Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Wright, Chloe Antonia
Doctor of Philosophy - PhD
University of British Columbia
Human induced landscape change has had a profound and lasting impact on the earth, leading to habitat destruction and biodiversity loss. Understanding and reversing the consequences of anthropogenic landscape change is a priority for wildlife managers. An important step in preserving biodiversity is protecting high quality habitat, which is often inferred through resource selection functions (RSFs) and does not include an explicit link between habitat and fitness, potentially leading to incorrect conclusions about what constitutes high-quality habitat for a species. ​In this dissertation, I evaluated the effects of landscape change on mule deer (Odocoileus hemionus) migration and survival in south-central British Columbia, Canada, and estimated whether RSFs correlated with survival and therefore inferred habitat quality. Specifically, I studied deer spring migration in a forested ecosystem disturbed by timber harvesting, identified how exposure to landscape disturbances affected deer survival, and integrated resource selection functions and survival models to quantify habitat quality across seasons and deer ages. To meet these objectives, I placed global positioning system collars on 201 adult female mule deer, 270 fawns, and 134 neonates from 2018 – 2022. ​I found that during spring migration, deer did not time their movements to match the pace of forage green-up, likely because green-up did not occur in a way that was conducive for deer to track. Potentially, timber harvesting has created a mosaic of early and late green-up patches, affecting the order and timing of green-up. Additionally, I found mortality risk increased in the winter when deer used areas with higher road densities and deeper snow. I also found deer that used recent burns and cutblocks had a reduced mortality risk in the summer compared to those that used these disturbances less often. ​Finally, I found that for neonates, fawns, and adults in the summer, selection positively correlated with survival, and RSFs accurately inferred habitat quality. However, for adults in the winter, I observed no relationship between predicted probabilities of use and survival. The common assumption that selection correlates with survival was not met for all deer, highlighting the importance of incorporating survival or fitness-based metrics into models that measure habitat quality.
SURVIVAL AND MOVEMENTS OF MULE DEER (ODOCOILEUS HEMIONUS) IN SOUTHERN BRITISH COLUMBIA by Chloe Antonia Wright B.Sc., University of Florida, 2011 M.Sc., University of Montana, 2018 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Chloe Wright, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: SURVIVAL AND MOVEMENTS OF MULE DEER (ODOCOILEUS HEMIONUS) IN SOUTHERN BRITISH COLUMBIA submitted by Chloe Antonia Wright in partial fulfillment of the requirements of the degree of Doctor of Philosophy . Dr. Adam T. Ford, Department of Biology, Irving K. Barber Faculty of Science Supervisor Dr. Karen E. Hodges, Department of Biology, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Michael Noonan, Department of Biology, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Sarah Gergel, University of British Columbia - Vancouver University Examiner Dr. Ryan Brook, University of Saskatchewan External Examiner Additional Committee Members include: Dr. Robert Serrouya, Department of Biology – Adjunct, Irving K. Barber Faculty of Science Supervisory Committee Member iii Abstract Human induced landscape change has had a profound and lasting impact on the earth, leading to habitat destruction and biodiversity loss. Understanding and reversing the consequences of anthropogenic landscape change is a priority for wildlife managers. An important step in preserving biodiversity is protecting high quality habitat, which is often inferred through resource selection functions (RSFs) and does not include an explicit link between habitat and fitness, potentially leading to incorrect conclusions about what constitutes high-quality habitat for a species. In this dissertation, I evaluated the effects of landscape change on mule deer (Odocoileus hemionus) migration and survival in south-central British Columbia, Canada, and estimated whether RSFs correlated with survival and therefore inferred habitat quality. Specifically, I studied deer spring migration in a forested ecosystem disturbed by timber harvesting, identified how exposure to landscape disturbances affected deer survival, and integrated resource selection functions and survival models to quantify habitat quality across seasons and deer ages. To meet these objectives, I placed global positioning system collars on 201 adult female mule deer, 270 fawns, and 134 neonates from 2018 – 2022. I found that during spring migration, deer did not time their movements to match the pace of forage green-up, likely because green-up did not occur in a way that was conducive for deer to track. Potentially, timber harvesting has created a mosaic of early and late green-up patches, affecting the order and timing of green-up. Additionally, I found mortality risk increased in the winter when deer used areas with higher road densities and deeper snow. I also found deer that used recent burns and cutblocks had a reduced mortality risk in the summer compared to those that used these disturbances less often. iv Finally, I found that for neonates, fawns, and adults in the summer, selection positively correlated with survival, and RSFs accurately inferred habitat quality. However, for adults in the winter, I observed no relationship between predicted probabilities of use and survival. The common assumption that selection correlates with survival was not met for all deer, highlighting the importance of incorporating survival or fitness-based metrics into models that measure habitat quality. v Lay Summary Humans have profoundly transformed ecosystems globally, leading to habitat and biodiversity loss. Understanding the consequences of these changes and protecting high quality habitat is important to management agencies, but defining high quality habitat is difficult. I evaluated the effects of landscape change on mule deer migration and survival in south-central British Columbia, Canada, and tested whether commonly used statistical models (resource selection functions; RSFs) infer habitat quality. During spring migration, deer did not time their movements to match the pace of forage green-up, potentially because timber harvesting has disrupted the natural order and timing of green-up. Additionally, roads and deep snow increased deer mortality risk in winter, and recent burns and cutblocks reduced mortality risk in summer. Finally, for some deer, RSFs positively correlated with survival, and measured habitat quality. However, this relationship was not true for all deer, highlighting the importance of incorporating survival into models measuring habitat quality. vi Preface Contributions This dissertation is part of a larger project, the Southern Interior Mule Deer project, that was formed through initial discussions in 2016-2017 with Andrew Walker, Craig McLean, Chris Procter, Jesse Zeman, Dr. Adam Ford, and Dr. Sophie Gilbert. I was not a part of these discussions. This project was supported by grants from the Habitat Conservation Trust Foundation, the Forest Enhancement Society of British Columbia, BC Timber Sales, the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Foundation for Innovation, BC Ministry of Transportation and Infrastructure, and from fellowships from the University of British Columbia and NSERC. Additional funding and support was provided by our project partners the BC Wildlife Federation (and many local hunting and fishing groups that provided support therein), BC Wildlife Branch (Ministry of Forests, Lands, Natural Resource Operations, and Rural Development, Ministry of Forests), Okanagan Nations Alliance, University of Idaho, and the Bonaparte Indian Band. Many volunteers and personnel from our project partners helped make this field work possible. I was directly involved in all fieldwork conducted between March 2018 and December 2021. Fieldwork conducted January 2022 – May 2023 was conducted by Chloe Howarth, Andrew Walker, Craig McLean, TJ Gooliaff, Chris Procter, Alexandra Francis, and Kieran Braid. The ideas and analyses used in Chapters 2, 3, and 4 are based on discussions and collaboration with Dr. Adam Ford. vii I performed all software coding and data analyses, and created all figures, maps, and tables for this thesis and subsequent manuscripts. All writing is my own, with edits from my committee and advisor. Manuscripts Modified versions of Chapters 2, 3, and 4 will be submitted for peer review and journal publication. Permits Part of this work involved capturing and collaring mule deer. All animal capture and handling methods were approved by the University of British Columbia’s Animal Care Committee (Protocols A18-0300 and A22-0257), the BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development, and the BC Ministry of Forests (Permits MRPE18-290018 and MRPE23-800846). viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiii List of Figures ........................................................................................................................... xviii Acknowledgements ....................................................................................................................xxv Chapter 1: Introduction ................................................................................................................1 1.1 Anthropogenic Landscape Change ............................................................................... 1 1.2 Effects of Anthropogenic Landscape Change on Species ........................................... 2 1.3 Conserving Species Through Habitat Protection ......................................................... 5 1.4 Defining Habitat Quality Through Resource Selection ............................................... 6 1.5 Determining More Meaningful Ways to Evaluate Habitat Quality ............................. 9 1.6 Study Species ............................................................................................................. 10 1.6.1 Anthropogenic Disturbances Affecting Mule Deer in BC .................................... 12 1.7 Study Areas ................................................................................................................ 15 1.8 General Approach ...................................................................................................... 17 1.9 Research Objectives ................................................................................................... 17 Chapter 2: Mule Deer Spring Migration in a Disturbed Forest Ecosystem ...........................21 2.1 Introduction ................................................................................................................. 21 2.2 Methods....................................................................................................................... 26 2.2.1 Deer Capture ......................................................................................................... 26 2.2.2 Measuring Migration ............................................................................................ 26 2.2.3 Greenscape and Green Wave Surfing ................................................................... 27 2.2.3.1 Characterizing the Greenscape ..................................................................... 27 2.2.3.2 Green Wave Surfing ..................................................................................... 28 2.2.4 Migration Route Fidelity....................................................................................... 29 2.2.4.1 Estimating Space Use and Overlap ............................................................... 29 2.2.4.2 Creating Covariates ....................................................................................... 30 2.2.4.3 Model Fitting ................................................................................................ 31 ix 2.2.5 Resource Selection ................................................................................................ 31 2.2.5.1 Determining Use and Availability ................................................................ 31 2.2.5.2 Creating Covariates ....................................................................................... 32 2.2.5.3 Determining Cutblock and Wildfire Age Groups ......................................... 34 2.2.5.4 Collinearity and Scaling ................................................................................ 35 2.2.5.5 Candidate Model Development and Fitting .................................................. 36 2.3 Results ......................................................................................................................... 37 2.3.1 Migration Summary .............................................................................................. 37 2.3.2 Greenscape and Green Wave Surfing ................................................................... 38 2.3.3 Migration Route Fidelity....................................................................................... 38 2.3.4 Resource Selection ................................................................................................ 39 2.4 Discussion ................................................................................................................... 41 2.5 Figures......................................................................................................................... 49 2.6 Tables .......................................................................................................................... 54 Chapter 3: The Effect of Anthropogenic Land Use Changes and Weather on Mule Deer Survival .........................................................................................................................................56 3.1 Introduction ................................................................................................................. 56 3.2 Methods....................................................................................................................... 60 3.2.1 Deer Capture ......................................................................................................... 60 3.2.2 Determining Cause-Specific Mortality Sources ................................................... 62 3.2.3 Determining Survival Time Frames...................................................................... 63 3.2.3.1 Defining Seasons .......................................................................................... 63 3.2.3.2 Defining Time Frames .................................................................................. 64 3.2.4 Determining Space Use in Short and Long Time Frames .................................... 65 3.2.5 Landscape and Weather Covariates ...................................................................... 66 3.2.5.1 Creating Covariates Included in All Seasons................................................ 66 3.2.5.2 Creating Seasonal Weather Covariates ......................................................... 68 3.2.6 Addressing Covariation Among Predictor Variables ........................................... 69 3.2.7 Survival Modeling ................................................................................................ 71 3.2.7.1 Cox Proportional Hazards Model ................................................................. 71 3.2.7.2 Model Development and Fitting ................................................................... 72 x 3.3 Results ......................................................................................................................... 74 3.3.1 Deer Capture ......................................................................................................... 74 3.3.2 Kaplan-Meier Survival Estimates ......................................................................... 74 3.3.3 Cause-Specific Mortality ...................................................................................... 75 3.3.3.1 Adult Winter-Spring Models ........................................................................ 75 3.3.3.2 Adult Summer-Fall Models .......................................................................... 76 3.3.3.3 Fawn Winter-Spring Models......................................................................... 77 3.3.3.4 Neonate Summer Models .............................................................................. 78 3.4 Discussion ................................................................................................................... 80 3.5 Figures......................................................................................................................... 86 3.6 Tables .......................................................................................................................... 91 Chapter 4: Do resource selection functions correlate with mule deer survival across age classes? ..........................................................................................................................................95 4.1 Introduction ................................................................................................................. 95 4.2 Methods....................................................................................................................... 99 4.2.1 Deer Capture and Monitoring ............................................................................... 99 4.2.2 Determining Migratory Strategies ...................................................................... 101 4.2.3 Resource Selection Functions ............................................................................. 102 4.2.3.1 Quantifying Availability ............................................................................. 102 4.2.3.2 Creating Covariates ..................................................................................... 103 4.2.3.3 Collinearity and Scaling .............................................................................. 105 4.2.3.4 Model Development and Fitting ................................................................. 105 4.2.4 Survival Modeling .............................................................................................. 106 4.2.4.1 Creating Weather Covariates ...................................................................... 106 4.2.4.2 Instantaneous Covariates ............................................................................ 107 4.2.4.3 Seasonal Covariates .................................................................................... 107 4.2.4.4 Choosing Seasonal or Instantaneous Covariates ......................................... 108 4.2.4.5 Model Development.................................................................................... 108 4.2.4.6 Model Fitting .............................................................................................. 109 4.2.5 Population Growth-Based Habitat Quality ......................................................... 111 4.2.5.1 Mapping Survival and Population Growth ................................................. 111 xi 4.2.5.2 Quantifying Changes in Habitat Quality Under Different Weather Scenarios 112 4.3 Results ....................................................................................................................... 112 4.3.1 Deer Capture and Pregnancy Rates..................................................................... 112 4.3.2 Resource Selection .............................................................................................. 113 4.3.2.1 Winter-Spring ............................................................................................. 113 4.3.2.2 Summer-Fall ............................................................................................... 114 4.3.2.3 Neonates ...................................................................................................... 114 4.3.3 Survival Modeling .............................................................................................. 116 4.3.3.1 Winter-Spring ............................................................................................. 116 4.3.3.2 Summer-Fall ............................................................................................... 117 4.3.3.3 Neonates ...................................................................................................... 118 4.3.4 Population Growth-Based Habitat Quality ......................................................... 119 4.3.4.1 Population Growth Under Different Weather Scenarios ............................ 119 4.3.4.2 Population Growth in Ungulate Winter Range Units ................................. 119 4.4 Discussion ................................................................................................................. 120 4.5 Figures....................................................................................................................... 127 4.6 Tables ........................................................................................................................ 133 Chapter 5: Conclusion ...............................................................................................................138 5.1 Research Findings and Significance ......................................................................... 138 5.2 Management Implications ......................................................................................... 142 5.3 Limitations of Study Methods .................................................................................. 145 5.4 Future Directions ...................................................................................................... 149 Bibliography ...............................................................................................................................152 Appendices ..................................................................................................................................179 Appendix A – Cutblock and Wildfire Burn Ages During Migration ................................. 179 Appendix B – Sensitivity Analysis for Stopover Classification ......................................... 181 Appendix C – Migration Route Fidelity ............................................................................. 189 Appendix D – Model Output from Migratory Resource Selection ..................................... 191 Appendix E – Cutblock and Wildfire Burn Ages During Winter-Spring and Summer-Fall 204 xii Appendix F – Additional Survival Model Information and Results (Chapter 3) ............... 209 Appendix G – Additional Resource Selection Results (Chapter 4) .................................... 226 Appendix H – Random Intercepts vs. Random Slopes in Resource Selection Functions .. 247 Appendix I – Additional Survival Analysis Information (Chapter 4) ................................ 253 xiii List of Tables Table 2-1. Model set used to evaluate resource selection functions in corridors and stopovers within spring migration routes for mule deer in three study areas in south-central British Columbia, Canada during 2018-2021. Each model also contained a random intercept for individual deer identification and a random slope for each fixed covariate. ................................ 54 Table 3-1. Covariates and predictions used in Cox proportional hazards models for adult, fawn and neonate mule deer summer-fall and winter-spring mortality risk in south-central British Columbia, Canada in 2018-2022. We predicted that the effect of the covariate would be the same for all age classes within each season, although the strength of the effect might vary by age class........................................................................................................................................................ 91 Table 3-2. Mortality sources of GPS collared adult (≥ 1-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) mule deer in south-central British Columbia, Canada during 2018 – 2022............................................................................................................................................ 94 Table 4-1. Model set used to evaluate resource selection functions during summer-fall and winter-spring for adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in three study areas in south-central British Columbia, Canada during 2018-2023. Each model also contained a random intercept for individual deer identification. . 133 Table 4-2. Model selection results including the AICc value, ΔAICc score, and model weight (ωi) comparing relative support for the effects of daily and seasonal resource selection function (RSF) and weather values on survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023. The RSF and weather value (seasonal or daily) with the lowest AICc value was used in the next stage of survival modeling. ....................................................................................... 135 Table 4-3. Model selection results including the number of parameters (K), AICc model weight (ωi) and model log likelihood (LL) comparing relative support for 4 models and testing the relationship between weather, resource selection function (RSF) output, and survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023. The RSF model represented either the Ideal Selection or Ecological Trap hypotheses, depending on the direction of the predicted effect. The WSI x RSF model represented either the Non-ideal Selection or Weather Mediated Ecological trap hypothesis, depending on the direction of the predicted effects. The weather only model represented the Weather only hypothesis. ....................................................................... 136 Table 4-4. Beta estimates (β) and 95% confidence intervals (CI) for the top ranked parameters in Cox Proportional Hazards models testing the relationship between weather, resource selection function (RSF) output, and survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023............................................................................................................................................. 137 xiv Table C-1. Top ranking models that explained variation in spring migration route overlap over successive years for mule deer in south-central British Columbia, Canada during 2018-2021. Each model also contains a random intercept for individual deer ID. Only the top models that together accounted for < 1.00 of the cumulative AICc weight are shown here. ......................... 189 Table D-1. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Boundary study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 191 Table D-2. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Cache Creek study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 193 Table D-3. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the West Okanagan study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 195 Table D-4. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Boundary study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 197 Table D-5. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Cache Creek study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 199 xv Table D-6. Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the West Okanagan study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. ................................................................................................................... 201 Table D-7. Number and percentage of used and available points within the 3 wildfire covariates included in the top generalized linear mixed model predicting mule deer resource selection within migration corridors in south-central British Columbia, Canada during 2018-2021. New burns were areas burnt 1-10 years prior, old burns were areas burnt >10 years prior, and never burned areas were never burnt. ................................................................................................... 203 Table F-1. Models developed for winter-spring Cox proportional hazards models used to determine the effects of habitat use and weather on adult and fawn mule deer mortality risk in south-central British Columbia during 2018-2022. .................................................................... 209 Table F-2. Models developed for summer-fall Cox proportional hazards models used to determine the effects of habitat use and weather on adult and neonate mule deer mortality risk in south-central British Columbia during 2018-2022. .................................................................... 211 Table F-3. Model output for univariate Cox Proportional Hazard Models used to choose the covariate included in subsequent modeling for 4 groups of correlated covariates. .................... 213 Table F-4. All models ranked above the null model for the adult winter-spring 2 day analysis...................................................................................................................................................... 214 Table F-5. All models ranked above the null model for the adult winter-spring 7 day analysis...................................................................................................................................................... 215 Table F-6. All models ranked above the null model for the adult winter-spring 30 day analysis...................................................................................................................................................... 216 Table F-7. All models ranked above the null model for the adult summer-fall 2 day analysis. 217 Table F-8. All models ranked above the null model for the adult summer-fall 7 day analysis. 218 Table F-9. All models ranked above the null model for the adult summer-fall 30 day analysis...................................................................................................................................................... 219 Table F-10. All models ranked above the null model for the fawn winter-spring 2 day analysis...................................................................................................................................................... 220 Table F-11. All models ranked above the null model for the fawn winter-spring 7 day analysis...................................................................................................................................................... 221 xvi Table F-12. All models ranked above the null model for the fawn winter-spring 30 day analysis...................................................................................................................................................... 222 Table F-13. All models ranked above the null model for the neonate intrinsic analysis. .......... 223 Table F-14. All models ranked above the null model for the neonate summer 2 day analysis. 224 Table F-15. All models ranked above the null model for the neonate summer 7 day analysis. 225 Table G-1. Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ..................................... 229 Table G-2. Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ......................... 230 Table G-3. Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ..................................... 231 Table G-4. Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ............................................... 232 Table G-5. Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ..................................... 233 Table G-6. Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ..................................... 234 Table G-7. Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ......................... 235 Table G-8. Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. ..................................... 236 Table G-9. Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. .............. 237 xvii Table G-10. Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. .............. 238 Table G-11. Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. .............. 239 Table G-12. Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. These deer had no used locations in old burns, so that covariate is not included. ........................................ 240 Table G-13. Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2020 – 2021. The reference level is grassland vegetation and unburned areas. ......................... 241 Table G-14. Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2020 – 2021. The reference level is grassland vegetation. ......................................................... 242 Table G-15. Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. .............. 243 Table G-16. Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. .............. 244 Table G-17. Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. .............. 245 Table G-18. Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. .............. 246 Table H-1. K-fold cross validation values for models including all random slopes, random slopes on continuous variables only, and a random intercept only. All k-fold validations were run using the cutblock vegetation x elevation + road density + NDVI + burns model. A fit closer to 1.00 indicates better model predictive performance. .................................................................. 248 xviii List of Figures Figure 1-1. Three study study areas in south-central British Columbia, Canada where I studied mule deer migration, survival, and habitat quality during winter 2018/19 – winter 2022/23. Red polygons indicate all wildfire burns occurring between 2012 and 2022. ..................................... 19 Figure 1-2. The three age classes of mule deer included in this study: A) Adult female (≥ 1-year-old), B) fawn (6-month – 1-year-old), and C) neonate (0 – 12-week-old). Photo credits belong to TJ Gooliaff for all 3 pictures. ........................................................................................................ 20 Figure 2-1. A) Map of 355 mule deer spring migration routes in three study areas (Boundary: purple, Cache Creek: pink, West Okanagan: yellow) across south-central British Columbia, Canada during 2018-2021. B) Elevation of mule deer locations during spring migration as a function of days since the start of spring migration. The black line shows the output of a generalized additive model fit of the data. Map was created in R using a Google maps basemap........................................................................................................................................................ 49 Figure 2-2. Density plots summarizing the three metrics that characterize the greenscape for all migratory mule deer during spring migration in south-central British Columbia during 2018-2021. A) Green-up rate is the speed at which green-up occurred at each individual cell during migration. B) Green-up duration is the total number of days green-up lasted within migration routes. C) Green-up order represents the progression of green-up within migration routes, where n < 1 = less consecutive (not a wave) and n > 1 = more consecutive (a wave). The dashed line at 1 indicates where this transition from non-wave to wave occurs. ................................................ 50 Figure 2-3. Date a cell was used relative to the date of peak instantaneous rate of green-up (IRG) of that cell for migratory mule deer in south-central British Columbia, Canada during 2018-2021. The grey line represents a theoretically perfect surfer who uses a cell on the date of peak IRG of that cell. The dashed blue line is a linear regression created using the data points in each year (Date used ~ peak IRG). Numbers above each panel are the year those points were observed. .. 51 Figure 2-4. Standardized selection coefficients (± 95% confidence intervals) for continuous covariates from generalized linear mixed models used to determine resource selection for mule deer during migration in A) corridors and B) stopovers in the Boundary, Cache Creek, and West Okanagan study areas in south-central British Columbia, Canada during 2018-2021 Only significant results are shown (P≤ 0.05); missing values indicate the covariate was not significant for that study area or movement type. See Tables D-1 – D-6 for full model output. ................... 52 Figure 2-5. Relative selection strength of vegetation types at varying elevations compared to selecting uncut forests at intermediate elevations in the A) Boundary (1,180 m), B) Cache Creek (1,120 m), and C) West Okanagan (1,320 m) study areas for mule deer during migration in stopovers and corridors in south-central British Columbia, Canada during 2018-2021. Values above the dashed line at one indicate deer are more likely to select for that vegetation type and elevation compared to uncut forests at intermediate elevation, and values below one indicate deer are less likely to select for that vegetation type and elevation. The relative selection strength changes as the comparison value changes, but the order of selection of each vegetation type at a xix specific elevation does not change. See Tables D-1 – D-6 for full model output, including standard errors. .............................................................................................................................. 53 Figure 3-1. Map of three study areas in south-central British Columbia, Canada where we GPS collared adult female (≥ 1 year-old), and male and female fawn (6-months to 1-year-old) and neonate (0 – 12-weeks-old) mule deer between 2018 – 2022. The red polygons denote areas burned by wildfires between 2012 and 2022, the yellow polygons denote individual mule deer summer ranges between 2018 and 2022, and the blue polygons denote individual mule deer winter ranges between winters 2017-2018 and 2022-2023. ......................................................... 87 Figure 3-2. Relative risk of mortality and 95% confidence intervals of predicted effects from Cox proportional hazards modeling used to determine which landscape and weather factors impact adult and neonatal deer mortality risk in south-central British Columbia, Canada during 2018-2022. Lines above the dashed line at one indicate an increasing relative risk of mortality. The numbers above each panel are the lower 2.5 and upper 90th percentile of the observed covariate value that generated the predicted risk curve. These plots represent the significant interaction terms remaining after model selection and model averaging for A) adult winter-spring 2-day analysis, B) adult winter-spring 7-day analysis, C) and D) neonate summer 2-day analyses, E) Neonate summer 7-day time frame analysis. Road density refers to the average road density attributed to locations within the specified time frame. The proportion of unharvested forest and cutblocks refers to the proportion of locations in that vegetation type within the specified time frame. ............................................................................................................................................ 88 Figure 3-3. Hazard ratios and 95% confidence intervals of parameters from Cox proportional hazards modeling used to determine which landscape and weather factors impact adult, fawn, and neonate mule deer mortality risk in south-central British Columbia, Canada during 2018-2022. Only significant parameters that were not involved in an interaction are shown. Hazard ratios above the dashed line at one indicate an increasing risk of mortality as the covariate increases, and ratios below one indicates a decreasing risk of mortality as the covariate increases. Abbreviations are as follows: Distance to Road: mean distance to nearest road, Road Density: mean road density, Ruggedness: mean ruggedness, Snow Depth: mean snow depth, Snow Depth.tt: time varying coefficient for snow depth, Summer-fall Temp: mean maximum daily temperature in summer-fall, Winter-spring Temp.1: mean maximum daily temperature from 4 Dec – 11 Feb, Winter-spring Temp.2: mean maximum daily temperature from 12 Feb – 24 March, Winter-spring Temp.3: mean maximum daily temperature from 25 March – 20 May, Recent Burns: Proportion of burns 1-14 years-old, Recent Burns.tt: time varying coefficient for proportion of burns 1-14 years-old, Recent Cutblocks: proportion of cutblocks 1-16 years old, Recent Cutblocks.tt: time varying coefficient for proportion of cutblocks 1-16 years-old, Open Areas – proportion of open areas. ................................................................................................. 89 Figure 3-4. Relative risk of mortality and 95% confidence intervals for time varying covariates from Cox proportional hazards models used to determine which landscape and weather factors impact adult and fawn mule deer mortality risk in south-central British Columbia, Canada during 2018-2022. These parameters all violated the proportional hazards assumption and were changed to include time varying coefficients so the change in effect over time was included in the model. Lines above the dashed line at one indicate an increased risk of mortality from that value at that xx time, and lines below one indicates a decreased risk of mortality from that value at that time. Each panel represents the significant interaction terms remaining after model selection and model averaging for A) adult winter-spring 30 day analysis - snow depth, B) adult summer-fall 30 day analysis – cutblocks 1-16 years-old, C) adult summer-fall 30 day analysis – burns 1-14 years-old, D – F) fawn winter-spring 30 day analysis – average maximum temperature. This covariate was broken up to have a different value for each of three different time periods, early winter (D), winter (E), spring (F). ................................................................................................ 90 Figure 4-1. Relationships between predicted habitat quality as determined by resource selection functions (RSFs) and survival probability of mule deer in south-central British Columbia, Canada during 2018 – 2023 under 4 hypotheses: A) Ideal Selection: survival probability is positively correlated with RSF based habitat quality, B) Non-ideal Selection: selection is positively correlated with survival, but selection patterns contain no buffer for abnormal weather conditions, so survival decreases under poor weather conditions, C) Ecological Trap: selection probability is negatively correlated with survival probability, and poor weather might worsen this relationship, D) Weather Only hypothesis: weather conditions alone influence survival probability. The lines in each panel are not indicative of the predicted magnitude of the effect, but rather are drawn for illustrative purposes. ............................................................................ 127 Figure 4-2. Parameter estimates ± 95% confidence intervals of standardized continuous covariates from the best models of adult, fawn and neonate resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 in the A) West Okanagan, B) Cache Creek, and C) Boundary study areas. Covariates included are normalized difference vegetation index (NDVI), aspect (North and East), topographic wetness index (TWI), terrain ruggedness, curvature, road density (km/km2), and slope. ........................................................................................................................................... 128 Figure 4-3. Relative selection strength of forest only vegetation types from the best models of resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 for A) deer in the West Okanagan, B) deer in Cache Creek, C) deer in the Boundary), and D) resident neonates in the Boundary at high elevation. The selection of each vegetation type is compared to unharvested forests at low and high elevations for that study area, age class, and movement strategy. Values above 1 indicate deer selected for that vegetation type more than unharvested forests at that elevation, and values below 1 indicate the opposite. High and low elevations were determined by low (5th percentile) and high (95th percentile) values of elevation among collared deer within each study area, age class, and movement strategy. For clarity, panels B, C, D only include forested vegetation, and panel A includes grasslands because no other forest types were used by resident neonates. To relative selection strength of all vegetation types, see Figure G-1. Note that cutblock ages were different in each season. Winter-spring ages: Recent cutblocks: 1-18 years old, medium cutblocks: 19-41 years old, old cutblocks: 42+ years old, recent burns: 1-11 years old, old burns: 12+ years old. Summer-fall ages: Recent cutblock: 1-16 years, old cutblock: 17+ years, recent burn: 1-14 years, old burns: 15+ years ....................................................................................... 129 Figure 4-4. Predicted seasonal survival curves based on the most parsimonious Cox proportional hazards model (see Table 4-3) for explaining mule deer survival in south-central British xxi Columbia, Canada during 2018 – 2023. Shaded ribbons show 95% confidence intervals. A) adult winter-spring survival as a function of winter-spring severity index, B) summer-fall adult survival as a function of precipitation and RSF values, C) overwinter fawn survival as a function of RSF values, and D) summer-fall neonate survival as a function of RSF values. Low and high values of each covariate were chosen using low (5th percentile) and high (95th percentile) values of that covariate among each collared age class and season. ...................................................... 130 Figure 5-5. Mule deer A) winter-spring and B) summer-fall ranges with the proportion of predicted spatial estimates of population growth (λ) above 1 (i.e., growth habitat) under four weather scenarios in south-central British Columbia, Canada during 2018-2023. A mild winter-spring refers to an average daily winter-spring severity index (WSI) of 0, severe winter-spring refers to an average daily WSI of 53, drought summer-falls have 0 mm of average daily precipitation, and rainy summer-falls have an average daily precipitation of 8.12 mm. ............ 131 Figure 5-6. Mean proportion of designated ungulate winter range units (UWR) with spatialized estimates of mule deer population growth (λ) > 1, and maps of spatialized mule deer λ estimates and UWRs on winter-spring range in the A) West Okanagan, B) Cache Creek, and C) Boundary study areas in south-central British Columbia, Canada. The bar plots contain estimates of λ during the best-case weather scenario (rainy summer-fall: average daily precipitation = 8.12 mm and mild winter-spring: average daily winter-spring severity index [WSI] = 0), and the worst-case weather scenario (drought summer-fall: average daily precipitation = 0 mm and a severe winter-spring: WSI = 53). The paired map shows spatial estimates of growth habitat (λ > 1) and sink habitat (λ < 1) during the best-case weather scenario for residents. ................................... 132 Figure A-1. Results from a generalized additive mixed effects model comparing mule deer used and available locations during spring 2018-2021 in relation to cutblock age in south-central British Columbia, Canada. Lines above zero indicate mule deer selection for those cutblock ages and lines below zero indicate avoidance for those cutblock ages. The solid line is the predicted output and the dashed lines are the 95% confidence intervals. ................................................... 179 Figure A-2. Results from a generalized additive mixed effects model comparing mule deer used and available locations during spring 2018-2021 in relation to wildfire burn age in south-central British Columbia, Canada. Lines above zero indicate mule deer selection for those burn ages and lines below zero indicate avoidance for those burn ages. The solid line is the predicted output and the dashed lines are the 95% confidence intervals. .............................................................. 180 Figure B-1. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the Boundary study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). ................................ 183 Figure B-2. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the West Okanagan study area of south-central British Columbia, Canada 2018-2021. Each color represents a different xxii value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). ..... 184 Figure B-3. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the Cache Creek study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). .......................... 185 Figure B-4. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the Boundary study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). .......................................... 186 Figure B-5. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the West Okanagan study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). ..... 187 Figure B-6. Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the Cache Creek study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). ................................ 188 Figure C-1. Predicted Bhattacharyya’s affinity (BA) scores from the top linear mixed effects model (Study Area × Difference in Start Date) used to determine which factors influence spring migration route overlap in two successive years for mule deer in south-central British Columbia, Canada during 2018-2021. Negative start date values indicate deer started spring migration earlier in year two than year one, zero indicates deer started spring migration on the same day in both years, and positive start date values indicate deer started spring migration later in year two than year one. All coefficients were significant (P< 0.05) except for the interaction term between difference in start date and the Cache Creek study area. ........................................................... 190 Figure E-1. Results from a generalized additive mixed effects model comparing mule deer used and available locations during summer 2018-2022 in relation to cutblock age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those cutblock ages and curves below zero indicate avoidance for those cutblock ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed line at x = 16 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 16 years old) from old (> 16 years old) cutblocks. .............................................................................................................. 205 xxiii Figure E-2. Results from a generalized additive mixed effects model comparing mule deer used and available locations during winter 2018-2022 in relation to cutblock age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those cutblock ages and curves below zero indicate avoidance for those cutblock ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 18 and x =41 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 18 years old) from medium aged (> 18 and ≤ 41 years old), and old (> 41 years old) cutblocks. ............................ 206 Figure E-3. Results from a generalized additive mixed effects model comparing mule deer used and available locations during summer 2018-2022 in relation to wildfire burn age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those burn ages and curves below zero indicate avoidance for those burn ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 14 and x =29 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 14 years old) from medium aged (> 14 and ≤ 29 years old), and old (> 29 years old) burns. There was low use of medium aged burns in all age classes and time periods, so we combined medium and old aged burns into one category (old; > 14 years old) to aid in survival model convergence. ................................. 207 Figure E-4. Results from a generalized additive mixed effects model comparing mule deer used and available locations during winter 2018-2022 in relation to wildfire burn age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those burn ages and curves below zero indicate avoidance for those burn ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 11 and x =28 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 11 years old) from medium aged (> 11 and ≤ 28 years old), and old (> 28 years old) burns. There was low use of medium aged burns in all age classes and time periods, so we combined medium and old aged burns into one category (old; > 11 years old) to aid in survival model convergence. ................................. 208 Figure G-1. Relative selection strength of vegetation types from the best models of resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 for A) adults and fawns in the winter-spring, B) adults in the summer-fall, C) neonates in the summer-fall in Cache Creek and the West Okanagan, and D) neonates in the summer-fall in the Boundary. The selection of each vegetation type is compared to unharvested forests at low and high elevations for that study area, age class, and movement strategy. Values above 1 indicate deer selected for that vegetation type more than unharvested forests at that elevation, and values below 1 indicate the opposite. High and low elevations were determined by low (5th percentile) and high (95th percentile) values of elevation among collared deer within each study area, age class, and movement strategy, and are listed in Table 4-1. Note that cutblock ages were different in each season. Winter-spring ages: Recent cutblocks: 1-18 years old, medium cutblocks: 19-41 years old, old cutblocks: 42+ years old, recent burns: 1-11 years old, old burns: 12+ years old. Summer-fall ages: Recent cutblock: 1-16 years, old cutblock: 17+ years, recent burn: 1-14 years, old burns: 15+ years ........................... 226 xxiv Figure G-2. Results from a sensitivity analysis investigating the effect of the number of available points on our results in the RSF analysis from Chapter 4. The plot shows coefficient values for adult winter RSF models in the Boundary study area using varying numbers of available points to used points. There were 198 173 used locations during this period which led to 990 865, 1 981 730, 3 963 460, 9 908 650, and 19 817 300 available locations for the 5, 10, 20, 50, 100 available:used ratio, respectively. In this analysis, we used a 10:1 ratio, which is denoted in black in the figure. We attempted to include a 250:1 ratio but were computationally limited using a computer with 128GB of memory. .................................................................... 227 Figure G-3. Map of NDVI layer used in RSF analysis with non-treed areas overlaid in black. These non-treed areas include shrubs, grasslands, urban areas, and recent cutblocks. .............. 228 Figure H-1. A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for summer adult migratory models. ....................................................................................................................... 249 Figure H-2. A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for summer adult resident models. .......................................................................................................................... 250 Figure H-3. A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for winter adult models...................................................................................................................................................... 251 Figure H-4. A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for winter fawn models..................................................................................................................................................... 252 Figure I-1. Plots of scaled Schoenfeld residuals for the RSF covariate from Cox proportional hazards models analyzing the relationship between RSF based habitat quality and survival for adult female mule deer in A) early summer-fall (May 20 – September 15) and B) late summer-fall (September 16 – November 17) in south-central British Columbia, Canada during 2018-2023. Initially, we ran the whole summer-fall period in one model, but this covariate violated the proportional hazards assumption. We split the summer-fall into 2 periods based off these plots of scaled Schoenfeld residuals, which showed that the RSF covariate was significant up until about day 119 (plot A). After this date (plot B) the covariate was no longer significant. ................... 253 xxv Acknowledgements This research (both the field work and writing) took place on the ancestral and unceded territories of the Syilx Okanagan Nation and Secwépemc Nation. I was lucky enough to have my PhD be a part of the largest, collaborative mule deer research project in the history of British Columbia, the Southern Interior Mule Deer Project (aka SIMDeer). Therefore, there are roughly 1 million people I am forever indebted to for their help, knowledge, generosity, and kindness as I navigated my way through my PhD. First, I thank my advisor Dr. Adam Ford for providing the support and guidance I needed to make it through this degree. Thank you for your insightful ideas and writing help, trying to get my brain out of the weeds I often got entangled in, weathering my many breakdowns, and providing pretty much any field equipment I needed. Thank you to my committee members Drs. Karen Hodges, Rob Serrouya, and Michael Noonan for prompting me to think more about my analyses and for your help making this dissertation clearer and more rigorous. Thank you to Dr. Sophie Gilbert for serving on my committee during the first 2.5 years of my PhD. As Adam’s first PhD student I have been lucky enough to work with every graduate student (and lab manager) he has hired thus far, and I couldn’t be more grateful for these friends who listened to my complaints, gave advice and feedback, and just generally were A+ people to be around. Thank you to Emily Sunter, Danielle Gauthier, Sultana Majid, Jakob von Andrian, Laura Balyx, Mateen Hessami, Sophie May Watts, Niki Love, Siobhan Darlington, Melanie Dickie, Peter White, Jacob Hubner, Jenna Scherger, Carmen Richter, Angus Smith, Chloe Howarth, Landon Birch, and Mitch Brunet. A special shoutout to the COVID crew, Laura, Pete, Mateen, Jake, Siobhan, and Jenna, for providing friendship and laughs during a time that would have been infinitely darker without them. We will one day build that tunnel. xxvi This project would never have got off the ground without the support and guidance from BC’s Best Bios (and associates): Andrew Walker, Craig McLean, TJ Gooliaff, Chris Procter, Francis Iredale, Sean O’Donovan, Alexandra Francis, Holger Bohm, and their many co-op students and technicians including Kieran Braid, Colin Brewer, and Kaitlin Hancock. They are the busiest biologists I know, but they always had time to support SIMDeer (and me), and for that I am forever grateful. I spent a lot of time with Andrew, Craig, and TJ to capture deer and find dead deer, but we also made some of the best memories from my PhD together (float, South Slopes, Johnnys, hunting, soup). There are no other people I’d rather go to Thone with. The BC Wildlife Federation was another driving force behind SIMDeer and my PhD and I can’t thank them enough. Thank you to Jesse Zeman for never finding a problem he can’t solve and never taking no for an answer. Thank you to the BCWF staff for hosting our website, providing excellent communication materials, and organizing webinars. Thank you to the over 100 clubs that started this whole project by being concerned about the downward trajectory of mule deer in the province and doing something about it. Also thank you to Sam Foster for being the best co-PhD student around. Your enthusiasm and positive attitude in the face of some garbage circumstances is impressive and inspires me to try to be a little bit more like you (hasn’t happened yet, but I’m trying). Thank you for Clover trapping with me during a time when you had a million other things you needed to do. My PhD was also made possible through the help of Kristen Mancuso, Addison Fosberry, Sterling Peterson, and Cailyn Glasser from the Okanagan Nations Alliance, and Melanie Minnabarriet, Fawn Pierro-Zabotel, Amanda Murrell-Hughes, and Bert William from Bonaparte Indian Band. Thank you for letting us conduct this work on Syilx and Secwépemc unceded territories and for your help Clover trapping and investigating mortalities. xxvii My PhD relied heavily on volunteers for field work, housing, and logistics, and would have fallen apart without the help of some amazing people who had no reason to help, other than they care deeply about mule deer and wildlife in BC. Thank you to Dave Carleton, Brian Hancock, Grant Hiebert, Bernice and Graham Anderson, Elias Torheiden, Darryl Frasier, and Brad and Alisa Siemens for going above and beyond whenever I needed help. Your generosity is inspiring. Neonate capture was my favorite fieldwork, but it required an army of volunteers to get done. In addition to almost everyone mentioned above, thank you to David Bell, Kayla Zaretzki, Brooklyn Maher, Shannon Werden, and the Mussel crew for spending days walking transects to find little deer. Also thank you to Telus, Chris Spencer, and Adam Foss for funding, producing, and filming this fieldwork and making the best form of outreach (and keepsake) from my PhD – the documentary “Community for the Wild”. Thank you to work study students Reese Embree, Cynthia Kielpinski, and Emily Matthew, and thank you to Siobhan for hiring these wonderful students and dealing with all the paperwork so I didn’t have to. Thank you to my gym family at CrossFit FYP for providing a supportive environment and a healthy outlet for the myriad of emotions I experienced during this degree. How anyone can get through a PhD without thrusters, the echo bike, and burpees is beyond me. Julia, John, and Lorina Keery have been immensely supportive of me and my PhD since the day I arrived in Kelowna 5.5 years ago and I am so grateful to call them my friends. I’m not sure how I (and my cats!) would have made it through my PhD without them. My cats Poppet, Beatrix, and Tod provided love, cuddles, and laughter at all times, and I am so sad Poppet isn’t here to cross the finish line with me. Thank you to my friends from afar Emily Oja, Andy Beauchine, Margaret Abrams, Kirsten Sax, Casey Reit, and the Molsberry xxviii family for their love, support, memes, and visits that kept me sane over the years. I also want to thank myself for not giving up, even when this finishing this PhD felt impossible. Finally, my mom, dad, and brother Dylan were invaluable to finishing this degree. They have supported me every step of the way, and provided tropical vacays each winter, both of which were vital to my happiness. When I say I couldn’t have done it without them, please know I am serious. Also, I promise I will never go back to school ever again. 1 Chapter 1: Introduction Where an animal lives is critical to its fitness because this area determines the resources available to the animal, the predator and competitor assemblage, and the predominant weather patterns, all of which can affect survival and reproduction (McLoughlin et al. 2006, Hurley et al. 2017, Sandford et al. 2017). The term habitat has been used variably in the ecological literature (Block and Brennan 1993, Hall et al. 1997), but the way I define habitat in this dissertation is: the concept that describes the resources (e.g., food, water, cover) and environmental conditions (e.g., temperature, precipitation) that contribute to species fitness (Morrison et al. 1992, Hall et al. 1997, Mitchell and Hebblewhite 2012, Kirk et al. 2018). Habitat provides a fundamental framework for studying the relationships between species and their environments and forms the foundation upon which many ecological theories and management paradigms are based. For example, the habitat-fitness link forms the basis for the Ideal Free Distribution (Fretwell and Lucas 1969), Marginal Value Theorem (Charnov 1976), density-dependent population growth (Ferrer and Donazar 1996), and touches on behavioral concepts like habitat selection and migration (Middleton et al. 2018, Matthiopoulos et al. 2019, Kauffman et al. 2021). 1.1 Anthropogenic Landscape Change For millennia, people have profoundly transformed ecosystems at a global scale (Seto et al. 2012, Waters et al. 2016), ushering in a new epoch – the Anthropocene (Steffen et al. 2007, Waters et al. 2016). Anthropogenic landscape changes have resulted in habitat degradation and the loss of biodiversity, which shifts how animals behave (Gaynor et al. 2018, Tucker et al. 2018, Burton et al. 2024), use resources (Hebblewhite and Merrill 2008, Courbin et al. 2014), and interact with other species (Guiden et al. 2019), which ultimately affects their abundance (Dirzo et al. 2014, Hill et al. 2019). Additionally, human activities have caused the Earth’s climate to 2 change rapidly, which is changing species’ habitats and geographic ranges (Thuiller 2004). Anthropogenic landscape change has irreversibly modified some species-habitat relationships and caused new ecological patterns and processes to emerge (Ellis 2011). Habitat degradation and loss is caused by many human activities, sometimes acting in a cumulative manner (Foley et al. 2005). Globally, conversion of native habitats to support agriculture has the largest human ‘footprint’ on the globe, occupying about 38% of Earth’s terrestrial surface (Foley et al. 2011). In addition, over 50% of the world’s population live in cities (United Nations 2019), where human activities are densely concentrated and destroy habitat for many species (McKinney 2006). Some impacts are more diffuse but perhaps more widespread, including forest harvesting, oil and gas extraction, wind, hydroelectric and solar energy development, and transportation infrastructure (Foley et al. 2005). Other changes to ecosystems happen more indirectly, such as climate change or fire suppression, but also have detrimental or long-lasting consequences for species (Backer et al. 2004, du Toit et al. 2022). 1.2 Effects of Anthropogenic Landscape Change on Species Habitat loss due to anthropogenic activities is the one of the largest drivers of biodiversity declines globally (Brook et al. 2008). In the most direct sense, when habitat is lost individuals that rely on the habitat die, and populations decline (Bender et al. 1998). However, there are also many ways in which landscape changes can indirectly affect species, typically by altering their behavior or movements (Gunderson et al. 2017). For example, greater sage-grouse (Centrocercus urophasianus) and mule deer (Odocoileus hemionus) change their behavior by avoiding areas surrounding oil and gas well pads in otherwise suitable winter habitat (Doherty et al. 2008, Sawyer et al. 2009a), and wolverines (Gulo gulo), woodland caribou (Rangifer tarandus 3 caribou), and mountain goats (Oreamnos americanus) avoid areas with high backcountry winter recreation (Seip et al. 2007, Richard and Côté 2016, Heinemeyer et al. 2019). Migratory species are particularly vulnerable to habitat loss and degradation because they travel long distances that may cross jurisdictional boundaries (Kauffman et al. 2021). These migratory species are critical for ecosystem functioning because these animals make highly predictable and pulsed movements across large distances, enabling them to regularly transport nutrients, energy, and other organisms along their migratory route (Bauer and Hoye 2014). Concerningly, seasonal migratory behaviors are being lost across taxa including birds (Sanderson et al. 2006), fish (Gresh et al. 2000), and mammals (Harris et al. 2009), largely due to anthropogenic landscape changes. For example, dams have prevented the seaward migration of Atlantic salmon (Salmo salar) populations in the Bay of Fundy, Canada (Fraser et al. 2007). Additionally, bighorn sheep (Ovis canadensis) in the Teton Range of Wyoming, USA, stopped migrating to low elevation winter ranges because permanent human settlements were built on their winter ranges, and because roads and fences were constructed across their migratory routes (Courtemanch et al. 2017). Habitat loss also impacts species interactions (Tylianakis et al. 2008). For example, wolves (Canis lupus) preferentially use linear features such as roads and seismic lines to move and hunt (Dickie et al. 2017, Gable et al. 2023). As a result, their prey should avoid these features (Mumma et al. 2017) or face greater predation and encounter risk (Whittington et al. 2011). In landscapes with high densities of linear features habitat change has had a negative effect on the prey species (Johnson-Bice et al. 2023), but not so for the wolves. This result highlights the often variable and species-specific effects of landscape changes. 4 In addition, human activities that lead to habitat loss for one species may have positive effects on another species. For example, in boreal forests of North America, timber harvesting has created early successional habitat with high quality forage for some ungulates, like moose (Alces alces; Serrouya et al. 2011). However, this same forest harvesting has led to drastic woodland caribou population declines (Wittmer et al. 2005, Serrouya et al. 2019). Global climate change has mixed effects on populations. Generally, climate change has resulted in rising temperatures globally, altered precipitation regimes, and more common extreme weather events (IPCC 2021). As a result, some species such as polar bears (Ursus maritimus) are predicted to lose their habitats almost entirely and possibly go extinct (Stirling and Derocher 2012). For other species, the effects of climate change could be more subtle and result in behavioral shifts or altered species interactions that ultimately lead to population declines. For example, in Isle Royal National Park, USA increasingly warmer summers are associated with increased winter tick (Dermacentor albipictus) levels on moose, which then face greater predation rates by wolves because moose with high tick levels are weaker and less successful in escaping from a cursorial predator (Hoy et al. 2021). Finally, in some areas, climate change could lead to ‘no analog communities’, in which novel ecological communities would arise (Williams and Jackson 2007, Hobbs et al. 2009). These novel areas represent a serious challenge for forecasting species responses to climate change because we lack past observational data to determine in which climates species can or cannot occur (Williams and Jackson 2007). As explored in this section, the effects of anthropogenic landscape changes on species habitats, and therefore species’ survival, abundance, distribution, and behavior, are complex, variable, and often species and location dependent. Therefore, it is important that we further our understanding of emergent species-habitat relationships in the Anthropocene. This includes 5 studying specific behaviors critical to both population persistence and ecosystem function that are easily affected by habitat changes, such as migrations, and investigating how use of habitats in a widely disturbed landscape might ultimately affect survival and population growth of a species. 1.3 Conserving Species Through Habitat Protection Given the many negative effects of human activity on species behavior, movements, and ultimately survival, the world is facing a biodiversity crisis, with almost half a million species facing extinction in the coming decades (Johnson et al. 2017). To limit future extinctions, it will be crucial to restore degraded areas and protect natural habitat (Mokany et al. 2020). To emphasize this point, recent calls have been made for protected areas to comprise 30% of the globe by 2030 (Dinerstein et al. 2019), and more ambitiously, protect 50% of the Earth’s terrestrial surface by 2050 (Dinerstein et al. 2017). However, in the past we have fallen short of previous habitat protection targets at a global scale (Butchart et al. 2010, Tittensor et al. 2014). There are many reasons for this lack of progress, including that responses to biodiversity decline are being offset (and more) by rising land use pressures related to increasing human population sizes (Johnson et al. 2017). Additionally, funding for global conservation is inadequate, particularly in developing countries that rely on international aid for biodiversity conservation projects (McCarthy et al. 2012). Therefore, there are many ecoregions where conserving large, unaltered tracts of land is not possible (Dinerstein et al. 2017), and other, more nuanced approaches to habitat protection are needed. In landscapes where habitat protection is difficult due to competing interests for land and resources, managers may need to prioritize those areas critical to the recovery and persistence of a species. This targeted approach can help minimize conflict and reduce the financial costs 6 associated with establishing and maintaining protected areas (Bohorquez et al. 2019). Under this idea, endangered species legislation from many countries provides protection for the portion of a species’ habitat considered “necessary for the survival or recovery” (Species at Risk Act, SC 2002, c 29), or “critical to survival” (Environment Protection and Biodiversity Conservation Act 1999) of listed species. Before this critical or high-quality habitat can be protected, it must first be identified, which requires a rigorous definition. Generally, it is recommended that critical habitat should explicitly consider the resources (e.g., food, water, cover) and environmental conditions (e.g., temperature, precipitation) required to ensure the persistence of a species or population (Hall et al. 1997, Rosenfeld and Hatfield 2006). Therefore, the primary criteria to define critical habitat should include an explicit link between fitness and habitat (Heinrichs et al. 2010). However, such a link may not exist for many species, resulting in less rigorous definitions of critical habitat for both federally listed species (Hodges and Elder 2008, Camaclang et al. 2015) and non-listed species that are in decline (Johnson 2007). 1.4 Defining Habitat Quality Through Resource Selection Habitat quality often is inferred by measuring spatial variation in species abundance or density through species distribution models (Guisan and Thuiller 2005) or by resource selection functions (Manly et al. 2002, Elith and Leathwick 2009, McLoughlin et al. 2010). These quantitative models and functions link species counts or occurrences to different environmental factors, with the idea that organisms are observed at places that help them survive and reproduce (i.e., areas of high-quality habitat; (Matthiopoulos et al. 2015, Northrup et al. 2022)). Therefore, using resource selection models to measure habitat quality relies on the assumption that there is a 7 positive correlation between an individual’s habitat selection and the fitness benefit associated with that habitat (i.e., that animals correctly identify and select for high quality habitat). Implicit with the assumption that there is a positive correlation between habitat selection and fitness comes the theoretical expectation that animals are behaving in a way that follows the predictions of an ideal and free distribution (IFD; Fretwell and Lucas 1969, Avgar et al. 2020, Northrup et al. 2022). The IFD states that individuals select between habitat patches such that their fitness is maximized at the time of selection, resulting in variation in population density among habitats in proportion to the fitness value of each habitat (Fretwell and Lucas 1969). Given a choice of habitats to select, those that are selected by individuals disproportionately to their availability will have a higher average population density (Avgar et al. 2020). The IFD is based on the idea that at a stable state, fitness is equal among habitats, so comparing species densities between habitats provides information on the fitness payoffs of those habitats as perceived by individuals at the time of selection (Northrup et al. 2022). Furthermore, under the IFD, individuals are continuously choosing which habitat to occupy based on the fitness-density relationship. If the per capita fitness is no longer equal among habitats because population density declined in one habitat, then individuals will select the habitat with higher fitness until the per capita fitness is again equal. Therefore, habitats with higher densities, or those that are selected disproportionately to their availability, are inferred to be the habitats with the higher average expected fitness at the time of selection (Northrup et al. 2022). While the IFD holds true for some species (Haugen et al. 2006, Haché et al. 2013), it does not for others (Holmes et al. 1996, Beckmann and Berger 2003, Piper 2011). There are many assumptions implicit within the IFD, including that individuals act to maximize foraging efficiency, have perfect knowledge of resource quality in the landscape, and can enter and use 8 any habitat without cost, and these assumptions may not be true for many species and systems. Furthermore, species may follow other distributions such as the ideal despotic distribution (IDD), which is seen in territorial species where dominant individuals inhabit patches where their fitness is maximized and force subordinates into lower quality patches (Holmes et al. 1996). Under the IDD, individual fitness is not equal across the landscape and higher quality habitat supports fewer individuals than poor quality habitat (Fretwell and Lucas 1969, Holmes et al. 1996). However, unlike the IFD, the IDD does not make any explicit predictions, thus making it challenging to test in studies. In addition to the IDD, other departures from IFD theory occur because of interspecific relationships. Food webs comprised of competitors and predators are common in nature, and these relationships can lead organisms to use suboptimal habitat to avoid negative encounters (Hugie and Dill 1994, Morris 2003). Even if free from interspecific competition and predators, the IFD may not hold true for species because individuals are unlikely to always select the highest quality habitat available (Arlt and Pärt 2007, Sadoti and Vierling 2010). Non-ideal selection may be caused by individuals having imperfect knowledge of habitat patches (Lima and Zollner 1996), lack of cues indicating habitat quality at the time of selection (Orians and Wittenberber 1991), or a mismatch in the relationship between the cues used to indicate habitat quality and actual habitat quality (Orians and Wittenberber 1991, Schlaepfer et al. 2002). An ecological trap is an extreme form of non-ideal selection, which occurs when individuals select habitat that decreases their fitness (Schlaepfer et al. 2002, Kristan 2003, Battin 2004, Arlt and Pärt 2007). Ecological traps can arise when rapid environmental changes cause a mismatch in the cues developed over evolutionary time to select habitats, and the fitness benefit of that habitat (Gates and Gysel 1978, Schlaepfer et al. 2002, Kristan 2003, Battin 2004). These rapid environmental changes are typically caused by 9 human activity and include introducing novel species to the area (Schlaepfer et al. 2005), changes in land use (Hawlena et al. 2010), and urbanization (Witherington 1997). 1.5 Determining More Meaningful Ways to Evaluate Habitat Quality While behavior and occurrence often are used to measure habitat quality, a truer definition of habitat should move beyond behavior and include links between individual fitness and population persistence and growth. Linking spatial variation in habitat use to spatial variation in fitness is difficult, given the complexities in measuring fitness (McLoughlin et al. 2006, 2007, Gaillard et al. 2010). However, evaluating the relationship between habitat and a basic components of fitness, survival, is a more achievable yet still rigorous way to define habitat quality (Garshelis 2000, Mitchell and Hebblewhite 2012, Gaillard et al. 2010). To further refine assessments of habitat quality using survival data, studies could evaluate the habitat-survival relationship within varying age classes (Kane et al. 2017). Different life stages make variable contributions to population growth (Cooch et al. 2001), and individuals rely on different habitats during different times of the year and at other life stages (Boyce et al. 2003, Rossman et al. 2015). Therefore, including varying age classes in an analysis would strengthen inferences about habitat quality at the population level (Gaillard et al. 2010). Studies on large mammals using global positioning system (GPS) collars often are used to collect data on the movement and locations of individuals. These collars also collect survival information for each individual, so it is surprising that, compared to straightforward resource selection studies, there are fewer studies that link habitat use or selection with survival (Matthiopoulos et al. 2015). However, survival analyses require a lot of data because deaths are the parameter of interest, and not the total number of collared animals (Hosmer et al. 2008). Therefore, many studies face trade-offs between collecting high resolution location data and the 10 battery life needed for the collar to remain active long enough to capture a mortality event. This tradeoff is especially true in long-lived species with higher survival rates where many years are needed to collect enough data to detect any relationship between habitat and survival. 1.6 Study Species Mule deer are an ideal species for which to evaluate the effects of landscape change on behavior and fitness because they migrate long distances for a terrestrial mammal (Sawyer et al. 2016), are sensitive to changes in habitat quality (Bishop et al. 2009, Monteith et al. 2014), and occupy landscapes with multiple disturbances (Sawyer et al. 2009a, Lendrum et al. 2012), which could preclude ideal selection of habitats. Throughout their range in western North America, mule deer are subject to a variety of anthropogenic disturbances such as energy development, urbanization, invasive species, timber harvesting, fire suppression, and climate change (Jensen et al. 2023). Mule deer are affected by these disturbances, exhibiting altered movement and behavior patterns (Lendrum et al. 2013, Aikens et al. 2020a), shifts in preferred resources (Lendrum et al. 2012), reduced survival rates across age classes (Peterson et al. 2017), and ultimately decreased population sizes (Monteith et al. 2014, Northrup et al. 2021). Generally, the continent-wide mule deer population has been in a downward trajectory for the last several decades due to these multiple and cumulative human-induced factors that have reduced habitat quality (Jensen et al. 2023). Additionally, predator populations have increased in recent decades, which may also be limiting mule deer populations in some regions (Robinson et al. 2002). Mule deer have some of the longest terrestrial migrations for ungulates observed in North America (> 150 km) and are at high risk for encountering anthropogenic disturbances along their migratory routes (Sawyer et al. 2016). While other ungulate species exhibit behavioral plasticity in their migratory behavior by changing their routes or migratory tactic (i.e., resident vs. migrant) 11 between years (Eggeman et al. 2016), mule deer rarely switch between resident and migratory behavior within their lifetime and exhibit high fidelity to their migration routes between seasons and years (Sawyer et al. 2019). This inflexibility with regards to whether and where they migrate can have negative implications for their ability to adapt to sudden landscape changes (Morrison & Bolger, 2012; Sawyer et al., 2013). Better understanding female mule deer spring migrations is of concern because there is evidence mule deer time their spring migrations to track the growth of nutritious and easily digestible spring forage (Aikens et al. 2017). This ‘green-wave surfing’ could lead to increased nutritional condition of both the females and their young (Monteith et al. 2014, Shallow et al. 2015), which has broader implications for population growth (Middleton et al. 2018). However, this behavior has not been observed in all mule deer populations, potentially because landscape change is decreasing a deers’ ability to track forage growth (Aikens et al. 2020a). Most evidence for green wave surfing comes from the intermountain west, which has a semiarid climate and is dominated by sagebrush-steppe at low elevations, montane, sub-alpine forests at high elevations, and with energy development as a significant anthropogenic disturbance (Lendrum et al. 2012; Wyckoff et al. 2018). Therefore, there is a knowledge gap surrounding mule deer spring migrations in other parts of their range where timber harvesting, forest fires, and steep elevational gradients drive the distribution and timing of forage availability. Mule deer in southern British Columbia (BC), Canada are not unique in their apparent population decline. As early as the 1950s, biologists in southern BC noted the rapid, large-scale changes being made to the landscape, including timber harvest, road development, and urbanization, and the subsequent negative effects these changes were likely to have on mule deer populations (Spalding 1956). Fire suppression and climate change have emerged as further 12 threats. Additionally, predator populations have changed over the last 50 years with wolves recolonizing portions of southern BC (Pletscher et al. 1997) and cougar (Puma concolor) populations potentially increasing, supported by expanding white-tailed deer (Odocoileus virginianus) populations (Robinson et al. 2002, Cooley et al. 2008). Given the interrelated nature of these changes, it is not known which have had the largest impact on mule deer in BC. Below I expand on the 4 largest anthropogenic changes in southern interior British Columbia: (1) roads, (2) wildfire and wildfire suppression, (3) timber harvesting, (4) climate change, and describe their potential impacts on mule deer behavior and survival. I do not explore how hunting might be impacting this mule deer population because there is limited harvest of female mule deer in south-central BC. Male-dominated harvest could affect mule deer population sizes through a reduction in pregnancy rates. However, buck:doe surveys conducted post-harvest have showed no evidence that male harvest exceeds biologically acceptable levels (Andrew Walker, Ministry of Forests, personal communication). 1.6.1 Anthropogenic Disturbances Affecting Mule Deer in BC Roads – Paved highways and roads negatively affect mule deer by fragmenting winter and summer ranges (Ito et al. 2013), interrupting migration routes (Sawyer et al. 2009b), and increasing mortality due to deer-vehicle collisions (Sawyer et al. 2012). In BC there are approximately 719,000 km of roads, but 92% of these are unpaved resource roads (Environmental Reporting BC 2018), which pose different threats to mule deer than paved roads. Unpaved roads are preferentially used by predators (Kertson et al. 2011, Knopff et al. 2014), enabling predators to move faster and more efficiently (Dickson et al. 2005, Dickie et al. 2017), which in turn causes increased encounter rates between predators and mule deer (Whittington et al. 2011) and increased predation risk for mule deer (Lendrum et al. 2018, Dickie et al. 2020). 13 This relationship had only been supported by indirect observations until recently, when video evidence of wolves using roads to hunt and kill ungulates was published (Gable et al. 2023). Wildfire and wildfire suppression – Wildfire is a common natural disturbance in many forested ecosystems, including BC (Heyerdahl et al. 2012), that creates landscape heterogeneity and affects ungulate nutritional resources by altering the composition, quality, and abundance of forage plants (Proffitt et al. 2016, Romme et al. 2016). Post-fire plant communities often differ in species composition from unburned areas, and can provide ungulates with high quality nutrition through increased nitrogen availability (Greene et al. 2012, Raynor et al. 2015) and enhanced nutrient concentration in young plant tissues (Van De Vijver et al. 1999). Typically, ungulate nutritional quality and abundance declines with successional age post-fire (Proffitt et al. 2019). Therefore, it is likely that decades of fire suppression in BC (Heyerdahl et al. 2012) have decreased the nutritional forage quality and abundance for mule deer (Hobbs and Spowart 1984, Lehmkuhl et al. 2001, Proffitt et al. 2019). Over 44,000 km2 of forests burned in wildfires in BC between 2017 – 2023. Projections from climate change models suggest these large-scale wildfires will be common in the future (Liu et al. 2010), which has the potential to increase forage quality for mule deer (Greene et al. 2012) and reduce predation risk from ambush predators, like cougars, on summer and autumn ranges (Altendorf et al. 2001, Doherty et al. 2022). However, wildfires remove canopy cover, and on winter ranges this removal can cause increased snow depths, which are energetically expensive and difficult for smaller ungulates to move through and can decrease survival through reduced body condition (Parker et al. 1984, Jackson et al. 2021), and increased susceptibility to predation (Gilligan et al. 2023). Additionally, many post-fire landscapes are salvage logged, 14 which results in increased road construction (Lindenmayer and Noss 2006) and can delay post-fire regeneration (Puig-Gironès et al. 2020), thus reducing the benefits of wildfire to mule deer. Timber harvesting – Forest openings created by timber harvest create early successional habitat and can be nutritionally beneficial to mule deer (Collins and Urness 1983, Hayden et al. 2008). However, cutblocks can also result in increased road construction, spread of non-native species (Hobbs and Huenneke 1992), and decreased understory plant species diversity (Royo and Carson 2006). Increased road densities have been associated with increased predation risk (Whittington et al. 2011) and altering the species composition of the understory can negatively affect the nutritional quality of browse species for ungulates (Alldredge et al. 2002, Cook 2002). Furthermore, timber harvesting removes canopy cover, which could be detrimental to mule deer on winter range when snow depths are a limiting factor. Climate change – Mule deer are sensitive to seasonal and annual fluctuations in weather. Generally, mule deer in northern temperate regions like BC have higher survival rates in years with less severe winters, characterized primarily by low snowfall, and warm, moderately wet summers (Gilligan et al. 2023). Climate change in this region is predicted to result in temperatures rising 2-3oC by 2050, and more precipitation falling as rain rather than snow in the winter (Zhang et al. 2019). Projections for changes in precipitation amounts are harder to predict and are thus more variable, but Canada is predicted to have increased spring and winter precipitation and slightly lower summer precipitation (Zhang et al. 2019). The direct effects of climate change on mule deer populations are speculative, but given that many aspects of plant phenology are tied to climate, mule deer migrations, habitat quality, and survival rates are likely to be affected by climate change (Gilligan et al. 2023). For example, reduced summer precipitation and earlier snowmelt may limit the nutritional benefits of summer forage and be 15 harmful for mule deer populations (Marshal et al. 2008). However, climate shifts could be beneficial for mule deer if winters have reduced snowpacks, because this reduced snowpack would result in lower energy expenditure in the winter (Parker et al. 1984, Hurley et al. 2017). 1.7 Study Areas I studied mule deer populations in the Boundary, Cache Creek, and West Okanagan regions of south-central British Columbia, Canada (Figure 1-1). These areas were chosen before I started my PhD by funders and collaborators partly due to logistical reasons and partly because they differed in amount of recently burned area. There were also regional differences in climate, elevation, and species composition, but all three areas had white-tailed deer, bighorn sheep, and moose. Predators of mule deer in all three areas included cougars, bobcats (Lynx rufus), wolves, coyotes (Canis latrans), and black bears (Ursus americanus). The Boundary was the only study area with a viable Grizzly bear (U. arctos) population. In general, low elevation forests were predominantly Douglas fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa), with an understory of Saskatoon serviceberry (Amelanchier alnifolia) and redstem ceanothus (Ceanothus sanguineus). High elevation forests were predominantly Douglas fir, lodgepole pine (P. contorta), and trembling aspen (Populus tremuloides), with an understory of pinegrass (Calamagrostis rubescens) and bluebunch wheatgrass (Agropyron spicatum). The Boundary study area was 8,820 km2 and ranged in elevation from 500 – 2,300 m. Low elevation winter range (500 – 1,200 m) had mild winters, with average temperatures between -3.7oC and -4.6oC (ClimateBC, Wang et al. 2016), and average winter snowfall of 13.1 – 27.2 cm. High elevation summer range (1,200 – 2,300 m) had warm, moderately dry summers, with average temperatures between 9.3oC – 14.5oC and average summer rainfall totals of 30.9 – 16 48.2 cm. Portions of summer range in this area also supported Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa), western hemlock (Tsuga heterophylla), western red cedar (Thuja plicata), black huckleberry (Vaccinium membranaceum), and Utah honeysuckle (Lonicera utahensis). Between 2012 and 2022, 282 km2 of this study area burned in wildfires, and between 1950 and 2022, 2,530 km2 of cutblocks have been harvested. The largest fire in the study area was the Gladstone Park wildfire in 2021 that covered 80 km2. The Cache Creek area was 7,800 km2 and ranged in elevation from 300 – 1,800 m. Winters on low elevation winter range (300 – 1,160 m) were mild, with average winter temperatures of -5.6oC to -3.0oC (Wang et al. 2016), and average snowfall of 4.6 – 9.5 cm. Portions of winter range in this study area also supported grasslands with sagebrush (Artemisia tridentata). High elevation summer range (1,160 – 1,800 m) had warm, dry summers, with average temperatures of 13.1oC to 14.3oC, and average summer rainfall of 21.5 – 26.0 cm. Between 2012 and 2022 3,590 km2 of this study area burned in wildfires, and 2,350 km2 of cutblocks were created between 1950 and 2022. The largest fire in the study area was the Elephant Hill wildfire in 2017 that burned 2,000 km2. The West Okanagan study area was 6,350 km2 and ranged in elevation from 300 – 2,300 m. Low elevation winter range (300 – 1,100 m) had mild winters, with average temperatures of -3.5oC to -1.7oC (Wang et al. 2016), and average snowfall of 5.2 – 13.0 cm. Portions of winter range in this study area also supported sage-steppe grasslands consisting of fescue (Festuca spp.), and sagebrush. High elevation summer range (1,100 – 2,300 m) had moderately warm, dry summers, with average temperatures of 11.1oC to 13.9oC, and average summer rainfall of 12.7 – 24.4 cm. Portions of summer range in this area also supported Engelmann spruce, subalpine fir, and black huckleberry. Between 2012 and 2022 283 km2 of the study area burned in wildfires, 17 and 1,800 km2 of cutblocks were created between 1950 and 2022. The largest wildfire in the study area was the Testalinden Creek fire that burned 52 km2 in 2015. 1.8 General Approach My dissertation relies on GPS collar data collected from adult female (≥ 1-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) mule deer (Figure 1-2) during 2018 – 2023 to answer questions about how landscape change can affect mule deer migrations, survival, and population growth. These GPS collars collected location data every 2.25 – 4.25 hours (depending on the age class) and were also equipped with motion-sensitive triggers to detect mortalities that I investigated to determine cause of death. I used remotely sensed data to measure vegetation, terrain, disturbance, and weather patterns on the landscape, which I then tied back to mule deer behavior and survival. 1.9 Research Objectives In this dissertation, I asked the question “How do anthropogenic disturbances affect mule deer migrations, survival, and population growth in south-central BC, Canada?”. In chapters 2-4, I use “we” instead of “I” to acknowledge the many collaborators who contributed to my PhD. In Chapter 2, I studied mule deer spring migration in an area dominated by coniferous forest, where cutblocks and fires could be disrupting migration patterns. Specifically, I (1) described mule deer spring migrations in this understudied ecosystem, (2) tested the Green Wave Hypothesis to determine if mule deer in southern BC track spring green-up during migration, (3) tested the hypothesis that mule deer exhibit fidelity to their spring migratory route, and (4) identified environmental features associated with stopovers and corridors during migration. In Chapter 3, I identified how exposure to landscape disturbances affects mule deer survival. I (1) quantified adult, fawn, and neonate mule deer survival rates and mortality sources, 18 and (2) investigated how landscape features, disturbance types, and weather conditions combined to affect mortality risk across all study areas over different time frames, with the intent to provide management recommendations to increase survival rates. I hypothesized that habitat used over short and long time frames would differentially affect survival. I predicted that features associated with predation risk would affect mule deer survival over short time frames, and features associated with habitat nutritional quality would affect mule deer survival over long time frames. In Chapter 4, I integrated resource selection functions and survival models to quantify habitat quality across seasons and age classes. Specifically, I (1) determined spatial resource selection and measured spatially and temporally varying weather conditions and (2) tested their relationships with 3 key vital rates of population growth, neonate, fawn, and adult survival rates. I specifically tested 4 hypotheses regarding how habitat selection and weather patterns correlate with mule deer survival rates including (a) Ideal Selection Hypothesis, (b) Non-ideal Selection Hypothesis, (c) Ecological Trap Hypothesis, and (d) Weather Only Hypothesis. In Chapter 5, I summarize the results from chapters 2-4, highlight their contributions to the ecological literature, and provide management recommendations to increase mule deer survival in south-central BC based off these results. Finally, I acknowledge the limitations of my data and analyses, and provide ideas for future research. 19 Figure 1-1. Three study areas in south-central British Columbia, Canada where I studied mule deer migration, survival, and habitat quality during winter 2018/19 – winter 2022/23. Red polygons indicate all wildfire burns occurring between 2012 and 2022. 20 Figure 1-2. The three age classes of mule deer included in this study: A) Adult female (≥ 1-year-old), B) fawn (6-month – 1-year-old), and C) neonate (0 – 12-week-old). Photo credits belong to TJ Gooliaff for all 3 pictures. 21 Chapter 2: Mule Deer Spring Migration in a Disturbed Forest Ecosystem . 2.1 Introduction Migration, the movement of animals between distinct seasonal ranges (Dingle and Drake 2007), allows animals to exploit seasonally abundant resources while avoiding unfavorable weather and minimizing the risk of predation (Alerstam et al. 2003). However, the benefits of migration extend beyond the migratory species and provide ecosystem services (Gresh et al. 2000, Kauffman et al. 2021). Unfortunately, migrations are declining rapidly worldwide (Bolger et al. 2008, Wilcove and Wikelski 2008, Kauffman et al. 2021), threatening the population persistence of some species (Berger et al. 2008). Long distance terrestrial migrations, such as those made by ungulates, are of special concern because they are easily interrupted by anthropogenic disturbances (Berger 2004, Sawyer et al. 2013, Wyckoff et al. 2018, Kauffman et al. 2021). Mapping these migrations can help guide effective conservation (Kauffman et al. 2021), but understanding what landscape features are associated with ungulate migration is central to conserving the habitat needed to manage populations and ecological processes (Monteith et al. 2018). For many ungulates, plant phenology is a key driver of timing and pathways used for migration (Holdo et al. 2009, Merkle et al. 2016, Aikens et al. 2017, Middleton et al. 2018). While new plant growth has a high nutrient quality, it typically occurs with low biomass (Hobbs et al. 1981, Baker and Hobbs 1982, Van Soest 1982). In contrast, older plant material – with higher carbon to nitrogen ratios and defenses – occurs with high biomass (Hobbs et al. 1981, Baker and Hobbs 1982, Van Soest 1982). Therefore, energy intake is maximized at intermediate plant biomass (hereafter peak green-up), which optimizes quality and abundance of forage (Fryxell 1991, Hebblewhite et al. 2008). 22 In temperate regions, plant growth in the spring occurs later at high elevations due to colder temperatures and delayed snow melt. This delay means that green-up ‘moves’ up elevational gradients as a resource wave. This gradual green-up over space and time provides an opportunity for ungulates to exploit the resource wave during spring migration (i.e. The Green Wave Hypothesis [GWH]; Drent et al. 1978). Individuals who use habitat patches on the date of peak green-up during migration are ‘surfing the green wave’ and obtain higher quality forage than those who do not track green-up during migration (Bischof et al. 2012, Merkle et al. 2016). Though ‘surfing’ implies a continuous flow of movement, observed migrations often vary between periods of rapid, linear movement (i.e., corridors) and slower, more tortuous movement (i.e., stopovers) phases (Alerstam and Hedenström 1998, Sawyer et al. 2009b, Sawyer and Kauffman 2011). Corridor and stopover locations may occur in different habitat types (Monteith et al. 2018), suggesting that animals require different resources during these migratory phases (Alerstam et al. 2003, Monteith et al. 2018). Some ungulates, like mule deer (Odocoileus hemionus) in Wyoming, USA use stopover sites to slow their migration speed to remain in sync with peak vegetation green-up (Sawyer and Kauffman 2011). This use of stopovers as extended foraging bouts enables mule deer to maximize energy intake during spring migration, which could translate to improved fitness (Mysterud et al. 2001, Middleton et al. 2018). However, not all ungulates that use stopovers exhibit a close relationship between timing of migration and green-up (Bischof et al. 2012, Geremia et al. 2019), indicating there are other reasons to use stopovers. The functionality of corridors and stopovers can be altered by anthropogenic disturbances (Lendrum et al. 2012). For example, climate change and resource extraction can alter the rate, duration, and order of green-up on the landscape (“greenscape”; Aikens et al. 2020b), impacting 23 an ungulate’s ability to track spring green-up (Aikens et al. 2017). Climate-change induced drought causes green-up to occur over a shorter time period and to progress nonconsecutively across landscapes, thus making it more difficult for ungulates to follow the timing of peak green-up and reducing the foraging benefits of migration (Aikens et al. 2020a). Other disturbances, like timber harvesting and wildfire, may also affect the greenscape through reduced canopy cover and altered species composition (Pettorelli et al. 2005a, Christianson et al. 2013, Gilbert et al. 2017), but little research has been done to study these effects on ungulate migration. Mule deer have some of the longest terrestrial migrations observed in North America (> 150 km) and therefore are at high risk for encountering anthropogenic disturbances along their migratory routes (Sawyer et al. 2016). Additionally, mule deer populations are declining in some parts of North America (Unsworth et al. 1999, Forrester and Wittmer 2013), thus underscoring the need to further study their spring migrations (Kauffman et al. 2021), a time critical for mule deer to gain the resources necessary for successful recruitment of young (Monteith et al. 2011). Mule deer are a partially migratory species, where some individuals migrate, and others are year-round residents (Peterson et al. 2022). Other partially migratory ungulates exhibit behavioral plasticity in their migratory behavior by changing their routes or migratory tactic (i.e., resident vs. migrant) between years (Eggeman et al. 2016), which can be beneficial because migration is not always the most advantageous movement strategy (Nicholson et al. 1997). However, mule deer rarely switch between resident and migratory behavior within their lifetime and exhibit high fidelity to their migration routes between seasons and years (Sawyer et al. 2019). This inflexibility with regards to whether and where they migrate can have negative implications for their ability to adapt to sudden landscape changes (Morrison and Bolger 2012, Sawyer et al. 2013). 24 There is also conflicting evidence as to the relationship between mule deer spring migration timing and plant phenology. Mule deer in Wyoming surfed the green wave (Aikens et al. 2017), while those in Colorado, USA ‘jumped’ the green-wave and arrived on summer range before peak green-up occurred (Lendrum et al. 2014). The Greenscape Hypothesis predicts that this discrepancy between populations is related to the rate, duration, and order of green-up within each locale (Aikens et al. 2017, 2020b). Most fine-scale information about mule deer migrations comes from the intermountain west of the US, which has a semiarid climate and is dominated by sagebrush-steppe at low elevations, montane, sub-alpine forests at high elevations, and with oil and gas development as a significant anthropogenic disturbance (Lendrum et al. 2012, Wyckoff et al. 2018). Therefore, there is a knowledge gap surrounding mule deer spring migrations in other parts of their range where timber harvesting, forest fires, and steep elevational gradients drive the distribution and timing of forage availability. Our goal was to study mule deer spring migration in south-central British Columbia (BC), Canada, an area dominated by coniferous forest where clearcuts (hereafter cutblocks) from large scale timber harvest have become the primary source of disturbance since the 1950s. Concurrently, historic frequent, low intensity fires have vanished from the landscape and have been replaced by large, high intensity fires (Heyerdahl et al. 2012, Kirchmeier-Young et al. 2019, Hagmann et al. 2021). Our first objective was to describe mule deer spring migrations in this understudied ecosystem by providing information about the proportion of the population that migrated, and the timing, location, and distance traveled of those migrations. Our second objective was to determine if mule deer in southern BC track spring green-up during migration (surf the green-wave), and characterize the greenscape by determining the duration, speed, and order of green-up on the landscape (Aikens et al. 2017). 25 Our third objective was to determine if mule deer in south-central BC exhibited the same fidelity to their spring migratory route as has been observed elsewhere (Sawyer et al. 2019). We predicted mule deer would exhibit high route overlap because mule deer rely heavily on memory to guide migratory movements (Merkle et al. 2019), which can make them less flexible than if they relied on perceptual cues from their environment to choose their migratory route (Bracis and Mueller 2017, Sawyer et al. 2019). We also predicted younger mule deer would exhibit less fidelity to migration routes than older mule deer (Mueller et al. 2013), and mule deer with longer routes, greater levels of new disturbances, and more variable NDVI values would exhibit less fidelity (Morrison and Bolger 2012, Joly et al. 2021, Morrison et al. 2021). Finally, we predicted mule deer with variable start and end dates and durations of migration between years would exhibit less fidelity than those who migrated on the same days for the same length of time (Morrison et al. 2021). Finally, our fourth objective was to identify environmental features associated with stopovers and corridors during migration. We predicted that corridors would be associated with areas that facilitate fast, directed movements, such as dry, gentle slopes, with little snow accumulation (Monteith et al. 2018). However, stopovers would be associated with areas that facilitated foraging, such as dry south facing areas that green up earlier than surrounding locations (Monteith et al. 2018). This resource selection function will help identify habitats that are crucial for maintaining connectivity and function of the migratory route. 26 2.2 Methods 2.2.1 Deer Capture We captured adult female (≥1-year-old) and fawn (6-month – 1-year-old) male and female deer using helicopter net-gunning (Carpenter and Innes 1995), chemical immobilization (Miller et al. 2009) administered via a dart gun, and Clover trapping (Cadsand et al. 2010) in March 2018 and December – January 2018-2021. We fitted deer with a 700-g (adult) or 450-g (fawn) Iridium GPS radio-collar (Vectronics Aerospace, Berlin, Germany) that was programmed to record a location every 4.25 hours. All capture and handling procedures were approved by the University of British Columbia Animal Care Committee (protocol A18-0300) and the province of British Columbia (permit MRPE18-290018). 2.2.2 Measuring Migration We defined migration as movement between distinct seasonal ranges (Jesmer et al. 2018) and considered multiple trips between ranges within a season as non-migratory behavior (Singh et al. 2012). We visually examined net squared displacement (NSD; Bunnefeld et al. 2011, Monteith et al. 2018) using Migration Mapper v. 1.3 (Merkle et al. 2022) to determine each mule deer’s migratory strategy (resident or migrant), and if they were migratory, delineate the start and end dates of spring migration. NSD curves of migratory individuals were represented by an upside down ‘U’ shape, whereas the NSD curves of resident individuals were represented by low values and no discernable pattern. We determined the timing of migration by visually inspecting each deer’s yearly movements and considered the start of migration to be the date of the last location within their winter range before a one-way, directional movement was made to their summer range. We considered the end of migration to be the date of the first location within their 27 summer range. We used the sequence of GPS locations taken between the start and end dates of spring migration for all analyses. This chapter was exclusively about mule deer spring migration strategies and resource selection, so we did not include data on non-migratory (resident) mule deer in any subsequent analysis. If a mule deer died during migration, we included them when determining the mean start dates of spring migration, but not in other analyses. 2.2.3 Greenscape and Green Wave Surfing To estimate forage quality across space and time we used the remotely sensed Normalized Difference Vegetation Index (NDVI; MOD13Q1; 250-m spatial resolution; 16-day temporal resolution; 2010-2021 Pettorelli et al. 2005a). We first pre-processed the NDVI data and fit double logistic curves to the time series, as outlined by Bischof et al. (2012), using the R package irg (Robitaille 2021). We calculated the first derivative of the fitted NDVI curves to derive the Instantaneous Rate of Green-up (IRG), which is a curve that ranges from 0 to 1 and peaks when vegetation growth is most rapid (i.e., intermediate biomass or peak green-up; Bischof et al. 2012, Aikens et al. 2017). We also calculated the spring scale parameter, which is estimated when pre-processing the NDVI time series, by measuring the number of days it takes for the logistic curve to change from the midpoint (date of peak IRG) to ¾ of the asymptote (Aikens et al. 2020b). 2.2.3.1 Characterizing the Greenscape To characterize the greenscape, or the way the ‘green wave’ moves across the landscape, we calculated three metrics: green-up rate, green-up duration, and green-up order (Aikens et al. 2017, 2020b) for each mule deer-migration-year. To calculate green-up rate we took the inverse of the spring scale parameter for each raster cell where the GPS relocation of a telemetered mule 28 deer occurred during spring migration, then calculated a mean rate (days-1) for each mule deer-year. We assumed that a mule deer’s location in a raster cell meant that the deer interacted with the resources in this cell. As such, we refer to co-located mule deer GPS locations and raster cells as ‘use’ of the resources in that cell. We extracted the date of peak IRG for each cell used during spring migration and took the difference between the 0.975 and 0.025 quantiles for each mule deer-year to calculate green-up duration in days. To calculate green-up order, we calculated the semi-variance by measuring half of the squared difference in the date of peak green-up across pairs of locations at different spatial scales (Jesmer et al. 2018, Aikens et al. 2020b). If green-up occurred in a wave-like progression, we would expect to see later dates of peak green-up at farther distance lags, which would result in an increasing steepness in semivariance. If green-up is truncated, then the steepness in the semivariogram decreases or reaches an asymptote. Therefore, the shape of the semivariogram represents the wave-like pattern of green-up. To quantify the shape of the variogram in a way that was comparable to similar studies (Aikens et al. 2020b) we modeled the semivariance in the date of peak green-up as a function of the distance lag raised to a power (n). Where n > 1, the shape of the curve represented a perfect resource wave with more consecutive green-up order. If n < 1 then the shape of the curve therefore represented less consecutive green-up order. 2.2.3.2 Green Wave Surfing To determine whether mule deer tracked spring green-up during migration as predicted by the GWH we compared the date a mule deer used a habitat patch during migration to the date of peak green-up (peak IRG) of that patch (Aikens et al. 2017). Following the standard approach for testing the GWH, we used linear regression to determine whether the date of use of a cell by mule deer depended on the date of peak IRG of the cell. If mule deer track green-up perfectly as 29 predicted by the GWH, we expect a strong, positive relationship between dates of use and peak IRG, with a slope of one and intercept of zero. We classified each regression as 1) surfed perfectly: the 95% confidence intervals (CI) of the slope overlapped one and the 95% CIs of the intercept overlapped zero; 2) surfed imperfectly (i.e., better than random): the slope was positive and 95% CIs of the slope did not overlap zero; 3) did not surf: where the slope was negative or had 95% CIs that overlapped zero (Aikens et al. 2017). 2.2.4 Migration Route Fidelity 2.2.4.1 Estimating Space Use and Overlap To estimate a utilization distribution (UD) for each individual spring migration route we used the dynamic Brownian bridge movement model (dBBMM; Kranstauber et al. 2012). The dBBMM creates a UD by modeling the space use between two continuous locations as Brownian movement, a continuous random walk, in which movement is diffusive and equally likely in any direction (Horne et al. 2007). Creating a UD using the dBBMM estimates the uncertainty associated with the actual movement path of a mule deer. We created dBBMMs using the move package (Kranstauber et al. 2023) in Program R, using a 30 m by 30 m raster template and a GPS collar error of 10 m. We quantified migration route overlap between successive years using Bhattacharyya’s affinity (BA; Bhattacharyya 1943, Fieberg and Kochanny 2005), which gave us insight into how both space use and intensity of use along the route changed between years. BA values range between zero (no overlap) and one (complete overlap). 30 2.2.4.2 Creating Covariates We created several covariates to determine what might impact migration route overlap including age of the mule deer, percent new cutblocks within the first year’s route, difference in start date between the two years, difference in duration between the two years, NDVI variation, study area, area of the UD, distance traveled, and years compared. Our age class covariate included only a comparison between either fawn/yearling overlap or adult/adult overlap because we were not able to distinguish age classes beyond fawn (6-months at capture) and adult (≥1 years at capture). We acquired the BC cutblock layer from the BC Geographic Data Catalogue (https://catalogue.data.gov.bc.ca/) and calculated the percentage of new cutblocks created within the first year’s UD. For example, for a 2020/2021 BA score we calculated the percentage of cutblocks created within the 2020 UD in 2020. To determine if timing of migration between years impacted route overlap, we subtracted the Julian start date and length in days of the second year’s migration from the first year’s migration to calculate the difference in start date and length in days. A negative number for difference in start date and length indicated an earlier start and faster migration in year 2 than year 1, respectively. To determine if variation in NDVI within the first years migration route affected subsequent overlap we used the fitted NDVI curves to calculate the coefficient of variation (CV) of NDVI values within the first year’s migration route. Finally, we wanted to see if migration route length or area affected overlap between years, so we calculated the area (km2) of the UD in year 1, and length (straight line distance between start and end points, km) of the migration route in year 1. This analysis was exploratory, so we combined these covariates into a set of 80 candidate models where each covariate was included in one additive and one interaction model with one other covariate, until all combinations were met. 31 2.2.4.3 Model Fitting We ran each model as a linear mixed effects model with individual mule deer identification as a random effect using the lme4 package (Bates et al. 2015) in Program R. We used Akaike’s Information Criterion scores corrected for small sample size (AICc) to determine the best model and considered models within 2 ΔAICc units from the top model as competing for the top model (Burnham and Anderson 2002). If models within this threshold only differed from the top model by one additional parameter, we considered that additional parameter to be uninformative and unsupported by the data (Burnham and Anderson 2002). We conducted model selection using the package AICcmodavg (Mazerolle 2023) in Program R. 2.2.5 Resource Selection 2.2.5.1 Determining Use and Availability We used the UDs from the dBBMMs created previously as the basis of our RSF, instead of using raw GPS locations, following the approach of Monteith et al. (2018). We made this decision because mule deer spend approximately 95% of their time in stopovers when migrating (Sawyer and Kauffman 2011), and therefore there are very few GPS locations that occur in corridors. Modeling resource selection without differentiating between corridors and stopovers would essentially be an RSF of stopovers. Additionally, some mule deer migrated very quickly (1-3 days total), so for these mule deer there were not enough used locations to accurately estimate parameters (Street et al. 2021). Following Monteith et al. (2018) we classified stopover sites as the highest 12% of the values within the UD to ensure that stopovers represented areas where mule deer stopped forward movement (Sawyer et al. 2009b). We recognize this decision could significantly affect 32 our results and interpretation, so we conducted a sensitivity analysis where we varied the stopover site classification value between 1% and 20% by 1% increments (total of 20 different values; Appendix B). We clipped these stopover sites from the UDs, rescaled them to represent values between 0 and 1, and cast 20 points per site in a random but probabilistic way based on the relative probability of use. The movement corridors were these UDs with the stopover sites clipped out. We rescaled the corridor UDs to values between 0 and 1, and cast 200 points per UD in a random but probabilistic way based on the relative probability of use. These randomly chosen points were the used points in our stopover and corridor analyses, respectively. Although our approach to define used locations may result in biased parameter estimate confidence intervals, it accounts for the uncertainty associated with the actual movement path and fixed our sample size problem (Monteith et al. 2018). To define availability for both corridor and stopover locations we buffered each individual UD by twice the median step length of all mule deer during migration (4.5 km). Due to lack of consistency in spatial layers between the USA and Canada, we clipped all UDs and available polygons at the border. We paired each used corridor and stopover point with 10 random points chosen within the available area unique to each mule deer-migration-year (Fieberg et al. 2021). 2.2.5.2 Creating Covariates We used spatial data layers available from public sources to quantify the resources available to mule deer during migration. Several landscape variables are important to mule deer during spring migration including elevation, slope, plant phenology, aspect, human disturbance, wetness, and vegetation type (Sawyer and Kauffman 2011, Lendrum et al. 2012, Monteith et al. 2018, Eckrich et al. 2019). We obtained a 20-m digital elevation model (DEM) from the BC 33 Geographic Data Catalogue and resampled it to 30-m resolution to better align with our other data layers. From the DEM we calculated slope (degrees) and aspect (north and east) using the raster package in R (Hijmans 2023). We also used the DEM to derive a relative measure of surface curvature (McNab 1989), with large positive values representing convex cells (ridgelines) and large negative values representing concave cells (valley bottoms), using the spatialEco package in R (Evans 2021). We calculated the topographic wetness index (TWI), a measure of how local slope and upslope area contribute to water flow (Sørensen et al. 2006), from the DEM using ArcGIS Spatial Analyst Tools (Environmental Systems Research Institute, Redlands, California, USA). We used BC and Washington road layers from the BC Geographic Data Catalogue and the Washington Geospatial Open Data Portal (https://geo.wa.gov/). We merged these layers and created a distance to road layer (km) and road density layer (within 2.25 km, the median step length during migration; km/km2). We included Washington roads even though mule deer locations in Washington were excluded because some locations were right on the Washington/BC border, and we wanted to accurately measure road metrics for these locations. We derived metrics of plant phenology that characterized spring from 2011-2021 (Monteith et al. 2018). We used the fitted NDVI curves created previously to calculate the 2 plant phenology layers. The first metric was the median start date of spring, which we defined as the Julian date that the NDVI values for a particular cell exceeded 10% of the baseline in the spring (Pettorelli et al. 2005a). We calculated the start of spring for each year, then used the median value of all years. The second metric was the date of peak green up, which we calculated as the median date of peak IRG. 34 To identify vegetation types used by deer during migration, we used the Vegetation Resource Inventory (VRI) layer from the BC Geographic Data. We classified the layer into shrub, grassland, forested, and other (contains exposed rock/rubble, bodies of water, urban areas, perennial snow and ice; DataBC 2022). We then classified the forested vegetation types into 5 categories depending on the age at which that forest block was harvested (see below). 2.2.5.3 Determining Cutblock and Wildfire Age Groups Based on previous research (Mumma et al. 2021, Hessami 2022), we expected mule deer would have a non-linear selection of post-fire and post-harvest forests. To integrate a potential non-linear response to age-since-disturbance, we first fit generalized additive mixed models to estimate the age thresholds in which deer change their selection probability. We expected to see an inverse quadratic form but did not want to bias the expectation by fitting exponential terms to a linear model. We used all mule deer locations during the spring season (March 15 – June 15) as our used locations. We created minimum convex polygons (MCP) around the locations in each study area, buffered each MCP by 2.25 km (the median step length during migration), and randomly chose 10 quadrature points for each used location within the buffered MCP. We created 4 cutblock age layers, with each one representing the age of cutblocks in each year we had locations (2018-2021). Because we did not know what time of year cutblocks were made, our minimum cutblock age was one year; cutblocks made in 2020 did not exist in the 2020 layer but were one year old in the 2021 layer. We extracted the cutblock age for all used and available locations for the appropriate year and ran a GAMM with use as a function of cutblock age and a random intercept for mule deer ID using the mgcv package in R (Wood 2011). We plotted the model output and chose breakpoints for cutblock age groups where the line crossed zero (Figure A-1; Mumma et al. 2021). The cutblock age groups we identified were: young (1-3 35 years old), mid aged (4-16 years old), old (17-36 years old), very old (36+ years old), and uncut forests (never been harvested according to the layer). We used the fire perimeter layer from the BC Geographic Data Catalogue and reclassified it into young burns (1-10 years old) and old burns (11+ years old) using the same method as described previously for selection of cutblocks ages (Figure A-2). 2.2.5.4 Collinearity and Scaling We estimated RSFs separately for each study area (n = 3) and movement type (n=2; corridor and stopover), which resulted in 6 model sets. Before modeling, we screened all predictor variables for collinearity (|r| > 0.6) by study area and movement type using Pearson’s correlation coefficient. In Cache Creek the two plant phenology metrics were correlated with each other, and in the Boundary and West Okanagan the plant phenology metrics were correlated with each other and elevation. Spring tends to start later at higher elevations due to the cooler temperatures and greater snow accumulation. It is this delayed timing that can potentially allow ungulates to follow new plant growth as they migrate to higher elevation summer ranges (Sawyer and Kauffman 2011, Aikens et al. 2017). Therefore, the actual timing of green-up of a particular location is less important to a migrating ungulate than the relative timing of green-up compared to near-by locations at comparable elevations. To provide a measure of the start of spring green-up independent of elevation, we ran a linear regression for start of spring as a function of elevation in each study area, and extracted the residuals (West Okanagan: stopovers – r2 = 0.43, P < 0.001, y = 18.63 + 0.057x, corridors – r2 = 0.44, P < 0.001, y = 19.01 + 0.057x; Boundary: stopovers – r2 = 0.44, P < 0.001, y = 40.93 + 0.043x, corridors – r2 = 0.42, P < 0.001, y = 43.08 + 0.041x; Monteith et al. 2018). 36 We extracted residuals from a linear regression between date of peak IRG and elevation to calculate the date of peak IRG independent of elevation in the Boundary and West Okanagan. However, the two plant phenology metrics were correlated with each other in both study areas and movement types even after accounting for elevation, indicating that sites that greened up earlier also reached peak IRG earlier. We chose to keep the start of spring variable in all study areas and movement types because we predicted that spring migration would be more closely associated with sites that greened up earlier, allowing mule deer to leave winter range earlier (Sawyer and Kauffman 2011, Monteith et al. 2018). 2.2.5.5 Candidate Model Development and Fitting We developed a set of candidate models (Table 2-1) that we predicted would best explain resource selection during spring migration. We included a quadratic term for elevation because we hypothesized mule deer would prefer intermediate elevations during winter and spring (Russell et al. 2015, Spitz et al. 2020). We also included an elevation × vegetation type interaction because we hypothesized that mule deer may prefer to use different vegetation types at different elevations; at higher elevations new cutblocks (no canopy cover) may still have snow cover, whereas at lower elevations these new cutblocks may green up earlier than vegetation types with more canopy cover (Pettorelli et al. 2005b). We scaled all continuous variables using the mean and standard deviation of each variable except distance to road, which we scaled using the exponential decay function 𝑒𝑒−𝛼𝛼𝛼𝛼 where d is the distance to the road and α is the inverse of the approximate distance at which a mule deer’s response to roads likely decreases (Skarin et al. 2018). We chose this function because we predicted that the influence of roads on mule deer resource selection would decrease quickly and non-linearly with increasing distance at a rate of α = 0.002 (Nielsen et al. 2009). 37 We used generalized linear mixed models (GLMMs) with a binomial distribution and logit link to estimate resource selection following the methods outlined by Muff et al. (2020) using the package glmmTMB (Brooks et al. 2017). We set the random intercept variance to 106 to avoid intercept shrinkage toward the mean and included a random intercept term for individual mule deer ID, and a random slope term that varied by mule deer ID for all fixed terms in each model. We weighted each available point with W = 1000 and used points with W = 1 (Fithian and Hastie 2013, Muff et al. 2020). We used AICc (Burnham and Anderson 2002) to determine the best model in each set (2 movement types, 3 study areas, 6 total) using the AICcmodavg package (Mazerolle 2023) as described previously. We used k-folds cross validation (Boyce et al. 2002, Roberts et al. 2016) with k = 5 to assess the predictive strength of the top model from each model set. 2.3 Results 2.3.1 Migration Summary We captured 99 fawns (64 female, 35 male) and 143 adult female mule deer that lived long enough to potentially migrate from 2018 – 2022. Of those 242 mule deer, 71 fawns (45 female, 26 male) and 107 adult females were migratory, and we observed 355 spring migrations between 2018 – 2021 (Figure 2-1a). We included male migrations only for the first year they were collared (e.g., their first spring migration). The mean start date of spring migration for all years was May 9 (standard deviation [SD] = 15 days), and mean end date was May 19 (SD = 13 days). The minimum, mean, and maximum straight-line distances traveled were 7.7 km, 47.2 km (SD = 20.8 km), and 108.1 km, respectively. Migration lasted on average 10 days (SD = 10.5 38 days, range = 10 days – 80 days). Mule deer spent a mean of 8.7 days (SD = 11.3) in stopovers during migration. 2.3.2 Greenscape and Green Wave Surfing Across all study areas and years, the mean green-up duration during spring migration was 74 days (SD = 35 days), mean green-up rate was 0.045 days-1 (SD = 0.016 days-1), and mean green-up order was 0.34 (SD = 0.49; Figure 2-2). Five out of the 355 spring migrations occurred in one day, and therefore we were unable to categorize green-wave surfing for those mule deer-years. Out of the remaining 350 migrations, zero mule deer surfed perfectly (i.e., the 95% CI of the slope overlapped one and the 95% CIs of the intercept overlapped zero), 125 (35.7%) mule deer surfed imperfectly, and 225 (64.3%) mule deer did not surf (Figure 2-3). 2.3.3 Migration Route Fidelity If a deer was collared for two consecutive spring migrations, then it contributed one overlap pair to the data set. Therefore, the 41, 35, and 22 mule deer collared for two, three, and four spring migrations, respectively, resulted in 177 overlap pairs. We observed no switching of migratory strategies between years. The mean BA value was 0.59 (SD = 0.15). The top model indicated that overlap was lower in the Boundary (β = -0.17, 95% CI = -0.11– -0.22) and Cache Creek (β = -0.06, 95% CI = -0.11– -0.002) than the West Okanagan, but this difference depended on the variation in the start date of migration and study area (Table C-1). In the West Okanagan, mule deer that started their migrations 12 days later in year two than year one exhibited 10% less overlap between the two years (β = -0.07, 95% CI = -0.03 – -0.11), whereas mule deer in the Boundary exhibited 5.8% more overlap (β = 0.10, 95% CI = 0.05 – 0.14). There was no effect of 39 the timing of the start of migration in Cache Creek (β = 0.04, 95% CI = -0.01 – 0.08; Figure C-1). 2.3.4 Resource Selection The top ranked resource selection model for all six sets was topographic × general vegetation + roads + spring + burns (Table 1-1). In all sets this model received an AICc weight of 1 and the next ranked model in any set was ≥ 54.23 AICc away. The k-folds cross validation revealed adequate fit for RSFs for all models (mean Spearman’s rank coefficient: Boundary corridor: 0.93, Boundary stopover: 0.98, Cache Creek corridor: 0.77, Cache Creek stopover: 0.67, West Okanagan corridor: 0.90, West Okanagan stopover: 0.63). Mule deer selected similar attributes in migration corridors in the three study areas. Overall, corridors were associated with dry (Boundary and Cache Creek), steep (Boundary and West Okanagan), and south-facing areas (Boundary and West Okanagan Figure 2-4, Tables D-1 – D-3). Mule deer in Cache Creek also selected for areas closer to roads that had an earlier Julian date of green-up, and mule deer in the West Okanagan selected for west-facing areas. The quadratic term for elevation and the interaction between vegetation type and elevation was significant in each study area, indicating that mule deer selected for intermediate elevations, but the strength of selection depended on the vegetation type (Figure 2-5). Two of the four most common significant landscape features (slope and TWI) had similar selection strengths between study areas, and one was selected more strongly in one study area over another (north aspect). Odds of selection increased by 5.8% and 4.2% with every 8.8 degree increase in slope (1 unit) in the Boundary and West Okanagan, respectively. With every 1-unit decrease in wetness (TWI), odds of selection increased by 3.7% in the Boundary and in Cache Creek. Mule deer were 1.34 and 1.92 times more likely to select completely south facing areas 40 compared to completely north facing areas in the Boundary and West Okanagan, respectively. Mule deer were more likely to select unburned areas than old burns in all study areas, but this result was likely due to a low number of both used and available locations in old burns compared to unburned areas (Table D-7). We define low, intermediate, and high elevations as the 25th, 50th, and 75th quantiles of used elevations in each study area. Low elevations were 960 m, 1070 m, 1040 m, intermediate elevations were 1180 m, 1320 m, 1120 m, and high elevations were 1400 m, 1450 m, 1360 m in the Boundary, West Okanagan, and Cache Creek, respectively. In corridors, mule deer selected for more open vegetation types at low to intermediate elevation in all three study areas (Figure 2-5). In the Boundary and West Okanagan, mule deer were 2.2 and 1.3 times more likely to select grasslands at intermediate elevations than uncut forests at low elevations. In Cache Creek, mule deer were 1.1 times more likely to select grasslands and 1.3 times more likely to select young cutblocks at low elevation compared to uncut forests at the same elevation. At intermediate elevations, mule deer were least likely to use very old cutblocks, shrub, and other vegetation types in the Boundary, young, old, and mid-aged cutblocks in the West Okanagan, and old cutblocks, uncut forests, and other vegetation types in Cache Creek. In stopovers, mule deer selected for similar landscape features in all three study areas (Figure 2-4). Stopovers were characterized by being dry and south facing in all three study areas, and steep in the Boundary and Cache Creek (Tables D-4 – D-6). In the West Okanagan, mule deer also selected sites that had a later Julian date of green-up (after correcting for elevation). In Cache Creek mule deer also selected for slopes that curved outwards. The quadratic term for elevation and the interaction between vegetation type and elevation was significant in each study 41 area, indicating that mule deer selected intermediate elevations, but the strength of selection depended on the vegetation type (Figure 2-5). The significant landscape features had a larger effect on mule deer selection in stopovers than in corridors. Mule deer were 1.92, 3.23, and 1.96 times more likely to select completely south facing areas compared to completely north facing areas in the Boundary, West Okanagan, and Cache Creek respectively. Furthermore, odds of selection increased by 25.9% and 11.8% with every 8.8 degree increase in slope (1 unit) in the Boundary and Cache Creek, respectively. The landscape feature with the smallest effect on selection was site wetness (TWI); for every 1-unit decrease in wetness, mule deer odds of selection increased by 7.6% − 12.9% in all three study areas. Mule deer were more likely to select unburned areas than old burns in all study areas (Table D-4 – D-6). In stopovers, mule deer selected grasslands at intermediate elevations in all three study areas (Figure 2-5). In Cache Creek, mule deer were 1.2 times more likely to select grasslands at intermediate elevation compared to uncut forests at low elevation. In the Boundary, mule deer were 3.5 times more likely to select grasslands at intermediate elevation compared to uncut forests at low elevation. In the West Okanagan, mule deer were 4.3 times more likely to select grasslands at intermediate elevation than uncut forests at low elevation. Aside from vegetation types included in the other category, at intermediate elevations mule deer were least likely to use very old cutblocks and shrub vegetation in the Boundary, young and very old cutblocks in the West Okanagan, and young and old cutblocks in Cache Creek. 2.4 Discussion For ungulates, migrations are typically a means for individuals to maximize access to seasonally and spatially variable pulses in high quality forage. However, while three-quarters of 42 the mule deer we collared in south-central BC migrated, almost two thirds of these deer migrated with no relationship between movement timing and optimal forage availability. Compared with other landscapes in the species range (Aikens et al. 2017, 2020b), the green-up of vegetation along mule deer migration routes in BC was slow at the individual patch level, short in duration at the landscape level, and did not occur in consecutive order. In support of the Greenscape Hypothesis, these phenological conditions are predicted to weaken the ability of migratory ungulates to surf the green wave (Aikens et al. 2017). Below we discuss the implications of asynchrony between mule deer migrations and forage, as well as other key characteristics of this migration. Mule deer migrations in south-central BC were short to moderate in both time spent migrating and distance traveled when compared to other mule deer migrations. Sawyer et al. (2016) recorded mule deer migrations in Wyoming from 20 – 264 km, and classified them into three categories based on the distance traveled: short (< 50 km), moderate (50 – 150 km), and long ( > 150 km). The migrations in our study were similar to those reported in regions outside Wyoming: 25 km to 35 km in Utah (van de Kerk et al. 2021), 36 km to 53 km in Colorado (Lendrum et al. 2013), 3.5 to 66 km in Idaho (Brown 1992), 10 to 104 km in the Greater Yellowstone Area (Gogan et al. 2019), and 38 km in Oregon (Schuyler et al. 2019). Long distance migrants may provide fitness benefits for themselves and other, shorter distance migrants (Sawyer et al. 2016). Their early departure from winter range can reduce competition for forage (Bishop et al. 2009), and increase their access to early spring green-up (Monteith et al. 2011). However, long distance migrants are observed only in a few locations (e.g., Wyoming) and it is not known why most mule deer migrations are shorter. 43 Despite the fitness benefits that may arise from green wave surfing (Mysterud et al. 2001, Middleton et al. 2018), no mule deer in our study tracked spring green-up perfectly during spring migration and 64% of mule deer migrated randomly with respect to green-up timing. These results contrast with mule deer in Wyoming, where 31% surfed perfectly and 2% migrated randomly (Aikens et al. 2017). Our results are more consistent with studies on red deer (C. elaphus) in Norway (Bischof et al. 2012), caribou (Rangifer tarandus) in Newfoundland (Laforge et al. 2021), and mule deer in Colorado (Lendrum et al. 2014) in which migrants arrived on summer range before peak green-up. One possible explanation for this lack of surfing in our study was the short duration and lack of spatial continuity in green-up (Aikens et al. 2017). Indeed, green-up occurred in a wave like or consecutive pattern in only 26 out of 355 migration routes in our study. It is therefore not surprising that mule deer do not surf the green wave in south-central BC, because there currently is no wave. One possible reason behind this lack of a green wave is that migrations did not occur over a linear elevational gradient (Figure 2-1b), which is a key landscape element that drives green-up to occur in a wave-like fashion (Bischof et al. 2012). Therefore, a green wave may never have existed along mule deer migration routes. However, populations with a similar phenological landscape to our study areas are predicted to be dominated by resident mule deer (Aikens et al. 2020b), but about 75% of the mule deer we collared were migratory. Given that transmission of knowledge between individuals about the green wave gives rise to migration within an ungulate population (Jesmer et al. 2018), we speculate that the landscape in south-central BC historically promoted the sequential tracking of spring green-up, and therefore migration, but large-scale anthropogenic alteration of habitat in recent decades may have decoupled migration timing from peak forage quality. 44 Plant phenology is affected by shifts in temperature and precipitation (Aikens et al. 2020a), and in mountainous environments amount of snow accumulation can impact spring green-up patterns (Pettorelli et al. 2005b). Deep snowpack causes spring to arrive later with more rapid green-up than in low snowpack areas (Christianson et al. 2013). Historically south-central BC was largely contiguous coniferous forest (Klenner et al. 2008), but industrial scale timber harvesting began in the 1950’s and in the last 20 years alone almost 20% of the forests in our study areas have been harvested (DataBC). New and regenerating cutblocks have little to no canopy cover and accumulate more snow than unharvested areas at the same elevation (Gilbert et al. 2017). Therefore, cutblocks may have created a mosaic of early and late green up patches, which would affect the continuity, or green-up order, in this region (Lundquist et al. 2013). While forest harvest practices may be changing the phenological characteristics of the landscape, they had no immediate impact on mule deer fidelity to migration routes, highlighting how inflexible mule deer are with their migratory routes. The only factors that impacted route fidelity were timing of migration and study area. Mule deer in the Boundary exhibited the lowest overlap, and mule deer in the Boundary and West Okanagan exhibited lower overlap when migration began on a different day in year two compared to year one, but in opposite ways. This lack of a clear relationship between route overlap and predictor variables across study areas suggests there are some individual differences between mule deer that we were not able to measure that might better explain yearly variation in migration route use. For example, our measure of age was coarse (yearling vs. adult), and a finer scale measure of age might help explain study area level differences in route fidelity if the age structures were not similar between study areas (Mueller et al. 2013, Jesmer et al. 2018). Importantly, the actual differences in route fidelity were small; overlap remained high (Boundary mean = 49.3%, Cache Creek mean 45 = 60.6%, West Okanagan mean = 65.7%; Figure C-1) between years, and we observed no switching between resident and migratory behavior. Other ungulate species can switch between migrant and resident strategies or use different seasonal ranges between years, and therefore would have 0% migration route overlap in those years (Hinkes et al. 2005, Fieberg et al. 2008, Morrison and Bolger 2012, White et al. 2014, Eggeman et al. 2016, Lowrey et al. 2020). This strong fidelity to migration routes provides a unique opportunity to identify areas for conservation (Williams et al. 2021), however, it is important to note that > 25% of our collared deer did not migrate, and therefore conservation actions in these areas would not benefit the whole population. To help identify areas that are important to migratory individuals, we evaluated resource selection in both stopovers and corridors. Corridors are primarily used for rapid movement (Sawyer et al. 2009b) and thus we predicted areas selected within migratory corridors would facilitate quick movement (Monteith et al. 2018, Jakes et al. 2020). Mule deer in at least two out of three study areas selected for dry, south-facing slopes that reduce the amount of snow within the corridor. Snow is energetically expensive to walk through (Parker et al. 1984), so these areas would promote fast, easy movements. However, if mule deer were solely selecting locations within corridors to reduce the cost of locomotion, we would expect them to select gentle slopes and low elevations (Lendrum et al. 2012, Monteith et al. 2018), which we did not observe. In addition to selecting steep slopes, mule deer selected more open vegetation types, such as grasslands and young cutblocks, at intermediate elevations. Given that mule deer perceive predators at large distances using visual and auditory cues in open areas (Lingle and Wilson 2001), these selection patterns may be an anti-predator strategy. Furthermore, cougars are the main source of mule deer mortality in this system (see chapter 3) and cougar kill sites are associated with dense vegetation that obscures 46 visibility (Laundré and Hernández 2003, Blake and Gese 2016), which makes it easier to stalk and capture their prey without being detected. Cougars also use intermediate slopes (Blake and Gese 2016, Dellinger et al. 2020), so mule deer use of steep slopes during migration could be a way to separate themselves from cougars. Migration corridors are risky for mule deer as they are moving quickly through unfamiliar terrain (Nicholson et al. 1997), potentially spending less time being vigilant (Robinson and Merrill 2013). Therefore, tradeoffs may exist between selecting areas that are easy to move through and areas that provide refugia from predators. In long distance migrations, mule deer use stopovers to migrate synchronously with spring green-up and maximize forage intake while sacrificing movement speed during migration (Sawyer and Kauffman 2011, Monteith et al. 2018). However, mule deer in our study areas do not exhibit a tight relationship between timing of spring green-up and migration, and therefore are not using stopovers to wait out the arrival of spring further along their route. Mule deer spent on average 8 days in stopovers during migration, much less than the roughly 20 days spent in stopovers in Wyoming where mule deer closely track plant phenology during migration (Sawyer and Kauffman 2011). Despite spending less time within stopovers, we still expected mule deer to select for landscape features that support high quality forage, as mule deer are likely foraging within these sites. In agreement with this expectation, stopovers were characterized by dry, south facing locations within grasslands at intermediate elevations. While grasslands at intermediate elevations could accumulate more snow during the winter due to their lack of canopy cover, the topography would facilitate fast snowmelt (D’Eon 2004), and earlier spring green-up (Monteith et al. 2018). Mule deer lose fat and protein over winter and generally enter spring migration in poor body condition (Monteith et al. 2013). Therefore, by selecting stopovers with access to 47 newly emergent forage, which is easily digestible and high in protein (Albon and Langvatn 1992, Hebblewhite et al. 2008), mule deer can begin to combat these nutritional losses during migration. Mule deer in south-central BC may not time their migration to perfectly exploit plant phenology, potentially because matching this timing is difficult to do in the current landscape, but they did select for stopover sites that contained the highest quality forage available at the time they migrated. We made many decisions within our RSF modeling that could have significantly affected our results. Most of these decisions, such as choosing the number of available locations or choosing the used/available weight value, were chosen based off values from the literature. However, analyzing coefficient variation as a result of these values could lead to a better understanding of how these decisions impacted our results. To this end, we conducted a sensitivity analysis for the corridor and stopover RSFs by varying the value we used to define a stopover from the highest 1% to the highest 20% of UD values within the migratory route. We found that varying the stopover value overall had little effect on corridor RSF results (Appendix B, Figures B-1 – B-3). However, varying the stopover value had a large effect on stopover RSF results, particularly at lower stopover value amounts (i.e., < 5%). As the stopover value increased, the coefficients stabilized for most covariates (Appendix B, Figures B-4 – B-5). Overall, our RSF results were robust to the choice of stopover value within the range of 5% to 20%. Migration corridors and stopovers are crucial not only for mule deer to move and acquire forage during migration, but also to maintain connectivity between seasonal ranges, a critical conservation concern for population persistence (With and Crist 1995, Berger 2004, Coulon et al. 2004, O’Grady et al. 2006). Therefore, it is vital to understand where these migration routes 48 occur, and what landscape features are associated with them, so they can be appropriately managed. We found mule deer selected for similar habitats within corridors and stopovers, but the strength of selection was greater in stopovers than corridors (Avgar et al. 2017, Fieberg et al. 2021). Therefore, mule deer are perhaps would be more sensitive to disturbance or habitat alteration within these segments of their migration route. Given limited conservation resources, and in the absence of clearly identified corridor bottlenecks (Kauffman et al. 2021), managers could prioritize protecting stopover habitat. Migration plays a key role in the population dynamics of mule deer and can have cascading effects within their food web and surrounding environment (Fryxell et al. 1988, Kauffman et al. 2021) (Fryxell et al., 1988; Kauffman et al., 2021). The information provided here is crucial to ensuring that migration routes are not truncated or lost in the coming decades (Kauffman et al. 2021). . 49 2.5 Figures Figure 2-1.3A) Map of 355 mule deer spring migration routes in three study areas (Boundary: purple, Cache Creek: pink, West Okanagan: yellow) across south-central British Columbia, Canada during 2018-2021. B) Elevation of mule deer locations during spring migration as a function of days since the start of spring migration. The black line shows the output of a generalized additive model fit of the data. Map was created in R using a Google maps basemap. 50 Figure 2-2.4Density plots summarizing the three metrics that characterize the greenscape for all migratory mule deer during spring migration in south-central British Columbia during 2018-2021. A) Green-up rate is the speed at which green-up occurred at each individual cell during migration. B) Green-up duration is the total number of days green-up lasted within migration routes. C) Green-up order represents the progression of green-up within migration routes, where n < 1 = less consecutive (not a wave) and n > 1 = more consecutive (a wave). The dashed line at 1 indicates where this transition from non-wave to wave occurs. 51 Figure 2-3.5Date a cell was used relative to the date of peak instantaneous rate of green-up (IRG) of that cell for migratory mule deer in south-central British Columbia, Canada during 2018-2021. The grey line represents a theoretically perfect surfer who uses a cell on the date of peak IRG of that cell. The dashed blue line is a linear regression created using the data points in each year (Date used ~ peak IRG). Numbers above each panel are the year those points were observed. 52 Figure 2-4.6Standardized selection coefficients (± 95% confidence intervals) for continuous covariates from generalized linear mixed models used to determine resource selection for mule deer during migration in A) corridors and B) stopovers in the Boundary, Cache Creek, and West Okanagan study areas in south-central British Columbia, Canada during 2018-2021 Only significant results are shown (P≤ 0.05); missing values indicate the covariate was not significant for that study area or movement type. See Tables D-1 – D-6 for full model output. 53 Figure 2-5.7Relative selection strength of vegetation types at varying elevations compared to selecting uncut forests at intermediate elevations in the A) Boundary (1,180 m), B) Cache Creek (1,120 m), and C) West Okanagan (1,320 m) study areas for mule deer during migration in stopovers and corridors in south-central British Columbia, Canada during 2018-2021. Values above the dashed line at one indicate deer are more likely to select for that vegetation type and elevation compared to uncut forests at intermediate elevation, and values below one indicate deer are less likely to select for that vegetation type and elevation. The relative selection strength changes as the comparison value changes, but the order of selection of each vegetation type at a specific elevation does not change. See Tables D-1 – D-6 for full model output, including standard errors. 54 2.6 Tables Table 2-1. Model set used to evaluate resource selection functions in corridors and stopovers within spring migration routes for mule deer in three study areas in south-central British Columbia, Canada during 2018-2021. Each model also contained a random intercept for individual deer identification and a random slope for each fixed covariate. Model Name Covariates1 Intercept Only None Topographic E + E2 + C + N + E + TWI + S General vegetation (GV2) G + SH + O + F Cutblock vegetation (CB.V) G + SH + O + CB_Y + CB_M + CB_O + CB_VO + UF Topographic + general vegetation E + E2 + C + N + E + TWI + S + GV Topographic + general vegetation + roads E + E2 + C + N + E + TWI + S + GV + R_DS + R_DT Topographic + general vegetation + spring E + E2 + C + N + E + TWI + S + GV + SS Topographic + general vegetation + burns E + E2 + C + N + E + TWI + S + GV + B_Y + B_O Topographic + general vegetation + roads + spring + burns E + E2 + C + N + E + TWI + S + GV + R_DS + R_DT + SS + B_Y + B_O Topographic × general vegetation E×GV + E2×GV + C + N + E + TWI + S Topographic × general vegetation + roads E×GV + E2×GV + C + N + E + TWI + S + R_DS + R_DT Topographic × general vegetation + spring E×GV + E2×GV + C + N + E + TWI + S + SS Topographic × general vegetation + burns E×GV + E2×GV + C + N + E + TWI + S + B_Y + B_O Topographic × general vegetation + roads + spring + burns E×GV + E2×GV + C + N + E + TWI + S + R_DS + R_DT + SS + B_Y + B_O Topographic + cutblock vegetation E + E2 + C + N + E + TWI + S + CB.V Topographic + cutblock vegetation + roads E + E2 + C + N + E + TWI + S + CB.V + R_DS + R_DT Topographic + cutblock vegetation + spring E + E2 + C + N + E + TWI + S + CB.V + SS Topographic + cutblock vegetation + burns E + E2 + C + N + E + TWI + S + CB.V + B_Y + B_O Topographic + cutblock vegetation + roads + spring + burns E + E2 + C + N + E + TWI + S + CB.V + R_DS + R_DT + SS + B_Y + B_O Topographic × cutblock vegetation E×CB.V + E2×CB.V + C + N + E + TWI + S Topographic × cutblock vegetation + roads E×CB.V + E2×CB.V + C + N + E + TWI + S + R_DS + R_DT Topographic × cutblock vegetation + spring E×CB.V + E2×CB.V + C + N + E + TWI + S + SS 55 Model Name Covariates1 Topographic × cutblock vegetation + burns E×CB.V + E2×CB.V + C + N + E + TWI + S + B_Y + B_O Topographic × cutblock vegetation + roads + spring + burns E×CB.V + E2×CB.V + C + N + E + TWI + S + R_DS + R_DT + SS + B_Y + B_O 1 Covariate abbreviations – E: elevation, E2: quadratic term for elevation, C: surface curvature, N: north aspect, E: east aspect, TWI: topographic wetness index, S: slope, G: grassland, SH: shrub, O: other, F: forest (all types), CB_Y: young cutblock (1 – 3 years old), CB_M: mid-aged cutblock (4 – 16 years old), CB_O: old cutblock (17 – 36 years old), CB_V: very old cutblock (36+ years old), UF: uncut forests (never been harvested), R_DS: road density, R_DT: distance to nearest road, SS: start of spring (residuals for the West Okanagan and Boundary study areas), B_Y: young wildfire burn (1 – 10 years old), B_O: old wildfire burn (10+ years old) 2 Abbreviations in italics contain all the covariates defined within that model. 56 Chapter 3: The Effect of Anthropogenic Land Use Changes and Weather on Mule Deer Survival 3.1 Introduction Humans have altered ecosystems at a spatial and temporal scale unlike any other species, with no signs of slowing down (Seto et al. 2012). This large human footprint has ushered in a new epoch – the Anthropocene (Steffen et al. 2007, Waters et al. 2016) in which human activities have irreversibly altered terrestrial biomes (Ellis 2011) and caused habitat loss and degradation for many species (Doak 1995, Bender et al. 1998). Reversing the negative effects of habitat loss has emerged as a priority for wildlife management and nature conservation agencies globally (WWF 2020) because it is one of the five main direct drivers of extinction (IPBES 2019). Determining what habitat to protect or restore to increase population growth can be difficult because the effects of some anthropogenic land use changes are unclear due to seasonal weather patterns. For example, after decades of fire suppression, large wildfires have become more common in western North America (Kirchmeier-Young et al. 2019), which have the potential to increase ungulate forage nutritional quality (Snobl et al. 2022) and reduce ungulate predation risk from ambush predators, like cougars (Puma concolor), on summer and autumn ranges (Altendorf et al. 2001, Doherty et al. 2022). However, wildfires remove canopy cover, and on winter ranges this lack of canopy causes increased snow depths, which are energetically expensive and difficult for smaller ungulates to move through, and can decrease survival through reduced body condition (Parker et al. 1984, Jackson et al. 2021), and increased susceptibility to predation (Gilligan et al. 2023). Despite these seasonal differences, wildfires are generally 57 thought to be a net positive for ungulate populations (Proffitt et al. 2016, 2019), but few studies have examined population-level survival estimates post-fire (Volkmann et al. 2020). Similarly, timber harvest is a widespread driver of change that could variably affect ungulate survival. Early successional forests created by timber harvesting create high quality forage for moose (Alces alces) and deer (Odocoileus spp) that could increase survival rates (Serrouya et al. 2011). However, timber harvesting leads to the increased construction of roads, which increase predation risk (Whittington et al. 2011, Dickie et al. 2020), and the spread of non-native species (Hobbs and Huenneke 1992), which reduce the nutritional benefits of timber harvesting (Hayes et al. 2022). Additionally, timber harvesting on winter ranges results in increased snow depths, which have the same negative impacts on survival as in recent burns. Given this complexity, it is important to identify which vegetation features, weather conditions, and anthropogenic disturbances affect ungulate survival before making management plans to protect or restore habitat. Choosing the most meaningful scale to study the effects of habitat use on ungulate survival can be difficult because ecological processes act at different spatial and temporal scales (Gaillard et al. 2010). In regions where seasonal fluctuations in resources and weather patterns drive survival and reproductive success, many researchers study habitat-survival relationships at a seasonal time scale (Brodie et al. 2013, Eacker et al. 2016, Schuyler et al. 2019, Oates et al. 2021). Additionally, analyzing the relationship between resources used at different critical life-history events (e.g., nesting, calving or migration) and survival can provide a greater understanding of species habitat requirements during that specific period (Gaillard et al. 2010). However, analyzing habitat-survival relationships over varying time frames could provide additional insight because some forms of mortality risk accumulate with time, whereas 58 others are more ephemeral or stochastic. For example, a chronic lack of nutrition in adult ungulates will take weeks or months before body condition degrades to a point where diseases, predation (e.g., predation sensitive foraging, Sinclair and Arcese 1995), and organ function affects survival (Cook et al. 2004, Couturier et al. 2009, Parker et al. 2009, Nielsen et al. 2012, 2013, Jackson et al. 2021). Unlike nutritional risk, we do not expect ungulate predation risk to ‘accumulate’ over weeks or months, but rather to be a function of decisions both predators and prey make over hours and days (Sih 1992, Fenn and MacDonald 1995, Hopcraft et al. 2005, Thaker et al. 2011). For neonatal ungulates, hours of exposure to acute weather events may cause lethal hypothermia or heat stress from unusually cold and hot weather, respectively (Gilbert and Raedeke 2004, Wilson and Krausman 2008). Therefore, only summarizing weather conditions or vegetation types used over an entire season may obfuscate the relationship between habitat and mortality risk that occurs at smaller time frames, and some key habitat features that affect survival may be missed. Mule deer (Odocoileus hemionus) populations in south-central British Columbia (BC), Canada have been declining for at least 30 years (Ministry of Forests, [MOF] 2014), but the cause is not clear. In this region, there have been simultaneous changes to the landscape associated with increasing habitat alterations and predator densities (Robinson et al. 2002, Lea 2008). Southern BC is dominated by coniferous forest where clearcuts (hereafter cutblocks) from timber harvest have become the primary source of disturbance since the 1950s, with over 600,000 km of resource roads being built to create these cutblocks. Until the last 75 years, this landscape experienced high frequency (5–10-year return interval), low intensity fires (Heyerdahl et al. 2012). Since the 1950s, fire suppression has resulted in a lack of fires across much of this area, but in the last decade, large scale, high intensity wildfires have become common 59 (Kirchmeier-Young et al. 2019). One of the largest fires, the Elephant Hill wildfire, burned 2,000 km2 between July and October 2017 (Crowley et al. 2019), across a broad range of elevations and vegetation types. In addition, wolves have recolonized portions of southern BC (Pletscher et al. 1997) and cougar populations may have increased, supported by expanding white-tailed deer (Odocoileus virginianus) populations (Robinson et al. 2002, Cooley et al. 2008). Our objectives were to quantify adult (≥1-year-old), fawn (6-month – 1-year-old), and neonate (0 –12-week-old) mule deer survival rates and mortality sources in south-central BC. We captured deer and monitored survival in three study areas that contained varying amounts of recently burned (≤ 10 years) area to first evaluate the effect of wildfire at the landscape level on mule deer survival. We predicted that adult, fawn, and neonate survival would be higher in the Cache Creek study area, which contained the entirety of the 2017 Elephant Hill wildfire (50% of the study area burned in the last 10 years), compared to the other two study areas (<5% of the study area burned in the last 10 years; Figure 3-1). We then investigated how landscape features, disturbance types, and weather conditions combined to affect mortality risk across all study areas over different time frames, with the intent to provide management recommendations to increase survival rates. We chose to focus solely on temporal scales and not incorporate varying spatial scales into our analysis because we wanted to explicitly study the relationship between amount of time exposed to disturbance features and weather conditions and survival. We hypothesized that different habitat characteristics would influence risk at short and long time frames. We predicted that over short time frames (2-7 days), mule deer mortality risk would be negatively affected by mule deer exposure to landscape features that increase catchability (sensu Hopcraft et al. 2010) for their primary predator, 60 cougars. These catchability features include proximity to roads, flatter terrain, deep snow, and closed canopies (Table 3-1). Over longer time frames (30 days), we predicted mortality risk would be reduced by exposure to landscape features associated with increased nutritional condition, such as productive forage. These nutrition-associated features include recent cutblocks, burns, and weather conditions that promote plant growth, such as summer precipitation (Table 3-1). For neonates, we predicted short-term exposure to seasonally cold temperatures in the summer would increase mortality risk. We did not expect adults and fawns to be affected by short-term changes in extreme weather, however, we did expect longer-term weather to affect their mortality risk. Specifically, we predicted that deep snow in the winter would increase mortality risk (Table 3-1) 3.2 Methods 3.2.1 Deer Capture We captured adult female (≥1-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) deer during 2018 – 2022. We caught adults and fawns using helicopter net-gunning (Webb et al. 2008), chemical immobilization (Mich et al. 2008) administered via a dart gun, and Clover trapping (Cadsand et al. 2010) in five sessions occurring in March 2018 and December – March 2018-2022. Deer were fit with a 700-g (adult) or 450-g (fawn) Iridium GPS radio-collar (Vectronics Aerospace, Berlin, Germany) that was programmed to record a location every 4.25 hours and transmit all locations via the Iridium service. Collars were equipped with a 6-hour motion sensor to detect mortalities. We collected 10mL of blood from all female deer to determine pregnancy using pregnancy-specific protein-B levels (Duquette et al. 2012). 61 In May 2019 – 2021 we reprogrammed all adult female collars to collect a location every 1.25 – 2.5 hours, depending on their collar type (adult collars = 1.25 hours, fawn collars = 2.5 hours). We monitored adult female locations daily from mid-May to mid-June for signs indicative of parturition including reduced daily movement speeds and rates, and lower net displacement (Peterson et al. 2018a, Wright et al. 2019, 2021). Once a potential birth area was identified, we grid searched the area in teams of 3 – 10 people to locate the neonate(s). We also captured some neonates opportunistically by visually locating them while driving around the study areas. These neonates were not associated with a collared mother. We blindfolded, sexed, and weighed all neonates, and if they weighed > 3.2 kg they were fit with a 145-g expandable Globalstar GPS radio-collar (< 4.5% of their body weight [Sikes et al. 2011], Vertex Mini, Vectronics Aerospace, Berlin, Germany). The collars were programmed to record a location every 2.25 hours that were stored on board the collar and transmit 1 location daily via the Globalstar service. Collars were equipped with a 4-hour motion sensor to detect mortalities. We estimated neonate age at capture by noting the behavior and umbilical and hoof characteristics of the deer (Grovenburg et al. 2014), and by examining the collared mother’s movement rates (Peterson et al. 2018a). We linearly regressed neonate mass at capture vs. age at capture separately for each study area and sex, and used the estimated regression coefficients to predict birth mass (Shallow et al. 2015, Eacker et al. 2016). All capture and handling procedures were approved by the University of British Columbia Animal Care Committee (protocol A18-0300) and the province of British Columbia (permit MRPE18-290018). 62 3.2.2 Determining Cause-Specific Mortality Sources All collars transmitted mortality events via e-mail and text message if the collar remained stationary longer than the programmed motion sensor switch. We attempted to reach mortality locations < 2 days after receiving the notification and at the mortality site we recorded the location of the carcass, predator sign (tracks, scat, hair), and vegetation. We used characteristics associated with predator killing from the literature (Elgmork 1982, Woodruff 2006, Murphy and Ruth 2009) to assign a possible predator. If there was no obvious cause of death and the carcass was not in active decay, we conducted a field necropsy or sent the carcass to the provincial wildlife veterinarian lab for a necropsy, depending on the feasibility of transporting the carcass to the road. If significant scavenging occurred before we arrived, there was no obvious cause of death, or not enough of the carcass was left behind, we classified the mortality cause as unknown. We censored all adult and fawn mortalities that occurred within 14 days of capture to account for potential capture related mortalities (adults: n = 9, fawns: n = 17; Webb et al. 2008). For neonates, there is little evidence that capture and handling leads to increased rates of maternal abandonment (Powell et al. 2005), and natural neonatal mortality is highest in the first few weeks of life (Monteith et al. 2014, Wright et al. 2019). Therefore, we censored only one neonate mortality that occurred within one day of capture, because after release that neonate ran quickly away in the opposite direction of its mother. If collars broke and stopped sending GPS locations remotely, we right censored those deer from the analyses on the last date we received a location from the collar. 63 3.2.3 Determining Survival Time Frames 3.2.3.1 Defining Seasons In northern temperate regions mule deer survival rates are strongly influenced by annual weather patterns (Monteith et al. 2014), particularly snow depths in winter (Hurley et al. 2017), so we divided survival models into two periods. To delineate these seasons, we used modeled daily 1-km2 Snow Data Assimilation System (SNODAS; National Operational Hydrologic Remote Sensing Center 2004) data as a measure of winter snow conditions (Hurley et al. 2017, Anton et al. 2022). We extracted these data to all mule deer GPS locations and calculated the mean first snow free day (modeled snow depth = 0 cm) in the spring (March – May) and the mean first snow fall day (modeled snow depth > 0 cm) in the fall (September – November) across all years of this study. To account for collared deer inhabiting a wide elevational range, we considered the start date of the winter-spring period to be 30 days after the mean first snow fall day and the start date of summer-fall period to be 30 days after the first mean snow free day. Therefore, summer-fall was defined as May 20 – November 16 and winter-spring was November 17 – May 19. Hereafter we refer to neonates as deer caught within their first week of life as neonates and fawns as deer caught when they were ~6 months old. We estimated neonate survival in the summer from birth to 12 weeks of age. We call this season ‘summer’ to connote the difference in timing from the summer-fall season used for adults. Because fawns were caught in early December each year, their survival models began on December 4 (first date we had ≥ 30 collared fawns; Pollock et al. 1989) and went until the end of winter-spring, May 19. Fawns age up to adults in June each year, so there was no summer-fall season for fawns. 64 3.2.3.2 Defining Time Frames Habitat used at different time scales can impact ungulate mortality risk (McKay et al. 2021) and choosing the time frame in which to study the habitat-survival relationship can greatly affect the results. We chose 2-day and 7-day windows within each season to represent short time frames, and 30-day windows as long time frames. When deer died their GPS relocations from their last 24h were almost entirely clustered around the mortality location. Therefore, we chose 2 days as the ‘short’ time frame, instead of 1 day, because we wanted to ensure we incorporated landscape features used immediately preceding death, and including an additional day of locations before death would capture this information. We chose 7 days as an additional short time frame because this is the mean time between kills for cougars (Knopff et al. 2010, Clark et al. 2014), the main source of mortality for mule deer in this system. We chose 30 days as the ‘long’ time frame to retain as many individuals as possible in this analysis. Deer had to be alive and collared for ≥ 75% of the days within each time frame and season to be included in that time frame. Therefore, a longer time frame would result in fewer deer and mortalities in the analysis. For example, there are 3 fawn mortalities that are excluded at 30 days, versus 11 that would be excluded at 60 days. We used only the two short time frames for neonates because 78% of deaths in this age class occurred before they were 30 days old. Therefore, for each adult and fawn we created three, non-overlapping time frames, and for each neonate we created two non-overlapping time frames in which to summarize their habitat use. We started backwards from either the day they died or the last day of the season (if they lived through that season) and grouped their locations in 2-day, 7-day, or 30-day intervals. For example, summer-fall contained 182 days for adults which resulted in six 30-day time 65 frames, 27 7-day time frames, and 91 2-day time frames per deer, given the deer lived through the whole summer-fall season. 3.2.4 Determining Space Use in Short and Long Time Frames To assess the impact of landscape features on deer mortality risk, we used remotely sensed vegetation, terrain, road, and weather data to develop spatial covariates. Adult and fawn collars had high fix and satellite transmission success rates, collecting on average 5.6±1.1 locations per deer per day, that rotated through all 24 hours of the day. We extracted all layers to the GPS location data for adults and fawns, with the assumptions that these 5.6 locations per day were representative of a mule deer’s daily movements and that their location in a raster cell meant that the deer interacted with the resources in this cell. Neonate collars sent 1 location per deer per day with a mean success rate of 65.3% (hereafter transmitted locations), and these were our only location data for collars we could not recover in the field. Neonate collars collected more locations daily than adult and fawn collars (mean = 10.3±1.5 locations per deer per day), but we could only access these data if the collar was recovered (hereafter called collar locations). We recovered 108 of 134 neonate collars. Therefore, the number of locations collected for neonates was not consistent. We did not want to subsample the collar locations to one location per day because this was a waste of fine scale movement data. We also did not want to censor neonates from the analysis if they had only transmitted locations. This censoring would have biased our results because unrecovered collars were more likely to be on deer who survived through our monitoring period. To account for this discrepancy, we created 99% minimum convex polygons (MCP) to estimate space use for all neonates instead of using raw location data. MCPs made from collar 66 locations were bigger than MCPS made from transmitted locations because MCPs increase in size as the number of locations increases (Hansteen et al. 1997). Therefore, we calculated the mean area (km2) and radius (km, 𝑟𝑟 = �𝐴𝐴𝜋𝜋 ) of the MCPs created from collar locations by age (weeks) and time frame. We then buffered the MCPs made from transmitted locations by the radius corresponding to the appropriate week and time frame. We recognize that using a different season definition and method for defining space use render these results as non-comparable to the adults but given the differences in neonate life history and data collection methods we felt they were necessary. 3.2.5 Landscape and Weather Covariates We predicted vegetation, terrain, and road covariates would affect mortality risk in all seasons and were therefore included in summer-fall, summer, and winter-spring models. We summarized the vegetation, terrain, and road layer values to get (1) the proportion of locations (adults/fawns) or space use estimate (neonates) within each vegetation type per time frame per season, and (2) the mean terrain or road layer value within that time frame per season. However, summer (i.e., heat, rain) and winter (i.e., cold, snow) have unique weather conditions that we predicted would affect deer mortality risk within each season but would not be important or exist in the other season (i.e., snow depth). Therefore, we used different weather covariates within each seasonal model. 3.2.5.1 Creating Covariates Included in All Seasons The covariates included in all seasons were mean road density, mean distance to road, mean ruggedness, mean distance to cutblock edge, proportion of unharvested forest, and 67 proportion of differently aged wildfire burns and cutblocks. We obtained a 20-m digital elevation model (DEM) from the BC Geographic Data Catalogue (https://catalogue.data.gov.bc.ca) and resampled it to 30-m resolution to align with most of the other environmental data used in this study. From the DEM we calculated a measure of terrain ruggedness (Vector Ruggedness Measure; Sappington et al. 2007) using the spatialEco package in R (Evans 2021). We used BC and Washington road layers from the BC Geographic Data Catalogue and the Washington Geospatial Open Data Portal (http s://geo.wa.gov/). We merged these layers and created a distance to road layer (km) and road density layer (within 500 m; km/km2; Sawyer et al. 2006). To determine how vegetation classes might impact mule deer survival we used the Vegetation Resource Inventory (VRI) and consolidated cutblocks layers from the BC Geographic Data Catalogue to classify vegetation types into shrub, grassland, unharvested forest, and harvested forest (DataBC 2022). We reclassified all grassland and shrub vegetation within the VRI layer to one class called Open Areas. We used the consolidated cutblocks layer to create a cutblock edge layer by extracting the edges of the cutblock polygons and buffering them by 100 m. We also obtained the wildfire perimeter layer from the BC Geographic Data Catalogue to determine deer selection of wildfire burns. Based on previous research (Mumma et al. 2021, Hessami 2022), we expected that mule deer would have a non-linear selection of post-fire and post-harvest forests. For example, moose avoid young cutblocks, select for mid-aged blocks, and avoid older blocks (Mumma et al. 2021). To integrate a potential non-linear response to age-since-disturbance, we first fit generalized additive mixed models to estimate the age thresholds in which deer change their selection probability. We expected to see an inverse quadratic form but did not want to bias the expectation by fitting exponential terms to a linear model. We then plotted the predicted 68 response of the GAMM to determine breakpoints in selection and avoidance of age-since-disturbance. We performed separate analyses for fire and cutblocks in each season. We found that mule deer selected cutblocks aged 1-16 years old in the summer-fall, cutblocks aged 1-18 years old in the winter-spring, burns aged 1-14 years old in the summer-fall, and burns aged 1-11 years old in the winter-spring. Mule deer avoided cutblocks aged 17+ years old in the summer-fall, cutblocks aged 19-41 years old in the winter-spring, burns aged 15+ years old in the summer-fall and burns aged 12+ years old in the winter-spring. Finally, deer selected for cutblocks aged 42+ years old in the winter-spring. Based on these breakpoints (Figures E-1 – E-4), we transformed the environmental data on cutblock and fire age into categorical values representing recent and old-aged burns in the both seasons, recent and old-aged cutblocks in the summer-fall, and young, mid, and old aged cutblocks in the winter-spring. However, we refer to these categories by their age groups for clarity. For more details on how we chose these age categories see Appendix E. 3.2.5.2 Creating Seasonal Weather Covariates We used Google Earth Engine to download modeled daily precipitation, minimum temperature, and maximum temperature grids from Daymet V4 (Thornton et al. 2023). These weather data occurred at a resolution of 1-km2. Summer temperatures in BC are unlikely to be severe enough to impact adult mule deer survival (Duvuvuei et al. 2023, Gilligan et al. 2023) but may impact neonate survival (Gilbert and Raedeke 2004, Wilson and Krausman 2008, Warbington et al. 2017). Therefore, in adult summer-fall models we included only precipitation covariates, but in neonate summer models we included both precipitation and temperature covariates. For both age classes we summarized precipitation values by first calculating the mean 69 value per day, and then summed these daily values to get the mean total precipitation (mm) per time frame. For neonates we summarized minimum and maximum temperature values by first calculating the mean values per day and then calculated the mean minimum and maximum temperature (oC) per time frame. In the winter-spring we assumed most precipitation would be in the form of snow, so we used 1-km2 SNODAS data to create winter-spring precipitation covariates. Snow depths > 40 cm increase adult mule deer energy expenditure (Parker et al. 1984) and restrict movements (Kelsall and Prescott 1971, Armleder et al. 1994, Poole and Mowat 2005). Therefore, we first calculated the mean snow depth per time frame using the SNODAS data, then also counted the proportion of days the daily mean snow depth was > 40 cm per time frame. Fawns are smaller than adults and may be negatively impacted by snow depths < 40 cm, so we also calculated the proportion of days the mean daily snow depth was > 30cm for fawns. Minimum and maximum temperatures could affect adult and fawn survival in the winter-spring (Gilligan et al. 2023), so we used the 1-km2 Daymet V4 temperature grids to calculate the average minimum and maximum temperatures per time frame. There could also be a threshold temperature at which the risk of mortality increases (Parker and Robbins 1984), so we calculated the proportion of days per time frame where the mean daily maximum temperature was < 0 oC or < -5 oC (Rivrud et al. 2016). 3.2.6 Addressing Covariation Among Predictor Variables Many of the covariates described previously were derived from the same data source (e.g., both road density and distance to road were created from the same road layer), so we expected a high degree of correlation. We wanted to use the covariate with the highest predictive power per age class and time frame (Hurley et al. 2017). We therefore screened all covariates for 70 collinearity (|r| < 0.6) and separated collinear covariates into 4 groups. Within each group, we ran individual covariates in a univariate Cox Proportional Hazards model (Therneau and Grambsch 2000), ranked models using Akaike’s Information Criterion scores corrected for small sample size (AICc), and chose the covariate within the top model as the covariate to represent that group for that age class and time frame. The groups of covariates were 1) roads (road density, distance to road), 2) summer temperature (mean minimum temperature, mean maximum temperature; neonates only), and 3) winter-spring temperature (mean minimum temperature, mean maximum temperature, number of days < 0oC, number of days < -5oC), 4) snow depth (mean snow depth, number of days with snow depth > 40 cm, number of days with snow depth > 30 cm). Road density was the top road covariate for adults and neonates during all time frames and seasons (Table F-3). Distance to road was the top road covariate for fawns in all time frames. The top winter-spring temperature covariate was the number of days below -5oC for adults at the 2 and 7-day time frames and mean daily maximum temperature at the 30-day time frame for adults and all time frames for fawns. Mean snow depth was the top snow depth covariate for adults during all time frames and fawns during the 7 and 30-day time frames. The top snow depth covariate for fawns at the 2-day time frame was the number of days where the mean snow depth was > 30cm. Mean daily maximum temperature was the top summer temperature covariate for neonates in all time frames. 71 3.2.7 Survival Modeling 3.2.7.1 Cox Proportional Hazards Model We estimated winter-spring and summer-fall adult, winter-spring fawn, and summer neonate survival rates using the Kaplan-Meier estimator (Pollock et al. 1989). We stratified estimates by study area and used log-rank tests to test for significant differences in survival (Pollock et al. 1989). We then used the Andersen-Gill formulation of the Cox Proportional Hazards model with either a study-based time scale (adults/fawns) or an age-based time scale (neonates) to determine the effect of landscape use on survival, pooling data across study areas (Johnson et al. 2004, Fieberg and DelGiudice 2009). This model is a formulation of the Cox proportional hazards model, a semi-parametric regression model for survival data (Therneau and Grambsch 2000), that can accommodate left and right censored data, and time-dependent covariates (Johnson et al. 2004). We can directly estimate the effect of covariates on the baseline risk of mortality by computing a hazard ratio from each model coefficient (hazard ratio = exp(βi)). A hazard ratio of 1 indicates no effect of the covariate on the risk of mortality, a hazard ratio < 1 indicates a reduced risk of mortality, and a hazard ratio > 1 indicates an increased risk of mortality. One of the main assumptions of the Cox Proportional Hazards model is the proportional changes in the baseline hazard caused by changes in covariates remain constant over time (Therneau and Grambsch 2000). We tested this proportional hazards assumption by using the cox.zph() function within the R package survival (Therneau 2023) which plots the scaled Schoenfeld residuals for each covariate against time. If a covariate violated this assumption, we examined the scaled Schoenfeld residual plot to determine the best way to create a time 72 dependent coefficient, β(t) (Therneau et al. 2023). If β(t) had a simple functional form we included a continuous time-dependent coefficient to the model, such as 𝛽𝛽(𝑡𝑡) = 𝑎𝑎 + 𝑏𝑏 log(𝑡𝑡). We used this approach for adult models in the summer-fall and winter-spring in the 30-day time frame (see results for more details). If β(t) changed in a more complex way over time, we included a step function which created different coefficients for the covariate over separate time intervals (Therneau et al. 2023). We used this approach for fawn models in the winter-spring in the 30-day time frame (see results for more details). If a parameter violated the proportional hazards assumption, we did not run models containing an interaction between that parameter and another; the addition of time dependent coefficients complicated the model and made interactions difficult to interpret and, in some cases, caused nonconvergence. 3.2.7.2 Model Development and Fitting We predicted variables that were related only to either bottom up or top down processes would be significant only at either the 2 and 7-day (top down) or 30-day (bottom up) time frames (Table 3-1). For example, we predicted roads would increase the risk of mortality for deer at the 2 and 7-day time frames because predators preferentially use roads (Table 3-1; Kertson et al. 2011, Dickie et al. 2017, 2020); roads do not increase forage quality or quantity and therefore are likely not related to nutrition and should not be significant within the 30-day time frame. However, some variables could be related to either process depending on the time frame they were significant. For example, recent cutblocks are relatively open and could expose deer to lower predation risk at short time frames (Altendorf et al. 2001). However, these recent cutblocks are often associated with increased forage quantity and quality in the summer (Hayes 73 et al. 2022), so they could reduce deer risk of mortality when used over a longer time frame because of the nutritional benefits they confer. Covariate predictions and citations are described in Table 3-1. We developed a set of winter-spring and summer-fall candidate models that we predicted would best explain mule deer survival in each season based on our hypotheses (Tables F-1, F-2). Survival modeling requires 15-20 deaths per covariate (Hosmer et al. 2008), so our models had 3 or fewer covariates. Neonatal ungulate survival is affected by intrinsic factors such as birth mass and sex (Rohm et al. 2007, Bishop et al. 2009, Hurley et al. 2011, Eacker et al. 2016, Wright et al. 2019). We first ran a set of models containing the intrinsic covariates sex, birth mass, Julian date of birth, study area, and mother’s migratory status (resident or migratory, see Section 2.2.2). We then chose the best approximating model as determined by Akaike’s Information Criterion scores corrected for small sample sizes (AICc: Burnham and Anderson 2002), and used this top model (hereafter intrinsic model) as the underlying structure for all models in Table F-2 (Rohm et al. 2007, Wright et al. 2019). Because we had to choose one model structure to carry forward, we considered the top model to be the highest ranked model where the 95% confidence interval of all beta parameters did not overlap 0. Neonate models had > 3 parameters, but given neonates had the largest sample size of mortalities, overparameterization of models in this age class was not considered to be an issue. Some adults were alive during multiple summer-falls and winter-springs, so we calculated robust standard errors through a cluster variable that accounted for this lack of independence (Therneau and Grambsch 2000). We ranked models using AICc (Burnham and Anderson 2002) to determine the best model in each set. If the top ranked model had an Akaike model weight (ωi) ≥ 0.90 we considered it to be the best model in the set and used it to make 74 predictions and draw conclusions (Symonds and Moussalli 2011). If no model had a ωi ≥ 0.90, we model averaged all parameters within models that accounted for 90% of the cumulative Akaike weights (Burnham and Anderson 2002, Symonds and Moussalli 2011). We considered parameters to be significant and informative if the model averaged 95% confidence intervals did not overlap 0. We ran all survival models within the survival package (Therneau 2023) and conducted model selection and averaging within AICcmodavg package (Mazerolle 2023) in Program R. 3.3 Results 3.3.1 Deer Capture We caught and fit GPS collars on 199 adult female deer (Boundary: 70, West Okanagan: 63, Cache Creek: 66), 183 6-month-old fawns (Boundary: 66, West Okanagan: 72, Cache Creek: 45), and 134 neonates (Boundary: 40, West Okanagan: 47, Cache Creek: 47). There were 61 female fawns (Boundary: 19, West Okanagan: 26, Cache Creek: 16) and 4 neonates (Boundary: 0, West Okanagan: 2, Cache Creek: 2) that lived long enough to become adults, for a total sample size of 264 adult female deer. 3.3.2 Kaplan-Meier Survival Estimates Kaplan-Meier survival estimates were 0.93 (95% confidence interval [CI] = 0.91 – 0.95) for adults in the winter-spring, 0.89 (95% CI = 0.86 – 0.92) for adults in the summer-fall, 0.62 (95% CI = 0.55 – 0.69) for fawns in the winter-spring and 0.41 (95% CI = 0.33 – 0.51) for neonates in the summer. Survival was not significantly different between study areas for adults in the winter-spring (P = 0.08), adults in the summer-fall (P = 0.20), fawns (P = 0.80), or neonates (P = 0.5). 75 3.3.3 Cause-Specific Mortality We observed 227 mortalities with the predominant cause of death being cougar predation for adults and fawns, and an unknown cause of death for neonates (Table 3-2). Within the 2-day time frame analyses there were 35 adult winter-spring, 52 adult summer-fall, 70 fawn, and 71 neonate mortalities. Within the 7-day time frame analyses there were 35 adult winter-spring, 52 adult summer-fall, 70 fawn, and 57 neonate mortalities. Within the 30-day time frame analyses there were 33 adult winter-spring, 52 adult summer-fall, and 67 fawn mortalities. 3.3.3.1 Adult Winter-Spring Models The most supported model for the adult winter-spring 2-day analysis was unharvested forest × mean road density (ωi = 0.99, second ranked model = 11.41 ΔAICc away; Table F-4), which predicted that in areas with higher road densities and less unharvested forest deer had a higher relative risk of mortality. However, within areas with lower road densities and less unharvested forest deer had a lower relative risk of mortality (road density: hazard ratio = 1.60, 95% CI = 1.04 – 2.46, unharvested forest: hazard ratio = 1.55, 95% CI = 1.13 – 2.15, interaction: hazard ratio = 0.52, 95% CI = 0.40 – 0.70; Figure 3-2a). There were 4 models within the top 90% of the cumulative Akaike weights and within 2.0 ΔAICc from the top model in the adult winter-spring 7-day analysis, so we used model averaging to determine parameter estimates (Table F-5). The significant parameters indicated that ruggedness increased the relative risk of mortality (hazard ratio = 1.65, 95% CI = 1.21 – 2.25) and unharvested forest and mean road density had the same interaction relationship as in the 2-day winter-spring models (road density: hazard ratio = 1.42, 95% CI = 1.01 – 1.99, unharvested forest: hazard ratio = 1.32, 95% CI = 0.89 – 1.97, interaction: hazard ratio = 0.57, 95% CI = 0.40 – 0.78; Figure 3-2b and 3-3). 76 Mean snow depth violated the proportional hazards assumption in the adult winter-spring 30-day analysis (P = 0.038). After examining the Schoenfeld residuals we determined that 𝛽𝛽(𝑡𝑡) = 𝑎𝑎𝑎𝑎 + 𝑎𝑎 𝑠𝑠𝑠𝑠𝑟𝑟𝑡𝑡(𝑡𝑡) fit the data best, so we included mean snow depth covariate and a square root time interaction for the covariate in each model that violated the assumption. There were 7 models within the top 90% of cumulative Akaike weights and within 2.0 ΔAICc from the top ranked model (Table F-6); after model averaging, we determined the significant parameters in this model set were snow depth (hazard ratio = 0.07, 95% CI = 0.00 – 1.13) and the square root time interaction (hazard ratio = 1.42, 95% CI = 1.11 – 1.82; Figure 3-3). According to these parameters, snow depths had no effect on mortality risk early in the winter-spring (November 17 – February 15), but after February 15, increasing snow depths increased the relative risk of mortality and this hazard increased as increased (Figure 3-4a). 3.3.3.2 Adult Summer-Fall Models The most supported model in the adult summer-fall 2-day analysis was cutblocks 1-16 years old + burns 1-14 years old (ωi = 0.89, second ranked model = 7.45 ΔAICc away; Table F-7), which predicted both covariates reduced the relative risk of mortality (cutblocks 1-16 years old: hazard ratio = 0.67, 95% CI = 0.48 – 0.92, burns 1-14 years old: hazard ratio = 0.52, 95% CI = 0.31 – 0.89; Figure 3-3). There were 10 models within the top 90% of the cumulative Akaike weights and within 8.0 ΔAICc from the top ranked model in the adult summer-fall 7-day analysis (Table F-8), so we used model averaging to determine parameter estimates. The significant parameters indicated ruggedness increased the mortality risk (hazard ratio = 1.16, 95% CI = 1.03 – 1.32), and cutblocks 1-16 years old (hazard ratio = 0.69, 95% CI = 0.52 – 0.91) and burns 1-14 years-old (hazard ratio = 0.62, 95% CI = 0.41 – 0.95) reduced the mortality risk (Figure 3-3). 77 In the adult summer-fall 30-day analysis, both cutblocks 1-16 years-old (P= 0.05) and burns 1-14 years-old (P = 0.02) violated the proportional hazards assumption. After examining the Schoenfeld residuals for both covariates we determined that 𝛽𝛽(𝑡𝑡) = 𝑎𝑎𝑎𝑎 + 𝑎𝑎 𝑙𝑙𝑙𝑙𝑙𝑙(𝑡𝑡) fit the data best for cutblocks 1-16 years-old and 𝛽𝛽(𝑡𝑡) = 𝑎𝑎𝑎𝑎 + 𝑎𝑎 𝑠𝑠𝑠𝑠𝑟𝑟𝑡𝑡(𝑡𝑡 + 10) fit best for burns 1-14 years-old, so we included each covariate and their respective covariate time interaction in models that violated the assumption. There were 4 models within the top 90% of the cumulative Akaike weights and within 5.18 ΔAICc from the top ranked model in the adult summer-fall 30-day analysis (Table F-9). Significant covariates after model averaging were cutblocks 1-16 years old (hazard ratio = 0.20, 95% CI = 0.05 – 0.77) and its associated log time interaction (hazard ratio = 1.39, 95% CI = 1.01 – 1.92), and burns 1-14 years-old (hazard ratio = 2.03, 95% CI = 0.84 – 4.91) and its associated square root time interaction (hazard ratio = 0.87, 95% CI = 0.79 – 0.96; Figure 3-3). Based on these covariates, cutblocks 1-16 years-old reduce mortality risk early in the summer (May – July) but have no effect later in the summer (Figure 3-4b), while burns 1-14 years-old have no effect on mortality risk early in the summer but reduce mortality risk later in the summer (July – November; Figure 3-4c). 3.3.3.3 Fawn Winter-Spring Models The fawn winter-spring 2-day analysis had two models within the top 90% of the cumulative Akaike weights and within 1.96 ΔAICc from the top ranked model (Table F-10). After model averaging, the two significant covariates predicted that ruggedness increased the risk of mortality (hazard ratio = 1.46, 95% CI = 1.30 – 1.63), and that being farther away from roads reduced the risk of mortality (hazard ratio = 0.11, 95% CI = 0.03 – 0.39; Figure 3-2). The fawn winter-spring 7-day analysis had three models within the top 90% of cumulative Akaike 78 weights (Table F-11). The significant model averaged parameters predicted that ruggedness increased the risk of mortality (hazard ratio = 1.46, 95% CI = 1.23 – 1.73), and that being farther away from roads reduced the risk of mortality (hazard ratio = 0.28, 95% CI = 0.09 – 0.94; Figure 3-3). The maximum temperature variable violated the proportional hazards assumption within the fawn winter-spring 30-day analysis (P = 0.003). We examined the Schoenfeld residuals and decided to create a step function, where we created different maximum temperature covariates over three time intervals (early winter: Dec 5 – Feb 12, winter: Feb 13 – Mar 24, spring: Mar 25 – May 20). We included this step function instead of the regular maximum temperature variable in all models where it violated the proportional hazards assumption. The most supported model was the step function for maximum temperature with an Akaike weight of 1.0 and the next ranked model was 70.87 ΔAICc away (Table F-12; early winter: hazard ratio = 0.42, 95% CI = 0.11 – 1.68, winter: hazard ratio = 0.07, 95% CI = 0.03 – 0.13, spring: hazard ratio = 0.018, 95% CI = 0.00 – 0.08; Figure 3-3). These covariates predicted that maximum temperature had no effect on mortality risk in early winter, but in winter and spring, higher maximum temperatures reduced the mortality risk (Figure 3-4d,e). 3.3.3.4 Neonate Summer Models The top model in the neonate intrinsic analysis was sex + mother’s migratory status (ωi = 0.50) and showed that male neonates had an increased risk of mortality compared to females (hazard ratio =1.79, 95% CI = 1.12 – 2.87) and neonates born to migratory mothers had a higher risk of mortality compared to neonates born to resident mothers (hazard ratio = 2.00, 95% CI = 1.11 – 3.60). The second ranked model only included mother’s migratory status (ΔAICc = 3.95, 79 ωi = 0.07) and third ranked model included all 5 covariates in an additive form (ΔAICc = 3.98, ωi = 0.07), of which birth mass (hazard ratio = 0.93, 95% CI = 0.72 – 1.20), date of birth (hazard ratio = 0.99, 95% CI = 0.93 – 1.06), and study area (Cache Creek: hazard ratio = 1.49, 95% CI = 0.81 – 2.77; West Okanagan: hazard ratio = 0.93, 95% CI = 0.51 – 1.72) were all non-significant (Table F-13). Therefore, we considered the top ranked model to be the most supported in the analysis and carried these two covariates forward in the 2 and 7-day analyses. There were 14 models within the top 90% of cumulative Akaike weights and within 6.30 ΔAICc from the top-ranked model in the neonate summer 2-day analysis (Table F-14). The significant terms that existed after model averaging predicted that increasing road densities (hazard ratio = 0.72, 95% CI = 0.55 – 0.93) and maximum daily temperatures reduced the relative risk of mortality (hazard ratio = 0.72, 95% CI = 0.53 – 0.98). There were also significant interactions between road density (hazard ratio = 0.67, 95% CI = 0.51 – 0.88) and cutblocks 1-16 years-old; (hazard ratio = 1.16, 95% CI = 0.92 – 1.47, interaction: hazard ratio = 1.36, 95% CI = 1.10 – 1.67; Figure 3-2c), and road density and unharvested forest (road density: hazard ratio = 0.76, 95% CI = 0.58 – 0.99, unharvested forest: hazard ratio = 1.08, 95% CI = 0.84 – 1.40, interaction: hazard ratio = 0.79, 95% CI = 0.64 – 0.98; Figure 3-2d). There were 15 models within the top 90% of the summed Akaike weights and within 5.74 ΔAICc from the top-ranked model for the neonates summer 7-day analysis (Table F-15). The significant covariates remaining after model averaging predicted that increasing road densities (hazard ratio = 0.72, 95% CI = 0.52 – 0.98), use of open areas (hazard ratio = 0.54, 95% CI = 0.33 – 0.90), and maximum daily temperatures reduced the relative risk of mortality (hazard ratio = 0.67, 95% CI = 0.45 – 0.99; Figure 3-3). There was also a significant interaction between cutblocks 17+ years-old (hazard ratio = 1.13, 95% CI = 0.84 – 1.51) and road density (hazard 80 ratio = 0.67, 95% CI = 0.48 – 0.92, interaction: hazard ratio = 1.40, 95% CI = 1.02 – 1.90; Figure 3-2e). 3.4 Discussion Determining how habitat affects ungulate survival is complex due to seasonal differences in space use and weather, and the varying temporal scales at which habitat features could impact survival. Here, we explored how mule deer habitat use at varying time frames may reveal different landscape features, mechanisms of disturbance and weather patterns that affect survival. In accordance with our predictions, winter-spring adult and fawn mortality risk at short time frames increased when they used features thought to increase predation risk (areas closer to roads and more rugged terrain), while at long time frames their mortality risk increased when they were exposed to weather conditions thought to decrease body condition (deeper snow and cooler temperatures). However, summer-fall adult mortality risk at both time frames was reduced when deer used areas with features that could reduce predation risk and increase nutritional quality (cutblocks 1-16 years-old and burns 1-14 years-old), highlighting that some habitat features can affect mortality risk across many temporal scales. Interestingly, despite this finding, we saw no significant differences in survival between study areas, even though almost 50% of one study area was burned in wildfires < 5 years before our data were collected. Finally, neonate mortality risk was the subject of substantial model uncertainty, thus weakening our ability to make management recommendations to reduce neonate mortality risk. We observed that higher road densities increased adult female mortality risk and areas closer to roads increased fawn mortality risk only when measured within a short time frame in the winter-spring, which we predicted would occur because of the increased predation risk associated with roads. Given that we observed 108 predation events and only 6 vehicle collisions 81 (Table 3-2), this association between predation risk and roads is likely justified in our study. Roads are preferentially used by predators (Kertson et al. 2011, Knopff et al. 2014), as roads enable them to move faster and more efficiently (Dickson et al. 2005, Dickie et al. 2017), which in turn causes increased encounter rates between predators and prey (Whittington et al. 2011) and increased predation risk (Lendrum et al. 2018, Dickie et al. 2020). This relationship had only been supported by indirect observations until recently, when video evidence of wolves using roads to hunt and kill ungulates was published (Gable et al. 2023). Additionally, increased terrain ruggedness, another feature typically associated with predation risk (Lingle 2002, Bonar et al. 2016), also increased mule deer mortality risk only when used within short time frames in the winter-spring. Interestingly, we predicted that use of rugged terrain would reduce mule deer mortality risk because it can act as escape terrain (Bonar et al. 2016), but our results showed the opposite relationship. Cougars, the predominant source of mortality in our system, select rugged terrain (Kunkel et al. 2013, Knopff et al. 2014, Morrison et al. 2015), which likely increased the encounter risk, and therefore mortality risk, in these rugged areas. As winter progresses, female mule deer catabolize fat and lean body tissue to meet the energetic demands of survival and gestation (Monteith et al. 2013). Severe winters, characterized by deep snow and cold temperatures, increase the energetic demands of thermoregulation and locomotion (Parker et al. 1984, 2009, Hobbs 1989) and reduce available forage (Hobbs 1989, DelGiudice et al. 2013). These factors exacerbate the decrease in mule deer body condition and make them more vulnerable to starvation or predation (Bishop et al. 2009, Gilbert et al. 2020, Jackson et al. 2021). Additionally, deep snow can reduce a mule deer’s ability to escape from predators, regardless of their body condition (Vucetich et al. 2012). In our study, deer mortality 82 risk increased when they were exposed to deep snow in late winter and spring (mid-February – March) when summarized over a long time frame only. Therefore, we speculate that deep snow in late winter contributed to a nutritional decline of adults (Mautz 1978), and subsequently made them more susceptible to cougar predation. If snow depths had made deer more susceptible to predation without affecting their nutritional state, we would expect that deep snow would have strong effects on mortality risk over short time frames, which we did not observe. In addition to snow depth, colder temperatures in late winter-spring increased fawn mortality risk over a long time frame, indicating these temperatures potentially had an effect on the nutritional state of fawns. However, lower critical temperatures for mule deer are < -20oC (Parker and Robbins 1984, DelGiudice et al. 2002), but we observed increased mortality risk for fawns in late winter/spring when average minimum temperatures were > -8.0oC. Given that colder temperatures increased fawn mortality risk only in winter and spring, we speculate that this increase in mortality risk was not due to thermoregulation constraints, but rather the growth of new spring forage at the end of winter. In areas with late springs or cooler early growing seasons, the growth of critically important nutritious plants is delayed (Kie 1999, Post and Stenseth 1999), which could reduce the ability of fawns to recover from the nutritional deficiencies of winter (Parker et al. 2009, Kautz et al. 2020). During the summer-fall, cutblocks 1-16 years-old and burns 1-14 years-old reduced adult mortality risk in all time frames. At short time frames, this effect could be attributed to a reduction in predation risk linked to greater visibility, which reduces risk for some prey (Lingle and Wilson 2001, Ford et al. 2014, Lendrum et al. 2018). Mule deer rely on sight to assess potential threats more than do white-tailed deer and black-tailed deer (Odocoileus hemionus columbianus; Lingle and Wilson 2001, Heffelfinger and Heffelfinger 2023), and therefore could 83 be more adept at avoiding predators within open areas where they can see farther distances. However, recent burns and cutblocks are also associated with high quality forage (Gilligan et al. 2023), so use of these areas could reduce mule deer mortality risk by increasing their body condition. In the summer-fall, adult female mule deer need to replenish fat and protein reserves lost over the winter (Bårdsen and Tveraa 2012, Monteith et al. 2013) while also consuming enough energy to support the metabolically expensive lactation period (Moen 1978). Wildfires and timber harvesting can create a mosaic of vegetation that benefits mule deer (Hobbs and Spowart 1984, Roerick et al. 2019, Hayes et al. 2022), particularly for the first 10 years following the disturbance. During this early seral stage, the forest canopy is diminished, promoting the growth of nutritious forbs and shrubs (Gilligan et al. 2023) and increasing sightlines and escape terrain. Thus, adult female mule deer using these areas over a long time frame in the summer-fall may have had access to forage with high digestible energy content and subsequently increased their intake rates, which would increase their ability to withstand the energetic demands of provisioning offspring in the summer (Merems et al. 2020). We found that adult use of burns 1-14 years-old during summer-fall range reduced their mortality risk, but we did not find that the study area with the largest amount of fire on summer-fall range had the highest survival rates. There were twice as many collared deer using burns 1-14 years-old in the summer in Cache Creek than the other two areas, but despite their home-range scale exposure to burns, the overall survival of the Cache Creek population was similar to the other study areas. There are at least 5 possible reasons why we did not see elevated survival in the Cache Creek study area: 1) we do not have pre-fire survival rates in Cache Creek, so it is possible the fires increased survival rates compared to baseline levels in Cache Creek, 2) deer could be top-down limited in Cache Creek, in which predators regulate survival rates and 84 increases in forage quality post-fire have no impact on survival rates (Ballard et al. 2001), 3) these fires also occurred on winter ranges, where the loss of canopy may be detrimental and negate the nutritional benefits of post-fire forage, 3) the large fires in Cache Creek likely had more variable fire severity than the smaller fires in other study areas, which could differentially impact forage quantity, quality and predation risk (Greene et al. 2012, Lewis et al. 2024), and 5) burned areas in Cache Creek were salvage logged, which leads to increased road construction (Lindenmayer and Noss 2006, Hebblewhite et al. 2009), which decreases adult survival in the winter. These data gaps could be filled by collecting survival data before disturbance occurs, understanding how fires in different seasonal ranges can mediate effects on ungulate survival, and linking post-fire activities to survival. Neonate survival is affected by maternal condition (Bishop et al. 2009, Lamb et al. 2023), microhabitat bed site characteristics (Shallow et al. 2015), localized predator densities (Monteith et al. 2014), and stochastic weather conditions (Gilbert and Raedeke 2004, Wilson and Krausman 2008) as contributing factors. However, neonate survival studies have found many conflicting results regarding these parameters. For example, maternal body condition has variable effects on neonate birth mass and survival probabilities including positively affecting neonate birth mass and survival (Bishop et al. 2009, Monteith et al. 2014, Lamb et al. 2023), having no impact on birth mass but reducing neonate survival (Peterson et al. 2018b), affecting birth mass but not neonate survival probability (Shallow et al. 2015) and having no impact on birth mass or survival (Monteith et al. 2014). We found that six variables affected neonate mortality risk: road density, temperature, and proportions of unharvested forest, open areas, and cutblocks (1-16 years-old and 17+ years-old). We could speculate about the many ways these features might influence predator densities 85 and search patterns. However, there were 15 models within the top 90% of the summed Akaike weights for the 2-day and 7-day model sets, indicating there was considerable uncertainty as to which of the models were actually the best at predicting neonate mortality risk (Burnham and Anderson 2002), and we hesitate to base inference and management recommendations from these results. It is likely that neonate mortality risk was affected by some combination of maternal body condition, vegetation, and predator densities, but our relatively coarse measure of habitat use did not capture or reflect these variables. By using different time frames to investigate habitat-performance relationships for a declining mule deer population we were able to identify varying landscape characteristics that affected mortality risk. If we had investigated only the relationship between habitats used within a long time frame (30 days) and mortality risk we would have missed the importance of roads to adults and fawns in the winter-spring because they affected mortality risk only over short time frames (2 and 7 days). Conversely, if we had investigated only the relationship between habitats used over short time frames and mortality risk, we would not be aware of the effect that harsh and long winters can have on adult and fawn mortality. Choosing other time frames could have resulted in other landscape characteristics affecting survival, and we recommend future studies continue to investigate the relationship between habitat and survival at varying time frames. Given these results, we recommend that managers in south-central BC decommission and restore roadways on winter range and discourage policies that promote extinguishing wildfires on summer ranges if these fires do not threaten human settlements or safety. However, we caution that wildfires might not scale up to population level increases in survival, as shown by our findings in the Cache Creek study area. More research is warranted on the effects of wildfire on mule deer population growth. We hesitate to recommend increasing production of 86 cutblocks even though they reduce mortality risk, because roads are built to access these cutblocks. Therefore, we recommend focusing on promoting natural disturbance regimes historically found on the landscape (Hayes et al. 2022). 87 3.5 Figures Figure 3-1.8Three study areas in south-central British Columbia, Canada where we GPS collared adult female (≥ 1 year-old), and male and female fawn (6-months to 1-year-old) and neonate (0 – 12-weeks-old) mule deer between 2018 – 2022. The red polygons denote areas burned by wildfires between 2012 and 2022, the yellow polygons denote individual mule deer summer ranges between 2018 and 2022, and the blue polygons denote individual mule deer winter ranges between winters 2017-2018 and 2022-2023. 88 Figure 3-2.9Relative risk of mortality and 95% confidence intervals of predicted effects from Cox proportional hazards modeling used to determine which landscape and weather factors impact adult and neonatal deer mortality risk in south-central British Columbia, Canada during 2018-2022. Lines above the dashed line at one indicate an increasing relative risk of mortality. The numbers above each panel are the lower 2.5 and upper 90th percentile of the observed covariate value that generated the predicted risk curve. These plots represent the significant interaction terms remaining after model selection and model averaging for A) adult winter-spring 2-day analysis, B) adult winter-spring 7-day analysis, C) and D) neonate summer 2-day analyses, E) Neonate summer 7-day time frame analysis. Road density refers to the average road density attributed to locations within the specified time frame. The proportion of unharvested forest and cutblocks refers to the proportion of locations in that vegetation type within the specified time frame. 89 Figure 3-3.10Hazard ratios and 95% confidence intervals of parameters from Cox proportional hazards modeling used to determine which landscape and weather factors impact adult, fawn, and neonate mule deer mortality risk in south-central British Columbia, Canada during 2018-2022. Only significant parameters that were not involved in an interaction are shown. Hazard ratios above the dashed line at one indicate an increasing risk of mortality as the covariate increases, and ratios below one indicates a decreasing risk of mortality as the covariate increases. Abbreviations are as follows: Distance to Road: mean distance to nearest road, Road Density: mean road density, Ruggedness: mean ruggedness, Snow Depth: mean snow depth, Snow Depth.tt: time varying coefficient for snow depth, Summer-fall Temp: mean maximum daily temperature in summer-fall, Winter-spring Temp.1: mean maximum daily temperature from 4 Dec – 11 Feb, Winter-spring Temp.2: mean maximum daily temperature from 12 Feb – 24 March, Winter-spring Temp.3: mean maximum daily temperature from 25 March – 20 May, Recent Burns: Proportion of burns 1-14 years-old, Recent Burns.tt: time varying coefficient for proportion of burns 1-14 years-old, Recent Cutblocks: proportion of cutblocks 1-16 years old, Recent Cutblocks.tt: time varying coefficient for proportion of cutblocks 1-16 years-old, Open Areas – proportion of open areas. 90 Figure 3-4.11Relative risk of mortality and 95% confidence intervals for time varying covariates from Cox proportional hazards models used to determine which landscape and weather factors impact adult and fawn mule deer mortality risk in south-central British Columbia, Canada during 2018-2022. These parameters all violated the proportional hazards assumption and were changed to include time varying coefficients so the change in effect over time was included in the model. Lines above the dashed line at one indicate an increased risk of mortality from that value at that time, and lines below one indicates a decreased risk of mortality from that value at that time. Each panel represents the significant interaction terms remaining after model selection and model averaging for A) adult winter-spring 30 day analysis - snow depth, B) adult summer-fall 30 day analysis – cutblocks 1-16 years-old, C) adult summer-fall 30 day analysis – burns 1-14 years-old, D – F) fawn winter-spring 30 day analysis – average maximum temperature. This covariate was broken up to have a different value for each of three different time periods, early winter (D), winter (E), spring (F). 91 3.6 Tables Table 3-1.2Covariates and predictions used in Cox proportional hazards models for adult, fawn and neonate mule deer summer-fall and winter-spring mortality risk in south-central British Columbia, Canada in 2018-2022. We predicted that the effect of the covariate would be the same for all age classes within each season, although the strength of the effect might vary by age class. Time Frame Covariate Prediction [Winter-spring/Summer-fall]a Explanation b Short (2 and 7-day time frame) Ruggedness -/- More rugged terrain helps deer evade predators [1, 2] Roads c +/+ Roads help predators access deer [3, 4] Distance to Edge +/+ Predators kill deer at edges of cutblocks [5-7] Recent Cutblocks d +/- Winter-spring – Snow depths would be higher in open areas in the winter, which makes deer more vulnerable to predation [8-11]; Summer-fall - Deer have better sightlines to avoid predators in open areas [12] Recent Burns +/- Grassland/Shrub +/- Old Cutblocks -/+ Winter-spring – Snow depths would be lower in closed canopy vegetation, thus making deer less vulnerable to predation [8-11]; Summer-fall - Predators are better at ambushing in a dense vegetation [13-15] Old Burns -/+ Medium Cutblocks -/NA Unharvested forest -/+ Snow Depth c +/NA Deep snow leads to increased predation because prey are more vulnerable [8-10] Winter-spring Temperature c 0/NA Cold temperatures experienced over short time frames should not impact mortality risk Precipitation c NA/0 Amount of precipitation experienced over a short time frame should not impact mortality risk Summer Temperature e +/NA Warm temperatures in the summer should decrease neonate mortality risk [26, 27] 92 Time Frame Covariate Prediction [Winter-spring/Summer-fall]a Explanation b Long (30-day time frame) Ruggedness 0/0 Rugged terrain experienced over long time frames should not impact mortality risk Roads 0/0 Roads experienced over long time frames should not impact mortality risk Distance to edge 0/0 Amount of edge experienced over long time frames should not impact mortality risk Recent Cutblocks +/- Winter-spring - Snow depths are higher in open areas in the winter which results in deer being in worse body condition [10, 13, 16, 17]. Summer-fall - Open areas have higher quality forage which results in deer being in better condition [13, 18-21] Recent Burns +/- Grassland/Shrub +/- Old Cutblocks -/+ Winter-spring - Snow depths are lower in vegetation with higher canopy cover, which results in deer being in better body condition [10, 17]; Summer-fall - Areas with dense canopy cover have lower quality forage, which results in deer being in worse body condition [22-24] Old Burns -/+ Medium Cutblocks -/NA Unharvested forest -/+ Snow Depth c +/NA Deep snow leads to increased predation because deer are in worse body condition [10, 13, 16, 17] Winter-spring Temperature c -/NA Cold temperatures are energetically expensive and therefore deer are in worse body condition [13, 16, 20] Precipitation c NA/+ Summer-fall precipitation affects summer-fall mortality risk by impacting forage quality and quantity [13, 20, 21] a Positive (+) indicates the covariate would increase the risk of mortality, negative (-) indicates the covariate would decrease the risk of mortality, zero (0) indicates that covariate will have no effect, and NA indicates that covariate does not exist for that season. 93 b Citations in brackets: 1) Bonar et al. (2016), 2) Lingle (2002), 3) Dickie et al. (2017), 4) Dickie et al. (2020), 5) MacAulay et al. (2022), 6) Holmes and Laundré (2006), 7) Laundré and Hernández (2003), 8) DelGiudice et al. (2002), 9) Gilligan et al. (2023), 10) Parker et al. (1984), 11) Huggard (1993), 12) Laundré (2010), 12) Altendorf et al. (2001), 13) Jackson et al. (2021), 14) Wilmers et al. (2013), 15) Benson et al. (2016), 16) Bishop et al. (2009) , 17) Gilbert et al. (2020) 18) Monteith et al. (2013), 19) Hurley et al. (2011), 20) Hurley et al. (2017), 21) Hurley et al. (2014) 22) Proffitt et al. (2019), 23) Proffitt et al. (2016), 24) Hayes et al. (2022), 25) Cook et al. (2004), 26) Gilbert and Raedeke (2004), 27) Warbington et al. (2017) c. Actual covariate varies depending on time frame and age class (see text). d. Winter-spring ages: Recent cutblocks: 1-18 years old, medium cutblocks: 19-41 years old, old cutblocks: 42+ years old, recent burns: 1-11 years old, old burns: 12+ years old. Summer-fall ages: Recent cutblock: 1-16 years, old cutblock: 17+ years, recent burn: 1-14 years, old burns: 15+ years e. Neonates only 94 Table 3-2.3Mortality sources of GPS collared adult (≥ 1-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) mule deer in south-central British Columbia, Canada during 2018 – 2022. Cause of Mortality Adult Fawn Neonate Cougar 37 24 2 Possible Cougar 3 1 0 Bear1 6 0 19 Possible Bear 0 0 5 Coyote 8 14 0 Possible Coyote 2 1 1 Wolf 1 8 0 Possible Wolf 1 0 0 Unknown Canid 1 5 0 Unknown Predation 3 1 8 Unlicensed Harvest 2 3 0 Vehicle Collision 5 1 0 Starvation 0 2 0 Health 4 0 4 Other2 3 0 0 Unknown 11 10 313 1 Either a black bear or grizzly bear. 2 Includes one drowning, one train collision, and one entrapment in a barbed wire fence. 3 It is likely most of these are predation. We only observed 4 non-predation mortalities for neonates, which were easily identifiable even a few days after the mortality occurred. However, to be conservative we labeled all mortalities as unknown if we didn’t find definitive evidence of predation. 95 Chapter 4: Do resource selection functions correlate with mule deer survival across age classes? 4.1 Introduction Habitat is a critical concept in ecology and forms the foundation upon which many key principles and theories are built. For example, habitat plays a pivotal role in determining species distributions (Rosenzweig 1995, Elith and Leathwick 2009), abundance (Venier and Fahrig 1996), intra- and interspecies interactions (MacArthur and Levins 1964, Janssen et al. 2007), and population dynamics (Pulliam and Danielson 1991, Johnson et al. 2020). Additionally, understanding species habitat requirements are crucial for conservation, as habitat protection is a fundamental strategy used to manage populations and restore biodiversity (Noss et al. 1997). The term habitat has been used in many ways in the ecological literature (Block and Brennan 1993, Hall et al. 1997, Garshelis 2000, Mitchell and Hebblewhite 2012, Kirk et al. 2018, Darracq and Tandy 2019). Many authors have argued that the definition of habitat should explicitly consider the resources (e.g., food, water, cover) and environmental conditions (e.g., temperature, precipitation) that contribute to species fitness, and not simply describe where organisms are found (Morrison et al. 1992, Hall et al. 1997, Morrison 2009, Mitchell and Hebblewhite 2012, Kirk et al. 2018), which is the definition of habitat we use in this paper. Species habitat, and habitat quality, are often inferred through the use of species distribution models (SDM) or resource selection functions (RSF; Manly et al. 2002, Guisan and Thuiller 2005, McLoughlin et al. 2010), which relate spatial variation in habitat quality to spatial heterogeneity in species occurrence or density (Hirzel and Lay 2008). However, using these models to assess habitat quality assumes there is a positive relationship between an individuals’ selection of resources and the fitness benefits conferred by those resources (Fretwell and Lucas 96 1969, Matthiopoulos et al. 2015, Northrup et al. 2022). Included with this assumption is the theoretical expectation that individuals behave in ideal and free distributions (Fretwell and Lucas 1969), where individuals select between resource patches such that their fitness is maximized at the time of selection, resulting in variation in population density among resource patches in proportion to the fitness value of each patch. When species do not behave in an ideal and free distribution, then using an SDM or RSF to infer habitat or habitat quality is not appropriate. For some populations, empirical tests of the ideal and free distribution have failed to explain the relationship between density, selection, and habitat (Beckmann and Berger 2003, Johnson and Seip 2008, Piper 2011). Generally, a positive relationship between selection and fitness is not observed when species exhibit certain behavioral traits such as territoriality (Fretwell and Lucas 1969, Van Horne 1983), site fidelity (Faille et al. 2010), or sociality (Pays et al. 2012). Additionally, individuals are unlikely to always select the highest quality habitat available (Arlt and Pärt 2007). Non-ideal selection is caused by individuals having imperfect knowledge of habitat patches (Lima and Zollner 1996), or a lack of cues indicating habitat quality at the time of selection (Orians and Wittenberber 1991). An ecological trap, where individuals select habitat that decreases their fitness, is the most extreme form of non-ideal selection. Ecological traps are typically caused by rapid environmental changes that cause a mismatch in the cues developed over evolutionary time to select habitats, and the fitness benefit of that habitat (Gates and Gysel 1978, Schlaepfer et al. 2002, Battin 2004). Even if a species does behave in an ideal and free distribution and an RSF accurately predicts habitat quality, species rely on different habitats at varying life history stages (Boyce et al. 2003, Rossman et al. 2015). Given that different life stages make variable contributions to population growth (Mills 2013), habitats used at one life stage may have greater influence on 97 population dynamics than others (Cooch et al. 2001, Kane et al. 2017). Therefore, assessing habitat using a single age class could lead to incorrect conclusions about habitat requirements at the population level (Gaillard et al. 2010). Annual and seasonal variation in weather patterns can also have a large effect on habitat. For example, areas used by female mule deer (Odocoileus hemionus) in the summer typically occur at high elevations and contain high quality forage as deer seek to replenish fat reserves lost during the previous winter, late stages of pregnancy, and lactation (Gilligan et al. 2023, Monteith et al. 2023). In contrast, in the winter mule deer use areas with lower snow depths and higher forage availability, such as open canopy areas in low elevations (Serrouya and D’Eon 2008, Gilligan et al. 2023). These winter areas are prioritized by mule deer to minimize energy expenditure caused by movement through deep snow and to access forage (Parker et al. 1984, Gilbert et al. 2017). In years with extreme weather conditions such as a summer drought or severe winter, the composition of seasonal habitat on the landscape is altered. Lower plant species richness during a drought could cause mule deer dietary breadth to decrease (Parker et al. 1999, Lashley and Harper 2012, Folks et al. 2014), thereby shifting the vegetation types deer use during the summer. In a severe winter, deep snow would bury key nutritional resources in open canopy areas, causing deer to shift selection to areas with high canopy cover (Hundertmark et al. 1990, Poole and Mowat 2005). Therefore, annual and seasonal weather patterns can affect species habitat requirements and also affect habitat quality. Mule deer populations in south-central British Columbia (BC), Canada have been declining for at least 30 years (Ministry of Forests, [MOF] 2014), potentially due to habitat alteration in the form of large scale timber harvest, road development, and decades of fire suppression (Heyerdahl et al. 2012). These changes may have reduced mule deer populations 98 because roads increase predation risk (Johnson-Bice et al. 2023), fire suppression reduces forage quality and quantity (Snobl et al. 2022), and timber harvest on winter range results in increased snow depths due to canopy removal, which increases the energetic demands of winter (Parker et al. 1984). Additionally, climate change may cause warmer temperatures and more variable precipitation (Mote and Salathé 2010), which could be beneficial for mule deer if winters became less severe (Hurley et al. 2017) or harmful if drought conditions are more prevalent in the summer (Marshal et al. 2008). Given these population declines and uncertainty surrounding future weather patterns, there is a need to determine what constitutes mule deer habitat, how it might interact with weather, and how managers might alter resources to increase population growth. Here, we determine spatial resource selection and measure spatially and temporally varying weather conditions and test their relationships with 3 key vital rates of population growth, neonate (0 – 12 weeks-old), fawn (6-months – 1-year-old) and adult (≥1-year-old) mule deer survival rates. We considered predictions under four different hypotheses for this analysis (Figure 1; DeCesare et al. 2014). Under the Ideal Selection Hypothesis, habitat selection behavior would result in selection that is positively correlated with survival. If this hypothesis is supported, resource selection functions accurately predict mule deer habitat. We also included a Non-ideal Selection Hypothesis in which selection would positively correlate with survival, but their selection patterns contain no buffer for more extreme weather conditions, so survival would decrease under poor weather conditions (Arlt and Pärt 2007, DeCesare et al. 2014). If this hypothesis is supported, resource selection functions alone do not predict mule deer habitat but must be combined with weather conditions. 99 Alternatively, under the Ecological Trap Hypothesis, mule deer select resources that reduce their survival probability and resource selection functions predict mule deer sink habitat, or habitat where deer are found but potential for survival is low (Mitchell and Hebblewhite 2012). Under this hypothesis we had two predictions regarding spatially and temporally varying weather patterns. Mule deer either select habitat in a way such that variable weather does not further reduce survival in highly selected areas, or the ecological trap is exacerbated by poor weather, and deer survival probabilities are even lower in poor weather. Finally, under the Weather Only Hypothesis, we hypothesized that resource selection would not be correlated with survival and that seasonal weather conditions alone influence mule deer survival. Under this hypothesis, resource selection functions do not predict mule deer habitat or habitat quality. We tested these 4 hypotheses separately for each age class and season (summer-fall, winter-spring). We then integrated survival rates as a function of resource selection probabilities and/or weather, and combined these estimates to map population growth (lambda)-based habitat quality in a spatially explicit framework (DeCesare et al. 2014, Kane et al. 2017). These maps can be used to identify source and sink habitat for a declining population of mule deer and predict population growth under varying scenarios of seasonal weather. 4.2 Methods 4.2.1 Deer Capture and Monitoring We captured adult female (≥1-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) male and female deer during 2018-2023. We caught adults and fawns using helicopter net-gunning (Webb et al. 2008), chemical immobilization (Mich et al. 2008) administered via a dart gun, and Clover trapping (Cadsand et al. 2010) in six sessions occurring 100 in March 2018 and December – January 2018-2023. Deer were fit with a 700-g (adult; Survey 2D) or 450-g (fawn; Survey 1D) Iridium GPS radio-collar (Vectronics Aerospace, Berlin, Germany) that was programmed to record a location every 4.25 hours and transmit all locations via the Iridium service. Collars were equipped with a 6-hour motion sensor to detect mortalities. We collected 10mL of blood from adult female deer to determine pregnancy using pregnancy specific protein-B levels (PSPB; Duquette et al. 2012). On a subset of adult females we used a portable ultrasound to count the number of fetuses (fetal rate; Monteith et al. 2018). The mean date of conception was November 14 (C. Wright, unpublished data) so when determining pregnancy and fetal rates we excluded all adult female deer caught before December 15 each year because PSPB levels and signs of pregnancy via ultrasonography are undetectable until 30 days post conception (Willard et al. 1998). In May 2019 – 2021 we reprogrammed all adult female collars to collect a location every 1.25 – 2.5 hours, depending on their collar type (adult collars = 1.25 hours, fawn collars = 2.5 hours). We monitored adult female locations daily from mid-May to mid-June for signs indicative of parturition including reduced daily movement speeds, net displacement, and movement rates (Peterson et al. 2018a, Wright et al. 2019, 2021). Once a potential birth cluster was identified, we grid searched the area in teams of 3 – 10 people to locate the neonate(s). We also captured some neonates opportunistically by visually locating them while driving around the study areas. These neonates were not associated with a collared mother. We blindfolded, sexed, and weighed all neonates, and those > 3.2 kg were fit with a 145-g expandable Globalstar GPS radio-collar (< 4.5% of their body weight; [Sikes et al. 2011], Vertex Mini, Vectronics Aerospace, Berlin, Germany). The collars were programmed to record a location every 2.25 hours that were stored on board the collar and transmit 1 location daily via 101 the Globalstar service. Collars were equipped with a 4-hour motion sensor to detect mortalities. We estimated neonate age at capture and birth date by noting the behavior and umbilical and hoof characteristics of the deer (Grovenburg et al. 2014), and by examining the collared mother’s movement rates (Peterson et al. 2018a). All capture and handling procedures were approved by the University of British Columbia Animal Care Committee (protocols A18-0300 and A22-0257) and the province of British Columbia (permits MRPE18-290018 and MRPE23-800846). We censored all adult and fawn mortalities that occurred within 14 days of capture to account for potential capture-related mortalities (adults: n = 9, fawns: n = 23; Webb et al. 2008). For neonates, there is little evidence that capture and handling leads to increased rates of maternal abandonment (Powell et al. 2005), and natural neonatal mortality is highest in the first few weeks of life (Monteith et al. 2014, Wright et al. 2019). Therefore, we censored only one neonate mortality that occurred within one day of capture, because after release that neonate ran quickly away in the opposite direction of its mother. If deer did not die during the study, we right censored them either at the end of the study (March 31, 2023) or on the last date we received a location from the collar if the collar malfunctioned or the battery died. 4.2.2 Determining Migratory Strategies Mule deer in our study areas are partially migratory, which likely impacts both resource selection (Spitz et al. 2020) and survival (Hebblewhite and Merrill 2011). Thus, we estimated separate resource selection functions for migratory and resident deer in the summer, when deer are located on separate ranges. We defined migration as movement between distinct seasonal ranges (Jesmer et al. 2018) and we considered multiple trips between ranges within a season as non-migratory behavior (Singh et al. 2012). We visually examined net squared displacement 102 (Bunnefeld et al. 2011, Monteith et al. 2018) using Migration Mapper v. 1.3 (Merkle et al. 2022) to determine each deer’s migratory strategy (resident or migrant). Neonates with a known, collared mother were assigned their mother’s migratory strategy. If neonates captured opportunistically lived until November, we used Migration Mapper to determine their movement strategy. There were 6 opportunistically captured neonates that did not live long enough to migrate, so we used expert knowledge of our system to determine if those neonates were migratory or resident deer based on their capture locations. 4.2.3 Resource Selection Functions 4.2.3.1 Quantifying Availability We determined third order seasonal resource selection (within home range; Johnson 1980) for adult, fawn, and neonate deer by comparing resource attributes at GPS points where deer were located (used locations) to attributes at randomly generated points (available locations). We defined summer-fall as May 20 – November 16 and winter-spring as November 17 – May 19, based on mean first snow free and first snow fall dates (See Chapter 3). Most neonates died before summer-fall ended, so we restricted their locations to the first 12 weeks of life. Additionally, fawns were caught in December each year, so their winter-spring period extended from December 4 (first date we had ≥ 30 collared fawns) to May 19. We defined the area of availability separately in each season by creating 100% minimum convex polygons around each deer’s winter-spring and summer-fall locations, and then buffered these polygons by twice the median step length between locations (1.6 km for neonates, 2.7 km for adults and fawns). We created 10 available locations for each used location within these unique areas of availability (Fieberg et al. 2021). 103 4.2.3.2 Creating Covariates We predicted terrain, vegetation type, and roads would impact mule deer resource selection in both seasons, so we downloaded remotely sensed data from various public repositories that represented these variables and extracted the data to each used and available location. We obtained a 20-m digital elevation model (DEM) from the BC Geographic Data Catalogue and resampled it to 30-m resolution to better align with our other data layers. From the DEM we calculated slope (degrees) and aspect (north and east) using the raster package in R (Hijmans et al. 2022). We also used the DEM to derive a relative measure of surface curvature (McNab 1989, 1993), with large positive values representing convex cells (ridgelines) and large negative values representing concave cells (valley bottoms), and a measure of terrain ruggedness (Vector Ruggedness Measure; Sappington et al. 2007) using the spatialEco package in R (Evans 2021). We calculated the topographic wetness index (TWI), a measure of how local slope and upslope area contribute to water flow (Sørensen et al. 2006), from the DEM using ArcGIS Spatial Analyst Tools (Environmental Systems Research Institute, Redlands, California, USA). We used BC and Washington road layers from the BC Geographic Data Catalogue and the Washington Geospatial Open Data Portal (http s://geo.wa.gov/). We merged these layers and created a road density layer (within 500 m; km/km2). To determine the vegetation classes mule deer selected during each season we used the Vegetation Resource Inventory (VRI) and consolidated cutblocks layers from the BC Geographic Data Catalogue to classify vegetation types into shrub, grassland, unharvested forest, harvested forest, and other (contains exposed rock/rubble, bodies of water, urban areas, perennial snow and ice; DataBC 2022). To obtain another measure of vegetation, we downloaded the remotely sensed Normalized Difference Vegetation Index (NDVI; MOD13Q1; 250-m spatial 104 resolution; 16-day temporal resolution) from Google Earth Engine. We created one NDVI layer by calculating the mean of all layers during the summer-fall months across all years in which we had GPS location data (2018-2023). We also obtained the wildfire perimeter layer from the BC Geographic Data Catalogue to determine deer selection of wildfire burns. Based on previous research (Mumma et al. 2021, Hessami 2022), we expected that mule deer would have a non-linear selection of post-fire and post-harvest forests. For example, moose avoid young cutblocks, select for mid-aged blocks, and avoid older blocks (Mumma et al. 2021). To integrate a potential non-linear response to age-since-disturbance, we first fit generalized additive mixed models to estimate the age thresholds in which deer change their selection probability. We expected to see an inverse quadratic form but did not want to bias the expectation by fitting exponential terms to a linear model. We then plotted the predicted response of the GAMM to determine breakpoints in selection and avoidance of age-since-disturbance. We performed separated analyses for fire and cutblocks. We found that mule deer selected recent cutblocks and burns (cutblocks: summer-fall = 1-16 years old, winter-spring = 1-18 years old; burns: summer-fall = 1-14 years old, winter-spring = 1-11 years old), avoided mid-aged to old cutblocks and burns (cutblocks: summer-fall: = 17+ years old, winter-spring = 19-41 years old; burns: summer-fall = 15+ years old, winter-spring = 12+ years old), and selected old cutblocks in the winter-spring (winter-spring: 41+ years old). Based on these breakpoints, we transformed the environmental data on cutblock and fire age into categorical values representing recent and old-aged burns in the both seasons, recent and old-aged cutblocks in the summer-fall, and young, mid, and old aged cutblocks in the winter-spring. However, we refer to these categories by their age groups for clarity. For more details on how we chose these age categories see Appendix E. 105 4.2.3.3 Collinearity and Scaling We estimated RSFs separately for each study area (n = 3), age class (n =3), season (n = 2), and movement type in the summer-fall (resident or migrant), which resulted in 18 model sets. Before modeling, we screened all predictor variables for collinearity (|r| < 0.6) using Pearson’s correlation coefficient and did not include correlated variables in the same model. In all age classes and seasons, road density and distance to road were the only correlated covariates. We wanted to use the road covariate with the highest predictive power, so we ran each road covariate in a univariate logistic regression model with a binomial distribution, ranked models using Akaike’s Information Criterion scores corrected for small sample size (AICc), and chose the covariate within the top model as the covariate used in the RSF. In all age classes and seasons, road density was the top ranked covariate (distance to road was ≥ 1012.8 ΔAICc away) and was used in RSF modeling. 4.2.3.4 Model Development and Fitting We developed a set of candidate models (Table 4-1) that we hypothesized would best explain resource selection. We included an elevation × vegetation type interaction to potentially account for mule deer using different vegetation types at different elevations. We scaled all continuous variables using the mean and standard deviation of each variable across all age classes, seasons, study areas, and movement type to enable comparison of beta coefficients across groups. We used generalized linear mixed models with a binomial distribution and logit link to estimate resource selection with individual mule deer ID as a random intercept using the package 106 glmmTMB (Brooks et al. 2017). We weighted each available point with W = 1000 and used points with W = 1 (Fithian and Hastie 2013, Muff et al. 2020). We used AICc to determine the best model and considered models within 2 ΔAICc units from the top model as competing for the top model (Burnham and Anderson 2002). If models within this threshold only differed from the top model by one additional parameter, we considered that additional parameter to be uninformative and unsupported by the data (Burnham and Anderson 2002). We conducted model selection using the package AICcmodavg (Mazerolle 2023) in Program R. We used k-folds cross validation (Boyce et al. 2002) with k = 5 to assess the predictive strength of the top model from each model set. We spatially mapped the top RSF model on the exponential scale for each study area, age class, season, and migratory strategy by predicting the top model over all spatial covariates included within that model. We divided the continuous selection probabilities into 30 equal area divisions (Morris et al. 2016) with 1 representing low selection probability areas and 30 representing high selection probability areas. 4.2.4 Survival Modeling 4.2.4.1 Creating Weather Covariates We extracted modeled daily precipitation, minimum temperature, and maximum temperature grids downloaded from Daymet V4 (Thornton et al. 2023), and modeled daily snow depth downloaded from the Snow Data Assimilation System (National Operational Hydrologic Remote Sensing Center 2004) to each deer GPS location. In the winter-spring, we calculated a winter-spring severity index (WSI) by assigning each day one point if the minimum temperature was <-17.8oC and one point if the snow depth was > 38.1 cm, and cumulatively summing these points over the season (DelGiudice et al. 2002, Kautz et al. 2020). Summer-fall temperatures in 107 BC are unlikely to be severe enough to impact adult mule deer survival (Duvuvuei et al. 2023, Gilligan et al. 2023) but may impact neonate survival (Gilbert and Raedeke 2004, Wilson and Krausman 2008). Therefore, in the summer-fall we extracted daily precipitation values to all adult GPS locations and extracted daily precipitation and daily maximum temperatures to all neonate GPS locations. We also extracted each predicted value from the fitted RSF to the corresponding deer GPS location. 4.2.4.2 Instantaneous Covariates We predicted that weather and selection probabilities could have either instantaneous or cumulative effects on deer survival probabilities (Gaillard et al. 2010, DeCesare et al. 2014). Therefore, we calculated mean spatial selection probabilities and weather values by date (instantaneous effect) and by season (cumulative effect). Seasonal start and end dates were the same as in the RSF modeling. Daily mean RSF values were calculated for each deer using their GPS locations. Daily mean weather values were calculated for each deer by calculating the mean precipitation/temperature value per day in the summer-fall, and cumulatively summing the WSI values by day in the winter-spring. We used a summed WSI index for comparison to other ungulate winter survival studies (DelGiudice et al. 2002, Kautz et al. 2020). 4.2.4.3 Seasonal Covariates We created seasonal RSF values by taking the mean of the instantaneous RSF values during each season. However, not all deer lived to the end of each season, and weather patterns change over the course of a season, so we could not calculate seasonal weather values individually by deer without biasing our survival models. For example, in the winter-spring, deer that died one month into the season would have a lower WSI value than a deer that lived through 108 the whole winter-spring, even if they lived in the same area. To account for this discrepancy, we calculated one seasonal weather covariate for each study area, age class, movement strategy and year, and all respective individuals received that seasonal value. Therefore, we measured total summer-fall precipitation by calculating the average daily precipitation for each study area, age class, movement strategy and year, then summed those daily values for the summer-fall season. We also pooled all neonate locations and calculated average summer-fall temperatures for each study area and year. We calculated seasonal WSI values by similarly calculating the average daily winter-spring temperature and snow depth within each study area, age class, and year, then cumulatively summing the WSI values during the winter-spring season, to receive one seasonal WSI value per study area, age class, and year. 4.2.4.4 Choosing Seasonal or Instantaneous Covariates To determine whether survival outcomes were better predicted by instantaneous or cumulative values of each covariate, we ran univariate Cox proportional hazards models (Therneau and Grambsch 2000) with each model containing either the daily or seasonal value of that covariate and ranked models using AICc. We chose the time frame within the top ranked model to represent that covariate for the respective age class and season. We found no differences in survival between study areas and years using univariate Cox proportional hazards models, so we pooled survival data by age class. 4.2.4.5 Model Development We developed three candidate models that represented our hypotheses (Figure 4-1), with statistical predictions for each one. For the first two models, a different hypothesis is supported depending on the direction of the predicted effect. Model one contained a main effect for 109 resource selection probability and represented our Ideal Selection Hypothesis (a positive relationship between probability of selection of resources and survival) and Ecological Trap Hypothesis (a negative relationship between probability of selection of resources and survival). Model two contained an interaction term between the seasonal weather covariate and resource selection probability and represents the Non-ideal Hypothesis (the positive relationship between selection and survival is reduced by poor weather) and the weather mediated ecological trap (the negative relationship between selection and survival is reduced by poor weather). Model three contained a main effect for seasonal weather and represented the Weather Only hypothesis (mule deer survival probability is driven by weather). Model four was the null model, with only an intercept term. 4.2.4.6 Model Fitting We used the Andersen-Gill formulation of the Cox proportional hazards model with either a study-based time scale (adults and fawns) or an age-based time scale (neonates) to determine the effect of selection probabilities and weather conditions on survival (Johnson et al. 2004, Fieberg and DelGiudice 2009). This model is a formulation of the Cox proportional hazards model, a semi-parametric regression model for survival data (Therneau and Grambsch 2000), that can accommodate left and right censored data and time-dependent covariates (Johnson et al. 2004). We can directly estimate the effect of covariates on the baseline risk of mortality by computing a hazard ratio from each model coefficient (hazard ratio = exp(βi)). A hazard ratio of 1 indicates no effect of the covariate on the risk of mortality, a hazard ratio < 1 indicates a reduced risk of mortality, and a hazard ratio > 1 indicates an increased risk of mortality. 110 One of the main assumptions of the model is that the proportional changes in the baseline hazard caused by changes in covariates remain constant over time (Therneau and Grambsch 2000). We tested this proportional hazards assumption by using the cox.zph() function within the R package survival (Therneau 2023) which plots the scaled Schoenfeld residuals for each covariate against time. If a covariate violated this assumption, we chose to split the season up into n separate time frames such that the proportional hazards assumption held true within those shorter time frames (Therneau et al. 2023). We used this approach for adult summer-fall models, see results for details. Some adults were alive during multiple years, so we calculated robust standard errors through a cluster variable that accounted for this lack of independence (Therneau and Grambsch 2000). To avoid a complex three-way interaction, we chose to include an interaction between RSF values and summer-fall temperature in the neonate weather mediated model, based on previous work in our system (Chapter 3) and the literature (Gilbert and Raedeke 2004, Wilson and Krausman 2008). We used AICc to evaluate the relative support for each hypothesis separately by season (summer-fall and winter-spring) and age class (neonate, fawn, adult). We considered models within 2 ΔAICc units from the top model as competing for the top model (Burnham and Anderson 2002). If models within this threshold only differed from the top model by one additional parameter, we considered that additional parameter to be uninformative and unsupported by the data (Burnham and Anderson 2002). We conducted model selection using the package AICcmodavg (Mazerolle 2023) in Program R. 111 4.2.5 Population Growth-Based Habitat Quality 4.2.5.1 Mapping Survival and Population Growth Using the top seasonal model for each age class, we predicted cumulative seasonal survival probabilities under different conditions by using both low (5th percentile) and high (95th percentile) values of each covariate within that model. We also used the top model to spatially map predicted survival probabilities at the end of each season as a function of each pixel’s attributes. For models that contained a significant weather covariate, we created two separate maps of predicted survival probabilities by using a low (5th percentile) and high (95th percentile) value of that weather covariate. In addition to spatially mapping survival estimates for each season and age class, we also created separate maps for each study area and movement strategy because we evaluated RSFs separately for these groups. We used the spatial survival estimates of each age class, mean pregnancy rates, and mean fetal rates to map population growth rates (λ; lambda), using the recruitment mortality equation (Hatter and Bergerud 1991, Serrouya et al. 2017, Hatter 2020) where 𝜆𝜆 = 𝑆𝑆1 − 𝑅𝑅 In the equation S = annual adult survival, which we calculated by multiplying the two seasonal adult survival maps together, and R = recruitment, which we calculated by multiplying the overwinter fawn survival map, neonate survival map, pregnancy rates (average probability of an adult female being pregnant), and half the fetal rate (average number of female fetuses per adult female, assuming a 50:50 male female birth ratio; Robinson et al. 2002, DeCesare et al. 2012) together. Migratory deer summer range occurred in areas where deer do not live in the winter. Therefore, predicted overwinter fawn survival was very low in these ranges, which artificially 112 deflated λ estimates. To account for this data anomaly, we used the mean predicted overwinter fawn survival estimate in migratory summer ranges when spatially predicting λ for migratory deer. 4.2.5.2 Quantifying Changes in Habitat Quality Under Different Weather Scenarios We estimated coarse seasonal deer home ranges by creating minimum convex polygons around GPS locations, pooling data across years for each deer-season. We then spatially joined these polygons together, creating one winter-spring range polygon per study area and two summer-fall range polygons per study area (residents and migrants). Within these seasonal ranges, we measured the proportion with λ > 1 (hereafter called growth habitat), and summarized differences in the proportions under varying weather conditions (e.g., high rainfall, low WSI). We repeated the previous step for Ungulate Winter Range Units (UWR), areas where timber companies must retain forest stands that meet certain requirements of forest type (e.g., Douglas-fir), stand age, and canopy cover based on the amount of snow that the area typically receives. 4.3 Results 4.3.1 Deer Capture and Pregnancy Rates We caught and fit GPS collars on 201 adult female deer, 270 6-month-old fawns (142 females, 128 males), and 134 neonates (72 females, 62 males) from March 2018 – December 2022. There were 69 female fawns and 15 female neonates that we monitored through to adults, for a total sample size of 285 adult female deer used in the analysis. We tested 146 and 108 adult female deer for pregnancy and fetal rates, respectively, with 137/146 being pregnant (pregnancy rate = 0.94) and 78/108 carrying twins (mean = 1.72 fetuses per adult female). 113 4.3.2 Resource Selection The top ranked resource selection model for all age classes, movement strategies, and study areas was topographic × cutblock vegetation + road density + NDVI + burns. In all sets this model received an AICc weight of 1 and the next ranked model in any set was ≥ 28.28 AICc. The k-folds cross validations revealed excellent fit (Spearman’s rank coefficient [ρ] = 0.6-0.93) for all RSFs except neonate migrants in the West Okanagan (ρ = 0.31). 4.3.2.1 Winter-Spring Overall, resource selection in the winter-spring was similar across study areas and age classes. For example, in the winter-spring, adult and fawn deer in all study areas selected lower values of NDVI, south facing aspects, dry, and less rugged areas (Tables G-1 – G-6; Figure 4-2), and all deer except fawns in Cache Creek selected steeper slopes. All age classes in the West Okanagan selected burns of any age over unburned areas in the winter-spring, while in Cache Creek they preferred unburned areas over burns of any age in the winter-spring (Table G-2, Table G-3, Table G-5, Table G-6). Fawns in the Boundary selected for burns 1-11 years-old over unburned areas and burns 12+ years-old, while adults preferred both burns of any age over unburned areas in the winter-spring (Table G-1, Table G-4). The vegetation types deer selected in the winter-spring changed as elevation increased, with very few patterns of similarity across study areas and ages (Figure 4-3, Figure G-1). Generally, at high elevations (1,300 m – 1,870 m), adults and fawns showed higher selection probabilities for closed canopy forests (unharvested and cutblocks 42+ years old) in all study areas. At low elevations (460 – 670 m), all age classes except adults in Cache Creek and fawns in the West Okanagan showed higher selection probabilities for more open canopy forests (cutblocks 1-18 and 19-41 years old). 114 4.3.2.2 Summer-Fall Unlike winter-spring, the resources selected by deer in the summer-fall varied by movement type, age class and study area. Migratory adults in all 3 study areas selected south and west facing, dry, and less rugged areas, and all adult residents selected areas with higher road densities (Figure 4-2, Tables G-7 – G-12). Resident adults in the Boundary and West Okanagan also selected areas with lower NDVI that were less rugged and more concave (e.g., valley bottoms). Adults in all study areas selected for burns of any age over unburned areas in the summer-fall except for migratory deer in Cache Creek, who selected for unburned areas over any age class of burns. The vegetation types adult deer selected in the summer-fall changed as elevation increased (Figure 4-3, Figure G-1). In all 3 study areas migratory deer were more likely to select cutblocks 1-16 years-old at high elevations (1,400 – 2,000 m) than any other forest type, and in Cache Creek and West Okanagan resident deer exhibited the same pattern of selection (high elevations = 1,300 m – 1,500 m). Similarly, at low elevations (migrants = 670 – 820 m, residents = 410 – 600 m), all adults in the Boundary, migrants in Cache Creek and residents in the West Okanagan were more likely to select cutblocks 1-16 years-old than any other forest type. 4.3.2.3 Neonates All migrant neonates selected lower values of NDVI, and all resident neonates selected more gentle slopes in concave areas, with no other similarities between all 3 study areas (Figure 4-2, Tables G-13 – G-18). Migrant neonates in the West Okanagan and Cache Creek selected less rugged terrain, while resident neonates in those study areas selected more rugged terrain. 115 Resident neonates in the Boundary and West Okanagan selected areas with lower values of NDVI on north facing aspects. We combined burns 1-14 years-old and burns 15+ years-old into one burn category for all neonate models except Boundary migrants, because neonates in other study areas had 0 used locations in either burns 15+ years-old (Cache Creek) or burns 1-14 years-old (West Okanagan). Boundary resident neonates had 0.7% of their used locations in any burn category, so we omitted burn age from those models. In the West Okanagan all neonates selected for unburned areas over burns, while in the Boundary migrant neonates selected for burns 15+ years-old over burns 1-14 years-old and unburned areas. In Cache Creek, resident neonates selected for burns over unburned areas, while migratory neonates selected for unburned areas over burns. Neonate deer had < 2% of used locations in the other vegetation category, causing model non-convergence. Therefore, in all neonate RSF models except West Okanagan residents, we combined vegetation in the other category with vegetation in the shrub category, which had the next fewest number of used locations. In West Okanagan resident neonate models, we combined other, shrub, cutblocks 1-16 years-old and cutblocks 17+ years-old into one category because 99.7% of their used locations were in grassland or unharvested forests. The vegetation types neonates selected for in the summer changed as elevation increased (Figure 4-3, Figure G-1). Migratory and resident neonates in the West Okanagan and Cache Creek were more likely to select forests with dense canopy cover (cutblocks 17+ years-old and unharvested forest) compared to cutblocks 1-16 years-old at both high (migrants = 1,290 – 1,940 m, residents = 940 – 1,200 m) and low (migrants = 770 – 1,040 m, residents = 370 – 600 m) elevations. In the Boundary, migratory neonates selected for cutblocks 1-16 years-old above all other forest types at both high and low elevations, and resident neonates also selected for cutblocks 1-16 years-old 116 at high elevations. However, at low elevations, resident neonates were more likely to select for unharvested forest above all other forest types. 4.3.3 Survival Modeling 4.3.3.1 Winter-Spring We used seasonal values of the RSF score and daily values of WSI in adult winter-spring survival models based on the results from the univariate modeling (Table 4-2). Model selection results indicated support for the weather only model for adult deer in the winter (Table 4-3). The WSI × RSF model also received some support, but the additional parameters in this model as compared to the weather model were not significant (RSF: β = 0.0060, 95% confidence interval [CI] = -0.11 – 0.12; RSF x WSI: β = -0.0022, 95% CI = -0.0050 – 0.00057), so we did not consider this model to be a better fit. Consistent with predictions from the Weather Hypothesis, each day the WSI increased by 1 unit, mortality risk for adult female deer increased by 1.01 (Table 4-4). Predictions of adult survival rates varied from 0.86 (95% CI = 0.81 – 0.93) to 0.94 (95% CI = 0.92 – 0.96) in the winter according to low (5th percentile) and high (95th percentile) values of daily WSI among collared adults (Figure 4-4a). Based on the results from the univariate modeling, we used seasonal values of the RSF score and daily values of WSI in fawn winter survival models (Table 4-2). Model selection results indicated support for the RSF model for fawns in the winter (Table 4-3). The WSI ×RSF model also received some support, but the additional weather parameters were not significant (WSI: β = 0.020, 95% CI = -0.035 – 0.076; RSF x WSI: β = -0.0012, 95% CI = -0.0035 – 0.0011), so we did not consider this model to be a better fit. Consistent with predictions from the Ideal Selection Hypothesis, fawn mortality risk over the winter decreased by 0.94 for each 1-117 point increase in RSF values (Table 4-4). Predictions of fawn survival rates varied from 0.48 (95% CI = 0.37 – 0.62) to 0.68 (95% CI = 0.60 – 0.77) according to low and high values of seasonal RSF values among collared fawns (Figure 4-4c). 4.3.3.2 Summer-Fall For adult summer-fall survival models, the RSF covariate was significant but violated the proportional hazards assumption (P = 0.035). Therefore, we chose to split the summer-fall into 2 time periods in which the proportional hazards assumption held true. The first summer-fall period was May 20 – September 15 and the second summer-fall period was September 16 – November 19. We chose these dates based on the plot of the scaled Schoenfeld residuals (Figure I-1). Within the first summer-fall period, univariate modeling indicated daily values of both the RSF and precipitation values were the most supported by the data (Table 4-2). When testing our 3 hypotheses, the Precipitation × RSF model received strong support (Table 4-3). Consistent with predictions from the Non-Ideal Selection Hypothesis, adult female mortality risk decreased as they used areas with higher RSF values, but the magnitude of this decrease depended on the amount of precipitation (Table 4-4). When using areas with lower RSF values, mortality risk was higher when there was higher rainfall (e.g., > 10 mm) compared to no rain, but as deer used areas with higher RSF values their mortality risk was higher when there was no rain compared to high rainfall periods. Predictions of adult survival rates varied from 0.81 (95% CI = 0.70 – 0.93) to 0.99 (95% CI = 0.99 – 1.00) in the summer-fall according to low and high values of daily precipitation and RSF values among collared adults (Figure 4-4b). 118 In the second summer-fall period, we used the seasonal RSF and daily values of precipitation, based on univariate modeling results (Table 4-2). There was no support for any of our hypotheses during the second summer-fall period (Table 4-3). The resource selection and weather models were within 0.21 ΔAICc from the null model and neither contained significant parameters, so we considered the null model to be the top model during this period. When spatially projecting lambda values across the study areas we used the Kaplan-Meier survival estimate during this time (0.97, 95% CI = 0.96 – 0.99) in place of a modeled survival raster. 4.3.3.3 Neonates We used daily values of both the RSF and weather variables in neonate survival models based on the results from the univariate modeling (Table 4-2). Model selection results indicated support for the RSF only model for neonates (Table 4-3). The temperature × RSF model also received some support, but the additional parameters in this model as compared to the resource selection model were not significant (precipitation: β = 0.0076, 95% CI = -0.054 – 0.069; temperature: β = -0.16, 95% CI = -0.44 – 0.13; RSF x temperature: β = 0.0019, 95% CI = -0.011 – 0.015), so we did not consider this model to be a better fit. Consistent with predictions from the Ideal Selection Hypothesis, neonate mortality risk decreased by 0.96 for each 1-point increase in RSF values (Table 4-4). Predictions of neonate survival rates varied from 0.23 (95% CI = 0.13 – 0.42) to 0.58 (95% CI = 0.47 – 0.73) according to low and high values of daily RSF values among collared neonates (Figure 4-4d). 119 4.3.4 Population Growth-Based Habitat Quality 4.3.4.1 Population Growth Under Different Weather Scenarios Spatial predictions of population growth varied by study area, movement strategy, and seasonal weather patterns. Resident deer had higher proportions of ‘growth habitat’ (i.e., λ > 1) within their seasonal ranges than migrant deer (Figure 4-5). A severe winter (average daily WSI = 53) reduced the proportion of growth habitat in winter range by 28%, 16%, and 10% in the Boundary, Cache Creek, and West Okanagan study areas respectively, when compared to a mild winter (average daily WSI = 0; Figure 4-5). Additionally, a drought summer-fall (average daily precipitation = 0 mm) reduced the proportion of growth habitat for migrant summer-fall range by 21%, 18% and 31% in the Boundary, Cache Creek, and West Okanagan study areas respectively, when compared to a rainy summer-fall (average daily precipitation = 8.12 mm). For resident deer, a drought summer-fall reduced the proportion summer-fall range with λ > 1 by 32%, 18%, and 5% in the Boundary, Cache Creek, and West Okanagan study areas respectively, when compared to a rainy summer-fall. 4.3.4.2 Population Growth in Ungulate Winter Range Units Designated Ungulate Winter Range units (UWR) contained varying amounts of growth habitat depending on the study area, winter severity, and summer-fall precipitation. During years with a low winter severity index and high summer-fall precipitation values (i.e., the ‘best’ case weather scenario), UWRs in Cache Creek (n = 10) contained the highest proportion of growth habitat for migrants and residents, followed by UWRs in the West Okanagan (n = 346), and UWRs in the Boundary (n = 251; Figure 4-6). However, in years with severe winters and low summer-fall precipitation (i.e., the ‘worst’ case weather scenario), UWRs in the West Okanagan 120 had the highest proportion of growth habitat for residents, followed by Cache Creek, and the Boundary (Figure 4-6). 4.4 Discussion In this paper, we tested if there was a correlation between RSFs and survival for mule deer in varying seasons and age classes. For neonates and fawns, our results supported the Ideal Selection Hypothesis in which selection was indicative of survival benefits gained from habitat, and thus RSFs predicted survival. For adults in the summer-fall, we observed support for the Non-Ideal Selection Hypothesis because poor weather conditions (no rainfall) reduced the survival benefits conferred by selected habitat. We interpret this result to indicate a flaw in resource selection because their selection of habitat did not anticipate how poor weather conditions might affect survival in that habitat (Arlt and Pärt 2007, DeCesare et al. 2014). Finally, for adults in the winter-spring, the Weather Hypothesis was supported; there was no relationship between predicted probabilities of use and survival, so RSFs did not predict winter-spring habitat. Using resource selection models to measure habitat relies on the assumption that individuals can select resources ideally and freely to maximize their fitness with regards to food, predation risk, weather, and other dimensions of their niche. Here, we showed that this assumption was valid for some age classes and seasons but not all, highlighting the difficulties in using RSFs alone to predict species habitat. Despite only conferring survival benefits to fawns, winter-spring resource selection was similar across study areas and age classes. This similarity in selection is likely because there are a small range of suitable environments where mule deer can live in the winter-spring due to limiting snow depths in the mountainous terrain that characterizes the areas in our study (Gilbert et al. 1970, Parker et al. 1984). This type of terrain concentrates winter-spring resources in 121 similar, low elevation, south facing, and steep areas. Similar resources are used by mule deer in the winter across northern North America (Serrouya and D’Eon 2008, Anderson et al. 2012, Coe et al. 2018). There was variation in the vegetation classes selected at different elevations, but most deer selected high canopy cover at high elevations (unharvested forest and cutblocks 42+ years-old) and lower canopy cover at low elevations (cutblocks 1-18 years-old and 19-41 years-old). Additionally, adults and fawns selected lower values of NDVI, which in this conifer dominated landscape represented open canopy areas (correlation with canopy cover = 0.68; Figure G-3). These selection patterns emphasize the need for deer to balance their ability to access forage in the winter with their ability to escape deep snow (Smith et al. 2015, Coe et al. 2018, Anton et al. 2022). Despite adults and fawns selecting similar resources in the winter-spring, adult RSFs did not correlate with survival probabilities. One reason for this discrepancy could be the larger number of mortalities observed for fawns (n = 105) compared to adults (n = 48) in the winter-spring. The effect size of selection probabilities on survival was relatively small for fawns and adults (adults: β = -0.07, 95% CI = -0.15 – 0.02). Therefore, we may have needed a larger adult sample size (more deaths) to reject the null hypothesis that selection probabilities do not affect survival (Steidl et al. 1997, Hosmer et al. 2008). Alternatively, the null hypothesis could be correct and therefore suggest as deer get older, they deer lose their ability to select resources in the winter-spring that convey survival benefits. This would be a perplexing result because ungulates are generally thought to increase their knowledge of a system as they age (Merkle et al. 2019, Wiskirchen et al. 2022). Finally, we could be missing some finer scale information about adults that further refines their resource selection patterns and better tracks their habitat requirements. For example, females that reared neonates through summer and those that lost 122 neonates early in summer have different energetic constraints in winter, which could result in differing selection patterns and survival probabilities (Bergman et al. 2018, Monteith et al. 2023). However, we were not able to test for this because we did not know which of our collared females successfully reared young the previous summer. Unlike winter-spring resource selection, summer-fall selection was highly variable. We would expect selection of resources to differ between migratory and resident deer in the summer-fall because these movement strategies are thought to confer contrasting benefits (Hebblewhite and Merrill 2009, Peterson et al. 2022). The variability in selection we observed between adults and neonates of the same movement strategy is likely due to their respective habitat requirements. Neonatal mule deer are hiders, spending the first 3 weeks of life bedded down to avoid predation (Hurley et al. 2023). Neonates are also fully reliant on nursing until they are at least 4 weeks old and are not fully weaned until at least 18 weeks old (Tollefson et al. 2011). Therefore, their selection patterns should prioritize reducing predation risk rather than accessing forage (Linnell et al. 1999, Pitman et al. 2014). Neonates generally showed higher selection strength for unharvested forests compared to cutblocks 1-16 years-old and grasslands at high and low elevations, which could confer greater hiding cover. Neonates in the Boundary, however, showed higher selection strength for cutblocks 1-16 years-old, which is the opposite of what we expected because they generally have less hiding cover than closed canopy forests. While the mechanism for these selection patterns is unclear, neonate selection was positively correlated with survival. Given that neonates are presumably born without knowledge of their environment, this ability to select resources with fitness benefits shortly after birth indicates strong maternal transmission and rapid learning (Jakopak et al. 2019). 123 Adult female mule deer should select habitat within their summer-fall range that provides hiding cover for their recent and high quality forage for lactation demands and fat deposition (Pitman et al. 2014, Gilligan et al. 2023, Monteith et al. 2023). Adults in all study areas strongly selected for open habitats (grasslands, cutblocks 1-16 years-old, shrubs, and burned areas), which are associated with increased forage quality (Gilligan et al. 2023), but not reduced predation risk for their neonates (Wilmers et al. 2013, Benson et al. 2016, Jackson et al. 2021). However, these selection pattens were measured within adult female home ranges (i.e., 3rd order selection), and it is possible at larger spatial scales (i.e., 2nd order selection – location of home ranges in a geographic range) adults select closed canopy vegetation types, increasing the availability of less risky habitats within their summer-fall range for their neonates. Adult summer-fall resource selection did positively correlate with survival, but they did not select resources in a way that might buffer the effects of a summer-fall drought. This non-ideal selection may be the result of rapid climate change (Schlaepfer et al. 2002), where deer in south-central BC have not adapted to select resources that can withstand the dry summer-falls that are becoming increasingly common (Gullacher et al. 2023). Additionally, mule deer exhibit high site fidelity (Sawyer et al. 2019, Smedley et al. 2019), a trait that leads to non-ideal selection because individuals remain in an area even if new disturbances arise and make those areas more risky or nutritionally depleted (Faille et al. 2010). Interestingly, areas with a low selection probability had a higher predicted survival probability in drought years compared to rainy years, the opposite of what we observed in areas with a high selection probability. However, survival was still lower in these low selection probability areas compared to highly selected areas even in a drought year (Figure 4-4). 124 Spatially projecting predicted survival probabilities for each age class and combining these spatial estimates to map growth-based habitat quality gave insights into the effect of seasonal weather patterns on population growth. When weather patterns shifted from a mild winter-spring and rainy summer-fall to an extreme winter-spring and drought summer-fall, we saw up to a 54% decrease in the amount of habitat that promoted population growth. This large decrease in habitat quality highlights the potential effects of climate change on mule deer populations through changes in weather alone. Given that we did not explicitly measure potential carryover effects of weather from one season to the next, this reduction in population growth due to weather could be even more pronounced (Monteith et al. 2014, Hurley et al. 2017). Another benefit to spatially mapping growth-based habitat quality is the ability to easily identify areas that should be the target of habitat conservation or restoration actions (Franklin et al. 2000, Heinrichs et al. 2010, Kane et al. 2017). In BC, designating forest stands as UWRs is the primary management action used to keep these stands from being intensively harvested, which would remove canopy cover that is valuable in the winter. Mapping these UWRs onto our spatial estimates of growth-based habitat quality enabled us to determine the effectiveness of the placement of these UWRs. In Cache Creek there is room for improvement, as there are many remaining forest stands with growth habitat that are not designated as UWRs (Figure 4-6). In contrast, in the West Okanagan the current UWRs overlap with much of the forested growth habitat in the study area, with few remaining unprotected timber stands. The only significant portion missing is on land governed by the Penticton Indian Band, which is not harvested by timber companies. The Boundary study area highlights that dense canopy is not the only vegetation type that mule deer need in the winter-spring; much of the growth habitat in this 125 region occurred on south-facing grassy hills. However, there was room to expand current UWRs to cover more forested growth habitat in the Boundary. It is important to note that our RSF models were estimated using only a random intercept for individual deer and did not include random slopes. Random intercept models account for variation in sample sizes between individuals (Gillies et al. 2006), but do not account for the pseudoreplication caused by having many data points from the same individual (Schielzeth and Forstmeier 2009, Muff et al. 2020). Furthermore, random intercept only RSFs produce overly confident or biased standard errors (see Figure 4-2; Schielzeth and Forstmeier 2009), and may produce biased parameter estimates (Fieberg et al. 2009). As a result, random intercept only models do not account for the variability in selection between individuals and therefore do not accurately reflect population-level inference (Gillies et al. 2006). We initially attempted to use random slopes as per Muff et al. (2020). However, these models did not converge for the neonate models (likely too few used points for some individuals) and the k-fold cross validation values for the adult and fawn top models indicated poor predicting power for some seasons and study areas, and all models had worse predictive power than the random intercept only model (Table H-1). Because our goal was to produce predictive maps that correlated well with deer GPS locations, we chose to use the random intercept only models. However, this decision means that our RSF assumed every deer had the same selection patterns and our experimental unit was each location and not each deer (Wagner et al. 2011). We estimated 6 different RSFs per study area, and the maps of predicted probabilities of use and survival for each age class, season, and movement strategy often produced inconsistent spatial predictions of high-quality habitat. By combining these conflicting maps into one map that represented demography-based habitat quality we consolidated this complexity into a more 126 interpretable outcome (Heinrichs et al. 2010). Overall, this research highlights the strengths and weaknesses of widespread resource selection modeling. We found that for some age classes, density and/or selection positively correlated with survival and therefore resource selection modeling accurately construed mule deer habitat for those age classes. However, this common assumption was not met for all deer, highlighting the importance of actually incorporating survival or fitness-based metrics into models that measure species habitat. 127 4.5 Figures Figure 4-1.12Relationships between predicted habitat quality as determined by resource selection functions (RSFs) and survival probability of mule deer in south-central British Columbia, Canada during 2018 – 2023 under 4 non-mutually exclusive hypotheses: A) Ideal Selection: survival probability is positively correlated with RSF based habitat quality, B) Non-ideal Selection: selection is positively correlated with survival, but selection patterns contain no buffer for abnormal weather conditions, so survival decreases under poor weather conditions, C) Ecological Trap: selection probability is negatively correlated with survival probability, and poor weather might worsen this relationship, D) Weather Only hypothesis: weather conditions alone influence survival probability. The lines in each panel are not indicative of the predicted magnitude of the effect, but rather are drawn for illustrative purposes. 128 Figure 4-2.13Parameter estimates ± 95% confidence intervals of standardized continuous covariates from the best models of adult, fawn and neonate resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 in the A) West Okanagan, B) Cache Creek, and C) Boundary study areas. Covariates included are normalized difference vegetation index (NDVI), aspect (North and East), topographic wetness index (TWI), terrain ruggedness, curvature, road density (km/km2), and slope. 129 Figure 4-3.14Relative selection strength of forest only vegetation types from the best models of resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 for A) deer in the West Okanagan, B) deer in Cache Creek, C) deer in the Boundary), and D) resident neonates in the Boundary at high elevation. The selection of each vegetation type is compared to unharvested forests at low and high elevations for that study area, age class, and movement strategy. Values above 1 indicate deer selected for that vegetation type more than unharvested forests at that elevation, and values below 1 indicate the opposite. High and low elevations were determined by low (5th percentile) and high (95th percentile) values of elevation among collared deer within each study area, age class, and movement strategy. For clarity, panels B, C, D only include forested vegetation, and panel A includes grasslands because no other forest types were used by resident neonates. To relative selection strength of all vegetation types, see Figure G-1. Note that cutblock ages were different in each season. Winter-spring ages: Recent cutblocks: 1-18 years old, medium cutblocks: 19-41 years old, old cutblocks: 42+ years old, recent burns: 1-11 years old, old burns: 12+ years old. Summer-fall ages: Recent cutblock: 1-16 years, old cutblock: 17+ years, recent burn: 1-14 years, old burns: 15+ years See panel D 130 Figure 4-4.15Predicted seasonal survival curves based on the most parsimonious Cox proportional hazards model (see Table 4-3) for explaining mule deer survival in south-central British Columbia, Canada during 2018 – 2023. Shaded ribbons show 95% confidence intervals. A) adult winter-spring survival as a function of winter-spring severity index, B) summer-fall adult survival as a function of precipitation and RSF values, C) overwinter fawn survival as a function of RSF values, and D) summer-fall neonate survival as a function of RSF values. Low and high values of each covariate were chosen using low (5th percentile) and high (95th percentile) values of that covariate among each collared age class and season. 131 Figure 5-5.16Mule deer A) winter-spring and B) summer-fall ranges with the proportion of predicted spatial estimates of population growth (λ) above 1 (i.e., growth habitat) under four weather scenarios in south-central British Columbia, Canada during 2018-2023. A mild winter-spring refers to an average daily winter-spring severity index (WSI) of 0, severe winter-spring refers to an average daily WSI of 53, drought summer-falls have 0 mm of average daily precipitation, and rainy summer-falls have an average daily precipitation of 8.12 mm. 132 Figure 5-6.17Mean proportion of designated ungulate winter range units (UWR) with spatialized estimates of mule deer population growth (λ) > 1, and maps of spatialized mule deer λ estimates and UWRs on winter-spring range in the A) West Okanagan, B) Cache Creek, and C) Boundary study areas in south-central British Columbia, Canada. The bar plots contain estimates of λ during the best-case weather scenario (rainy summer-fall: average daily precipitation = 8.12 mm and mild winter-spring: average daily winter-spring severity index [WSI] = 0), and the worst-case weather scenario (drought summer-fall: average daily precipitation = 0 mm and a severe winter-spring: WSI = 53). The paired map shows spatial estimates of growth habitat (λ > 1) and sink habitat (λ < 1) during the best-case weather scenario for residents. 133 4.6 Tables Table 4-1.4Model set used to evaluate resource selection functions during summer-fall and winter-spring for adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in three study areas in south-central British Columbia, Canada during 2018-2023. Each model also contained a random intercept for individual deer identification. Model Name Covariates1 Intercept Only None Topographic (T2) Elevation (E) + Curvature (C) + North (N) + East (Es) + Terrain Wetness Index (TWI) + Slope (S) + Ruggedness (VRM) General vegetation (GV) Grassland (G) + Shrub (Sh) + Other (O) + Forest (F) Cutblock vegetation (CV) G + Sh + O + Recent Cutblocks (CB.Y3) + Medium Cutblocks (CB.M) + Old Cutblocks (CB.O) + Unharvested Forest (UF) Topographic + general vegetation T + GV Topographic + general vegetation + road density T + GV + Road Density (R.DS) Topographic + general vegetation + NDVI T + GV + NDVI Topographic + general vegetation + burns T + GV + Recent Burns (B.Y4) + Old Burns (B.O) Topographic + general vegetation + road density + NDVI + burns T + GV + R.DS + NDVI + B.Y + B.O Topographic × general vegetation (T × GV) E×G + E×Sh + E×O + E×F + C + N + Es + TWI + S Topographic × general vegetation + road density T × GV + R.DS Topographic × general vegetation + NDVI T × GV + NDVI Topographic × general vegetation + burns T × GV + B.Y + B.O Topographic × general vegetation + road density + NDVI + burns T × GV + R.DS + NDVI + B.Y + B.O Topographic + cutblock vegetation T + CV Topographic + cutblock vegetation + road density T + CV + R.DS Topographic + cutblock vegetation t + NDVI T + CV + NDVI Topographic + cutblock vegetation + burns T + CV + B.Y + B.O Topographic + cutblock vegetation t + road density + NDVI + burns T + CV + R.DS + R.DT + NDVI + B.Y + B.O Topographic × cutblock vegetation (T × CV) E×G + E×Sh + E×O + E×CB.Y + E×CB.M + E×CB.O + E×UF + C + N + Es + TWI + S 134 Model Name Covariates1 Topographic × cutblock vegetation + road density T × CV + R.DS Topographic × cutblock vegetation + NDVI T × CV + NDVI Topographic × cutblock vegetation + burns T × CV+ B.Y + B.O Topographic × cutblock vegetation + road density + NDVI + burns T × CV + R.DS + NDVI + B.Y + B.O 1 Covariates are defined the first time they are used and represented by their abbreviation each time after. 2 Abbreviations in bold italics are groups of covariates used in multiple models. These groups are defined the first time they appear in the table and represented by their abbreviation each time after. 3 Summer-fall: recent cutblocks = 1-16 years old, old cutblocks = 17+ years old. Winter-spring: recent cutblocks = 1-18 years old, medium cutblocks = 19-41 years old, old cutblocks = 41+ years old 4 Summer-fall: recent burns = 1-14 years old, old burns = 15+ years old. Winter-spring: recent burns = 1-11 years old, old burns = 12+ years old 135 Table 4-2.5Model selection results including the AICc value, ΔAICc score, and model weight (ωi) comparing relative support for the effects of daily and seasonal resource selection function (RSF) and weather values on survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023. The RSF and weather value (seasonal or daily) with the lowest AICc value was used in the next stage of survival modeling. Age Class Season Model RSF Weather AICc ΔAICc ωi AICc ΔAICc ωi Adult Winter-spring Daily 614.46 1.99 0.27 609.85 0.00 0.72 Seasonal 612.47 0.00 0.73 611.77 1.92 0.28 Summer-fall: period 1 Daily 622.78 0.00 0.97 629.16 0.00 0.62 Seasonal 629.72 6.95 0.03 630.12 0.96 0.38 Summer-fall: period 2 Daily 217.85 2.21 0.25 215.73 0.00 0.62 Seasonal 215.64 0.00 0.75 216.73 1.00 0.38 Fawn Winter-spring Daily 1126.32 3.90 0.12 1127.07 0.00 0.58 Seasonal 1122.41 0.00 0.88 1127.74 0.67 0.42 Neonate Summer-fall Daily 594.55 0.00 0.88 595.40 0.00 0.81 Seasonal 598.61 4.06 0.12 598.27 2.87 0.19 136 Table 4-3.6Model selection results including the number of parameters (K), AICc model weight (ωi) and model log likelihood (LL) comparing relative support for 4 models and testing the relationship between weather, resource selection function (RSF) output, and survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023. The RSF model represented either the Ideal Selection or Ecological Trap hypotheses, depending on the direction of the predicted effect. The WSI x RSF model represented either the Non-ideal Selection or Weather Mediated Ecological trap hypothesis, depending on the direction of the predicted effects. The weather only model represented the Weather only hypothesis. Model K AICc ΔAICc ωi LL Adult winter-spring WSI1 1 609.85 0.00 0.47 -303.92 WSI × RSF 3 610.71 0.86 0.31 -302.35 RSF 1 612.47 2.62 0.13 -305.24 Null 0 612.94 3.09 0.10 -306.47 Adult summer-fall – period 1 Precipitation × RSF 3 616.65 0.00 0.95 -305.33 RSF 1 622.78 6.13 0.04 -310.39 Null 0 628.33 11.68 0.00 -314.17 Precipitation 1 629.16 12.50 0.00 -313.58 Adult summer-fall – period 2 RSF 1 215.64 0.00 0.28 -106.82 Precipitation 1 215.73 0.09 0.27 -106.87 Null 0 215.85 0.21 0.26 -107.93 Precipitation × RSF 3 216.45 0.81 0.19 -105.23 Fawn winter-spring RSF 1 1,122.41 0.00 0.54 -560.21 WSI × RSF 3 1,123.51 1.10 0.31 -558.75 Null 0 1,126.02 3.60 0.09 -563.01 WSI 1 1,127.07 4.65 0.05 -562.53 Neonate summer RSF 1 594.55 0.00 0.50 -296.27 Temperature × RSF + precipitation 4 594.97 0.42 0.41 -293.48 Temperature + precipitation 2 598.40 3.85 0.07 -297.20 Null 0 601.40 6.85 0.02 -300.70 1Winter-spring severity index 137 Table 4-4.7Beta estimates (β) and 95% confidence intervals (CI) for the top ranked parameters in Cox Proportional Hazards models testing the relationship between weather, resource selection function (RSF) output, and survival of adult female (≥ 1 year-old), fawn (6-month – 1-year-old) and neonate (0 – 12-weeks-old) mule deer in south central British Columbia, Canada from 2018-2023. Age Class Season Parameter β 95% CI Adult Winter-fall WSI 0.016 0.0031 – 0.029 Summer-fall (period 1) RSF -0.034 -0.074 – 0.0057 Precipitation 0.23 0.053 – 0.41 Precipitation × RSF -0.025 -0.045 – -0.0061 Summer-fall (period 2) NA NA NA Fawn Winter-fall RSF -0.060 -0.11 – -0.012 Neonate Summer RSF -0.043 -0.71 – -0.015 138 Chapter 5: Conclusion . 5.1 Research Findings and Significance In this dissertation I evaluated the effects of landscape change on mule deer (Odocoileus hemionus) populations in south-central British Columbia (BC), Canada, and provided management recommendations and tools to aid in the conservation of this species. Within this dissertation I filled multiple research gaps regarding mule deer specifically, and the habitat-fitness relationship more broadly. Mule deer spring migration has primarily been studied in the intermountain west of the US, which is dominated by sagebrush-steppe at low elevations, montane, sub-alpine forests at high elevations, and where oil and gas development is a primary source of anthropogenic disturbance (Kauffman et al. 2023). In Chapter 2, I added to our knowledge of mule deer spring migrations by studying migration in an understudied ecosystem, coniferous forests altered by timber harvesting and large-scale wildfires. I used GPS collar data from 178 migratory mule deer to observe 355 spring migrations, which on average began on May 9, ended May 19, lasted 10 days and covered 47 km. This distance and duration are comparable to migratory mule deer populations in Utah, Idaho, Colorado and Oregon, USA (Brown 1992, Lendrum et al. 2013, Schuyler et al. 2019, van de Kerk et al. 2021), but are shorter than those observed in Wyoming (> 150 km and 2 months long; Sawyer et al. 2016). Mule deer migrate to access seasonally available forage and escape harsh winter weather on seasonal ranges, but recent evidence suggests that mule deer also access preferred forage during migration by timing their spring movements to match the pace of forage green-up (“surf the green wave”; Aikens et al. 2017), potentially increasing summer fat deposition (Middleton et 139 al. 2018). However, this potential nutritional benefit of migration routes is not universally true for all mule deer populations, because I found that mule deer in BC did not surf the green wave, with 64% of mule deer migrating with no relationship to the timing of peak green-up. Compared with other landscapes in their range (Aikens et al. 2017, 2020b) the green-up of vegetation along mule deer migration routes in BC was slow at the individual patch level, short in duration at the landscape level, and did not occur in consecutive order. Together, these characteristics make tracking spring green-up difficult for mule deer (Aikens et al. 2020b) and therefore, it is perhaps not surprising that mule deer do not surf the green wave in south-central BC, because there is not a measurable wave to surf. Possibly, this lack of a green wave is due to mule deer not migrating over a linear elevational gradient, which is a key landscape element that drives green-up to occur in a wave-like fashion (Bischof et al. 2012). Additionally, it is possible that cutblocks have created a mosaic of early and late green up patches, which would affect the continuity, or green-up order, in this region. Overall, the lack of green-wave surfing coupled with a short migration length and duration suggests that the spring migration period is less crucial, in terms of foraging and fat deposition, for mule deer populations in BC than it is for mule deer inhabiting sagebrush-steppe habitat. Despite not tracking green-up closely during spring migration, conserving mule deer migration pathways is still a critical management action because mule deer exhibit high fidelity to these routes, which they use to travel to their summer ranges. Therefore, within chapter 2, I also evaluated what resources mule deer select in forested landscapes during migration, by quantifying resource selection in corridors (directed, fast movements) and stop-overs (slower, tortuous movement). Mule deer selected for similar resources during each movement type: open 140 canopy vegetation at elevations ≥1,120 m and ≤ 1,320 m, steep, dry, south facing slopes; deer avoided both old and new burned areas. While understanding the effects of habitat alteration or climate change on mule deer behavior is an important first step, directly linking these changes to survival outcomes is crucial to ensure management actions protect or restore the appropriate habitat. Linking landscape change to mule deer survival is complicated because habitat use at different time scales can impact survival (Gaillard et al. 2010). Additionally, some changes, such as wildfires and timber harvesting, differentially affect survival depending on the season. In chapter 3, I investigated how landscape features, disturbance types, and weather conditions combined to affect seasonal mule deer mortality risk over different time frames. I also quantified adult (≥ 1.5-year-old), fawn (6-month – 1-year-old), and neonate (0 – 12-week-old) mule deer survival rates in three study areas which contained varying amounts of recently burned area to evaluate the effect of wildfire at the landscape level on mule deer survival. I predicted that survival would be significantly higher in the Cache Creek study area, where > 50% of the area was burned in wildfires in 2017 and 2021. I found that survival was not significantly different between study areas for any age class, indicating that recent wildfires did not increase seasonal survival rates in Cache Creek as compared to the less burned study areas. Importantly, I did not have pre-burn survival estimates in Cache Creek, so it is possible that survival has increased since the fire but is not currently greater than the other study areas. However, it is also possible that deer are predator limited in Cache Creek and increasing survival through habitat management is not possible unless predator densities are also reduced. Overall, this result highlights that while wildfires have been conclusively linked to increased forage nutritional quality (Snobl et al. 2022) and increased body 141 condition (Proffitt et al. 2016), making the connection to increased survival rates, and therefore population growth rates, is difficult. However, linking wildfires to mule deer survival is necessary to understand the impacts of wildfire on mule deer populations. I also found that adult and fawn mortality risk increased in the winter when deer used areas with higher road densities and deeper snow depths, results that are not surprising given that roads are linked to increased predation risk (Whittington et al. 2011, Dickie et al. 2017) and deep snow is associated with increased energetic expenditure and reduced body condition (Parker et al. 1984, Jackson et al. 2021). I also found adults that used recent burns and cutblocks more often had a reduced mortality risk in the summer compared to those that used these disturbances less often. Early successional forests contain high quality forage for mule deer, which could increase survival through increased body condition (Gilligan et al. 2023), and reduce predation risk through greater sight lines and visibility (Altendorf et al. 2001, Doherty et al. 2022). Finally, I found that six different metrics affected neonate mortality risk, and these models were the subject of significant model uncertainty, suggesting that none of our covariates were strong predictors of neonate mortality risk. Resource selection functions (RSF) are often used to infer species habitat and habitat quality (McLoughlin et al. 2010), which relies on the assumption that there is a positive correlation between an individual’s habitat selection and the fitness benefit associated with that habitat (Matthiopoulos et al. 2015). However, in a rapidly changing landscape this positive correlation may not exist or could become an ecological trap, in which there is a negative correlation between habitat selection and fitness (Battin 2004). In chapter 4, I wanted to determine if resource selection functions accurately predicted mule deer habitat and habitat 142 quality by relating seasonal spatial selection probabilities to survival probabilities for adult, fawn, and neonate mule deer. I found that for neonates and fawns, selection was indicative of survival benefits gained from habitat, and thus RSFs predicted actual habitat quality. However, for adults in the summer, I found that while their selection probabilities did positively correlate with survival, this relationship was affected by summer rainfall. I interpreted this result to indicate a flaw in resource selection because their selection of habitat did not anticipate how poor weather conditions might affect survival in that habitat (Arlt and Pärt 2007, DeCesare et al. 2014). Finally, for adults in the winter, I observed no relationship between predicted probabilities of use and survival, so RSFs did not predict winter habitat or habitat quality. I also spatially projected predicted survival probabilities for each age class and combined these spatial estimates to map growth-based habitat quality, which gave insights into the effect of seasonal weather patterns on population growth. When weather patterns shifted from a mild winter and rainy summer to an extreme winter and drought summer, I saw up to a 54% decrease in the amount of habitat that promoted population growth. Overall, this chapter highlights the strengths and weaknesses of widespread resource selection modeling. I found that for some age classes, density and/or selection positively correlated with survival and therefore resource selection modeling accurately construed mule deer habitat for those age classes. However, this common assumption was not met for all deer, highlighting the importance of incorporating survival or fitness-based metrics into models that measure species habitat. 5.2 Management Implications Mule deer populations in south-central BC have declined in the last 50 years, with some local populations having stabilized at a lower number than was historically present, and other 143 populations still declining. Mule deer are an important food source for Indigenous and non-Indigenous communities in BC and generate over $130 million per year through hunting activity (Responsive Management 2013). Therefore, each chapter of my dissertation was designed in part to provide recommendations to wildlife managers in BC to help increase mule deer population sizes. In chapter 2, I identified the landscape resources deer selected for within stopovers and corridors along their spring migration route. Based off these results, managers should protect open canopy vegetation types within dry, south-facing, steep slopes at elevations ≥ 1,120 m and ≤ 1,320 m, because these resources were selected by deer in both corridors and stopovers. However, there were some differences in selection between stopovers and corridors, and deer showed a higher strength of selection in stopovers, indicating they might be more selective within these segments of their migration route. Therefore, managers with limited resources could prioritize conserving stopover locations because mule deer may be more sensitive to disturbance when in stopovers. Additionally, I mapped spring migration routes, none of which have been previously visualized with high resolution GPS data. These migration routes were diffuse throughout the study areas (Figure 2-1a), but there were localized regions within each study area where many deer migrated through the same area. Without a map of migration routes on the landscape, policymakers and managers would not know where these migrations or high use areas occur, and the effects new landscape changes might have on migratory mule deer. Therefore, mapping migratory routes is a crucial first step for conserving migrations because they provide planners and policymakers the information needed to develop corridor and connectivity plans, review 144 impacts of potential developments, and consider new policies to protect migration routes (Kauffman et al. 2021). In chapter 3, I identified several landscape changes and weather patterns that impact adult and fawn survival. Based off these results, I recommend that managers in south-central BC decommission and restore roadways on winter range and allow wildfires to burn when it is safe to do so. I hesitate to recommend increasing production of new cutblocks even though they reduce mortality risk, because roads are built to access these cutblocks. Therefore, I recommend focusing on promoting natural disturbance regimes historically found on the landscape (Hayes et al. 2022). Further, while managers are not able to mitigate the effects of harsh winters on adult and fawn survival, the results from this chapter provide evidence that population sizes might decrease following a winter with deep snow and prolonged cold temperatures in the spring. If there are several such severe winters in a row, managers might consider reducing harvest allocations, which is one of the only immediate actions they could enact to reduce mule deer mortalities. Finally, in chapter 4 I made spatial projections of survival probabilities for each age class and combined these spatial estimates to map growth-based habitat quality in each study area. Age-specific RSF maps may have inconsistent values over space, so by combining these different data sources into a single map, I consolidated this complexity into a more interpretable outcome (Heinrichs et al. 2010). Therefore, these maps will enable managers to easily identify areas that should be the target of habitat conservation or restoration actions (Franklin et al. 2000, Heinrichs et al. 2010, Kane et al. 2017) In BC, designating a forest stand as an Ungulate Winter Range unit (UWR) is the primary management action used to keep these stands from being intensively harvested. By mapping 145 UWRs onto my spatial estimates of growth-based habitat quality I was able to determine the effectiveness of the placement of these UWRs in each study area. In Cache Creek there is room for improvement, as there were many remaining forest stands with growth habitat that are not designated as UWRs. In contrast, in the West Okanagan the current UWRs overlap with much of the forested growth habitat in the study area, with few remaining unprotected timber stands. The only significant portion missing is on land governed by the Penticton Indian Band, which is not harvested by timber companies. The Boundary study area highlights that dense canopy is not the only vegetation type that mule deer need in the winter; much of the growth habitat in this region occurred on south-facing grassy hills. However, there was room to expand current UWRs to cover more forested growth habitat in the Boundary. 5.3 Limitations of Study Methods Though my final dataset was large, containing 605 unique collared individual mule deer and 291 mortality events across 3 age classes and 5 years, my dissertation had some limitations. Because I used GPS collar data collected at a large spatial and temporal scale, I relied on remotely sensed data as covariates in every chapter. Despite advances in technology, these data can have a low degree of accuracy for many reasons including 1) they are collected at a large spatial and temporal resolution, often larger than the scale at which variation exists on the ground, 2) atmospheric interference can affect the quality of the data, 3) the processing of raw data and interpretation of the imagery involves algorithms that can propagate inaccuracies throughout the dataset, and 4) there is limited ground truthing data used to validate remotely sensed data (Kerr and Ostrovsky 2003, Turner et al. 2003, Horning 2010). For example, the weather data I used in chapters 3 and 4 is derived from a collection of algorithms and software that extrapolate data from daily meteorological stations to produce gridded estimates of daily 146 weather parameters at a 1-km by 1-km scale (Thornton et al. 2023). Clearly, weather patterns on the ground change within a smaller spatial and temporal scale, and the high cloud cover in BC during the winter can make these readings less accurate. Ideally, I would have ground-truthed or validated some of the remotely sensed layers I used in this dissertation. Unfortunately, that was beyond the scope of what was possible given my timeline and budget but would be a fruitful avenue to explore for future mule deer research in BC. In chapter 3, I used normalized difference vegetation index (NDVI) data to approximate vegetation quality across space and time, and all the green wave surfing analyses rely on this single measure of forage quality. NDVI is highly correlated with biomass in open canopy systems (Garroutte et al. 2016), but the presence of dense canopy cover (Pettorelli et al. 2005a), and complex topography (Matsushita et al. 2007) can diminish this correlation. Given that my study areas are largely comprised of coniferous forests and variable topography, the accuracy and correlation of NDVI with forage biomass is a concern. However, Hebblewhite et al. (2008) found that ground estimates of herbaceous biomass were correlated with NDVI in a similarly treed and mountainous environment in the Canadian Rocky Mountains. Furthermore, we used NDVI to calculate metrics that described the distance from peak green-up at a given location, and not as a relative measure of fresh plant biomass, which should be more robust to these potential issues with NDVI data on this landscape (Bischof et al. 2012). I made many subjective decisions within my analyses that could have significantly affected my results, particularly within my RSF modeling. Most of these decisions, such as choosing the number of available points or choosing the used/available weight value, were chosen based off values in the literature. However, evaluating coefficient variation as a result of these subjective decisions could lead to better understanding of how important these decisions 147 were in my models and results. Therefore, I ran two sensitivity analyses (Appendix B and Figure G-2) and found that changes to one of these inputs had little effect on the RSF results (Figure G-2), but the other input did affect the RSF results (Appendix B). These variable results highlight the importance of sensitivity analyses when creating models with subjective inputs. To explore sensitivity fully within this dissertation, I could have also run sensitivity analyses on (1) the number of used and available points chosen in the RSF in Chapter 2, (2) the distance used to buffer minimum convex polygons to delineate the ‘available’ area for the RSFs in Chapters 2 and 4, (3) the used/available weighting value for the RSFs in Chapter 2 and 4, and (4) the time frames chosen in Chapter 3. Another limitation of my dissertation is that my study methods were observational and not experimental, thus limiting any direct causal inference about landscape change and mule deer survival and behavior. At best, my results from chapters 3 and 4 lend insights into correlations between anthropogenic landscape changes, selection probability, and mule deer survival. I attempted to set up a ‘landscape experiment’ in chapter 3 in which I compared mule deer survival rates in a heavily recently burned study area to those in study areas with limited recent burns. However, it is not possible to determine if wildfires increased mule deer survival in Cache Creek compared to pre-burn survival rates. Furthermore, there are many differences between the 3 study areas, so even if I had observed higher survival rates in Cache Creek it would not be possible to state conclusively that this increase was due to wildfires. Additionally, the RSF models I created in Chapter 4 were estimated using only a random intercept and did not include random slopes because the random slopes models did not predict as well as the random intercept only models. Random intercept models account for variation in sample sizes between individuals (Gillies et al. 2006), but do not account for pseudoreplication 148 (Schielzeth and Forstmeier 2009, Muff et al. 2020), and produce overly confident or biased standard errors (see Figure 2; Schielzeth and Forstmeier 2009). As a result, random intercept only models do not account for the variability in selection between individuals and therefore do not accurately reflect population-level inference (Gillies et al. 2006). In Appendix H, I explored a potential compromise between full random slopes models and random intercept only models by including random slopes for the continuous covariates only. These models greatly improved the k-fold cross validation values compared to the full random slopes models, with some models predicting as well as the random intercept only models (Table H-1). Incorporating random slopes in this manner would allow a balance between a model that predicts deer use well and a model that includes information about population level deer selection patterns. I recognize that using different RSF models could change the outcome of the survival models and therefore the conclusions of this chapter. Finally, despite a large sample size, there was one instance in chapter 4 where a larger sample size of mortalities might have helped strengthen my results. Despite adults and fawns selecting similar resources in the winter, adult RSFs did not correlate with survival probabilities. One reason for this discrepancy could be the larger number of mortalities observed for fawns (n = 105) compared to adults (n = 48) in the winter. The effect size of selection probabilities on survival was relatively small for fawns and adults. Therefore, we may have needed a larger adult sample size (more deaths) to reject the null hypothesis that selection probabilities do not affect survival (Steidl et al. 1997, Hosmer et al. 2008). This result highlights how seemingly large datasets in ecological survival modeling can still be inadequate to detect small effect sizes, particularly when the sample size must account for seasonal differences in survival. 149 5.4 Future Directions My dissertation provides at least 5 areas that could provide productive inquiry for future research. Within chapter 2, I determined that mule deer in southern BC do not surf the green wave during spring migration, likely because green up does not occur in a wave-like fashion. I speculate that this lack of a green wave is due to the extensive network of cutblocks on the landscape, which might have altered the pattern of green up (Aikens et al. 2020a). Given that NDVI data exists from 2000 onwards, future work could support or refute this theory by quantifying the rate, duration, and order of green-up on the landscape over time, as cutblock density increases. This analysis could also be conducted in other landscapes where different anthropogenic disturbances might have altered the greenscape and reduced ungulate tracking ability. Additionally, future research should prioritize establishing a link between green wave surfing and fitness consequences for mule deer. Theoretically, accessing forage at peak nutritional quality during migration should increase mule deer body condition (Merems et al. 2020) and therefore increase survival probability (Monteith et al. 2014), but this relationship has not been linked conclusively. Elk (Cervus canadensis) that time their migration movements with peak green-up have higher levels of body fat the following fall, which is linked to increased population growth via the effect of body fat on pregnancy rates (Middleton et al. 2018). However, mule deer pregnancy rates are less sensitive to variation in body condition than elk (Tollefson et al. 2010, Monteith et al. 2023), thus making this linkage between green wave surfing and population performance unlikely for mule deer. Additionally, many mule deer populations spend < 2 weeks migrating each spring (Brown 1992, Lendrum et al. 2013, Schuyler et al. 2019, van de Kerk et al. 2021), which could potentially be too short of a time frame to see 150 increased fat deposition. If there is no relationship between green wave surfing and population growth, researchers and managers with limited resources should study other, more impactful, behavioral decisions of mule deer. In chapter 3 I identified many landscape features that were correlated with mule deer survival rates to provide management recommendations that could increase mule deer population growth in south-central BC. Future research could expand upon and strengthen my results and inferences by investigating the relationship between these features, forage nutritional quality, and predator kill site selection. For example, I hypothesized that increased road densities on winter ranges reduced mule deer survival because roads are used by predators to hunt mule deer more effectively. If future research found that cougars (Puma concolor) and coyotes (Canis larans) use roads to hunt mule deer in our system, then the correlation I observed between roads and survival is further supported, and the strength of evidence to promote the rehabilitation of roadways in southern BC is increased. Similarly, quantifying mule deer forage quality and cougar kill site frequency in recent burns and cutblocks could provide evidence regarding the mechanisms behind these habitat features reducing mule deer mortality risk. Additionally, more evidence is needed to determine the relative strength of top-down and bottom-up forces in this system, which could help provide further management recommendations. I collected data on several life history characteristics that can be used to determine where the mule deer population is relative to carrying capacity (K) including pregnancy rates of adults, number of fetuses per adult female (fetal rates), and neonate birth mass (Bowyer et al. 2005, Pierce et al. 2012). Based off these three metrics I might conclude that mule deer are top-down regulated because I observed high pregnancy and fetal rates, and relatively high neonate birth masses compared to nearby populations (Shallow et al. 2015), which are all 151 indicative of top-down regulation (Bowyer et al. 2005). However, more life history characteristics are needed to determine where the population is relative to K, particularly for mule deer, an ungulate whose pregnancy and fetal rates are largely insensitive to variation in nutrition (Bowyer et al. 2005, Monteith et al. 2023). Future studies should collect data regarding the body condition of adult females and diet quality, and assess whether fawn mortality is compensatory or additive (Pierce et al. 2012). In chapter 4, I created a map of population growth-based habitat quality to help managers assess where ‘good’ habitat currently exists for mule deer in southern BC. To build upon this work, future research could use my results as the foundation for work that forecasts relationships between future landscape conditions, weather, and mule deer population growth (Heinrichs et al. 2010). This approach would hinge on the assumption that mule deer resource selection would not change in the future as the landscape is altered but would still provide an important first step for understanding how future changes might impact mule deer populations. Globally, the rate at which humans are altering ecosystems, from vegetation characteristics to climate, is unparalleled and shows no signs of slowing down (Seto et al. 2012). Therefore, my dissertation research created an important baseline measure of fitness-based habitat quality that can also be used to determine how these inevitable future changes might impact a species that is valuable for both human and ecological communities. . 152 Bibliography Aikens, E. O., M. J. Kauffman, J. A. Merkle, S. P. H. 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Sawyer, S. E. Albeke, S. L. Garman, and M. J. Kauffman. 2018. Evaluating the influence of energy and residential development on the migratory behavior of mule deer: Ecosphere 9. Zhang, X., G. Flato, M. Kirchmeier-Young, L. Vincent, H. Wan, X. Wang, R. Rong, J. Fyfe, G. Li, and V. V. Kharin. 2019. Changes in temperature and precipitation across Canada. Pages 112–193 in E. Bush and D. S. Lemmen, editors. Canada’s changing climate report. Government of Canada, Ottawa, Ontario, Canada. 179 Appendices Appendix A – Cutblock and Wildfire Burn Ages During Migration Figure A-1.18Results from a generalized additive mixed effects model comparing mule deer used and available locations during spring 2018-2021 in relation to cutblock age in south-central British Columbia, Canada. Lines above zero indicate mule deer selection for those cutblock ages and lines below zero indicate avoidance for those cutblock ages. The solid line is the predicted output and the dashed lines are the 95% confidence intervals. 180 Figure A-2.19Results from a generalized additive mixed effects model comparing mule deer used and available locations during spring 2018-2021 in relation to wildfire burn age in south-central British Columbia, Canada. Lines above zero indicate mule deer selection for those burn ages and lines below zero indicate avoidance for those burn ages. The solid line is the predicted output and the dashed lines are the 95% confidence intervals. 181 Appendix B – Sensitivity Analysis for Stopover Classification We conducted a sensitivity analysis for the corridor and stopover RSFs to determine the impact of our decision to define stopovers as the highest 12% of the utilization distribution (UD) values within the migratory route. We conducted a sensitivity analysis for both corridor and stopover RSFs, despite only varying the stopover definition, because the stopover definition affected both the corridor UD and stopover UD. The definition affected both corridors and stopovers because we clipped the stopover site (as defined by the stopover value) out of the corridor UD before randomly, but probabilistically, selecting our used points within the corridor UD. Therefore, as the stopover site increased in size (because we used more UD values to define a stopover), the less ‘corridor’ UD remained in which to select our used points. We varied the stopover value from 1% to 20%, conducted the same steps as described in section 2.2.5 to set up the RSF analyses, ran the top model topographic × general vegetation + roads + spring + burns for each stopover value, and plotted the standardized coefficient estimates from the model output (Figures B-1 – B-6). We found that varying the stopover value overall had little effect on corridor RSF results. There was some variation, with the coefficient estimates of categorical covariates being slightly larger at stopover values > 12% and ≤ 20%, but the 95% confidence intervals (CI) overlapped each other, and the point estimates had the same sign (negative or positive) indicating the basic interpretation of the RSF results did not change. However, varying the stopover value had a large effect on stopover RSF results, particularly at lower stopover value amounts (i.e., < 5%). As the stopover value increased, the coefficients stabilized for most covariates. In the Boundary and Cache Creek models, there were some point estimates for categorical covariates that had opposite signs depending on the stopover 182 value used, but many of these 95% CI’s overlapped 0. Therefore, our interpretation of the results would not have changed because these estimates were not significant. Overall, our stopover RSF results were robust to the choice of stopover value. It is possible using stopover values >20% could affect our RSF results, but given the stabilization we saw for most covariates, we do not believe it would have a large effect on the interpretation and management recommendations resulting from our work. Conducting this sensitivity analysis was crucial to examining the robustness of our conclusions to one model input (stopover definition), but ideally, we would conduct more sensitivity analyses to determine the effects of other model inputs such as the number of available points, number of used points, used/available point weighting, and our method of defining used points. 183 Figure B-1.20Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the Boundary study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). 184 Figure B-2.21Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the West Okanagan study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). 185 Figure B-3.22Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration corridors in the Cache Creek study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in black representing the value we used in the analysis (highest 12% of the values within the UD). 186 Figure B-4.23Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the Boundary study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). 187 Figure B-5.24Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the West Okanagan study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). 188 Figure B-6.25Standardized selection coefficients from a sensitivity analysis conducted for resource selection functions within mule deer migration stopovers in the Cache Creek study area of south-central British Columbia, Canada 2018-2021. Each color represents a different value used to classify stopovers within the migration route dBBMM, with 0.12 in red representing the value we used in the analysis (highest 12% of the values within the UD). 189 Appendix C – Migration Route Fidelity Table C-1.8Top ranking models that explained variation in spring migration route overlap over successive years for mule deer in south-central British Columbia, Canada during 2018-2021. Each model also contains a random intercept for individual deer ID. Only the top models that together accounted for < 1.00 of the cumulative AICc weight are shown here. Model k AICc ΔAICc Cumulative weight Log Likelihood Study Area × Difference in Start Date 8 -219.37 0.00 0.98 118.12 Study Area × NDVI variation 8 -208.17 11.20 0.99 112.51 Study Area + Difference in Start Date 6 -207.15 12.23 0.99 109.82 Study Area + NDVI variation 6 -206.61 12.76 0.99 109.55 Study Area + Year 7 -206.61 12.76 0.99 110.64 Study Area 5 -206.58 12.79 0.99 108.47 Study Area × UD1 Area 8 -206.02 13.35 0.99 111.44 1 Area of the utilization distribution (UD) created using a dynamic Brownian bridge movement model for individual spring migration routes. 190 Figure C-1.26Predicted Bhattacharyya’s affinity (BA) scores from the top linear mixed effects model (Study Area × Difference in Start Date) used to determine which factors influence spring migration route overlap in two successive years for mule deer in south-central British Columbia, Canada during 2018-2021. Negative start date values indicate deer started spring migration earlier in year two than year one, zero indicates deer started spring migration on the same day in both years, and positive start date values indicate deer started spring migration later in year two than year one. All coefficients were significant (P< 0.05) except for the interaction term between difference in start date and the Cache Creek study area. 191 Appendix D – Model Output from Migratory Resource Selection Table D-1.9Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Boundary study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard Error Statistic P fixed NA Elevation -0.55 0.14 -3.87 0.00 fixed NA Elevation2 -1.06 0.08 -13.32 0.00 fixed NA Surface curvature 0.00 0.01 -0.23 0.82 fixed NA North -0.04 0.02 -2.54 0.01 fixed NA East 0.02 0.04 0.45 0.65 fixed NA Topographic wetness index -0.04 0.01 -3.25 0.00 fixed NA Slope 0.06 0.02 2.74 0.01 fixed NA Other -0.56 0.10 -5.48 0.00 fixed NA Mid age cutblock -0.38 0.10 -3.70 0.00 fixed NA Old cutblock -0.58 0.09 -6.76 0.00 fixed NA Very old cutblock -1.33 0.16 -8.12 0.00 fixed NA Young cutblock -0.57 0.13 -4.37 0.00 fixed NA Uncut forests -0.37 0.08 -4.64 0.00 fixed NA Shrub -1.02 0.19 -5.30 0.00 fixed NA Road density 0.02 0.05 0.38 0.71 fixed NA Distance to road 0.09 0.09 0.99 0.32 fixed NA Young wildfire burn 0.10 0.26 0.38 0.70 fixed NA Old wildfire burn -1.78 0.36 -4.87 0.00 fixed NA Start of spring residuals 0.06 0.04 1.56 0.12 fixed NA Elevation × Other 0.75 0.11 6.61 0.00 fixed NA Elevation × Mid age cutblock 0.40 0.11 3.63 0.00 fixed NA Elevation × Old cutblock 0.28 0.11 2.56 0.01 fixed NA Elevation × Very old cutblock 0.24 0.14 1.78 0.08 fixed NA Elevation × Young cutblock 0.55 0.12 4.43 0.00 fixed NA Elevation × Uncut forests 0.57 0.11 5.39 0.00 fixed NA Elevation × Shrub 0.37 0.14 2.70 0.01 fixed NA Elevation2 × Other 0.46 0.06 8.21 0.00 fixed NA Elevation2 × Mid age cutblock 0.57 0.06 9.46 0.00 192 Effect Group Term Estimate Standard Error Statistic P fixed NA Elevation2 × Old cutblock 0.57 0.06 9.72 0.00 fixed NA Elevation2 × Very old cutblock 0.65 0.07 8.76 0.00 fixed NA Elevation2 × Young cutblock 0.56 0.07 7.75 0.00 fixed NA Elevation2 × Uncut forests 0.50 0.05 9.51 0.00 fixed NA Elevation2 × Shrub 0.47 0.08 6.26 0.00 rand DeerID Intercept 1000.00 NA NA NA rand DeerID.1 Surface curvature 0.08 NA NA NA rand DeerID.2 North 0.10 NA NA NA rand DeerID.3 Slope 0.14 NA NA NA rand DeerID.4 Elevation 0.73 NA NA NA rand DeerID.5 Elevation2 0.45 NA NA NA rand DeerID.6 East 0.28 NA NA NA rand DeerID.7 Topographic wetness index 0.04 NA NA NA rand DeerID.8 Other 0.49 NA NA NA rand DeerID.9 Shrub 1.01 NA NA NA rand DeerID.10 Mid age cutblock 0.58 NA NA NA rand DeerID.11 Old cutblock 0.41 NA NA NA rand DeerID.12 Very old cutblock 0.85 NA NA NA rand DeerID.13 Young cutblock 0.71 NA NA NA rand DeerID.14 Uncut forests 0.40 NA NA NA rand DeerID.15 Road density 0.34 NA NA NA rand DeerID.16 Distance to road 0.58 NA NA NA rand DeerID.17 Young wildfire burn 1.55 NA NA NA rand DeerID.18 Old wildfire burn 1.77 NA NA NA rand DeerID.19 Start of spring residuals 0.28 NA NA NA 193 Table D-2.10Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Cache Creek study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard error Statistic P fixed NA Elevation -0.87 0.21 -4.08 0.00 fixed NA Elevation2 -0.83 0.12 -7.13 0.00 fixed NA Surface curvature -0.03 0.02 -1.33 0.18 fixed NA North -0.03 0.03 -1.08 0.28 fixed NA East 0.00 0.03 0.04 0.96 fixed NA Topographic wetness index -0.04 0.01 -4.58 0.00 fixed NA Slope 0.00 0.02 -0.17 0.87 fixed NA Other -2.50 0.43 -5.86 0.00 fixed NA Mid age cutblock 0.00 0.06 -0.04 0.97 fixed NA Old cutblock -0.03 0.06 -0.58 0.56 fixed NA Very old cutblock -0.02 0.08 -0.24 0.81 fixed NA Young cutblock -0.08 0.10 -0.83 0.40 fixed NA Uncut forests -0.10 0.04 -2.28 0.02 fixed NA Shrub 0.02 0.07 0.28 0.78 fixed NA Road density -0.09 0.07 -1.21 0.23 fixed NA Distance to road -0.40 0.10 -3.85 0.00 fixed NA Young wildfire burn 0.22 0.21 1.06 0.29 fixed NA Old wildfire burn -0.93 0.36 -2.58 0.01 fixed NA Start of spring -0.11 0.03 -4.11 0.00 fixed NA Elevation × Other -2.06 0.97 -2.12 0.03 fixed NA Elevation × Mid age cutblock -0.02 0.09 -0.21 0.84 fixed NA Elevation × Old cutblock -0.01 0.09 -0.06 0.95 fixed NA Elevation × Very old cutblock -0.03 0.11 -0.30 0.76 fixed NA Elevation × Young cutblock -0.35 0.12 -3.02 0.00 fixed NA Elevation × Uncut forests -0.05 0.05 -1.04 0.30 fixed NA Elevation × Shrub -0.06 0.07 -0.84 0.40 fixed NA Elevation2 × Other -0.58 0.31 -1.85 0.06 fixed NA Elevation2 × Mid age cutblock -0.15 0.09 -1.59 0.11 194 Effect Group Term Estimate Standard error Statistic P fixed NA Elevation2 × Old cutblock -0.42 0.11 -3.85 0.00 fixed NA Elevation2 × Very old cutblock -0.55 0.15 -3.67 0.00 fixed NA Elevation2 × Young cutblock 0.27 0.16 1.72 0.09 fixed NA Elevation2 × Uncut forests 0.06 0.03 1.81 0.07 fixed NA Elevation2 × Shrub -0.03 0.04 -0.67 0.50 random DeerID Intercept 1000 NA NA NA random DeerID.1 Surface curvature 0.12 NA NA NA random DeerID.2 North 0.19 NA NA NA random DeerID.3 Slope 0.13 NA NA NA random DeerID.4 Elevation 1.47 NA NA NA random DeerID.5 Elevation2 0.78 NA NA NA random DeerID.6 East 0.18 NA NA NA random DeerID.7 Topographic wetness index 0.02 NA NA NA random DeerID.8 Other 0.00 NA NA NA random DeerID.9 Shrub 0.36 NA NA NA random DeerID.10 Mid age cutblock 0.30 NA NA NA random DeerID.11 Old cutblock 0.24 NA NA NA random DeerID.12 Very old cutblock 0.49 NA NA NA random DeerID.13 Young cutblock 0.52 NA NA NA random DeerID.14 Uncut forests 0.24 NA NA NA random DeerID.15 Road density 0.50 NA NA NA random DeerID.16 Distance to road 0.69 NA NA NA random DeerID.17 Young wildfire burn 1.40 NA NA NA random DeerID.18 Old wildfire burn 1.60 NA NA NA random DeerID.19 Start of spring residuals 0.18 NA NA NA 195 Table D-3.11Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the West Okanagan study area in corridors. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard error Statistic P fixed NA Elevation -0.36 0.08 -4.43 0.00 fixed NA Elevation2 -0.56 0.07 -7.80 0.00 fixed NA Surface curvature 0.01 0.01 0.74 0.46 fixed NA North -0.22 0.03 -8.20 0.00 fixed NA East -0.05 0.02 -2.55 0.01 fixed NA Topographic wetness index -0.01 0.01 -1.10 0.27 fixed NA Slope 0.04 0.02 2.25 0.02 fixed NA Other -0.33 0.19 -1.70 0.09 fixed NA Mid age cutblock -0.35 0.08 -4.31 0.00 fixed NA Old cutblock -0.51 0.09 -5.89 0.00 fixed NA Very old cutblock -0.22 0.08 -2.60 0.01 fixed NA Young cutblock -0.57 0.14 -4.11 0.00 fixed NA Uncut forests -0.15 0.06 -2.51 0.01 fixed NA Shrub -0.41 0.13 -3.23 0.00 fixed NA Road density 0.07 0.05 1.48 0.14 fixed NA Distance to road -0.15 0.13 -1.11 0.27 fixed NA Young wildfire burn -1.19 0.46 -2.59 0.01 fixed NA Old wildfire burn -2.39 0.68 -3.49 0.00 fixed NA Start of spring 0.02 0.03 0.88 0.38 fixed NA Elevation × Other 0.14 0.13 1.13 0.26 fixed NA Elevation × Mid age cutblock 0.05 0.06 0.83 0.41 fixed NA Elevation × Old cutblock 0.40 0.09 4.29 0.00 fixed NA Elevation × Very old cutblock 0.37 0.07 5.25 0.00 fixed NA Elevation × Young cutblock -0.06 0.08 -0.69 0.49 fixed NA Elevation × Uncut forests 0.01 0.05 0.11 0.91 fixed NA Elevation × Shrub 0.21 0.08 2.58 0.01 fixed NA Elevation2 × Other -0.07 0.06 -1.06 0.29 196 Effect Group Term Estimate Standard error Statistic P fixed NA Elevation2 × Mid age cutblock 0.21 0.04 5.62 0.00 fixed NA Elevation2 × Old cutblock 0.03 0.06 0.50 0.62 fixed NA Elevation2 × Very old cutblock -0.10 0.05 -1.95 0.05 fixed NA Elevation2 × Young cutblock 0.24 0.06 3.84 0.00 fixed NA Elevation2 × Uncut forests 0.18 0.03 5.49 0.00 fixed NA Elevation2 × Shrub 0.22 0.07 3.44 0.00 random DeerID Intercept 1000 NA NA NA random DeerID.1 Surface curvature 0.08 NA NA NA random DeerID.2 North 0.21 NA NA NA random DeerID.3 Slope 0.14 NA NA NA random DeerID.4 Elevation 0.52 NA NA NA random DeerID.5 Elevation2 0.53 NA NA NA random DeerID.6 East 0.14 NA NA NA random DeerID.7 Topographic wetness index 0.04 NA NA NA random DeerID.8 Other 1.00 NA NA NA random DeerID.9 Shrub 0.71 NA NA NA random DeerID.10 Mid age cutblock 0.38 NA NA NA random DeerID.11 Old cutblock 0.41 NA NA NA random DeerID.12 Very old cutblock 0.44 NA NA NA random DeerID.13 Young cutblock 0.71 NA NA NA random DeerID.14 Uncut forests 0.21 NA NA NA random DeerID.15 Road density 0.37 NA NA NA random DeerID.16 Distance to road 1.07 NA NA NA random DeerID.17 Young wildfire burn 2.90 NA NA NA random DeerID.18 Old wildfire burn 3.21 NA NA NA random DeerID.19 Start of spring residuals 0.20 NA NA NA 197 Table D-4.12Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Boundary study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard error Statistic P fixed NA Elevation 0.19 0.25 0.75 0.46 fixed NA Elevation2 -1.28 0.12 -10.91 0.00 fixed NA Surface curvature -0.01 0.02 -0.76 0.45 fixed NA North -0.21 0.04 -4.66 0.00 fixed NA East 0.07 0.06 1.14 0.25 fixed NA Topographic wetness index -0.14 0.02 -7.16 0.00 fixed NA Slope 0.23 0.03 8.12 0.00 fixed NA Other -0.90 0.21 -4.25 0.00 fixed NA Mid age cutblock -0.87 0.19 -4.47 0.00 fixed NA Old cutblock -0.92 0.16 -5.66 0.00 fixed NA Very old cutblock -3.14 0.54 -5.85 0.00 fixed NA Young cutblock -0.86 0.28 -3.06 0.00 fixed NA Uncut forests -0.45 0.14 -3.26 0.00 fixed NA Shrub -2.17 0.41 -5.26 0.00 fixed NA Road density -0.04 0.08 -0.52 0.60 fixed NA Distance to road 0.23 0.29 0.78 0.44 fixed NA Young wildfire burn -0.33 0.48 -0.69 0.49 fixed NA Old wildfire burn -6.04 1.61 -3.75 0.00 fixed NA Start of spring 0.10 0.07 1.53 0.13 fixed NA Elevation × Other -0.03 0.12 -0.28 0.78 fixed NA Elevation × Mid age cutblock -0.10 0.11 -0.95 0.34 fixed NA Elevation × Old cutblock -0.41 0.11 -3.79 0.00 fixed NA Elevation × Very old cutblock -0.99 0.15 -6.42 0.00 fixed NA Elevation × Young cutblock -0.33 0.13 -2.60 0.01 fixed NA Elevation × Uncut forests -0.04 0.10 -0.36 0.72 fixed NA Elevation × Shrub 0.16 0.14 1.20 0.23 fixed NA Elevation2 × Other 0.11 0.07 1.55 0.12 fixed NA Elevation2 × Mid age cutblock 0.61 0.07 8.80 0.00 fixed NA Elevation2 × Old cutblock 0.40 0.07 5.71 0.00 198 Effect Group Term Estimate Standard error Statistic P fixed NA Elevation2 × Very old cutblock 0.70 0.09 7.43 0.00 fixed NA Elevation2 × Young cutblock 0.28 0.09 3.11 0.00 fixed NA Elevation2 × Uncut forests 0.24 0.06 3.80 0.00 fixed NA Elevation2 × Shrub 0.37 0.09 4.14 0.00 random DeerID Intercept 1000 NA NA NA random DeerID.1 Surface curvature 0.10 NA NA NA random DeerID.2 North 0.32 NA NA NA random DeerID.3 Slope 0.20 NA NA NA random DeerID.4 Elevation 1.72 NA NA NA random DeerID.5 Elevation2 0.74 NA NA NA random DeerID.6 East 0.44 NA NA NA random DeerID.7 Topographic wetness index 0.09 NA NA NA random DeerID.8 Other 1.31 NA NA NA random DeerID.9 Shrub 2.07 NA NA NA random DeerID.10 Mid age cutblock 1.25 NA NA NA random DeerID.11 Old cutblock 1.00 NA NA NA random DeerID.12 Very old cutblock 2.20 NA NA NA random DeerID.13 Young cutblock 1.73 NA NA NA random DeerID.14 Uncut forests 0.83 NA NA NA random DeerID.15 Road density 0.56 NA NA NA random DeerID.16 Distance to road 2.17 NA NA NA random DeerID.17 Young wildfire burn 2.75 NA NA NA random DeerID.18 Old wildfire burn 6.14 NA NA NA random DeerID.19 Start of spring residuals 0.49 NA NA NA 199 Table D-5.13Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the Cache Creek study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard error Statistic P fixed NA Elevation -1.70 0.30 -5.69 0.00 fixed NA Elevation2 -2.07 0.30 -7.01 0.00 fixed NA Surface curvature -0.07 0.03 -2.05 0.04 fixed NA North -0.23 0.06 -3.81 0.00 fixed NA East 0.00 0.05 -0.05 0.96 fixed NA Topographic wetness index -0.10 0.02 -5.82 0.00 fixed NA Slope 0.11 0.05 2.33 0.02 fixed NA Other -1.57 0.61 -2.55 0.01 fixed NA Mid age cutblock -0.16 0.17 -0.93 0.35 fixed NA Old cutblock -0.54 0.18 -2.94 0.00 fixed NA Very old cutblock -0.12 0.17 -0.69 0.49 fixed NA Young cutblock -0.29 0.24 -1.20 0.23 fixed NA Uncut forests -0.25 0.10 -2.49 0.01 fixed NA Shrub -0.14 0.17 -0.85 0.40 fixed NA Road density 0.01 0.12 0.08 0.94 fixed NA Distance to road -0.21 0.17 -1.24 0.22 fixed NA Young wildfire burn 0.16 0.22 0.72 0.47 fixed NA Old wildfire burn -5.15 2.17 -2.38 0.02 fixed NA Start of spring -0.04 0.05 -0.75 0.45 fixed NA Elevation × Other 1.55 0.91 1.71 0.09 fixed NA Elevation × Mid age cutblock 0.53 0.17 3.12 0.00 fixed NA Elevation × Old cutblock 0.41 0.17 2.36 0.02 fixed NA Elevation × Very old cutblock -0.37 0.22 -1.64 0.10 fixed NA Elevation × Young cutblock 0.34 0.23 1.51 0.13 fixed NA Elevation × Uncut forests -0.11 0.10 -1.09 0.27 fixed NA Elevation × Shrub 0.58 0.16 3.61 0.00 fixed NA Elevation2 × Other 0.27 0.43 0.63 0.53 fixed NA Elevation2 × Mid age cutblock -0.92 0.20 -4.63 0.00 200 Effect Group Term Estimate Standard error Statistic P fixed NA Elevation2 × Old cutblock -0.09 0.18 -0.54 0.59 fixed NA Elevation2 × Very old cutblock -1.16 0.30 -3.85 0.00 fixed NA Elevation2 × Young cutblock -0.51 0.33 -1.56 0.12 fixed NA Elevation2 × Uncut forests 0.15 0.07 2.33 0.02 fixed NA Elevation2 × Shrub -0.16 0.11 -1.49 0.14 random DeerID Intercept 1000 NA NA NA random DeerID.1 Surface curvature 0.20 NA NA NA random DeerID.2 North 0.42 NA NA NA random DeerID.3 Slope 0.31 NA NA NA random DeerID.4 Elevation 1.95 NA NA NA random DeerID.5 Elevation2 1.99 NA NA NA random DeerID.6 East 0.30 NA NA NA random DeerID.7 Topographic wetness index 0.08 NA NA NA random DeerID.8 Other 1.06 NA NA NA random DeerID.9 Shrub 0.89 NA NA NA random DeerID.10 Mid age cutblock 1.02 NA NA NA random DeerID.11 Old cutblock 1.13 NA NA NA random DeerID.12 Very old cutblock 1.05 NA NA NA random DeerID.13 Young cutblock 1.38 NA NA NA random DeerID.14 Uncut forests 0.65 NA NA NA random DeerID.15 Road density 0.88 NA NA NA random DeerID.16 Distance to road 1.11 NA NA NA random DeerID.17 Young wildfire burn 1.45 NA NA NA random DeerID.18 Old wildfire burn 4.43 NA NA NA random DeerID.19 Start of spring residuals 0.36 NA NA NA 201 Table D-6.14Results from the top generalized linear mixed model predicting mule deer resource selection during migration in south-central British Columbia, Canada during 2018-2021. Specifically, this output is from the top model in the West Okanagan study area in stopovers. This model was estimated using the glmmTMB package with a fixed random intercept variance and random slopes for all fixed parameters except the interaction terms because the model did not converge when they were included. The model contained grassland and unburned areas as reference covariates. Effect Group Term Estimate Standard error Statistic P fixed NA Elevation -0.84 0.25 -3.34 0.00 fixed NA Elevation2 -2.04 0.37 -5.47 0.00 fixed NA Surface curvature -0.01 0.03 -0.55 0.58 fixed NA North -0.40 0.06 -6.69 0.00 fixed NA East -0.02 0.06 -0.28 0.78 fixed NA Topographic wetness index -0.08 0.02 -3.60 0.00 fixed NA Slope 0.07 0.04 1.49 0.14 fixed NA Other -2.54 0.55 -4.64 0.00 fixed NA Mid age cutblock -0.38 0.20 -1.88 0.06 fixed NA Old cutblock -1.37 0.25 -5.56 0.00 fixed NA Very old cutblock -1.28 0.24 -5.27 0.00 fixed NA Young cutblock -1.91 0.41 -4.65 0.00 fixed NA Uncut forests -0.17 0.14 -1.25 0.21 fixed NA Shrub -1.36 0.25 -5.37 0.00 fixed NA Road density 0.14 0.10 1.44 0.15 fixed NA Distance to road -0.10 0.37 -0.27 0.79 fixed NA Young wildfire burn -3.96 1.48 -2.68 0.01 fixed NA Old wildfire burn -3.14 0.91 -3.46 0.00 fixed NA Start of spring 0.14 0.05 2.95 0.00 fixed NA Elevation × Other 1.11 0.23 4.84 0.00 fixed NA Elevation × Mid age cutblock 0.55 0.13 4.30 0.00 fixed NA Elevation × Old cutblock 1.28 0.19 6.86 0.00 fixed NA Elevation × Very old cutblock 0.27 0.14 2.03 0.04 fixed NA Elevation × Young cutblock 0.34 0.16 2.16 0.03 fixed NA Elevation × Uncut forests 0.13 0.10 1.26 0.21 fixed NA Elevation × Shrub 0.61 0.15 4.02 0.00 fixed NA Elevation2 × Other 0.75 0.13 5.75 0.00 fixed NA Elevation2 × Mid age cutblock 0.26 0.08 3.15 0.00 fixed NA Elevation2 × Old cutblock -0.18 0.12 -1.53 0.13 fixed NA Elevation2 × Very old cutblock 0.24 0.10 2.30 0.02 202 Effect Group Term Estimate Standard error Statistic P fixed NA Elevation2 × Young cutblock 0.52 0.12 4.43 0.00 fixed NA Elevation2 × Uncut forests 0.33 0.07 4.98 0.00 fixed NA Elevation2 × Shrub 0.74 0.12 6.17 0.00 random DeerID Intercept 1000.00 NA NA NA random DeerID.1 Surface curvature 0.18 NA NA NA random DeerID.2 North 0.48 NA NA NA random DeerID.3 Slope 0.35 NA NA NA random DeerID.4 Elevation 1.86 NA NA NA random DeerID.5 Elevation2 2.98 NA NA NA random DeerID.6 East 0.48 NA NA NA random DeerID.7 Topographic wetness index 0.13 NA NA NA random DeerID.8 Other 1.92 NA NA NA random DeerID.9 Shrub 1.35 NA NA NA random DeerID.10 Mid age cutblock 1.33 NA NA NA random DeerID.11 Old cutblock 1.57 NA NA NA random DeerID.12 Very old cutblock 1.63 NA NA NA random DeerID.13 Young cutblock 2.10 NA NA NA random DeerID.14 Uncut forests 0.85 NA NA NA random DeerID.15 Road density 0.82 NA NA NA random DeerID.16 Distance to road 3.03 NA NA NA random DeerID.17 Young wildfire burn 4.82 NA NA NA random DeerID.18 Old wildfire burn 2.44 NA NA NA random DeerID.19 Start of spring residuals 0.37 NA NA NA 203 Table D-7.15Number and percentage of used and available points within the 3 wildfire covariates included in the top generalized linear mixed model predicting mule deer resource selection within migration corridors in south-central British Columbia, Canada during 2018-2021. New burns were areas burnt 1-10 years prior, old burns were areas burnt >10 years prior, and never burned areas were never burnt. Study Area Type Total Number of Points Percent of Points New Burn Old Burn Never Burned New Burn Old Burn Never Burned Boundary available 6599 3805 199511 3.14 1.81 95.04 Boundary used 1527 244 19224 7.27 1.16 91.56 Cache Creek available 130171 1457 88337 59.18 0.66 40.16 Cache Creek used 13449 132 8419 61.13 0.60 38.27 West Okanagan available 5998 2904 273075 2.13 1.03 96.84 West Okanagan used 1342 180 26678 4.76 0.64 94.60 204 Appendix E – Cutblock and Wildfire Burn Ages During Winter-Spring and Summer-Fall We chose the age groups by season for cutblocks by using generalized additive mixed models (GAMM). We created a resource selection function for cutblock age, because we expected a nonlinear relationship between selection and age of cutblocks (Mumma et al. 2021). We separated all mule deer locations by season (summer-fall: May 20 – November 16, winter-spring: November 17 – May 19) and considered these our used locations. We created minimum convex polygons (MCP) around the locations in each study area, buffered each MCP by 2.25 km, and randomly chose 10 available locations for each used location within the buffered MCP. We created 5 cutblock age layers, with each one representing the age of cutblocks in each year we had locations (2018-2022). Because we did not know what time of year cutblocks were made, our minimum cutblock age was one year; e.g., cutblocks made in 2020 did not exist in the 2020 layer but were one year old in the 2021 layer. We extracted the cutblock age for all used and available locations for the appropriate year and season and ran a GAMM with use as a function of cutblock age and a random intercept for mule deer ID using the mgcv package in R (Wood, 2011). Due to low availability of cutblocks > 50 years old, we had to set all cutblocks older than this to be 50 years old for the model to converge. We plotted the model output and chose breakpoints for cutblock age groups where the line crossed zero (Figures E-1 and E-2; Mumma et al. 2021). We created wildfire burn age class categories in the same manner using the fire perimeter layer from the BC Geographic Data catalogue. Due to low availability of burns > 40 years old, we had to set all burns older than this to be 40 years old for the model to converge (Figures E-3 and E-4). There was also low use of medium aged burns in all age classes and time periods, so we combined medium and old aged burns into one category to aid in model convergence. 205 Figure E-1.27Results from a generalized additive mixed effects model comparing mule deer used and available locations during summer 2018-2022 in relation to cutblock age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those cutblock ages and curves below zero indicate avoidance for those cutblock ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed line at x = 16 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 16 years old) from old (> 16 years old) cutblocks. 206 Figure E-2.28Results from a generalized additive mixed effects model comparing mule deer used and available locations during winter 2018-2022 in relation to cutblock age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those cutblock ages and curves below zero indicate avoidance for those cutblock ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 18 and x =41 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 18 years old) from medium aged (> 18 and ≤ 41 years old), and old (> 41 years old) cutblocks. 207 Figure E-3.29Results from a generalized additive mixed effects model comparing mule deer used and available locations during summer 2018-2022 in relation to wildfire burn age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those burn ages and curves below zero indicate avoidance for those burn ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 14 and x =29 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 14 years old) from medium aged (> 14 and ≤ 29 years old), and old (> 29 years old) burns. There was low use of medium aged burns in all age classes and time periods, so we combined medium and old aged burns into one category (old; > 14 years old) to aid in survival model convergence. 208 Figure E-4.30Results from a generalized additive mixed effects model comparing mule deer used and available locations during winter 2018-2022 in relation to wildfire burn age in south-central British Columbia, Canada. Curves above the solid horizontal line at zero indicate mule deer selection for those burn ages and curves below zero indicate avoidance for those burn ages. The solid curved line is the predicted output and the dashed curved lines are the 95% confidence intervals. The vertical dashed lines at x = 11 and x =28 shows were selection changed from positive to negative, and thus the age at which we separated recent (≤ 11 years old) from medium aged (> 11 and ≤ 28 years old), and old (> 28 years old) burns. There was low use of medium aged burns in all age classes and time periods, so we combined medium and old aged burns into one category (old; > 11 years old) to aid in survival model convergence. 209 Appendix F – Additional Survival Model Information and Results (Chapter 3) Table F-1.16Models developed for winter-spring Cox proportional hazards models used to determine the effects of habitat use and weather on adult and fawn mule deer mortality risk in south-central British Columbia during 2018-2022. Model number Model Structure 1 Ruggedness 2 Road Covariate1 3 Distance to Edge 4 Road Covariate + Edge 5 Road Covariate + Ruggedness 6 Road Covariate × Ruggedness 7 Cutblocks (1-18 years-old) 8 Burns (1-11 years-old) 9 Open Areas 10 Cutblocks (1-18 years-old) + Burns (1-11 years-old) 11 Cutblocks (42+ years old) 12 Burns (12+ years-old) 13 Cutblocks (19-41 years-old) 14 Unharvested forest 15 Cutblocks (42+ years old) + Burns (12+ years-old) 16 Road Covariate + Cutblocks (1-18 years-old) 17 Road Covariate × Cutblocks (1-18 years-old) 18 Road Covariate + Burns (1-11 years-old) 19 Road Covariate × Burns (1-11 years-old) 20 Road Covariate + Open Areas 21 Road Covariate × Open Areas 22 Road Covariate + Cutblocks (42+ years old) 23 Road Covariate × Cutblocks (42+ years old) 24 Road Covariate + Cutblocks (19-41 years-old) 25 Road Covariate × Cutblocks (19-41 years-old) 26 Road Covariate + Burns (12+ years-old) 27 Road Covariate × Burns (12+ years-old) 28 Road Covariate + Unharvested forest 29 Road Covariate × Unharvested forest 30 Snow Depth2 + Temperature Covariate3 31 Temperature Covariate 32 Snow Depth 33 Cutblocks (1-18 years-old) + Snow Depth 34 Cutblocks (1-18 years-old) × Snow Depth 210 Model number Model Structure 35 Burns (1-11 years-old) + Snow Depth 36 Burns (1-11 years-old) × Snow Depth 37 Open Areas + Snow Depth 38 Open Areas × Snow Depth 39 Cutblocks (42+ years old) + Snow Depth 40 Cutblocks (42+ years old) × Snow Depth 41 Cutblocks (19-41 years-old) + Snow Depth 42 Cutblocks (19-41 years-old) × Snow Depth 43 Unharvested forest + Snow Depth 44 Unharvested forest × Snow Depth 1 Road covariate could be either road density or distance to road. 2 Total Snow Depth Covariate could be either mean snow depth, total number of days where the mean snow depth was > 40 cm or total number of days where the mean snow depth was > 30 cm. 3 Temperature Covariate could either be the mean daily maximum temperature, mean daily minimum temperature, number of days where the mean temperature was below 0oC, or the number of days where the mean temperature was below -5oC. 211 Table F-2.17Models developed for summer-fall Cox proportional hazards models used to determine the effects of habitat use and weather on adult and neonate mule deer mortality risk in south-central British Columbia during 2018-2022. Model number Model Structure 1 Ruggedness 2 Road Covariate1 3 Edge 4 Road Covariate + Edge 5 Road Covariate + Ruggedness 6 Road Covariate × Ruggedness 7 Road Covariate + Cutblocks (1-16 years-old) 8 Road Covariate × Cutblocks (1-16 years-old) 9 Road Covariate + Burns (1-14 years-old) 10 Road Covariate + Open Areas 11 Road Covariate × Open Areas 12 Road Covariate × Burns (1-14 years-old) 13 Road Covariate + Cutblocks (17+ years-old) 14 Road Covariate × Cutblocks (17+ years-old) 15 Road Covariate + Burns (15+ years-old) 16 Road Covariate × Burns (15+ years-old) 17 Road Covariate + Unharvested forest 18 Road Covariate × Unharvested forest 19 Summer-fall Weather Covariate2 20 Cutblocks (1-16 years-old) 21 Burns (1-14 years-old) 22 Open Areas 23 Burns (1-14 years-old) + Cutblocks (1-16 years-old) 24 Cutblocks (1-16 years-old) + Summer-fall Weather Covariate 25 Cutblocks (1-16 years-old) × Summer-fall Weather Covariate 26 Burns (1-14 years-old) + Summer-fall Weather Covariate 27 Burns (1-14 years-old) × Summer-fall Weather Covariate 28 Open Areas + Summer-fall Weather Covariate 29 Open Areas × Summer-fall Weather Covariate 30 Cutblocks (17+ years-old) 31 Unharvested forest 32 Burns (15+ years-old) 33 Cutblocks (17+ years-old) + Burns (15+ years-old) 34 Cutblocks (17+ years-old) × Cutblocks (1-16 years-old) 35 Unharvested forest × Cutblocks (1-16 years-old) 36 Burns (1-14 years-old) × Burns (15+ years-old) 1 Road covariate could be either road density or distance to road. 212 2 The summer-fall weather covariate is total precipitation for adults. For neonates, this covariate could either be total precipitation, average daily minimum temperature or average daily maximum temperature. 213 Table F-3.18Model output for univariate Cox Proportional Hazard Models used to choose the covariate included in subsequent modeling for 4 groups of correlated covariates. Covariate Group Age Class Season Top Ranked Covariate Akaike Model Weight (ω) Roads 2- day 7-day 30-day 2-day 7-day 30-day Adult Summer-fall Road Density Road Density Road Density 0.51 0.52 0.52 Adult Winter-spring Road Density Road Density Road Density 0.91 0.71 0.56 Neonate Summer-fall Road Density Road Density NA 0.89 0.93 NA Fawn Winter-spring Distance to Road Distance to Road Distance to Road 0.64 0.64 0.58 Winter-spring temperature Adult Winter-spring # of days below <-5C # of days below <-5C Mean daily maximum temperature 0.37 0.37 0.48 Fawn Winter-spring Mean daily maximum temperature Mean daily maximum temperature Mean daily maximum temperature 0.27 0.57 1 Winter-spring snow depth Adult Winter-spring Mean snow depth Mean snow depth Mean snow depth 0.49 0.44 0.62 Fawn Winter-spring Number of days where mean snow depth was > 30cm Mean snow depth Mean snow depth 0.38 0.4 0.81 Summer temperature Neonate Summer Mean daily maximum temperature Mean daily maximum temperature NA 0.95 0.98 NA 214 Table F-4.19All models ranked above the null model for the adult winter-spring 2 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Uncut Forest × Road Density 3 407.09 0.00 0.99 -200.54 0.99 Road Density × Cutblocks (1-18 years) 3 418.50 11.41 0.00 -206.25 1.00 Road Density + Ruggedness 2 419.61 12.52 0.00 -207.80 1.00 Road Density × Ruggedness 3 421.02 13.93 0.00 -207.51 1.00 Ruggedness 1 423.15 16.06 0.00 -210.58 1.00 Open Areas + Road Density 2 426.85 19.76 0.00 -211.42 1.00 Open Areas × Road Density 3 427.50 20.41 0.00 -210.75 1.00 Road Density 1 427.59 20.50 0.00 -212.79 1.00 Open Areas 1 428.71 21.62 0.00 -213.36 1.00 Uncut Forest + Road Density 2 428.73 21.64 0.00 -212.36 1.00 Road Density + Burns (12+ years) 2 428.85 21.76 0.00 -212.43 1.00 Road Density + Cutblocks (1-18 years) 2 429.36 22.28 0.00 -212.68 1.00 Road Density + Burns (1-11 years) 2 429.39 22.30 0.00 -212.69 1.00 Road Density + Cutblocks (42+ years) 2 429.43 22.34 0.00 -212.71 1.00 Road Density + Distance to Edge 2 429.59 22.50 0.00 -212.79 1.00 Road Density + Cutblocks (19-41 years) 2 429.59 22.50 0.00 -212.79 1.00 Road Density × Cutblocks (19-41 years) 3 429.67 22.58 0.00 -211.83 1.00 Open Areas + Mean Snow Depth 2 430.04 22.95 0.00 -213.02 1.00 Open Areas × Mean Snow Depth 3 430.18 23.09 0.00 -212.09 1.00 Road Density × Burns (12+ years) 3 430.18 23.09 0.00 -212.09 1.00 Null 0 430.44 23.36 0.00 -215.22 1.00 215 Table F-5.20All models ranked above the null model for the adult winter-spring 7 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Road Density + Ruggedness 2 422.19 0.00 0.27 -209.09 0.27 Uncut Forest × Road Density 3 422.27 0.08 0.26 -208.13 0.54 Ruggedness 1 422.66 0.47 0.22 -210.33 0.75 Road Density × Ruggedness 3 424.12 1.93 0.10 -209.06 0.86 Road Density × Cutblocks (1-18 years) 3 426.14 3.95 0.04 -210.07 0.90 Open Areas 1 429.30 7.11 0.01 -213.65 0.90 Mean Snow Depth 1 429.67 7.48 0.01 -213.84 0.91 Open Areas + Mean Snow Depth 2 429.68 7.49 0.01 -212.84 0.92 Open Areas × Road Density 3 430.03 7.85 0.01 -212.02 0.92 Open Areas + Road Density 2 430.09 7.90 0.01 -213.05 0.93 Null 0 430.37 8.18 0.00 -215.18 0.93 216 Table F-6.21All models ranked above the null model for the adult winter-spring 30 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Mean Snow Depth + Mean Snow Depth.tt 2 389.15 0.00 0.30 -192.58 0.30 Cutblocks (1-18 years) + Mean Snow Depth + Mean Snow Depth.tt 390.89 1.74 0.13 -192.44 0.43 Open Areas + Mean Snow Depth + Mean Snow Depth.tt 3 390.98 1.83 0.12 -192.49 0.55 Cutblocks (42+ years) + Mean Snow Depth + Mean Snow Depth.tt 3 391.12 1.96 0.11 -192.56 0.66 Burns (1-11 years) + Mean Snow Depth + Mean Snow Depth.tt 3 391.12 1.97 0.11 -192.56 0.77 Uncut Forest + Mean Snow Depth + Mean Snow Depth.tt 3 391.13 1.97 0.11 -192.56 0.89 Cutblocks (19-41 years) + Mean Snow Depth + Mean Snow Depth.tt 3 391.15 2.00 0.11 -192.57 1.00 Ruggedness 1 399.39 10.24 0.00 -198.69 1.00 Road Density + Ruggedness 2 401.30 12.15 0.00 -198.65 1.00 Road Density × Ruggedness 3 403.06 13.91 0.00 -198.52 1.00 Maximum Daily Temperature 1 403.63 14.48 0.00 -200.81 1.00 Uncut Forest × Road Density 3 404.86 15.70 0.00 -199.42 1.00 Open Areas 1 405.00 15.84 0.00 -201.50 1.00 Null 0 405.12 15.97 0.00 -202.56 1.00 217 Table F-7.22All models ranked above the null model for the adult summer-fall 2 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Cutblocks (1-16 years) + Burns (1-14 years) 2 615.97 0.00 0.89 -305.99 0.89 Burns (1-14 years) 1 623.43 7.45 0.02 -310.71 0.91 Cutblocks (1-16 years) 1 624.34 8.37 0.01 -311.17 0.92 Cutblocks (17+ years) × Cutblocks (1-16 years) 3 624.55 8.58 0.01 -309.28 0.93 Burns (1-14 years) + Precipitation 2 625.09 9.12 0.01 -310.55 0.94 Road Density + Burns (1-14 years) 2 625.28 9.31 0.01 -310.64 0.95 Cutblocks (1-16 years) + Precipitation 2 625.82 9.85 0.01 -310.91 0.96 Road Density + Cutblocks (1-16 years) 2 625.92 9.94 0.01 -310.96 0.96 Uncut Forest × Cutblocks (1-16 years) 3 625.97 10.00 0.01 -309.98 0.97 Ruggedness 1 626.01 10.04 0.01 -312.00 0.97 Cutblocks (1-16 years) × Precipitation 3 626.04 10.07 0.01 -310.02 0.98 Burns (1-14 years) × Precipitation 3 626.78 10.81 0.00 -310.39 0.98 Road Density × Cutblocks (1-16 years) 3 627.23 11.26 0.00 -310.62 0.99 Road Density + Ruggedness 2 627.99 12.02 0.00 -311.99 0.99 Uncut Forest 1 628.16 12.19 0.00 -313.08 0.99 Road Density × Uncut Forest 3 628.75 12.78 0.00 -311.38 0.99 Road Density × Ruggedness 3 629.46 13.49 0.00 -311.73 0.99 Null 0 629.51 13.54 0.00 -314.76 0.99 218 Table F-8.23All models ranked above the null model for the adult summer-fall 7 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Cutblocks (1-16 years) + Burns (1-14 years) 2 621.19 0.00 0.58 -308.60 0.58 Cutblocks (1-16 years) + Precipitation 2 625.75 4.55 0.06 -310.87 0.64 Cutblocks (1-16 years) 1 625.75 4.56 0.06 -311.88 0.70 Road Density + Cutblocks (1-16 years) 2 626.26 5.07 0.05 -311.13 0.75 Burns (1-14 years) 1 626.72 5.53 0.04 -312.36 0.78 Burns (1-14 years) + Precipitation 2 627.29 6.10 0.03 -311.65 0.81 Cutblocks (1-16 years) × Precipitation 3 627.46 6.27 0.03 -310.73 0.84 Road Density + Burns (1-14 years) 2 627.86 6.67 0.02 -311.93 0.86 Road Density × Cutblocks (1-16 years) 3 627.95 6.76 0.02 -310.97 0.88 Ruggedness 1 629.16 7.97 0.01 -313.58 0.89 Burns (1-14 years) × Precipitation 3 629.24 8.05 0.01 -311.62 0.90 Null 0 629.48 8.28 0.01 -314.74 0.91 219 Table F-9.24All models ranked above the null model for the adult summer-fall 30 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Cutblocks (1-16 years) + Cutblocks (1-16 years).tt + Burns (1-14 years) + Burns (1-14 years).tt 4 619.79 0.00 0.61 -305.89 0.61 Burns (1-14 years) + Burns (1-14 years).tt 2 623.07 3.28 0.12 -309.53 0.73 Road Density + Burns (1-14 years) + Burns (1-14 years).tt 3 623.23 3.44 0.11 -308.61 0.84 Burns (1-14 years) + Burns (1-14 years).tt + Precipitation 3 624.97 5.18 0.05 -309.48 0.89 Road Density + Cutblocks (1-16 years) + Cutblocks (1-16 years).tt 3 626.14 6.35 0.03 -310.07 0.91 Cutblocks (1-16 years) + Cutblocks (1-16 years).tt 2 626.27 6.48 0.02 -311.14 0.94 Cutblocks (1-16 years) + Cutblocks (1-16 years).tt + Precipitation 3 628.25 8.46 0.01 -311.12 0.95 Null 0 629.42 9.63 0.00 -314.71 0.95 220 Table F-10.25All models ranked above the null model for the fawn winter-spring 2 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Distance to Road + Ruggedness 2 661.91 0.00 0.73 -328.95 0.73 Distance to Road × Ruggedness 3 663.87 1.96 0.27 -328.94 1.00 Ruggedness 1 673.78 11.87 0.00 -335.89 1.00 Distance to Road × Cutblocks (1-18 years) 3 682.43 20.52 0.00 -338.21 1.00 Distance to Road + Burns (1-11 years) 2 687.34 25.43 0.00 -341.67 1.00 Distance to Road × Cutblocks (19-41 years) 3 687.51 25.60 0.00 -340.75 1.00 Distance to Road × Burns (1-11 years) 3 687.52 25.61 0.00 -340.76 1.00 Distance to Road 1 688.97 27.06 0.00 -343.49 1.00 Distance to Road + Burns (12+ years) 2 689.65 27.74 0.00 -342.82 1.00 Distance to Road + Cutblocks (1-18 years) 2 690.29 28.39 0.00 -343.15 1.00 Distance to Road + Distance to Edge 2 690.67 28.76 0.00 -343.33 1.00 Distance to Road + Cutblocks (42+ years) 2 690.73 28.82 0.00 -343.36 1.00 Open Areas + Distance to Road 2 690.86 28.95 0.00 -343.43 1.00 Uncut Forest + Distance to Road 2 690.89 28.98 0.00 -343.44 1.00 Distance to Road + Cutblocks (19-41 years) 2 690.95 29.04 0.00 -343.47 1.00 Distance to Road × Burns (12+ years) 3 691.24 29.34 0.00 -342.62 1.00 Open Areas × Distance to Road 3 691.85 29.94 0.00 -342.92 1.00 Distance to Road × Cutblocks (42+ years) 3 692.72 30.81 0.00 -343.36 1.00 Uncut Forest × Distance to Road 3 692.83 30.92 0.00 -343.41 1.00 Null 0 697.19 35.28 0.00 -348.59 1.00 221 Table F-11.26All models ranked above the null model for the fawn winter-spring 7 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Distance to Road + Ruggedness 2 683.95 0.00 0.59 -339.97 0.59 Distance to Road × Ruggedness 3 685.95 2.00 0.22 -339.97 0.81 Ruggedness 1 686.59 2.64 0.16 -342.29 0.97 Distance to Road × Burns (1-11 years) 3 693.41 9.46 0.01 -343.70 0.98 Maximum Daily Temperature 1 695.14 11.19 0.00 -346.57 0.98 Distance to Road + Burns (1-11 years) 2 695.20 11.25 0.00 -345.60 0.98 Distance to Road × Cutblocks (1-18 years) 3 695.24 11.30 0.00 -344.62 0.98 Distance to Road + Cutblocks (1-18 years) 2 695.43 11.48 0.00 -345.71 0.98 Cutblocks (1-18 years) 1 695.81 11.87 0.00 -346.91 0.99 Distance to Road 1 696.22 12.28 0.00 -347.11 0.99 Cutblocks (1-18 years) + Burns (1-11 years) 2 696.40 12.45 0.00 -346.20 0.99 Cutblocks (1-18 years) + Mean Snow Depth 2 697.05 13.10 0.00 -346.52 0.99 Null 0 697.21 13.26 0.00 -348.60 0.99 222 Table F-12.27All models ranked above the null model for the fawn winter-spring 30 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Maximum Daily Temperature (3 time periods) 3 591.53 0.00 1 -292.77 1 Burns (1-11 years) + Mean Snow Depth 2 662.40 70.87 0 -329.20 1 Mean Snow Depth 1 662.61 71.08 0 -330.30 1 Ruggedness 1 662.68 71.15 0 -330.34 1 Cutblocks (1-18 years) + Mean Snow Depth 2 663.19 71.65 0 -329.59 1 Distance to Road + Ruggedness 2 663.35 71.82 0 -329.68 1 Uncut Forest × Mean Snow Depth 3 663.59 72.05 0 -328.79 1 Burns (1-11 years) × Mean Snow Depth 3 663.65 72.12 0 -328.83 1 Cutblocks (42+ years) + Mean Snow Depth 2 664.05 72.51 0 -330.02 1 Uncut Forest + Mean Snow Depth 2 664.15 72.62 0 -330.07 1 Cutblocks (19-41 years) + Mean Snow Depth 2 664.54 73.01 0 -330.27 1 Cutblocks (1-18 years) × Mean Snow Depth 3 665.04 73.51 0 -329.52 1 Null 0 665.06 73.53 0 -332.53 1 223 Table F-13.28All models ranked above the null model for the neonate intrinsic analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Migratory + Sex 2 617.56 0.00 0.50 -306.78 0.50 Migratory 1 621.50 3.95 0.07 -309.75 0.57 Birth Mass + Date of Birth + Sex + Migratory + Study Area 6 621.54 3.98 0.07 -304.75 0.64 Sex 1 621.60 4.04 0.07 -309.80 0.71 Sex + Birth Mass 2 621.79 4.23 0.06 -308.89 0.77 Study Area + Sex 3 622.69 5.13 0.04 -308.34 0.81 Study Area + Migratory 3 623.05 5.50 0.03 -308.52 0.84 Migratory + Date of Birth 2 623.08 5.53 0.03 -309.54 0.87 Migratory + Birth Mass 2 623.26 5.70 0.03 -309.63 0.90 Sex × Birth Mass 3 623.36 5.80 0.03 -308.68 0.93 Sex + Date of Birth 2 623.51 5.95 0.03 -309.75 0.96 Migratory × Date of Birth 3 623.81 6.25 0.02 -308.90 0.98 Study Area × Migratory 5 626.09 8.53 0.01 -308.03 0.99 Null 0 626.47 8.92 0.01 -313.24 0.99 224 Table F-14.29All models ranked above the null model for the neonate summer 2 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Road Density × Cutblocks (1-16 years) 5 608.63 0.00 0.34 -299.31 0.34 Road Density × Uncut Forest 5 609.57 0.94 0.21 -299.78 0.55 Road Density + Open Areas 4 612.05 3.42 0.06 -302.02 0.61 Road Density + Uncut Forest 4 612.12 3.48 0.06 -302.05 0.67 Road Density 3 612.79 4.16 0.04 -303.39 0.71 Open Areas + Maximum Daily Temperature 4 613.48 4.85 0.03 -302.73 0.74 Road Density + Cutblocks (1-16 years) 4 613.65 5.02 0.03 -302.82 0.77 Road Density + Burns (1-14 years) 4 613.83 5.20 0.03 -302.91 0.79 Road Density × Open Areas 5 614.01 5.38 0.02 -301.99 0.82 Maximum Daily Temperature 3 614.07 5.44 0.02 -304.03 0.84 Road Density + Cutblocks (17+ years) 4 614.29 5.66 0.02 -303.14 0.86 Road Density + Ruggedness 4 614.75 6.12 0.02 -303.37 0.87 Road Density + Distance to Edge 4 614.79 6.16 0.02 -303.39 0.89 Burns (1-14 years) × Maximum Daily Temperature 5 614.93 6.30 0.01 -302.45 0.90 Open Areas × Maximum Daily Temperature 5 615.00 6.37 0.01 -302.49 0.92 Road Density × Cutblocks (17+ years) 5 615.08 6.45 0.01 -302.53 0.93 Road Density × Burns (1-14 years) 5 615.40 6.77 0.01 -302.69 0.94 Burns (1-14 years) + Maximum Daily Temperature 4 615.44 6.81 0.01 -303.71 0.95 Road Density × Ruggedness 5 615.92 7.29 0.01 -302.95 0.96 Cutblocks (1-16 years) + Maximum Daily Temperature 4 615.95 7.32 0.01 -303.97 0.97 Open Areas 3 616.08 7.44 0.01 -305.03 0.98 Null 2 617.56 8.93 0.00 -306.78 0.98 225 Table F-15.30All models ranked above the null model for the neonate summer 7 day analysis. Model K AICc Delta AICc AICc Weight Log Likelihood Cumulative Weight Road Density + Open Areas 3 464.83 0.00 0.22 -229.40 0.22 Road Density × Open Areas 4 465.33 0.51 0.17 -228.64 0.40 Open Areas + Maximum Daily Temperature 3 465.45 0.62 0.16 -229.71 0.56 Open Areas × Maximum Daily Temperature 4 467.04 2.21 0.07 -229.50 0.63 Open Areas 2 467.33 2.50 0.06 -231.66 0.70 Road Density × Uncut Forest 4 469.03 4.20 0.03 -230.49 0.73 Cutblocks (1-16 years) + Maximum Daily Temperature 3 469.16 4.33 0.03 -231.56 0.75 Road Density × Cutblocks (17+ years) 4 469.18 4.36 0.03 -230.57 0.78 Road Density 2 469.25 4.43 0.02 -232.62 0.80 Maximum Daily Temperature 2 469.48 4.66 0.02 -232.73 0.82 Road Density + Uncut Forest 3 469.62 4.79 0.02 -231.80 0.84 Road Density × Cutblocks (1-16 years) 4 470.17 5.35 0.02 -231.06 0.86 Road Density + Burns (15+ years) 3 470.42 5.59 0.01 -232.19 0.87 Road Density + Cutblocks (1-16 years) 3 470.42 5.59 0.01 -232.19 0.89 Road Density + Distance to Edge 3 470.56 5.74 0.01 -232.27 0.90 Burns (1-14 years) + Maximum Daily Temperature 3 470.86 6.04 0.01 -232.42 0.91 Road Density + Burns (1-14 years) 3 470.94 6.12 0.01 -232.46 0.92 Road Density + Cutblocks (17+ years) 3 471.06 6.23 0.01 -232.51 0.93 Uncut Forest × Cutblocks (1-16 years) 4 471.16 6.34 0.01 -231.56 0.94 Cutblocks (1-16 years) × Maximum Daily Temperature 4 471.17 6.35 0.01 -231.56 0.95 Road Density + Ruggedness 3 471.26 6.43 0.01 -232.62 0.96 Cutblocks (1-16 years) 2 472.13 7.31 0.01 -234.06 0.96 Burns (1-14 years) × Maximum Daily Temperature 4 472.21 7.39 0.01 -232.08 0.97 Road Density × Burns (15+ years) 4 472.43 7.61 0.00 -232.19 0.97 Road Density × Burns (1-14 years) 4 472.85 8.02 0.00 -232.40 0.98 Road Density × Ruggedness 4 473.16 8.34 0.00 -232.56 0.98 Null 1 473.28 8.46 0.00 -235.64 0.98 226 Appendix G – Additional Resource Selection Results (Chapter 4) Figure G-1.31Relative selection strength of vegetation types from the best models of resource selection as determined using generalized linear mixed models for mule deer in south-central British Columbia, Canada during 2018-2023 for A) adults and fawns in the winter-spring, B) adults in the summer-fall, C) neonates in the summer-fall in Cache Creek and the West Okanagan, and D) neonates in the summer-fall in the Boundary. The selection of each vegetation type is compared to unharvested forests at low and high elevations for that study area, age class, and movement strategy. Values above 1 indicate deer selected for that vegetation type more than unharvested forests at that elevation, and values below 1 indicate the opposite. High and low elevations were determined by low (5th percentile) and high (95th percentile) values of elevation among collared deer within each study area, age class, and movement strategy, and are listed in Table 4-1. Note that cutblock ages were different in each season. Winter-spring ages: Recent cutblocks: 1-18 years old, medium cutblocks: 19-41 years old, old cutblocks: 42+ years old, recent burns: 1-11 years old, old burns: 12+ years old. Summer-fall ages: Recent cutblock: 1-16 years, old cutblock: 17+ years, recent burn: 1-14 years, old burns: 15+ years. 227 Figure G-2.32Results from a sensitivity analysis investigating the effect of the number of available points on our results in the RSF analysis from Chapter 4. The plot shows coefficient values for adult winter RSF models in the Boundary study area using varying numbers of available points to used points. There were 198 173 used locations during this period which led to 990 865, 1 981 730, 3 963 460, 9 908 650, and 19 817 300 available locations for the 5, 10, 20, 50, 100 available:used ratio, respectively. In this analysis, we used a 10:1 ratio, which is denoted in black in the figure. We attempted to include a 250:1 ratio but were computationally limited using a computer with 128GB of memory. 228 Figure G-3.33Map of NDVI layer used in RSF analysis with non-treed areas overlaid in black. These non-treed areas include shrubs, grasslands, urban areas, and recent cutblocks. 229 Table G-1.31Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -8.907 0.041 -217.204 <0.001 Elevation 0.933 0.007 125.975 <0.001 Curvature -0.038 0.003 -14.504 <0.001 North -0.156 0.003 -45.335 <0.001 East 0.014 0.003 4.508 <0.001 TWI -0.161 0.004 -42.595 <0.001 Slope 0.198 0.003 77.547 <0.001 Ruggedness -0.027 0.002 -13.123 <0.001 Other -0.477 0.010 -45.942 <0.001 Cutblocks 42+ years -1.091 0.057 -19.001 <0.001 Cutblocks 1-18 years -0.799 0.014 -57.915 <0.001 Unharvested forest -0.396 0.008 -49.504 <0.001 Shrub -0.495 0.016 -31.069 <0.001 Cutblocks 19-41 years -0.537 0.013 -40.066 <0.001 Road density -0.029 0.003 -10.982 <0.001 Burns 1-11 years 0.095 0.009 10.765 <0.001 Burns 12+ years 0.587 0.010 57.171 <0.001 NDVI -0.954 0.004 -250.295 <0.001 Other x Elevation -0.402 0.010 -41.393 <0.001 Cutblocks 42+ years x Elevation -0.555 0.058 -9.563 <0.001 Cutblocks 1-18 years x Elevation -1.000 0.017 -58.249 <0.001 Unharvested forest x Elevation -0.498 0.008 -61.060 <0.001 Shrub x Elevation -0.347 0.021 -16.765 <0.001 Cutblocks 19-41 years x Elevation -0.928 0.019 -47.898 <0.001 Random Intercept 0.391 230 Table G-2.32Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.740 0.086 -113.789 <0.001 Elevation -0.218 0.008 -29.082 <0.001 Curvature 0.075 0.003 27.865 <0.001 North -0.188 0.003 -63.575 <0.001 East 0.165 0.003 54.393 <0.001 TWI -0.122 0.003 -42.584 <0.001 Slope 0.120 0.002 50.865 <0.001 Ruggedness -0.073 0.002 -33.514 <0.001 Other 0.106 0.024 4.511 <0.001 Cutblocks 42+ years -1.368 0.032 -42.248 <0.001 Cutblocks 1-18 years -0.301 0.015 -19.466 <0.001 Unharvested forest 0.061 0.011 5.579 <0.001 Shrub -0.815 0.025 -32.430 <0.001 Cutblocks 19-41 years -1.727 0.051 -34.024 <0.001 Road density 0.110 0.002 52.774 <0.001 Burns 1-11 years 1.060 0.007 162.773 <0.001 Burns 12+ years 0.654 0.009 69.268 <0.001 NDVI -0.605 0.003 -177.844 <0.001 Other x Elevation 0.159 0.014 11.477 <0.001 Cutblocks 42+ years x Elevation -1.162 0.044 -26.608 <0.001 Cutblocks 1-18 years x Elevation -1.311 0.011 -118.174 <0.001 Unharvested forest x Elevation -0.510 0.008 -65.911 <0.001 Shrub x Elevation -0.131 0.022 -5.913 <0.001 Cutblocks 19-41 years x Elevation -0.711 0.052 -13.589 <0.001 Random Intercept 0.840 231 Table G-3.33Output from generalized linear models quantifying winter-spring adult female mule deer resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.906 0.070 -141.412 <0.001 Elevation -0.731 0.007 -106.621 <0.001 Curvature 0.001 0.003 0.428 0.669 North -0.229 0.003 -83.367 <0.001 East -0.002 0.003 -0.599 0.549 TWI -0.137 0.003 -49.109 <0.001 Slope 0.115 0.003 42.333 <0.001 Ruggedness -0.032 0.002 -13.230 <0.001 Other -2.100 0.153 -13.744 <0.001 Cutblocks 42+ years 0.210 0.015 14.324 <0.001 Cutblocks 1-18 years -0.782 0.017 -46.812 <0.001 Unharvested forest 0.441 0.008 55.057 <0.001 Shrub 0.571 0.011 53.274 <0.001 Cutblocks 19-41 years -0.701 0.020 -35.449 <0.001 Road density -0.026 0.003 -9.250 <0.001 Burns 1-11 years -0.246 0.007 -37.341 <0.001 Burns 12+ years -1.482 0.071 -20.921 <0.001 NDVI -0.445 0.004 -121.330 <0.001 Other x Elevation 0.290 0.080 3.621 <0.001 Cutblocks 42+ years x Elevation 0.653 0.042 15.597 <0.001 Cutblocks 1-18 years x Elevation -0.451 0.032 -13.999 <0.001 Unharvested forest x Elevation 0.076 0.008 9.010 <0.001 Shrub x Elevation 0.373 0.008 45.811 <0.001 Cutblocks 19-41 years x Elevation -0.526 0.044 -11.881 <0.001 Random Intercept 0.636 232 Table G-4.34Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.340 0.027 -342.599 <0.001 Elevation 0.641 0.012 53.040 <0.001 Curvature -0.091 0.004 -21.583 <0.001 North -0.189 0.006 -32.700 <0.001 East 0.070 0.005 13.382 <0.001 TWI -0.159 0.006 -25.988 <0.001 Slope 0.263 0.004 62.380 <0.001 Ruggedness -0.024 0.003 -7.295 <0.001 Other -0.295 0.018 -16.571 <0.001 Cutblocks 42+ years -0.853 0.090 -9.507 <0.001 Cutblocks 1-18 years -0.542 0.025 -22.060 <0.001 Unharvested forest 0.062 0.013 4.680 <0.001 Shrub -0.369 0.027 -13.658 <0.001 Cutblocks 19-41 years -0.426 0.023 -18.737 <0.001 Road density 0.023 0.004 5.517 <0.001 Burns 1-11 years -0.079 0.015 -5.294 <0.001 Burns 12+ years 0.277 0.017 16.629 <0.001 NDVI -0.779 0.007 -119.662 <0.001 Other x Elevation -0.138 0.017 -8.022 <0.001 Cutblocks 42+ years x Elevation 0.144 0.110 1.306 0.191 Cutblocks 1-18 years x Elevation -0.704 0.031 -22.394 <0.001 Unharvested forest x Elevation -0.197 0.013 -14.683 <0.001 Shrub x Elevation -0.426 0.035 -12.169 <0.001 Cutblocks 19-41 years x Elevation -0.545 0.036 -15.128 <0.001 Random Intercept 0.252 233 Table G-5.35Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.852 0.066 -148.509 <0.001 Elevation -0.158 0.012 -13.498 <0.001 Curvature 0.033 0.004 7.815 <0.001 North -0.133 0.005 -25.721 <0.001 East 0.169 0.005 32.080 <0.001 TWI -0.150 0.005 -28.291 <0.001 Slope 0.138 0.004 34.383 <0.001 Ruggedness -0.065 0.004 -18.321 <0.001 Other 0.304 0.037 8.209 <0.001 Cutblocks 42+ years -1.196 0.067 -17.735 <0.001 Cutblocks 1-18 years -1.162 0.041 -28.224 <0.001 Unharvested forest 0.248 0.018 13.915 <0.001 Shrub -0.336 0.037 -9.113 <0.001 Cutblocks 19-41 years -2.994 0.144 -20.782 <0.001 Road density 0.205 0.004 52.033 <0.001 Burns 1-11 years 0.748 0.013 58.000 <0.001 Burns 12+ years 0.710 0.015 46.683 <0.001 NDVI -0.481 0.006 -80.004 <0.001 Other x Elevation 0.294 0.023 12.773 <0.001 Cutblocks 42+ years x Elevation -2.167 0.083 -26.060 <0.001 Cutblocks 1-18 years x Elevation -0.387 0.032 -12.063 <0.001 Unharvested forest x Elevation -0.130 0.012 -10.765 <0.001 Shrub x Elevation -0.534 0.026 -20.535 <0.001 Cutblocks 19-41 years x Elevation 0.516 0.102 5.056 <0.001 Random Intercept 0.673 234 Table G-6.36Output from generalized linear models quantifying fawn winter-spring mule deer resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.974 0.063 -157.806 <0.001 Elevation -0.613 0.014 -43.821 <0.001 Curvature -0.061 0.006 -10.388 <0.001 North -0.097 0.005 -18.279 <0.001 East -0.076 0.005 -13.929 <0.001 TWI -0.128 0.005 -23.611 <0.001 Slope -0.003 0.005 -0.605 0.545 Ruggedness -0.047 0.005 -9.631 <0.001 Other -2.106 0.342 -6.161 <0.001 Cutblocks 42+ years 0.090 0.032 2.778 0.005 Cutblocks 1-18 years -0.516 0.029 -18.113 <0.001 Unharvested forest 0.618 0.015 40.714 <0.001 Shrub 0.203 0.022 9.074 <0.001 Cutblocks 19-41 years 0.687 0.025 27.358 <0.001 Road density -0.128 0.006 -21.435 <0.001 Burns 1-11 years -0.463 0.016 -29.548 <0.001 Burns 12+ years -0.024 0.068 -0.352 0.725 NDVI -0.607 0.008 -78.696 <0.001 Other x Elevation 0.684 0.220 3.114 0.002 Cutblocks 42+ years x Elevation 2.049 0.081 25.271 <0.001 Cutblocks 1-18 years x Elevation -0.594 0.055 -10.830 <0.001 Unharvested forest x Elevation 0.065 0.016 4.015 <0.001 Shrub x Elevation 0.260 0.019 13.973 <0.001 Cutblocks 19-41 years x Elevation -0.773 0.056 -13.700 <0.001 Random Intercept 0.544 235 Table G-7.37Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.193 0.030 -309.410 <0.001 Elevation 0.604 0.014 44.722 <0.001 Curvature -0.057 0.003 -17.255 <0.001 North -0.015 0.004 -4.333 <0.001 East -0.029 0.004 -8.091 <0.001 TWI -0.046 0.004 -11.245 <0.001 Slope 0.174 0.003 56.250 <0.001 Ruggedness -0.081 0.003 -26.950 <0.001 Other -0.520 0.020 -25.910 <0.001 Cutblocks 17+ years -0.454 0.019 -24.218 <0.001 Cutblocks 1-16 years 0.181 0.017 10.749 <0.001 Unharvested forest -0.475 0.015 -32.030 <0.001 Shrub -0.221 0.025 -8.694 <0.001 Road density -0.252 0.004 -66.886 <0.001 Burns 1-14 years 0.668 0.011 59.196 <0.001 Burns 15+ years 0.699 0.017 42.190 <0.001 NDVI -0.355 0.005 -72.789 <0.001 Other x Elevation -0.300 0.015 -19.682 <0.001 Cutblocks 17+ years x Elevation 0.195 0.016 12.403 <0.001 Cutblocks 1-16 years x Elevation 0.004 0.015 0.227 0.821 Unharvested forest x Elevation -0.177 0.014 -12.852 <0.001 Shrub x Elevation 0.079 0.019 4.171 <0.001 Random Intercept 0.208 236 Table G-8.38Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.168 0.048 -192.013 <0.001 Elevation 0.376 0.011 33.654 <0.001 Curvature -0.061 0.006 -10.655 <0.001 North -0.293 0.006 -49.604 <0.001 East -0.011 0.005 -2.089 0.037 TWI -0.067 0.005 -12.437 <0.001 Slope -0.008 0.005 -1.550 0.121 Ruggedness -0.071 0.005 -15.358 <0.001 Other -0.500 0.024 -20.871 <0.001 Cutblocks 17+ years -0.170 0.022 -7.879 <0.001 Cutblocks 1-16 years -0.025 0.024 -1.047 0.295 Unharvested forest -0.315 0.014 -22.141 <0.001 Shrub -1.675 0.050 -33.190 <0.001 Road density 0.033 0.005 7.399 <0.001 Burns 1-14 years 0.283 0.018 16.004 <0.001 Burns 15+ years 0.071 0.023 3.111 0.002 NDVI -0.630 0.007 -91.301 <0.001 Other x Elevation 0.385 0.024 16.094 <0.001 Cutblocks 17+ years x Elevation -0.460 0.034 -13.678 <0.001 Cutblocks 1-16 years x Elevation -0.453 0.032 -14.114 <0.001 Unharvested forest x Elevation -0.001 0.014 -0.081 0.936 Shrub x Elevation 0.357 0.073 4.907 <0.001 Random Intercept 0.249 237 Table G-9.39Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.154 0.019 -486.200 <0.001 Elevation -0.226 0.012 -18.876 <0.001 Curvature -0.018 0.004 -4.324 <0.001 North -0.057 0.003 -21.497 <0.001 East -0.125 0.003 -44.243 <0.001 TWI -0.027 0.003 -9.434 <0.001 Slope -0.147 0.003 -44.083 <0.001 Ruggedness -0.109 0.004 -28.559 <0.001 Other -1.062 0.039 -27.351 <0.001 Cutblocks 17+ years -0.078 0.018 -4.316 <0.001 Cutblocks 1-16 years 0.289 0.017 17.151 <0.001 Unharvested forest -0.042 0.014 -2.945 0.003 Shrub 0.260 0.021 12.105 <0.001 Road density 0.077 0.002 31.821 <0.001 Burns 1-14 years 0.399 0.012 32.010 <0.001 Burns 15+ years 0.385 0.021 18.379 <0.001 NDVI -0.437 0.004 -117.969 <0.001 Other x Elevation -0.054 0.024 -2.211 0.027 Cutblocks 17+ years x Elevation 0.377 0.015 25.580 <0.001 Cutblocks 1-16 years x Elevation 0.437 0.014 31.435 <0.001 Unharvested forest x Elevation 0.019 0.012 1.585 0.113 Shrub x Elevation -0.457 0.022 -20.909 <0.001 Random Intercept 0.107 238 Table G-10.40Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -9.980 0.060 -166.512 <0.001 Elevation 0.313 0.009 33.002 <0.001 Curvature -0.035 0.006 -5.820 <0.001 North 0.048 0.006 7.490 <0.001 East 0.365 0.007 53.874 <0.001 TWI 0.072 0.005 13.801 <0.001 Slope -0.069 0.005 -12.902 <0.001 Ruggedness -0.065 0.004 -14.722 <0.001 Other -0.525 0.039 -13.448 <0.001 Cutblocks 17+ years -0.122 0.063 -1.941 0.052 Cutblocks 1-16 years 0.739 0.037 19.831 <0.001 Unharvested forest 0.309 0.015 20.893 <0.001 Shrub -0.660 0.030 -21.710 <0.001 Road density 0.047 0.005 9.272 <0.001 Burns 1-14 years 0.757 0.014 53.675 <0.001 Burns 15+ years 0.921 0.013 68.436 <0.001 NDVI -0.636 0.007 -84.867 <0.001 Other x Elevation 0.367 0.029 12.525 <0.001 Cutblocks 17+ years x Elevation 0.335 0.046 7.222 <0.001 Cutblocks 1-16 years x Elevation -0.220 0.031 -7.152 <0.001 Unharvested forest x Elevation 0.055 0.010 5.519 <0.001 Shrub x Elevation 0.152 0.024 6.359 <0.001 Random Intercept 0.293 239 Table G-11.41Output from generalized linear models quantifying adult mule deer migratory summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -8.436 0.053 -159.174 <0.001 Elevation -0.259 0.009 -27.307 <0.001 Curvature -0.154 0.005 -29.255 <0.001 North -0.061 0.003 -22.161 <0.001 East -0.069 0.003 -23.282 <0.001 TWI -0.063 0.002 -26.741 <0.001 Slope -0.068 0.005 -14.666 <0.001 Ruggedness -0.082 0.005 -15.018 <0.001 Other -2.559 0.101 -25.280 <0.001 Cutblocks 17+ years -0.588 0.011 -52.726 <0.001 Cutblocks 1-16 years 0.171 0.010 16.689 <0.001 Unharvested forest -0.217 0.008 -25.693 <0.001 Shrub 0.388 0.013 30.288 <0.001 Road density 0.225 0.003 64.742 <0.001 Burns 1-14 years -1.258 0.008 -163.560 <0.001 Burns 15+ years -3.022 0.114 -26.495 <0.001 NDVI 0.274 0.005 57.894 <0.001 Other x Elevation -0.064 0.097 -0.662 0.508 Cutblocks 17+ years x Elevation 0.335 0.018 18.225 <0.001 Cutblocks 1-16 years x Elevation 0.002 0.017 0.101 0.92 Unharvested forest x Elevation -0.079 0.012 -6.421 <0.001 Shrub x Elevation 0.044 0.014 3.202 0.001 Random Intercept 0.404 240 Table G-12.42Output from generalized linear models quantifying adult mule deer resident summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2018 – 2023. The reference level is grassland vegetation and unburned areas. These deer had no used locations in old burns, so that covariate is not included. Covariate Estimate Standard Error z value P Intercept -9.585 0.059 -163.614 <0.001 Elevation -0.743 0.009 -79.329 <0.001 Curvature 0.022 0.006 3.912 <0.001 North -0.068 0.004 -16.638 <0.001 East -0.071 0.004 -16.472 <0.001 TWI 0.007 0.003 2.283 0.022 Slope -0.207 0.005 -42.240 <0.001 Ruggedness 0.046 0.003 14.457 <0.001 Other -0.288 0.138 -2.088 0.037 Cutblocks 17+ years -0.514 0.019 -26.649 <0.001 Cutblocks 1-16 years 0.024 0.021 1.139 0.255 Unharvested forest -0.226 0.014 -15.761 <0.001 Shrub 0.034 0.020 1.723 0.085 Road density 0.230 0.004 60.326 <0.001 Burns 1-14 years 0.202 0.011 18.705 <0.001 NDVI 0.288 0.005 57.264 <0.001 Other x Elevation 0.776 0.071 10.958 <0.001 Cutblocks 17+ years x Elevation -0.510 0.043 -11.910 <0.001 Cutblocks 1-16 years x Elevation 0.731 0.028 26.028 <0.001 Unharvested forest x Elevation -0.897 0.013 -69.611 <0.001 Shrub x Elevation 0.185 0.014 13.357 <0.001 Random Intercept 0.295 241 Table G-13.43Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2020 – 2021. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -10.65 0.19 -55.32 <0.001 Elevation 0.41 0.11 3.67 <0.001 Curvature 0.02 0.01 1.45 0.15 North -0.01 0.01 -0.43 0.67 East -0.18 0.01 -12.23 <0.001 TWI -0.03 0.02 -2.01 0.04 Slope 0.40 0.01 28.28 <0.001 Ruggedness 0.05 0.01 4.12 <0.001 Other -1.01 0.11 -9.47 <0.001 Cutblocks 17+ years 0.76 0.10 7.30 <0.001 Cutblocks 1-16 years 1.62 0.10 16.13 <0.001 Unharvested forest 0.70 0.09 7.71 <0.001 Road density -0.16 0.02 -9.61 <0.001 Burns 1-14 years -2.99 0.71 -4.21 <0.001 Burns 15+ years 2.97 0.07 45.10 <0.001 NDVI -0.79 0.02 -35.10 <0.001 Other x Elevation 0.30 0.12 2.46 0.01 Cutblocks 17+ years x Elevation 0.67 0.12 5.68 <0.001 Cutblocks 1-16 years x Elevation 0.58 0.12 5.03 <0.001 Unharvested forest x Elevation 0.23 0.11 2.07 0.04 Random Intercept 0.84 242 Table G-14.44Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the Boundary study area in British Columbia, Canada during 2020 – 2021. The reference level is grassland vegetation. Covariate Estimate Standard Error z value P Intercept -10.87 0.13 -80.79 <0.001 Elevation -0.12 0.05 -2.44 0.01 Curvature -0.19 0.02 -9.90 <0.001 North 0.14 0.02 7.43 <0.001 East -0.04 0.02 -2.10 0.04 TWI -0.03 0.02 -1.49 0.14 Slope -0.11 0.02 -6.55 <0.001 Ruggedness -0.06 0.02 -4.05 <0.001 Other 1.24 0.11 11.30 <0.001 Cutblocks 17+ years 1.41 0.12 12.27 <0.001 Cutblocks 1-16 years 2.67 0.11 24.24 <0.001 Unharvested forest 1.69 0.07 25.21 <0.001 Road density 0.15 0.02 9.12 <0.001 NDVI -1.30 0.03 -48.57 <0.001 Other x Elevation 3.91 0.13 30.04 <0.001 Cutblocks 17+ years x Elevation 3.49 0.13 27.18 <0.001 Cutblocks 1-16 years x Elevation 6.15 0.19 32.22 <0.001 Unharvested forest x Elevation 1.15 0.06 18.23 <0.001 Random Intercept 0.45 243 Table G-15.45Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -8.60 0.15 -58.35 <0.001 Elevation -1.24 0.09 -13.56 <0.001 Curvature 0.06 0.01 4.69 <0.001 North -0.03 0.01 -3.51 <0.001 East 0.12 0.01 12.69 <0.001 TWI -0.04 0.01 -5.37 <0.001 Slope -0.01 0.01 -0.91 0.36 Ruggedness -0.13 0.02 -8.87 <0.001 Other 1.29 0.05 23.63 <0.001 Cutblocks 17+ years -0.35 0.07 -4.80 <0.001 Cutblocks 1-16 years -0.01 0.07 -0.12 0.90 Unharvested forest 0.87 0.05 19.14 <0.001 Road density 0.23 0.01 22.50 <0.001 Burn 15+ years -1.09 0.08 -13.12 <0.001 NDVI -0.51 0.02 -28.45 <0.001 Other x Elevation -0.65 0.10 -6.64 <0.001 Cutblocks 17+ years x Elevation 1.11 0.09 11.71 <0.001 Cutblocks 1-16 years x Elevation 0.60 0.10 6.10 <0.001 Unharvested forest x Elevation -0.66 0.09 -7.56 <0.001 Random Intercept 0.83 244 Table G-16.46Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the West Okanagan study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -11.89 0.32 -37.31 <0.001 Elevation -1.51 0.07 -20.83 <0.001 Curvature -0.09 0.02 -3.89 <0.001 North 0.07 0.02 3.67 <0.001 East 0.31 0.02 14.76 <0.001 TWI -0.11 0.02 -6.53 <0.001 Ruggedness 0.08 0.01 8.10 <0.001 Slope -0.25 0.02 -13.24 <0.001 Unharvested forest 0.67 0.10 6.49 <0.001 Other -1.31 0.29 -4.46 <0.001 Road density -0.11 0.02 -6.93 <0.001 NDVI -0.42 0.03 -15.10 <0.001 Burn of any age -1.04 0.09 -11.71 <0.001 Other x Elevation 1.78 0.38 4.73 <0.001 Unharvested forest x Elevation -0.12 0.06 -1.88 0.06 Random Intercept 0.90 245 Table G-17.47Output from generalized linear models quantifying neonate mule deer migratory summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value p value Intercept -8.19 0.14 -58.58 <0.001 Elevation -1.47 0.21 -7.01 <0.001 Curvature -0.59 0.05 -12.60 <0.001 North 0.03 0.02 2.16 0.03 East 0.03 0.02 2.10 0.04 TWI -0.10 0.01 -9.39 <0.001 Slope -0.13 0.04 -3.49 <0.001 Ruggedness -0.25 0.07 -3.39 <0.001 Other 0.73 0.06 12.26 <0.001 Cutblocks 17+ years -1.84 0.10 -17.56 <0.001 Cutblocks 1-16 years -1.78 0.08 -21.49 <0.001 Unharvested forest -0.12 0.04 -2.85 0.00 Road density 0.44 0.02 23.02 <0.001 Burn 1-14 years -1.45 0.08 -17.56 <0.001 NDVI -0.95 0.02 -38.48 <0.001 Other x Elevation -3.30 0.31 -10.65 <0.001 Cutblocks 17+ years x Elevation 2.30 0.27 8.58 <0.001 Cutblocks 1-16 years x Elevation 1.54 0.25 6.11 <0.001 Unharvested forest x Elevation -1.19 0.21 -5.62 <0.001 Random Intercept 0.61 246 Table G-18.48Output from generalized linear models quantifying neonate mule deer resident summer-fall resource selection in the Cache Creek study area in British Columbia, Canada during 2019 – 2021. The reference level is grassland vegetation and unburned areas. Covariate Estimate Standard Error z value P Intercept -12.00 0.24 -49.44 <0.001 Elevation -1.47 0.06 -26.13 <0.001 Curvature -0.04 0.02 -1.75 0.08 North -0.06 0.02 -4.05 <0.001 East -0.13 0.02 -8.03 <0.001 TWI -0.02 0.01 -2.01 0.04 Slope -0.58 0.02 -26.84 <0.001 Ruggedness 0.14 0.01 11.65 <0.001 Other -2.28 0.32 -7.07 <0.001 Cutblocks 17+ years -0.02 0.07 -0.33 0.74 Cutblocks 1-16 years -0.48 0.12 -4.10 <0.001 Unharvested forest 0.51 0.05 9.60 <0.001 Road density 0.96 0.02 49.87 <0.001 Burn 1-14 years 1.54 0.04 34.94 <0.001 NDVI 0.26 0.03 10.00 <0.001 Other x Elevation -0.42 0.20 -2.09 0.04 Cutblocks 17+ years x Elevation 1.24 0.27 4.63 <0.001 Cutblocks 1-16 years x Elevation -0.91 0.59 -1.54 0.12 Unharvested forest x Elevation -0.35 0.05 -7.02 <0.001 Random Intercept 0.79 247 Appendix H – Random Intercepts vs. Random Slopes in Resource Selection Functions When doing the analysis for chapter 4, we initially ran adult and fawn models with random slopes on all variables. However, the poor fit of the West Okanagan and Cache Creek resident adult summer models prompted us to explore the k-fold cross validation values when using a random intercept only model. The increase in k-fold values for all models when using random intercepts only led us to use random intercept only models as the basis for this analysis. However, a possible compromise between the two techniques for a future version of this paper could be to include random slopes for the continuous covariates only and not include them for the categorical covariates. As seen in Table H-1, including random slopes for the continuous covariates only greatly improved the k-fold cross validation values for all models, with some models predicting as well as the random intercept only models. Incorporating random slopes in this manner would allow us to maintain a balance between a model that predicts deer use well and a model that includes information about population level deer selection patterns. Figures H-1 through H-4 compare parameter coefficients and 95% confidence intervals from the random intercept models and the random slopes on continuous covariates models. As expected, the categorical covariates generally have the same beta estimates and small confidence intervals in both models. For the continuous covariates, the confidence intervals are much greater, many parameters became non-significant in the random slopes models, and the sign (negative or positive) of the point estimate for 4 parameters switched depending on the model used. However, the point estimates (which are used to create a spatial map of the RSF) were not drastically different for most parameters. We recognize that using different RSF models could change the outcome of the survival models and therefore the conclusions of this chapter. 248 Table H-1.49K-fold cross validation values for models including all random slopes, random slopes on continuous variables only, and a random intercept only. All k-fold validations were run using the cutblock vegetation x elevation + road density + NDVI + burns model. A fit closer to 1.00 indicates better model predictive performance. Study Area Age Class Season Migratory k-fold correlations Random slopes – all Random slopes – continuous only Random intercept Boundary Adult Winter NA 0.83 1.00 1.00 Fawn NA 0.85 0.99 0.99 Adult Summer Yes 0.68 0.91 0.99 No 0.55 0.85 0.92 Cache Creek Adult Winter NA 0.97 0.95 0.97 Fawn NA 0.70 0.89 0.91 Adult Summer Yes 0.84 0.85 0.92 No 0.46 0.52 0.61 West Okanagan Adult Winter NA 0.94 0.98 0.99 Fawn NA 0.76 0.89 0.97 Adult Summer Yes 0.63 0.94 0.92 No 0.07 0.56 0.76 249 Figure H-1.34A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for summer adult migratory models. 250 Figure H-2.35A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for summer adult resident models. 251 Figure H-3.36A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for winter adult models. 252 Figure H-4.37A comparison of parameter estimates and 95% confidence intervals resulting from resource selection function models containing no random slopes (the models used in chapter 4) and models containing random slopes on the continuous covariates only for winter fawn models 253 Appendix I – Additional Survival Analysis Information (Chapter 4) Figure I-1.38Plots of scaled Schoenfeld residuals for the RSF covariate from Cox proportional hazards models analyzing the relationship between RSF based habitat quality and survival for adult female mule deer in A) early summer-fall (May 20 – September 15) and B) late summer-fall (September 16 – November 17) in south-central British Columbia, Canada during 2018-2023. Initially, we ran the whole summer-fall period in one model, but this covariate violated the proportional hazards assumption. We split the summer-fall into 2 periods based off these plots of scaled Schoenfeld residuals, which showed that the RSF covariate was significant up until about day 119 (plot A). After this date (plot B) the covariate was no longer significant.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ford, Adam
Survival and movements of mule deer (Odocoileus hemionus) in southern British Columbia
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