From occurrence to eco-evolutionary dynamics Assessing connectivity in a changing world through modelling and landscape genetics Dissertation by Jan O. Engler FROM OCCURRENCE TO ECO-EVOLUTIONARY DYNAMICS:ASSESSING CONNECTIVITY IN A CHANGING WORLD THROUGH MODELING AND LANDSCAPE GENETICS Dissertation zur Erlangung des Doktorgrades (Dr. rer. nat.) der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vorgelegt von Jan Oliver Engler aus Oberhausen Bonn, November 2015 THESIS COMMITTEE Prof. Dr. J. WOLFGANG WÄGELE (Erstgutachter) Director ZFMK Zoological Research Museum Alexander Koenig Prof. Dr. BERNHARD MISOF (Zweitgutachter) Director ZMB Zoological Research Museum Alexander Koenig Prof. Dr. NIKO BALKENHOL (Fachnahes Mitglied) Head of Department of Wildlife Sciences Büsgen Institut, University of Göttingen Prof. Dr. GABRIELE M. KÖNIG (Fachfernes Mitglied) Professor of Pharmaceutical Biology Institute of Pharmaceutical Biology, University of Bonn Angefertigt mit Genemigung der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn. Die Arbeit wurde am Zoologischen Forschungsmuseum Alexander Koenig in Bonn durchgeführt Tag der mündlichen Prüfung: 25.05.2016 Erklärung Hiermit versichere ich, Jan O. Engler, dass ich diese Arbeit selbständig verfasst, keine anderen Quellen und Hilfsmittel als die angegebenen verwendet, und die Stellen der Arbeit, die anderen Werken dem Wortlaut oder dem Sinn nach entnommen sind, unter Angabe der Quelle kenntlich gemacht habe. Ich versichere außerdem, dass ich die beigefügte Dissertation nur in diesem und keinem anderen Promotionsverfahren eingereicht habe und, dass diesem Promotionsverfahren keine endgültig gescheiterten Promotionsverfahren vorausgegangen sind. Bonn, den 18.11.2015 Jan O. Engler From occurrence to eco-evolutionary dynamics: assessing connectivity in a changing world through modeling and landscape genetics Date of defense: 25.05.2016 Thesis, University of Bonn, Bonn, Germany (2016) With references, with summaries in Englisch and German Contents List of Figures List of Tables Aims & Scope 1 Chapter 1: General Introduction 7 What is connectivity? 9 Connectivity in Conservation and Environmental Planning 11 Connectivity in Landscape Genetics 13 Chapter 2: Conception of Potential Connectivity Models 17 Environmental Information 19 Occurrence Information 21 The SDM 21 The Connectivity Model 23 Linking genetic information with PCMs 23 PART A–PCM’S IN CONSERVATION &ENVIRONMENTAL PLANNING 27 Chapter 3: Accounting for the ‘network’ in the Natura 2000 network: A response to Hochkirch et al. 2013 29 Commentary 31 Chapter 4: Missing the target? A critical view on butterfly conservation efforts on calcareous grasslands in south-western Germany 35 Introduction 37 Material and methods 39 Study sites 39 Field sampling design 41 Classification of butterfly species 42 Statistical analysis 45 Resistance surface modeling 46 Results 49 Species decline 49 Degradation of functional groups 50 Changes in relative trait diversity 51 Connectivity modeling 52 Discussion 53 Chapter 5: Coupling satellite data with species distribution and connectivity models as a tool for environmental management and planning in matrix-sensitive species 59 Introduction 61 Material and methods 65 Study area and data sampling 65 Satellite data 67 Potential Connectivity Model 68 Results 72 Estimated condition status of Colognes’ sand lizard populations basedon field observations 72 Distribution of potential habitats 72 Predicted connectivity between populations 73 Discussion 74 Applicability of the approach 74 Data requirements and limitation for further applications 78 Conclusion 79 PART B–PCM’S IN LANDSCAPE GENETICS 83 Chapter 6: Comparative landscape genetics of three closely related sympatric Hesperid butterflies with diverging ecological traits 85 Introduction 87 Material and methods 89 Ethics statement 89 Study area and species 89 Molecular data and genetic cluster analysis 90 Modelling landscape effects on genetic differentiation 92 Comparing connectivity estimates with genetic data 95 Results 96 Genetic structures 96 Genetic clustering results 97 Species Distribution Models 97 Landscape effects of genetic differentiation 98 Discussion 102 Diverging responses to identical landscape conditions 103 Accounting for FST and Dest in landscape genetic studies 106 Conclusion 107 Supplementary material 109 Chapter 7: A statistical learning approach to improve ecological inferences in landscape genetics by accounting for spatial nonstationarity of genetic differentiation 117 Introduction 119 Material and methods 122 The statistical learning approach 122 Empirical examples 123 Results 125 Discussion 126 Supplementary material 131 PART C–IMPROVING SDMS USING CONTEMPORARY GENETIC INFORMATION 135 Chapter 8: Genes to the niche! How contemporary DNA can help to refine niche theory for predicting range dynamics in the Anthropocene 137 Niche models, theory, and the recent integration of genetic information 139 Why do we need to consider genetic information in the ENM concept? 140 Integrating genetic information to understand the processes behind range dynamic patterns 141 Gene flow and functional connectivity 141 Spatial genetic structure 142 Density blocking 143 Hybridization 144 Source-sink dynamics 145 Using genetic data to enhance the ENM concept 146 Concluding remarks 149 Box 1: Integration of niche theory and genetic information 150 Box 2: What can genetic information add to our understanding of the realized niche? 151 Box 3: What can genetic information add to our understanding of the niche dynamics during colonization? 153 GENERAL DISCUSSION 159 The necessity to include standart connectivity assessments in conservation management and policy 161 Benefits and caveats of PCMs in landscape genetics 163 From genes to ranges and back: is niche theory ready for the Anthropocene? 166 Personal outlook 167 SUMMARIES 169 Summary 171 Zusammenfassung 175 ACKNOWLEDGEMENTS 181 REFERENCES 187 CURRICULUM VITAE 225 List of Figures Figure 1.1: Illustration of the differences between structural connectivity and functional connectivity for mobile and matrix-sensitive species 10 Figure 2.1: The potential connectivity model (PCM) framework 19 Figure 4.1: Location of the study area near Trier in south-western Germany as well as habitat suitability and potential connectivity maps from the same region. 53 Figure 4.2: Trait space spanned by the first two principal components for either vineyard fallows and calcareous grasslands. 54 Figure 5.1: Two-step conceptional framework for performing potential connectivity models. 64 Figure 5.2: Potential distribution and connectivity of the sand lizard in the city of Cologne. 76 Figure 6.1: Locations of populations studied for all three Thymelicus species in southwestern Germany and adjoining areas in France and Luxemburg. 92 Figure 6.2: SDM output for Thymelicus lineola and T. sylvestris as well as T. acteon. 98 Figure 6.3: Schematic illustration about the gradual effects forcing the three Thymelicus species. 102 Material 6.S1 Figure 1: Consensus tree inferred by using the Neighbor- Joining method accounting for the 50% majority rule. 110 Figure 6.S1: Estimation of the number of panmictic clusters for each species. 113 Figure 6.S2: Scatterplots showing the difference of isolation by distance patterns with isolation by resistance patterns in the two species that show a spatial genetic structure. 114 Figure 7.1: A fictive study area that comprises seven localities where genetic information from a species was obtained but where relationships to landscape elements are unknown. 121 Figure 7.2: Spatial composition of regions where one of the three landscape elements is locally driving gene flow in the study region. 122 Figure 7.3: Linear relationships between genetic distance with effective distances from land use, topography, and climate together with the optimized solution for the Hesperid butterfly Thymelicus sylvestris. 126 Figure 7.4: The optimized solution plotted as a network of comparisons across the study area from T. sylvestris. 127 Figure 7.5: Linear relationships between genetic distance with effective distances from range shape and climate together with the optimized for the wolverine. 128 Figure 7.6: The optimized solution plotted as a network of comparisons across the North American range of the wolverine. 129 Figure 8.1: The fraction of the BAM plot where species occur. 140 Figure 8.2: Expanding the ENM concept with genetic information. 147 Figure 8.B1.1: The BAM diagram as an abstract visualization of the ENM concept in geographic space. Biological and ecological aspects than can be quantified with genetic information can be linked to certain compartmentd of this concept. 150 Figure 8.B2.1: Integrating information on contemporary gene flow into niche modeling and theory. 151 Figure 8.B2.2: Accounting for spatial genetic structure in niche modeling. 152 Figure 8.B3.1: Two examples of biological invasions where density blocking may play a key role are Rubus alceifolius and Ambrosia artemisiifolia. 153 Figure 8.B3.2: Invasion success due to increased adaptive potential following hybridization may result in different niche dynamics. 155 List of Tables Table 4.1: Presence-absence data of all butterfly species recorded. 43 Table 4.2: Factor loadings as well as Eigenvalues and cumulative explained variance for each of the three Principal components extracted. 52 Table 4.3: Percentage of the