

Precipitation change (%/century)

4

Temperature change (ºC/century)

-10

-20

-30

-40

BAMBU (CSIRO2-A2)

-10

BAMBU (NCAR-PCM-A2) -20

BAMBU (RCA3 RCM) GRAS (HadCM3-A1FI)

-30

-60

-50

0

1

2

3

4

5

6

7

8

-60

Temperature change (ºC/century)

AR4 AOGCMs-A2 0

1

2

3

4

5

6

7

8

Temperature change (ºC/century)

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.

AR4 AOGCMs-B1

-40

AR4 AOGCMs-A1B -50

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).

•

•

•

-6

-5

-4

-3

-2

December-February precipitation change

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

-7

-25

-20

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

-8

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

-15

-1

0

1

June-August precipitation change

-10

-5

RCA3 1961-1990

0

5

10

15

20

RCA3 2071-2100 300 270 240 210 180 150 120 90 60 30

a

b

c

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.

Pi

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

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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)

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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

1996

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2004

50 cm 60 cm 70 cm 80 cm 90 cm 100 cm 120 cm No permafrost 0

100 m

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

2C

100 km

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.

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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

Zooplankton

Sticklebacks

Macroinvertebrates Macroinvertebrates

Zooplankton

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.

a

Figure 3. Winter nest of pine processionary moth. Photo: F. Goussard, INRA.

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Paris FRANCE

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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

a

b

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Orleans

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Orleans

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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 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

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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.

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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

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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

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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)

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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

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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

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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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Figure 4. Plant species richness (above) and impressions (below) of Vilcanota, Perú. Photos: P. Sowell.

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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

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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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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).

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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.

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Figure 10. Plant species richness along a GLORIA altitudinal gradient in Apolobamba (red dot).

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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.

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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

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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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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).

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Figure 15. Fumarole heated soils near the Socompa summit harbour compact mats of dozens of species of bryophytes and lichens.

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Figure 18. Profiles of soil temperatures to -190 cm depth show the strongly buffered temperature range at increasing depths.

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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.

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References

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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.

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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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85

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

,

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.

1

2

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

Chapter

4

LAND USE CHANGES AND THEIR IMPACTS

Land Use, Its Change and Effects on Biodiversity

,

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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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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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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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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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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Patch Analyst is an ArcGIS extension.

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

a

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

b

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

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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).

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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,

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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

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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

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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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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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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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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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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.

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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

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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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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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A

M A JO R

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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N I T RO G E N

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

B

AB

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

B

B

AB

T>4

T 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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129

IG

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.

Chapter

6

BIOLOGICAL INVASIONS

Are the Aliens Taking Over? Invasive Species and Their Increasing Impact on Biodiversity

,

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

A R E

T H E

A L I E N S

TA K I N G

OV E R ?

I N VA S I V E

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).

134

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

E U RO PE

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.

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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).

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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 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

"

"

"

Important shipping canals

Nort No rthe hern rn corrido corridor

2

"

Main watersheds

Baltic Sea North Sea

Centrall co Centra corr rridor idor

15

"

1

Od er

Elb e

"

" " " 11

"

"

"

"13 "

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"

Pripyat

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4

Dnie pe

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tula Vis

Azov Sea

" 10

Caspian Sea

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12

Rhône

" "

Loire

14

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"

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Sein e

Rh ine Marne

7

6

"

5

" 8

Danube

Black Sea

Western Wester corr co rrid idor or Southern co corr rridor idor

Mediterranean Sea

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).

"

3

Other main waterways

Aquatic RAT (risk-based DSS)

"

White Sea

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-

I N T RO D UC T I O N S

V I A

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

6

7

21 20 1

12

3

19

15

14

11

8

16 17 14

18 2

4

13 10

9, 10

9 7

15-21

8

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).

#### # ### # ## ## # #

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

# # # # # # # # # # ## # ### ## # # # ## ##

#

#

#

#

# # #

#

#

# # # # # #

##

#

#

0

100

# #

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.

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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 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:

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B Y

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H E R AC L E U M

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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

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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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

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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

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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

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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 (30,000 20,000-30,000

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