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A Spatially Explicit Individual-Based Approach Using Grizzly Bears As A

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A Spatially Explicit Individual-Based Approach Using Grizzly Bears As A COMPREHENSIVE CONSERVATION MODELING: A SPATIALLY EXPLICIT INDIVIDUAL-BASED APPROACH USING GRIZZLY BEARS AS A CASE STUDY by Vickie Marie Backus A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Geography The University of Utah August 2006 Copyright © Vickie Marie Backus 2006 All Rights Reserve ABSTRACT This dissertation illustrates how a mechanistic bottom-up approach to constructing a spatially explicit individual-based model (IBM) provides the proper theoretical and operational frameworks for constructing population viability analysis (PVA) models that avoid many of the substantive and theoretical criticism of the conventional demographic models used in PVA. Using JavaTM, such a model is developed for the grizzly bear population of the Cabinet-Yaak Ecosystem. This approach, coupled with spatially explicit landscape data, permits direct linkage of a population’s viability with geographic attributes. This linkage extends the effectiveness of PVA as a tool to guide conservation and landscape management decisions. The individual-based perspective facilitates pedigree analysis and the direct calculation of individual kinship coefficients and other genetic measures. This permits the definition of viability to be expanded to include genetic criteria, and PVA to be used as a tool to predict the timing, scope, and intensity of population augmentation actions necessary to maintain a population’s genetic integrity. Spatially explicit IBMs as an approach to population genetics are explored in depth. Results suggest geography plays an important role in population genetic processes and raise questions about the adequacy of relying solely either on effective population size or heterozygosity for developing in situ conservation strategies. Technical tools for exploring population genetics from an individual-based theoretical framework are developed and discussed. This research shows how the modern technologies of geographical information systems (GIS), remotely sensed imagery, and object-oriented programming, along with principles from complex adaptive systems can be integrated with more traditional ecology, wildlife biology, and genetics to create a comprehensive PVA model. The model is comprehensive in its inclusion of important aspects of species conservation: habitat protection, population genetics, and population demographics, along with the species’ behavior and ecology, and is comprehensive in the type of conservation issues it can address. CONTENTS ABSTRACT……………………………………………………………………………………………………………………… iv LIST OF TABLES………………………………………………………………………………………..……………………. vii LIST OF FIGURES………………………………………………………………………………………………….………… ix ACKNOWLEDGMENTS…………………………………………………………………………………..………………… x Chapter 1. INTRODUCTION ……………………………………………………………………………………………………… 1 1.1 Study area………………………………………………………………………………………………………. 8 1.2 Contributions………………………………………………………………………………………………….. 11 1.3 Summary………………………………………………………………………………………………………… 13 2. REVIEW OF LITERATURE ………………………………………………………………………………………… 16 2.1 Theory…………………………………………………………………………………………………………….. 16 2.2 Previous modeling used in bear research ………………………………………………………… 24 2.3 Modeling ……………………………………………………………………………………………………….. 34 2.4 Object-oriented design and programming...……………………………………….……………… 41 2.5 Bear behavior .………………………………………………………………………….……………..…….. 43 2.6 Summary ………………………………………………………………………………………...…………….. 47 3. INDIVIDUAL-BASED MODELING AND INBREEDING ………………………………………………….. 50 3.1 Introduction…………………………………………………………………..……………………………….. 50 3.2 Inbreeding and its consequences and measures……………………………………………… 51 3.3 Inbreeding and conservation……………………………………………………………………………. 55 3.4 Inbreeding and individual-based modeling………………………………………….……………. 61 4. INDIVIDUAL-BASED MODELING AND CONSERVATION GENETICS ……………………………… 65 4.1 Introduction ……………………………………………………….…………..………….…..….………….. 65 4.2 Conventional approaches for analysis of genetic variation ..…………….….……..…….. 66 4.3 Individual-based modeling …………….…………………….…………………………….…..………. 72 4.4 Illustrative examples ...………..…………………………………………………..….……...…...…….. 80 5. INDIVIDUAL-BASED MODELING: A MODELING FRAMEWORK FOR GRIZZLY BEAR CONSERVATION …………………………………………………………………………………………………….. 111 5.1 Introduction ……………………………………………………….…………..………….…..….………….. 111 5.2 Preliminary model development …………………………………….….…….…………….……….. 114 5.3 Model design………………………………………………………………………………………………….. 117 5.4 Model implementation………………………………..…………………………………………………… 120 5.5 Model outputs and conclusion…………………………………………………………………………. 190 6. CONCLUSION...………………………………………………………………………………………………………. 197 6.1 Research summary……………………………………………………………………..………………….. 197 6.2 Potential uses…………………………………………………………………………………………………. 203 6.3 Future research………………………………………………………………………………………………. 206 6.4 Conclusion………………………………………………………………………………………………………. 208 Appendices GRIZZLY BEAR VEGETATIVE FOODS………………………………………………………………………… 209 A. MODEL OUTPUT FILES – ONE SIMULATION RUN……………………………………………………… 217 B. REFERENCES…………………………………………………………………………………………………………………. 224 vi LIST OF FIGURES Figure Page 1.1 Study area boundary………………………………………………………………………………………… 9 2.1 Class inheritance for a bear population…………………………………………………………….. 42 4.1 Geographically explicit landscapes used in simulations……………………………………… 82 4.2 Diagram of individual-based model…………………………………………………………………… 84 4.3 Variables and methods for the Animal class……………………………………………………… 87 4.4 Variables and methods for the MaleAnimal and FemaleAnimal classes……………… 88 4.5 Series 1 model results……………………………………………………………………………………… 97 4.6 Series 2 model results……………………………………………………………………………………... 102 4.7 Spatial distribution of modeled population after 100 generations……………………… 108 5.1 Hypothesized boundary between grizzlies inhabiting the Yaak and Cabinet portions of the study area………………………………………………………………………………… 116 5.2 Model diagram…………………………..……………………………………………………………………. 121 5.3 Inheritance relationships of the bear-related classes…………………………………………. 128 5.4 Relationship of spatially explicit data layers to HabitatCell class………………………… 139 5.5 Relationship of spatially explicit data to mortality risk grid…………………………………. 141 5.6 Process used to translate a remote sensing classification scheme into individual bear food category layers…….…………………………………………………………………………… 171 5.7 Mortality locations for all model runs………………………………………………………………… 193 LIST OF TABLES Table Page 4.1 Descriptive statistics for population average heterozygosity (Series 1)………………. 99 4.2 Descriptive statistics for population average heterozygosity variance (Series 1)…. 99 4.3 Descriptive statistics for the percentage of lost founder allelles (Series 1)…………. 99 4.4 Descriptive statistics for the mean kinship (Series 1)………………………………………… 100 4.5 ANOVA average heterozygosity (Series 1)………………………………………………………….. 100 4.6 ANOVA average heterozygosity variance (Series 1)……………………………………………. 100 4.7 ANOVA percentage of lost founder alleles (Series 1)………………………………………….. 100 4.8 ANOVA mean kinship (Series 1)………………………………………………………………………… 101 4.9 Descriptive statistics for population average heterozygosity (Series 2)……………….. 104 4.10 Descriptive statistics for average heterozygosity variance (Series 2)…………………… 104 4.11 Descriptive statistics for percentage lost founder alleles (Series 2)……………………. 104 4.12 Descriptive statistics for mean kinship (Series 2)……………………………………………… 105 4.13 ANOVA population average heterozygosity (Series 2)…………………………………………. 105 4.14 ANOVA average heterozygosity variance (Series 2)…………………………………………….. 105 4.15 ANOVA percentage lost founder alleles (Series 2)……………………………………………… 105 4.16 ANOVA mean kinship (Series 2)………………………………………………………………………… 105 4.17 Proportion of population in 10 inbreeding categories (Series 2)…………………………. 107 5.1 Model events…………………………………………………………………………………………………… 120 5.2 Crosswalk between SILC-1 size classes and structural classes for single tree species cover types………………………………………………………………………………………….. 154 5.3 Crosswalk between SILC-1 size classes and structural classes for mixed tree species cover types………………………………………………………………………………………….. 155 5.4 Plant associations corresponding to PNV used for transitional grassland cells……. 160 5.5 Very low cover grasslands………………………………………………………………………………… 160 Table Page 5.6 Grassland and low/moderate cover grassland………………………………………………….. 161 5.7 Moderate/high cover grasslands………………………………………………………………………. 161 5.8 Montane parklands/subalpine meadows………………………………………………………….. 163 5.9 Percent cover of important bear foods occurring with a constancy of at least 10% by Aquatic Response Units (ARU)……………………………………………………………………… 164 5.10 Preferred bear food items as suggested by the literature…………………………………… 172 5.11 Lower, upper, and accumulation thresholds used for GDD model…………………….. 176 5.12 Climate scenarios used in the model………………………………………………………………… 180 5.13 Relative percentages used when resampling to larger grid size…………………………. 182 5.14 Summary of extinction times for each model run………………………………………………. 191 5.15 Population-level genetic measures calculated at last generation with > 1 bear in population……………………………………………………………………………………………………….. 194 5.16 Descriptive summary statistics for the population genetic measures in Table 5.15.………………………………………………………………………………………………….……………. 195 viii ACKNOWLEDGMENTS Thanks to my committee members for serving on my committee and giving me the latitude to pursue my research largely unconstrained. Extra thanks goes to Dr. Michael Gilpin
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