Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change [Elektronische Ressource] / Damaris Zurell. Betreuer: Boris Schröder

Damaris Zurell

Informations

• Publié par: universitat_potsdam
• Publié le: 01 janvier 2011
• Nombre de lectures: 7
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

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