PREDICTIVE ECOLOGICAL MODELING OF GREY WOLF (Canis lupus) MOVEMENT USING AGENT-BASED MODELING AND GIS by ALYSSA C. TEWS B.S., Rhodes College, 2016 A thesis submitted to the Graduate Faculty of the University of Colorado Colorado Springs in partial fulfillment of the requirements of the degree of Master of Arts Department of Geography and Environmental Studies 2020 0 © 2020 ALYSSA C. TEWS ALL RIGHTS RESERVED i This thesis for the Master of Arts degree by Alyssa C. Tews has been approved for the Department of Geography and Environmental Studies by Steve Jennings, Chair David Havlick Diep Dao Date: December 15, 2020 ii Tews, Alyssa C. (M.A., Applied Geography) Predictive ecological modeling of grey wolf (Canis lupus) movement using agent-based modeling and GIS Thesis directed by Associate Professor Steve Jennings, Emeritus ABSTRACT There is a wide-spread loss of large predators being witnessed across ecosystems globally. The large, apex predators are either displaced from habitat destruction, or killed directly by anthropogenic disturbances. We need apex predators for tri-trophic cascades, mesopredator control, and to promote biodiversity in ecosystems. Previous ecology research highlights how a tri-trophic cascade, or a series of dynamic interactions between predator, prey, and vegetation, is vital for allowing the ecosystem to be resilient and sustainable to disturbances. Technology equips us with new methods of exploring ecosystem functions, animal behavior, and how changing landscapes affect animal movement. By constructing an ABM for grey wolves in North America, we experimented with different predator efficiencies to test how grey wolves could possibly recolonize Moffat County, Colorado. Results from the ABM showed patterns of wolf occupation along low-elevation, river valleys, the wolves did not impact or disrupt prey population demographics, and wolves were able to recolonize the region despite the different predator efficiencies. Although the model results cannot be validated with current wolf data in Colorado, the results are similar to previous wolf habitat and occupation studies. Future improvements to this model could be implemented and used for another region with an established wolf population. iii ACKNOWLEDGEMENTS Many people helped in the creation and completion of this thesis. I could not have written this model without the assistance of George Mudrak, who gave coaching in NetLogo coding and offered ideas when I hit roadblocks. I am grateful for the encouragement from my committee board members, Dr. Dao and Dr. Havlick, and the intellectual curiosity of my peers who inspired me to keep searching for answers. A special thank you goes to my advisor, Dr. Jennings who had infinite patience with my thesis writing and helped me find the silver lining in the long modeling process. I also want to thank my family for giving support in pursuing my graduate studies, and to my friends who celebrated each accomplishment along the way. Lastly, I could not have kept my sanity without Kyle Kane listening to my wolf ramblings and helping to formulate my model logic. iv TABLE OF CONTENTS CHAPTER I. INTRODUCTION………………………………………………………………….…1 Purpose of the Study……………………………………………………………..……2 Research Questions…………………………………………………………….....2 Scope of the Study……………………………………………………………...……3 Data Limitations………………………………………………………………….3 Other Limitations…………………………………………………………...……3 II. REVIEW OF THE LITERATURE………………………………………………….4 Wolf Biogeography………………………………………………………………......4 Human-Wolf Conflicts……………………………………………………………….9 Wolves and Trophic Cascades……………………………………………………….10 Ecological Modeling using ABM……………………………………………………13 III. RESOURCES & METHODS………………………………………………..………19 Data Resources……………………………………………………………………….19 Study Area………………………………………………………………………...…19 O.D.D. Protocol…………………………………………………………...…………25 IV. RESULTS……………………………………………………………………...…….38 V. DISCUSSION & CONCLUSION……………………………………………...……60 REFERENCES………………………………………………………………………64 APPENDIX I. GLOSSARY…………………………………………………………………………70 v LIST OF TABLES 1. Table of 20% kill probability experiment results………………………………..………39 2. Table of 10% kill probability experiment results……………………………………..…46 3. Table of 3% kill probability experiment results………………………….………...……53 vi LIST OF FIGURES 1. Map of Moffat County, CO……………………………………………………………...20 2. NetLogo representation of Moffat County, CO…………………………….……………23 3. NetLogo Moffat County with environmental barriers……………………………...……24 4. Flowchart of wolf ABM…………………………………………………………………28 5. 20% Kill Probability Map: Run #1……………………………………………...……….41 6. 20% Kill Probability Map: Run #2…………………………………………...………….42 7. 20% Kill Probability Map: Run #3………………………………………………………43 8. 20% Kill Probability Map: Run #4…………………………………………………...….44 9. 20% Kill Probability Map: Run #5…………………………………………………...….45 10. 10% Kill Probability Map: Run #1………………………………………………………48 11. 10% Kill Probability Map: Run #2………………………………………………………49 12. 10% Kill Probability Map: Run #3………………………………………………………50 13. 10% Kill Probability Map: Run #4……………………………………………...……….51 14. 10% Kill Probability Map: Run #5………………………………………………………52 15. 3% Kill Probability Map: Run #1…………………………………………………..……55 16. 3% Kill Probability Map: Run #2………………………………………………..………56 17. 3% Kill Probability Map: Run #3…………………………………………………..……57 18. 3% Kill Probability Map: Run #4…………………………………………………….….58 19. 3% Kill Probability Map: Run #5……………………………………………………..…59 vii CHAPTER I INTRODUCTION In today’s conservation and restoration research, we are witnessing a gradual loss of large predators in response to urban growth and increased human-wildlife conflicts (Fan et al 2016). Successful restoration projects focus on the re-instatement of ecosystem processes, such as predator-prey interactions in trophic cascades in hopes that ecosystem cycles and relationships can be reshaped to become more sustainable in the future (Fraser et al. 2015). Although the concept and intrinsic value of ecosystem services are widely critiqued, there is a growing body of scientific literature which connects the ecosystem condition and processes to different components of biodiversity, including diverse trophic cascades (Schroter et al. 2014). The restoration of apex predators is crucial for future ecosystems to be robust and support richer biodiversity. Apex predators are vital to regulating prey populations and preventing mesopredator release in less-resilient ecosystems. The grey wolf (Canis lupus) is an ideal predator for re-introduction studies because of their high mobility, distinct territories, and natural low-density (Bangs et al. 2005). Also, the generalist nature of the grey wolf allows the predator to find suitable habitat in nearly every environment where humans can tolerate wolf presence (Mladenoff et al. 1997, 1999; Bangs et al. 2005). Modeling animal movement is a rising technology used to predict and simulate how organisms disperse across particular terrains and obstacles (Dodge et al. 2016). Computational studies on animal movement have yielded agent-based models (ABM) to illustrate how organisms can interact with the environment and disperse based upon different variables (Tang & Bennett, 2010). The grey wolf’s ability to successfully recolonize former territories and disperse into adjacent habitats gives us the opportunity to predict where wolves may travel and 1 how wolves may alter the ecosystem. This study will analyze the potential spatial patterns of grey wolves dispersing into their formerly occupied habitat at the southwest extent of their historic range in Colorado, USA. Specifically, the purpose of this ABM model is to highlight or identify habitat conditions in Moffat County, Colorado that would be suitable for grey wolves. In Colorado, Moffat County is located in the northwest corner of the state and in early January of 2020, wildlife officials confirmed evidence of a wolf pack presence (Colorado Parks and Wildlife, 2020). Research Questions With the disappearance of large carnivores from their historic ranges, ecosystems will continue to degrade and lose biodiversity richness (Estes et al. 2011). However, re-introduction programs offer an opportunity to introduce a highly mobile, successful predator to re-shape and re-vitalize North America’s ecosystems. Specific to grey wolves, re-introduction into northwest Colorado and expanding the grey wolf home could promote diverse, tri-trophic cascades and increase species interaction within ecosystems. The aim of this study is to simulate predator efficiency relating to survival rates and to show potential dispersal across Moffat County. Specifically, this research will (1) create an agent-based model to simulate potential spatial and behavioral patterns of wolf movement, (2) apply the grey wolf agent-based model to test spatial habitat constraints in Moffat County. By doing this, the research will evaluate if GIS and agent- based modeling (ABM) can simulate and predict where and if a grey wolf pack would disperse across Colorado. This research is in support of future restoration projects and the recovery of ecosystem processes. If a goal of restoration is the recovery of self-sustainable, dynamic, and resilient 2 communities, restoration practices must consider the re-establishment of ecological networks and trophic cascades (Fraser et al. 2015). Scope of the Study The model constructed and analyzed for this research represents Moffat County located in northwestern Colorado, USA. Moffat County has an area of approximately 12,310 km2. The two categories of organisms represented