Integrating Dynamic and Statistical Modelling Approaches in Order to Improve Predictions for Scenarios of Environmental Change

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Integrating Dynamic and Statistical Modelling Approaches in Order to Improve Predictions for Scenarios of Environmental Change Institut für Erd- und Umweltwissenschaften Arbeitsgruppe Environmental Modelling Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change Kumulative Dissertation zur Erlangung des akademischen Grades "doctor rerum naturalium" (Dr. rer. nat.) in der Wissenschaftsdisziplin "Geoökologie" eingereicht an der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam von Damaris Zurell aus Templin Potsdam, den 05.05.2011 Published online at the Institutional Repository of the University of Potsdam: URL http://opus.kobv.de/ubp/volltexte/2011/5684/ URN urn:nbn:de:kobv:517-opus-56845 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56845 if tha eva does owt for nowt do it for thi sen Yorkshire saying Contents Contents CONTENTS I SUMMARY V ZUSAMMENFASSUNG VII 1 GENERAL INTRODUCTION 1 1.1 Motivation and objectives 2 1.2 State of the art 6 1.2.1 Correlative species distribution models 6 1.2.2 Mechanistic models of species distributions 11 1.2.3 ‘Hybrid’ models of species distributions 15 1.3 Thesis structure 16 2 THE VIRTUAL ECOLOGIST APPROACH: SIMULATING DATA AND OBSERVERS 19 2.1 Abstract 20 2.2 Introduction 20 2.3 The virtual ecologist approach 23 2.4 Past use of VE 26 2.4.1 Testing and improving sampling schemes and methods 26 2.4.2 Testing and comparing models 28 2.5 Discussion 33 2.5.1 Limitations 34 2.5.2 The role of mechanistic models 35 2.5.3 Future directions 36 3 STATIC SPECIES DISTRIBUTION MODELS IN DYNAMICALLY CHANGING SYSTEMS: HOW GOOD CAN PREDICTIONS REALLY BE? 39 i Contents 3.1 Abstract 40 3.2 Introduction 40 3.3 Methods 42 3.3.1 Dynamic population model 42 3.3.2 Sampling by virtual ecologist 46 3.3.3 Statistical modelling 46 3.4 Results 48 3.4.1 Dynamic population model 48 3.4.2 Statistical models 49 3.5 Discussion 54 3.5.1 Prediction accuracies under climate change 54 3.5.2 Model comparison 55 3.5.3 Effects of ecological properties and processes 55 3.5.4 Limitations and extensions 57 3.5.5 Perspectives and research needs in species distribution modelling 57 3.5.6 Conclusion 59 4 UNCERTAINTY IN PREDICTIONS OF RANGE DYNAMICS: BLACK GROUSE CLIMBING THE SWISS ALPS 61 4.1 Abstract 62 4.2 Introduction 62 4.3 Methods 65 4.3.1 Species data 65 4.3.2 Environmental predictors 65 4.3.3 Climate change scenarios 66 4.3.4 Species distribution model 66 4.3.5 Individual-based model 67 4.3.6 Sensitivity analysis 69 4.4 Results 70 4.4.1 Statistical modelling and range predictions 70 4.4.2 Population dynamics 72 4.4.3 Sensitivity analysis 73 4.5 Discussion 76 4.5.1 Black grouse population and range dynamics 77 4.5.2 Robustness of range predictions 79 ii Contents 4.5.3 Challenges in species distribution modelling 81 4.5.4 Conclusions 83 5 PREDICTING TO NEW ENVIRONMENTS: TOOLS FOR VISUALISING MODEL BEHAVIOUR AND IMPACTS ON MAPPED DISTRIBUTIONS 85 5.1 Abstract 86 5.2 Introduction 86 5.3 Demonstrating prediction problems: simulated species 88 5.4 New tools for visualisation 89 5.5 Summary 94 6 SYNTHESIS 95 6.1 Summary of achievements 96 6.1.1 Virtual ecologists 96 6.1.2 Range predictions by correlative models 98 6.1.3 Range predictions by dynamic models 100 6.2 Challenges in dynamic range predictions 102 6.2.1 Niche conservatism 102 6.2.2 Circularity 103 6.2.3 Model specification 103 6.2.4 Model complexity 104 6.2.5 Response vs. effect traits 105 6.2.6 Data availability 106 6.3 Quo vadis? 107 A THE VIRTUAL ECOLOGIST APPROACH: SIMULATING DATA AND OBSERVERS – SUPPLEMENTARY MATERIAL 113 B STATIC SPECIES DISTRIBUTION MODELS IN DYNAMICALLY CHANGING SYSTEMS: HOW GOOD CAN PREDICTIONS REALLY BE? – SUPPLEMENTARY MATERIAL 123 C UNCERTAINTY IN PREDICTIONS OF RANGE DYNAMICS: BLACK GROUSE CLIMBING THE SWISS ALPS – SUPPLEMENTARY MATERIAL 127 iii Contents D PREDICTING TO NEW ENVIRONMENTS: TOOLS FOR VISUALISING MODEL BEHAVIOUR AND IMPACTS ON MAPPED DISTRIBUTIONS – SUPPLEMENTARY MATERIAL 135 BIBLIOGRAPHY 153 DANKSAGUNG 173 iv Summary Summary Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. v Summary Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments. vi Zusammenfassung Zusammenfassung Das Vorkommen von Arten wird zunehmend bedroht durch Klima- und Landnutzungswandel. Robuste Vorhersagen der damit verbundenen Arealveränderungen sind ausschlaggebend für die Erarbeitung dynamischer und nachhaltiger Naturschutzstrategien. Habitateignungsmodelle erstellen statistische Zusammenhänge zwischen dem Vorkommen einer Art und relevanten Umweltvariablen und erlauben zügige Einschätzungen potentieller Arealveränderungen. Dabei werden jedoch transiente Dynamiken weitgehend ignoriert sowie demographische Prozesse und biotische Interaktionen. Daher wurden Vorschläge laut, diese statistischen Modelle mit mechanistischeren Ansätzen zu koppeln. In der vorliegenden Arbeit zeige ich zwei verschiedene Möglichkeiten solcher Kopplung auf. (i) Ich beschreibe den sogenannten ‚Virtuellen Ökologen’-Ansatz als mächtiges Validierungswerkzeug, in dem mechanistische Modelle virtuelle Testflächen bieten zur Erforschung verschiedener Probenahmedesigns oder statistischer Methoden sowie spezifischer Fragestellungen. Auch verwende ich diesen Ansatz, um systematisch zu untersuchen wie sich transiente Dynamiken sowie Arteigenschaften und ökologische Prozesse auf die Vorhersagegüte von Habitateignungsmodellen auswirken. So kann ich entscheidende Prozesse identifizieren welche in zukünftigen Modellen Berücksichtigung finden sollten. (ii) Darauf aufbauend koppele ich Vorhersagen von Habitateignungsmodellen mit einem individuen-basierten Populationsmodell, um die Entwicklung des Schweizer Birkhuhnbestandes unter Klimawandel vorherzusagen. Durch die explizite Berücksichtigung der Wechselwirkungen zwischen Habitat und demographischer Prozesse lassen sich direktere Aussagen über Populationsentwicklung und damit verbundener Extinktionsrisiken treffen. Allerdings führen verschiedene Simulationen auch zu hoher Variabilität zwischen Vorhersagen, was die Bedeutung von Sensitivitätsanalysen unterstreicht, um Unsicherheiten und Robustheit von Vorhersagen einzuschätzen. Außerdem identifiziere ich Restriktionen in der Datenabdeckung des Umweltraumes als möglichen Grund für kontrastierende Vorhersagen verschiedener Habitateignungsmodelle. Wenn die Nische einer Art nicht vollständig durch Daten beschrieben ist, kann dies zu unrealistischen Vorhersagen der Art-Habitat-Beziehung führen. Insgesamt trägt meine Arbeit erheblich bei zu einem besseren Verständnis der Auswirkung verschiedenster Unsicherheitsfaktoren auf Vorhersagen von Arealveränderungen und zeigt Wege auf, mit diesen umzugehen. Abschließend erstelle ich einen vorläufigen Leitfaden für Vorhersagemodelle und identifiziere Kernpunkte für weitere Forschung
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