PREDICTIVE ECOLOGICAL MODELING of GREY WOLF (Canis Lupus)

PREDICTIVE ECOLOGICAL MODELING OF GREY WOLF (Canis lupus)

MOVEMENT USING AGENT-BASED MODELING AND GIS

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

ALYSSA C. TEWS

This thesis for the Master of Arts degree by

Alyssa C. Tews

has been approved for the

Department of Geography and Environmental Studies

Steve Jennings, Chair

David Havlick

Diep Dao

Date: December 15, 2020

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.

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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.

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

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

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

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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 in this model are the grey wolf and ungulate species in

Colorado, including elk, deer, bighorn sheep, and pronghorn. One time-step of the model is representing three months, with four time-steps, or ticks, equaling one year.

The initial and most important limitation to this study is the lack of documented wolf occupation in Colorado. Although there have been wolf sightings in Colorado and a wolf pack was presence was confirmed in Moffat County in early 2020, there is no source of spatial data for where the grey wolves are dispersing to. In order to create and simulate wolf movement, the agent-based modeling (ABM) was preformed using NetLogo. In regard to NetLogo as a software, there were several limitations for this research study. NetLogo’s processing speed is greatly hindered by the complexity of behaviors and number of agents in the model. For modelling the prey population in the wolf ABM, only a portion of the ungulate population was able to be included because if the true population numbers were used, then the model could not execute. NetLogo also is very demanding for the amount of memory space used on computers, and the memory space was limited for this research study.

CHAPTER II

REVIEW OF THE LITERATURE

Wolf Biogeography

The grey wolf (Canis lupus) represents one of the iconic, megafauna of North America and has become an important topic for debate in ecology and conservation. Globally, the wolf is one of the most widely distributed large carnivores and most popular predator for conservation studies (Ripple et al. 2014). In the U.S., the total grey wolf population is estimated to be between

13,000 to 15,000 individuals (USFWS, 2018), with a substantial population residing in Alaska

(approximately 7,000 – 11,000 individuals) and the lower 48 states being home to nearly 5,000 wolves. Currently, the largest population of grey wolves is found in Canada and is an estimated

60,000 wolves, whereas the European grey wolf populations are roughly more than 12,000 individuals (not including Russia) (Kaczensky et al. 2014; International Wolf Center, 2019).

Similar to nearly all large carnivores, the grey wolves’ history has been greatly impacted by anthropogenic disturbances and direct persecution. Particular to grey wolves, humans have strictly enforced decades of systematic eradication, and human influence continues to shape the wolves’ population growth.

First seen in Eurasia’s geologic record approximately 500-800 thousand years ago (kya), this dominant, large predator dispersed to North America via the Bering Strait land bridge in the late Pleistocene approximately 13 – 21 kya (Fan et al. 2016). In a study done by Leonard and colleagues (2005), genetic analysis between historic and current samples revealed the North

American ancient wolf population supported approximately several hundred thousand individuals. In the Wisconsin area alone, scientists estimated population sizes to have ranged between 3,000-5,000 wolves with active dispersal between the north into the present-day country

4 of Canada (Leonard et al. 2005; Mladenoff et al. 2009). However, a significant bottleneck event coincided with the Wisconsin glacial maximum (23 kya), and the ancient wolf populations were severely depleted. A potential glacial refugia is hypothesized to exist in the southern continental

US and Mexico region, which was south of the glacial sheets covering Canada (Leonard et al.

2005). This hypothesis is supported by Leonard and colleagues (2005) using mitochondrial DNA from extinct and extant canid samples, and this ancient gray wolf population was the only surviving North American grey wolf until the glacial maximum ended.

Once the Wisconsin glacial sheet retreated (12-6 kya), the surviving, ancient wolf population slowly recolonized the continent and the canid species came to occupy nearly all ecosystems in the region (Fan et al. 2016; Mladenoff et al. 2009). This range expansion coincided with high speciation rates, which resulted in five sub-species of grey wolf existing across North America: the Arctic wolf (C.l. arctos), Northwestern wolf (C.l. occidentalis), Great

Plains wolf (C.l. nubilus), Eastern wolf (C.l. lycaon), and Mexican grey wolf (C.l. baileyi)

(Leonard et al. 2005). As a habitat generalist and an opportunistic carnivore, the North American wolf species’ historic distribution ranged from Alaska to Mexico. This carnivore specialized to prey upon large ungulates including white-tailed deer (Odocoileus virginianus), elk (Cervus elaphus), caribou (Rangifer tarandus), moose (Alces alces), and bison (Bison bison) (Potvin et al. 2005; Mladenoff et al. 2009, 1995). Once the wolf populations recovered from their severe glacial bottle-neck event, the canid predator was highly successful in recolonizing its former territory and filling empty niches from the prior glaciation event.

Prior to Euro-American settlement in the 1800s, wolves encompassed a wide distribution across the Holarctic region. However, with Euro-American colonization and introduction of agriculture, significant human-wildlife conflicts arose in regard to wolf and livestock

5 depredation. Wolves took the persona of being the frightening “monsters” of the wilderness, and many settlers in North America came to fear and hate the canid. These conflicts gave rise to government-mediated predator control programs and the subsequent eradication of wolves

(Muhly & Musiani, 2008). Methods to eliminate or cull wolves included trapping, direct persecution via hunting, and poisoning carcasses. These lethal methods not only killed wolves, but also impacted other large carnivores such as the brown bear and mountain lion (Humane

Society of the United States, 2019). From decades of persecution and habitat fragmentation, the grey wolf was extirpated from much of their historic range and was reduced to occupy only 3% of its original habitat by the end of the 20th century (Ripple et al. 2014 Mlandenoff et al. 1995).

Wolf populations were not able to begin recovering until being federally listed as endangered and protected in the lower 48 states under the Endangered Species Act (ESA) of

1974 (Leonard et al. 2005; US Fish and Wildlife Service et al. 1974). When the U.S. Fish and

Wildlife Service (USFWS) and Department of Interior implemented the Northern Rocky

Mountain Wolf Recovery Plan, three regions for species re-introduction were proposed for wolf re-introduction based upon their prior wolf occupation: northwest Montana, central Idaho, and the Greater Yellowstone area (Yellowstone Center for Resources, 2019). To initiate the NRM recolonization, 30 grey wolves from Canada were released in 1994 to the Yellowstone National

Park in Wyoming and Idaho (Yellowstone Center for Resources, 2019). This “experimental” or reintroduced population was highly successful and resulted in dispersal through the Northern

Rockies and to the neighboring states of Minnesota, Montana, and Wisconsin (Leonard et al.

2005; Mladenoff et al. 2009). Overall, natural dispersal from Canada coupled with the re- introduction in mid-1990s has resulted in wolves rapidly expanding their range in the West

(Mech, 2017). Although once exterminated from nearly all of the contiguous U.S., the current

6 population of grey wolves in the lower 48 states is able to disperse to just about any state (Mech,

2017).

Today, grey wolves are delisted from the ESA in several states in accordance with the wolves’ growing populations in different states and successful recolonization. In areas which have established populations, the mean rate of increase for wolf populations is up to 20% per year and maturing 1-4-year-old wolves of both sexes can disperse hundreds of kilometers to find new territories (Mech, 2017). U.S. Congress removed grey wolves’ protected status in the NRM

(Montana, Idaho, Wyoming), WGL (Michigan, Minnesota, Wisconsin), northern Utah, eastern

Oregon, and eastern Washington (Mech, 2017). Grey wolves are still protected in their historic

SW range (Colorado, New Mexico) to aid efforts in establishing the Mexican grey wolf reintroduction efforts in New Mexico (US Fish and Wildlife et al. 2004). Wolves are routinely discussed at local, state, and federal political levels in regard to future de-listing in all 50 states

(Bangs et al. 2005). According to wolf-biologist Mech (2017), grey wolves will most likely not need to be relisted in the ESA. When wolves are entirely removed from the ESA, the individual state wildlife-management agencies are therefore responsible for handling and mitigating the human-wolf conflicts such as livestock depredation and wolf harvesting (Mech, 2017).

Since the successful wolf re-introductions of the 1990s, scientific literature has focused on identifying where grey wolf populations may disperse to and how the re-established wolf may affect degraded ecosystems. These studies aim to define the amount of available wolf habitat, potential wolf population limits, and the availability of wolf dispersal corridors in the Northern

Rocky Mountains (Oakleaf et al. 2006). The difficulty in predicting wolf re-colonization lies with their generalist nature and the human-wildlife conflict that arises. Unlike most large predators, wolves are highly mobile. Wolf dispersal can span across distances of several hundred

7 kilometers, and individuals traveling over 1000 km have been documented (Geffen et al. 2004).

Being labeled as habitat generalists, wolves are known to only avoid swamps and tropical rain forests, thus occupation on the landscape is not well predicted by land cover or landscape diversity (Potvin et al. 2005; Geffen et al. 2004). Previous wolf dispersal studies identify the temperate-steppe-mountain division of the Bailey’s ecoregion layer to be the ideal habitat for wolves (Bailey, 1995; Oakleaf et al. 2006; Potvin et al. 2005). After the successful re- introduction efforts and dispersal events from Canadian wolves, the formerly regionally extirpated canid continues to expand its range and reclaim former territory in the U.S.

Thus far, the main predictors of grey wolf occupation are higher degree of forest habitat, lower human population density, higher prey density, and human tolerance to wolf presence (Bangs et al. 2005). Specific thresholds for prey and human densities are found to influence the presence of wolves. In the study done by Potvin and colleagues (2006) to analyze available wolf habitat in northern Michigan, the prey biomass of 2.4 deer/km2 was the lowest deer density to sustain an individual wolf. Typically, the average adult wolf eats approximately 5 kg of prey biomass per day (Bangs et al. 2005). Anthropogenic influences such as road and human densities were deemed unsuitable if they approached or exceeded <0.7km/km2 and <4 humans/km2 in portions of the Great Lakes (Oakleaf et al. 2006; Potvin et al. 2005). Overall, remoteness has a positive effect on wolf survival because of the reduced conflict with humans (Mladenoff et al. 2009).

However, with the increasing human population and anthropogenic influences, wolves must live in and disperse through areas occupied by people and their livestock to ensure long-term viability in the Northern Rockies (Bangs et al. 2005).

Human-wolf Conflicts

Overall, the greatest hinderances to grey wolf survival and successful dispersal is the wolf’s historic reputation for killing domestic animals and human intolerance (Bangs et al. 2005;

McLane et al. 2011). Most Americans do not support killing wolves to protect livestock, according to a new national study (Humane Society of the United States, 2019). However, as

Bangs and colleagues (2005) point out, livestock producers typically have the strongest dislike for wolves in comparison to other categories of today’s urbanized society. Previous studies identified the most common documented cause of radio-collared wolf death to be caused by an agency-controlled response to livestock depredation incidents (Bangs et al. 2005). According to a

2017 public attitudes study, lethal predator controls such as shooting animals from aircraft (aerial gunning), leg-hold traps, poisons and other lethal control methods are unpopular with the

American public (Humane Society of the United States, 2019).

Livestock depredation events on cattle and sheep typically follow an April-September seasonality pattern for when grazing is most dispersed and young livestock are most vulnerable

(Bangs et al. 2005). Wolf attacks upon livestock are usually sporadic and randomly dispersed, and few livestock producers witnessed multiple losses in regional analysis of livestock depredation (Bangs et al. 2005). If there is to be another long-term, successful wolf re- introduction, conservation efforts are potentially impeded by: the attitudes and behaviors of people, human-caused mortality, habitat connectivity, and future anthropogenic development and land cover change (McLane et al. 2011; Ripple et al. 2014). Due to domestication, hyperabundant exotic ungulates (livestock) are present in much of the world. These livestock are a potential, easy prey base and thus a continuing source of conflict between humans and large carnivores (Ripple et al. 2014). If humans are to coexist with these apex predators, control

9 measures and government agencies must be prepared to handle conflict between livestock producers and grey wolves.

Wolves and Trophic Cascades

Further re-introduction of grey wolves to their former range is paramount to conservation ecology and future sustainable ecosystems in the Colorado portion of the SW region. Restoration efforts are becoming more needed as increasing anthropogenic activity negatively impacts human health, economic well-being, biodiversity, and standards of living (Fraser et al. 2015).

Shown in both aquatic and terrestrial systems, an ecosystem with diverse multi-trophic species and high trophic transfer efficiency is more sustainable and resilient to adverse effects (Fraser et al. 2015). Thus, restoration efforts should prioritize goals of re-establishing food web structure, increasing biodiversity, and enhancing ecosystem services (Fraser et al. 2015). Wolves especially have shown complex effects upon the trophic cascades and biodiversity via predation of ungulates (Winnie & Creel, 2017; Mladenoff et al. 1995; Beschta & Ripple, 2012).

In the restoration case study of the Greater Yellowstone area, re-introduction of the grey wolf fostered regrowth and functional integrity of riparian ecosystems that had suffered greatly from ungulate over-browsing. When wolves were extirpated from the Yellowstone area in the mid-1920s, removal of the apex predator caused the ungulate population to exponentially increase and degrade riparian habitat (Beschta & Ripple, 2013, 2012). The 70-year absence of wolves in Yellowstone park had elk populations reach over 15,000 individuals, and riparian plant ecosystems succumbed to accelerated streambank erosion along alluvial channels (Beschta &

Ripple, 2013). This increase in elk populations was double over the estimated carrying capacity in Yellowstone National Park and elk culling events were held in effort to curb the ungulate population (Beschta & Ripple, 2013). Scientists documented that between 1935-1989, young

10 willow, aspen, and other woody species were unable to grow above a height of 100 cm due to intensive elk browsing (Beschta & Ripple, 2012; Winnie & Creel, 2017). After wolf re- introduction, however, elk densities decreased dramatically in the northern range of Yellowstone, thus greatly decreasing browsing pressure that was previously limiting the growth and recruitment of riparian vegetation (Beschta & Ripple, 2013). Researchers also documented a change in elk browsing location and behavior, noticing a change in elk habitat use, vigilance, group sizes, and seasonal movement patterns in response to wolf presence (Beschta & Ripple,

2012). Because of the behavioral changes and difference in browsing rates, ecologists and park managers were able to document herbivory release (i.e., increased vegetation recruitment and growth) in certain regions of the park and noted how wolf presence created a behavior-mediated alteration in Yellowstone’s trophic cascade (Beschta & Ripple, 2012, 2013).

With the regrowth of plant communities, riparian biodiversity has shown to increase as an added benefit of increased vegetation. Specifically, riparian songbird and beaver populations are repopulating the Yellowstone streams. Beavers, dubbed the “ecosystem engineers,” influence the frequency and duration of the stream overflow, water table elevation, and channel morphology

(Beschta & Ripple, 2005). Prior to wolf re-introduction, only one beaver colony was documented in 1995-1998 surveys; however, 18 colonies were documented in 2015 and their presence has already morphed Yellowstone’s hydrology by altering the streamline and rate of waterflow

(Beschta & Ripple, 2005). The increase in biodiversity and ecosystem robustness is largely credited to wolves rejoining the large predator guild in Yellowstone and creating top-down control on prey species (Beschta & Ripple, 2013; Fraser et al. 2015).

Aside from the reintroduction in Yellowstone, another tri-trophic cascade involving wolves has been documented and thoroughly studied at Isle Royale National Park since 1958

(Vucetich et al. 2012). A tri-trophic cascade describes a diverse, multi-species interaction between the primary producers, herbivores, and the predators in which the different trophic levels influence one another (Beschta & Ripple, 2005). At Isle Royale, scientists conducted multi-year tree-ring analyses to characterize the predator and prey system between wolves and moose (McLauren & Peterson, 1994). Prior to wolves existing on the island, moose had no predator control and overshot the island’s carrying capacity on several occasions. Specifically,

Isle Royale’s moose population crashed in 1934 due to an acute lack of food, increased again after the decrease in intraspecific competition, and then died back once more in the 1940s from lack of resources (Vucetich et al. 2012) Wolves colonized Isle Royale via natural dispersal, most likely crossing an ice bridge sometime between 1948 – 1950 which resulted in a heavy predation on the Isle’s moose population (Vucetich et al. 2012). Between 1958 and 1980, wolf predation had a substantial impact on moose abundance and rates of browsing (Vucetich et al. 2012). Over three decades, balsam fir trees from widely separated areas of the island displayed cyclic intervals of ring growth suppression that accompanied elevated moose densities and vegetation release during successful wolf predation (McLauren & Peterson, 1994). Because the vegetation dynamics appear to be more intimately linked to the wolf-moose interaction than to seasonal weather patterns, ecologists view this case study as evidence for top-down control (McLauren &

Peterson, 1994).

Large carnivores have the dual role of potentially limiting both large herbivores through predation and mesocarnivores through intraguild predation, thus structuring ecosystems along multiple food pathways (Ripple et al. 2014). Shifts in flora and fauna communities consequent to the cascading effects of wolf population demographics have been found across a variety of areas of North America, representing a wide range of productivity. Wolves are the most important

12 predator of cervids in the Northern Hemisphere, and predation upon the ungulates indirectly influences the soil and plant composition in the regions of wolf occupation (Ripple et al. 2014;

Mech et al. 2017; Estes et al. 2011). Specifically, the absence of apex predators has a marked, characteristic signal of reduced tree growth rate or recruitment failure in the dominant tree species (Estes et al. 2011). Large carnivores influence the nature and strength of ecosystem functioning, and when an apex predator is removed from the ecosystem, the process is referred to as “trophic downgrading” (Estes et al. 2011). Cervid irruptions, after wolf and other predator declines, were first documented in various ecosystems of western North America (Estes et al.

2011; Beschta & Ripple, 2013). The extinction or extirpation of apex predators reduces the food chain length, and thus alters the biodiversity, abundance, and composition of other organisms in the ecosystem (Estes et al. 2011). In order to facilitate the functionality and resilience of ecosystems on a holistic scale, the recovery of large predator populations is an ideal way promote herbivory release and instate mesopredator control (Wallach et al. 2010).

Ecological Modeling using ABM

The variables and interacting processes which determine future species distribution and diversity have been the central questions and focus to biogeographers and ecologists alike. This quandary is further complicated by climate change and human-wildlife conflicts (Sanchez et al.

2014). The distribution of species diversity across space and time reflects the net result of speciation, extinction, dispersal, and species interaction (i.e., predation) in the species population and metapopulations (Jonsson et al. 2016; Darwin, 1859). For dispersal specifically, this movement phenomenon facilitates the re-distribution of organisms via colonization events, range shifts, and genetic flow between populations (Jonsson et al. 2016). With the advancements in

13 technology and spatial software, the emerging subdiscipline of movement and spatial ecology within the geographic information sciences (GISci) has generated new data to further understand dispersal and species distributions and possible predictions for future populations (Dodge et al.

2016, Laffan et al. 2012; Seidel et al. 2018).

Movement ecology, according to Dodge and colleagues (2016), is defined as research on how, why, when, and where animals, plants, and microorganisms move. These studies correspond to dispersal events such as migration and natal dispersal (Dodge et al. 2016; Jonsson et al. 2016). Movement ecology data can be collected using either the Eulerian or Lagrangian methodology (Dodge et al. 2016; Seidel et al. 2018). The Eulerian method utilizes fixed locations such as mobile phone cell towers. In contrast, the Lagrangian approach to data collection uses moving objects (e.g., GPS collars, or by video-tracking) for location information

(Dodge et al. 2016; Wegmann et al. 2016). For animal dispersal studies using movement analysis, the real-time movement data is combined with remotely sensed data on vegetation type, climate, and biomass to create movement models such as migration patterns and corridor analysis (Jonsson et al. 2016).

An example of a movement ecology model within GIS is the least-cost analysis. The purpose of cost-analysis is to represent the species-specific factors such as mortality risk, behavioral aversion, or energy expenditure that impede movement across a landscape

(Etherington & Perry, 2016). Cells, or pixels in a gridded map, have assigned values or “cost” based upon environmental values and represent the difficulty associated with traversing different parts of a landscape. Cells with low cost relate to preferred habitat and lower mortality risks.

Thus, the animals’ movement typically reflects how the individual will seek out the most abundant food resources and move to them, as long as it can perceive those resources and there

14 are not any harmful impacts outweighing the benefits of moving (McLane et al. 2011). The accumulated cost illustrates the dispersal ability of an organism as a function of the total distance and the costs encountered (Etherington & Perry, 2016). Maps can be generated to visualize regions of preferred versus avoided habitat based upon the cost-analysis. Conservation studies prioritize these preferred habitats, or wildlife corridors, and commonly use least cost-analysis to advocate for land preservation and wildlife habitat connectivity (Golicher et al. 2012; Seidel et al. 2018).

Similar to movement ecology, spatial ecology studies the spatial distribution or spread of organisms, populations, and landscapes. Spatial ecology takes further consideration to how distributions affect the ecological processes involved and how one process is connected to another (Laffan et al. 2015). Spatial ecology has gained popularity in application for evidence- based studies and influencing environmental policy regarding biodiversity, ecosystem services, and sustainable environmental management (Laffan et al. 2015, 2014). With the convenience of

GIS technologies, spatial ecology models can be summarized at any scale of management interest and can be used in direct conjunction with biodiversity conservation planning (Chee &

Elith, 2012).

One such spatial ecology model which incorporates a bottom-up methodology and has been tailored to ecological and social systems is the agent-based model (ABM) (Tang & Bennett,

2010). This modeling method incorporates autonomous individual elements of a computer simulation, called “agents” which can have properties, states, and behaviors (Wilensky & Rand,

2015). The agents in ABM can respond to stimuli and report back information which can either be binary or a multi-valued state. The goal of ABMs is to capture the structural and functional complexity of ecological systems, and to then simulate the emergent, non-linear, and adaptive

15 phenomena which are observed in the real world (Tang & Bennett, 2010; Wilensky & Rand,

2015). ABM methodology encodes the behavior of individual agents in simple rules, which allows us to observe the results of those agent’s interactions (Wilensky & Rand, 2015). An important feature of ABM is how the model can describe the individuals, not aggregates, so the relationship between the real world and the computational model can more be more closely matched. Additionally, a computational model can help eliminate ambiguities that usually arise in textual models where others may interpret descriptions differently than others. Thus, the model is viewed in a “glass box” as opposed to a “black box,” where the hypothesized theories can be examined and observed through the agents’ interactions (Wilensky & Rand, 2015). ABM provides an unambiguous simulation of a specific query and can be utilized in many discussions concerning topics such as public forums and policy analysis. This “glass box” approach to modeling enables all interested parties to examine the model all the way down to its most basic components and thus used for informing the public (Wilensky & Rand, 2015).

ABM has gained popularity within the spatial studies due to its capability of integrating

GIS, statistics, and adaptive learning. Specific to animal studies, remotely sensed data allows for location-specific environmental variables to be tested and increase the features that are relevant for species distribution and fitness (Ficetola et al. 2014; Zengeye & Murwira, 2016). In an animal movement ABM, geographic and location-dependent information are represented as the environment/stationary agents as opposed to motile agents such as wolves, prey, or humans.

Thus, the stationary, environmental agents generate spatial patterns of results as opposed to spatially homogenous aggregate results which are given in equation-based models (Wilensky &

Rand, 2015). The movement path of agents in ABMs result from the dynamic interplay of four basic components: the internal state of the organism, its motion capacity, its navigational

16 capacity, and the external environment. With these four components, the ABM is capable of simulating possible emergent movement patterns critical to determining how wildlife populations may respond to disturbances such as landscape change (McLane et al. 2011).

Ecologists have been quick to utilize ABMs in combination with remote sensing and/or in situ data to create models of critical habitat for species survival (McLane et al. 2011) After selection of appropriate variables, the model generates a map of a species’ realized or potential distribution and the threshold values which can determine the geographic range based upon suitability (Golicher et al. 2012). This process is similar to the least-cost map for animal movement, except the ABM functions beyond the equation-based model which produces a static result. The end result of ABM modeling consists of a map of locations to which the agents (i.e. wolves) have a probability or general possibility of dispersing to and recolonizing the region.

Also, agent interactions in ABM occur temporally, thus the model moves beyond a static snapshot of the system and projects a dynamic understanding of the overall system’s behavior

(Wilensky & Rand, 2015). By having agents sense and react to their changing environment, a more robust and realistic simulation of the environment can be generated using ABM methodology.

In regard to learning, ABM allows individual agents to have a history of interactions

(e.g., with the environment and/or other agents) and can change their behaviors and strategies based on previous events. Agents are goal-driven and try to fulfill specific objectives, they are aware of and can respond to changes in their environment, they can move within that environment, and they can be designed to learn and adapt their state and behavior in response to stimuli from other agents and their environment. The dynamic interplay between agents is readily accommodated, realistic environmental conditions can be approximated, and hypothetical

17 scenarios can be simulated. An ABM specifically developed for use in determination of critical habitat is one that explicitly incorporates these individual fitness-seeking behaviors of animal movement in a spatially-realistic representation of the environment that is then subjected to alternate scenarios of land-use development (McLane et al. 2011). In an evolutionary sense, it is possible for agents to learn and hence modify their behavior as a result of continual interaction with a particular group of agents (Wilensky & Rand, 2015). Overall, ABM is a unique opportunity to further enhance technology with ecological theories so that our understanding of ecosystem processes can be advanced with the changing tolerance of humans to large predators

CHAPTER III

RESOURCES & METHODS

Using the ESRI Geographical Information Systems (GIS) software ArcPro 2.5.0 and

NetLogo 6.1.1, this study creates a multi-faceted model with vegetation, wolf, and human data to simulate and predict grey wolf dispersal into new and/or historic territories (ESRI 2020;

Wilensky, 2016).

Study Area

Moffat County was specifically chosen to model grey wolf dispersal in Colorado due to the grey wolf sighting and pack confirmation in early 2020 (Colorado Parks and Wildlife, 2020).

As of 2019, grey wolves are considered to be endangered in Colorado and are federally protected and managed by the U.S. Fish and Wildlife Service (Colorado Parks & Wildlife 2020). Since

Moffat is the only known Colorado County to have a successful, recent incident of dispersal from the northern Rocky Mountain corridor, the county’s spatial heterogeneity is of high interest as the grey wolves navigate the terrain and encounter anthropogenic factors.

The total area of Moffat County is 12,310 km2, has the elevation range of 1,522 m –

2,940 m, and the slope of elevation can vary between 0° - 74°. Three main rivers, the Little

Snake River, Yampa River, and Green River are located in the county (see Figure 1). The main town is Craig, located in the southeastern corner, with a population of 9,500 (U.S. Census

Bureau, 2010). Other towns include Dinosaur and Maybell (see Figure 1). Moffat County is in the Wyoming Basin province ecoregion, in which the most common vegetation is sagebrush, greasewood, and Saltbush shrubs (USGS, 2015). Foothill regions that flank adjacent mountains are dominated by montane sagebrush, interspersed with deciduous and conifer woods. The

19 montane and subalpine forests include conifer and aspens (USGS, 2015; Bailey, 1995). The amount of agriculture in Moffat is approximately 953, 100 acres, or 30% of Moffat County according to the 2017 Census of Agriculture by the US Department of Agriculture (USDA)

(USDA, 2017).

Figure 1. Map of Moffat County, CO

Figure. 1 A map of Moffat County with the rivers and main towns labeled.

The Wyoming Basin province supports some of the largest U.S. populations of game species and this region encompasses the largest migration routes in North America reported for mule deer and pronghorn (USGS, 2015). Prey species, including white-tailed deer, mule deer, and elk, have populations ranging in the thousands. The ungulate population is estimated by

Game Management Units (GMUs) for Moffat county and include GMUs 1, 2, 3, 4, 5, 10, 11, 12,

13, 201, 211, and 301 (Colorado Parks & Wildlife, 2019a, 2019b, 2019c, 2019d). Within these

GMUs, the deer population was estimated at 78,250 individuals, elk populations were estimated to be 68,950 individuals, pronghorn were recorded at 23,480 individuals, and the moose population in Moffat totaled 260 individuals in the post-hunt estimates of 2019 (Colorado Parks

& Wildlife, 2019a, 2019b, 2019c, 2019d). In total, the ungulate prey population in Moffat county is approximately 170,940 individuals.

Environmental GIS Data

To simulate the land usage and vegetation in the Colorado landscape, land cover data was acquired at coarse resolution (30m²) via open access from the National Land Cover Database

(NLCD) from USGS. Using ArcGIS products, the initial 16-classification for the land cover was simplified into six categories: Forest, Urban, Agriculture, Grassland, Barren, and Water. This initial simplification allowed for the data to be in a manageable size and format for later NetLogo modeling. The land cover and vegetation classes were then clipped to dimensions of Moffat county and each classification was converted into vector shapefiles (See Figure 2). Since there was a restriction on data size and complexity, the vegetation classifications remained static throughout all model simulations and did not have land conversion or land change. Also, the model did not include seasons which would impact the vegetation resources available to agents.

Abiotic variables such as elevation and slope were calculated with GIS ArcPro software. The

Digital Elevation Model (DEM) illustrated impassible barriers to wolves where the elevation exceeded 2150 m, and slope greater than 55° (See Figure 3). These were all variables specific to grey wolf avoidance seen in the study in Banff National Park, Canada and were considered as biotic variables similar to the Rocky Mountain Range in Colorado (Paquet et al. 1999)

Additional, anthropogenic variables such as human population densities are also documented to prohibit wolf movement and survival and were included in the Colorado model.

Previous studies in the Great Lakes region of Michigan identify wolves having a relatively low tolerance threshold to human densities (< 0.4 humans/km²) (Oakleaf et al. 2006; Potvin et al.

2005). However, the model does not include domestic animals such as livestock or household pets or roads. By extracting these abiotic and biotic variables, the NetLogo model simulation of grey wolf agents moving across the model of quasi-Colorado landscape can give insight to the environmental barriers that could mitigate or benefit wolf dispersal capabilities in the real world.

Figure 2. NetLogo’s representation of Moffat County, CO

Figure 2. NetLogo’s representation of the study region, Moffat County, CO. The six vegetation classifications are displayed: forest (green), grassland (light green), water (blue), urban (red), agriculture (orange), and barren (brown).

Figure 3. NetLogo’s Moffat County with environmental barriers

Figure 3. Elevation barriers to wolf agents show in grey. The elevation and slope barrier are regions that have elevations greater than 2150 m and slopes greater than 55°.

ODD Model Description

The model description follows the ODD (Overview, Design concepts, Details) protocol for describing agent-based models (Grimm et al. 2005; 2006). The ODD protocol begins with three elements that provide an overview to what the model is and how it is designed, followed by an element of design concepts that depicts the ABM’s essential characteristics. The ODD ends with three elements that provide the details necessary to make the description complete

(Railsback & Grimm, 2019). The ODD protocol is utilized so other interested parties can simulate the model with the same parameters with the hopes of replicating the results found here.

Also, the ODD protocol allows for clarification in model setup, design, and input variables but not to overly complicate the model description.

1. Purpose and patterns: A statement of the question or problem addressed by the model. This

section includes a statement of the patterns used to determine how useful the model is for its

purpose.

The purpose of the model is to create a spatially explicit simulation of potential grey wolf dispersal in Moffat County, Colorado. The movement model is based upon the aggregated impacts of (1) wolf agents having low tolerance for anthropogenic disturbances, (2) wolf agents’ prior hunting experiences associated with spatial memory and (3) prey agents’ avoidance behavior to any nearby wolf agents. From grey wolf studies, the literature cites avoidance patterns of grey wolves to anthropogenic factors and specific characteristics in the physical landscape. In the Great Lakes areas, grey wolves have a low threshold of <4 humans/km2

(Oakleaf et al. 2006; Potvin et al. 2005). Wolves are also documented to prefer elevations below

1850 meters and slopes greater than 45° in the Northern Rocky Mountains (Paquet et al. 1999).

In order to translate these thresholds to Colorado’s Rocky Mountains, the physical impassible

25 barriers were set to anything greater than 2150 m in elevation and slopes greater than 55°.

Overall, grey wolves are habitat generalists, and their habitat occupancy is not determined by vegetation type, but rather the prey population density and human tolerance. Thus, this model simulation will attempt to show adaptive decision-making of wolf agents choosing where to move based upon their avoidance of humans, prey hunting memory and emerging learned behavior. Specifically, this movement model is designed to predict where grey wolves will disperse in this particular Colorado county.

2. Entities, state variables, and scales: The outline of the model, including what kinds of things

are represented in the model (entities) and what variables are used to characterize them (state

variables). The scales are defined both temporally and spatially.

Wolf agents are given the properties of spatially explicit memory, best-choice for a hunting location, a predator efficiency, bioenergetic measurement, and tolerance of human densities.

Wolf agents also are initially assigned a social “pack” which gives restrictions on reproduction and has wolf agents stay within a radius to their other pack members. To mimic the complex social behavior of grey wolves, wolf agents are given the additional variables of sex, social status, and age. Prey agents are given the properties of avoiding wolf agents, age, and bioenergetics. Humans are represented either as an “urban human” or a “farmer,” and all human agents are stationary on their allocated patches. One tick of the model represents 3 months of time, and the individual cells (or patches) in the NetLogo grid are scaled to be 350 m x 350 m of real space. For all model simulations, the value of bioenergetic quantities were made by educated guesses and the values can be changed in the NetLogo Interface.

3. Process overview and scheduling: A description of the processes executed by the model’s

entities, and details on how the observer, or the creator of the model, records and measures

the data output. The model’s schedule lists the order in which the processes are executed by

the computer.

The model is initialized with a user-defined number of wolf packs and maximum number of wolves per pack. The wolf packs are distributed across the Moffat County landscape randomly and an individual wolf agent’s social status is determined by the wolf agent’s age. Wolf agents make the decision of where to move based upon their hunting memory (see Hunt-Prey submodel) and tolerance to human agents (see Avoid-Anthro submodel). Wolf packs will try to reproduce every 2 ticks of the model if they have suitable conditions (see Reproduce-Wolves submodel).

The number of prey agents is also user-defined at model setup, and the prey agents are randomly dispersed across Moffat County. Prey agents will avoid wolf agents (see Avoid-Wolves submodel) and move across the landscape to consume vegetation resources. For model simplicity, prey agents did not prefer certain vegetation classifications nor did the prey agents have physical barriers to impede their movement. Prey agents will reproduce every 2 ticks if the bioenergetic requirements are met. In regard to the vegetation patches, the patches will regrow their vegetation resource amount at each time step.

The model has a series of global updates and observer processes every time step or tick. The global updates include calculating the amount of resources available at each patch, aging the prey and wolf agents, and determining the pack alphas if a wolf pack is missing a male and/or female alpha wolf agent (see Update-Alphas submodel). The observer processes involve the creation and aging of kmarkers (See list of terms in Appendix) in addition to determining the survival probability of a wolf pack’s litter of pups (see Pup-Survival section in Reproduce-

Wolves submodel).

The execution schedule for the model is as follows: (1) load GIS data, (2) load NLCD vegetation data, (3) agent initiation, (4) GO, (5) update vegetation, (6) update pack alphas, (7) update prey, and (8) update patches. A flowchart for a visual is shown in Figure 4.

Figure 4. Flowchart of wolf ABM

4. Design concepts: Discusses why the model concepts were implemented as they were.

Illustrates that design decisions were based on knowledge of the system being modeled and

careful consideration of the model’s purpose.

Basic Principles—

The basic principle of this simulation is to replicate possible grey wolf dispersal in

Moffat County, Colorado, with the influence of hunting memory and anthropogenic avoidance behaviors. To simulate how grey wolves may be more efficient at killing certain prey, different predator efficiencies (3%, 10%, 20%) were adapted from previous wolf studies so that agent dispersal could be modelled effectively and show potential limitations for wolf population growth (Mech & Peterson, 2003; Benson et al. 2017). The dispersal behavior of grey wolves is aggregated from other wolf behavior studies conducted in the Great Lakes region and the

Northern Rocky Mountains in Canada (Paquet et al. 1996; Potvin et al. 2005). The reproduction of wolf agents is loosely modeled after the wolf data in Yellowstone National Park and regulated so that reproduction occurs only within a wolf pack that has high enough energy to support the young, the wolf pack is a certain amount of distance away from human disturbances, and the wolf pack has more than three wolf agents. At initialization, the model starts with five wolf packs as to mimic the population density seen in Yellow Stone National Park (Yellowstone

Center for Resources, 2019). Also, the wolf reproduction only occurs every 2 time-steps, which translates to every 6 months in real time. To mimic the mortality caused by anthropogenic disturbances, wolf agents have a probability of dying that increases when the wolf agent is in proximity to a human agent. The chance of mortality by proximity to human agents increases if

29 the human density increases within a distance of the wolf agent. For prey agents, the prey agents have a low tolerance for wolf agents. This makes the prey agents flee from any wolf agent and also choose to congregate with similar prey agents instead.

Emergence—

For this model of aggregated behaviors of both wolf and prey agents, the important outputs are recorded in the “ph-memory” or patch-hunting memory table to record wolf occupation and the number of kmarkers created. By recording the coordinates where each individual wolf agent moves in the NetLogo grid and storing it in the ph-memory table, a map displaying wolf occupation is created in order to illustrate if the wolves move towards or away from certain regions. If there is a kmarker for a wolf pack, the emergent behavior would be for the wolf pack to stay within the same area of the kmarker. In contrast, if there are no kmarkers for a wolf pack, then one would expect for the wolf pack to move across the landscape until a successful kill (kmarker) is made.

Adaptation—

The wolf agents and prey agents in the model both make decisions based upon their response to the environmental conditions. For wolf agents, dispersal or moving into a new patch is determined by several factors: the human density in relation to the anthro-tolerance, the physical barriers, and if there is an active kmarker in the region. If the human density exceeds the anthro-tolerance or the elevation and slope are not suitable to the wolf agent, the wolf agent will find a new patch that has the preferred human-density and topography conditions. The decision for the wolf agent to move based upon the presence of an active kmarker is more complicated since the temporal decay of the kmarker determines if the wolf is able to “remember” where the kmarker was previously. Once the kmarker is “forgotten” or becomes inactive, the wolf does not

30 factor the inactive kmarker into its decision process of where to move. The adaptation behavior for prey agents is simply to avoid patches that have high density of wolf agents.

Objectives—

The objective of a wolf agent is to find a pack, hunt prey, and to successfully reproduce. The wolf agent’s decision to move is based upon the prior hunting success and avoiding the human agents around them. A prey agent’s objective is to consume vegetation, avoid wolf agents, and to reproduce. The kmarkers and patches have no objectives in this model.

Learning—

In order to hunt prey, wolf agents have a memory of where a previous successful kill was made (kmarker). The memory has a temporal decay to simulate the wolf agent forgetting over time. Wolf agents will try to return to active kmarkers until the kmarker is inactive.

Sensing—

In the model, wolf and prey agents are given different distances that they can sense other agents and their surrounding environment. Wolf agents are able to sense prey and human agents within a radius of five patches. Wolf agents are able to locate kmarkers until the kmarker becomes “forgotten” or inactive after a certain period of time (see Hunt-Prey submodel). Prey agents are able to sense other agents, including wolf agents, in a radius of 2 patches. All distance measurements are estimated interactions made from an educated best guess and can be further refined in future work.

Interaction—

Male and female wolf agents interact to establish a pack and attempt to reproduce. Prey agents and wolf agents have an antagonistic interaction when the wolf agents hunt and kill prey.

The prey agents interact with the vegetation patches by consuming the vegetation available on

31 the patch. Human agents indirectly kill wolf agents, and wolf agents attempt to avoid all human agents and anthropogenic patches. In this model, human agents have no interaction with prey agents because the primary focus is the between anthropogenic avoidance and wolf-prey relations.

Stochasticity—

Within the system, there are many processes dependent upon stochastic decisions. The stochastic patch vegetation amount and regrowth rates vary between vegetation types to mimic the different food resources that can be available for prey agents to consume. Wolf reproduction also has a random number of pups born per litter which ranges from zero to five pups. The temporal decay of the kmarkers is random per wolf agent, so that wolf agents may have different memory capacities than others. Prey agents being able to sense wolf agents is randomly set between zero (no awareness) to 3 (hyper awareness) so that some prey agents are easier to hunt than others. Also, all prey and wolf agents have their age randomly assigned at initiation.

Collectives—

There are several collective groups in this model. For wolf agents, the sex, age, and social status determine if the wolf agent is an alpha or not. Human agents can either be an urban or a farmer human. Farmer human agents are different than urban human agents in the way that one farmer agent occupies each agriculture patch, whereas there can be multiple urban human agents at an urban patch. The vegetation classes of forest, grassland, agriculture, urban, water, and barren all form the patch collectives. Kmarkers also have the collective status of either being active or inactive.

Observation—

At each time step, the model records the wolf population size, the prey population size, the number of kmarkers created, and what patches were occupied by wolf agents. If the wolf population reaches zero, the wolf population exceeds 500 agents, or the prey population exceeds

100 thousand agents, the model is stopped and all data for the ph-memory table is exported.

These model parameters or stop commands function so that the model does not operate infinitely and does not exceed the amount of computational memory space available for this study. If the model was not restricted by data memory space, the stop commands could be altered to adjust for different scenarios.

5. Initialization: Describes how initial values of environmental variables are set. Initial variables

pertaining to concepts such as bioenergetic constraints and agent fecundity are formed from

best guess estimates and can be better adapted with further model improvement.

The predator efficiency: 3%, 10%, 20%

Initial number of wolf packs: 5 wolf packs

Number of wolves per pack: 8 wolves

Pack member distance: 6 patches

Wolf energy gained from food: 50 units

Wolf fecundity: 45 units

The tolerance of wolves to humans: 0.049 humans/m²

The initial count of prey: 15000 prey agents

The avoiding behavior of prey to wolves: 95%

Prey energy gained from food: 30 units

Prey fecundity: 25 units

Human population: 13000 humans (Census Bureau, 2017)

6. Input Data: Lists the data files read into the model.

The data required for the model initiation are the elevation via DEM data, vegetation classifications, and human population density.

7. Sub-models: Describes the major processes in more detail.

Hunt-Prey Submodel: The process of wolf agents hunting prey and thereafter deciding where to move is an amalgamation of several commands. The first command for wolf agents to process involves finding if any active kmarkers exist for that particular wolf. This process is built from two procedures: (1) the model determines if an active kmarker associated with the individual wolf agent still exists, and (2) if that particular patch containing the active kmarker is within the wolf’s vision and the patch meets the elevation requirements for the wolf to enter the patch. The next procedure for the wolf agent is determining if the patch containing the active kmarker is within the allotted pack-member-distance from at least one wolf in the wolf agent’s wolf pack. If the patch containing the active kmarker is within the pack-member-distance, this patch is deemed a valid hunting patch since it has a previous kmarker, the wolf agent is able to sense the kmarker, and the patch is within distance to the wolf agent’s wolf pack. When the wolf agent must decide which direction to move, if there is a valid hunting patch with a kmarker, the wolf will calculate which valid patch has more kmarkers. The wolf agent will then move towards the valid patch containing the highest number of active kmarkers. If there are no valid patches and/or the wolf agent does not have any active kmarkers, the wolf agent will instead move towards the closest

34 member in the wolf pack. This hunting process allows for one wolf individual to facilitate the movement of the overall pack towards an area of active kmarkers and for the wolf pack to stay within the allotted distance of one another.

Avoid-Anthro Submodel: The process of wolves avoiding patches associated with human activity is based upon the wolf literature and the particular wolf agent moving until finding a suitable patch. As previously mentioned, grey wolves in North America typically do not tolerate human densities at or above 0.4 humans/km². To translate this tolerance into the NetLogo model and the spatial dimensions of each vegetation patch, the human tolerance for wolf agents is set to

0.049 humans/m². Although this metric for Moffat County does not convert to the human population according to the 2017 Census, this variable was calculated by determining the spatial dimensions of the NetLogo patch (350m x 350m = 122500m²) and converting the 0.4 human/km² to be in meters. The conversion equation is shown as:

0. 4 푘푚 푥 = This conversion, however, may1000000 have error 푚 since122500 the human 푚² density in Moffat County is not equally distributed across the county. Human agents are only present at urban and agriculture patches, and the human density overall is set by the UI slider bar on the Interface panel. When a wolf agent enters a patch, the wolf-happy? is set to FALSE if the patch has human densities at or above 0.049 human/m². If the wolf-happy? is set to FALSE, the wolf agent will continue to move to different patches until wolf-happy? is set to TRUE. The wolf-happy? will only report TRUE if the patch has human densities below the wolf agent’s tolerance level and the patch is suitable for the elevation and slope barriers.

Reproduce-Wolves Submodel: In order for wolf agents to reproduce, there are several conditions that must be satisfied. First, wolf reproduction only occurs within wolf packs that

35 have both a female alpha and a male alpha present. Also, the wolf pack in question must have more than 3 wolves, have a distance away from any human agent greater than 2 patches, and the total pack energy must be greater than 50 units. If these requirements are met, the wolf pack will attempt to produce one litter of zero to five pups every 2 steps (6 months). The newly created wolf agents have their social status assigned to “pups” and the wolf pups will disperse from their natal pack if the wolf pack in question exceeds 8 wolves. Wolf pups become “yearlings” after 4 ticks (12 months), and adults after reaching two years of age (8 ticks).

Avoid-Wolves Submodel: The behavior of prey agents avoiding wolf agents is adapted from the common Schelling model of segregation, which was originally developed by the economist

Thomas Schelling in 1971 (Schelling, 1971). From the Segregation model, the prey agents avoid or leave the patch vicinity when there are too many non-similar agents to themselves. In other words, the prey agents are intolerant to both wolf and human agents and prefer to be in areas that have higher densities of prey agents. NetLogo executes this behavior by having the prey agents calculate how many agents are similar in breed to themselves (e.g., “wolf” breed versus “prey” breed) in a specified radius, and then compare this count to the total of all agents in that same radius. In this model, all prey species (e.g., deer, moose, elk) are categorized as prey agents. If the prey agent is located in a patch having a proportion of 95% prey agents, the prey agent is satisfied and the prey-happy? reports TRUE. In contrast, if the prey agent enters a patch that does not meet the 95% my-similar-wanted, the prey agent reports prey-happy? as FALSE and the prey agent moves until the Boolean reporter can become TRUE.

Update-Alphas Submodel: In the event of a wolf pack losing or not having a female and/or male alpha wolf, the wolf agents have a series of procedures to reorganize the wolf pack social structure. At model initiation, all five of the created wolf packs have the procedure ensure-pack-

36 has-alphas so that a male and female alpha is generated. If an alpha male or female is disposed in an already-established wolf pack, the alpha position is given to a wolf agent (of the correct gender) who has their social status as an “adult” and is 4 years or older. If the wolf pack is being newly created (i.e., natal dispersal for wolf pups), a wolf pack is formed with the procedure join- a-pack in which transient wolves can join-a-pack until the limit of 8 wolves is reached. If a wolf pack falls below 3 members or cannot fulfill the missing gendered alpha, the wolf pack is disbanded with the procedure pack-die.

CHAPTER IV

RESULTS

20% Kill Probability Experiment:

For the experiment of the wolf agents having the maximum predator efficiency (20% kill probability), the model revealed scenarios in which the wolf populations had the quickest growth demographically and the least bioenergetic constraints. The BehaviorSpace data output for the experiment with 20% kill probability had the highest averages corresponding to population growth: the final count of wolves averaged 148.4 agents, the number of wolf pups born per iteration had an average of 128 pups, and the average kmarker count of 56.2 kmarkers was greater than the other two experimentations (Table 1). By having the highest final count of wolves, number of wolf pups born, and the highest kmarker average—the 20% kill probability illustrates a scenario in which grey wolves have the greatest prolific opportunity to recolonize and disperse across the landscape. Additionally, the average number of wolf packs was greater for this experiment (15.2 wolf packs) and further reflects the population growth when the wolf agents have the maximum chance of catching a prey agent.

Looking at the population numbers, the wolf agents did not experience a bottleneck effect or a decrease in wolf numbers from model initiation, but instead showed a steady, positive growth. The first iteration of the model in particular yielded 307 wolf agents and 169 successful kills (kmarkers) which was the maximum amounts witnessed between all experiments. For each iteration, the prey population exceeded the stop command of 100 thousand agents and triggered the model to end simulation. In other words, prey population maxed out the simulation parameters even with the wolves having the highest predator efficiency. When comparing this experiment to the other kill probability experiments, this setup produced the shortest model

38 duration and averaged 4.55 years before the prey population triggered the stop command.

Overall, the data illustrates the situation where wolves had the least bioenergetic constraints and reproduction was not limited as much as the other experiments.

Table 1. Table of 20% kill probability experiment results

In comparing the maps of wolf occupation, having the highest predator efficiency allowed a higher occurrence for kmarkers to be created and the wolf agents to display clustering patterns.

Unlike the other experiment kill probabilities, three iterations of the 20% kill probability revealed wolf occupation near or around Craig(See Figures 5, 6, 9). Craig has the highest human density within Moffat County, and the presence of wolves around the town center may suggest that wolves could be inclined to approach human dense areas if the area supports high prey densities. However, it is important to note that the current model does not include a variable of human intolerance to wolf presence. Another interesting effect of the wolf packs not being limited by low bioenergy is seen in Run #2 (Figure 6) which shows wolf occupation and kmarkers the most scattered compared to any other experimental result and does not show wolf occupation along river valleys, which is contrary to the general spatial pattern seen in this model simulation. From this experiment, two maps in particular show dense clustering patterns in three regions: the intersection of Yampa River and Little Snake River, southeast of Dinosaur National

Monument, and along Highway 40 between Dinosaur and Maybell (See Figures 5 & 9) Three maps also display wolf occupation to be following the three rivers of Green River, Yampa River,

39 and Little Snake River (Figures 5, 7, 8). These geographic trends match previous grey wolf studies in which grey wolves prefer low elevation, river valleys.

Figure 5. 20% Kill Probability: Run #1

Figure 6. 20% Kill Probability: Run #2

Figure 7. 20% Kill Probability: Run #3

Figure 8. 20% Kill Probability: Run #4

Figure 9. 20% Kill Probability: Run #5

10% Kill Probability Experiment: In order to model the different possibilities of grey wolf dispersal in Moffat County, the second experiment had a predator efficiency set to 10% so that the grey wolf agents were not as successful in hunting and could hopefully be more realistic in fulfilling their bioenergetic requirements. The grey wolf agents did not experience any extinction events; however, the 10% predator efficiency did result in two iterations having a wolf population decrease and lower population demographic compared to the model initiation. The wolf population decrease was particularly noticeable for model run #2 which started with 18 wolves across 5 wolf packs but ended with only 7 wolf agents belonging to 2 wolf packs (Table 2). Overall, the experiment with

10% kill probability had data results that averaged lower than the 20% kill probability, but higher than the minimum of 3% kill probability. The data results from the BehaviorSpace revealed the

10% kill probability to have the longest model duration time (7.1 years) when compared to the other two experimental trials.

Table 2. Table of 10% kill probability experiment results 10% Kill Probability run # # of steps model duration (in years) initial count of wolves final count of wolves count of wolf births # of wolf packs final count prey kmarker count 1 35 8.75 19 159 170 27 105565 97 2 35 8.75 18 7 3 5 103763 7 3 26 6.5 30 188 169 17 112443 44 4 14 3.5 16 115 101 6 111734 6 5 32 8 20 18 7 5 110411 3 min 14 3.5 16 7 3 5 103763 3 max 35 8.75 30 188 170 27 112443 97 average 28.4 7.1 20.6 97.4 90 12 108783.2 31.4

For the wolf occupation maps in the 10% kill probability experiment, only two iterations of the experiment had spatial patterns of true clustering patterns and high kmarker frequency for suitable wolf conditions. Specifically, the model runs #3 and #1 showed clustering patterns which suggests certain regions may be suitable or preferred by the grey wolf agents (Figures 10

& 12). These regions are along the Yampa River and Little Snake River, near Dinosaur, and the

46 area southeast of Dinosaur National Monument—which is similar to the 20% kill probability simulations. For run #3, the clustering was seen most notably in the region southeast of Dinosaur

National Monument (Figure 12). In run #1, the clustering of kmarkers is noted particularly along the Little Snake River and the Yampa River (See Figure 10). Both the Yampa and Little Snake rivers have agriculture patches following the waterways and may promote high wolf occupation by the high prey density and lower elevation and slopes. When looking at the population center of Craig, the first iteration for the 10% kill probability (run #1) showed wolf occupation near

Craig but no kmarkers (Figure 10). This may suggest that a wolf pack was generated in the Craig region at the model initiation, but the wolves moved away from the higher human densities to find better conditions for hunting prey.

In the model simulation of 10% predator efficiency, there were two distinct iterations which lacked the spatial patterns of wolf occupation along river valleys. The map results for run

#2 and run #4 (Figures 11 & 14) show little or no wolf occupation along a river valley which is unlike other model simulations. This model phenomenon also shows scattered wolf occupation northwest of Craig, which may suggest a wolf pack was initiated near the town but then dispersed west to find suitable conditions. The low kmarkers may have been the result of a wolf pack continually moving away from Craig, but not towards an area of high prey density.

Figure 10. 10% Kill Probability: Run #1

Figure 11. 10% Kill Probability: Run #2

Figure 12. 10% Kill Probability: Run #3

Figure 13. 10% Kill Probability: Run #4

Figure 14. 10% Kill Probability: Run #5

3% Kill Probability Experiment: On average, the experiment with 3% kill probability yielded 86.6 wolves at model completion, a total of 68.8 wolf pups born, and 5.2 kmarkers per simulation (Table 3). These results were the lowest when compared to the other two experiment setups. No model simulation resulted in grey wolf population extinction nor had a grey wolf population exceed the carrying capacity of 500 wolf agents. The average amount of wolf packs in this experiment was 10.8 wolf packs, with the limit of 8 wolves per wolf pack. One interesting result with the 3% kill probability was the amount of wolf pups born, despite the low number of kmarkers. This observation may be the result of the wolf packs having a high pack-energy and the prey agents killed yielding a high bioenergy for the wolf agents. When translating the number of model steps into time in years, the experiment averaged 5.75 years until the prey population exceeded their population limit.

Table 3. Table of 3% kill probability experiment results 3% Kill Probability run # # of steps model duration (in years) initial count of wolves final count of wolves count of wolf births # of wolf packs final count prey kmarker count 1 17 4.25 29 106 80 9 110935 2 2 20 5 25 110 88 15 111587 8 3 32 8 23 71 64 10 111877 7 4 26 6.5 26 94 81 13 110725 6 5 20 5 25 50 31 7 110281 3 min 17 4.25 23 50 31 7 110281 2 max 32 8 29 110 88 15 111877 8 average 23 5.75 25.6 86.2 68.8 10.8 111081 5.2

When looking at the maps of wolf occupation in Moffat County for the 3% kill probability experiment, there appears to be patterns or general trends where the wolves are found. In all five iterations of this experiment, the maps show some level of wolf occupation along the Yampa River despite the nearby agriculture and urban patches in the region. This may suggest that the conditions along the Yampa River are below the grey wolves’ anthro-tolerance and have suitable prey densities and environmental conditions. Additionally, three maps show

53 similar wolf occupation and kmarkers along the Little Snake River which has less urban and agriculture patches than the Yampa River (See Figures 15, 16, 18). The only iteration of the 3% predator efficiency that did not produce wolf occupation along the Little Snake River was observed in run #3 (Figure 17). None of the maps in this iteration showed wolf packs to be near or clustered around the town of Craig. The only areas that showed wolf occupation and kmarkers near or around urban areas were Maybell and Dinosaur (See Figures 16, 18, 19).

Figure 15. 3% Kill Probability: Run #1

Figure 16. 3% Kill Probability: Run #2

Figure 17. 3% Kill Probability: Run #3

Figure 18. 3% Kill Probability: Run #4

Figure 19. 3% Kill Probability: Run #5

CHAPTER V DISCUSSION & CONCLUSION

Classic ecological models face conflict when trying to predict the changing environment and complex behaviors of the organisms in the ecosystem. To combat this challenge, future ecologists should embrace ABM so that predictive tools can become stronger. With ABM, the agents can sense and react not only to their proximate environment, but also to their surrounding agents. The aggregated behaviors that can be produced will illustrate a more realistic result or pattern seen in real-world situations. With the predictive power of ABMs, researchers can enhance the current methodology and understanding of ecosystem processes so that policy makers can be informed of potential possibilities without the extraneous expenses.

The purpose of my Moffat County, grey wolf model is to investigate how the environmental barriers, hunting success, and anthropogenic avoidance behaviors could impact or shape wolf movement. Previous research has already noted that grey wolves avoid elevations and slopes above a certain range, are not tolerant of human densities greater than a particular level, and that recolonization success has a strong link to the prey densities and hunting success rates.

From the model results, we can notice how wolves clustered and remained in river valleys that had low elevation. Also, the elevation barriers seem to have influenced occupation along the northern and southern borders of the county. The wolf agents did not show much movement across the landscape, or display a particular draw towards something; however, the model showed some evidence of the wolf agents moving away from the town of Craig. Most importantly, the grey wolves did not show any indication of causing a disruption or top-down control on the prey agents. In all model iterations, neither prey nor wolf populations were eradicated, despite the different experimental values of the kill probability. This suggests that grey wolves are highly resilient, and even with moderately large population densities, wolf

60 presence may not cause a massive disruption, and the grey wolves can survive in most places where humans can tolerate their presence.

An interesting result witnessed in all model iterations was the model termination caused by the prey stop command. The prey triggering the stop command could suggest three explanations: (1) the model should have a higher prey carrying capacity for the stop command,

(2) the prey population is not affected by top-down control from the grey wolf agents in the model, (3) and the model does not encapsulate the true prey avoidance or fear behaviors towards the grey wolf agents as they would in real world scenarios. A previous wolf study conducted by

Dellinger and colleagues in 2018 concluded that the newly recolonized wolves in Washington

State did not have the typical, strong influence on deer mortality; and in contrast, human influence via hunting had a greater impact on the deer population than the grey wolves

(Dellinger et al. 2018). The results by Dellinger and colleagues contradict other wolf studies, but the data for recolonizing wolves not exerting as strong of a pressure on prey populations is helpful in analyzing how much human disturbances (i.e., recreational hunting) may outweigh the standard predator-prey relationship. By having the model termination be caused by the prey overabundance, this could suggest that the grey wolves in the model simulations were similar to the Washington wolves in not exerting a strong influence over the prey demographics. For future insight as to how the model simulates prey population dynamics, the model should be tested without wolf agents to analyze how realistic the prey population is modeled.

In regard to the aggregate behaviors influencing wolf dispersal, wolf agents did show signs of occupation around urban and agriculture patches despite the anthro-tolerance levels.

This result is surprising because of the avoidance behavior for wolf agents and the heightened mortality rate when wolf agents are in proximity to human agents. However, a possible

61 explanation could be agriculture patches have the highest amount of resources for prey agents to consume and grey wolf survival is closely linked to prey abundance. By the prey agents being attracted to the higher vegetation resources, this may cause higher prey densities in to be seen in the agriculture patches. This model assumption could be in error, since not all agriculture patches are equal in resource amounts available for prey to consume (e.g., a dairy farm versus a crop field). Despite the wolves avoiding human agents present at the agriculture patches, the wolf agents may occupy these regions due to the higher prey densities. Overall, the wolf occupation near agriculture and urban patches suggests that according to this model prey density is a greater draw for wolves than the deterrent of human densities.

For model improvement and complexity, the prediction power would be enhanced by including more anthropogenic influences, program a stronger avoidance behavior in prey agents, and to incorporate more of the socially complex behaviors that grey wolves exhibit between wolf packs. Wolf biologists have identified a threshold for road densities at 0.7 km/km2 which deters wolves from the area. These studies were conducted in the Great Lakes region and helped identify suitable habitat for the existing grey wolf population. Roads are also important to include in the model because they are the most common cause of wolf mortality via roadkill or direct persecution by humans. In regard to the prey behavior, ecological studies in Yellowstone have documented how the ungulate populations have altered their foraging behavior and location due to wolf presence. If future iterations of the ABM included a map for how prey density changes in response to wolf presence, the model analysis could give further insight to how wolves affect prey behavior and recolonization success. For the wolf agents themselves, the wolf behavior and dispersal would be more realistic if intraspecies competition between wolf packs was implemented. Wolves are highly territorial creatures and aggression is commonly seen

62 between packs in the same region. With the addition of territorial behaviors between wolf packs, the model could give more insight to how specific wolf packs interact spatially and how the mortality could be influenced. By including the interspecies competition and territoriality, the population growth and expansion could be more realistic about the wolf mortality caused between wolf individuals.

Although this model is not perfect and does not have wolf population data to validate its predictions, this model can be used to create informed communication between policy makers and ecologists. With many current ecosystem simulations, there is a poor relation between computational models and their real-life counterparts (Varley & Boyce, 2005). However, by improving our computational methodology and refining the data input, ABMs can highlight regions that would be important for preservation or limited anthropogenic disturbances.

Specifically, the ABM prediction could help inform urban development planning to avoid areas more suitable for wolf occupation and to influence the grazing allotment patterns so that livestock depredation can be minimal. Another way the model could give insight is for hunting licenses to be regulated based upon wolf densities in certain GMUs. By providing a starting conversation tool, the ABM could highlight important regions to prioritize and control grey wolf dispersal.

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APPENDIX

GLOSSARY Term Explanation Patch Attributes veg-type Patches can have one of six vegetation types: forest, grassland, agriculture, urban, water, or barren. The vegetation types are derived from the NLCD classification data. veg-amount Patches that are classified as the forest, grassland, and agriculture vegetation types (veg-type) have differential resource amounts (veg-amounts) or carrying capacity for prey agents. The maximum vegetation amount (veg-amount) for a forest patch is 12 units, grassland patches is 15 units, and agriculture patches is 20 units. elevation From DEM data of the study region, patches are given an elevation variable (in meters) to simulate the physical barriers of the landscape. slope From DEM data of the study region, patches are given a slope variable (in degrees) to simulate the physical barriers of the landscape. hash-id The x-coordinate and y-coordinate of the individual patch. Each patch in NetLogo has coordinates, with the center patch of (0,0) human-density Patches that are classified as urban and agriculture have a calculated human density, which is based upon the human population number (slider bar) divided by the total number of urban and agriculture patches. Patch Commands regrow-vegetation If the vegetation amount (veg-amount) is greater than 0, the patch will regrow the vegetation by adding a random number ranging from 0 - 10 to the current veg-amount. update-patches If the human population count (slider bar) is changed, the patches will recalculate and update the human density at the urban and agriculture patches. slope-barrier If the patch has a slope greater than 45°, the agent cannot enter the patch. elevation-barrier If the patch has an elevation greater than 1850 m, the agent cannot enter the patch. water-barrier If the patch is classified as the water vegetation type, the agent cannot enter the patch.

Wolf Agent Attributes

70 energy To simulate bioenergetics, the wolf agent must consume prey agents to obtain energy. The amount of energy gained is stochastic and the energy is spread equally among individuals in a wolf pack. At each tick, the individual wolf agent will lose energy to imitate metabolism processes. If the individual wolf's energy reaches below zero, the wolf will die. pack-id To differentiate the wolf packs, there is a unique identification number assigned to each pack. pack-energy Each wolf pack has an energy variable. If a wolf agent within a pack makes a successful kill (kmarker), then a portion of the energy from the kill will go into the pooled wolf pack energy. In order for the PUP-SURVIVAL Boolean reporter to be true, the average total wolf pack energy must be greater than 50 units. alpha In order for a wolf pack to exist, there must be one male alpha wolf agent and one female alpha wolf agent. Alphas must have an "adult" social status and be at least 4 years old. sex Wolf agents have 50/50 chance for male or female social-status The social status of a wolf agent is directly related to the wolf agent's age. "Pups" are wolf agents with age = 0, "yearlings" age = 1, and "adults" age >= 2. anthro-tolerance Wolf literature cites grey wolves can tolerate human densities at or below 0.45 humans/km² wolf-happy? Boolean operator. If the current patch the wolf agent is on has human densities greater than the wolf's anthro-tolerance, this reporter will be FALSE. ph-memory Every time a wolf enters a patch, the patch hash-id is listed with the individual wolf. This creates a table of all the patches that were occupied by wolves at some point in the model simulation. Wolf Agent Commands avoid-anthro Wolf agents will determine if their current patch has a human density which the wolves can tolerate. If the human density at the patch is greater than or equal to the anthro-tolerance, the wolf agent will have the Boolean reporter "wolf-happy?" be FALSE and the wolf agent will then "flee" from the patch. create-wolf-pack In the setup commands, the initial wolf packs are created. The number of wolf packs, number of wolves per pack, and distance between pack members are determined by slider bars in the Interface tab. death If the individual wolf agent's energy reaches 0 and/or the wolf's age is greater than 10 years, the wolf agent dies. die-grim-reaper To simulate anthropogenic disturbances such as road mortality or random culling events, the individual wolf agents have a higher probability of being killed if the wolf agent is within the radius of 5 patches to a human agent.

71 disperse-pups If the wolf pack is larger than 8 individuals, the "pups" in the pack will leave the natal wolf pack to either create a new pack or to join a different wolf pack. ensure-wolf-pack-alpha If a wolf pack is missing one or both alpha wolf agents at the model initiation, an adult wolf agent is created to fulfill the missing alpha spot. This command is only given at the setup procedure of the code with "create wolf pack". find-new-hunting-patch An aggregated behavior command for wolf agents. If the individual wolf agent has an active kmarker within the pack- member-distance, the wolf agent will move towards the patch containing the active kmarker. Otherwise, the wolf agent will move closer to a wolf agent within the same pack. find-new-spot If the Boolean operator for "wolf-happy?" is FALSE, the individual wolf agent will move to a different, random patch. flee If the wolf agent is on a patch that exceeds the wolf agent's anthro-tolerance, the wolf agent will leave its current patch. The wolf agent will continue to flee until it the Boolean reporter "avoid-anthro" reports TRUE. join-a-pack If there is a transient wolf agent and a nearby wolf pack containing less than 8 individuals, the transient wolf agent may join the pack in closest proximity. pack-die If there are no viable male or female alphas for a wolf pack, the entire pack will disband and the wolf individuals will no longer belong to that pack. reproduce-wolves Wolf reproduction can only occur within a wolf pack and the Boolean reporter "pup-survival" must be TRUE. The number of pups born per litter is a random number ranging from 0-5 pups. set-social-status When there is a successful wolf reproduction, the wolf agents are given a social status based upon the age of the wolf individual. See social-status for categorization details. update-pack-alphas If the male and/or female alpha in a pack die, the next wolf individual of the correct sex and is at least 4 years old will become the next alpha. If there are no available options in the wolf pack, the wolf pack will disband by the "pack-die" command. where-am-i Each time a wolf agent enters a patch, the coordinates of the patch are recorded with the wolf individual. This data is listed in the ph-memory table.

Prey Agent Attributes energy To simulate bioenergetics, the prey agent must consume vegetation resources (veg-amount) to obtain energy. At each tick, the individual prey agent will lose energy to imitate metabolism processes. If the individual prey's energy reaches below zero, the prey agent will die. 72 similar-nearby Prey agents have the ability to sense the breed of other agents in a nearby radius. This command calculates the number of agents with the identical breed (prey = prey) in the nearby radius. The distance radius (number of patches) that the individual prey agent can sense is determined by a random number 1-5. This adds stochasticity to the prey agents being able to avoid wolf agents. total-nearby Similar to the similar-nearby, the prey agent is able to sense nearby agents in a distance radius. The total-nearby allows the prey agent to calculate the ratio of agents that are similar in breed versus the agents that are not similar in breed. prey-happy? This Boolean operator will report FALSE if the "my-%-similar- wanted" is not equal to the proportion of similar-nearby divided by total-nearby. my-%-similar-wanted Determined by the slider bar. Prey Agent Commands death If the individual prey agent's energy reaches 0 and/or the prey agent's age is greater than 10 years, the prey agent dies. eat-vegetation The prey agent consumes the patch resources (veg-amount) at each tick of the model. This is to simulate prey bioenergetics and metabolism. move The prey agent will move at each iteration of the model. Prey agents cannot enter unsuitable patches by the slope-barrier, elevation-barrier, and water-barrier. The direction of prey movement is randomly selected. move-unhappy-prey If the Boolean operator for "prey-happy?" is FALSE, then the individual prey agent will then "find new patch" or move to a different patch to find more suitable conditions. find-new-patch This command is part of the "move-unhappy-prey". The individual prey agent will leave its current patch and move to a new patch in order for the Boolean operator "prey-happy?" to report TRUE.

Human Agent Attributes human-density The human density is calculated by taking the human population count (slider bar) and dividing it by the total number of agriculture and urban patches. human-type The human agents can either be an urban human (stationed on urban patches), or a farmer (stationed on agriculture patches).

Successful Kills (kmarkers) associated-wolf When a wolf agent makes a successful kill, the individual wolf agent is recorded as the "associated wolf" who made the kill.

73 associated-wolf-pack If the "associated wolf" is part of a pack, the wolf pack id is recorded with the information about the kmarker. age This acts as a temporal decay for the kmarkers. At creation, the kmarker is given a random age (0-5). For each tick of the model, the kmarker decreases in age by one unit. When the kmarker's age is below zero, the kmarker is "inactive" or has been forgotten and is no longer used for wolves' "find-new-hunting- patch" patch-veg-type For spatial reference, the vegetation type of the patch of where the kmarker was created is recorded.

Reporters pup-survival This reporter determines if the wolf-reproduction command will result in a litter of pup wolf agents. To have this Boolean reporter be TRUE, the wolf pack must be greater than 2 patches in distance away from any human agent, the total-pack-energy must be greater than 50 units, and the wolf pack must have more than 3 individual wolf agents. common-veg-type This reporter calculates the most common vegetation type the kmarkers are created on. The reporter finds the vegetation type that has the highest frequency and lists it as the "common-veg- type".