Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Hubner, Jacob
Master of Science - MSc
University of British Columbia
Fatal and difficult to monitor, Chronic Wasting Disease (CWD) continues to spread throughout cervid populations in North America. British Columbia (BC) is one of the few remaining provinces/states without a confirmed CWD case; however, the disease has been detected less than 40 kilometers away from the BC border in both Montana and Alberta. Some of these cases are approaching the remnant herd ranges of BC’s endangered mountain caribou – a species that is also vulnerable to CWD infection. BC has a robust pre-emptive monitoring program, though it does not currently cover the full extent of the border with other infected areas. To improve detection of CWD-infected animals in BC and assess vulnerability to non-detection of infected animals, we developed connectivity models to identify areas where cervids are likely to cross the border. Using archived telemetry data for mule deer, white-tailed deer, moose, and elk, we built summer and winter resource selection functions. Seasonal migration patterns were simulated using circuit theory-based connectivity analyses (Circuitscape and Omniscape). By simulating movement from CWD-positive locations towards British Columbia, key movement corridors with a higher probability were identified for use by infected cervids, as well as likely routes towards extant caribou populations. It was found that the current west end of the monitoring area (MU 4-07) has poor relative connectivity and extending the testing area further west may enhance CWD management in BC. In addition, several key corridors used by multiple species were identified, most within the Rocky Mountain range. Identifying these CWD ‘hotspot’ corridors can assist managers in more efficient testing or public education initiatives. The same telemetry data was also used to quantify how the probability of an infected animal entering BC may change with distance to the border. Considering both current prevalence and seasonal movement distances, it was found to be unlikely (< 1%) that an individual infected animal will cross the border from a currently identified CWD-positive location during a single season. This analysis framework is flexible and can be used to reassess CWD risk as more data is collected, or model the spread of other cervid diseases.
Keeping Ahead of Chronic Wasting Disease: An Assessment of Trans-Boundary Cervid Movement in British Columbia by Jacob Hubner B.Sc., Dalhousie University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2023 © Jacob Hubner, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Keeping Ahead of Chronic Wasting Disease: An Assessment of Trans-Boundary Cervid Movement in British Columbia Submitted by Jacob Hubner in partial fulfillment of the requirements of the degree of Master of Science . Dr. Adam T. Ford, Biology Department, Irving K. Barber Faculty of Science Supervisor Dr. Jason Pither, Biology Department, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Michael Noonan, Biology/Mathematics Department, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Frederic Menard, Biochemistry and Molecular Biology/Chemistry Department, Irving K. Barber Faculty of Science University Examiner Dr. Marie Auger-Méthé, Statistics Department, Institute for the Oceans and Fisheries (University of British Columbia) External Examiner iii Abstract Fatal and difficult to monitor, Chronic Wasting Disease (CWD) continues to spread throughout cervid populations in North America. British Columbia (BC) is one of the few remaining provinces/states without a confirmed CWD case; however, the disease has been detected less than 40 kilometers away from the BC border in both Montana and Alberta. Some of these cases are approaching the remnant herd ranges of BC’s endangered mountain caribou – a species that is also vulnerable to CWD infection. BC has a robust pre-emptive monitoring program, though it does not currently cover the full extent of the border with other infected areas. To improve detection of CWD-infected animals in BC and assess vulnerability to non-detection of infected animals, we developed connectivity models to identify areas where cervids are likely to cross the border. Using archived telemetry data for mule deer, white-tailed deer, moose, and elk, we built summer and winter resource selection functions. Seasonal migration patterns were simulated using circuit theory-based connectivity analyses (Circuitscape and Omniscape). By simulating movement from CWD-positive locations towards British Columbia, key movement corridors with a higher probability were identified for use by infected cervids, as well as likely routes towards extant caribou populations. It was found that the current west end of the monitoring area (MU 4-07) has poor relative connectivity and extending the testing area further west may enhance CWD management in BC. In addition, several key corridors used by multiple species were identified, most within the Rocky Mountain range. Identifying these CWD ‘hotspot’ corridors can assist managers in more efficient testing or public education initiatives. The same telemetry data was also used to quantify how the probability of an infected animal entering BC may change with distance to the border. Considering both current prevalence and seasonal movement distances, it was found to be unlikely (< 1%) that an individual infected animal will cross the border from a currently identified CWD-positive location during a single iv season. This analysis framework is flexible and can be used to reassess CWD risk as more data is collected, or model the spread of other cervid diseases. v Lay Summary Chronic Wasting Disease affects deer, elk, and moose and is difficult to manage. Now that it is near the border of British Columbia, Canada (BC), it is valuable to assess how the landscape connectivity of the border area may relate to where and when the disease may first reach BC, and how likely it is that it will be detected quickly. We used habitat selection models and connectivity analyses to assess these questions, using animal collar GPS data from other studies. The models identified several corridors where animals carrying CWD are more likely to enter BC. We also found that while the current risk of a CWD-infected animal crossing is low, it would be in BC’s best interest to require more areas of the Kootenays region to submit harvested deer for disease testing. We hope our methods can lay a path for other regions at risk of CWD infection. vi Preface A modified version of this thesis to Environment and Climate Change Canada (ECCC) as part of the deliverables for Report on CWD risk to BC caribou populations (submitted April 13th, 2022). This report was titled “Modeling Transboundary Movement of Cervids to Predict Chronic Wasting Disease Risk to Mountain Caribou”. The contents of the report have since been refocused, improved, and expanded upon for this thesis. To match the style of a journal publication, I have elected to use the plural ‘we’ throughout the course of this thesis. Data Availability Statement: This thesis utilised existing data from previous and ongoing studies (Table 1). Some of these datasets are publicly available while others may only be available upon request from the original authors. Available data and code that support the findings of this study may be found in a public Open Science Framework (OSF) repository at http://doi.org/10.17605/osf.io/nwv8g vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................... xi Acknowledgements .................................................................................................................... xxi Introduction ................................................................................................................................... 1 Background Information ............................................................................................................. 8 Chronic Wasting Disease ............................................................................................................ 8 Species of Interest ..................................................................................................................... 11 Mule Deer (Odocoileus hemionus) ....................................................................................... 12 White-tailed deer (Odocoileus virginianus).......................................................................... 13 Elk (Cervus canadensis) ....................................................................................................... 14 Moose (Alces alces) .............................................................................................................. 15 Caribou (Rangifer tarandus) ................................................................................................. 15 Connectivity Models ................................................................................................................. 16 Methods ........................................................................................................................................ 18 Study Area ................................................................................................................................ 18 Datasets ..................................................................................................................................... 20 Spatial Data and Raster Manipulation ...................................................................................... 24 Resource Selection Functions ................................................................................................... 27 viii Objective 1: Evaluate CWD Monitoring Effectiveness ............................................................ 32 Objective 2a: Identify High-Likelihood Border Crossing Locations ....................................... 32 Objective 2b: CWD Risk To Caribou ....................................................................................... 34 Objective 3: Estimate Risk of a Border Crossing Event ........................................................... 35 Results .......................................................................................................................................... 38 Resource Selection Functions ................................................................................................... 38 Objective 1: Evaluate CWD Monitoring Effectiveness ............................................................ 40 Objective 2a: Identify High-Likelihood Border Crossing Locations ....................................... 45 Objective 2b: CWD Risk To BC Caribou Herds ...................................................................... 54 Objective 3: Estimate Risk of a Border Crossing Event ........................................................... 57 Discussion..................................................................................................................................... 64 Resource Selection Functions and Validation .......................................................................... 64 Objective 1: Evaluate CWD Monitoring Effectiveness ............................................................ 66 Objective 2: Identify High-Likelihood Border Crossing Locations ......................................... 68 Objective 3: Estimate Risk of a Border Crossing Event ........................................................... 69 Method Applications ................................................................................................................. 70 Limitations ................................................................................................................................ 71 Moving Forward ....................................................................................................................... 73 References .................................................................................................................................... 75 Appendix ...................................................................................................................................... 96 ix List of Tables Table 1 Datasets collected for analysis, including general location and relevant statistics. Full citations and access can be found in the References section. ...................................................... 21 Table 2 Source information for collected spatial layers used as predictor variables in habitat selection model creation for all species. Layers without source entries are sourced from the same layer as the layer above. Full citations and access can be found in the References section. SD = species dependent. EVI stands for Enhanced Vegetation Index satellite data. ............................ 25 Table 3 Summary of the tested resistance surface transformations. .......................................... 31 Table 4 Top models for summer and winter seasons for elk (Cervus canadensis), moose (Alces alces), mule deer (Odocoileus hemionus), and white-tailed deer (Odocoileus virginianus) in Region 4 of BC. Asterisks indicate the top model did not include that predictor variable. ‘L95% CI’ and ‘U95% CI’ are the lower and upper 95% confidence intervals respectively. K-Fold validation results are also included .............................................................................................. 39 Table 5 BC townships and communities located nearest to notable movement corridors with high relative levels of connectivity. The letters correspond to those found on Figure 16. .......... 52 Table 6 BC border-crossing event risk percentages from existing areas of positive CWD reports near the BC border. Areas are ordered starting at the northernmost location and moving counterclockwise around the border. Risk values are proportions from 0 to 1, and are based on the risk buffers created for this analysis (Figures 19 to 22). The risk value is defined as the probability that a single animal would make a seasonal movement that would cause it to cross the border, assuming that an animal is at that location. Estimated CWD prevalence is based on full province data, rather than the specific areas. ............................................................................... 63 x Table 7 Risk of a crossing event in table form, based on the methods in Objective 3. Animal movement is assumed to be unimpeded by geography. Risk values were estimated using collected telemetry data in the East Kootenay area. .................................................................. 110 Table 8 A detailed list of decision points with more than one viable option for input. Each parameter is listed with the value chosen, its justification for that selection, possible consequences of that decision and any tests made to explore sensitivity and uncertainty. ....... 119 xi List of Figures Figure 1 A map depicting CWD monitoring protocol in the Kootenay region of BC as of December 2022. Colour depicts whether each management unit has mandatory CWD testing for deer harvested from that area. The MUs in yellow are referred to as the mandatory testing area (MTA) in this paper. Map generated using ArcMap Pro 2.2.0 ...................................................... 4 Figure 2 A visual workflow for the methods used to achieve the objectives of this analysis. Grey boxes depict final results, while dotted lines depict new data (angled boxes) being brought in for a particular step. Trapezoids depict steps where manual input was required. ................... 19 Figure 3 Map depicting this project’s area of interest, including BC’s Wildlife Management Region 4 with a 100-kilometer extension into the United States and Alberta. Map was created using ArcMap Pro (Esri Inc., 2020) with the default basemap as a background. ........................ 20 Figure 4 Map of the area of interest and locations of collected telemetry data. The area includes the entire Region 4 area of British Columbia and extends 100 km south into the United States and east into Alberta. Points are coloured by species. Approximated local positive CWD detections are also included, based on maps from Environment and Parks Alberta (2021) and Montana FWP (2022). Map was created using ArcMap Pro (Esri Inc., 2020) with the default basemap as a background. ............................................................................................................ 23 Figure 5 Visual description for methodology in calculating the probability of a crossing event for the risk buffer (P) based on a given distance (x). Box A depicts the conversion from a cumulative density function (CDF) to an inverted CDF. Box B depicts using the values from the inverted CDF for a circular distance-based raster. The cell values of the blue raster are calculated by comparing the distance between the cell and the centerpoint of the circle, and relating that to the value of the generated seasonal movement distance CDF for a single cervid species, inverted xii in order to generate the correct statistic. This scenario assumes a free and random choice of migration direction. ...................................................................................................................... 37 Figure 6 Results of the ‘binned’ validation for the Omniscape results of 5 possible transformations used to create resistance surfaces for Circuitscape and Omniscape analyses. Each Omniscape result was reclassified into 10 equal area bins (1-10, 10 being best) before extracting the values with the test data. Mean score was first collected for individuals, then a mean was taken for all individuals of a species. ........................................................................................... 41 Figure 7 Results of the ‘binned’ validation for the Omniscape results of 5 possible transformations used to create resistance surfaces for Circuitscape and Omniscape analyses. Each Omniscape result was rescaled from 0-1 before extracting the values with the test data. Median score was first collected for individuals, then a mean was taken for all individuals of a species. Extreme high outliers were removed for improved visual comparison of the means. ................ 42 Figure 8 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and total harvest rates and CWD samples from 2019-2021 for the two species with mandatory CWD testing in Region 4: Odocoileus hemionus (a and c) and Odocoileus virginianus (b and d). Current density calculated by Omniscape can be interpreted as the relative amount of connectivity for a given species within the entire area of that MU. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. .............................................................................................................. 43 Figure 9 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and total harvest rates and CWD samples from 2019-2021 for the two species with mandatory CWD testing in Region 4: Odocoileus hemionus (a and xiii c) and Odocoileus virginianus (b and d). Current density calculated by Circuitscape can be interpreted as the relative risk of CWD incursion based on general connectivity and geographical relevance to current CWP positive locations. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Only MUs that are in contact with the BC border were included in this figure. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ................................................................................... 44 Figure 10 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for elk (Cervus canadensis). Current density value indicates concentrated areas of movement. Map generated using ArcMap Pro 2.2.0. ............................... 46 Figure 11 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for moose (Alces alces). Current density value indicates concentrated areas of movement. Map generated using ArcMap Pro 2.2.0 ................................ 47 Figure 12 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for mule deer (Odocoileus hemionus). Current density value indicates concentrated areas of movement. Map generated using ArcMap Pro 2.2.0 ................. 48 Figure 13 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for white-tailed deer (Odocoileus virginianus). Current density value indicates concentrated areas of movement. Map generated using ArcMap Pro 2.2.0 ....... 49 Figure 14 Combined directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for all four analyzed cervid species, using the summed value of Figures 10 to 13. Current density value indicates overlapping areas of concentrated movement. Map generated using ArcMap Pro 2.2.0 .............................................. 50 xiv Figure 15 Concurrent directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for all four analyzed cervid species, using the maximum value among Figures 10 to 13 for each cell. Current density value indicates concentrated areas of movement. Map generated using ArcMap Pro 2.2.0 ................................ 51 Figure 16 A map of the Kootenay region of BC highlighting the high-current density border crossing areas specified in Table 6 above, using Figure 14 as a background. Map generated using ArcMap Pro 2.2.0 ......................................................................................................................... 52 Figure 17 Concurrent directed Circuitscape results from known positive CWD detection locations (as of May, 2021) to existing Kootenay mountain caribou (Rangifer tarandus caribou) ranges for mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), moose (Alces alces), and elk (Cervus canadensis) combined, using the summed value of each individual analysis. Current density value indicates overlapping areas of concentrated movement. Map generated using ArcMap Pro 2.2.0 .............................................................................................. 55 Figure 18 Least-cost path (LCP) results from known positive CWD detection locations (as of May, 2021) to existing Kootenay mountain caribou (Rangifer tarandus caribou) ranges for mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), moose (Alces alces), and elk (Cervus canadensis). Summed current density from Figure 16 is used as a background for comparison. Maps generated using ArcMap Pro 2.2.0 .......................................................... 56 Figure 19 Map depicting the distance-based risk of a British Columbian boundary-crossing event within a seasonal migration by elk (Cervus canadensis). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 ....................................................................................................................................... 58 xv Figure 20 Map depicting the distance-based risk of a British Columbian boundary-crossing event within a seasonal migration by moose (Alces alces). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 ....................................................................................................................................... 59 Figure 21 Map depicting the distance-based risk of a British Columbian boundary-crossing event within a seasonal migration by mule deer (Odocoileus hemionus). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 ......................................................................................................................... 60 Figure 22 Map depicting the distance-based risk of a British Columbian boundary-crossing event within a seasonal migration by white-tailed deer (Odocoileus virginianus). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 ............................................................................................................... 61 Figure 23 Test of collinearity for all collected environmental prediction layers. Values further from zero indicate high positive or negative collinearity. The full study area-wide rasters were evaluated against each other. ....................................................................................................... 96 Figure 24 Omniscape result for elk (Cervus canadensis) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 ................................................... 97 Figure 25 Omniscape result for moose (Alces alces) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 ................................................... 98 xvi Figure 26 Omniscape result for mule deer (Odocoileus hemionus) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 ........................... 99 Figure 27 Omniscape result for white-tailed deer (Odocoileus virginianus) using the chosen ‘c8’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 .................. 100 Figure 28 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and total submitted CWD samples from 2019-2021 for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. A log-scaled y-axis is used, allowing for linear trend lines. X-axes values are hidden for clarity as they are arbitrary and change between species. ......................................................................... 101 Figure 29 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean days spent hunting per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ....................................................................................................................................... 102 Figure 30 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean count of animals harvested per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility xvii of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ............................................................................................................ 103 Figure 31 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean rate of hunter success (harvests per day spent hunting) for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ...................................................................................................... 104 Figure 32 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and total submitted CWD samples from 2019-2021 for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. A log-scaled y-axis is used, allowing for linear trend lines. X-axes values are hidden for clarity as they are arbitrary and change between species. ......................................................................... 105 Figure 33 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean days spent hunting per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ....................................................................................................................................... 106 Figure 34 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean count of animals harvested per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. xviii Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ............................................................................................................ 107 Figure 35 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean rate of hunter success (harvests per day spent hunting) for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. ...................................................................................................... 108 Figure 36 Comparisons of Circuitscape maps using 5, 10, and 50 evenly spaced ground nodes. Different rows depict different species results. No meaningful differences in path selection were noted, though using more ground nodes created better contrast visually. ................................. 109 Figure 37 RSF projection for elk (Cervus canadensis) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ............................................................................................ 111 Figure 38 RSF projection for elk (Cervus canadensis) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ............................................................................................ 112 Figure 39 RSF projection for moose (Alces alces) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values xix represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ....................................................................................................................... 113 Figure 40 RSF projection for moose (Alces alces) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ....................................................................................................................... 114 Figure 41 RSF projection for mule deer (Odocoileus hemionus) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ............................................................................................ 115 Figure 42 RSF projection for mule deer (Odocoileus hemionus) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 ............................................................................................ 116 Figure 43 RSF projection for white-tailed deer (Odocoileus virginianus) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 .................................................................................... 117 Figure 44 RSF projection for white-tailed deer (Odocoileus virginianus) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 .................................................................................... 118 xx Figure 45 A picture showing the difference in resource selection function results when the number of available points used is adjusted. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. .................... 120 Figure 46 A picture showing the difference in resource selection function results when the number of available points used is adjusted and the binomial distribution is substituted for the Poisson distribution. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. ......................................................... 121 Figure 47 A picture showing the difference in resource selection function results when the model weights on the available points is adjusted. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. ............. 122 Figure 48 Results of resistance surface transformation validation for mule deer (Odocoileus hemionus) when the percentile cutoff of the RSF projection for use as source areas is adjusted. Values between to 70th and the 95th percentile were tested, separated by colour. The 90th percentile was used in this thesis. In all cases, the same transformation received the highest score. .......................................................................................................................................... 123 Figure 49 A comparison of the Circuitscape results for moose (Alces alces) using a 150m cell resolution (on the left) and a 1050m resolution (on the right). .................................................. 123 xxi Acknowledgements The study area for this project covers a large area and overlaps with numerous First Nations’ unceded territories. The analysis of the data itself took place on Syilx Nation territory. This research was made possible through grants from a number of sources including the NSERC-CRSNG Foundation, Environment and Climate Change Canada, and University of British Columbia Okanagan. Despite starting this project at the beginning of the pandemic lockdown, I still made the journey to the campus lab day in and day out. It wasn’t just for the office space. Each of my local labmates made the tough parts of this thesis a lot less tough: Peter White, Chloe Wright, Siobhan Darlington, Jenna Scherger, Mateen Hesami, Laura Balyx, Angus Smith, and Jakob Andrian. All of them were all-stars who were kind enough to share their knowledge and laugh at my bad jokes. Plenty of help from the non-local lab members as well, particularly Clayton Lamb, Matt Williamson, and Melanie Dickie. A thank you to my gracious committee members who helped turn this jumble of ideas into something worth being proud of: Dr. Michael Noonan, Dr. Jason Pither, and my supervisor Dr. Adam Ford. Thank you for all the ideas, opportunities, and guidance. I thank Pat Stent and Luke Vander Vennen for their help with collecting the telemetry data from the far corners of the digital archives and all parties who were willing to share their unpublished data. Cait Nelson, Andrew Walker, and Margo Robie were endlessly helpful for collecting data on CWD and hunting stats in my study area. My brilliant friend Sebastian xxii McCoshen was somehow able to help me wrap my head around circle geometry when designing the risk buffer methodology. Another thanks to Brian Arquilla who convinced me down the graduate research road in the first place. My family was always able to keep me feeling smart on the days I felt like I didn’t belong here. Above all, endless appreciation to Morgan Ryder for being a beacon of light and laughter at the end of each day. Introduction The importance of landscape connectivity has been growing steadily, both as a focal point for ecology and in international treaties, broad-scale land management, urban development, and wildlife restoration efforts (Taylor et al., 1993; CBD, 2010; Moore and Shadie, 2007; Keely et al., 2018). Understanding the degree to which the landscape impedes or facilitates movement of organisms in the context of land management can improve the opportunity for both animals and plants to disperse, often through the construction or rehabilitation of natural corridors between isolated habitat patches (Beckmann et al., 2012; Damschen et al., 2019). Habitat connectivity allows for gene flow and the promotion of increased genetic diversity, as well as improving access to resources (Lamy et al., 2013; Jangjoo et al., 2016; Noss, 1991) and is associated with greater population persistence (Palstra and Ruzzante, 2008) and biodiversity (Thompson et al., 2017). Because it often increases diversity and stability, connectivity is promoted in conservation and policy, yet it can also have negative impacts on biodiversity. These negative impacts include: the spread of invasive species (Milt et al., 2018); human-wildlife conflict (Lamb et al., 2020); the spread of diseases (Borg et al., 2017; Fong, 2017). Diseases can be transported by their hosts to new individuals or entirely new populations via migration or exploration events. Barriers to movement can therefore slow the spread of diseases. For example, roads, urban areas, and highway fencing (i.e. structures associated with habitat fragmentation and a reduction in habitat connectivity) have prevented the spread of a mange outbreak in wild bighorn sheep by acting as movement barriers in the South Okanagan area of British Columbia, thereby enhancing the overall viability of the population (Hering, 2020). Managing wildlife diseases that infect multiple host species becomes increasingly difficult when accounting for different life histories 2 and management constraints (Hering et al., 2021). Understanding the link between the spread of wildlife diseases, human activity, and landscape structure has reached a new level of urgency with the continued outbreaks of zoonotic diseases such as Lyme disease, malaria, multiple bird flus, and COVID-19 (Rahman et al., 2020; Reaser et al., 2021). For the purposes and context of this thesis, we will define connectivity in general as: “degree to which the landscape facilitates or impedes movement among resource patches” (Taylor et al., 1993). Specifically, this thesis will be addressing the connectivity of the land for animals, as well as for the spread of diseases, which can have characteristics separate from the animals that carry them (e.g. contact between animals is important for disease connectivity, but may not be necessary to describe animal movement connectivity). Potential sources for future zoonoses are of concern as well. One of the most important potential zoonoses in North American, Asian, and European wildlife is Chronic Wasting Disease (CWD) (Pritzkow, 2022). CWD is a fatal prion disease affecting members of the deer family (cervids) (Saunders et al., 2012). Untreatable due to its dense protein structure, CWD causes the slow degradation of brain tissue in infected animals, eventually leading to premature death (Moreno and Telling, 2018). CWD has a symptom onset measured in years and is highly persistent in the environment, making its spread particularly difficult to control (Wille and Requena, 2018). Infectious agents can be found in the saliva, waste products, and carcasses of animals, providing multiple modes of direct, indirect, and environmental transmission to other individuals. CWD has historically remained specific to cervids, but there is concern that new variants may one day be transmitted to other taxonomic families and even humans via ingestion of infected meat (Osterholm et al., 2019). CWD has not been documented in humans that consume cervid meat, but agencies recommend against eating CWD-infected animal products 3 because of the potential for pathogen spillover (e.g. variant Creutzfeldt-Jakob disease from Bovine Spongiform Encephalopathy) (Pape et al., 2006; Reaser et al., 2021). The first detection of CWD was in Colorado in 1967, and it has since spread across North America, Scandinavia, and South Korea (Sohn et al., 2002; Benestad et al., 2016). Four of the five North American cervid species have been detected as CWD-positive (Williams and Young, 1992). CWD arrived in Canada via farmed elk transport to Saskatchewan in the 1980/1990’s and from there spread into Canadian wild populations (Williams and Miller, 2002). In British Columbia (BC), Canada, there have been no detections of CWD though it is found nearby both east (Alberta) and south (Montana and Idaho) of the border. The BC Wildlife Health Program began a CWD testing program in 2001 and has now expanded it out to include eight management units (MUs) in the Kootenay region where head submission is mandatory for harvested deer (Nelson and Schwantje, 2019). These MUs form a contiguous area along the border nearest to most close positive CWD detections, which we will refer to as the mandatory testing area (MTA) for the remainder of this thesis (Figure 1). Harvested animals from outside the MTA can be submitted voluntarily, though the infrastructure to do this is less well developed when compared to the MTA. The extent of BC adjacent to CWD-positive regions is several hundred kilometers long, making comprehensive disease monitoring of the entire border area unfeasible. Efforts to monitor and manage future CWD outbreaks in BC would thus benefit from an understanding of the location and risk of trans-border movement by cervids into the province. While people may illegally move infected cervids or prion-infected material (e.g. live farmed deer, harvested deer carcasses, contaminated feed, and deer products) into BC, the natural movement of animals may also bring CWD into BC. (Nelson and Schwantje, 2019; Williams and 4 Figure 1 A map depicting CWD monitoring protocol in the Kootenay region of BC as of December 2022. Colour depicts whether each management unit has mandatory CWD testing for deer harvested from that area. The MUs in yellow are referred to as the mandatory testing area (MTA) in this paper. Map generated using ArcMap Pro 2.2.0 5 Miller, 2002). Understanding the intensity and location of these natural movements may thus help inform monitoring and mitigation of CWD infections. Identifying high-likelihood-of-use corridors (referred to as ‘high-likelihood corridors’ in this thesis) for cervid movement into BC will help prioritize sampling efforts (i.e. monitoring) and mitigation (i.e. response) efforts (Nelson and Schwantje, 2019). In Alberta, models have been created to predict CWD spread in the prairies, but these models do not incorporate foothills and mountainous geographies (Nobert et al., 2016). Once positive cases are detected within BC, government-led response plans will be put into action, but the effectiveness of these actions depends on how early CWD is detected (Nelson and Schwantje, 2019). There is a clear and urgent need to support CWD planning and management in BC, given the data available for such efforts. Because CWD is not yet found in BC, the tools needed to support these efforts rely on predictions from models considered to represent the fundamental process of disease spread. One of the best tools available to understand the location where the natural spread of CWD is likely to occur the fastest is through modeling connectivity and the natural movement of animals that can carry it. In addition to the location of border-crossing locations, there is a need to understand the likelihood of a border crossing event occurring. For example, the detection of CWD 70 kilometers from the BC border in Libby, MT would be less of an immediate concern if the animals afflicted typically travel less than 70 kilometers. However, a recent study on mule deer movements in southeast BC found that translocated urban mule deer (relevant given that the CWD-positive deer in Libby are urban deer) are able to exceed this distance within a year of movement under certain circumstances (up to 150 km in the case of Wright et al., 2020). In addition, rare events of uncharacteristically long migration can occur, potentially carrying 6 disease farther than estimates of normal home ranges and migrations (~300 kilometers for a white-tailed deer in the case of Moll et al., 2021). If the probability of a crossing event is sufficiently high, CWD may be ‘functionally’ in BC even if it has not yet been detected. To address knowledge gaps in where and how likely CWD is to enter BC, we used seasonal migration connectivity modeling to assess the movement of wild animals in the Rocky Mountain region. Corridors detected in these analyses are expected to best facilitate animal movement and have higher densities of migrating or resident animals. These qualities would also improve the rate of transfer and spread of CWD in these areas. Specifically, the models will address three objectives: Objective 1: Evaluate CWD Monitoring Effectiveness. To evaluate the effectiveness of CWD surveillance efforts in BC and spatial coverage of the MTA, we will quantify the relationship between CWD sampling efforts and connectivity among 39 Wildlife Management Units in Southeastern BC. Ideally, an effective surveillance program would receive more samples from areas with higher connectivity, and thus higher risk, when compared to lower risk areas. Objective 2: Identify High-Likelihood Border Crossing Locations. (a) To identify high-likelihood border crossing areas from CWD-positive locations into BC, we will create species-specific connectivity models. Due to increased geographical restriction in the Rocky Mountains, we expect that movement corridors along the BC/AB border will be both more numerous, but concentrated, when compared to crossing locations along the BC/USA border. (b) Given that caribou are the last remaining North American cervid to have no positive CWD detections, an additional analysis will be conducted to establish whether high-likelihood routes to extant caribou populations in BC utilise the same corridors as the results of the general corridor analysis. 7 Objective 3: Estimate Risk of a Border Crossing Event. To estimate the probability of a boundary-crossing movement by a CWD-infected animal from a known CWD-positive locale, we will establish a species-specific border ‘buffer zone’ that will quantify likelihood of trans-boundary movement based on an animal’s distance from the border. 8 Background Information To provide the ecological context for this thesis research we will briefly review key life history information on CWD, the five species of cervids in BC, and current state of connectivity models needed to complete these objectives. Chronic Wasting Disease CWD is a prion disease currently found in multiple areas around the globe, including Canada, the USA, Scandinavia, and South Korea (Saunders et al., 2012; Otero et al., 2021; Sohn et al., 2002). Prions are mutated cellular proteins that can spontaneously form from a misfolding of a common cellular protein known as PrP (Wille and Requena, 2018). Though misfolding events are rare, these particular prions have the ability to re-fold existing healthy PrP proteins into the prion shape (often denoted as PrPSc, referencing the related prion disease Scrapie), multiplying within the cell at an exponential rate (Pruisner, 1982; Pruisner 1998). The unique structure of the prion is highly robust, incorporating many strong beta-linkages (Wille and Requena, 2018). This makes PrPSc highly resistant to all external influences, including cellular proteases and environmental degradation (Brown and Gadjusek, 1991; Jung et al., 2003). Prions are able to transmit between cells, moving throughout an organism and multiplying wherever it goes. PrP is found in high concentrations in lymph and neuron cells. In these cells, PrPSc is able to multiply and aggregate to the extent that it can interfere with cell function. The exact mechanisms for this interference are currently unknown but it is theorized that the aggregated proteins act as a cellular plaque (Moreno and Telling, 2018). Eventually these cells will cease to function and lyse, creating empty spaces within the brain tissue. The slow loss of neural cells leads to multiple cognitive and physical deficiencies, such as a lack of fear, reduced muscle strength, paralysis, drooling, and confusion. These symptoms take years to 9 develop but eventually lead to death by starvation or predation. There is currently no cure once an organism has been infected with prions. CWD is closely related to other known prion diseases such as scrapie (affecting sheep), Bovine spongiform encephalopathy (BSE) (affecting cattle), and Creutzfeldt-Jakob disease (affecting humans) (Pruisner, 1998). All prion diseases create similar symptoms and are fatal. However, their ability to spread is variable. Creutzfeldt-Jakob disease (CJD) has a very low probability of transferring to others, usually arising spontaneously in humans. CWD has a risk of becoming zoonotic in the future (Saunders et al., 2012). Many of the largest epidemics throughout human history have zoonotic origins, such as HIV, plague, Lyme disease, malaria, SARS, and COVID-19 (Rahman et al., 2020). While there are no cases of CWD transmitting from cervids to humans as of yet, other prion diseases have been able to infect humans directly. Consumption of BSE-infected beef in Britain caused over 200 infections of variant Creutzfeldt-Jakob disease in humans, and there is evidence that the prions persist in the British population today (de Marco et al., 2010). Variants of CWD can be created from small changes in protein sequence, and multiple variants are already present in infected populations (Ishida et al., 2020). It has been shown that CWD can infect human PrP protein through in vitro experimentation, though considerable medical intervention was required to make this possible (Belay et al., 2004). New variants of CWD could one day make natural transmission from cervids to humans a reality. CWD prions can reach new hosts via multiple anthropogenic and natural venues, including the transportation of animal carcasses or farmed infected deer, transportation of infected feed (e.g. hay), and the natural movements of infected deer leading to contact with uninfected animals (Nelson and Schwantje, 2019; Williams and Miller, 2002). Spread through 10 animal or feed transport is unpredictable, but can be managed by direct government intervention and regulation. Understanding and predicting the natural movement of diseases requires an understanding of the vectors that carry them, particularly in areas where disease has not yet been detected. Infected individuals can release prions to the environment through their urine, faeces, saliva, and through post-mortem decomposition (Nelson and Shwantje, 2019; Miller et al., 2004). Prions are resilient enough to remain infectious outside the body for years and bind tightly to surface soils, preventing them from seeping into the ground via rain events (Johnson et al., 2006, Wyckoff et al., 2016). Tests on the closely related Scrapie, have found the PrPSc prion can persist in the environment for at least fifteen years (Georgsson et al., 2006). While there are no direct host vectors for transmitting CWD, predators have shown to carry active prions from prey and deposit them in other environments through their feces (Nichols et al., 2015). A relatively small number of prions to be successfully transferred for the infection to be unavoidable and infected animals are able to transmit prions well before they begin to suffer symptoms. CWD can also be transmitted vertically from mother to offspring (Nalls et al., 2013). These mechanisms of transmission are most effective for animals that herd and congregate throughout the year, carrying the disease along seasonal migrations and into winter gathering sites. The spread of CWD in North America has been in progress for decades. The disease was first detected in captive mule deer in Colorado, 1967 (Williams and Young, 1992). By 1979 it had spread from Colorado to Wyoming and continued to move into other neighbouring states. It was not until 1996 that it was detected in Canada from a captive elk farm in Saskatchewan (Williams and Miller, 2002). Detections of the disease increased rapidly throughout the United States and Western Canada, though it is unclear if this was truly the movement of CWD or a 11 result of more animals being tested as concern grew (Miller et al., 2000). As of 2020, the disease has been found in twenty-five states and four Canadian provinces, and shows no signs of slowing (Michigan DNR, 2020). As a precaution, BC began testing for CWD in the Kootenay and Peace River jurisdictions in 2001. Harvested deer within the MTA must have brain and/or lymph tissue sent to a testing facility before consumption. Other cervid species are not mandatory but are accepted for testing. Over 4000 tissue samples have been analyzed since the implementation of the testing program. In addition to testing, other measures include a ban on imported cervids and cervid-derived products such as urine, and outreach programs to inform the public of CWD symptoms. Species of Interest In order to make predictions about disease contraction and transmission, the movements of the disease hosts must first be considered. Understanding the movements and spatial scales of wildlife is central to developing effective wildlife management strategies (Allen and Singh, 2016). Quantifying habitat choice and seasonal movements of an animal requires the characterization of how that animal selects for resources. (Boyce et al., 2002). Animals can display patterns on multiple scales from the species as a whole to the unique behaviours of the individual, with seemingly conflicting behaviours at these different scales (Wiens, 1989). Over time, broad-scale patterns of movement for large mammals have become better understood with the use and refinement of animal tracking technologies (Hebblewhite and Haydon, 2010). Radio collar telemetry enables ecologists to examine movements at scales from local to global (Kays et al., 2015). In British Columbia, key differences in spatial distributions and seasonal migration behaviour between the five cervid species lead to specialized management considerations. 12 Diseases are more likely to travel faster within a single species than between related species, being in naturally closer proximity with each other with more frequent direct interactions between members. Seasonal migrations are conducted by many species in the interest of maximizing forage quality and minimizing predation (Swingland and Greenwood 1983). For cervids, this often involves a climb in elevation during the summer months to follow new vegetation growth. Due to a combination of migrants and range-resident individuals, summer and winter ranges for populations will typically show overlap due to individual variation in migration strategy and distance (Chapman et al., 2011). Cervids typically congregate into smaller ranges during the winter (i.e. ‘deer yards’) for increased forage opportunity and defence against predators (Janousek et al., 2021; Makarova, 1996; Messier and Barrette, 2011). Close proximity to other individuals may allow for increased rates of physical contact and shared foraging, leading to an increased risk for CWD transmission. Physical interactions during mating, such as males checking female urine or challenging other males, are commonplace, while physical interactions between different species are less likely to occur. However, overlapping winter ranges can lead to shared browsing between species, increasing the chance of interspecies infection (Martinka, 1968). Mule Deer (Odocoileus hemionus) Mule deer have historically been consistent seasonal migrants with an affinity for higher elevations in the summer months with a return to valley bottoms in the winter (D’Eon and Serrouya, 2005). A study on mule deer in the Okanagan showed the average annual displacement to be 25 kilometers (± 16.8), with average home ranges of 4 km2 (Wright et al., 2020). Range preference can be sex-dependent in mule deer (Nicholson et al., 1997). For example, females have been shown to prefer higher levels of canopy closure than males, and prefer steeper 13 slopes for fawn protection against predators (Robinson et al., 2006; Main and Coblentz, 1996). Selection of winter range considers multiple factors, the most important being a viable food source. Mule deer diet is composed mostly of the browsing of trees, with Douglas fir (Pseudotsuga menziesii) being the preferred species (Waterhouse et al., 1993). Elevation, solar duration, and coniferous tree cover (for snow interception purposes) are found to be strong selection factors for winter range (D’Eon and Serrouya, 2005). Douglas fir is also considered as one of the best species for snow interception (Koot et al., 2015). This strong affiliation to Douglas fir, both young and mature, is the foundation for the provincial establishment of Mule Deer Winter Range (MDWR) in BC (Robinson et al., 2006). It is during the winter range contractions of mule deer populations that CWD infections are mostly likely to develop, especially during mating. Males will often taste the estrus (urine) of females directly during this time, increasing direct interactions between individuals. Higher levels of shared browsing and water sources at this time also increase potential for infection. White-tailed deer (Odocoileus virginianus) White-tailed deer are also prone to seasonal movement, but rather than utilizing steeper slopes in the summer, they tend to remain in valley bottoms year-round (Kinley, 2019). While it is possible for an individual to travel over 100 kilometers between summer and winter ranges, studies have estimated the average to be about 10 kilometers (Kinley, 2019; Grovenburg et al., 2009). A recent study captured the movements of an anomalous individual that travelled over 300 kilometers in a single month (Moll et al., 2021). A study of white-tailed deer in Region 4 estimated home ranges of 2-3 hectares, depending on habitat (Smith, 1977). While white-tailed deer congregate in the winter, they also exhibit higher levels of group fidelity and exclusiveness than other cervid species (Tierson et al., 1985). This conservative 14 behaviour may prevent new immigrants from passing on disease, or transmission from mixed herds in the winter. It may also keep them from associating with herds of other species in the winter, improving their resistance to disease spread. Elk (Cervus canadensis) The seasonal movements of elk are highly variable compared to deer and are subject to change over the course of an individual’s life (Woods, 1991). Patterns of elk movement can range from fully separate summer and winter ranges, to using a subset of their summer range in the winter, to wandering a smaller range throughout the year, to remaining relatively stationary within a herd year-round (Hebblewhite and Merrill, 2011). Individuals that do migrate will move to elevations even higher than mule deer to access new growth throughout the summer months. Studies of elk in BC have shown average annual home range sizes of 190 km2, though this can vary considerably (Range: 50-1000 km2) (Gillingham and Parker, 2008). Another study showed the average distance between summer and winter ranges to be 37.9 kilometers (Poole and Mowat, 2005). Elk are also able to withstand deeper snowpack and less canopy closure than deer due to their size (Poole and Mowat, 2005). Winter ranges can be large in size and don’t require canopy cover (Amor, 2019). Elk have been shown to possess a level of agency as to whether they migrate or not, possibly based on resident herd size (Hebblewhite and Merrill, 2011). Year-round herding populations have potential for a more rapid transfer of disease between individuals. Migrating individuals who return to an infected herd in the winter may also be at significant risk. While elk can often have overlapping winter range areas with mule deer, mule deer prefer separation from species larger than themselves and tend to occupy different areas according to 15 aspect, lowering the potential for interspecies infections (Stewart et al., 2010). Differences in foraging strategies also reduce direct transmission risks (Coe et al., 2004). Moose (Alces alces) Of the North American cervid species, moose are the most unpredictable in terms of seasonal movement. Studies vary in the ratio of residential to migratory moose, and ranges can be up to 75 kilometers apart and can vary dramatically depending on location (Demarchi, 2003). A study of moose in BC has shown an annual range size of 195.28 km2 with significant individual variance (39-899 km2) (Gillingham and Parker, 2008). Factors in range selection have been historically difficult to determine, but it is thought that snow quality may affect migratory decisions (Ball et al., 2001). Where snow depth is not prohibitive, forage quality significantly determines winter habitat (MacCracken et al., 1997). Similar to elk, there is strong evidence for increased calf survival for seasonal migrants due to better prey (including human) avoidance (White et al., 2014). Moose migratory behaviour has a strong genetic (or early impressioning) link and does not typically change throughout an individual’s lifetime (Sweanor and Sandegren, 1988). It is thus more difficult to predict how moose are likely to spread CWD, but their relatively solitary nature would imply a far less rapid rate of infection. As for contact with other cervids, Moose have been shown to be outcompeted by elk in the Rocky Mountains, where willow is consumed by both species (Hurd, 1999). Shared resources and nearby competition raise the probability of interspecific disease transmission. Caribou (Rangifer tarandus) Caribou share the herding dangers that elk face from CWD, but on a much more severe scale. Caribou are almost exclusively herding animals, and CWD is likely to spread quickly with 16 animals in such close proximity. A CWD-infected herd of over 2000 reindeer (very similar to caribou) in Norway was culled completely, as it was impossible to isolate the infected animals due to the long onset of CWD (Mysterud and Rolandsen, 2018). Caribou have yet to test positive for CWD in Canada, but the disease continues to move towards the home range of caribou herds in Alberta via mule and white-tailed deer. Caribou typically have large home ranges in the hundreds or even thousands of square kilometers (though this varies greatly) and would thus be able to spread CWD prions across large distances through shared grazing (Roberts et al., 2003; Wilson et al., 2019). A number of stressors are already affecting caribou such as human encroachment on home ranges and increased predation from wolves (often due to human roads and survey lines creating more movement opportunities for predators) (Latham et al., 2011; Apps and McLellan, 2006). When CWD begins affecting these already dwindling populations, it will only hasten caribou decline in Canada. resources and nearby competition raise the probability of interspecific disease transmission. Connectivity Models Tracking the movements of animals allows wildlife biologists to assess landscape effects on habitat connectivity. Connectivity models are often focussed around landscape resistance surfaces (Zeller et al., 2012). The lower the resistance value, the more likely an animal is to be able to move into or through an area (Zeller et al., 2016). Landscape resistance surfaces that indicate ease of passage by an animal can be created from habitat preference models, such as a specialized resource selection function. Resistance surfaces can be used by multiple software platforms to identify likely movement routes. Of the available connectivity modeling platforms, Circuitscape, and the closely related Omniscape, are two of the most notable connectivity 17 applications available, and have been used in hundreds of studies to assess connectivity (McRae et al., 2016a; Dixon et al., 2019; McRae et al., 2016b). Unlike Omniscape, Circuitscape requires source and destination locations to model the flow of movement through a resistance surface. In the context of CWD, Circuitscape can be applied by marking areas of known CWD infections and putting the endpoints within or ‘behind’ BC. However, for modelling animal movement pathways into BC without prior knowledge of source locations for CWD, there are no defined starts and ends. In this case, Omniscape allows researchers to model connectivity with fewer assumptions about the source and destination of animal movement at the study area scale (McRae et al., 2016b). Together, we used Circuitscape and Omniscape to address both directional movement from CWD positive areas to BC and more generalized movement throughout our study area. Circuit theory forms the basis of Circuitscape and Omniscape. In ecology, circuit theory is a relatively new concept, and many studies incorporating it have experimented with its uses and how it compares to other known methods of modeling. Poor et al. (2012) compared model types for modeling migration in pronghorn antelope and found that circuit theory-based modelling was appropriate for predicting movement between locations where more than one single ‘best’ corridor is of interest. A literature review on circuit theory has identified a niche in assessing the relative frequency of routine animal movements along multiple potential paths (Dickson et al., 2019). The review found modeling with Circuitscape was a sound method when predicting both population-level dispersal patterns and identifying potential pathways for spread of invasive diseases. Reed et al. (2017) found that empirical models using Circuitscape outperformed models based on expert opinion, though each had better predictions in specific environmental variables. 18 Methods The three objectives of this analysis are based on a shared set of telemetry data. Multiple steps taken in the analysis worked towards completing multiple objectives. Figure 2 depicts the workflow and relationships between the Objectives, Methods and the Results of this thesis. Study Area To assess high-likelihood border crossing routes, the study area was chosen to encapsulate all known CWD-positive detections near the BC border at the time of the study’s inception (Figure 3). In Alberta, CWD has been found throughout the prairies and northwestern boreal parkland and continues to move west towards the British Columbian border within the Rocky Mountains, which have been identified as an important area for connectivity in North America (Pither et al., 2021, Belote et al., 2022). The nearest positive detection of CWD in Alberta was found in the Eden Valley area, approximately 35 kilometers from the BC border. The northmost detection near the border is in the Cochrane area. To the south, CWD cases have been confirmed as close as Libby, Montana (USA), approximately 70 kilometers away. The current suite of MUs with mandatory CWD testing are located in Resource Management Unit 4 (also known as Region 4), which covers the Kootenay Mountains region. Region 4 shares its border with the USA, (including Washington, Idaho, and Montana), as well as bordering southern Alberta, making it the most at-risk area for CWD-infected animals to enter the province (Nelson and Schwantje, 2019). A 100-kilometer buffer into the USA and Alberta was added to the study area, the distance being chosen as it encompasses both all the collected telemetry data and a relatively uniform distribution of nearby positive CWD detections (as of January 1, 2021) in these BC-adjacent regions. MUs 8-14 and 8-15 were also included in the southwest corner. 19 Figure 2 A visual workflow for the methods used to achieve the objectives of this analysis. Grey boxes depict final results, while dotted lines depict new data (angled boxes) being brought in for a particular step. Trapezoids depict steps where manual input was required. 20 Figure 3 Map depicting this project’s area of interest, including BC’s Wildlife Management Region 4 with a 100-kilometer extension into the United States and Alberta. Map was created using ArcMap Pro (Esri Inc., 2020) with the default basemap as a background. Datasets Telemetry datasets were provided largely by Region 4 Wildlife Biologists Patrick Stent and Luke Vander Vennen in September, 2020. These datasets were stored in multiple repositories, including government databases and those made available from previous studies (Table 1). These datasets spanned four cervid species, mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), moose (Alces alces), and elk (Cervus canadensis). 21 Table 1 Datasets collected for analysis, including general location and relevant statistics. Full citations and access can be found in the References section. Species Study area Author Number of animals Start year End year Fix rate Cervus canadensis Southwest Alberta Boyce and Ciuti, 2020; Ciuti et al., 2012 170 2007 2013 2 hours Tobacco Valley, BC/MN Ethan Lula, Unpublished 6 2015 2017 23 hours Rocky Mountain Trench, BC Phillips and Szkorupa, 2011 36 2007 2010 9 hours Rocky Mountain Trench, BC Szkorupa, Unpublished 14 2014 2018 23 hours Lardeau Valley, BC Poole and Bark, 2003 8 2003 2005 2-24 hours Sparwood, BC Poole and Lamb, 2020 74 2016 2020 13 hours Alces alces Flathead, BC Poole and Stuart-Smith, 2004 48 2001 2003 2 hours Odocoileus hemionus Kootenay Region, BC Stent, 2019 150 2015 2020 23 hours Odocoileus virginianus Kootenay National Park, BC Kinley, 2019 11 2012 2016 1 hour Creston & Koocanusa, BC Vander Vennen, Unpublished 24 2021 2022 4.25-6 hours Another dataset, containing Southwestern Alberta elk data, was sourced from Movebank (Boyce and Ciuti, 2020; downloaded November 2020). Additional recently collected unpublished white-tailed deer data was included as well from Wildlife Biologist Luke Vander Vennen. In total ten datasets were made available. No appropriate data were located for the assessment of mountain caribou (Rangifer tarandus caribou). The datasets were standardized to UTC time and the UTM Zone 11N (Datum: WGS84) coordinate reference system. Datasets from studies that failed to report their time zone (33% of total data) or datum (23% of data) were assumed to have used UTC-8:00 and WGS84 respectively. The datasets consist largely of female animals, with only one elk dataset and one white-tailed deer dataset including any males (n=48 22 for elk and n=2 for white-tailed deer). The combined total number of animals is 517, with the majority being elk (n=286, see Table 1). The fix rates ranged from 1 hour to 23 hours, with most datasets using 23-hour fix rates. The datasets were aggregated and filtered to remove duplicate points, mortality events, and points with dilution-of-precision (DOP) greater than 15 for 3D fixes and 10 for 2D fixes. These thresholds are based on the methods presented by Bjørneraas et al. (2010) but given that DOP thresholds can be arbitrary (Fleming et al., 2020), they were both increased by five for a more conservative retention based on the age of some of the data. DOP was not provided by all datasets, and for those cases no data were removed in this way. Though removing data based on DOP can lead to habitat bias (i.e. areas with gps-impeding terrain may have more points dropped than more open areas), this step removed a maximum of 6.7% of data from any individual animal, with only two animals losing more than 5%. Mortality events, time intervals less than 20 minutes (likely to be a double-count given the much longer fix rates), animals with median fix rates greater than 30 hours (i.e. 20% higher than the highest fix rate in the data), and points with speeds exceeding 12 kilometers per hour (unusually fast as a consistent pace, likely to be a satellite inaccuracy) per hour were also removed. These cutoff values were based on correspondence with a professional biologist (C. Lamb, 2021, personal communications). This cleaning process reduced the total available data by 3.3%. The combined dataset was split into four smaller sets, one for each species. Data columns that were not needed for analysis were removed. 5% of the individuals (rounding up) from each species were set aside for resistance surface validation, while 95% remained in use for modelling. From this point in the methodology on, all further manipulations were conducted on each species separately. All movement data were divided into seasons based on month. Each 23 Figure 4 Map of the area of interest and locations of collected telemetry data. The area includes the entire Region 4 area of British Columbia and extends 100 km south into the United States and east into Alberta. Points are coloured by species. Approximated local positive CWD detections are also included, based on maps from Environment and Parks Alberta (2021) and Montana FWP (2022). Map was created using ArcMap Pro (Esri Inc., 2020) with the default basemap as a background. 24 individual was split into four standardized season windows (summer range [June 1 to August 31], fall migration [September 1 to October 31], winter range [November 1 to March 31], and spring migration [April 1 to May 31]). To standardize methods for all species, all animals used the same set of seasonal cutoff dates determined from examining 5-day range sizes and looking for periods of increased range size, indicating consistent directional movement. The summer and winter datasets were used in the following analyses to establish areas of summer and winter range, while the spring and fall datasets were set aside along with the previous 5% of individuals for use in validating the results of the connectivity analyses that simulate movement between summer and winter ranges. Spatial Data and Raster Manipulation To create predictive data for habitat selection modeling, we assembled a suite of environmental variables in raster format from publicly available geographic databases (Table 2). To ensure analyses were consistent across the area of interest, all environmental variables used needed to be available in the regions of BC, Alberta, Montana, Idaho, and Washington. To create the digital elevation model, separate images were collected and standardized (30-meter resolution, CRS: UTM 11N [Datum: WGS84]) before being merged (Government of Canada, 2015; USGS, 2021). Topography is a consistent feature of RSFs in literature with a high likelihood of being significant for third-order selection (Boyce et al., 2003; D’Eon and Serrouya, 2005; Gillingham and Parker, 2008; Poole and Mowat, 2005; Johnson et al., 2000). Layers for slope and aspect were calculated from elevation. To apply linear properties to aspect with only a single predictor variable, slope southwestness was derived by taking the cosine (multiplied by -1 to create southness rather than northness) of the aspect rotated 30 degrees counterclockwise, as 25 Table 2 Source information for collected spatial layers used as predictor variables in habitat selection model creation for all species. Layers without source entries are sourced from the same layer as the layer above. Full citations and access can be found in the References section. SD = species dependent. EVI stands for Enhanced Vegetation Index satellite data. Layer Abbreviation Original Source Year of Data Collection Hypothesized Relationship with Species Elevation ELE Natural Resources Canada, 2015; USGS, 2021 2017 (CAN); 2003 (USA) - Slope SLP Natural Resources Canada, 2015; USGS, 2021 2017 (CAN); 2003 (USA) - Aspect Southwestness SWT Natural Resources Canada, 2015; USGS, 2021 2017 (CAN); 2003 (USA) + Mean of Annual EVI EVM USGS, 2021 2000-2022 SD Standard Deviation of Annual EVI EVS USGS, 2021 2000-2022 SD Distance to Main Roads DTR CEC, 2020 2015 + Distance to Water DTW CEC, 2020 2015 - Forest Road Density FRD GeoBC, 2017; Altalis, 2020; Transport Canada, 2016; USFS, 2021; USFS, 2021a; USFS, 2021b 2017; 2020; 2016; 2014; 2017; 2017 - per the calculation for solar-radiation aspect index (Roberts and Cooper, 1989). Terrain ruggedness index (TRI) was also calculated (Riley et al., 1999) but was dropped due to a high collinearity with slope in the study area (see Appendix Figure 23). To assess forest cover and forage quality, publicly available Enhanced Vegetation Index (EVI) data for the study area was collected via Google Earth Engine (USGS, 2021). Two images were taken for each year between 2000 and 2020, the first with values of annual mean EVI and the second with standard deviation of all images over the course of that year. These image sets were separately averaged to create a raster depicting the mean annual mean EVI and a raster depicting the mean annual standard deviation of EVI. Annual mean EVI can indicate the amount 26 of consistent green foliage over the course of a year (Huete et al., 2002), thus high values would represent evergreen areas while low values represent areas continually devoid of vegetation. Annual standard deviation represents the amount of change in green foliage throughout the year, thus high values would indicate areas with high levels of new vegetative growth and nutritional value for browsing and grazing animals. Potential browsing areas, both with and without canopy cover, are often used for cervid RSFs (D’Eon and Serrouya, 2005; Poole and Mowat, 2005; Johnson et al., 2000; Poole and Stuart-Smith, 2007). Distance to main roads and distance to water sources (D’Eon and Serrouya, 2005; Johnson et al., 2000; Poole and Stuart-Smith, 2007) were calculated using a land cover layer provided by the CEC (2020) with a Euclidean distance function in ArcMap Pro (Esri Inc., 2020). Creation of a forest road density layer first required the isolation of forest road data in BC, Alberta, and the USA. All three areas had unique methods of data storage requiring different methods to unify the dataset for the study area. Road data from BC (GeoBC, 2017) was filtered to include only recreation roads, service roads, restricted roads, and ‘unknown’ roads which were visually confirmed to be largely within Crown land. Two datasets were collected for Alberta, each containing unique sets of roads (Altalis, 2020; Transportation Canada, 2016). Only the Transport Canada dataset was properly classified, and ‘resource/recreation’ roads were isolated. A 15m-radius buffer was created from the Transport Canada dataset in order to remove overlapping lines in the Altalis dataset. Highways and all roads east of the Alberta foothills were removed. Forest road data from the USA were also sourced from multiple datasets (USFS, 2021; USFS, 2021a; USFS, 2021b) but lacked properly distinctive classification. To remove rural and urban roads, lines below 900 meters of elevation were removed. While this removed most highways and cities from the dataset, Montana areas of Kalispell, Eureka, and the Blackfeet 27 Indian Reserve required additional removal which was done by hand. Results from BC, Alberta and the USA were merged together, and a line density function was run with a 5-kilometer radius using Arcmap Pro (Esri Inc., 2020). All collected layers were clipped to the study area, re-projected to UTM Zone 11N (Datum: WGS84) and resampled to a 30m resolution. The layers were then stacked to ensure consistent resolutions and extents. These layers were then checked for high levels of correlation (>0.7 as per Poole and Mowat, 2005) using the ‘layerStats’ function from the raster package (Hijmans, 2020). Resource Selection Functions Our resource selection functions used binomial distributions of locations that are actively used by animals (denoted as ‘Used’), and existing but available locations (denoted as ‘Available’) (Boyce and McDonald, 1999) to quantify preference to habitat predictors. The ‘Used’ locations were represented by the observations from the collar data and have a binomial value of 1, while available locations are given a value of 0. For each used and available location, we extracted environmental data from the suite of raster layers (Table 2) using the ‘extract’ function of the ‘raster’ package in R (Hijmans, 2020). These environmental data were set as the predictor variables in the RSF modeling, while the Used/Available values were set as the response variable. To quantify availability, a 95th quantile minimum convex polygon (MCP) was created for each season-individual using the package adehabitatHR (Calenge, 2006). Within each MCP, we created five random locations (as per Gillingham and Parker, 2008; Baasch, 2008) for every used location. These random locations were coded as available locations. Additional MCP manipulation was required specifically for white-tailed deer, due to the exceptionally straight 28 annual movement patterns of their telemetry data in one area. To quantify availability for these unusual movements, circles with a radius equal to the mean seasonal displacement for all individuals were used in place of the traditional MCP. These custom MCPs were applied to all whitetail individuals for consistency. Raster values were extracted for these used points as well. Prior to modelling, the predictor variables were center scaled or, in the case of the ‘distance-to’ terms, transformed using a degrading exponential function to reduce all values beyond 500 meters as ‘0’ (Whittington et al., 2022), to keep the coefficient terms comparable to each other during analysis. From these data, a series of conditional logistic regression models were created to estimate the resource selection function, using the ‘glmmTMB’ function (Brooks et al., 2017). These models use a binomial distribution with a ‘logit’ link. The suite of environmental variables can be found on Table 2, with the addition of a quadratic term on elevation and interaction effect between southwestness and slope. This interaction was based on the prediction that an animal on flatter ground should not have a preference for aspect, while an animal on a steeper slope might. This preference for aspect on steeper terrain occurs because solar exposure affects forage availability and thermal conditions for the animal (Keating et al., 2007; D’Eon and Serrouya, 2005). Animal ID was used as a random intercept. For the respective weighted logistic regression approach, the likelihood for the ‘available’ samples (i.e. used = 0) were given a weight of 1000, while the ‘used’ points (used = 1) were given a weight of 1 (Fithian and Hastie, 2013). The weights were added to approximate infinitely weighted ‘available’ points, allowing for proper model convergence for models that are not fully specified (i.e. there may be missing, redundant or extraneous predictors in the model). We tested each possible combination of predictors using the ‘MuMIn’ package’s ‘dredge’ function (Barton, 2020), then selected the best-fitting model for 29 each species based on the lowest possible Bayesian Information Criterion (BIC), as BIC is more accurately able to correct for overfitting (Schwarz, 1978). An overfit model results in poor predictions when used on new data (Babyak, 2004). Thus, a general model is more useful for predictive applications. The top model for each species-season was then assessed for performance using k-fold cross-validation (k=5) (Fushiki, 2009) using the ‘MoveTools’ package in R (Merkle, 2022). Cross-validation evaluates the stability of the coefficients and the predictive power of the used points (expected to be strong) and the randomly generated available points (expected to be weak or non-existent). Once the best-fitting model was established, the coefficients were applied back to the environmental raster layers and summed to project the RSF model on a logit scale. To project the RSF across the study area, the coefficients from the best-fitting model were extracted and multiplied by their respective environmental layer. These layers were scaled using the same scaling parameters as the data used in the GLMM. All layers were then summed into a single projection. The output of the RSF projection is a single layer depicting habitat preference across the study area. The preference layer was then transformed to create a resistance surface, where higher values represent areas that are more difficult for the animal to move through (Zeller et al., 2012). Following Zeller et al. we tested five functions to transform the projected RSF to a resistance surface (Table 3). In each function, resistance surfaces were scaled from 1 to 1000, with 1000 representing maximum resistance to movement. In addition, water bodies (CEC, 2020), major roads (CEC, 2020), and urban areas (Data BC, 2016; Altalis, 2022; US Census Bureau, 2021) were given maximum resistance to movement (as done in Belote et al., 2022 for water and de Rivera et al., 2022 for roads; Pither et al., 2021 for both). For each transformation, the summer and winter resistance surfaces were combined, taking the minimum value for each 30 raster cell location between the two to represent an area being favourable during summer or winter season, if not both. Given that seasonal migrations typically function as a means to get from summer to winter range, this combined resistance surface reflects the desirable non-migratory locations for animals on the landscape. In total, 20 resistance surfaces were tested (five transformations for each of the four species). As part of this transformation, the resolution of the maps was increased from 30 meters to 150 meters to reduce computational load. Increasing the cell resolution of Circuitscape resistance surfaces has been shown as an effective method that can retain accuracy when habitat patches are sufficiently large (Koen et al., 2019; McRae and Beier, 2007). To validate the resistance surfaces, each raster was run in Omniscape to create non-directional connectivity coverage of the entire study area, allowing fair validation of the test data regardless of location (Landau et al., 2021, McRae et al., 2016b). To improve runtimes for the connectivity analyses, Omniscape, and later Circuitscape, (Anantharaman et al., 2020) was run in the Julia language environment (Bezanson et al., 2017). Omniscape requires two non-raster parameters that are specified by the user: Block size indicates the amount of area averaging the program can do to reduce computing time, while moving window radius determines the area within reach of a simulated animal. The Omniscape analyses were run with a block size of 11 to facilitate reasonable runtimes (< 5 hours per transformation-species combination). The moving window radius determines how ‘far’ the analysis searches for source areas to connect to (McRae et al., 2016b). Belote et al. (2022) found that large window size produced more concentrated current density in Omniscape analyses, but had a higher tendency to ignore finer-scale heterogeneity on landscapes. Given our relatively fine cell resolution and focus on specific species, we opted for a species-specific approach to determining window radius size.31 Table 3 Summary of the tested resistance surface transformations. Transformation Equation Source Negative Linear R = x*-1 Braaker et al., 2014 Natural Log R = ln(x*-1) Braaker et al., 2014 Exponential R = (x*-1)^5 Braaker et al., 2014 c4 R = 100-99*((1-exp(-4*x))/(1-exp(-4))) Keely et al., 2016 c8 R = 100-99*((1-exp(-8*x))/(1-exp(-8))) Keely et al., 2016 To calculate window radius, the mean home range area was calculated for each species continuous time-movement modeling (Calabrese et al., 2016). A circle was created matching the calculated area. The diameter of that circle was used as the window radius (i.e. an estimate of the average distance traveled between home ranges). The source areas were created from the 90th percentile of the winter and summer RSF projections for each species combined, scaled 1-100 before combining. The final output of this analysis was a single cumulative current density raster for each species and transformation. The data used to validate the Omniscape results included both the test data from the 5% of individuals set aside at the beginning of the project, as well as the spring and fall data that was not used in constructing the resistance surfaces. For each species, the five resulting Omniscape rasters were reclassified into equal-area bins of value 1 to 10 (similar to Braaker et al., 2014 and Zeller et al., 2018), with 10 representing the highest current values. Values from these rasters were then extracted using that species’ reserved validation datapoints. A mean extracted value was taken for each individual, then a between individual mean was taken as the final assessor of model fit. To reinforce the results of this method, a second method of deriving validation values was completed (Zeller et al., 2018). In this method, rather than converting the map to equal area bins, the Omniscape rasters were scaled from 0-1, which then provided the extracted values for 32 the test datapoints. The median value was taken per individual to account for abnormal value distributions and outliers before taking the mean between individuals. The transformed resistance layer with the highest score for each species would then be brought forward into subsequent analyses, as the chosen transformation represents a more accurate calibration of a species’s sensitivity to moving through unfavourable habitat in the context of Circuitscape simulation. Objective 1: Evaluate CWD Monitoring Effectiveness To evaluate the effectiveness of the harvest-led CWD monitoring strategy in BC, mean cumulative current density value was extracted from both Omniscape and Circuitscape results for each MU within the study area. The study area included all Region 4 MUs as well as 8-14 and 8-15 in the southwest corner. The Omniscape results were used to avoid bias from MUs near source and ground points that occur in Circuitscape outputs, whereas Circuitscape values better represent the optimal corridors for infected cervids from CWD-positive areas. The mean cumulative current density statistics were compared to, for each MU, the count of CWD testing samples since 2019 (the year the mandatory submission requirements expanded to the current MTA) (BC Wildlife Health Program et al., 2022), and hunting statistics from 2020-2021 including hunter effort, hunter days, and hunter kills (BC Wildlife and Habitat, 2022). All hunting statistics are based on survey feedback from randomized population samples. Scatter plots were created to assess correlation between these factors. Objective 2a: Identify High-Likelihood Border Crossing Locations Circuitscape was run on the previously chosen resistance surfaces in order to identify corridors with a high-likelihood of use by cervids. Circuitscape requires the establishment of source and destination nodes and models the directed flow from one to the other (Anantharaman et al., 2020). In this thesis, source nodes were located in areas with known positive CWD 33 infections in Montana and Alberta within 100 kilometers of the border. Destination nodes were set as follows: To establish directional flow towards BC, two separate Circuitscape analyses were run for each species, one south to north and one east to west. The east-west analysis used source nodes in Alberta with ground nodes evenly distributed along lines 100 kilometers west of the west western edge of the area of interest within BC. The south-north analysis used source nodes in Montana and ground nodes set along lines 100 kilometers north of the northern edge of the area of interest within BC. To ensure bias was not being created by the number of ground nodes used, three Circuitscape analyses were run per species per direction using 5, 10, and 50 evenly spaced ground nodes along the lines. After confirming that the results of differing ground node numbers produced significantly similar results (i.e. Pearson correlation coefficient >0.7, see Appendix Figure 36), the 50 ground node results were used for further analysis. The resulting raster maps from the Circuitscape analyses were then combined, taking the maximum value for each location was retained to create a Bidirectional Connectivity Map. In these Bidirectional Connectivity Maps, areas of high current density are predicted to have higher probability of CWD flow into BC than areas of low connectivity. Bidirectional Connectivity is thus not a map of individual animal movement per se, but reflects how variation in movement direction and concentration may affect the speed of disease transmission among individuals. To account for interspecies disease transmission effects on CWD movement towards BC, we combined all four species’ Circuitscape maps using two different methods. To remove the extreme bias of high values at the source and ground nodes, each image was first reclassified into 100 equal-area bins (i.e., each value from 1 to 100 had the same number of pixels within the image). To examine areas where multiple species overlap, all images were then summed together. High values would indicate areas where multiple species have strong overlapping 34 connectivity in that area. The second method examines the overall coverage of Region 4 by all four species; the images were binned using the same method, then the maximum value among all four images was taken for each cell. High values in this map indicate that at least one cervid species has high levels of connectivity in that respective area. Objective 2b: CWD Risk To Caribou Circuitscape-based connectivity modeling was used to predict disease movement towards extant caribou herd ranges within BC. Polygons outlining the ranges of the Central Selkirks (28 animals as of 2021), Columbia South (4 animals as of 2020), and Columbia North herds (184 animals as of 2021) (i.e. all ranges within Region 4 and containing extant caribou populations) (BC FLNRO and BC MoE, 2022; BC Caribou Recovery Program, 2021) were converted to rasters, each with a unique numerical identifier. Any cell within these areas could then be used as a ground node in connectivity analysis. Circuitscape was run on the same resistance surfaces and source nodes used in the Objective 2a analysis, with the ground points replaced with the caribou herd ranges. All other parameters in the Circuitscape analysis were kept consistent with previous analyses. Individual species maps were then summed to create a single map highlighting multi-species corridors. To contrast the Circuitscape analysis, least-cost paths (LCPs) were also estimated from existing CWD locations to caribou ranges. Ideally, the LCP should generally follow high current density areas on the current density maps, while also more explicitly showing the most efficient route in areas where there are many movement options. The LCP analysis was conducted using the Linkage Mapper package in ArcMap (version 2.2; Esri, 2020), connecting the various known positive CWD detection locations with the Central Selkirk and Columbia North caribou herd polygons. The linear resistance surfaces were used in this analysis (as per Benz et al., 2016), as 35 the large number of low values in the ‘e5’ and ‘c8’ interfered with the analysis’s ability to detect optimal paths. The resulting .csv file from Step 3 of the Arcmap Tool “Build Network and Map Linkages” was manually altered to exclude links to between source nodes, links between herd polygons, and links to Columbia South, as all probable paths would logically require crossing through the Central Selkirks herd range first. Individual species results were combined to create a single map for comparison to the Circuitscape product. Objective 3: Estimate Risk of a Border Crossing Event To analyze the distance-based risk of a border crossing event by a CWD-infected animal, the telemetry data used for the connectivity analyses was repurposed to estimate average seasonal migration distance. The data were split into species, then individuals within a species, then further broken up by year. In order to measure the average length of a seasonal migration, the data were finally split into half-year periods with breakpoints of February 1st and August 1st. Individual half-years that did not have data within 45 days of both breakpoints were removed. Minimum convex polygons (MCPs) were created from these datasets using 100% of the points to estimate range size. From these estimations, the greatest length of each home range polygon was derived and recorded. These lengths are a generalized representation of the distance an animal can travel while moving between winter and summer ranges. These estimations do not take into consideration the total distance travelled within the home range and thus in some cases this may be underestimated. However, given that Region 4 and surrounding area has a similar restrictive geography, using the full distance travelled may lead to overestimation when calculating the risk buffers. A single cumulative distribution function (CDF) was created from the collection of spring and fall maximum migration distances. The CDF shows the probability of a seasonal movement 36 being within a given distance. We then generated a complement of the CDF (i.e. 1 - x) to estimate the probability of an animal traveling at least a given distance over a single season (see Box A Figure 5). Next, we simulated the probability of crossing a hypothetical border by intersecting a chord (i.e., perpendicular line) and various distances from the center of a circular raster. This circular, 100-meter resolution raster was given a radius of 200 kilometers, exceeding the distance any individual animal travelled in a single season during the study. The cell values of this raster were based on the inverted CDF, such that there was a higher probability of movement closer to the center of the 200 km radius circle. In others, we spatialized a 2-dimensional, inverted CDF. We then bisected the circle with a chord at 100m increments starting at the center and moving outward. For each bisection, we summed the raster values in the smaller section and divided this value by the sum of the cells in the entire raster (See Box B of Figure 5), to give a probability that the hypothetical border would be crossed. This approach requires the assumption that the border is a straight line, perpendicular to the animal. This assumption is effectively correct along the United States/Canada border but less accurate along the BC/AB border. The irregularities in shape may alter the probability of crossing a border. Based on the simulated border crossing probabilities, we then created a raster for each species to show an empirical estimate of cervid movement into BC at different distances from the border, extending into AB and MT. We converted the linear distance from the border into a species-specific probability of border crossing based on the simulations (see Appendix Table 7). Further, we examined areas with a known positive CWD detection, and calculated the risk of an animal from these areas moving into BC in a single season. Finally, we multiplied the probability of a crossing event with local CWD prevalence rates to calculate the risk of a CWD-positive border crossing event, naive to local deer abundance (Alberta, 2022; Montana FWP, 2022). 37 Figure 5 Visual description for methodology in calculating the probability of a crossing event for the risk buffer (P) based on a given distance (x). Box A depicts the conversion from a cumulative density function (CDF) to an inverted CDF. Box B depicts using the values from the inverted CDF for a circular distance-based raster. The cell values of the blue raster are calculated by comparing the distance between the cell and the centerpoint of the circle, and relating that to the value of the generated seasonal movement distance CDF for a single cervid species, inverted in order to generate the correct statistic. This scenario assumes a free and random choice of migration direction. 38 Results Resource Selection Functions Of the top performing models, three out of eight included all predictor terms, and only one removed more than one term (Table 4). All terms in all top models were found to be significant in the top models, save for forest road density in the moose summer model (p = 0.052). The two most common removed terms were the quadratic elevation term and the interaction between slope and southwestness. Removing the quadratic elevation term created a linear elevation term for the moose winter model, whereas in summer the elevation term was removed completely. While this missing term alone would suggest that moose will prefer any elevation during the summer season, both the strong terms for slope and EVI kept moose at lower elevations without the need for an elevation term. Both top models that did not include the interaction term (Moose-Summer and White-tailed Deer-Winter) had strong negative correlation effects on slope and only a very small effect for southwestness itself, implying a strong preference for low or no slopes. On flatter ground, aspect begins to become meaningless, explaining the lack of a preference for aspects. Mule deer were unique in having a positive quadratic relationship with elevation, which is kept in check at high elevations by other predictor term effects. The positive relationship was likely due to the limitations of the quadratic effect shape attempting to fit a preference for middle elevations. Six of the eight models had a relatively high mean K-fold value, indicating model stability. Of the two lowest scores, summer mule deer was the more stable, with values ranging from 0.49 to 0.73. Consistent lower scores imply there may be an important predicting variable that is not included in the current model or may be a result of generalist behaviour utilising a wide range of the tested predictor variables. Winter white-tailed deer had the lowest score of 0.346, caused by 39 Table 4 Top models for summer and winter seasons for elk (Cervus canadensis), moose (Alces alces), mule deer (Odocoileus hemionus), and white-tailed deer (Odocoileus virginianus) in Region 4 of BC. Asterisks indicate the top model did not include that predictor variable. ‘L95% CI’ and ‘U95% CI’ are the lower and upper 95% confidence intervals respectively. K-Fold validation results are also included. 40 unstable individual scores ranging from -0.006 to 0.697. This implies a lack of consistency in habitat preference across individual white-tailed deer, reinforced by both white-tailed deer models having the highest K-fold standard errors (0.193 for summer and 0.127 for winter). It could also imply a significant difference between subgroups within the data, such as males and females. Each species was tested separately for their highest-scoring transformation (Figures 6 and 7). For all species both validation methods resulted in the same transformation being selected (‘c8’ for white-tailed deer and ‘e5’ for the rest). Each species used its highest-validating transform for all subsequent Circuitscape and Omniscape analyses. Objective 1: Evaluate CWD Monitoring Effectiveness Mean Omniscape cumulative current density for each MU in Region 4 showed a positive correlation with the number of submitted samples across all species (R2 of 0.41 to 0.62, Figure 8). MUs without mandatory testing showed relatively low submitted samples, and sample numbers were not correlated with relative connectivity (R2 ranged from <0.01 to 0.26, though only due to uniformly low values forming a ‘straight’ line). Some of these MUs have similar or greater levels of harvest numbers (Figures 8 and 9) as MUs with mandatory testing. Mule deer connectivity correlated the most closely with harvest numbers and hunter success rate, particularly in MUs without mandatory testing (R2 = 0.41). Many of the patterns found in Figure 8 are not repeated in Figure 9 due to the marked differences between CWD’s current presence in the area of interest and a uniform distribution of the disease. One MU of note is 4-03 (the area south of Cranbrook and east to the Columbia River. It contains the MacGillivray Ranges, see Figure 16 for locations), which has the largest sample submission 41 Figure 6 Results of the ‘binned’ validation for the Omniscape results of 5 possible transformations used to create resistance surfaces for Circuitscape and Omniscape analyses. The x-axis shows the 15 different species- transformation combinations tested. Each Omniscape result was reclassified into 10 equal area bins (1-10, 10 being best) before extracting the values with the test data. Mean score was first collected for individuals, then a mean was taken for all individuals of a species. 42 Figure 7 Results of the ‘scaled’ validation for the Omniscape results of 5 possible transformations used to create resistance surfaces for Circuitscape and Omniscape analyses. The x-axis shows the 15 different species- transformation combinations tested. Each Omniscape result was rescaled from 0-1 before extracting the values with the test data. Median score was first collected for individuals, then a mean was taken for all individuals of a species. Extreme high outliers were removed for improved visual comparison of the means. 43 Figure 8 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and total harvest rates and CWD samples from 2019-2021 for the two species with mandatory CWD testing in Region 4: Odocoileus hemionus (a and c) and Odocoileus virginianus (b and d). Current density calculated by Omniscape can be interpreted as the relative amount of connectivity for a given species within the entire area of that MU. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 44 Figure 9 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and total harvest rates and CWD samples from 2019-2021 for the two species with mandatory CWD testing in Region 4: Odocoileus hemionus (a and c) and Odocoileus virginianus (b and d). Current density calculated by Circuitscape can be interpreted as the relative risk of CWD incursion based on general connectivity and geographical relevance to current CWP positive locations. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Only MUs that are in contact with the BC border were included in this figure. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 45 in Region 4. MU 4-03 shows high levels of Omniscape connectivity in both deer species. Hunter harvest was very high for white-tailed deer in 4-03 while other species were relatively low. Along the southern border, MU 4-07 is the western-most MU in the MTA. In both species of deer, this MU has medium to low levels of current density (Figures 8 and 9). For white-tailed deer, current density is much higher in 4-07’s western neighbours, 4-08 and 4-09. There are multiple unmonitored MUs north of 4-23 (i.e. the only MU in the MTA bordering Alberta) with a high-risk of travel by potentially infected mule deer (Figure 9). Despite lower levels of mean current density, MUs within the Rocky Mountains show similar levels of harvest and days spent hunting as border MUs to the west of the MTA (Figure 8; Appendices 29 and 30). Objective 2a: Identify High-Likelihood Border Crossing Locations The four species-specific Circuitscape analyses conducted produced unique patterns of connectivity for each of the four cervid species, while also identifying key areas of overlapping connectivity (Figures 10 to 13). The four species showed a varying preference for following valleys to navigate through the Rocky Mountains; due to their strong preference for low elevation, white-tailed deer showed the highest use of valley bottoms. Mule deer were on the other extreme, showing almost no hindrance by elevation. The lack of elevational constraints on mule deer resulted in their current density map to appear more homogenous compared to the other species. Moose and elk fell in between these two end points of patchiness and homogeneity. Multiple corridors on both the eastern and southern borders of BC were used by 2 or more cervid species (Figure 14; see Figure 16 for corridor names and locations). Of these, the Elk Valley Trench is of particularly high interest given its proximity to the positive CWD detections in southern Alberta. Other corridors were clearly favourable for some species and less 46 Figure 10 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for elk (Cervus canadensis). Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0. 47 Figure 11 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for moose (Alces alces). Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0 48 Figure 12 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for mule deer (Odocoileus hemionus). Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0 49 Figure 13 Directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for white-tailed deer (Odocoileus virginianus). Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0 50 Figure 14 Combined directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for all four analyzed cervid species, using the summed value of Figures 10 to 13. Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0 51 Figure 15 Concurrent directed Circuitscape results from known positive CWD detection locations (as of May, 2021) towards and into BC for all four analyzed cervid species, using the maximum value among Figures 10 to 13 for each cell. Current density value indicates concentrated areas of simulated animal movement, and thus enhanced rates of CWD spread. Map generated using ArcMap Pro 2.2.0 52 Figure 16 A map of the Kootenay region of BC highlighting the high-current density border crossing areas specified in Table 5 below, using Figure 14 (all species) as a background. Map generated using ArcMap Pro 2.2.0 Table 5 BC townships and communities located nearest to notable movement corridors with high relative levels of connectivity. The letters correspond to those found on Figure 22 above. Reference High Connectivity Area Nearby Populated Places A Saskatchewan River Blaeberry B Highway 1 through the Rocky Mountains Field, Golden C, D Highway 93 , Ball Pass Radium Hot Springs E Verdant Creek Radium Hot Springs F, G Aurora Creek, Cross River Radium Hot Springs H, I Albert River, Palliser River Invermere J Maiyuk Creek Fairmont, Canal Flats K Elk Valley Trench Elkford L Flathead River Valley None M Rocky Mountain Trench Fernie, other small townships N Purcell Mountains Yahk, Moyie, Cranbrook O Purcell Trench Creston 53 so for others. Moving along the Saskatchewan River Valley, much farther north than known CWD sources, was only shown to be of interest for species such as moose and white-tailed deer that adhered to low-elevation passes. The valleys containing Highway 1 and Highway 93 were used by moose and white-tailed deer due their lower elevations as well. The corridors that follow major highways through the Rocky Mountains are using easily accessible valley bottoms, which can be distanced from the highways that are usually built on the side of the valley. In these cases, the preference for utilizing low-elevation valleys outweighed the deterrence of major roads. Though mule deer were less likely to use corridors overall, there are some more general areas where current density was more focussed, such as the Elk Valley Trench, the west-moving cross-mountain passage from the Kananaskis area to the pass between the Windermere and Columbia Lakes, or southwest from the Spray Lakes towards Radium. Certain areas where movement corridors are relatively wide and unrestricted, such as the Flathead River Valley and the Rocky Mountain Trench, lack higher levels of cumulative current density despite having few or no barriers to movement. These corridors are more visible in the results for white-tailed deer (Figure 13) and elk (Figure 10) but have lower visibility for mule deer (Figure 12) and moose (Figure 11). Less constricted areas are more likely to present as wider swaths of medium values because movement is less restricted, and their usefulness for the purposes of vigilance, wild animal testing, or potential wildlife obstruction efforts should be assessed on a case-by-case basis (Hall et al., 2021). Many of the corridors identified were predicted to be used by multiple species (Figures 14 and 15). Figure 14 shows the summed current density values from all species, highlighting areas where multiple species’ corridors overlap. Figure 15 shows the max available among the four maps, highlighting any areas where there is animal movement being predicted and, equally 54 important, when there is none at all. Areas with low current density for all species include MU 4-07, and the mountain ranges immediately west and east of the Rocky Mountain Trench. This could potentially have the effect of funneling animals into the Rocky Mountain Trench. Objective 2b: CWD Risk To BC Caribou Herds Directing Circuitscape flow from known CWD occurrences towards caribou herd ranges yielded very similar results to the border connectivity analysis (Figure 17). High cumulative current density corridors across the border included all of the same areas identified in Objective 2a, with the addition of higher current flow in the area west of Kootenay Lake. Once within BC, high cumulative current density routes from the border to the caribou herd ranges included the Rocky Mountain Trench, the Cooper Creek pass between Kootenay and Duncan Lake, and both shores of Kootenay Lake towards the Central Selkirks herd range. Being the range nearest to current CWD-positive areas, the Central Selkirks herd is likely to be the area of ‘first contact’ when CWD begins spreading within BC. The Kootenay and Duncan Lakes on the eastern edge of the herd range act as impassable barriers to movement, forcing animals who try to move west to either go around the top of Duncan Lake or through the Cooper Creek pass between the lakes. This would be an important location to establish monitoring or movement barriers once CWD has entered the province. From Montana or Idaho, there are no lakes to act as barriers, though the Kootenay River may deter northern movements by ungulates west of Kootenay Lake. The least-cost path analyses revealed corridors that were more focused than in Circuitscape analyses, due to having a specific endpoint, rather than a general direction (Figure 18). Some routes were too far away to be calculated by this analysis such as the Blackfeet Reserve and Claresholm CWD-positive areas, as well as those from multiple areas to the Columbia South and North herd ranges. LCP analysis has a higher priority on optimization rather 55 Figure 17 Concurrent directed Circuitscape results from known positive CWD detection locations (as of May, 2021) to existing Kootenay mountain caribou (Rangifer tarandus caribou) ranges for mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), moose (Alces alces), and elk (Cervus canadensis) combined, using the summed value of each individual analysis. Current density value indicates overlapping areas of concentrated movement. Map generated using ArcMap Pro 2.2.0 56 Figure 18 Least-cost path (LCP) results from known positive CWD detection locations (as of May, 2021) to existing Kootenay mountain caribou (Rangifer tarandus caribou) ranges for mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), moose (Alces alces), and elk (Cervus canadensis). Summed current density from Figure 17 is used as a background for comparison. Maps generated using ArcMap Pro 2.2.0 57 than natural movement, thus many of the LCP corridors are more direct and less realistic than the Circuitscape analysis. LCP optimal corridors are more likely to cut through small passes in mountain ranges, even when there are nearby high current density pathways to use. From Libby MT, the western edge of Kootenay Lake is again predicted to be used by moose and mule deer, while the eastern edge is used by white-tailed deer and elk. While other species selected border crossing points near their starting locations, white-tailed deer were considerably more selective and only crossed through the Rocky Mountains at the major highways. All species cross the border at the Purcells trench, though elk were predicted to use the Purcells Mountains as well. Other notable corridors where Circuitscape and LCP analyses matched include the Kananaskis Lake and Palliser River, the Aurora and Verdant Creeks (elk only), Highway 1, and Highway 93. Objective 3: Estimate Risk of a Border Crossing Event Maximum seasonal movement distance varied greatly among the four species, with the maximum distance for elk (140.6 km) over twice as long as that of moose (60.9 km) or mule deer (68.57 km) (Figures 19 to 22). White-tailed deer had the second longest movement at 109.4 km (Figure 22). However the mean distance travelled by elk (26.6 km, SD = 17.3) is much more in line with moose (22.9 km, SD = 12.1) and mule deer (25.0 km, SD = 14.9) than that of white-tailed deer (22.1 km, SD = 28.2). We note that the standard deviation on white-tailed deer is large due to small sample size. Taking into account the buffer risk values alone, white-tailed deer have the highest risk of a border crossing event (Table 6), with the three highest-risk areas being Eden Valley (24.6%), Chain Lakes Reserve (21.4%), and Turner Valley (13.5%). The highest-risk area for Montana is the Blackfeet Reserve area (8.5% risk for white-tailed deer). However, incorporating the 58 Figure 19 Map depicting the distance-based risk (0-0.5) of a British Columbian boundary-crossing event within a seasonal migration by elk (Cervus canadensis). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 = 59 Figure 20 Map depicting the distance-based risk (0-0.5) of a British Columbian boundary-crossing event within a seasonal migration by moose (Alces alces). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 = 60 Figure 21 Map depicting the distance-based risk (0-0.5) of a British Columbian boundary-crossing event within a seasonal migration by mule deer (Odocoileus hemionus). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 = 61 Figure 22 Map depicting the distance-based risk (0-0.5) of a British Columbian boundary-crossing event within a seasonal migration by white-tailed deer (Odocoileus virginianus). Intensity of red colour shows probability of a single animal moving from that cell’s location to a location within the BC border during a single seasonal movement. The cell size is 100m x 100m. Map generated using ArcMap Pro 2.2.0 = 62 prevalence rates for CWD in these regions reduces these values considerably (0.4% - 0.9% for all the above examples). The CWD prevalence rates used in Table 6 were considered accurate according to sources at the time of this analysis (September 2022). By multiplying the overall buffer risk by the prevalence rate, white-tailed deer crossing risk from Eden Valley and Chain Lakes Reserve are reduced to 1.1% and 0.8% respectively. Mule deer from Eden Valley become the third-highest risk at 0.6%. Before moving to the discussion, it is important to clarify precisely how this study defines ‘risk of a crossing event’ to avoid misinterpretation. The buffer risk probabilities displayed on Figures 19 to 22 are the risk that any one individual animal of that species moves far enough, in the correct direction, and during a single season such that it crosses the border. It does not account for the density of animals on the landscape, and therefore we are not representing the actual probability of CWD entering BC in a single season. Framed differently, a 1% risk of crossing means that, given 1000 animals, 10 are likely to complete a crossing event. A 0.2% buffer risk prevalence means given 1000 deer, 2 are likely to both have CWD and complete a crossing event. 63 Table 6 BC border-crossing event risk percentages from existing areas of positive CWD reports near the BC border. Areas are ordered starting at the northernmost location and moving counterclockwise around the border. Risk values are proportions from 0 to 1, and are based on the risk buffers created for this analysis (Figures 19 to 22). The risk value is defined as the probability that a single animal would make a seasonal movement that would cause it to cross the border, assuming that an animal is at that location. Estimated CWD prevalence is based on full province data, rather than the specific areas. 64 Discussion CWD is a pervasive disease in many US states and Canadian provinces, and has the potential to disrupt food security, culture, recreation, and conservation objectives. One of the key knowledge gaps in managing CWD is a broad scale, landscape-level assessment of how this disease spreads (Winter and Escobar, 2020). Our study applies telemetry data collected in Region 4 of BC to estimate the likely locations and current risk levels of CWD crossing the BC border from known CWD-positive areas, and how those locations relate to BC’s current CWD monitoring plan. We were able to identify multiple corridors with high-likelihood of facilitating cervids moving into BC and, as CWD is transmitted via natural animal movement, we expect it to occur in these areas of the province first. With this information on corridors, wildlife health managers will have a better understanding of how to develop CWD monitoring and response plans. In addition, our crossing risk analysis shows that at this time, it is highly unlikely (< 1%) that a CWD-infected animal from any known CWD-positive area would cross the border into BC in a single season. Continued and increased monitoring of the areas near the BC border will be paramount to updating the model inputs and reassessing current risk levels accurately in the future. Below we discuss the context of our results as it relates to the underlying resource selection functions used in the connectivity models and our three objectives. Resource Selection Functions and Validation Across the four species in this study, only a few terms in the RSF models were consistent in all seasons. Higher mean annual EVI was selected in all summer models, while southwestness was selected in all winter models – both results matching expectations from previous studies (Klebenow, 1965; Stewart et al., 2010 for cover; Armleder et al., 1994; D’Eon and Serrouya, 2005 for southwestness). Mule deer were unique in selecting for high elevations in both seasons. 65 Typically, mule deer use low elevation areas in the winter and migrate to higher elevations in the spring (D'Eon and Serrouya, 2005; Nicholson et al., 1997; Robinson et al., 2006). In the winter, both elk and mule deer selected low EVI (i.e. areas with less year-round vegetation cover) and for south-facing slopes, suggesting that these species are utilising low canopy cover slopes with minimal snow depth for browsing (Stewart et al., 2010, Poole and Mowat, 2005). Other studies found that mule deer prefer mature coniferous forest in the winter as dense canopies can provide snow interception on the ground level (D’Eon and Serrouya, 2005, Poole and Mowat, 2005, Armleder, 1986). Our results may conflict with this finding by D’Eon and Serrouya (2005), differing results could be due to a difference between habitat types used for browsing versus security cover (Armleder, 1986), or a potential issue with using mean EVI as a proxy for forest characteristics. In winter, white-tailed deer were the only species that selected for areas of low EVI variance (i.e. stable vegetation levels throughout the year), implying that they use conifer forests with reduced subcanopy snowpack. Similar results were observed by Poole and Mowat (2005), who found that white-tailed deer movements are sensitive to snow depths in western Canada with Douglas fir-ponderosa pine stands. In the transformation of the RSF projections to resistance surfaces, the c8 (for white-tailed deer) and the e5 (for all other species) had the highest validation scores (Figures 6 and 7). These two transformations match the best-performing transformations in other studies (c8 in Zeller et al., 2018; e5 in Braaker et al., 2014). However, validation scores were universally lower in our study (60th - 80th percentile) compared to Zeller et al., 2018 (90th - 95th percentile). The lower validation performance in our study could be partially attributed to the extrapolation of the RSF models across a broader area when compared to Zeller’s study. Pither et al., (2021) found that telemetry data from large mammals correlated strongly with high-current density areas 66 calculated by Omniscape, though the relative accuracy was unreported. One possible reason for white-tailed deer having a different highest-scoring transformation is the stricter adherence to lower elevations within their range, especially in winter (Poole and Mowat, 2005; Long et al., 2010; Staudenmaier et al., 2021). The more linear and restricted movements of white-tailed deer through valleys may be better represented by the c8 transformation which enhances the resistance of high resistance areas even further than e5. Objective 1: Evaluate CWD Monitoring Effectiveness The presence of high-connectivity MUs along the BC border and encroaching cases in MUs beyond the MTA highlights the need for the inclusion of additional MUs into the MTA. Along the southern border, the western end of the MTA (i.e., southwest side of MU 4-07) has relatively low connectivity compared to its immediate neighbours both east and west. Low-connectivity areas are expected to take more time for CWD to move through compared to higher-connectivity areas. In this way, adding MU 4-08 (see Figure 1) to the MTA could increase the detection probability for potential CWD cases moving north from Idaho because movement is better facilitated along that route (Figure 8). Along the Canada/USA border, CWD surveillance outside of the MTA remains insufficient, with less than 5 samples for any species (Figure 9). These low numbers of samples imply hunters in the Kootenay region are unwilling to voluntarily submit deer heads for testing without incentives or enforcement, or lack the knowledge and infrastructure to submit samples reliably. If we were to assume that CWD has spread beyond the areas where cases have been reported in Idaho (USGS, 2023; Langenberg et al., 2012), expanding the MTA west into at least 4-08 should be considered at this time. Considering the distance-based risk with Circuitscape (Figure 9) also highlights how nearby CWD-positive locations disrupt the positive relationship between connectivity, harvest, 67 and monitoring (Figure 8). For example, though the MUs north of 4-23 show low connectivity relative to other MUs in BC, the proximity to existing CWD cases increases the risk of CWD incursion, despite lower levels of connectivity relative to other areas of the MTA. In an ideal case, altering the harvest and monitoring numbers within the MTA to achieve positive correlation between Circuitscape connectivity and harvest would increase the probability and speed of CWD detection. For both mule and white-tailed deer, Figure 9 shows that only one or two MUs per species need an improvement in sample rates to substantially improve the risk-sampling relationship, notably 4-07, 4-01, and 4-04. Each of these areas show low levels of hunter activity (Appendix Figure 33), likely due to their difficult terrain. The reduced accessibility may also affect cervids by lowering their local populations in these areas, adding to the lack of successful harvest. Figure 9 also shows some MUs that could be considered ‘over-sampled’ (i.e. low risk, high harvest/hunting activity), though adjusting these areas shouldn’t be a priority unless monitoring resources are limited. As of 2022 harvest rates in MUs 4-07 to 4-09 remain relatively low. In Alberta, it was found that 16 deer per 18km2 needed to be tested to have 80% testing confidence (Merrill et al., 2013). A predictive disease model for the Kootenay region determined a baseline of 600-700 annual samples within the current MTA to maintain a 95% confidence level of CWD presence/absence given a 0.5% prevalence rate (Imada, 2022). Cervid harvest in the Kootenay region would need to be increased in target monitoring areas to reach target numbers. A change in testing policy may therefore need to be coupled with a promotion of hunting in these areas to improve detection, through adjusting licensing and improving accessibility. Mysterud et al. (2020) predicts a marked improvement in detection probability by adjusting the harvested sex ratio to include more males and more mature individuals. Zimmer et al. (2010) found that 68 hunters in Alberta were interested in and willing to participate in extended seasons for herd reduction and CWD monitoring. A survey in Alberta showed the public is receptive to methods such as increasing tag limits and compensation for head submission (Durocher, 2022). These forms of improving harvest, and therefore monitoring effectiveness, can be implemented before CWD enters the province. Proactive harvest strategies set ahead of CWD detection can increase cervid resistance to widespread infection (Belsare et al., 2021). Harvest rates may also be improved by increasing road access and visibility from roads in target areas (Lebel et al., 2012). It is recommended that additional studies are taken to forecast the impact to wildlife from increased hunting pressure in higher-risk areas before decisions are made. Objective 2: Identify High-Likelihood Border Crossing Locations The highest-likelihood border crossing locations remained fairly consistent among the four species analyzed, even though routes to these crossing locations varied. Overlap of corridor use among species was common, particularly in the Rocky Mountains. Many of the corridors from Alberta connect into BC MUs north of the MTA. Given the low voluntary sampling rate from hunters (11.9 per MU samples outside of MTA versus 165.5 samples per MU inside MTA), it is likely that cervid populations outside the MTA are under-sampled north of the MTA and are thus at risk for harbouring CWD undetected (Imada, 2022). The multiple wide, accessible valleys north of the MTA may be used more by animals that have begun to deteriorate physically due to CWD infection (Edmunds et al., 2018; Williams and Miller, 2002). Compared to LCPs when assessing caribou herd risk, Circuitscape results indicated broader corridors and followed more intuitive routes, rather than the most strictly optimal paths of the LCP. Using Circuitscape also provided alternative pathways rather than a single one, better representing the more diffuse nature of animal movement in vivo. Similarly, Gallo et al., (2019) found that while both LCPs 69 were more straight-forward when it came to identifying important areas of concern for wildlife managers, Omniscape allowed a flexibility that would require rerunning and stacking multiple LCPs to achieve the same results. Movement corridors in the Rocky Mountains were often surrounded by rugged areas with high resistance to movement, masking the importance of the corridor itself when looking at mean current density for an MU. Synthesizing the results of the Circuitscape and MU-level analyses produced a better understanding of how to identify and manage areas of CWD concern. The synthesis of other model types with our own could reveal even more insights (Winter and Escobar, 2020). As discussed above, extending the MTA north to include MUs that contain high-likelihood corridors would need to be complemented by improving harvest rates. A complementary method to MU-level monitoring could be a more targeted monitoring system for movement corridors specifically. Several of the corridors highlighted by the Circuitscape analyses have nearby municipalities in BC (Table 5). These municipalities could be used as ideal locations for targeted CWD education programs and government-led testing initiatives, which may become more popular as the technology for live testing improves (Ferreira et al., 2021). Objective 3: Estimate Risk of a Border Crossing Event Overall, we found that there was a low current risk of a CWD-positive animal crossing the BC border from areas where cases have been reported in MT and AB. This calculation of risk includes estimates of movement distance and prevalence of CWD in local populations adjacent to BC. Typically, many deer have a high fidelity to their winter and summer ranges. If we assume that home range fidelity is sufficiently common (D’Eon and Serrouya, 2005; Kinley, 2019; Brough et al., 2017), then the measure of border-crossing risk for an animal's movement in a single season can be further thought of as annual or even lifetime risk of a crossing event. 70 With time, we expect the risk of CWD crossing the border to increase for at least two reasons. First, while we focused on modelling interseasonal movements we know that some animals in the study area are capable of rare, but sustained long-distance movements (Moll et al., 2021). It is unknown if CWD positive animals can conduct such movements, but with each year there is a greater chance of this rare event occurring. Second, the prevalence of CWD in areas outside of BC is growing, such that CWD positive animals are getting closer to BC’s borders and prevalence is increasing (USGS, 2023). With both increases in proximity and prevalence, the probability of CWD-positive animals crossing the BC border grows each year. In addition, we do not know how many animals occur in each source population, which can affect the overall probability of an animal from that population crossing the BC border. For example, if Eden Valley (0.8% estimated crossing risk for mule deer) had a local mule deer population of 100, we might predict at most 1 deer crossing that might be likely to cross the border. If the population were 1000, we might expect 8. So while prevalence is likely increasing in source populations (Alberta, 2022; Weisler et al., 2022), true risk needs to account for the actual number of CWD transmitters in the source population. Method Applications The connectivity models in our study are one of the first to examine disease transmission pathways in a region with extreme topographical variation, an important characteristic for all future CWD modeling in western North America. Many models have been developed to explain CWD spread or predict future patterns of CWD transmission (Winter and Escobar, 2020). Models investigating landscape connectivity and CWD transmission have typically been built in prairie regions (even when adjacent to the Rocky Mountains) where topography is less of a factor in directing animal movement (Nobert et al., 2016; Winter and Escobar, 2020; Winter, 71 2020; Jennelle et al., 2022; Grovenberg et al., 2011; Habib et al., 2011). At this time multiple states west of the mountain-prairie divide have detected CWD in wild populations, such as Utah and Idaho. On the west coast of North America, the six remaining provinces and states that do not currently have CWD are located even further west of the Rocky Mountains (USGS, 2023). Mountain-valley systems restrict the movement of most cervid species (Long et al., 2010; Winter and Escobar, 2020; Martinka, 1968; Staudenmaier et al., 2021), so there is an important opportunity to maximize preventative measures and monitoring of CWD in BC. Benz et al. (2016) used LCPs to identify elk migrations between ranges in southwestern Alberta, analyzing a smaller area than our own study and at a finer scale. Whereas LCP analyses produce highly specific corridors, our Circuitscape models better represent the varied and less directed nature of animal movement. The corridors created by Circuitscape were wider and farther reaching than those created by Benz et al., 2016. Now that the methods for Circuitscape models into BC have been created, it can then be utilized in future studies. Beyond updating CWD risk as new CWD-positive areas appear, our methods could also be used to predict the effectiveness of mitigation measures by wildlife managers by simulating barriers to movement via fencing, highways, cull zones, and other intentional methods of impeding animal movement (Alexander and Waters 2000; Wobeser 2002; Mysterud and Rolandsen 2019; Olsson and Widen, 2008; Hering, 2020). In the future, the code required to run these models could be implemented in a user-friendly package, such as a R Shiny App (Chang et al., 2023). Limitations There are at least two major external limitations to this study that bear consideration in management and application to other contexts. These limitations center around forming regional 72 predictions from (a) a limited sample of telemetry data smaller than the study area; and (b) a non-proportional demographic of collared animals. We made regional (183746 km2) predictions from telemetry data that varied from 9.67% of to 29.64% of the study area (based on an MCP of the full dataset for each species) and in this way, our models assume that unmonitored animals of the same species respond similarly to those in the telemetry data across the study area. This assumption is reasonable if animals in unsampled regions of our study area exhibit similar behaviours as those in our telemetry datasets. However, variation in animal movement patterns within and among populations may weaken these assumptions, particularly in areas of novel terrain (e.g. a mule deer from the prairies avoiding movement into the Rocky Mountains or urban white-tailed deer in Libby, Montana (Wright et al., 2020). More targeted telemetry projects to bolster broad-scope connectivity analyses are one way to reduce or remove the need for these assumptions. Differences in the fix rates between datasets could also lead to model bias for one area or type of terrain over another when applying them to the whole study area. A similar assumption was required for behavioural differences between sexes of a species. The overwhelming majority of the telemetry data used were collared females, whereas for some species such as mule deer, CWD is far more present in males than females due to increased levels of physical contact with both females and other males (Miller et al., 2008). If male cervids have unique movement or seasonal behaviours, they are largely unaccounted for in these analyses. In some regions, higher CWD prevalence is seen in males, highlighting the need to more studies to include males in their collaring project to ensure their demographic is being modeled properly. Sex bias may not be restricted to the telemetry data, but the acquired cervid harvest statistics as well. Generally, male cervids are targeted for harvest more than females, 73 potentially leading to further disconnect when comparing them to the results of our largely female telemetry data. Connectivity modeling involves many decisions that can be subjective and sensitive to change (Wade et al., 2015). While we were able to test variations in some parameters of our methods, there are more opportunities for sensitivity and uncertainty analyses that could have been applied including the number of available points and weights in the RSF modeling, as well as cell resolutions, source areas, window sizes, and search radii in the Circuitscape and Omniscape analyses. Many of the parameter values listed were selected based on existing literature, and analyzing variations on these values could lead to better understanding of how important these decisions are in our modeling context. While fully exploring sensitivity was not feasible for the scope of this thesis, figures addressing the sensitivity of available point numbers, modeling family, RSF weights, Omniscape source areas, and Circuitscape cell resolution for the mule deer winter dataset can be found in the Appendix. Overall, it was found that changes to these inputs did not significantly alter the results of the models. Moving Forward The methods in this analysis could be further improved by future data that could be broadly applicable to science and wildlife management as a whole. Here we recommend some subjects for future studies and data collection projects. This analysis relied on extrapolating results across a large region from a core of tangible data in its center. For broad scale analysis, it would be best for the initial telemetry data to represent a larger portion of the full study area, as well as better representing both male and female animals, in order to better represent the demographic landscape of CWD. We recommend the further development of GPS-tagging projects such as Southern Interior Mule Deer Project 74 (BC Wildlife Federation, 2022), with the strong encouragement to make the data publicly available for analyses such as these. Similarly, the results of some of the RSF’s conducted may imply that there may be additional terms that could be used to improve the model’s accuracy, especially for mule deer (Table 4). Future studies should consider testing additional or alternate predictor terms in model creation, such as tree species (Hermosilla et al., 2022), landscape heterogeneity (Kie et al., 2002), or snow cover (Poole and Mowat, 2005). 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[Ottawa]: Library and Archives Canada = Bibliothèque et Archives Canada. 96 Appendix Figure 23 Test of collinearity for all collected environmental prediction layers. Values further from zero indicate high positive or negative collinearity. The full study area-wide rasters were evaluated against each other. 97 Figure 24 Omniscape result for elk (Cervus canadensis) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 98 Figure 25 Omniscape result for moose (Alces alces) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 99 Figure 26 Omniscape result for mule deer (Odocoileus hemionus) using the chosen ‘e5’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 100 Figure 27 Omniscape result for white-tailed deer (Odocoileus virginianus) using the chosen ‘c8’ transformation. Current density value indicates concentrated areas of movement, whereas low values indicate difficulty of passage. Map generated using ArcMap Pro 2.2.0 101 Figure 28 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and total submitted CWD samples from 2019-2021 for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. A log-scaled y-axis is used, allowing for linear trend lines. X-axes values are hidden for clarity as they are arbitrary and change between species. 102 Figure 29 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean days spent hunting per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 103 Figure 30 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean count of animals harvested per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 104 Figure 31 Four figures showing, by MU, the correlation between species-specific mean Omniscape cumulative current density and mean rate of hunter success (harvests per day spent hunting) for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 105 Figure 32 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and total submitted CWD samples from 2019-2021 for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. A log-scaled y-axis is used, allowing for linear trend lines. X-axes values are hidden for clarity as they are arbitrary and change between species. 106 Figure 33 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean days spent hunting per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 107 Figure 34 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean count of animals harvested per year for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 108 Figure 35 Four figures showing, by MU, the correlation between species-specific mean Circuitscape cumulative current density and mean rate of hunter success (harvests per day spent hunting) for a) Odocoileus hemionus, b) Odocoileus virginianus, c) Alces alces, and d) Cervus canadensis. Hunting statistics were generated from 2019-2021 BC government random sample survey results. Model lines were generated using gamma general linear models to remove the possibility of predicting values below 0. X-axes values are hidden for clarity as they are arbitrary and change between species. 109 Figure 36 Comparisons of Circuitscape maps using 5, 10, and 50 evenly spaced ground nodes. Different rows depict different species results. No meaningful differences in path selection were noted, though using more ground nodes created better contrast visually. 110 Table 7 Risk of a crossing event in table form, based on the methods in Objective 3. Animal movement is assumed to be unimpeded by geography. Risk values were estimated using collected telemetry data in the East Kootenay area. 111 Figure 37 RSF projection for elk (Cervus canadensis) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 112 Figure 38 RSF projection for elk (Cervus canadensis) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 113 Figure 39 RSF projection for moose (Alces alces) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 114 Figure 40 RSF projection for moose (Alces alces) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 115 Figure 41 RSF projection for mule deer (Odocoileus hemionus) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 116 Figure 42 RSF projection for mule deer (Odocoileus hemionus) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 117 Figure 43 RSF projection for white-tailed deer (Odocoileus virginianus) in the summer season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 118 Figure 44 RSF projection for white-tailed deer (Odocoileus virginianus) in the winter season using the coefficients determined in the RSF for the Kootenay region of British Columbia. Higher selection values represent areas an individual is more likely to inhabit during the season. Map generated using ArcMap Pro 2.2.0 119 Table 8 A detailed list of decision points with more than one viable option for input. Each parameter is listed with the value chosen, its justification for that selection, possible consequences of that decision and any tests made to explore sensitivity and uncertainty. Analysis Step Parameter Value Justification Possible Effect Tested? Data Cleaning Autocorrelation Not accounted for Highly variable data sources and too much data lost to include it. Smaller coefficient variances and possibly means could change. No. RSF Random Effects Random intercept included, no random slope Models failed when random slope was included. Possibly too little data available for some individuals. Radom slopes account for more differences between individual animals and could adjust coefficient values. No. RSF Available Locations Generated 5 Gillingham and Parker, 2008; Baasch, 2008 Low numbers of available can potentially cause poor coefficient estimates. Values of 5-100 tested on winter mule deer. Little change in coefficients. RSF Modeling Distribution Binomial Commonly used in RSFs Poisson distributions can provide better coefficient estimates with fewer available needed in some cases. Tested on winter mule deer with available ranging 5-10. Little difference compared to binomial distribution. RSF Used/Available weights 1/1000 Muff et al., 2020; Fithian and Hastie, 2013 Modifying the weights could cause changes in the coefficient estimates. Values of 1-5000 tested on winter mule deer. Small changes occurred in some parameters at very low weights (<100). Resistance Surface Creation Transformation formula Species dependent (either '^5' or the 'c8' formula) 5 different transformations were tested in Omniscape. The change in the range of values affects how easily simulated animal movement can travel through high resistance areas. All 5 transformations were run and validated using independent data. Omniscape /Circuitscape Cell Resolution 150m Not enough memory available to run analysis at the original 30m resolution. Coarsened to ease computational load. Increasing the cell resolution could lead to narrow corridors being 'washed' out and become less available for use by simulated animals. Circuitscape was run at both 150m and 1050m resolution for moose. The results were largely similar at a courser resolution, though higher current densities were noted in the most prominent corridors. Omniscape Window size 11 This value eases computational density by assess movement based on larger blocks of cells. A size of 11 allowed for faster analysis. Large window sizes relative to the search radius can cause halo-like 'artefacting' in the resulting maps. Not enough computer memory available to test smaller window sizes. Omniscape Search Radius Species-dependent Generated using average AKDE home range size of our dataset. Changes to this parameter affects how far the simulated animals will 'see' quality habitat across high resistance terrain. No. Omniscape Source Area 90th Percentile of RSF projection Recommendation from Dr. Matt Williamson of Boise State University. Changing this value alters the amount of quality habitat available for the simulated animals to travel between and increases the amount of movement in less-preferred areas. Multiple source area percentage cutoffs (ranging form 70%-95% were tested in Omniscape for mule deer. Validation results altered were higher or lower in value, but the scoring order of the transformations remained consistent. Circuitscape Number of ground nodes 50 50 had slightly higher contrast based on tests. Could potentially affect the use of different corridors 5/10/50 ground nodes were tested. Little difference found. 120 Figure 45 A picture showing the difference in resource selection function results when the number of available points used is adjusted. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. 121 Figure 46 A picture showing the difference in resource selection function results when the number of available points used is adjusted and the binomial distribution is substituted for the Poisson distribution. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. 122 Figure 47 A picture showing the difference in resource selection function results when the model weights on the available points is adjusted. Points show mean coefficient value. This test was done on the mule deer (Odocoileus hemionus) winter dataset used in this thesis. 123 Figure 48 Results of resistance surface transformation validation for mule deer (Odocoileus hemionus) when the percentile cutoff of the RSF projection for use as source areas is adjusted. Values between to 70 th and the 95th percentile were tested, separated by colour. The 90th percentile was used in this thesis. In all cases, the same transformation received the highest score. Figure 49 A comparison of the Circuitscape results for moose (Alces alces) using a 150m cell resolution (on the left) and a 1050m resolution (on the right).
Thesis/Dissertation
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Biology
Vancouver : University of British Columbia Library
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Ford, Adam
Keeping ahead of chronic wasting disease : an assessment of trans-boundary cervid movement in British Columbia
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