Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Dickie, Melanie
Doctor of Philosophy - PhD
University of British Columbia
The study of ecological systems requires a nuanced understanding of the complex interactions that govern species dynamics. In this dissertation I investigate the intricate relationships between habitat alteration, climate change, and predator-prey dynamics that drive species declines in western Canada’s boreal forest, namely of woodland caribou (Rangifer tarandus). I explore the roles of habitat alteration and climate in facilitating the expansion of white-tailed deer (Odocoileus virginianus), a novel apparent competitor to caribou (Chapter 2). I demonstrate that deer densities are primarily influenced by winter severity, though habitat alteration and productivity can mediate this effect, suggesting an interaction where milder winters and human-altered habitats combine to support higher deer densities. These findings emphasize the significant role of climate in determining population abundance, while also highlighting the compounding effects of habitat alteration. In Chapter 3 I examine how resource density and exploitation efficiency influence wolf (Canis lupus) home ranges, a predator of caribou. Wolf home ranges were smaller in areas with increased linear feature density, though this relationship was weaker in more productive areas, indicating that wolves benefit most from linear features in less productive environments. These results suggest that increased access to prey in disturbed landscapes can reduce the area wolves need to survive, potentially linking to wolf densities; reinforcing the indirect role of habitat alteration in predator-prey dynamics. In Chapter 4, I synthesize existing knowledge on how the effects linear features cascade through the food web associated with caribou declines. While many studies explore how linear features affect the spatial behavior of caribou, their predators, and prey, few directly connect these changes to caribou demographic outcomes. This gap underscores the need for targeted research to inform effective conservation strategies and targets for woodland caribou. Additionally, I explore the response of prey species to predator reductions aimed to reduce predation on caribou, finding only a muted behavioral and demographic response by white-tailed deer, moose (Alces alces) and caribou; offering a deeper appreciation of complexities of the ecological mechanisms at play. Finally, I provide a suggested path forward for accommodating uncertainty within adaptive management plans when species are in precipitous declines; Intervention-forward adaptive management. Throughout my dissertation, I emphasize the importance of advancing ecological theory to better comprehend the variability and context-dependence of natural systems, ultimately contributing to more effective conservation strategies.
Evaluating the mechanisms linking anthropogenic habitat alteration to caribou declines in western Canada by Melanie Dickie A THESIS SUBMITTED IN PARTIAL FUFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY In THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November 2024 © Melanie Dickie, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: Evaluating the mechanisms linking anthropogenic habitat alteration to caribou declines in western Canada submitted by Melanie Dickie in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Adam Ford, Irving K. Barber School of Arts and Sciences Supervisor Dr. Michael Noonan, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Robin Steenweg, Environment and Climate Change Canada Supervisory Committee Member Dr. Robert Serrouya, Biodiversity Pathways Supervisory Committee Member Dr. Warren Cardinal-McTeague, UBC Faculty of Forestry University Examiner Dr. Fanie Pelletier, University of Sherbrooke External Examiner iii ABSTRACT The study of ecological systems requires a nuanced understanding of the complex interactions that govern species dynamics. In this dissertation I investigate the intricate relationships between habitat alteration, climate change, and predator-prey dynamics that drive species declines in western Canada’s boreal forest, namely of woodland caribou (Rangifer tarandus). I explore the roles of habitat alteration and climate in facilitating the expansion of white-tailed deer (Odocoileus virginianus), a novel apparent competitor to caribou (Chapter 2). I demonstrate that deer densities are primarily influenced by winter severity, though habitat alteration and productivity can mediate this effect, suggesting an interaction where milder winters and human-altered habitats combine to support higher deer densities. These findings emphasize the significant role of climate in determining population abundance, while also highlighting the compounding effects of habitat alteration. In Chapter 3 I examine how resource density and exploitation efficiency influence wolf (Canis lupus) home ranges, a predator of caribou. Wolf home ranges were smaller in areas with increased linear feature density, though this relationship was weaker in more productive areas, indicating that wolves benefit most from linear features in less productive environments. These results suggest that increased access to prey in disturbed landscapes can reduce the area wolves need to survive, potentially linking to wolf densities; reinforcing the indirect role of habitat alteration in predator-prey dynamics. In Chapter 4, I synthesize existing knowledge on how the effects linear features cascade through the food web associated with caribou declines. While many studies explore how linear features affect the spatial behavior of caribou, their predators, and prey, few directly connect these changes to caribou demographic outcomes. This gap underscores the need for targeted research to inform effective conservation strategies and targets for woodland caribou. Additionally, I explore the response of prey species to predator reductions aimed to reduce predation on caribou, finding only a muted behavioral and demographic response by white-tailed deer, moose (Alces alces) and caribou; offering a deeper appreciation of complexities of iv the ecological mechanisms at play. Finally, I provide a suggested path forward for accommodating uncertainty within adaptive management plans when species are in precipitous declines; Intervention-forward adaptive management. Throughout my dissertation, I emphasize the importance of advancing ecological theory to better comprehend the variability and context-dependence of natural systems, ultimately contributing to more effective conservation strategies. v LAY SUMMARY This dissertation focuses on the decline of woodland caribou in western Canada’s boreal forest, exploring how changes in habitat and climate, influence predator-prey relationships associated with caribou declines. I examine how habitat alteration and climate change facilitate white-tailed deer expansion into caribou range, how the availability of resources and how efficiently they are used affect the home ranges of wolves, a predator of caribou, and review the pathways in which features like roads and seismic lines impact caribou through the interactions implicated in their decline. I also investigate how predator reduction programs aimed to recover caribou influences the behavior and populations of other prey species. Finally, I propose a pathway forward for managing species in precipitous declines while continuing to learn about the ecological processes. My work highlights the need to refine our understanding of ecological processes to better manage and protect wildlife, ultimately leading to more effective conservation strategies. vi PREFACE I am responsible for conceptualization, study design, data collection, organization and analysis, and writing of this thesis. No animals were captured, handled, or directly involved in this research. All data used in this thesis were obtained through collaboration with research partners, who collected the data for separate research objectives that complied with ethical and legal guidelines. Data were collected with approved animal care under the University of Alberta (AUP00001309, AUP0000040, AUP00000102, AUP00001309) or University of Saskatchewan (AUP20130127) or were approved as Category A Invasiveness by the University of Alberta. Additionally, because my research was highly collaborative, several co-authors were involved in review, supervision, and editing of data chapters within this document (see list below). Chapters 2, 2 Addendum, 3, 4, and 6 are published in peer-reviewed journals with the contribution of co-authors, and Chapter 5 is currently being formatted for submission as well. I am solely responsible for the introduction (Chapter 1) and conclusions (Chapter 7), with support from my committee. I used generative AI, specifically ChatGPT, to assist with drafting text and supporting code during the preparation of this thesis. I reviewed, edited, and fact-checked all content generated by ChatGPT, and no AI-generated material was used in its final form without substantial modification. Chapter 2: Dickie, M., Serrouya, R., Becker, M., DeMars, C., Noonan, M. J., Steenweg, R., Boutin, S., & Ford, A. T. (2024). Habitat alteration or climate: What drives the densities of an invading ungulate? Global Change Biology, 30(4), e17286. https://doi.org/10.1111/gcb.17286 Chapter 2 Addendum: Dickie, M., Serrouya, R., Becker, M., DeMars, C., Noonan, M. J., Steenweg, R., Boutin, S., & Ford, A. T. (2024). The influence of habitat alteration is widespread, but the impact of climate cannot continue to be discounted. 1–3. https://doi.org/10.1111/gcb.17497 vii Chapter 3: Dickie, M., Serrouya, R., Avgar, T., McLoughlin, P., McNay, R. S., DeMars, C., Boutin, S., & Ford, A. T. (2022). Resource exploitation efficiency collapses the home range of an apex predator. In Ecology. https://doi.org/10.1002/ecy.3642 Chapter 4: Dickie, M., Love, N., Steenweg, R., Lamb, C. T., Polfus, J., & Ford, A. T. (2023). In search of evidence-based management targets: A synthesis of the effects of linear features on woodland caribou. Ecological Indicators, 154, 110559. https://doi.org/10.1016/j.ecolind.2023.110559 Chapter 6: Dickie, M., Ford, A. T., Steenweg, R., & Serrouya, R. (2023). Intervention-forward adaptive management in the face of extinction. Trends in Ecology & Evolution, 38(6), 505–506. https://doi.org/10.1016/j.tree.2023.01.016 viii TABLE OF CONTENTS ABSTRACT ................................................................................................................................ iii LAY SUMMARY .......................................................................................................................... v PREFACE .................................................................................................................................. vi TABLE OF CONTENTS ……………………………………………………………………………….viii LIST OF FIGURES ..................................................................................................................... x LIST OF TABLES ..................................................................................................................... xix ACKNOWLEDGEMENTS ....................................................................................................... xxiii CHAPTER 1: INTRODUCTION .................................................................................................. 1 CHAPTER 2: DISENTANGLING THE INFLUENCE OF ANTHROPOGENIC HABITAT ALTERATION FROM CLIMATE ON INVADING WHITE-TAILED DEER POPULATIONS IN WESTERN CANADA ................................................................................................................. 6 Introduction ............................................................................................................................ 6 Methods ................................................................................................................................10 Results ..................................................................................................................................16 Discussion .............................................................................................................................19 Literature Cited ......................................................................................................................24 Appendix S2.1: Seasonal and annual deer densities .............................................................33 Appendix S2.2: Quantifying snow season length and cumulative snowfall Seasonal and annual deer densities ............................................................................................................34 Appendix S2.3: Habitat productivity gradient in the study area ..............................................35 Appendix S2.4: Spatial heterogeneity in landscape attributes within camera clusters ............36 Appendix S2.5: Evaluating lags in annual weather and 5-year climate metrics ......................41 Appendix S2.6: Analyses reflecting individual cameras as the sample unit ............................45 Appendix S2.7: Additional details on Multiple Factor and Principle Component Analyses .....50 CHAPTER 2 ADDENDUM: THE INFLUENCE OF HABITAT ALTERATION IS WIDESPREAD, BUT THE IMPACT OF CLIMATE CANNOT CONTINUE TO BE DISCOUNTED .......................53 Literature Cited ......................................................................................................................57 Appendix S2A.1: Supplemental information ...........................................................................59 CHAPTER 3: HOW DOES ANTHROPOGENIC HABITAT ALTERATION AFFECT THE HOME RANGE SIZE OF AN APEX PREDATOR? ...............................................................................64 Introduction ...........................................................................................................................64 Methods ................................................................................................................................69 Results ..................................................................................................................................76 Discussion .............................................................................................................................79 Literature Cited ......................................................................................................................84 Appendix S3.1: Additional details on study area and wolf sampling intensity .........................92 Appendix S3.2: Evaluating the effect of primary productivity on moose density .....................99 Appendix S3.3: Evaluating the effect of pack size and sex on wolf home range size ........... 101 ix Appendix S3.4: Testing the support for the effect of linear feature density and primary productivity on wolf home range size ................................................................................... 107 CHAPTER 4: IN SEARCH OF EVIDENCE-BASED MANAGEMENT TARGETS: A SYNTHESIS OF THE EFFECTS OF LINEAR FEATURES ON WOODLAND CARIBOU ............................. 109 Introduction ......................................................................................................................... 109 Methods .............................................................................................................................. 112 Results ................................................................................................................................ 117 Discussion ........................................................................................................................... 124 Literature Cited .................................................................................................................... 129 Appendix S4.1: Methodological details for literature search ................................................. 136 Appendix S4.2: Consolidated Terms ................................................................................... 141 Appendix S4.3: General Results .......................................................................................... 147 Appendix S4.4: List of literature reviewed ............................................................................ 150 Appendix S4.5: Mechanistic Pathway Frequency Table ...................................................... 175 CHAPTER 5: UNINTENDED CONSEQUENCES: DOES THE REDUCTION OF AN APEX PREDATOR RELEASE DEMOGRAPHIC AND BEHAVIORAL CONSTRAINTS ON LARGE-MAMMAL PREY? ................................................................................................................... 176 Introduction ......................................................................................................................... 176 Methods .............................................................................................................................. 181 Results ................................................................................................................................ 192 Discussion ........................................................................................................................... 209 Literature Cited .................................................................................................................... 214 Appendix 5.1 Using Multi Factor Analysis to quantify climate dimensions ............................ 225 Appendix 5.2 The relationship between indices of forage availability and security ............... 226 Appendix 5.3 Using camera traps to estimate wolf temporal activity .................................... 227 CHAPTER 6: INTERVENTION-FORWARD ADAPTIVE MANAGEMENT IN THE FACE OF EXTINCTION .......................................................................................................................... 232 Literature Cited .................................................................................................................... 236 CHAPTER 7: CONCLUSIONS ................................................................................................ 238 The challenges of uncovering mechanistic pathways in ecology.......................................... 240 How do we proceed given the unpredictable nature of nature? ........................................... 242 Lessons learned along the way ........................................................................................... 247 BIBLIOGRAPHY ..................................................................................................................... 249 x LIST OF FIGURES Figure 2. 1: Location of camera clusters used to evaluate the relative effects of anthropogenic habitat alteration (HA) and climate on white-tailed deer. Dark grey shading depicts HA as mapped by Environment and Climate Change Canada, buffered by 500m (Environment and Climate Change Canada, 2017), and light grey shading depicts the mapping boundary in which HA was mapped. Camera clusters are coloured by the Climate Dimension 1 identified using a 5-year Multiple Factor Analysis, such that higher values (i.e. cooler colours) represent more severe winters. Cameras were placed randomly within a 12.5km by 4km area on trees with a minimum distance of 1-km apart using a random location generator, and all cameras faced north to avoid solar interference with images. The inset map depicts the study area in relation to Canada, whereas the outset images depict an example of random camera trap placement within each camera cluster. Map lines delineate study areas and do not necessarily depict accepted national boundaries............................ 9 Figure 2. 2: Average density of white-tailed deer (animals/km2) as a function of habitat alteration (%) and winter severity (Scaled Climate Dimension 1; such that winter severity increases as Dimension 1 increases). Density estimates are coloured by habitat productivity (ΔEVI) and symbols represent different years. The significant predicted relationship and 95% confidence intervals between Climate Dimension 1 and average deer densities from the global model presented in Table 2.2 is shown. Percent Alteration is displayed in it’s original units to aid in interpretation, whereas Climate Dimension 1 is displayed as scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). ........................................................19 Figure S2.1. 1: the relationship between estimated white-tailed deer densities (animals/km2) during the snow-free (april 16 - october 15), snow (october 16 - april 15) seasons, and annual density (april 16 to april 15 in the following year). A linear smoothed regression is displayed with 95% confidence intervals. ...........................................................................33 Figure S2.2. 1: example plot of snow water equivalent (swe; kg/m2) plotted over time, which was used to identify the length of the snow season and cumulative snowfall. Example demonstrates identifying the 2017/2018 snow season from lac la biche, in northeastern alberta. Each dot represents the daily swe, the black line connects each point, and he vertical lines represent the start (blue) and end (red) of the season. ..................................34 Figure S2.3. 1: the change in enhanced vegetation index (delta evi) across the study area, such that higher values represent higher primary productivity. Delta evi was calculated for each year by subtracting the evi during the leaf-off season (1 september – 1 october 1) from the leaf-on period when deciduous vegetation is at or near peak production (1 july – 1 august). xi An example from 2020 is shown. The inset map depicts the study area in relation to canada, and the mapping boundary used to measure habitat alteration as per figure 2.1. .35 Figure S2.4. 1: the percent habitat alteration and habitat productivity (change in the enhanced vegetation index; δevi) in each camera cluster in which white-tailed deer density was measured. Each point represents the percent habitat alteration from each 1km cell using the alberta human footprint inventory and saskatchewan footprint inventory, and the median δevi from each 500m cell using modis terra vegetation indices. The boxplot centreline represents the median, the lower and upper hinges correspond to the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values no further than 1.5 * interquartile range from the hinge. .....................................................................................38 Figure S2.4. 2: average white-tailed deer density (animals/km2) as a function of the coefficient of variation (cv) in habitat alteration (% alteration) and habitat productivity (δevi) in each camera cluster sampling area from 2017 to 2021. ..............................................................39 Figure S2.5. 1: average white-tailed deer density (animals/km2) as a function of annual principal component analysis (pca) dimension 1 without (top panel) and with (bottom panel) a one-year lag in annual climate metrics. .....................................................................................43 Figure S2.5. 2: average white-tailed deer density (animals/km2) as a function of the full monitoring period multi-factoral analysis (mfa) dimension 1 without (top panel) and with (bottom panel) a one-year lag in annual climate metrics. ...................................................44 Figure S2.6. 1: white-tailed deer density (animals/km2) at each camera as a function of winter severity (climate dimension 1; such that winter severity increases as dimension 1 increases). Density estimates are coloured by year. ..........................................................46 Figure S2.6. 2: white-tailed deer density (animals/km2) at each camera as a function of percent of the habitat altered within the 50km2 camera cluster sampling area (% alteration). Density estimates are coloured by year. .........................................................................................47 Figure S2.6. 3: white-tailed deer density (animals/km2) at each camera as a function of median habitat productivity (δevi) within the 50km2 camera cluster sampling area each year. Density estimates are coloured by year. ............................................................................47 Figure S2.7. 1: visualization of the multiple factor analysis (mfa) used to quantify climate across the 5-year monitoring season. Visualization of where the quantitative variables map across dimension 1 (dim 1) and dimension 2 (dim 2), coloured by metric type (panel a), coloured by the cos2 of each variable (panel b), and each variable grouped across years and colored by cos 2 (panel c). The corresponding variation of the data explained by these two dimensions. Climate metrics are measured for each season and year, and grouped by xii metric type (sfprcmax = maximum precipitation in the snow-free season; sfprcmean = mean precipitation in the snow-free season; sftmax = maximum temperature in the snow-free season; sftmin = minimum temperature in the snow-free season; sfvpmean = mean vapor pressure in the snow-free season; sprcmax = maximum precipitation in the snow season; sprcmean = mean precipitation in the snow season; sslen = length of snow season; sswecum = cumulative snow water equivalent in the snow season; sswemax = maximum snow water equivalent in the snow season; stmax = maximum temperature in the snow season; stmin = minimum temperature in the snow season; svpmean = mean vapor pressure in the snow season. .............................................................................................50 Figure S2.7. 2: visualization of the principle component analysis (pca) used to quantify annual climate. Visualization of where the quantitative variables map across dimension 1 (dim 1) and dimension 2 (dim 2), coloured by the cos2 of each variable. Climate metrics are measured for each season and year annually, and grouped by metric type (snowfreeprcmax = maximum precipitation in the snow-free season; snowfreeprcmean = mean precipitation in the snow-free season; snowfreetmamax = maximum temperature in the snow-free season; snowfreetmimin = minimum temperature in the snow-free season; snowfreevp_mean = mean vapor pressure in the snow-free season; snowsnowltotalcum = length of snow season; snowsnowfalltotalcum = cumulative snow water equivalent in the snow season; snowswemax = maximum snow water equivalent in the snow season; snowtmamax = maximum temperature in the snow season; snowtmimin = minimum temperature in the snow season; snowvp_mean = mean vapor pressure in the snow season. ..............................................................................................................................51 Figure S2.7. 3: principle component analysis (pca) dimensions 1 and 2 across years, coloured by camera clusters. ............................................................................................................52 Figure 2A. 1: Comparison of z-standardization (Z) transformation (panels a, b, and c) and minimum-maximum (MM) scaling (panels d, e, and f) of predictor variables, and resulting impacts on model predictions. Predicted densities of white-tailed deer (animals/km2) are shown as a function of percent habitat alteration at three levels of habitat productivity (change in the Enhanced Vegetation Index, ΔEVI; panels a and d), climate Dimension 1 (Dim 1), such that winter severity increases as Dim 1 increases, at three levels of habitat productivity (ΔEVI; panels b and e), and habitat alteration at three levels of Dim 1 (panels c and f). Note the y-axis varies across predictor variables, but is consistent across transformations, demonstrating that the estimates and precision are unchanged between the two transformations. The two methods for transforming data yield perfectly correlated xiii and 1: 1 data (R2 = 1 for all combinations; panels g, h, and i), though MM preserves the shape of the raw data. Barnas et al. (2024) contend that our model has overly large standard errors, yet the two transformations yield the same relative precision. MM results in estimates and standard errors that are correspondingly large because they represent a change in the predictor variable from the minimum to the maximum value. In contrast, estimates and standard errors resulting from Z are correspondingly small because a one-unit change in the predictor variable represents only a change of one standard deviation of the predictor. ......................................................................................................................55 Figure 3. 1: Conceptual diagram of how the Diffusion Facilitation Hypothesis and the Resource Exploitation Hypothesis predict contrasting relationships between movement cost and home range size. Under the Diffusion Facilitation Hypothesis, consumers in areas with lower movement cost (grey = lower cost, black = higher cost) have lower inter-patch travel times and earlier optimal leaving time (dashed lines). Diffusive movements as consumers leave earlier to exploit more patches leads to a negative correlation between movement cost and home range size. Under the Resource Exploitation Hypothesis, consumers in areas with lower movement costs (lower energy expenditure) require less energy acquisition to maintain positive energy balances. Consumers are able meet energetic requirements in smaller areas, leading to a positive correlation between movement cost and home range size. ........................................................................................................68 Figure 3. 2: Seasonal wolf home ranges, estimated using 95 % autocorrelated kernel density estimators, across a range of linear feature density (km/km2) and primary productivity (ΔEVI) in northwestern Canada. Grey lines represent linear features within the boreal forest, and wolf home ranges are coloured to represent the surrounding linear feature density within each home range. Data were collected from 36 individuals in British Columbia, 34 individuals from northern Alberta, 44 individuals from northeastern Alberta, and 28 individuals from Saskatchewan. In the inset map depicts the productivity gradient (ΔEVI) across the study area, calculated as the median EVI in the 2020 leaf-on season minus the median EVI value in the 2020 leaf-off season. Black lines represent the provinces and territories of Canada....................................................................................70 Figure 3. 3: Panel a: Moose densities (no./km2) across a primary productivity (ΔEVI) gradient in northern Alberta. Boundaries of the moose survey areas are coloured to represent the estimated moose densities within each moose survey area from aerial surveys. The productivity gradient (ΔEVI) across the study area, calculated as the median EVI in the 2020 leaf-on season minus the median EVI value in the 2020 leaf-off season. Black lines xiv represent the provinces and territories of Canada. Panel b: Estimated moose density (no./km2) as a function of primary productivity (average ΔEVI) within each moose survey area in northern Alberta. The solid line represents the predicted relationship between moose density and ΔEVI while including year, survey method and region as fixed effects and set at the reference conditions (year = 2014, method = distance sampling, region = boreal). Dashed lines represent 95 % Confidence Intervals of the predicted relationship. ..73 Figure 3. 4: Wolf seasonal home range area (km2), calculated using individual 95% autocorrelated density estimators, as a function of linear feature density (km/km2) and primary productivity (ΔEVI, scaled between 0 and 1) within each home range. Raw values are presented as points for each study sub-region, and the predicted relationship is shown for the reference categories (snow season and BC) with Monitoring Period set to the mean. BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. ..................................................................................................................79 Figure S3.1. 1: Landcover across the study area (Canada Centre for Remote Sensing, 2017). We combined landcover classes for visual purposes (coniferous = temperate and sub-polar needleleaf, shrubland = temperature shrubland and sub-polar shrubland, grassland = temperate grassland and sub-polar grassland, barren = barren lichen and barren, urban and cropland = cropland and urban. Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. ......................................................................................95 Figure S3.1. 2: Human Influence Index across the study area (Wildlife Conservation Society 2005). Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. .................................................................................................................................96 Figure S3.1. 3: Linear and polygonal features across the study area, within the ECCC mapping boundary (Environment and Climate Change Canada, 2012). Polygonal disturbances include cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances include roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, and unknown features. Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. 97 Figure S3.3. 1: Residuals of predicted and observed wolf seasonal home range area (km2) as a function of minimum estimated pack size at capture and sex (F = female, M=male). Point values represent individual seasonal estimates, bold horizontal line represents the median for each pack size, and box hinges represents the 25th and 75th percentiles. Point shapes represent study sub-regions; BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan.............................................................................. 104 xv Figure S3.3. 2: Deviation of each pack-year combination in estimated wolf seasonal home range area (km2), i.e. the intercept, as a function of estimated pack size in that year. Vertical bars represent 2 Standard Deviation Units in the deviation. The dotted horizontal line signifies zero deviation, which can be interpreted as that pack-year having an average home range size that does not deviate from the average home-range size. ......................................... 105 Figure 4. 1: Process model (see Box 1) showing pathway widths that are weighted by quantity of research focus. The number of analyses in each pathway are provided in Appendix 5. ........................................................................................................................................ 119 Figure 4. 2: The impact of linear features on caribou and the large-mammal community implicated in caribou declines (see Box 1). The proportion of analyses from peer-reviewed manuscript reviewed reporting positive, negative, or null effects of linear features are presented for the five most-studied species, partitioned by five linear feature types. Positive or negative effects indicate increased or decreased use, selection, movement, or density, whereas neutral indicate no response to linear features. The total number of analyses for each species and linear feature category are presented. ................................................. 121 Figure 4. 3: The relationship between road density (km/km2) and total disturbance (percent area; including 500-m buffers on anthropogenic disturbances) used to estimate a road density target (Panel A and B), and visual representation of the state of each herd relative to calculated road density target for the 6 Southern Mountain Caribou - central group herds in British Columbia (Panels C and D). Panels A and C were derived from ECCC data. Panel B and D were derived from Mann and Wright (2018) data. The blue dashed line represents the point at which the 35% maximum total disturbance target (Environment Canada, 2014) intersects the modelled relationship between total disturbance and road density. Grey shading in C and D represent roads for the respective data sources, and range boundaries match those used in ECCC. ............................................................................................. 123 Figure S4.3. 1: Impacts linear features on caribou, moose and predator species, by dependent metric. (A) The direct impacts of linear features on caribou, moose and a broad “predator” species, which encompasses the direct impacts of linear features on wolves, black bears and grizzly bears. (B) The direct impacts of linear features on the three most-studied predator species (in relation to caribou and linear features) in the reviewed papers are differentiated by dependent metrics used to study the impact. (C) The inferred impacts of linear features on caribou due to apparent competition and predation from the presence of prey (moose, deer, etc.) and predator species (wolves and bears), in addition to the direct xvi impacts of linear features on caribou, separated by ecotype (barren-ground (B-G), boreal, mountain and reindeer). ................................................................................................... 149 Figure 5. 1: A non-species specific example of predicted changes in the demography and behavior of ungulate prey over time in response to wolf reductions relative to reference areas. Predictions display both a reference area with low habitat alteration (low reference) and a reference area with habitat alteration more similar to the wolf reduction area (reference). Demographic responses include both density and distribution over time, contrasting the three areas. We predict that prey density and distribution will increase over time in areas with wolf reductions, whereas density will remain constant over time in both the reference areas. Behavioral responses are separated into space, (i.e., the use of habitats that provide more security or resources), and time, (i.e., temporal overlap with high predator activity periods). We predict that prey will transition from using areas with higher cover (i.e., security) and lower productivity (i.e., food resources) to areas with low cover and high productivity over time in the wolf reduction area, but remain constant over time in the two reference areas. Finally, we predict that prey will increase their use of high wolf activity periods over time in the wolf reduction area, resulting in increased temporal overlap. In contrast, we predict that temporal overlap in the low reference and reference areas will remain constant over time. ............................................................................................... 180 Figure 5. 2: Study area map depicting camera cluster deployments in relation to caribou ranges with wolf reductions (Cold Lake and East Side Athabasca River) and reference areas with similar habitat alteration (West Side Athabasca River; “reference”) and low habitat alteration (Saskatchewan Boreal Plains; “low reference”). ............................................................... 183 Figure 5. 3: Temporal activity of wolves monitored using camera traps from 2017 through 2023 in an area with wolf reduction, a reference area with high habitat alteration similar to the wolf reduction area (Reference) and a reference area with low habitat alteration (Low Reference). ...................................................................................................................... 191 Figure 5. 4: Density of moose, white-tailed deer, and caribou in the wolf reduction (WR) area and reference areas (Ref) over time. Wolf reductions implementation began in 2017. Colors represent camera cluster replicates in each treatment. Note the y-axis scale varies by species. ........................................................................................................................... 193 Figure 5. 5: Predicted average annual change in white-tailed deer, moose, and caribou density (animals/km2) across the study period in the low reference, reference, and wolf reductions areas, as a function of environmental covariates. The environmental covariate included varies per species, reflecting the most supported model from Table 5.1. ......................... 196 xvii Figure 5. 6: Proportion of camera traps in which each species (moose, white-tailed deer, and caribou) was detected for camera trap clusters in the wolf reduction (WR) area, reference area (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. ... 200 Figure 5. 7: The predicted biweekly total count of white-tailed deer as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. ............................................................................................................................. 202 Figure 5. 8: The predicted biweekly total count of moose as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. ....... 203 Figure 5. 9: The predicted biweekly total count of caribou as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. ....... 204 Figure 5. 10: The predicted biweekly total count of white-tailed deer, moose, and caribou as a function of the biweekly period in each treatment area (low reference, reference, and wolf reduction). Note the y-axis differs between panels. .......................................................... 205 Figure 5. 11: Temporal activity patterns of wolves and their ungulate prey (white-tailed deer, moose, and caribou). Temporal activity was quantified using camera traps from 2017 through 2023 in an area with wolf reduction, a reference area with high habitat alteration similar to the wolf reduction area (Reference) and a reference area with low habitat alteration (Low Reference). Grey shading represents overlap between wolves and their prey. ................................................................................................................................. 206 Figure 5. 12: Temporal overlap between ungulate prey (white-tailed deer, moose, and caribou) and high wolf activity periods in the wolf reduction (WR) area, a low habitat alteration xviii reference (Low Ref) area, and high-habitat alteration reference (Ref) area. Overlap was calculated for each camera cluster and year combination. Boxplots display the interquartile range (IQR), with the line inside the box representing the median and the whiskers extend to the smallest and largest values within 1.5 times the IQR. The boxplots are overlaid with each sample point, colored by the number of observations in which the overlap value was calculated. ........................................................................................................................ 207 Figure S5.2. 1: The relationship between habitat productivity (ΔEVI), and index of forage availability, and percent canopy cover, an index of security. Each point represents a camera trap location in a given year, and points are colored by cluster. Pearson’s Correlation R2 = -0.31. ..................................................................................................... 226 Figure S5.3. 1: Density of wolves in the wolf reduction (WR) area, reference areas (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. ............................... 229 Figure S5.3. 2: Density of wolves in the wolf reduction (WR) area, reference areas (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. ............................... 229 Figure S5.3. 3: The minimum number of wolves counted per day monitored (i.e. the total count) using camera traps in the wolf reduction (Wolf-Reduction) area, reference areas (Reference), and low-alteration reference area (Low-Reference) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. Wolf reductions occur from approximately the end of December to the end of March; the dashed vertical line represents March 30 in each year. .................................. 230 Figure 6. 1: Comparison of Idealized Adaptive Management (AM), Realized AM, and the newly proposed Intervention-forward AM. Under Idealized AM, management interventions (solid line; left Y axis) are implemented one at a time to facilitate learnings, and a positive demographic response (dashed line; right Y axis) occurs. Under Realized AM, management interventions are implemented (also ideally one at a time, though independent implementation is not always achieved), yet the demographic response continues to decline. Under Intervention-forward AM, all management interventions are implemented immediately to facilitate recovery, and are removed one at a time to facilitate learning only after a sufficient (positive) demographic response is observed. ....................................... 235 xix LIST OF TABLES Table 2. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and climate when evaluating the effects on white-tailed deer density. ............................................................17 Table 2. 2: Model coefficient estimates, standard errors (SE), and p-values of the global model used to test the relative effects of habitat alteration and climate, given habitat productivity, on average deer density (animals / km2). All independent metrics were scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). ...........................................................18 Table S2.4. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and productivity when evaluating the effects on white-tailed deer density; mean, coefficient of variation (CV), and their interaction. ..........................................................................................................40 Table S2.5. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to evaluate the support for including a one-year lag in climate metrics to test the effects on white-tailed deer density. ......................................................42 Table S2.6. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and climate when evaluating the effects on white-tailed deer density, where each camera is included as a sampling unit. .....................................................................................................................48 Table S2.6. 2: Model coefficient estimates, standard errors (SE), and p-values of the global model used to test the relative effects of habitat alteration and climate, given habitat productivity, on average deer density (animals / km2), where each camera is included as a sampling unit. All independent metrics were scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). .............................................................................49 Table 3. 1: The effect of linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. ..................................................................78 xx Table S3.1. 1: Number of individual wolves monitored for each season (snow and snow-free) and year across four study sub-regions in western Canada. BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. .............................98 Table S3.2. 1: The effect of primary productivity (ΔEVI) year, survey method and region on the estimated (log transformed) moose density (no./100 km2) from aerial surveys in Alberta from 2015 to 2020. Model estimates, standard error (SE) and P-values are shown. Distance sampling, 2014 and the boreal are set as the reference categories. Significant estimates (P-value <0.05) are bolded. .................................................................................................. 100 Table S3.3. 1: Akaike’s Information Criterion (AIC), degrees of freedom (df) and ΔAIC from the “base” model presented in the main manuscript to test the support for including pack size and sex in models of wolf seasonal home range area. The base model includes monitoring period, season, study sub-region, and linear feature density interacting with primary productivity (ΔEVI). .......................................................................................................... 102 Table S3.3. 2: The effect of pack size, sex, linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. .............................. 103 Table S3.4. 1: Models predicting seasonal wolf home range area (km2) to test the support for the effect of linear feature density and primary productivity (ΔEVI, scaled between 0 and 1). Models were compared using Akaike’s Information Criterion (AIC) to find the most supported random-effects structure, then the “basic structure” in which monitoring period, season, and study sub-region were included, and finally the “full structure” in which the environmental variables of interested were included. The model with lowest AIC is bolded for each component. Monitoring period represents the number of days each individual was monitored within the season, scaled between the maximum and minimum values. df = degrees of freedom. ......................................................................................................... 107 Table S3.4. 2: The additive effect of linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density xxi Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Nakagawa’s conditional R2 for the model was 0.73 and marginal R2 was 0.39. ..................................................................... 108 Table S4.1. 1: Search terms used to evaluate the volume of peer-reviewed literature with the goal of developing a threshold for managing linear features within mountain caribou range. Search terms were grouped into 12 priority groups (1 being high priority, 12 low priority) to provide inferences on hypothesized components of the effect of linear features on caribou populations. As search terms broadened, a number of papers were captured by multiple search term groups; * indicates partial overlap with previous search terms, ** indicates complete overlap with previous search terms. .................................................................. 136 Table S4.1. 2: Metadata table of information extracted from the peer-reviewed literature. Metadata are grouped into study design, methods, results and implications. ................... 139 Table S4.2. 1: The metrics used as response variables to test the impact of linear features within the reviewed literature, and the number of analyses for each of these identified metric. .............................................................................................................................. 142 Table S4.2. 2: The metrics used as explanatory variables to quantify linear features and test the impact of linear features within the reviewed literature, and the number of analyses for each of these identified metrics. ............................................................................................... 145 Table S4.2. 3: The linear feature classes used to test the impact of linear features within the reviewed literature, and the number of analyses for each linear feature class. ................. 146 Table S4.2. 4: The data types used to test the impact of linear features within the reviewed literature, and the number of analyses for each datatype. ................................................ 146 Table S4.5. 1: The number of analyses addressing each mechanistic pathway identified in the Conceptual Diagram (Box 1). ........................................................................................... 175 Table 5. 1: Comparing model support using Akaike’s Information Criterion, corrected for small sample sizes (AICc), for the effect of wolf reductions (treatment, such that wolf reductions is contrasted against two reference areas) and environmental covariates (climate Dimension 1 and 2, habitat productivity measured using the change in the Enhanced Vegetation Index, percent habitat alteration, the length of linear features (km)) as well as the additive effect of treatment and environmental covariates on the change in density of white-tailed deer, moose, and caribou over time. ......................................................................................... 194 xxii Table 5. 2: The effect of treatment (low reference, reference, and wolf reductions; such that wolf reductions is the intercept) and environmental covariates on the change in white-tailed deer, moose, and caribou densities (animals/km2) over time. The environmental covariate included varies per species, reflecting the most supported model from Table 5.1. ........... 195 Table 5. 3: Comparing model support using Akaike’s Information Criterion, corrected for small sample sizes (AICc),for the effect of wolf reductions (treatment, such that wolf reductions is contrasted against two reference areas) and environmental covariates (climate Dimension 1 and 2, habitat productivity measured using the change in the Enhanced Vegetation Index, percent habitat alteration, the length of linear features (km)) as well as the additive effect of treatment and environmental covariates on the change in distribution, as measured by the proportion of cameras occupied, of white-tailed deer, moose, and caribou over time. ...... 198 Table 5. 4: The effect of treatment (low reference, reference, and wolf reductions; such that wolf reductions is the intercept) and environmental covariates on changes in white-tailed deer, moose, and caribou distribution over time, as measured by the proportion of cameras occupied in each cluster-year combination. The environmental covariate included varies per species, reflecting the most supported model from Table 5.3. .......................................... 199 Table 5. 5: The effect of wolf reductions over time on the temporal overlap between white-tailed deer, moose, and caribou with high wolf activity periods relative to a low anthropogenic alteration reference area and a reference area with similar alteration. ............................. 208 Table S5.3. 1: The number of photos used to evaluate temporal activity patterns of wolves in the low reference area, reference area, and wolf reduction area over time. The number of photos is calculated per camera cluster. .......................................................................... 231 xxiii ACKNOWLEDGEMENTS First and foremost, I would like to express my deepest gratitude to my supervisor, Dr. Adam Ford. Thank you for providing a new perspective and lens through which to look at ecology and science more generally. Your patience and accommodation during my atypical PhD journey—navigating parenthood, maintaining an established home life, and overcoming the challenges brought by COVID-19—were invaluable. You pushed me to think more critically, to refine my ideas, and to communicate more effectively. I think we made a good team. To my committee members, Drs Robin Steenweg and Michael Noonan, thank you for being incredible sounding boards. Your guidance in statistics, ability to challenge me, and thoughtful feedback on communication played a key role in shaping this work. A special thank you to Dr. Robert Serrouya, who was a constant source of encouragement. We spent countless hours dissecting all aspects of this research, from the most intricate details of statistical models to broader questions about ecological theory. Your ability to distill complex concepts into clear, actionable advice was invaluable. Our discussions always left me feeling energized and more confident in my work, and I’m deeply grateful for your generosity with your time and expertise. I am incredibly fortunate to have worked with such a remarkable group of co-authors: Marcus Becker, Tal Avgar, Craig DeMars, Stan Boutin, Nicola Love, Clayton Lamb, Jean Polfus, Phil McLoughlin, and Scott McNay. The opportunity to blend pieces and people from my master’s degree and early research career into my PhD studies has allowed me to create something truly cohesive. The diversity in expertise and perspectives has shaped my work in ways I couldn’t have imagined, and I’m grateful for how these collaborations have come together so seamlessly. I look forward to continuing to build on these relationships. I also want to acknowledge the field technicians who supported data collection. The sheer volume of data involved in this work could never have been managed by a single person. Your hard work, dedication, and meticulous efforts in gathering data were the foundation upon which this thesis was built, and I am truly grateful for your contributions. xxiv A huge thank you to my colleagues at the Wildlife Science Centre and Alberta Biodiversity Monitoring Institute. Your wealth of skills and knowledge kept me motivated and helped me succeed through every stage of this journey. Beyond the professional help, the camaraderie, laughter, and encouragement you all provided made even the most difficult moments manageable and kept me motivated when the going got tough. I’m incredibly fortunate to be surrounded by such a talented and supportive group of people. Jim Herbers, Monica Kohler, Cynthia McClain, David Roberts and Marcus Becker - you are so appreciated. To my best friends, Christina Prokopenko and Natasha Crosland, who are closer to being my sisters than anything else: this feat required immense support, and you provided it in abundance. Your unwavering encouragement, love, and humor carried me through the toughest moments, and I am so incredibly grateful to have you in my life. I also thank my new friend, Ellie Bolas. In each stage of my life, there are one or two people who I remember when I look back. I may have only met you near the end of my PhD, and we may be many miles apart, but when I think of my PhD, you are one of those people. I’m so glad we met, and I look forward to staying connected and continuing to collaborate in the future. Most importantly, to my family: To my kiddo, Ryann, and my partner, Sean, thank you for your endless love and support. You were there for me when my patience was running thin, bringing me food and candy when I was in “leave-me-alone-to-work” mode, and giving me the absolute best breaks. I am also grateful to the herd of neighborhood children who helped keep Ryann active and sane while I worked from home. You all made this possible, and I am forever thankful. Finally, I gratefully acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada, Regional Industry Caribou Collaboration, MITACs, the Government of Alberta, the Canadian Society of Zoologists Anne Innis Dagg Research Grant, Environment and Climate Change Canada, Alberta Biodiversity Monitoring Institute, Biodiversity Pathways, and University of British Columbia Okanagan. 1 CHAPTER 1: INTRODUCTION Biodiversity is declining across the globe, threatening nature’s goods and services (Foley et al., 2005; Hooper et al., 2005). Landscape change through anthropogenic habitat alteration (hereafter termed habitat alteration) and climate change are cited as two leading causes of global biodiversity declines (Hoegh-Guldberg, 1999; Newbold et al., 2015). In spite of the broad agreement on the need to reverse these declines (e.g. the Convention on Biological Diversity and Aichi targets), the proximate mechanisms through which ecosystems are impacted by these two factors, their relative impacts, and their interactive effects are variable across systems, and elusive for many others (Mantyka-Pringle et al., 2013; Paniw et al., 2021). Additionally, these factors may exert direct effects (e.g. changes in resource availability or survival; Hoegh-Guldberg, 1999; Post & Forchhammer, 2008) as well as indirect effects (e.g. changes in community dynamics; Lindeman, 1942; Odum, 1968; Schmitz et al., 1997). Because of this latter reason, a community ecology approach is required to mitigate impacts on biodiversity. Developing a mechanistic understanding of the complex, multi-species pathways in which habitat alteration and climate change affect ecosystems is important because it will provide the evidence needed to effectively conserve and restore biodiversity. Predator-prey interactions are a crucial process in which perturbations such as habitat alteration and climate change can lead to reverberating effects across complex pathways. Predation can restrict prey distribution, limit prey abundance and even lead to evolutionary change in predators and prey (Lima & Dill, 1990; Welborn et al., 1996). Predator-prey interactions are determined by the abundance of predators, the abundance of prey, and the encounters between the two (Holling, 1959b; Solomon, 1949). Therefore, changes to the abundance or behaviour of either the predator or prey can ripple through the system. Determining the underlying mechanisms of predator-prey interactions can help predict the outcomes of perturbations on species abundance, distribution and behavior across space and time. 2 The impact of habitat alteration and climate change on species can be highly variable and context-dependent (Brooks et al., 2006; Paniw et al., 2021). In some areas, humans have been present at high densities for centuries, altering landcover and directly modifying populations through intense harvest (Allan et al., 2017). In other areas, human population density is much lower but can still have profound changes on the landscape through impacts associated with resource extraction. Compared to other countries, Canada contains vast areas with minimal human impact, particularly in the boreal forest (Coristine et al., 2019), but these areas still are affected by industrial footprint and global climate change. Within Canada’s boreal forest, boreal woodland caribou (Rangifer tarandus caribou, hereafter termed caribou) and their associated food web are ideal for evaluating the implications of habitat alteration and climate change. Caribou have undergone rapid range contractions and population declines (Johnson et al., 2015; Schaefer, 2003). Caribou have a pan-Canadian distribution that has been altered by natural resource extraction including oil, gas, and forestry (Environment and Climate Change Canada, 2017). These industries are important drivers of the Canadian economy; consequently, few species capture the challenges of species conservation like that of caribou, leading to debates over the relative importance of potentially mutually exclusive goals to either conserve wildlife or advance economic growth (Hebblewhite, 2017). Habitat alteration has been implicated as an ultimate cause of caribou declines by reducing critical caribou habitat (Johnson et al., 2020), changing inter-species interactions across the large-mammal boreal food web (Boutin et al., 2010; Mumma et al., 2018; Wittmer, Sinclair, et al., 2005). Proximately, caribou are in decline due to predation, which results in low survival of adult females and young (i.e., calves), despite high fecundity (McLoughlin et al., 2016; Wittmer, McLellan, et al., 2005). Habitat alteration is hypothesized to increase the abundance of non-caribou ungulates, specifically moose (Alces alces) and deer (Odocoileus spp.), which then sustains high populations of their predators, wolves (Bergerud & Elliot, 1986; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). The increased abundance of 3 predators on the landscape has increased incidental predation of caribou in highly altered areas compared to landscapes with less habitat alteration (Neufeld et al., 2021). Concomitantly, predator hunting efficiency and incursion into caribou habitat is facilitated via linear features (Dickie et al., 2017; Latham, Latham, Boyce, et al., 2011), which is hypothesized to increase predation on caribou. Theory predicts an increase in caribou predation due to these perturbations, both as a result of increased abundance of non-caribou ungulates resulting in an increase in predator density (i.e. increased resource availability and the numerical response; (Solomon, 1949), and increased predator movement efficiency (i.e. increased instantaneous search rate, a component of the functional response (Holling, 1959a) which increases encounters between predators and caribou. Although habitat alteration is linked to caribou declines across Canada (Johnson et al., 2020), the mechanisms in which habitat alteration reverberates through the boreal caribou food web are still under investigation (but see Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Much of the current understanding of habitat alteration on primary prey abundance comes from highly productive systems where disturbance is dominated by forestry (Courbin et al., 2014; Mumma et al., 2020; Seip, 1992; Wittmer, Sinclair, et al., 2005; Wittmer et al., 2007). Instead, boreal caribou ranges within western boreal forests are dominated by oil and gas exploration (Alberta Biodiversity Monitoring Institute, 2018a). Unlike forestry, these disturbances are less likely to contribute to increased early seral forage because they have minimal vegetation regeneration (Lee & Boutin, 2006) and limited direct footprint (less than 2 % of the landscape area). From a predator-prey lens, climate change likely increases boreal forest productivity (Zhu et al., 2016), and is also documented to facilitate the northern expansion of white-tailed deer which are becoming a larger component of wolf diet (Latham, Latham, McCutchen, et al., 2011). Furthermore, despite the understanding that anthropogenic linear features increase wolf movement, and the theoretical prediction that increased movement 4 influences wolf behaviour and abundance, the impacts of linear features on wolf space-use at a larger scale, as well as the impact on wolf abundance and kill rates remains unknown. Inherent in the pursuit of understanding and predicting how habitat alteration and changes in climate will influence predator-prey interactions associated with caribou declines, is the need for a baseline understanding of how predators influence their prey. While predation is a key processes that can influence the distribution, abundance, and behavior of their prey (Holt & Barfield, 2009; Schmitz et al., 1997), these top-down forces rarely act in isolation, often interacting with bottom-up factors like resource availability and climate (Hunter & Price, 1992; Sinclair & Krebs, 2002). The interplay between top-down and bottom-up forces complicates our ability to predict outcomes of predator-prey interactions in general, even more so when perturbed by changing habitat. Evaluating changes in prey abundance, distribution, and behavior as a result of natural or experimental changes in predator abundance offer the ability to understand the relative role of top-down and bottom-up factors (Sinclair et al., 2007; Todd & Hoffman, 1999; Walters & Holling, 1990). For example, through jurisdictional boundaries associated with different harvest policies, large-scale population declines as a result of disease outbreaks, predator reintroduction programs, or even predation reduction programs. In the context of boreal caribou, it is important to understand how predator reduction programs, aimed at reducing predation on caribou (Hervieux et al., 2014; Lamb et al., 2024; Serrouya et al., 2019), impact non-target prey species and how these effects may interact with habitat alteration and climate. By examining the responses of deer, moose, and caribou to predator reductions, we can begin to disentangle these complex interactions and better predict the cascading effects of ecosystem change. Without understanding and targeting the mechanisms of caribou declines, the ability for caribou recovery planning to develop cost-effective and long-term solutions to reverse caribou declines is hindered. In this dissertation I identify the mechanistic pathways in which habitat 5 alteration and climate influence predator and prey interactions that are implicated in caribou declines across western Canada’s boreal forest. Specifically, I: 1. Disentangle the influence of habitat alteration from climate on invading white-tailed deer populations in western Canada, providing an understanding of how habitat alteration and climate interact to increase primary prey density; 2. Evaluate how resource density and resource exploitation interact to influence wolf seasonal home ranges, providing an understanding of how habitat alteration influences wolf space-use and distribution, with implications for wolf abundance; 3. Synthesize existing knowledge on the effects of linear features on woodland caribou, with the goal of identifying an evidence-based management targets; 4. Evaluate the behavioral and demographic effects of predator reductions on white-tailed deer, moose, and caribou, providing an understanding of how prey species respond to predation (or lack there of); and 5. Propose a new adaptive management framework to maximize the likelihood of recovery while maintaining the scientific benefits of an experimental approach to management, given the urgency to halt and reverse the loss of biodiversity and the uncertain responses to management interventions that address various bottom-up and top-down forces. 6 CHAPTER 2: DISENTANGLING THE INFLUENCE OF ANTHROPOGENIC HABITAT ALTERATION FROM CLIMATE ON INVADING WHITE-TAILED DEER POPULATIONS IN WESTERN CANADA Introduction Disentangling the effects of anthropogenic habitat alteration and climate change on global biodiversity loss is a pressing ecological challenge (Caro et al., 2022; Williams et al., 2022), complicated by the fact that habitat alteration and climate change are often correlated in space and time. Both factors can alter species distribution and abundance, which can disrupt food webs (Bartley et al., 2019; Wang et al., 2020) and facilitate the spread of novel diseases and parasites (Patz et al., 2000), potentially leading to extirpations of native biota. For most species, anthropogenic habitat alteration (hereafter termed habitat alteration) is considered to have a stronger effect (Caro et al., 2022), though the relative influence of habitat alteration and climate is species- and context-specific (Tourani et al., 2023; Weiskopf et al., 2020). As climate change accelerates and its effects strengthen (Smith et al., 2015), it is incumbent to understand the relative contributions of habitat alteration and climate change to observed biodiversity changes, their interactive effects, and, importantly, the mechanisms through which these factors act. Such knowledge is essential for developing evidenced-based conservation actions that take into account predicted effects of climate change going forward (Dirzo et al., 2014; Gilbert et al., 2019; Mantyka-Pringle et al., 2013). Across North America, white-tailed deer (Odocoileus virginianus) have greatly expanded their distribution and abundance in the last century, with the potential to disrupt ecological processes (Côté et al., 2004; Patterson & Power, 2002; VerCauteren, 2003). The historic expansion of this species into mixedwood forests and grasslands of the United States and southern Canada was facilitated by food subsidies through the conversion of land for agriculture and forest harvest, reduced predation through intensive carnivore reductions, and 7 deer harvest management (Côté et al. 2004 and references therein). In many places, white-tailed deer have since become highly abundant, and are considered ecosystem disruptors via overgrazing, disease transmission, and altered predator-prey interactions (Côté et al., 2004; Greenspoon et al., 2023; Rooney, 2001). White-tailed deer have also expanded into boreal forests throughout Canada, where they have been implicated in the decline of boreal caribou – an ecotype of woodland caribou facing extirpation across much is its range (Rangifer tarandus caribou; Environment and Climate Change Canada, 2020; Latham et al., 2011). Increased prevalence and densities of white-tailed deer supports higher wolf densities (Latham, Latham, McCutchen, et al., 2011; Mech & Boitani, 2003b), leading to increased incidental predation on caribou (Holt, 1977; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Additionally, white-tailed deer expansion may increase the transmission of disease and parasites, including ticks, brainworm, chronic wasting disease, and COVID-19 (Christina, 2008; Hannaoui et al., 2017), potentially impacting human health and sustenance. At the northern edge of white-tailed deer range in Canada’s western boreal forests, changes in forage availability and physiological constraints imposed by harsh climates are implicated in the expansion of white-tailed deer (Dawe et al., 2014; Kennedy-Slaney et al., 2018; Laurent et al., 2020). In these northwestern boreal forests there is an underlying habitat productivity gradient as the highly productive Parkland ecosystem in the south transitions into less productive Boreal Plains and then Boreal Shield farther north (Dickie et al., 2022a). Generally, this productivity gradient occurs along a northwest-southeast axis. Though agriculture is rare in the Boreal Plains and Boreal Shield, land clearing for oil and gas exploration and forest harvest are hypothesized to increase forage availability beyond the baseline variation in productivity (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021), potentially supporting higher densities of deer (Fuller et al., 2022). However, concomitant with changes in habitat alteration, climate change has resulted in less severe winters that increase white-tailed deer survival, and longer growing seasons that increases food availability 8 (Beier & McCullough, 1990; Dawe & Boutin, 2016; Kennedy-Slaney et al., 2018). The covariation of habitat alteration and climate change in time can also play out over space. In western Canada, human activity tends to decrease with increasing latitude, while climate severity increases with latitude. Thus, we expect white-tailed deer densities to decrease with latitude, but it is currently unclear to what extent habitat alteration, climate, or their combined effects might be underpinning this pattern. Here, we assessed the relative influence of habitat alteration and climate change on deer densities by taking advantage of a decoupling of the typical habitat alteration–climate change pattern (i.e., decreasing alteration and increasing winter severity with increasing latitude) that occurs at the Alberta-Saskatchewan provincial border (Figure 2.1). Habitat alteration is on average 3.6-fold higher in Alberta’s eastern boreal plains than in the neighboring Saskatchewan western boreal plains (i.e., an east-west habitat alteration gradient; Environment and Climate Change Canada, 2017). Unlike habitat alteration, climate does not vary significantly across the provincial border for a given latitude. We leveraged this unique contrast in habitat alteration, along with a latitudinal gradient in climate, to evaluate factors affecting white-tailed deer density estimated from remote camera grids situated in 12 replicated 50km2 landscapes. If white-tailed deer densities are primarily driven by habitat alteration, we expected densities to be higher in study sites with higher habitat alteration (i.e., in Alberta). In contrast, if white-tailed deer densities are primarily driven by climate, we expected densities to be lower in study sites that are characterized by more severe winters (i.e. more northerly). If these factors interact beyond their additive effects, we expected deer densities to be highest in areas with high habitat alteration and less severe winters, and that the deer density would be lowest in areas with low habitat alteration and more severe winters. 9 Figure 2. 1: Location of camera clusters used to evaluate the relative effects of anthropogenic habitat alteration (HA) and climate on white-tailed deer. Dark grey shading depicts HA as mapped by Environment and Climate Change Canada, buffered by 500m (Environment and Climate Change Canada, 2017), and light grey shading depicts the mapping boundary in which HA was mapped. Camera clusters are coloured by the Climate Dimension 1 identified using a 5-year Multiple Factor Analysis, such that higher values (i.e. cooler colours) represent more severe winters. Cameras were placed randomly within a 12.5km by 4km area on trees with a minimum distance of 1-km apart using a random location generator, and all cameras faced north to avoid solar interference with images. The inset map depicts the study area in relation to Canada, whereas the outset images depict an example of random camera trap placement within each camera cluster. Map lines delineate study areas and do not necessarily depict accepted national boundaries. 10 Methods Study Area – The study area is located at the interface of the Boreal Plains and Boreal Shield ecozones (Ecological Stratification Working Group, 1996). Both ecozones are characterized by a mosaic of upland and wetland forests, though the plains contain relatively more broadleaf forest than the shield, whereas the shield has relatively more cover of barren areas, shrubland and grassland peatlands. The study area has a relatively low human influence index (Wildlife Conservation Society, 2005), with human population densities typically less than 1 person / km2. The majority of anthropogenic habitat alteration within the study area is related to the forestry and energy sectors, with some occurrence of agriculture in localized areas. Geophysical exploration within the boreal shield is less prominent than on the boreal plains due to the depth of the oil and gas deposits. The divergence between Saskatchewan and Alberta’s human land-use is a result of complex socio-political drivers (Herbert Emery and Kneebone, 2008). Camera Deployment – Beginning in 2017, we deployed twelve 50-km2 (12.5 km x 4-km) clusters of camera traps, with 25 cameras per cluster, across approximately 55,375 km2 in northeastern Alberta and northwestern Saskatchewan (Figure 2.1). Camera clusters were placed on both sides of the Alberta-Saskatchewan border to maximize the variation in habitat alteration (East-West gradient), and across a range of latitude to capture climatic variation (Northwest-Southeast axis). For example, minimum temperatures in the snow season varied from -35 to -45 ° Celsius and maximum temperatures in the snow free season varied from 28 to 39 ° Celsius. Initially, 6 camera clusters were placed in Alberta (i.e. high habitat alteration) in January of 2017. In fall and winter of 2017/2018, 4 additional clusters were added to sample areas with low habitat alteration in both eastern Alberta and western Saskatchewan. An additional cluster was added in Saskatchewan in 2019, and another in 2021. Finally, the southwestern most cluster in Alberta was moved in 2020 due to imminent forest harvest. Cameras collected data year-round to increase the cumulative detection probability, and were typically serviced once per year. 11 We used Reconyx Hyperfire2 and Recoynx PC900 camera models (Holmen, WI, USA). Cameras were programmed to take motion-triggered photos during day and night, and to take a time-lapse photo every 2 hours. Detailed camera trigger settings are documented in Alberta Biodiversity Monitoring Institute (2019). Camera clusters were placed within reasonable access to roads to facilitate camera servicing, such that roads typically bisect the shorter edge of the cluster and run parallel to the longest edge. Within clusters, cameras were placed randomly, with a minimum distance of 1-km apart using a random location generator. While this design results in clusters biased towards areas with roads, this bias was consistent across the strata of interest. Estimating Deer Density – We estimated deer density for each camera using the Time in Front of Camera method (TIFC; Becker et al., 2022). The TIFC model uses basic sampling logic in which the number of animals observed within a defined area, sampled using camera traps, is multiplied by the time those animals were observed, and divided by the area and time monitored, using the formula: 𝐷 =∑(𝑁 ∙ 𝑇𝐹)𝐴𝐹 ∙ 𝑇𝑂 Where density D is calculated as the total number of individuals observed N multiplied by the time in front of the camera field-of-view TF, divided by the area of the camera field-of-view AF multiplied by the total camera operating time TO. The units are animal-seconds per area-seconds, which simplifies to the number of animals per unit area. The probability of detecting an animal decreases as the distance from the camera increases, and this is species- and habitat-specific. To account for this, we modified the area monitored by the effective detection distance in which white-tailed deer, in each season, can be detected following the methods of Becker and colleagues (2022). Furthermore, we accounted for the probability of the animal leaving the field of view as a function of time between images when quantifying T0 (Becker et al., 2022). 12 Lastly, we added the average time between images in all series to the duration of each series to account for how long animals are typically in the field of view before and after the first and last images, respectively, are taken (Becker et al., 2022). TIFC is an appropriate density estimator for our system given white-tailed deer are unmarked populations, and our cameras were placed randomly (Becker et al., 2022), though like the example provided by Becker and colleagues our sampling likely violates the assumption of true random placement across microhabitats. We calculated the density of deer at each camera separately for each year, defined as April 16 to April 15 in the following year, roughly corresponding to camera servicing dates and time periods used to calculate effective detection distances (Becker et al. 2022). To obtain cluster-level density estimates, we then averaged the density across all cameras and calculated 90% confidence intervals (CIs) treating density estimates as a compound distribution of presence (1) and absence (0), and abundance given presence (Becker et al. 2022), similar to a zero-inflated log-normal distribution. While annual densities smooth over potential seasonal variation, for example resulting from seasonal migrations, snow-free and snow season density estimates were correlated, and correlated to annual estimates (Appendix S2.1). Environmental Variables – We quantified habitat alteration, here defined as the area modified by human land-use, using two approaches. First, we used a dataset extending across Alberta and Saskatchewan’s boreal forest where anthropogenic habitat alteration identified at a 1:50 000 scale using Landsat imagery is buffered by 500 m (hereafter termed “buffered disturbance”; (Environment and Climate Change Canada, 2017). This definition of anthropogenic habitat alteration is used by the Federal Recovery Strategy for boreal caribou (Environment and Climate Change Canada, 2020; Environment Canada, 2011). A known drawback to this dataset is that underestimates the footprint of linear disturbances, such as roads, pipelines, and seismic lines (Dickie, Bampfylde, Cody, et al., 2023). Therefore, we also quantified “direct alteration” by calculating the mean percent area altered by human land-use in each cluster using the best 13 available data from Saskatchewan and Alberta’s Footprint Inventories (Alberta Biodiversity Monitoring Institute, 2018; Government of Saskatchewan, 2021). We used the Alberta Human Footprint Inventory with the same vintage data available in Saskatchewan (i.e., 2018), and converted it into a 1-km by 1-km raster of the percent area disturbed by habitat alteration to match the Saskatchewan Footprint Inventory. To quantify spatial variation in climate, we conducted a multiple factor analysis (MFA) to reduce climate variables expected to impact deer demographic rates (survival and recruitment), and thus density, during the period when deer were monitored. The MFA accounted for sampling variables across years at the same location, grouping each of the variables over the 5-year monitoring period, and hence represents medium-term “climate” dimensions for each cluster. For each year, we extracted the maximum and mean precipitation, maximum and minimum temperature, and the mean vapor pressure (i.e., a measure of humidity) for two seasons, defined as snow-free (May 1 - August 31) and snow (September 1 - March 31) from DAYMET Version 4 (Thornton et al., 2022). We extracted the mean value of each variable for each pixel, then averaged the pixel-level average values for each camera cluster, season and year combination. Metrics of temperature, precipitation and humidity have been linked to over-winter survival, energy reserves available for reproduction, and nutrition of ungulates through energy balances associated with how these variables interact to influence winter severity and resource availability throughout the growing season (Delguidice et al., 2002; Michel et al., 2018; Patterson & Power, 2002). Additionally, we quantified snow season length by conducting a breakpoint analysis on snow water equivalent data to identify the period when the ground was continuously covered by snow (Appendix S2.2). We also used snow water equivalent data to estimate cumulative snowfall over the snow season. We used package FactoMineR (Lê et al., 2008) in R (R Core Team 2019) to conduct the MFA. Recognizing that annual variation in weather may contribute to annual variation in deer density, we additionally quantified spatio-temporal variability in weather (i.e., annual variation in “climate” dimensions) in addition to 14 evaluating the effect of medium-term climate (i.e., the 5-year medium-term “climate” dimensions). We used a principal component analysis (PCA) to identify annual weather dimensions for each cluster using package factoextra (Kassambara & Mundt, 2020) in R (R Core Team 2019), using the same variables described above. In addition to anthropogenic habitat alteration and climate, deer density is expected to vary depending on landscape context, such as primary productivity (Dawe et al., 2014; Kennedy-Slaney et al., 2018). To account for landscape context, we used the change in the Enhanced Vegetation Index (ΔEVI) as a measure of deciduous vegetation availability (Gagné et al., 2016), which has been found to impact the density of terrestrial herbivores in boreal forests, including moose (Alces alces) and deer species (Dickie et al., 2022a; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). We used MODIS Terra Vegetation Indices 16-Day Global data (500-m resolution) to extract the median EVI values during the leaf-on period when deciduous vegetation is at or near peak production (1 July – 1 August), and the leaf-off period when vegetation has senesced but snow has not yet accumulated (1 September – 1 October 1). We calculated the change in EVI (ΔEVI) by subtracting the leaf-off median EVI from the leaf-on median EVI (Appendix S2.3). To eliminate pixels covered by clouds or shadows, we retained only those classified as “VI produced, good quality” and removed pixels covered by water, following Hansen et al (2013). Analytical Framework – We used a two-stage modelling framework to first identify the metrics that best represented the two main variables of interest (habitat alteration and climate), and second to evaluate their relative effects on deer density while controlling for habitat productivity. For habitat alteration, we evaluated the relative support for direct habitat alteration versus buffered habitat alteration. For climate, we evaluated the relative support for the inclusion of the 5-year climate and annual climate dimensions, specifically contrasting the support for Dimension 1 only, Dimension 2 only, or the additive effect of Dimensions 1 and 2. We included 15 the metrics for habitat alteration and climate with the lowest Akaike Information Criterion, corrected for small sample size (AICc; Akaike 1974), in the final global model. If metrics fell within 2 AICc units, the simplest model formulation was included (Sutherland et al., 2023). We also explored the inclusion of heterogeneity in habitat alteration and productivity within camera clusters, measured using the coefficient of variation of each variable, but found no support for including the variation in habitat alteration or productivity rather than their means (Appendix S2.4). Because weather or climate may have a lag effect on deer densities (Patterson and Power 2002 and references therein), we additionally conducted the MFA and the PCA analyses to include a one-year lag in the climate metrics. We found little evidence to support the inclusion of a one-year lag, and so present these analyses in Appendix S2.5. Finally, we modelled average deer density in each camera cluster as a function of the top variables identified to quantify habitat alteration and climate, each interacting with ΔEVI, as well as the interaction between the top metrics of habitat alteration and climate. We calculated the marginal R2 to evaluate model performance using Nakagawa’s R2 (Nakagawa & Schielzeth, 2013). For all models we used a tweedie distribution and included temporal autocorrelation using an ar1 covariance structure in glmmTMB (Brooks et al., 2017). Because some cameras failed or were displaced from their field of view, we weighted each density estimate by the number of cameras operating in that cluster-year, thereby giving higher weight to clusters-year combinations where more cameras were operating. We checked that all covariates did not covary (i.e., all correlation coefficients were less than 0.6; the Pearson’s correlation between direct habitat alteration and habitat productivity was 0.37, between direct habitat alteration and climate dimension 1 was 0.38, and habitat productivity and climate dimension 1 was -0.41). We found no support for spatial autocorrelation based on model residuals using Moran’s I, which was estimated using package DHARMa (Hartig & Lohse, 2022; Moran’s I = -0.230, p-value = 0.120). Individual cameras within a cluster were considered replicated samples of mean deer density, however, the mean may not represent the variation in deer densities within cluster. We 16 therefore tested if using each individual camera density estimate as the sample unit influenced our results, and found that model interpretations and coefficients were similar between the analytical approaches (Appendix S2.6). Results Camera traps operated between 53,506 and 96,096 trap days from 2017 to 2021. During this time, we recorded 7,738, 9,775, 17,785, 19,071, and 24,547 images of white-tailed deer in each of the study years, respectively. For the MFA, Dimension 1 and 2 explained 40.37 % and 21.90% of the variance, respectively, for a total of 62.27 % of the climate variance explained across cameras clusters by these two axes (Appendix S2.7). Dimension 1 was primarily related to cumulative snow water equivalent, maximum snow water equivalent, and snow season length (each with square cosine > 0.8), such that increasing values of Dimension 1 represents longer, snowier winters. Dimension 2 was primarily related to the maximum snow season precipitation (each with square cosine > 0.60), and to a lesser extent snow free season mean precipitation, snow season minimum and maximum temperatures (square cosine > 0.2), having a less clear ecological interpretation than Dimension 1. For the PCA, Dimension 1 and 2 explained 38.25 % and 15.84% of the weather variance, respectively, for a total of 54.09 % (Appendix S2.7). Dimension 1 was primarily related to cumulative snow water equivalent and maximum snow water equivalent (square cosine >0.8), representing snowier winters, and axis 2 was primarily related to minimum snow season temperatures (square cosine > 0.4), with increasing values representing warmer winters. Both Dimension 1 and 2 varied across years, though clusters tended to follow the same annual patterns (Appendix S2.7). Of the competing habitat alteration models, percent direct habitat alteration was the best model, being 6 AIC units lower than the second-ranked model (Table 2.1). Of the competing climate models, the MFA Climate Dimension 1 and MFA Climate Dimension 1 and 2 models 17 were within 2 AICc units (Table 2.1). We therefore chose the simplest model structure of the two competing models, MFA Dimension 1, to include within the final modelling stage (Arnold, 2010). Table 2. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and climate when evaluating the effects on white-tailed deer density. Variable Metric df AICc Habitat alteration % direct habitat alteration 8 -10.159 % buffered habitat alteration 8 -4.537 Climate MFA Dimension 1 8 -11.119 MFA Dimension 2 8 -4.266 MFA Dimension 1 + MFA Dimension 2 9 -12.337 PCA Dimension 1 8 -5.291 PCA Dimension 2 8 -2.518 PCA Dimension 1 + PCA Dimension 2 9 -2.653 Results from the global model (marginal R2 = 0.90), which assessed for effects of habitat alteration and climate after accounting for habitat productivity, showed that deer densities were primarily influenced by winter severity (i.e., climate). Specifically, deer densities were significantly lower in areas with increasing values of climate Dimension 1 (β = -6.794, SE= 2.523; Table 2.2). When habitat alteration and productivity were held at their mean value, predicted deer densities declined from 1.83 deer/km2 to 0.35 deer/km2 when winter severity increased from the lowest value, to the median value (Figure 2.2). There was a tendency for increasing habitat alteration and habitat productivity to partially mediate the effect of climate Dimension 1 (% Habitat alteration: Climate Dimension 1 β = 3.871, SE= 2.357; Habitat 18 productivity : Climate Dimension 1: β = 4.586, SE= 3.818), though the effect was not statistically significant (Table 2.2). Table 2. 2: Model coefficient estimates, standard errors (SE), and p-values of the global model used to test the relative effects of habitat alteration and climate, given habitat productivity, on average deer density (animals / km2). All independent metrics were scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). Variable Estimate SE P-value Intercept 1.562 2.107 0.458 Habitat productivity -2.241 3.008 0.456 % Habitat alteration -0.328 2.060 0.873 Climate Dimension 1 -6.794 2.523 0.007 Habitat productivity : % Habitat alteration 0.553 2.290 0.809 Habitat productivity : Climate Dimension 1 4.586 3.818 0.230 % Habitat alteration : Climate Dimension 1 3.871 2.357 0.100 19 Figure 2. 2: Average density of white-tailed deer (animals/km2) as a function of habitat alteration (%) and winter severity (Scaled Climate Dimension 1; such that winter severity increases as Dimension 1 increases). Density estimates are coloured by habitat productivity (ΔEVI) and symbols represent different years. The significant predicted relationship and 95% confidence intervals between Climate Dimension 1 and average deer densities from the global model presented in Table 2.2 is shown. Percent Alteration is displayed in it’s original units to aid in interpretation, whereas Climate Dimension 1 is displayed as scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). Discussion In a rapidly changing world, one of the most pressing issues facing ecologists is disentangling the relative effects of habitat alteration and climate change on shifting animal communities. We contrasted the relative effects of habitat alteration and climate, measured here using an index of winter severity across the 5-year monitoring period, on white-tailed deer densities in a region where spatial variation in habitat alteration and climate were decoupled. Our data supports the climate hypothesis: white-tailed deer densities were significantly lower in areas where winter severity was higher. While there was a tendency for deer densities to increase with increasing habitat alteration the magnitude of this effect was approximately half that of climate, and was statistically insignificant. 20 For many wildlife species, more severe winters can lead to longer or more intense phases of energy deficits. These energy deficits manifest as poor body condition and put individuals at risk of abortions, reduced offspring condition, starvation, and increased risk of disease, parasites, and predation (Delguidice et al., 2002; Mysterud & Østbye, 2006; Tverra et al., 2007). While the physiological effects of severe winters could potentially be mediated by higher forage availability (Dumont et al., 2000, 2005; Tverra et al., 2007)—for example, in areas with high productivity or through forage subsidies associated with human land-use—this mechanism was not statistically supported by our results. Though we established a link between climate and deer densities, the ecological mechanisms behind this linkage remain unclear without detailed information about cause-specific mortality and recruitment. Future analyses could explore temporal variation in distribution and abundance as well as finer-resolution information on attributes of altered habitat (for example, vegetation regeneration and forage availability), or the relative influence of the various components of climate expected to influence survival and reproduction. Winter severity is expected to decline as climate change progresses (Notaro et al., 2014), and therefore white-tailed deer are expected to keep expanding northwards and increase in abundance (Dawe & Boutin, 2016; Fisher et al., 2020; Kennedy-Slaney et al., 2018). The deer densities we observed were similar to those estimated by Latham and colleagues (2011) for a nearby region in the mid to late 2000’s, suggesting that deer have continued to have a strong presence in this region, though we note that higher densities appear to be more common now, at least in the more southerly portions of the study area. Even over a small geographic area relative to the overall distribution of white-tailed deer, we found a large increase in deer densities (from 0 to 2 animals / km2), which suggests that even a small change in winter severity over time could trigger a substantial increase in deer densities in northern areas. However, forecasting future deer distribution and abundance using retrospective relationships (see examples Dawe and Boutin 2016; Kennedy-Slaney et al. 2018) is complicated by the mounting 21 frequency and intensity of wildfires that can drastically influence habitat composition and population demographics (Neilson et al., 2020), as well as the spatial variation in the velocity of climate change (Li et al., 2019). The annual unpredictability of overall climate change and extreme weather events underscores the need to evaluate the mechanisms in which weather and climate influences population demographics (i.e., disentangling how various components of climate link to reproduction, adult female survival, etc.), though long-term monitoring at the invasion front is needed to evaluate the drivers of abundance over time. When seeking to understand the drivers of a species’ abundance and distribution, it’s important to consider that ecological processes are often hierarchical (Tourani et al., 2023). Our finding that climate drives the population density of white-tailed deer, aligns with studies on factors affecting deer distribution (Dawe & Boutin, 2016; Kennedy-Slaney et al., 2018). In contrast, some studies, even within these same regions, have consistently demonstrated that habitat alteration influences relative habitat use (Fisher et al., 2020; Fuller et al., 2022), and thus emphasizes the significance of habitat composition and structure in shaping deer behavior (i.e., habitat use). While drivers of habitat use are often inferred to influence demographics, this is not always the case and is rarely tested (Avgar et al., 2020; Courbin et al., 2017). Localized habitat-driven processes are nested within a larger-scale framework that dictates overall population dynamics (Tourani et al., 2023). Climate may set the envelope within which white-tailed deer can persist in the boreal forests of western Canada, while habitat alteration may influence their abundance or finer-scale spatial distribution within this climatic envelope (Elmhagen et al., 2017; Kennedy-Slaney et al., 2018). Creating robust designs to study large-scale drivers of population abundance for wide-ranging species is notoriously difficult. The strength of our study design is underscored by two fundamental components. First, we were able to leverage a jurisdictional boundary to decouple spatial variation in climate and habitat alteration. Second, we leveraged the strengths of camera traps to estimate densities in an area where abundance is otherwise difficult to quantify. The 22 use of cameras to estimate population abundance for unmarked populations is undergoing rapid methodological development. Many such approaches are afflicted by low precision resulting from the small area monitored by each individual camera, coupled with low sample sizes, both of which contribute to high measurement error (Becker et al., 2022; Palencia et al., 2021). By treating each cluster of camera traps as a sample estimate of deer densities, we were able to contrast densities across landscape units of interest that varied in the environmental strata of interest. Despite poor precision inherent with many camera studies, particularly with unmarked populations (Palencia et al., 2021), the magnitude of effect sizes we documented overcame the lack of precision in estimates, supported by the similar results between analyses using the mean density at each cluster versus the individual camera. White-tailed deer – one of the largest contributors to terrestrial mammal biomass (Greenspoon et al., 2023) – are considered ecosystem disruptors that alter predator-prey dynamics and spread diseases (Hannaoui et al., 2017; Patz et al., 2000). Specifically, in the boreal forests of western Canada white-tailed deer are implicated in the decline of woodland caribou (Latham, Latham, McCutchen, et al., 2011). Increasing deer densities affect predator-prey dynamics by shifting the diets of predators and triggering a predator numerical response (Latham, Latham, McCutchen, et al., 2011; Serrouya et al., 2015). Wolves in particular have been shown to have densities an order of magnitude higher when deer are available as prey (Fuller et al., 2002). Habitat alteration is widely believed to be the ultimate cause of caribou declines, acting through increased predator hunting efficiency and disturbance-mediated apparent competition (Johnson et al., 2020; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Habitat restoration and protection are therefore proposed management actions to recover caribou populations, with the goal of reducing predation by restoring predator-prey dynamics. However, if climate is indeed favoring the northward expansion of white-tailed deer to a greater degree than habitat alteration, restoring habitat is less likely to be effective on its own as white-tailed deer will continue to support high abundances of wolves (Ford et al., 2021; Fuller 23 et al., 2002; Latham et al., 2011). 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Vertebrate population trends are influenced by interactions between land use, climatic position, habitat loss and climate change. Global Change Biology, 28(3), 797–815. https://doi.org/10.1111/gcb.15978 33 Appendix S2.1: Seasonal and annual deer densities We estimated annual deer densities from April 16 to April 15 of the following year. Annual density estimates may smooth over temporal variation, for example resulting from seasonal migrations. We therefore evaluated the density of white-tailed deer during two equal-length seasons that roughly represent the snow-free (April 16 - October 15) and snow (October 16 - April 15) time periods. Annual and seasonal estimates were strongly related (Figure S2.1.1). Annual density estimates were correlated with density estimates from the snow-free (Pearson’s r = 0.98) and snow (Pearson’s r = 0.88) seasons. Density estimates from the snow-free and snow season were likewise correlated (Pearson’s r = 0.86). Figure S2.1. 1: The relationship between estimated white-tailed deer densities (animals/km2) during the snow-free (April 16 - October 15), snow (October 16 - April 15) seasons, and annual density (April 16 to April 15 in the following year). A linear smoothed regression is displayed with 95% confidence intervals. 34 Appendix S2.2: Quantifying snow season length and cumulative snowfall Seasonal and annual deer densities We used DAYMET Version 4 snow water equivalent (SWE) data to quantify the length of each snow season for each camera cluster, and to calculate cumulative SWE (i.e., a measure of cumulative snowfall). For each camera cluster and year combination we extracted and plotted the median daily SWE in the 50km2 sampling area (Figure S2.2.1). We then used package ‘inflection’ in R (Christopoulos, 2022) to identify the start of the snow season as the inflection point where SWE begins accumulating from August to January, and the end of the snow season as the inflection point when SWE is no longer prevalent between January and July. Figure S2.2. 1: Example plot of snow water equivalent (SWE; kg/m2) plotted over time, which was used to identify the length of the snow season and cumulative snowfall. Example demonstrates identifying the 2017/2018 snow season from Lac la Biche, in northeastern Alberta. Each dot represents the daily SWE, the black line connects each point, and he vertical lines represent the start (blue) and end (red) of the season. 35 Appendix S2.3: Habitat productivity gradient in the study area Figure S2.3. 1: The change in Enhanced Vegetation Index (Delta EVI) across the study area, such that higher values represent higher primary productivity. Delta EVI was calculated for each year by subtracting the EVI during the leaf-off season (1 September – 1 October 1) from the leaf-on period when deciduous vegetation is at or near peak production (1 July – 1 August). An example from 2020 is shown. The inset map depicts the study area in relation to Canada, and the mapping boundary used to measure habitat alteration as per Figure 2.1. 36 Appendix S2.4: Spatial heterogeneity in landscape attributes within camera clusters We evaluated the relative effects of habitat alteration and climate, while accounting for habitat productivity, on white-tailed deer density by measuring each of these variables within 50km2 areas that were strategically placed to sample climate and habitat alteration independently. Because we were primarily interested in these landscape-scale drivers of deer abundance, rather than variation in within each of the 50km2 sampling areas, we measured the mean habitat alteration and alteration in each area. However, within these areas sampling areas, there is indeed heterogeneity within these variables (Figure S2.4.1). While in concept average deer density within these sampling areas should reflect the average landscape attributes, we evaluated if landscape heterogeneity within each camera cluster influenced average deer density (Figure S2.4.2). We included the variation in habitat alteration and productivity in each sampling area, measured using their Coefficient of Variation (CV), within the model comparison component of the two-stage modelling framework. Within each 50km2 camera cluster sampling area, we extracted the percent habitat alteration from each 1km cell using the Alberta Human Footprint Inventory and Saskatchewan Footprint Inventory, as well as the median ΔEVI from each 500m cell using MODIS Terra Vegetation Indices, and calculated the CV of each. For both habitat alteration and productivity, we then evaluated if the CV was more supported using AICc relative to the mean value. We also compared the relative support for the interaction between the mean value and the CV for each, such that the influence of heterogeneity may depend on the overall (mean) value. We used the same modelling formulation as in the main analyses, though models using CV rather than mean values did not converge when including the number of cameras as a weighting. We therefore removed the weighting to compare the support for models of mean and CV for each variable. For both habitat alteration and productivity, the mean value had a lower AICc, by more than 2 units, than their respective CVs as well as the interaction between the mean and CV 37 (Table S2.4.1). We therefore found no support for including the variation in habitat alteration or productivity, rather than their means, in the second modelling stage to evaluate the relative effects of habitat alteration and climate on deer densities. This suggests that overall habitat alteration and productivity best explains the average density of deer within each sampling area. It is possible that heterogeneity in landscape attributes may influence relative deer abundances at a finer-scale, for example the relative space-use of deer within each sampling area. 38 Figure S2.4. 1: The percent habitat alteration and habitat productivity (change in the Enhanced Vegetation Index; ΔEVI) in each camera cluster in which white-tailed deer density was measured. Each point represents the percent habitat alteration from each 1km cell using the Alberta Human Footprint Inventory and Saskatchewan Footprint Inventory, and the median ΔEVI from each 500m cell using MODIS Terra Vegetation Indices. The boxplot centreline represents the median, the lower and upper hinges correspond to the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values no further than 1.5 * Interquartile Range from the hinge. 39 Figure S2.4. 2: Average white-tailed deer density (animals/km2) as a function of the coefficient of variation (CV) in habitat alteration (% alteration) and habitat productivity (ΔEVI) in each camera cluster sampling area from 2017 to 2021. 40 Table S2.4. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and productivity when evaluating the effects on white-tailed deer density; mean, coefficient of variation (CV), and their interaction. Variable Metric df AICc Percent habitat alteration Mean 8 -14.983 CV 8 -11.191 Mean*CV 10 -9.985 Habitat Productivity (ΔEVI) Mean 8 -8.181 CV 8 -8.029 Mean*CV 10 -5.884 41 Appendix S2.5: Evaluating lags in annual weather and 5-year climate metrics To quantify spatial variation in climate we summarized climate variables expected to impact deer survival and recruitment during the 5-year period in which deer were monitored, and additionally explored annual variation in these metrics. However, survival and recruitment could be influenced by carry-over effects of previous years, for example through nutritional deprivation (DeMars et al., 2021; Mech et al., 1987; Parker et al., 2009). We therefore explored the possibility of lag effects by evaluating if including a one-year lag was supported within the model competition process. We extracted the same climate metrics for one year prior to when cameras were operating (i.e., 2016). We re-ran the Multi-factoral Analysis (MFA) including metrics for one year prior to the 5-year monitoring period, and tested if the lagged MFA climate dimensions were more supported than the MFA climate dimensions that did not include one-year prior for predicting deer densities using AICc. We also re-ran the Principal Component Analysis (PCA) including metrics one-year prior to monitoring, and tested if annual dimensions matched by year to annual deer densities, or matched to the following year’s deer densities, were most supported using AICc. Models in which a one-year lag in the annual PCA and the MFA had AICc within 2 units of models not including a one-year lag (Table S2.5.1; Figure S2.5.1; Figure S2.5.2). We therefore did not further investigate the influence one lags when evaluating the influence of climate on white-tailed deer densities. 42 Table S2.5. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to evaluate the support for including a one-year lag in climate metrics to test the effects on white-tailed deer density. Variable Metric No Lag One-Year Lag df AICc df AICc Principal Component Analysis Dimension 1 8 -5.291 8 -4.275 Dimension 2 8 -2.518 8 - Dimension 1 + Dimension 2 9 -2.653 9 -4.234 Multi-factoral Analysis Dimension 1 8 -11.119 8 -11.644 Dimension 2 8 -4.266 8 -4.139 Dimension 1 + Dimension 2 9 -12.337 9 -12.799 43 Figure S2.5. 1: Average white-tailed deer density (animals/km2) as a function of annual Principal Component Analysis (PCA) dimension 1 without (top panel) and with (bottom panel) a one-year lag in annual climate metrics. 44 Figure S2.5. 2: Average white-tailed deer density (animals/km2) as a function of the full monitoring period Multi-factoral Analysis (MFA) dimension 1 without (top panel) and with (bottom panel) a one-year lag in annual climate metrics. 45 Appendix S2.6: Analyses reflecting individual cameras as the sample unit We deployed twelve 50-km2 (12.5 km x 4-km) clusters of camera traps, with 25 cameras per cluster. Clusters of cameras were placed along a decoupled gradient of the two main variables of interest, habitat alteration and climate. Given this sampling design, each 50km2 cluster represents areas of interest along the habitat gradients sampled, and individual cameras represent sub-sample replicates of these areas. In this way, camera traps which monitor small slivers of space, act similarly to quadrats that are replicated across sampling areas of interest. Within the main analysis we summarized the density of deer in each 50km2 sampling areas using the mean deer density across the camera replicates per camera cluster, and used the mean density per cluster as the dependent variable, reflecting the cluster as the unit of replication. However, we recognize that the mean may not adequately represent deer density, particularly if there is skewed variation in deer density across cameras within a cluster. We therefore conducted the same analyses as described within the main analysis, but using each individual camera density estimate as the sample unit. Because we were interested in understanding what drives differences in densities across clusters, rather than habitat associations within each cluster, we assigned each camera within a cluster with the mean habitat alteration, climate, and habitat productivity metrics within the cluster. We followed the same two-stage approach as in the main analysis, and again included cluster and year as random intercepts; a random slope for cluster could not be specified because environmental variables were measured at the cluster-level. We also included temporal autocorrelation, and tested for spatial autocorrelation in the residuals of the final model. As with the main analysis, percent direct habitat alteration had a lower AICc than buffered habitat alteration (Table S2.6.1). The MFA Climate Dimension 1 and MFA Climate Dimension 1 and 2 models were within 2 AICc units (Table S2.6.1) and we chose the simplest model structure of the two competing models, MFA Dimension 1, to include within the final modelling stage. When accounting for habitat productivity, deer densities were significantly 46 lower in areas with increasing values of climate Dimension 1 (β = -7.429, SE= 2.466; Table S2.6.2). Figure S2.6. 1: White-tailed deer density (animals/km2) at each camera as a function of winter severity (climate dimension 1; such that winter severity increases as Dimension 1 increases). Density estimates are coloured by year. 47 Figure S2.6. 2: White-tailed deer density (animals/km2) at each camera as a function of percent of the habitat altered within the 50km2 camera cluster sampling area (% Alteration). Density estimates are coloured by year. Figure S2.6. 3: White-tailed deer density (animals/km2) at each camera as a function of median habitat productivity (ΔEVI) within the 50km2 camera cluster sampling area each year. Density estimates are coloured by year. 48 Table S2.6. 1: Akaike Information Criterion corrected for small sample size (AICc) and degrees of freedom for models used to select metrics to quantify habitat alteration and climate when evaluating the effects on white-tailed deer density, where each camera is included as a sampling unit. Variable Metric df AICc Habitat alteration % habitat alteration 8 2117.996 % buffered habitat alteration 8 2123.208 Climate MFA Dimension 1 8 2116.292 MFA Dimension 2 8 2123.545 MFA Dimension 1 + MFA Dimension 2 9 2114.346 PCA Dimension 1 8 2121.407 PCA Dimension 2 8 2124.125 PCA Dimension 1 + PCA Dimension 2 9 2123.219 49 Table S2.6. 2: Model coefficient estimates, standard errors (SE), and p-values of the global model used to test the relative effects of habitat alteration and climate, given habitat productivity, on average deer density (animals / km2), where each camera is included as a sampling unit. All independent metrics were scaled between 0 and 1 as x-minimum(x))/(maximum(x)-minimum(x). Variable Estimate SE P-value Intercept 1.942 2.041 0.341 Habitat productivity -2.812 2.873 0.328 % Habitat alteration -0.124 2.122 0.953 Climate Dimension 1 -7.429 2.466 0.003 Habitat productivity : % Habitat alteration 0.274 2.231 0.902 Habitat productivity : Climate Dimension 1 5.102 3.721 0.17 % Habitat alteration : Climate Dimension 1 3.816 2.626 0.147 50 Appendix S2.7: Additional details on Multiple Factor and Principle Component Analyses Figure S2.7. 1: Visualization of the Multiple Factor Analysis (MFA) used to quantify climate across the 5-year monitoring season. Visualization of where the quantitative variables map across Dimension 1 (Dim 1) and Dimension 2 (Dim 2), coloured by metric type (Panel A), coloured by the Cos2 of each variable (Panel B), and each variable grouped across years and colored by Cos 2 (Panel C). The corresponding variation of the data explained by these two dimensions. Climate metrics are measured for each season and year, and grouped by metric type (SfPrcMax = Maximum Precipitation in the snow-free season; SfPrcMean = Mean precipitation in the snow-free season; SfTMax = Maximum temperature in the snow-free season; SfTMin = Minimum temperature in the snow-free season; SfVpMean = Mean vapor pressure in the snow-free season; SPrcMax = Maximum precipitation in the snow season; SPrcMean = Mean precipitation in the snow season; SSLen = Length of snow season; SSweCum = Cumulative snow water equivalent in the snow season; SSweMax = Maximum snow water equivalent in the snow season; STMax = Maximum temperature in the snow season; STMin = Minimum temperature in the snow season; SVpMean = Mean vapor pressure in the snow season. 51 Figure S2.7. 2: Visualization of the Principle Component Analysis (PCA) used to quantify annual climate. Visualization of where the quantitative variables map across Dimension 1 (Dim 1) and Dimension 2 (Dim 2), coloured by the Cos2 of each variable. Climate metrics are measured for each season and year annually, and grouped by metric type (snowfreeprcmax = Maximum Precipitation in the snow-free season; snowfreeprcmean = Mean precipitation in the snow-free season; snowfreetmamax = Maximum temperature in the snow-free season; snowfreetmimin = Minimum temperature in the snow-free season; snowfreevp_mean = Mean vapor pressure in the snow-free season; snowsnowltotalCum = Length of snow season; snowsnowfalltotalCum = Cumulative snow water equivalent in the snow season; snowswemax = Maximum snow water equivalent in the snow season; snowtmamax = Maximum temperature in the snow season; snowtmimin = Minimum temperature in the snow season; snowvp_mean = Mean vapor pressure in the snow season. 52 Figure S2.7. 3: Principle Component Analysis (PCA) Dimensions 1 and 2 across years, coloured by camera clusters. 53 CHAPTER 2 ADDENDUM: THE INFLUENCE OF HABITAT ALTERATION IS WIDESPREAD, BUT THE IMPACT OF CLIMATE CANNOT CONTINUE TO BE DISCOUNTED Recently, we (Dickie et al., 2024) contrasted the effects of climatic factors and habitat alteration on the density of white-tailed deer, a species implicated in declines of woodland caribou (Latham et al., 2011). Barnas et al. (2024) provided a critique of our study, but misrepresented our overall findings, claiming we reported negligible effects of habitat change on deer. Although we reported stronger climate effects, we repeatedly highlighted that habitat alteration had positive effects on deer density (Appendix S2A.1). Barnas et al. (2024) also suggested a number of putative shortcomings to our study, but we submit that their conclusions are either unfounded or identical to that of Dickie et al. (2024). Barnas et al. (2024) argue that our method for transforming predictor variables affects model inferences. It does not. Model predictions are identical whether the data are minimum-maximum scaled, or z-standardized as proposed by Barnas et al. (2024). These results are unsurprising given the two transformations yield perfectly correlated data (Figure 2A.1). Barnas et al. (2024) then highlight that coefficients for the single-term predictors differ between the two transformations, with the z-standardization yielding similar effect sizes for climate and habitat alteration. Focusing on these coefficients for inference is misguided when interaction effects are included, because these coefficients are conditional (i.e., estimates when other interaction variables = 0). Estimated slopes for predictors are not invariant as the magnitude, and possibly direction, of slope coefficients change as other interaction variables change (Aiken et al., 1991; Mize 2019; Spake et al., 2023). Interaction effects are more appropriately evaluated by plotting predicted responses over the range of the focal predictor at meaningful values of the modifying predictor(s) (Mize 2019; Spake et al., 2023; Figure 2A.1). Barnas et al. (2024) then suggested our model was overfit, resulting in low precision. To support their claim, Barnas et al. (2024) used an information-theoretic approach and found a 54 reduced model was selected over our full model. Although information-theoretic approaches are favored for prediction, they are not optimal for causal inference (Bolker 2024), which was our objective. Our full model reflected our study design, which empirically sampled gradients of climate, habitat alteration, and habitat productivity - factors known a priori to influence deer density. Barnas et al. (2024) also suggested that our approach of using a single density estimate for each camera cluster contributed to an overfit model. As noted in Dickie et al. (2024), whether we specified each camera as the sample unit or used the collapsed data, our results did not fundamentally change. These outcomes suggest that our inferences are not sensitive to overfitting. Finally, we submit that even when considering Barnas et al.’s (2024) reduced model, management implications for woodland caribou remain unchanged, despite Barnas et al.’s (2024) unspecified claims to the contrary. Using their reduced model and setting habitat alteration to the minimum observed value, climate is still predicted to support white-tailed deer densities that are incompatible with self-sustaining caribou populations (Appendix S2A.1). As such, habitat protection and restoration alone are unlikely to halt the northward expansion of deer into caribou range, compelling managers to consider the effects of climate change, which are only predicted to increase in coming decades. 55 Figure 2A. 1: Comparison of z-standardization (Z) transformation (panels a, b, and c) and minimum-maximum (MM) scaling (panels d, e, and f) of predictor variables, and resulting impacts on model predictions. Predicted densities of white-tailed deer (animals/km2) are shown as a function of percent habitat alteration at three levels of habitat productivity (change in the Enhanced Vegetation Index, ΔEVI; panels a and d), climate Dimension 1 (Dim 1), such that winter severity increases as Dim 1 increases, at three levels of habitat productivity (ΔEVI; panels b and e), and habitat alteration at three levels of Dim 1 (panels c and f). Note the y-axis (a) (b) (c) (d) (e) (f) (g) (h) (i) 56 varies across predictor variables, but is consistent across transformations, demonstrating that the estimates and precision are unchanged between the two transformations. The two methods for transforming data yield perfectly correlated and 1: 1 data (R2 = 1 for all combinations; panels g, h, and i), though MM preserves the shape of the raw data. Barnas et al. (2024) contend that our model has overly large standard errors, yet the two transformations yield the same relative precision. MM results in estimates and standard errors that are correspondingly large because they represent a change in the predictor variable from the minimum to the maximum value. In contrast, estimates and standard errors resulting from Z are correspondingly small because a one-unit change in the predictor variable represents only a change of one standard deviation of the predictor. 57 Literature Cited: Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage Publications. Barnas, A., Bradley, A., Burton, C., Kathleen, C., Cote, S., Festa-Bianchet, M., Fryxell, J., St. Laurent, M.H., Fisher, J., (2024). The influence of habitat alteration on density of invading white-tailed deer should not be discounted: a response to Dickie et al., Global Change Biology. Bergerud, A., & Elliot, J. (1986). Dynamics of caribou and wolves in northern British Columbia. Canadian Journal of Zoology, 64, 1515–1529. Bolker, B.M. (2024). Multimodel approaches are not the best way to understand multifactorial systems. Entropy, 26, 506. https://doi.org/10.3390/e26060506 Environment and Climate Change Canada. (2020). Amended recovery strategy for the woodland caribou (Rangifer tarandus caribou), boreal population, in Canada. In Species at Risk Act Recovery Strategy Series (Issue 17, p. xiii + 143pp). www.canada.ca/en/environment-climate-change/services/species-risk-public-registry.html Fuller, T., Mech, D., & Cochrane, J. F. (2002). Chapter 6: Wolf population dynamics. In L. Mech & L. Boitani (Eds.), Wolves: Behavior, ecology, and conservation (pp. 161–191). University of Chicago Press. Messier, F. (1994). Ungulate population models with predation: A case study with the North American moose. Ecology, 75(2), 478–488. Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science, 6, 81–117. https://doi.org/10.15195/v6.a4 Serrouya, R., Dickie, M., Lamb, C., Van Oort, H., Kelly, A. P., DeMars, C., McLoughlin, P. D., Larter, N. C., Hervieux, D., Ford, A. T., & Boutin, S. (2021). Trophic consequences of terrestrial eutrophication for a threatened ungulate. Proceedings of the Royal Society B: 58 Biological Sciences, 288(1943). https://doi.org/10.1098/rspb.2020.2811Latham, A.D.M., Latham, M.C., McCutchen, N.A. & Boutin, S. (2011) Invading white‐tailed deer change wolf–caribou dynamics in northeastern Alberta. The Journal of wildlife management, 75, 204-212. Spake, R., Bowler, D. E., Callaghan, C. T., Blowes, S. A., Doncaster, C. P., Antão, L. H., Nakagawa, S., McElreath, R., & Chase, J. M. (2023). Understanding ‘it depends’ in ecology: A guide to hypothesising, visualising and interpreting statistical interactions. Biological Reviews, 98(4), 983–1002. https://doi.org/10.1111/brv.12939 59 Appendix S2A.1: Supplemental information Section S2A.1.1: “Negligible impact of habitat alteration” Barnas et al. (2024) states: “We generally agree with the authors’ conclusion that spatial variation in winter severity impacts deer population density, but we contend their conclusion on negligible impact of habitat alteration was compromised by their method of transforming explanatory variables and insufficient data for their model.”. However, in Dickie et al. (2024) we make at least six statements that acknowledge the importance of habitat alteration’s effect on deer densities, as well as our finding that such effects were weaker (but not absent) in our study, as illustrated by the following quotes:  “There was a tendency for increasing habitat alteration and habitat productivity to partially mediate the effect of climate Dimension 1 (% Habitat alteration: Climate Dimension 1 β = 3.871, SE = 2.357; Habitat productivity: Climate Dimension 1: β = 4.586, SE = 3.818), though the effect was not statistically significant.”  “While there was a tendency for deer densities to increase with increasing habitat alteration the magnitude of this effect was approximately half that of climate, and was statistically insignificant.”  “Our finding that climate drives the population density of white-tailed deer, aligns with studies on factors affecting deer distribution (Dawe & Boutin, 2016; Kennedy-Slaney et al., 2018). In contrast, some studies, even within these same regions, have consistently demonstrated that habitat alteration influences relative habitat use (Fisher et al., 2020; Fuller et al., 2022), and thus emphasizes the significance of habitat composition and structure in shaping deer behavior (i.e., habitat use).”  “Climate may set the envelope within which white-tailed deer can persist in the boreal forests of western Canada, while habitat alteration may influence their abundance or finer scale spatial distribution within this climatic envelope.” 60  “Habitat alteration is widely believed to be the ultimate cause of caribou declines, acting through increased predator hunting efficiency and disturbance-mediated apparent competition (Johnson et al., 2020; Serrouya et al., 2021).”  “if climate is indeed favoring the northward expansion of white-tailed deer to a greater degree than habitat alteration, restoring habitat is less likely to be effective on its own as white-tailed deer will continue to support high abundances of wolves (Ford et al., 2021; Fuller et al., 2002; Latham et al., 2011).” Further, the word negligible was not used once in Dickie et al. (2024). We therefore recognize that habitat alteration and climate both have an impact on white-tailed deer densities, though results presented in Dickie et al. (2024) suggest that deer densities were more variable as a result of observed differences in winter severity than that of habitat alteration. Section S2A.1.2: Management Implications Barnas et al. (2024) highlight the need for scrutiny, particularly when “conclusions are presented in social debates with direct impacts on conservation decisions by government authorities, as in woodland caribou conservation”. As such we used the reduced model proposed by Barnas et al. (2024) to understand the implications of climate effects on white-tailed deer populations even in the most conservative scenario presented where both habitat alteration and climate have equal but opposing impacts. To support self-sustaining caribou populations, the federal recovery strategy for boreal caribou has adopted a target density below 3.0 wolves/km2 (Environment and Climate Change Canada, 2020), though wolf densities estimated to be associated with self-sustaining caribou populations have varied between 6.5 wolves/km2 (Bergerud and Elliot, 1986; mountain systems) and 1.8 wolves/km2 (Serrouya et al., 2021; boreal systems). The reduced model resulting from the information-theoretic approach proposed by Barnas et al. (2024; Supplemental Materials 1) predicts 1.32 deer/km2 at the minimum observed 61 values of habitat alteration (corresponding to <1% altered habitat) and climate dimension 1 (i.e., if winter severity is at its lowest). Using the equations of Fuller et al. (2002; Figure 6.2): 𝑌 = 3.5 + 3.3𝑋1 where Y is predicted wolf density (wolves/1000km2) and 𝑋1 is the ungulate biomass index (deer/km2), a predicted density of 1.32 deer/km2 is estimated to result in a density of wolves of 7.9 wolves/1000km2. Similarly, we estimated wolf densities as by first converting deer biomass to moose biomass, and using the equation of Messier (1994): 𝑌 =58.7(𝑋2 − 0.03)0.76 + 𝑋2 where Y is predicted wolf density (wolves/1000km2) and 𝑋2 is moose density (/km2). Fuller et al. (2002) estimated that 1 moose/km2 is equivalent to approximately 6 deer/km2, and as such 1.32 deer/km2 is equivalent to 0.22 moose/km2 (Fuller et al., 2002), resulting in a density of 11.4 wolves/1000km2 (Messier 1994). Therefore, estimated densities of white-tailed deer in conditions with minimal habitat alteration and mild winters will result in estimated wolf densities well above targets associated with self-sustaining caribou, even without the consideration of other ungulate biomass. The original model presented by Dickie et al. (2024), and reiterated by Barnas et al. (2024), predicts even higher deer densities when habitat alteration is at its minimum and climate effects are least. Nonetheless, we used the model proposed by Barnas et al. (2024; where habitat and climate have similar magnitude, using the reduced, information theoretic approach) to illustrate our point in the most conservative manner. We again contest that while habitat alteration likely has an effect on white-tailed deer densities, and this effect likely interacts with that of climate and habitat productivity, there is a need to consider climate effects when designing management plans. These results highlight 62 that habitat protection and restoration alone may be insufficient to recover caribou populations to self-sustaining status, particularly at the southern edge of their distribution. 63 64 CHAPTER 3: HOW DOES ANTHROPOGENIC HABITAT ALTERATION AFFECT THE HOME RANGE SIZE OF AN APEX PREDATOR? Introduction Maximizing energy acquisition while minimizing energy expenditure is a fundamental trade-off faced by all organisms (Charnov, 1976). For mobile organisms, foraging theory centers around the behavioural decisions animals make to optimize this energetic trade-off, including the effort spent on searching for and exploiting resources, exposure to predation risk, and diet preferences (Brown, 1999; Pyke et al., 1977). These behavioural decisions can scale up into patterns of distribution and abundance for both consumers and resources (Fretwell & Lucas, 1969). Hence, the manner in which animals use space offers an information-rich paradigm through which we can understand aspects that limit the abundance of organisms. A key process that links individual movement to the abundance and distribution of species is the formation and maintenance of home ranges, i.e. the space used to meet life-history requirements (Burt, 1943). Home range size ties space use to regional density through the ‘packing’ of home ranges (Wang & Grimm, 2007). Much like trade-offs consumers face while foraging, the size of a home range reflects a trade-off between the costs and benefits associated with acquiring resources, such as food and mates, over a given period of time (McNab, 1963). For species that defend a portion of the home range, i.e. territory, the benefit of exclusive use of resources is balanced with the costs of defensive behaviours and aggressive interactions (Brown, 1964; Cubaynes et al., 2014). As such, territorial animals are expected to defend a home range large enough to meet their needs, but no larger (Calsbeek & Sinervo, 2002). For both territorial and non-territorial species, one key extrinsic determinant of home range size is forage resource availability, with home range size decreasing in more productive environments (Kodric-Brown & Brown, 1978; Snider et al., 2021). While resource availability is often considered synonymous with resource density, availability is also related to the consumer’s ability to exploit those resources through movement 65 (here termed “resource exploitation”; Pyke et al., 1977). Satiating processes can also decouple changes in resource density with changes in consumption rates, further complicating how resource density and exploitation influence home ranges. When resource density is low, resource exploitation likely becomes increasingly important for determining encounter and consumption rates, whereas when resource density is high, handling time can impose an increasingly large influence on consumption rates (Holling, 1959a). Despite the well-known inverse relationship between resource density and home range size, it is unclear how resource density and exploitation interact to influence home range formation and maintenance. Resource exploitation is influenced by movement costs imposed by habitat heterogeneity and landscape permeability (Johnson et al., 1992). Despite the understanding that habitat heterogeneity can dramatically impact movement (Tucker et al., 2018), the link between movement costs and the formation and maintenance of home ranges is unclear. Two hypotheses with opposing predictions can be invoked to predict how consumers will respond to variation in movement costs. Following marginal value theory, consumers in landscapes with low movement cost can reduce patch-residence times (Cowie, 1977; Ford, 1983), which leads to exploitation of additional patches. Consequently, travel between a greater number of patches leads to diffusive movements (Turchin, 1991) and the formation of large home ranges (Broadley et al., 2019), here termed the Diffusion Facilitation Hypothesis (Figure 3.1). However, following energetics theory, consumers in landscapes with low movement cost can instead expend less energy on travelling, and are therefore able to maintain a positive energy balance while exploiting lower resource density (Mitchell and Powell, 2004). A smaller area is therefore needed to maintain energetic demands, leading to the formation of small home ranges (Mitchell & Powell, 2004; Pyke et al., 1977), here termed the Exploitation Efficiency Hypothesis (Figure 3.1). The contrasting outcomes of how movement costs will affect home range size suggests that a more mechanistic understanding of resource exploitation is needed, particularly given the 66 important consequences for understanding trophic interactions in changing landscapes (Lewis & Murray, 1993; Morales et al., 2010). Anthropogenic habitat alteration has modified landscape permeability, having implications for the movement of many taxa across the globe (Tucker et al., 2018). In the boreal forest of North America, one of the most predominant forms of habitat alteration, particularly in western Canada, are linear features such as roads, seismic lines, or pipelines. Linear features facilitate the movement of predators like wolves (Canis lupus; Dickie et al., 2017, 2020a), which is hypothesized to increase their encounter rates with prey, and thus kill rates (McKenzie et al., 2012). Increased wolf kill rates have important consequences for woodland caribou (Rangifer tarandus), which are in decline across much of their range as a result of increased predation (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021; Wittmer, Sinclair, et al., 2005). Although it is increasingly recognized that linear features are important for facilitating wolf movement (Dickie et al., 2017), how this process scales up to broad patterns of habitat use is unclear. Under the Diffusion Facilitation Hypothesis, increased movement efficiency from linear features would lead to diffusion and large home ranges. Conversely, under the Exploitation Facilitation Hypothesis, increased movement efficiency from linear features would increase foraging efficiency and lead to small home ranges. If linear features increase the ability of wolves to exploit a given density of resources, there may be a disconnect between resource density and “effective” resource density (i.e. the resource density a forager is successfully able to exploit in a given time and space). In this study we explore how resource density and exploitation combine to affect the home range size of wolves. First, we evaluate if home range area (i.e. a metric of size) is best explained by primary productivity (i.e. a metric of resource density), linear feature density (i.e. a metric of landscape capacity for resource exploitation), or the interaction between productivity and linear feature density. We predicted that the effect of resource exploitation on home range size is dependent on resource density, such that increased access to resources becomes more 67 important when resource density is low. Next, we evaluate how resource exploitation affects home range size of wolves (Figure 3.1). If higher linear feature density facilitates diffusion across more resource patches (Diffusion Facilitation Hypothesis), we predict a positive relationship between home range area and linear feature density. Conversely, if higher linear feature density decreases the energy required to maintain a positive energy balance (Exploitation Efficiency Hypothesis), we predict a negative relationship between home range area and linear feature density. We contrast the support for these hypotheses using data from 142 GPS-collared wolves, distributed over more than 500,000 km2, across a broad gradient of linear feature density and primary productivity. 68 Figure 3. 1: Conceptual diagram of how the Diffusion Facilitation Hypothesis and the Resource Exploitation Hypothesis predict contrasting relationships between movement cost and home range size. Under the Diffusion Facilitation Hypothesis, consumers in areas with lower movement cost (grey = lower cost, black = higher cost) have lower inter-patch travel times and earlier optimal leaving time (dashed lines). Diffusive movements as consumers leave earlier to exploit more patches leads to a negative correlation between movement cost and home range size. Under the Resource Exploitation Hypothesis, consumers in areas with lower movement costs (lower energy expenditure) require less energy acquisition to maintain positive energy balances. Consumers are able meet energetic requirements in smaller areas, leading to a positive correlation between movement cost and home range size. 69 Methods Study Area We used GPS telemetry data collected from previous research programs located across four sub-regions of northwestern Canada: northeastern British Columbia (BC), northern Alberta (AB N), northeastern Alberta (AB NE), and Saskatchewan (SK; Figure 3.2). The study area spanned ecoregions of the Boreal Plains and Shield, as well as the Taiga Plains and Shield, and is represented by a mosaic of uplands and peatlands (Appendix S3.1). Forest cover is predominantly coniferous (black spruce Picea mariana, white spruce Picea glauca, jack pine Pinus banksiana and tamarack Larix laricina), with some deciduous and mixedwood forest (trembling aspen Populus tremuloides, white birch Betula papyrifera and balsam poplar Populus balsamifera). The Boreal and Taiga Shield are represented by wetter, less productive habitat and shorter fire return intervals than the Boreal and Taiga Plains. The study area is undergoing anthropogenic habitat alteration via the cutting of forests for timber and oil and gas exploration (Alberta Biodiversity Monitoring Institute, 2018a), and the conversion of forests to agriculture (Hobson et al., 2002). Population density within the study area was below 1 person/km2. Sub-regions varied between 0.1 and 0.8 people/km2 (Statistics Canada, 2017). Additional information about the habitat and human use of the study area are provided in Appendix S3.1. 70 Figure 3. 2: Seasonal wolf home ranges, estimated using 95 % autocorrelated kernel density estimators, across a range of linear feature density (km/km2) and primary productivity (ΔEVI) in northwestern Canada. Grey lines represent linear features within the boreal forest, and wolf home ranges are coloured to represent the surrounding linear feature density within each home range. Data were collected from 36 individuals in British Columbia, 34 individuals from northern Alberta, 44 individuals from northeastern Alberta, and 28 individuals from Saskatchewan. In the inset map depicts the productivity gradient (ΔEVI) across the study area, calculated as the median EVI in the 2020 leaf-on season minus the median EVI value in the 2020 leaf-off season. Black lines represent the provinces and territories of Canada. 71 Quantifying Home Range Size GPS telemetry data were collected between 2011 and 2017 from 36 individuals in 15 packs (BC), 34 individuals from 10 packs (AB N), 44 individuals from 16 packs (AB NE), and 28 individuals from 19 packs (SK). All animals were captured and handled using approved Animal Care protocols. One to seven individuals were monitored per pack per year, with a median of two individuals. Collars were programmed to retrieve GPS locations at varying intervals, from 5 minutes to 3 hours. We visually checked for location errors, and rarified data to the largest interval between fixes (i.e. 3-hour locations). Because individuals may shift their home ranges across seasons, we divided data into two seasons, roughly corresponding to the snow (November through April) and snow-free (May through October) seasons. We were unable to delineate the defended and undefended portion of the home range to quantify territory size. We therefore opted to evaluate wolf “home range” size as per (Ciucci et al., 1997; Mancinelli et al., 2018; Mattisson et al., 2013), while recognizing that a portion of the home range is defended. To quantify home range area we created 95% autocorrelated Kernel Density Estimators (aKDEs; Fleming et al., 2015; Noonan et al., 2019) for each individual in each season using the ctmm package in R (Calabrese et al., 2016). Prior to creating aKDEs, we removed any individual season combination with less than 50 locations and visually assessed and removed individual seasons with extra-territorial forays. As suggested by Calabrese et al (2016), we used Maximum Likelihood to fit independent identically distributed, Brownian motion, Ornstein–Uhlenbeck, integrated Ornstein–Uhlenbeck, and Ornstein–Uhlenbeck Foraging models, and selected the model with the lowest Akaike Information Criterion (AIC) score (Akaike, 1974). The number of seasonal individual home ranges per study sub-region, year, and season are presented in Appendix S3.1. Environmental Attributes We quantified environmental attributes available in each individual’s seasonal home range. To represent the area in which individuals defined their home range, we buffered the centroid of each seasonal home range by the average radius of seasonal home 72 ranges (mean = 50 km). We calculated the density of linear features within each of these buffered centroids using a publicly available dataset of disturbances (Environment and Climate Change Canada, 2012). This dataset uses LANDSAT imagery at a 30 m cell-size, which may under-estimate the density of linear features that are typically smaller than this cell-size. However, these data are consistently collected across boreal Canada to allow comparisons across study sub-regions. Increased moose (Alces alces) density has been associated with increased wolf density (Fuller, 1989; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). However, in the absence of robust prey density estimates within each study sub-region, we used primary productivity as a surrogate for resource density. Remote-sensing indicators of primary productivity have been shown to represent habitat quality for ungulates (Street et al., 2015). We used the Enhanced Vegetation Index (EVI) as a metric of primary productivity, and assumed that this metric was related to wolf prey density (Bergerud & Elliot, 1986; Street et al., 2015). We tested our assumption that primary productivity is an appropriate index of wolf prey density by evaluating the relationship between moose density and primary productivity in a sub-analysis (Figure 3.3; see Appendix S3.2 for methodological details). 73 a b Figure 3. 3: Panel a: Moose densities (no./km2) across a primary productivity (ΔEVI) gradient in northern Alberta. Boundaries of the moose survey areas are coloured to represent the estimated moose densities within each moose survey area from aerial surveys. The productivity gradient (ΔEVI) across the study area, calculated as the median EVI in the 2020 leaf-on season minus the median EVI value in the 2020 leaf-off season. Black lines represent the provinces and territories of Canada. Panel b: Estimated moose density (no./km2) as a function of primary productivity (average ΔEVI) within each moose survey area in northern Alberta. The solid line represents the predicted relationship between moose density and ΔEVI while including year, survey method and region as fixed effects and set at the reference conditions (year = 2014, method = distance sampling, region = boreal). Dashed lines represent 95 % Confidence Intervals of the predicted relationship. 74 We extracted EVI using the MODIS Terra Vegetation Indices 16-Day Global, with a 500m cell size. For each year the median EVI value from the leaf-on (July 1 – August 1) and leaf-off (September 1 – October 1) were extracted as measures of productivity at peak vegetation growth, and when deciduous vegetation has died back, but before snow falls, respectively. The leaf-off median EVI was subtracted from the leaf-on median EVI to calculate the change in EVI (ΔEVI). To remove pixels covered by clouds or shadows we retained only pixels classified as “VI produced, good quality”. Pixels covered by water were removed Hansen et al. (Hansen et al., 2013). Analytical Framework To test if wolf seasonal home range area was driven by resource density, resource exploitation, or the interaction, we compared the support for models including linear feature density only, ΔEVI only, or the interaction of linear feature density and ΔEVI in a model comparison framework using Akaike’s Information Criterion (AIC; Akaike, 1974). First, we determined the random effects structure to account for non-independence between multiple seasonal home ranges from the same individual and individuals in the same pack. We included all fixed effects and discriminated against animal as a random intercept, animal and pack as random intercepts, and animal nested within pack as a random intercept using Restricted Maximum Likelihood (Zuur et al., 2009). We then incorporated the random intercept structure with the lowest AIC in subsequent model selection. We additionally tested the additive effect of linear feature density and primary productivity to understand if the interaction effect was spurious. To account for unmeasured differences across the four sub-study regions, as well as differences among years and between the snow and snow-free season, we discriminated between models including fixed effects and interactions for study region, season, and year using Maximum Likelihood (Zuur et al., 2009). We included the monitoring period, defined as the number of days each individual was monitored, scaled between 0 and 1, as a fixed effect in all 75 models to account for variation in the duration of monitoring. We then incorporated the basic fixed effects model structure with the lowest AIC in subsequent model selection to compare the support for models including linear feature density only, ΔEVI only, or the interaction of linear feature density and ΔEVI. We scaled ΔEVI between 0 and 1 to place it on a similar scale to linear feature density, and scaled monitoring period between 0 and 1. Finally, to compare the support for the Diffusion Facilitation Hypothesis and Resource Exploitation Hypothesis we evaluated if there is a positive or negative slope, respectively in the above most supported model. We plotted the raw data and predicted response curves of home range size as a function of linear feature density and scaled ΔEVI using the most competitive models. We held other continuous variables constant at the mean, and categorical fixed effects at the reference category. We evaluated the predictive capacity of the final model using Leave One Out (LOO) model validation (Zuur et al., 2009). Pack Size and Sex Previous research has shown that wolves adapt territory size, not pack size, to local resource density (Kittle et al., 2015; Messier & Crête, 1985). However, it is possible that smaller home range supports a lower pack size and does not result in a numerical response of wolf population size to prey density. Furthermore, males and females typically differ in their home range size (Kodric-Brown & Brown, 1978). Sex and pack size (estimated as the minimum count of pack members) were estimated during captures but were not available for all individuals. Additionally, pack cohesion varies by season, and as such pack size at capture may not necessarily reflect seasonal variation in pack sizes (Metz et al., 2011). For these two reasons we used sub-analyses to test if our interpretations were sensitive to the effects of sex and pack size (Appendix S3.3), but did not incorporate sex and pack analyses into our primary analyses. First, using only individuals where pack size and sex were estimated, we tested if including these variables lowered the AIC score of the top competing model above. Second, we 76 modelled the residuals of the top competing model as a function of pack size and sex to evaluate if the discrepancy between predicted and observed home range area in our main analysis was influenced by these variables. We found no evidence that pack size or sex improved model fit or influenced the relationship between observed and predicted wolf seasonal home range area (Appendix S3.3). Data Accessibility All data are available from Dryad doi:10.5061/dryad.ns1rn8pth and code and data used for analyses are available from https://doi.org/10.5281/zenodo.5643218. Results Primary productivity (ΔEVI) as an index of prey density Average moose density increased by 1.12 times for every 100-unit increase in ΔEVI (β = 0.001, SE = <0.001, p = < 0.001; Figure 3.3, Appendix S3.2). Wolf home range size The top competing model included linear feature density, ΔEVI, and the interaction between linear feature density and ΔEVI, along with monitoring period, season, and study sub-region as basic fixed effects, and pack-year as a random intercept (Appendix S3.4). While the additive model of linear feature density and ΔEVI had similar support as the interaction model (Appendix S3.4), we provide effect sizes based on the interaction model which explicitly tests our hypothesis. See Appendix S3.4 for effect sizes and model output for the additive model. Seasonal wolf home range area significantly decreased as linear feature density and ΔEVI increased (Table 3.1, Figure 3.4). The effect of linear feature density diminished as ΔEVI increased (Figure 3.4). For example, when ΔEVI was at the lowest value observed, average wolf home ranges were 2.7 times larger (snow: 21,211 km2 to 7,760 km2; snow-free: 13,354 km2 77 to 4,885 km2) as linear feature density increased from 0 to the mean linear feature density (i.e., 0.18 km/km2). Comparatively, when ΔEVI was at its mean, the average wolf home range was only 1.5 times larger (snow: 4,878 km2 to 3,186 km2; snow-free: 3,071 km2 to 2,006 km2) as linear feature density increased from 0 to the mean linear feature density. Additionally, wolf home range size was significantly smaller, on average, in the snow-free season than the snow season, and increased with increasing monitoring period (Table 3.1). Linear feature density and ΔEVI were correlated at 0.44. Nakagawa’s conditional R2 for the model was 0.73 and marginal R2 was 0.40. The adjusted R2 from LOO model validation was 0.35. On average observed seasonal home ranges were 1.12 (SE = 0.07) times larger than predicted home ranges. 78 Table 3. 1: The effect of linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Coefficient Estimate - CI + CI Intercept 9.713 8.877 10.550 Monitoring Period 0.431 0.221 0.640 Snow free -0.463 -0.602 -0.324 AB NE -1.114 -1.432 -0.796 SK -1.352 -1.888 -0.817 AB N -1.484 -1.844 -1.125 Linear feature density -5.467 -9.403 -1.530 ΔEVI -2.660 -3.824 -1.497 Linear feature density * ΔEVI 5.704 -0.541 11.948 79 Figure 3. 4: Wolf seasonal home range area (km2), calculated using individual 95% autocorrelated density estimators, as a function of linear feature density (km/km2) and primary productivity (ΔEVI, scaled between 0 and 1) within each home range. Raw values are presented as points for each study sub-region, and the predicted relationship is shown for the reference categories (snow season and BC) with Monitoring Period set to the mean. BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Discussion We assessed whether resource density, resource exploitation, or the interaction between the two influenced the home range size of wolves, which are an apex predator in North America’s boreal forests. We found that wolf home range size was influenced by the interaction between linear feature density and primary productivity, suggesting that linear features increase the ‘“effective” density of resources by increasing the energy gained per unit time and distance, while reducing energy spent (Charnov, 1976; Pyke et al., 1977). We then evaluated the support for two competing hypotheses linking exploitation efficiency to home range size. Wolf home 80 range size decreased as linear feature density increased, supporting the Exploitation Efficiency Hypothesis. The effect of linear features on home range size diminished in more productive areas, suggesting that exploitation efficiency is of greater importance when resource density is low. Our results suggest that facilitated access to prey can increase regional consumer density, which is supported by studies that found that wolf habitat use and density increased with disturbance-mediated productivity (Kittle et al., 2017; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Increased movement is typically predicted to increase encounter rates between consumers and resources, therefore increasing kill rates (Holling, 1959a; Morales et al., 2010). Under the Diffusion Hypothesis, movement costs could even become so low that predators only consume the highest quality portion of the food item before moving to the next prey item, resulting in surplus killing (DelGuidice, 1998). However, under the Exploitation Efficiency Hypothesis kill rates may instead remain constant, or even decrease, if facilitated movement decreases the energy required to survive and reproduce, as suggested by our results. Although kill rates within each home range may not increase as energy requirements decreases, higher predation rates may still occur at a regional scale because small wolf home ranges may result in higher wolf densities as more home ranges can be ‘packed’ into the landscape (Wang & Grimm, 2007). More efficient predators may even be able to devote more energy to reproduction, leading to a feedback between the functional and numerical response (Holling, 1959b). In the case of prey populations limited by predation, an increased predation rate will decrease the density of prey (Holling, 1959a), having top-down consequences on the food chain. Increasing the exploitative efficiency of predators should decrease the prey density needed for a given predator density to be sustained (Rosenzweig & MacArthur, 1963), resulting in a system that moves away from a stable equilibrium, therefore becoming cyclic and more sensitive to stochastic events. Understanding how facilitated exploitation of a given resource 81 density effects kill rates, predation rates, and predator-prey interactions is fundamental to predicting responses to landscape change. Wolves are territorial (Mech & Boitani, 2003a), and as such intraspecific aggression associated with territorial defense decreases survival and is hypothesized to limit wolf populations (Cubaynes et al., 2014). Increased movement efficiency may also facilitate territory-defense behaviours such as patrolling and scent marking. Reducing the cost of these behaviours may allow some species to defend larger territories, thereby increasing access to resources (Moorcroft & Lewis, 2006). Sells and colleagues (2021) also observed smaller wolf territories in areas with higher road density, and posited that this effect was due to decreased cost of travel despite pointing out the perplexity that territory size should increase as a result of decreased defensive costs. Because smaller home ranges have a higher perimeter to volume ratio, the relative cost of territorial defense may in fact be higher with smaller home ranges. Our finding that increased linear feature density was related to smaller home ranges suggests that exploitation efficiency overcomes the added cost of higher defense costs. With the increased availability of movement datasets across taxa, future work should test the role of resource density and exploitation efficiency across territorial and non-territorial species to understand the relative role of territory defense. While we found that increased linear feature density decreased wolf home range size in our system, there may be non-linear effects that were not observed in our study. There is likely a point in which further increases in linear features no longer facilitate movement or even disrupt movement (Muhly et al., 2019). In this situation, diffusion facilitation may play a more prominent role and home range size may increase with increasing linear feature density. Further, increased linear feature density may even pose costs to wolves and their prey. For example, linear features may facilitate human access, thereby increasing hunter harvest of ungulates (Rempel et al., 1997). In this case, increased linear feature density likely does not represent increased exploitation efficiency, but instead depressed prey density. In systems where roads 82 that are used frequently by humans are the predominant linear feature, movement may not be facilitated and instead lead to increased home range size (Thompson et al., 2021). Because boreal western Canada has relatively low human population densities, human use of linear features is low (Appendix S3.1); particularly for seismic lines and pipelines (Pigeon et al., 2016). We posit that we did not observe non-linear patterns in our study system because the predominant linear features increase wolf hunting efficiency but infer relatively low costs to wolves and their prey. Our finding that home ranges are smaller in areas with increased resource density is predicated on the assumption that primary productivity is a measure of wolf prey density (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021; Street et al., 2015). We found that moose density, a primary prey species for wolves in these systems (Fuller & Keith, 1980; James et al., 2004), increased with increasing primary productivity, supporting this assumption. However, invading white-tailed deer (Odocoileus virginianus) populations are increasingly becoming a dominant prey species for wolves (Latham, Latham, McCutchen, et al., 2011; Latham et al., 2013). While primary productivity is a reasonable index of ungulate density in the boreal forest, other prey species such as beavers (Latham et al., 2013) are not accounted for in our analyses. Diet composition likely influences the caloric gains relative to the caloric costs associated with foraging and consumption, as well as the distribution and abundance of resource patches (Ford, 1983; MacArthur & Pianka, 1966). We expect that movement between resource patches, i.e. resource exploitation, becomes increasingly important for smaller prey with lower handing time. However, the relationship between consumer density, home range size, and the density of various prey species should be further explored. The link between resource density, resource exploitation, and home range size has applied implications for species management. In the system we present here, restoration of linear features is identified as a priority for the management and recovery of threatened woodland caribou (Environment Canada, 2014). The interaction between linear feature density 83 and primary productivity suggests that restoring linear features in areas of low productivity will have disproportionately large effects by simultaneously decreasing foraging efficiency and regional wolf density. In contrast, in high-productivity areas, decreasing vagility may reduce foraging efficiency (Johnson et al., 2019; Serrouya et al., 2020; Spangenberg et al., 2019), but will not effect home range size and thus regional density. Our works adds to the growing demand for studies to better link movement ecology and ecological theory to conservation (Allen & Singh, 2016). 84 Literature Cited Akaike, H., 1974. A new look at the statistical model identification. Autom. Control. IEEE Trans. 19, 716–723. Alberta Biodiversity Monitoring Institute, 2018. 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Springer. 10.1007/978-0-387-87458-6 92 Appendix S3.1: Additional details on study area and wolf sampling intensity Study Area Details – In this study we explore how resource density and exploitation combine to affect the home range size of wolves. Wolves use linear features to facilitate travel (Dickie et al., 2017; McKenzie et al., 2012). Facilitated travel increases encounter rates with prey, therefore increasing hunting efficiency, particularly for cursorial predators (Holling, 1959a; McKenzie et al., 2012). Linear feature density can therefore be considered a metric of the landscape’s capacity for wolves to exploit a given density of resources. These linear features span a large area with high variation in primary productivity, and thus high variation in primary prey density (Appendix S3.2). Because the study area is characterised by a broad gradient in both linear feature density, which is a metric of resource exploitation, and primary productivity, a metric of resource density, it provides an excellent opportunity to test the effect of a forager’s ability to exploit a given resource density on home range size. The primary productivity gradient observed across the study area reflects underlying landcover, human habitat alteration, and climatic gradients such as temperature and precipitation (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021; Zhu et al., 2016). While primary productivity is indeed a useful metric of moose density (Appendix S3.2), many of these habitat variables underlying primary productivity also directly effect moose densities (Maier et al., 2005). Therefore, in this Appendix we provide additional habitat information about the study area, including landcover, human influence, and human habitat alteration to facilitate understanding of the study area. All four sub-regions are characterized by a mosaic of upland and wetland forests, though the boreal shield has a larger cover of barren areas, shrubland and grassland peatlands (Figure S3.1.1). Broadleaf forests are associated with high primary productivity, whereas coniferous forests, grasslands and wetlands are associated with lower primary productivity (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Areas with increased human habitat alteration have higher primary productivity. For example, primary productivity increases 93 following forestry harvest, and remains elevated for more than a decade post harvest (Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). The study area is characterised by a low human influence index (Wildlife Conservation Society 2005; Figure S3.1.2). Human population densities range from 0.1 people / km2 in BC and SK, to 0.8 people / km2 in AB N and AB NE. Hunter harvest pressure within the region is low. For example, throughout the study period when moose hunter harvest data were publically available (2012 through 2015), 2% of the moose harvested in the Alberta were harvested within the Wildlife Management Units that overlapped by our study area (https://open.alberta.ca/publications/hunter-harvest-report-moose-estimated-resident-harvest-for-moose). The majority of human habitat alteration within our study area is consequence of the forestry and energy sectors, with some occurrence of agriculture in constrained areas. For example, within Alberta’s Lower Athabasca Region, human habitat alteration has directly impacted approximately 8.7% of the land (2.8% agriculture, 2.4% forestry, and 2.1% energy sector; Alberta Biodiversity Monitoring Institute, 2018). In general, human habitat alteration types are spatially correlated; areas with high densities of linear features tend to have high amounts of polygonal habitat alteration types as well (Figure S3.1.3). While polygonal habitat alteration has been found to impact wolf habitat selection, their movements are not influenced by these features (Dickie et al., 2020b). The three sub-regions predominantly located in the higher-productivity boreal plains and cordillera (AB N, AB NE, BC) are characterised by a mix of agriculture, energy and forestry sectors. Linear features in these regions include primary and secondary transportation roads, forestry roads, and geophysical lines (seismic lines and pipelines). In contrast, the sub-region predominantly within the boreal shield (SK) is characterised by low human land-use. Geophysical exploration within the boreal shield is less omnipresent than on the boreal plains due to the depth of the oil and gas deposits. Therefore, while the density of primary and 94 secondary roads is similar, linear features associated with geophysical exploration are less common in Saskatchewan. 95 Figure S3.1. 1: Landcover across the study area (Canada Centre for Remote Sensing, 2017). We combined landcover classes for visual purposes (coniferous = temperate and sub-polar needleleaf, shrubland = temperature shrubland and sub-polar shrubland, grassland = temperate grassland and sub-polar grassland, barren = barren lichen and barren, urban and cropland = cropland and urban. Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. 96 Figure S3.1. 2: Human Influence Index across the study area (Wildlife Conservation Society 2005). Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. 97 Figure S3.1. 3: Linear and polygonal features across the study area, within the ECCC mapping boundary (Environment and Climate Change Canada, 2012). Polygonal disturbances include cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances include roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, and unknown features. Ecozones are outlined in black and labelled. Provincial boundaries are provided in white. Sampling Intensity Details - GPS telemetry data were collected from 2011 to 2017 from 36 individuals in 15 packs (BC), 34 individuals from 10 packs (AB N), 44 individuals from 16 packs 98 (AB NE), and 28 individuals from 19 packs (SK). Collars were programmed to retrieve GPS locations at varying intervals, from 5 minutes to 3 hours. We visually checked for location errors, and rarified data to the largest interval between fixes (i.e. 3-hour locations). We divided data into two seasons, roughly corresponding to the snow (November through April) and snow-free (May through October) seasons. The number of seasonal individual home ranges per study sub-region, year, and season are presented in Table S3.1.1. Table S3.1. 1: Number of individual wolves monitored for each season (snow and snow-free) and year across four study sub-regions in western Canada. BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Year BC AB NE AB N SK Snow Snow-free Snow Snow-free Snow Snow-free Snow Snow-free 2011 0 0 0 0 19 0 0 0 2012 4 0 18 0 18 19 0 0 2013 15 11 24 17 10 8 22 0 2014 13 12 19 16 0 5 22 16 2015 9 5 0 22 0 0 7 14 2016 5 6 0 0 0 0 0 1 2017 0 2 0 0 0 0 0 0 99 Appendix S3.2: Evaluating the effect of primary productivity on moose density We used publicly available moose density estimates to evaluate if moose density increased as ΔEVI increased. Because moose are an important game species, they are consistently inventoried by the Government of Alberta using aerial surveys. This analysis was conducted across a restricted portion of our study area, where estimates of moose density were available. Moose density estimates were available across 37 survey units that covered the majority of wolf home ranges from Alberta, and extended beyond the monitored wolves (219, 361 km2). We extracted the mean ΔEVI for each survey unit following the methods in the main text, with ΔEVI matched to the year of the moose survey. In Canada’s western boreal forest, wolves primarily prey on moose (Alces alces), but also deer (Odocoileus spp.), and small prey such as beaver (Castor canadensis; (Latham, Latham, McCutchen, et al., 2011). Estimates for other prey species for this region were not available. We modelled moose density as a function of ΔEVI. The Government of Alberta predominantly surveyed moose using distance sampling (Buckland et al., 2004), but also used random block surveys (Gasaway et al., 1986). To account for possible variation due to survey methodology, we included survey method as a fixed effect. We included year as a fixed effect to account for possible yearly variation in moose density, as well as survey differences across years that may impact estimates such as observers and survey conditions. Finally, we included whether the survey area was within the parkland or boreal forest to account for habitat differences not measured using ΔEVI, for example differences in human land-use types in the boreal (forestry and energy disturbances) relative to the parkland (agriculture). We transformed moose density using the natural logarithm to meet residual-normality assumptions. Average moose density increased by 1.12 times for every 100-unit increase in ΔEVI (Table S3.2.1). Moose density also varied by year: densities were lower in 2014 compared to all years except 2017. Moose density was 2.8 times lower in the parkland region compared to the boreal (Table S3.2.1). Survey method did not influence estimated moose densities. 100 Table S3.2. 1: The effect of primary productivity (ΔEVI) year, survey method and region on the estimated (log transformed) moose density (no./100 km2) from aerial surveys in Alberta from 2015 to 2020. Model estimates, standard error (SE) and P-values are shown. Distance sampling, 2014 and the boreal are set as the reference categories. Significant estimates (P-value <0.05) are bolded. Coefficient Estimate SE P Intercept -4.615 0.442 <0.001 ΔEVI 0.001 <0.001 <0.001 Year 2015 0.896 0.284 0.004 Year 2016 0.651 0.260 0.019 Year 2017 0.574 0.300 0.066 Year 2018 0.627 0.254 0.020 Year 2019 0.889 0.256 0.002 Year 2020 1.058 0.480 0.036 Method (random block) 0.213 0.457 0.645 Region (parkland) -1.040 0.361 0.008 101 Appendix S3.3: Evaluating the effect of pack size and sex on wolf home range size More, smaller home ranges may not result in a numerical response of wolf population size to prey density or linear feature density if each home range supports a lower pack size. Furthermore, males and females typically differ in their home range size (Kodric-Brown & Brown, 1978). Therefore, to evaluate the sensitivity of our results to pack size or sex, we evaluated if including pack size and sex was supported in an Information Theoretic framework and if including these variables changed the interpretation of the main variables of interest; linear feature density, ΔEVI, and their interaction. Furthermore, we evaluated if the residuals from the model presented in the main manuscript were significantly influenced by pack size or sex, or if pack size influenced how much a given pack-year deviated from the mean observed home range size. We used AIC to test if including pack size or sex in the top competing model presented in the main results was supported. We compared the main model (hereby termed the “base” model), which included monitoring period, season, study sub-region, and linear feature density interacting with ΔEVI, to the base model with pack size included, with sex included, and with both pack size and sex included. The competing model with the lowest AIC score included pack size (Table S3.3.1). However, the model was less than 2 AIC units lower than the base model (Table S3.3.1). These results suggest that including pack size or sex when modelling home range size as a function of linear feature density and ΔEVI was not supported. Because models that included sex and pack size were within 2 AIC units of the base model, we further tested if pack size or sex significantly influenced home range size, and if including pack size or sex changed the interpretations of linear feature density and ΔEVI. The confidence intervals of pack size and sex both overlapped zero, suggested that neither variable significantly influenced home range size (Table S3.3.2). Furthermore, including pack size and sex also does not change the direction of the estimate or significance, as defined by confidence 102 intervals not overlapping zero, of the effect of linear feature density, ΔEVI, or their interaction (Table S3.3.2). Table S3.3. 1: Akaike’s Information Criterion (AIC), degrees of freedom (df) and ΔAIC from the “base” model presented in the main manuscript to test the support for including pack size and sex in models of wolf seasonal home range area. The base model includes monitoring period, season, study sub-region, and linear feature density interacting with primary productivity (ΔEVI). Model df AIC ΔAIC Base 11 457 0 Base + Sex 12 459 2 Base + Pack Size 12 456 -1 Base + Sex + Pack Size 13 458 1 103 Table S3.3. 2: The effect of pack size, sex, linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Coefficient Estimate - CI + CI Intercept 8.970 7.980 9.960 Monitoring Period 0.428 0.208 0.649 Males 0.036 -0.101 0.173 Pack Size 0.055 -0.006 0.116 Snow free -0.403 -0.551 -0.256 AB NE -0.861 -1.274 -0.447 SK -1.040 -1.668 -0.413 AB N -1.545 -2.095 -0.995 Linear feature density -7.141 -11.232 -3.050 ΔEVI -2.327 -3.491 -1.164 Linear feature density * ΔEVI 8.176 1.808 14.545 We also evaluated if the difference between predicted and observed home range sizes from the base model was significantly predicted by pack size or sex (Figure S3.31). The difference between observed and predicted individual-season home ranges was not significantly influenced by sex (β=-111.8, SE = 147.9) or pack size (β=75.9, SE = 34.8). Finally, we evaluated if the deviance in the pack-year intercept was related to the observed pack size specific to that year. There is potentially a trend of increased deviation (i.e. 104 larger home ranges) for individuals from larger packs (Figure S3.3.2), though this effect is insignificant (β=0.031, SE = 0.028). Figure S3.3. 1: Residuals of predicted and observed wolf seasonal home range area (km2) as a function of minimum estimated pack size at capture and sex (F = female, M=male). Point values represent individual seasonal estimates, bold horizontal line represents the median for each pack size, and box hinges represents the 25th and 75th percentiles. Point shapes represent study sub-regions; BC = British Columbia, AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. 105 Figure S3.3. 2: Deviation of each pack-year combination in estimated wolf seasonal home range area (km2), i.e. the intercept, as a function of estimated pack size in that year. Vertical bars represent 2 Standard Deviation Units in the deviation. The dotted horizontal line signifies zero deviation, which can be interpreted as that pack-year having an average home range size that does not deviate from the average home-range size. We found no support to include pack size or sex within our models evaluating the influence of linear features and productivity on wolf home range size. While we found no difference in home range size as a function of sex, reproductive status may be an important driver of home range that we were unable to evaluate. Furthermore, we were unable to account for age, which is related to the probability of dispersal. Dispersal events can result in increased home range area estimated from aKDEs, though we minimized this effect by removing extraterritorial behaviours from GPS data, as well as while modelling aKDEs, we removed all aKDEs that did not have a plateau in their variogram. Understanding if pack size influences home range size in this system is of paramount importance because it is possible that while there are more, smaller home ranges, each home range supports a lower pack size and does not result in a numerical response (Messier 1985). 106 Conversely, if pack size also increases in areas with higher linear feature densities as a result of increased predation rates increasing reproductive output, we have under-estimated the possible influence of linear features on the numerical response. We note that pack size at capture is typically poorly estimated because capture time is limited for animal welfare, reducing the confidence in pack size or minimum packs size at capture. Additionally, packs are dynamic across time and a point estimate from capture would not reflect seasonal pack sizes across seasons and years. For example, pack size may change between and among seasons as mortality and birth occurs, introducing unknown variation into pack size. Likewise, packs tend to be less cohesive in the snow-free period compared to the snow period, changing the effective pack size. 107 Appendix S3.4: Testing the support for the effect of linear feature density and primary productivity on wolf home range size Table S3.4. 1: Models predicting seasonal wolf home range area (km2) to test the support for the effect of linear feature density and primary productivity (ΔEVI, scaled between 0 and 1). Models were compared using Akaike’s Information Criterion (AIC) to find the most supported random-effects structure, then the “basic structure” in which monitoring period, season, and study sub-region were included, and finally the “full structure” in which the environmental variables of interested were included. The model with lowest AIC is bolded for each component. Monitoring period represents the number of days each individual was monitored within the season, scaled between the maximum and minimum values. df = degrees of freedom. Component Model df AIC Random Structure Animal-Year 11 797 Pack-Year 11 723 Animal-Year / Pack ID 12 799 Basic Structure Monitoring Period + Season 5 801 Monitoring Period + Study sub-region 7 775 Monitoring Period + Season + Study sub-region 8 743 Full Structure Linear feature density 9 731 ΔEVI 9 727 Linear feature density + ΔEVI 10 712 Linear feature density * ΔEVI 11 711 108 Table S3.4. 2: The additive effect of linear feature density (km/km2), primary productivity (ΔEVI, scaled between 0 and 1), monitoring period (number of days monitored, scaled between 0 and 1), season (snow and snow free), and study sub-region on seasonal wolf home range area (km2). Home range area was calculated using 95 % autocorrelated Kernel Density Estimators and log transformed. Model estimates and 95 % Confidence Intervals are shown for mixed-effects models with pack-year included as a random intercept. The snow season and British Columbia are set as the reference categories. AB NE = northeastern Alberta, AB N = northern Alberta, SK = Saskatchewan. Nakagawa’s conditional R2 for the model was 0.73 and marginal R2 was 0.39. Coefficient Estimate - CI + CI Intercept 9.240 8.580 9.90 Monitoring Period 0.436 0.226 0.646 Snow free -0.466 -0.605 -0.327 AB NE -1.117 -1.440 -0.797 SK -1.160 -1.650 -0.665 AB N -1.491 -1.850 -1.130 Linear feature density -1.975 -2.91 -1.040 ΔEVI -1.885 -2.68 -1.090 109 CHAPTER 4: IN SEARCH OF EVIDENCE-BASED MANAGEMENT TARGETS: A SYNTHESIS OF THE EFFECTS OF LINEAR FEATURES ON WOODLAND CARIBOU Introduction Habitat loss is a primary mechanism causing, and accelerating, species extinctions across the globe (Powers & Jetz, 2019). When human land-use is a main cause of habitat loss and alteration (hereafter ‘habitat alteration’), habitat restoration may be assumed as a means to reverse species declines – e.g., restore the vegetation community and the forest ecosystem will return to its former state (Ford, 2021; Suding, 2011). However, population dynamics are governed by a complex suite of ecological interactions, including predation, herbivory, and competition (DeCesare et al., 2010; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Consequently, the total effects of habitat alteration on a given focal species arises through a myriad of pathways in the ecological community (Estes et al., 2011). Clear targets, or indicators, to trigger changes in monitoring efforts and management interventions are essential components of effective habitat management (Cook et al., 2016; Roberts et al., 2022). These targets should reflect meaningful changes in the response of a species or system to a particular stressor, and ideally the mechanistic pathways linking stressors to responses. When relationships between habitat and demography are clear, they provide managers with a tractable means to monitor progress towards habitat management targets (Morellet et al., 2007; Perry et al., 2023). In some cases, the relationship between habitat alteration and demography is gradual, such that managers must define arbitrary or socially-derived targets. In other cases, these relationships may have abrupt transitions in which the probability of population persistence declines suddenly or becomes highly uncertain (Huggett, 2005). Abrupt transitions could occur because of density-dependent population growth, for example via an Allee effect, and through demographic stochasticity. The identification of these abrupt transitions (i.e., thresholds or ‘tipping points’) provides an 110 ecological-based approach to define management targets and reduce uncertainty in population management. Anthropogenic habitat alteration has been identified as a proximate cause of decline for many caribou (Rangifer tarandus) populations, including Threatened boreal and southern mountain woodland caribou populations (R.t. caribou; Banfield, 1974) in Canada (Environment Canada, 2012, 2014). To support the recovery of woodland caribou, managers adopted a habitat management target of a maximum of 35% total ‘disturbed’ habitat (Environment Canada, 2012, 2014). This management target is based on a continuum of risk, whereby populations have approximately 60% chance of being self-sustaining if the total disturbance within their range is less than 35% (Environment Canada, 2011), where disturbance accounts for natural disturbances such as wildfire, as well as anthropogenic habitat alteration from industrial activity like forestry and petroleum extraction. Though originally developed for boreal herds, the management target of a maximum of 35% disturbance has been adopted for both boreal and southern mountain caribou (hereafter SMC; Environment and Climate Change Canada, 2020; Environment Canada, 2012, 2014) despite differences in underlying ecology and uncertainty in the applicability of the underlying relationship between habitat alteration and demography among populations. Caribou habitat restoration efforts to date largely focus on linear features, such as roads and seismic lines, that are pervasive across the landscape but are no longer actively used for resource extraction. Many linear features, especially roads, do not naturally revegetate in a timely manner, making active decommissioning and access management a necessary, but costly, aspect of recovery planning (Lee and Boutin, 2006; Nagy-Reis et al., 2021). Roads and other linear features are ubiquitous features across the globe used to facilitate human access, even in otherwise relatively intact landscapes (Ibisch et al., 2016). Linear features alter the movement behavior of animals, leading to disrupted migratory pathways, barriers to gene flow, the spread of invasive species, and altered predator-prey 111 dynamics (Fahrig & Rytwinski, 2009; Forman & Alexander, 1998). Demographic responses to linear features have been used to develop management targets for some species. For example, road densities between 0.6-0.7 km/km2 are correlated with grizzly bear population declines and lower densities of bears (Boulanger & Stenhouse, 2014; Lamb et al., 2018). Linear features are linked to caribou declines by providing efficient travel corridors for predators (Dickie et al., 2017; McKenzie et al., 2012), bringing predators into caribou habitat (DeMars & Boutin, 2017; Dickie et al., 2020a), and thereby increasing predation. Despite the focus of research and habitat management on linear features, there remains uncertainty about the pathways through which linear features impact caribou, and what restoration targets are expected to have a meaningful, positive impact on caribou populations. We developed a framework to link linear features (i.e., a stressor) to caribou declines, directly and via community-level interactions, and compiled evidence for each of these pathways to support the development of habitat management targets for SMC populations, and woodland caribou more generally. We summarized the current state of knowledge on the effects that linear features have on caribou, the dominant predator species of mountain caribou (e.g., wolves Canis lupus, cougars Puma concolor, black bears Ursus americanus, and grizzly bears Ursus arctos; Wittmer, Sinclair, et al., 2005) as well as moose (Alces alces), a primary prey species of those predators. We then used this literature review to identify linear feature density thresholds in these behavioral and demographic pathways, and gauge if these thresholds would effectively support evidence-based caribou habitat management. To further support linear feature management and restoration planning, specifically for SMC populations as a case study, we translated the current maximum total disturbance target of 35% (Environment Canada, 2014) to a linear feature-based target, and compared this value to those identified in the literature. Finally, we discuss the appropriateness of area-based versus linear feature-based targets based on their sensitivity to data imperfections and ecological relevance. 112 Methods Mechanisms linking linear features to caribou declines We developed a process model to identify the mechanisms linking anthropogenic linear features to caribou declines (Box 1). We used this process model to guide our literature review, to choose key-words, and summarize reported effects. We also used this model to assess the relative support for each pathway linking linear features to caribou, and identify any knowledge gaps that are needed to effectively manage linear features within caribou range. We reviewed literature from all caribou populations, including Scandinavian reindeer. Given our focus on linear features, we note that this review is not an exhaustive review of the mechanisms leading to caribou declines, human-caused or otherwise, nor is it an exhaustive review of each of the pathways not mediated by linear features (for example, the impacts of prey space use on predator space use more generally). We searched peer-reviewed literature using Web of Science. Web of Sciences is an effective principal search system for scientific literature review because it provides access to multiple databases simultaneously, but we nonetheless recognize that our results may be impacted by search engine choice (Gusenbauer & Haddaway, 2020). We prioritized groupings of search terms that we predicted would link linear features to caribou demography, populations, and predation using the process model (Box 1; Appendix S4.1). 113 Box 4. 1: Process model depicting the mechanistic pathways evaluated to describe the effects of linear features (LFs; ultimate stressor) on caribou. LFs exert impacts on agents (colored boxes, “2-5”), which influence caribou populations (“1”) via proximate stressors (hollow boxes), connected by mechanistic pathways. Peer-reviewed literature was assigned to pathway(s), as follows: 1. Effects of LFs on caribou populations, without an explicit mechanism. 2. Effect of LFs on predators (e.g., wolves, black bears, etc.), which flows through changes to space use (a) or changes to predator density (b), for example through vehicle collisions. LFs alter predator space use (a) via habitat use, selection, and movement, which is hypothesized to modify their search rate (c). The predator’s search rate, predator density, and prey density combine to influence the predation rate (f, d, and s, respectively), which then directly influences caribou populations (h), and can in turn feed back into predator density (e) through changes in reproductive rates. 3. Effect of LFs on primary prey species of caribou predators (e.g., moose, deer, etc.), which flows through changes to space use (j) or direct changes to density (i). LFs alter the density of the primary prey species, for example via vehicle collisions, habitat loss in the case of creation of hard surfaces such as roads, or the creation of early seral habitat in the case of forest cutting for soft surfaces such as seismic lines (i). Changing prey density influences predator densities (s), as well as the availability of resources on the landscape and energetics (k), having implications to caribou survival and reproduction (m). LFs also alter primary prey space use (j), which can influence resource availability on the landscape and energetics (g), or influence caribou space use (l) for example via competitive exclusion. In addition, as prey avoid encounters and predators seek encounters, predator space use can influence space use of primary prey (u), and vice-versa (t). 4. Effect of LFs on caribou habitat. LFs directly reduce caribou habitat, and can have a zone of influence, further reducing access to otherwise viable habitat. Habitat loss and change results in changes to caribou space use (o), which in turn influences resource availability and energetics (n), thereby reducing caribou survival or reproduction (m). Changes to caribou space use can also result in increased predation (p) via increased encounter rates, and is in turn influenced by predator space use (r) via caribou avoidance of predators.5. Effect of LFs on humans. LFs can facilitate access into caribou habitat, resulting in human-caused mortality (q) from vehicle collisions, hunting, and poaching. 114 We recorded information about the study design (data type, independent metrics, and dependent metrics), observed effects, any reported thresholds in responses, and management recommendations (Appendix S4.1). To facilitate comparison across studies, we consolidated varying descriptions of study design, methods, and results into broad terms (Appendix S4.2). We also scored the relevance of each paper to the objectives of our research, and our subjective confidence in the study’s methods (high, medium, low). Manuscripts that scored low in “Relevance” or “Confidence” were filtered out of subsequent analysis or summary (Appendix S4.1). Manuscripts that used more than one method, study design, species, or statistical analysis were partitioned by analysis to capture each effect studied. When analyses were multi-species (i.e., co-occurrence of caribou and wolves), we recorded the effect for each species. We assigned each analysis to the corresponding pathway(s) in which linear features are predicted to influence caribou (Box 1). At least two co-authors reviewed each paper, and where discrepancies occurred in any of the variables recorded, the paper was co-reviewed to reach consensus. We focus on results of dependent metrics, linear feature types, species studied, impacts and thresholds identified, but see Appendix S4.3 for additional results. Many studies did not report sufficient detail to facilitate a formal meta-analysis, and as such we used a simple ordinal approach to summarize the direction of responses (see Fahrig & Rytwinski, 2009). Literature review to inform linear feature-based management targets For the targeted goal of identifying a threshold in the response to linear features to support a linear feature-based management target, we included both peer-reviewed and grey literature. Any thresholds identified within the peer-reviewed literature were recorded. Because highly applied and specific results may be under-represented in peer-reviewed literature, we also compiled grey literature by combining target websites and database records in conjunction with a more broad search using Google Scholar (Godin et al., 2015; Haddaway et al., 2015). We reviewed all reports from four funding and research agencies known to host research on 115 woodland caribou in Western Canada: British Columbia Oil and Gas Research and Innovation Society (BCOGRIS), Canada’s Oil Sands Innovation Alliance (COSIA), Alberta Biodiversity Monitoring Institute (ABMI), and Foothills Research Institute (fRI). We included all reports from the BCOGRIS “Boreal Caribou” program (http://www.bcogris.ca), the fRI Caribou program (https://friresearch.ca) and all reports from COSIA and ABMI with the search term “caribou”. We then systematically searched for unpublished research theses using Google Scholar and the search terms "Mountain caribou" AND "linear features”, or "Mountain caribou" AND "roads". While the literature review was generalized beyond mountain caribou, we targeted the grey literature search, with its more applied goal, to be most applicable to SMC populations. We searched the Abstract, Executive Summary, Figures, Tables, and Management Recommendations sections of each report or thesis for identified thresholds or management recommendations. We also searched for the terms “threshold”, “breakpoint”, “management”, “recommendation”. We recorded the geographic area, ecoregion, data type, linear feature type, if the report evaluated a mammal response to linear features, species, as well identified management recommendations and thresholds using the same definitions as the peer-reviewed literature. We again scored the relevance of each report and our confidence in the methods, and filtered out reports that scored low in “Relevance” or “Confidence” for subsequent analysis (Appendix S4.1). Translating the area-based target to linear feature density To translate the area-based maximum disturbance target of 35% disturbed to a linear feature-based target, we assessed the relationship between estimated road density and total disturbance across 6 SMC central group ranges (Burnt Pine, Kennedy Siding, Klinse-za, Narraway, Scott West, and Quintette). The predominant linear feature type within SMC ranges are roads associated with forestry activities and recreation, though some more northeastern ranges are also disturbed via seismic exploration (Canadian Wildlife Service, 2012). While other 116 linear features undoubtedly contribute to linear feature density within central SMC ranges, road density represents a more consistent metric across all ranges. We used data compiled by ECCC to calculate total disturbance by buffering all anthropogenic disturbances by 500m, converted to the percent area for each range, and road density as the total length of roads divided by the area of each range. We used the range boundaries as delineated in ECCC for each herd. The ECCC data were primarily created using 30-m resolution Landsat 5 satellite imagery, emulating the methods used to identify the 35% maximum disturbance identified in boreal populations (Canadian Wildlife Service, 2012; Environment Canada, 2011). Landsat imagery underestimates smaller features, particularly narrow roads and other linear features (Dickie, Bampfylde, Cody, et al., 2023; Pasher et al., 2013). We therefore assessed the sensitivity of our conclusions to the data source used to classify roads and other disturbances by comparing ECCC data to higher-resolution data compiled by Mann and Wright (2018). Mann and Wright (2018) used the Government of British Columbia’s Digital Road Atlas, circa 2018, to identify roads including primary, secondary, and tertiary roads. To understand the differences and similarities between the two data sources, we evaluated the Pearson’s correlation between each dataset for both total disturbance and road density, with herd was the sampling unit. We then tested the correlation between each metric (road density and total disturbance) within each of the two datasets with herd was the sampling unit, and used a linear model to regress road density against total disturbance to estimate the road density that corresponded to 35% total disturbance. Finally, we placed the current disturbance conditions in context with the calculated management target by calculating the difference between the road density within each SMC range and the estimated road density target, for each data source. Peer-reviewed and grey literature were searched as of August 2020. All analyses were conducted in R (Team, 2019). 117 Results We identified 108 manuscripts in the systematic peer-reviewed literature search (Appendix S4.3), 60% of which studied boreal woodland caribou. After filtering for relevance and confidence, and removing analyses that did not report on the effect of linear features (for example, measured disturbance in general), we used the results of 620 unique analyses from 54 manuscripts from the peer-reviewed literature, as well as 72 reports and 27 theses from the grey literature. The majority (85%) of manuscripts had multiple analyses that included additional species and/or interacting effects like season, habitat type, and species co-occurrence. See Appendix S4.4 for a full list of manuscripts and reports evaluated. Linear features were categorized into 5 main classes; general linear features (which typically occurred when multiple linear feature classes were evaluated simultaneously, but also included infrequently studied linear feature types such as railways), natural linear features (such as rivers and creeks), pipelines and powerlines, roads, and seismic lines (Table S4.2.3). While we recorded information on the degree of human activity on these linear features (such as traffic levels), there were insufficient analyses to further analyze how the response to linear features depended on human use of these linear features (Appendix S4.3). Mechanisms linking linear features to caribou declines The majority (65%) of manuscripts we reviewed focused solely on caribou response to linear features, and 28% of manuscripts used a multi-species approach. The most commonly-used dependent metrics for all species studied were grouped into five broad categories: space use (67%), movement (12%), demographics (12%), caribou-predator co-occurrence (6%), and caribou-primary prey co-occurrence (3%). Metrics of space use were used over five-times more than any other dependent metric in the evaluated peer-reviewed manuscripts (Appendix S4.3; Appendix S4.5). 118 There were 60 analyses that specifically evaluated caribou demography, 42% of which directly related linear features to demographics without specifying a pathway. Only 1% of analyses evaluated the effect of linear features on caribou predation rates (“h”), none evaluated how linear features affect the direct impact of humans on caribou populations (“q”), and none evaluated how the influence of linear features on resource availability or energetics in turn influenced caribou populations (“m”; Figure 4.1). The majority of analyses reviewed were relevant to 3 pathways in our process model: the pathway between linear features and caribou space use (“o”; 40% of analyses), predator space use (“a”; 32% of analyses), and prey space use (“j”; 8% of analyses; Figure 4.1). Together, 82% of all analyses examined metrics of space use (i.e., habitat use and selection). Approximately 3% of analyses evaluated how changes in predator space use from linear features in turn influenced predator search rate (“c”), and 1% evaluated how this in turn influenced predation rates (“f”), though none evaluated how changes in predator space use from linear features in turn influenced caribou space use (“r”). Approximately 2% of analyses evaluated how changes in caribou space use in turn influenced predation rates (“p”), while few (<1%) evaluated how changes in caribou space use influenced resource availability and energetics (“n”). Few (<1%) analyses evaluated how changes in primary prey space in turn influences resource availability and energetics (g), and none evaluated how changes in primary prey space use influenced caribou space use (“l”). No analyses evaluated how linear features impacted how predator space use influenced primary prey space use (“u”) or vice-versa (“t”). Few of the analyses reviewed (<1%) evaluated how linear features influenced the density of predators (“b”) or how changes in predator density influenced predation rates were mediated by linear features (“d”). No analyses evaluated how the influence of linear feature on predation rates in turn influences predator density (“e”). Few analyses (<1%) evaluated how linear features influences the density of primary prey (“i”), and none evaluated how this in turn influenced predation rates (“s”) or resource availability and energetics (“k”). 119 There were 36 analyses that could not be attributed to a single pathway. Approximately 3% of analyses evaluated how linear features influenced the co-occurrence of moose and caribou (“o”, “j”, and “l”), and few (<1%) tested how these changes in co-occurrence affected caribou mortality risk (“g”, “n”, and “m”). Few analyses (<1%) evaluated how linear features influenced the co-occurrence of caribou and predators (“o”, “a”, and “r”), and how these changes effected caribou mortality risk (“p” and “h”). Few analyses (<1%) evaluated how linear features interacted with moose space use (“o” and “l”) or predator space use (“o” and “r”) to influence caribou space use, and how caribou survival depended on moose density, without testing the causal pathway between (“k” and “m”). Figure 4. 1: Process model (see Box 1) showing pathway widths that are weighted by quantity of research focus. The number of analyses in each pathway are provided in Appendix S4.5. Caribou were predominantly negatively impacted by linear features (Figure 4.2). Negative impacts of linear features on caribou were reported 2.3 times more than both null and positive effects combined. Positive effects were typically reported in cases where linear features were associated with decreased overlap between caribou and predators or their primary prey, which following the process model predicts a positive outcome for caribou populations. The two most-studied categories of linear features were roads and seismic lines (combined, 77% of 120 analyses; Appendix S4.3). Of the 189 analyses that evaluated the relationship between roads and caribou, negative impacts were reported 2.86 times more than null and positive effects combined (Figure 4.2). Negative impacts of seismic lines on caribou were reported at approximately the same frequency as null impacts, but 5 times more than positive impacts. All other linear feature types combined had an 83% negative, 2% positive, and 15% null impact on caribou. Of the analyses that evaluated the impact of linear features on caribou demographics, 70% found negative impacts. The response of moose to linear features was highly variable across linear feature types, seasons, and sex (Figure 4.2). The number of analyses that reported a null effect (42%) of linear features on moose was similar to the number that reported a negative impact (38%), whereas 19.8% reported a positive effect. The majority (80%) of the analyses on the impact of linear features on moose focused on roads and seismic lines. Of the 81 analyses that examined the impact of linear features on moose, 63% included interacting metrics like season or habitat type, and all were multi-species. The majority of analyses that evaluated predator responses to linear features in the context of caribou predation found a positive effect, such that linear features were inferred to facilitate predation. This effect was largely driven by seismic lines; positive effects were reported 1.9 times more than null and negative effects combined (Figure 4.2). The majority of wolf-specific analyses that we reviewed used metrics of space use (90 of 159 analyses) or movement (46 of 159 analyses). The majority of analyses on wolves were spread between roads, seismic lines, and general linear features. Within these linear feature types, 54% of analyses reported positive impacts on wolves and 36% reported null impacts (Figure 4.2). Metrics of space use were also common for black bears (all 29 analyses) and grizzly bears (23 of 29 analyses; Appendix S4.3). Of the 58 analyses on bears, the majority (90%) focused on seismic lines and 67% reported positive impacts of these features on bears (Figure 4.2). Comparatively, only 3% of analyses evaluated the impact of roads on bears, in which positive 121 and null effects were reported equally. Metrics of caribou-predator co-occurrence and density were only reported for wolves, and constituted less than 15% of the 159 wolf analyses (Appendix S4.3). Figure 4. 2: The impact of linear features on caribou and the large-mammal community implicated in caribou declines (see Box 1). The proportion of analyses from peer-reviewed manuscript reviewed reporting positive, negative, or null effects of linear features are presented for the five most-studied species, partitioned by five linear feature types. Positive or negative effects indicate increased or decreased use, selection, movement, or density, whereas neutral indicate no response to linear features. The total number of analyses for each species and linear feature category are presented. Literature review to inform linear feature density targets Within the peer-reviewed literature, we found no instances of explicitly identified thresholds in the impact of linear features on the behavior or demography of caribou, their competitors, or 122 predators. In the grey literature, we found multiple reported thresholds of linear feature densities. The probability of caribou occurrence was reduced significantly when primary roads exceeded 0.01 km/km2 and when secondary roads exceeded 0.1 km/km2 across all ecoregions in Western Canada (Neveux, 2017). McCutchen (2007) identified a number of thresholds in which roads and seismic lines influenced the behavior of wolves in boreal Alberta using simulations, and noted that thresholds typically occurred at low linear feature densities. Most relevantly, McCutchen (2007) identified that in more than 65% of the cases in which simulated wolf kill rates and caribou survival rates reached asymptotes as a function of linear feature density, these asymptotes occurred prior to 1 km/km2. Translating the area-based target to a linear feature density Total disturbance was strongly correlated between the two datasets (Pearson’s R = 0.90), whereas road density was less so (Pearson’s R = 0.68). On average, total disturbance using the Mann and Wright (2018) data was 1.2 times higher than the ECCC data, whereas road density was 4.2 times higher than the ECCC data. Total disturbance and road density were highly correlated for the Mann and Wright (2018) data (Pearson’s R = 0.88), and less so for the ECCC data (Pearson’s R = 0.67). Total disturbance significantly predicted road density for both datasets (βECCC = 0.005; FECCC = 3.198; βMann and Wright = 0.018; FMann and Wright = 13.760). The 35% maximum total disturbance target corresponded to a road density of 0.18 km/km2 using the ECCC data, and 0.63 km/km2 using the Mann and Wright (2018) data (Figure 4.3). Of the 6 SMC central group herds assessed, only Kennedy Siding had total disturbance below the boreal 35% maximum total disturbance target (Figure 4.3). The density of roads within Kennedy Siding was also below the translated road density target regardless of the dataset used. While total disturbance in the Scott West herd was above the total disturbance target for both ECCC and Mann and Wright (2018) data, Scott West was below the translated 123 road density target for ECCC. No other herd was below the total disturbance or road density target using either dataset. Figure 4. 3: The relationship between road density (km/km2) and total disturbance (percent area; including 500-m buffers on anthropogenic disturbances) used to estimate a road density target (Panel A and B), and visual representation of the state of each herd relative to calculated road density target for the 6 Southern Mountain Caribou - central group herds in British Columbia (Panels C and D). Panels A and C were derived from ECCC data. Panel B and D were derived from Mann and Wright (2018) data. The blue dashed line represents the point at which the 35% maximum total disturbance target (Environment Canada, 2014) intersects the modelled relationship between total disturbance and road density. Grey shading in C and D represent roads for the respective data sources, and range boundaries match those used in ECCC. 124 Discussion Decades of applied research have contributed to a vast body of literature on the effects of habitat alteration on woodland caribou. In particular, many studies have evaluated the impact of linear features, ubiquitous features across western Canada’s boreal and montane forests that are at the forefront of habitat management discussions. Yet, we found little empirical evidence that mechanistically linked linear features to caribou demography, nor evidence for a threshold in behavioral or demographic responses to linear features that could be used to support a management target for land-use and restoration planning, beyond the 35% maximum total habitat disturbance management target. Many studies emphasized an impact of linear features on caribou without explicitly evaluating the links between the ultimate stressor (linear features) and the mechanistic links and proximate stressor(s) in which those impacts flow to caribou demography. For example, studies tested how linear features impact space use of caribou, and inferred resulting impacts on caribou populations without demonstrating demographic effects nor testing if changes in space use influenced resource availability, competition, and/or predation. The most studied mechanisms evaluated how linear features modify space use of caribou, their predators, and the primary prey of those predators. Although numerous studies show that the main predators of caribou use linear features and travel more efficiently on them (Dickie et al., 2017; Latham, Latham, Boyce, et al., 2011; McKenzie et al., 2012), it is unclear if these changes result in increased encounter rates with prey, and ultimately increased kill rates of caribou and other ungulates (but see Whittington et al., 2011), or how these effects compare to how disturbance influences prey abundance, and therefore predator abundance. For mechanisms that did receive ample research, limited generalities could be made because of variable outcomes reflecting context-dependencies. For example, (Mumma et al., 2018) found that female moose generally avoided roads, whereas male moose selected for areas with high density of roads during calving, but avoided roads during early and late winter. 125 The strength and direction of behavioral responses often depend on the animal’s sex, age, or reproductive status, landscape context, and even the scale in which they are measured (Avgar et al., 2020; Boyce et al., 2003; Prokopenko et al., 2017). Indeed, functional responses in habitat selection, i.e., how behavior changes based on the availability and quality of resources in different habitats (Muhly et al., 2019; Mumma et al., 2019; Mysterud & Ims, 1998), can greatly impact our ability to make generalizations about the impacts of habitat alteration on species. The variability and lack of support for each pathway indicate important areas of research to discern the contribution of these mechanisms to caribou declines and subsequent recovery. While behavioral studies are beneficial for identifying species-habitat associations that may have important implications for populations, they may not alone inform population recovery targets. For example, behavioral analyses showed that boreal woodland caribou avoid burned areas, yet use of these habitats does not appear to affect adult female survival (Konkolics et al., 2021). If wildlife managers based management decisions only on the behavioral analyses from Konkolics et al., (2021) they may erroneously shift their focus on wildfire mitigation, which would unlikely increase caribou survival. We caution against the tendency to interpret behavioral studies as having implications on demography, and in turn population recovery, without these linkages being tested empirically. To recover caribou populations, we recommend that habitat management targets link to demographic rates - population growth and its components, survival and recruitment - to ensure that habitat contributes positively to demography. Testing relationships between habitat alteration and demography is becoming increasingly possible through cross-population analyses. For example, Johnson et al. (2020) combined 58 study areas, finding that the effect of total anthropogenic habitat alteration had much larger effects on adult female caribou survival and recruitment than fire disturbance. To inform uncertainty around the demographic impacts of linear features on caribou, cross-population analyses evaluating the effect of linear feature density on population growth rate (see (Environment Canada, 2011), ideally calculating thresholds in which populations are stable, 126 would be ideal. Additionally, contrasting cause-specific mortality rates of caribou, caribou recruitment, or wolf kill rates of caribou in areas with low and high linear feature densities would inform these linkages, particularly if the contextual dependency of additional habitat disturbance (e.g., forestry) was additionally explored. However, demographic data across such large scales is not always feasible for large mammals, particularly for species that are not as intensely monitored and widely distributed as caribou. In these cases, research can focus on understanding how habitat alteration influences the survival and reproduction of telemetered individuals, as long as individual animals experience differing levels of habitat quality and human pressures. These studies also provide the opportunity to explicitly link behavior to vital rates. Additionally, monitoring vital rates as habitat restoration expands will provide a pseudo-experiment to test the effect of linear features on population demography. While it is challenging to collect the data needed to rigorously test these linkages, understanding the contribution of linear features relative to other features and cumulative effects on caribou vital rates is necessary to predict the effectiveness of linear feature restoration. Despite a substantial body of literature associating linear features with caribou declines, we found no peer-reviewed evidence of a linear feature density threshold. In the grey literature, we identified four potential thresholds from two studies, at which a change in the effect of linear features on caribou or their predators was observed: 0.1, 0.17, 0.63 and <1 km/km2 (McCutchen, 20007; Neveux, 2017). None of these thresholds, however, are explicitly supported in peer-reviewed literature. To support land-use and restoration planning, we transformed the current total disturbed habitat target of 35% (Environment Canada, 2014) to a road density target. The resulting road density target depended on the underlying data source used to quantify total percent disturbance and road density; 0.18 km/km2 from ECCC and 0.63 km/km2 from Mann and Wright (2018). The potential thresholds and targets identified through these two exercises ranged across an order of magnitude, suggesting substantial uncertainty for management targets as well as likely context-dependencies limiting the ability to extrapolate 127 specific targets across sub-populations. However, all are below 1 km/km2, which may serve as an upper limit for any potential maximum road density target, though mostly points to a maximum road density target that is substantially lower. Linear feature density differed more between data sources than did total area altered. The observed increase in both road density and total disturbance (i.e. area altered) from the ECCC to the Mann and Wright (2018) data partially reflects an increase in habitat alteration over time (Nagy-Reis et al., 2021), given these data represent the landscape approximately 6 years apart. However, the average 13% increase in total disturbance from ECCC to Mann and Wright (2018) was much less than the 350% average increase in road density. Landsat imagery has been found to underestimate habitat alteration in other regions, particularly narrow features such as roads and other linear features (Dickie, Bampfylde, Cody, et al., 2023; Pasher et al., 2013). Differences may also reflect the configuration of disturbances; as road density increases these features likely fall within the buffers of existing disturbance, resulting in a non-linear relationship. In other words, in relatively intact landscapes road density and total disturbance are highly correlated because roads are required infrastructure for access, but, when disturbance is high, additional roads disproportionately impact road density over total disturbance. Together, this suggests that management targets derived from road densities are more sensitive to differences in data compilation methodology than area-based targets, particularly when buffering is used. However, if compiled well, linear feature-based targets may be more sensitive to increased habitat alteration in areas where human land-use is already pervasive; area-based targets saturate in highly-disturbed areas as new disturbances fall within existing disturbed areas, particularly when buffers are used. When conflict between conservation and economic impacts is high (Hebblewhite, 2017), there may be additional scrutiny placed on the data underlying scientific analyses and evidence-based recommendations. As such, developing publicly-available datasets on key variables like linear 128 features and disturbance will remain a critical need for operators, decision makers, and scientists. Identifying management targets for linear features is important for supporting land-use and restoration planning, but may not reflect additional or alternative mechanisms of decline, such as increased wolf abundance as a result of forestry activities providing increased resources for primary prey species (Serrouya et al., 2011; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021) and cumulative effects (Sorensen et al., 2008; Theobald et al., 1997). As such, there may be a need for multiple management targets that address separate mechanisms of decline. The issue of density- versus area-based targets has been considered for other species, such as grizzly bears (Proctor et al., 2019), and area-based metrics are often preferred or used in conjunction with density-based metrics to guide species recovery (Fish and Wildlife Service, 2013). While well-defined demographic-disturbance relationships are a laudable goal, meaningful action should not cease in the face of uncertainty. Indeed, rigorous monitoring of the effectiveness of management actions can also inform causal mechanisms and any tipping points within these pathways. Despite a well-defined total disturbance target for woodland caribou, there has been little progress towards reducing disturbance to below this target, and many herds continue to lose habitat at an accelerating rate (Nagy-Reis et al., 2021). Once a target has been adequately identified, implementation of management towards this target is needed to achieve the desired outcomes. 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Oecologia 144, 257–267. https://doi.org/10.1007/s00442-005-0055-y 136 Appendix S4.1: Methodological details for literature search We evaluated the current volume of peer-reviewed literature supporting each of the mechanisms identified in Box 1 by using search terms associated with various components of the effect of linear features on caribou (Table S4.1.1). Table S4.1. 1: Search terms used to evaluate the volume of peer-reviewed literature with the goal of developing a threshold for managing linear features within mountain caribou range. Search terms were grouped into 12 priority groups (1 being high priority, 12 low priority) to provide inferences on hypothesized components of the effect of linear features on caribou populations. As search terms broadened, a number of papers were captured by multiple search term groups; * indicates partial overlap with previous search terms, ** indicates complete overlap with previous search terms. Group Scope Search terms # Papers 1 Linear Features & Caribou Demographics "Rangifer tarandus" and "road" and "survival" 9 "Rangifer tarandus" and "road" and "lambda" 2** "Rangifer tarandus" and "road" and "mortality" 10* 2 Linear Features & Caribou Populations "Rangifer tarandus" and "linear features" 8 "Rangifer tarandus" and "road" 56* "Rangifer tarandus" and "pipeline" 10* "Rangifer tarandus" and "seismic" 36* 3 Linear Features & Caribou-Predation "Rangifer tarandus" and "road" and "predation" 21** "Rangifer tarandus" and "seismic" and "predation" 21** "Rangifer tarandus" and "pipeline" and "predation" 3** 4 Linear Features & Caribou-Wolves "Rangifer tarandus" and "linear features" and "canis" 19* "Rangifer tarandus" and "seismic" and "canis" 15** "Rangifer tarandus" and "road" and "canis" 9** 5 Linear Features & Caribou-Bears "Rangifer tarandus" and "linear features" and "Ursus" 3** "Rangifer tarandus" and "road" and "Ursus" 6** "Rangifer tarandus" and "seismic" and "Ursus" 5** 6 Linear Features & "Rangifer tarandus" and "linear features" and "Puma" OR “cougar” 2 137 Group Scope Search terms # Papers Caribou-Cougars "Rangifer tarandus" and "seismic" and "Puma" OR “cougar” 1** "Rangifer tarandus" and "road" and "Puma" OR “cougar” 2 7 Linear Features & Wolves "Canis lupus" and "predation" and "road" 28* "Canis lupus" and "predation" and "seismic" 15* "Canis lupus" and "predation" and "linear features" 20* 8 Linear Features & Bears "Ursus" and "predation" and "road" 11* "Ursus" and "predation" and "seismic" 2** "Ursus" and "predation" and "linear features" 2* 9 Linear Features & Cougars "Puma" OR “cougar” and "predation" and "road" 9* "Puma" OR “cougar” and "predation" and "seismic" 1** "Puma" OR “cougar” and "predation" and "linear features" 2** 10 Linear Features & Wolf Functional Response "Canis lupus" and "road" and "functional" 8* "Canis lupus" and "seismic" and "functional" 7* "Canis lupus" and "road" and "movement" 15* "Canis lupus" and "seismic" and "movement" 13** 9 Linear Features & Wolf Numerical Response "Canis lupus" and "road" and "numerical" 1 "Canis lupus" and "seismic" and "numerical" 1** "Canis lupus" and "road" and "density" 59* "Canis lupus" and "seismic" and "density" 8** 10 Linear Features & Bear Functional Response "Ursus" and "road" and "functional" 5* "Ursus" and "seismic" and "functional" 0 "Ursus" and "road" and "movement" 32* "Ursus" and "seismic" and "movement" 4* 11 Linear Features & Bear "Ursus" and "road" and "numerical" 0 "Ursus" and "seismic" and "numerical" 0 "Ursus" and "road" and "density" 49* 138 Group Scope Search terms # Papers Numerical Response "Ursus" and "seismic" and "density" 5* 12 Linear Features & Cougar Functional Response "Puma" OR “cougar” and "road" and "functional" 2* "Puma" OR “cougar” and "seismic" and "functional" 0 "Puma" OR “cougar” and "road" and "movement" 22* "Puma" OR “cougar” and "seismic" and "movement" 2* 13 Linear Features & Cougar Numerical Response "Puma" OR “cougar” and "road" and "numerical" 1** "Puma" OR “cougar” and "seismic" and "numerical" 0 "Puma" OR “cougar” and "road" and "density" 23* "Puma" OR “cougar” and "seismic" and "density" 0 14 Linear Features & Predators "Canis lupus" and "road" 105* "Canis lupus" and "seismic" 19** "Ursus" and "road" 136* "Ursus" and "seismic" 11* "Puma" OR “cougar” and "road" 64* "Puma" OR “cougar” and "seismic" 3** 139 Table S4.1. 2: Metadata table of information extracted from the peer-reviewed literature. Metadata are grouped into study design, methods, results and implications. Component Data Recorded Details Study design Year Years in which data were collected Population Boreal, mountain, tundra, reindeer Region Western North America, eastern North America, Scandinavia Methods Data type Collars, camera traps, observational, tracks, scat, indigenous knowledge Category of linear disturbance Linear features (general), major roads, secondary roads, recreational trails, right of way (pipelines, transmission etc.), seismic lines Dependent Metric Habitat selection, movement, adult female survival, calf survival, kill rates, abundance, occupancy, encounters, probability of being killed LF Independent Metric Density, area disturbed, distance to, on/off, before/after Interacting Independent Metrics Environmental covariates, season Quantifies LFs? Does the paper explicitly quantify linear features? If Yes, value km/km2, distance to, or percent disturbed Other species studied Results Direct impact on caribou Observed impact (increase/decrease/null) of LFs on caribou Interacting direct impacts on caribou Observed impact (increase/decrease/null) of interacting variables on caribou Impact on other species Observed impact (increase/decrease/null) of LFs on other species Can data format identify caribou demographic threshold? Are the data presented in a format such that a meta-analysis could inform a threshold for linear feature impacts on caribou demography? Can data format identify other LF thresholds? Are the data presented in a format such that a meta-analysis could inform a threshold other than for Caribou demography? 140 Component Data Recorded Details Implications Identified threshold Km/km2, distance to, or percent disturbed Inferred implications for caribou Explicitly identified implications for caribou ecology or recovery Management recommendations Explicitly identified caribou management recommendations 141 Appendix S4.2: Consolidated Terms Overall, the evaluated peer- reviewed manuscripts used 109 dependent metrics, 43 independent metrics associated with linear features, 34 types of linear features, and 16 data types that were then consolidated into the metrics listed below. We consolidated the metrics of 620 analyses after filtering for relevance, confidence, linear feature type, and accounting for double-counting from co-occurrence metrics (see Results). We define dependent metrics as the measurements of caribou demography, movement, space use or co-occurrence that varied depending on the metric of linear feature presence that peer-reviewed manuscripts reported (Table S4.2.1). We consolidated the explanatory variables used to test the effect of linear features into 10 classes; amount disturbed, density, distance to, human activity, restoration, use, above vs. underground, time, zone of influence, and other (Table S4.2.2). Linear features were categorized into 5 main classes; linear features, natural linear features, pipelines and powerlines, roads, and seismic lines (Table S4.2.3). Analyses that reported infrequently-studied linear features (e.g. “railways”) or grouped the effects of linear features together (e.g. “seismic lines /tertiary roads”) were grouped into the “linear features” category. Natural linear features include rivers or streams. Analyses that specified road type by traffic volume or road type (e.g. “primary”, “secondary”, “tertiary”, “paved”, “unpaved”, etc.) were grouped into the “roads” category. Additionally, powerlines were grouped with pipelines for simplicity because functionally, these two categories operate similarly. Analyses that included the impact of non-linear anthropogenic disturbances (e.g. “mine” or “cutblock”) were grouped into a separate “disturbance” category. The consolidation of the different data types terms mainly consisted of grouping all the different survey types reported (e.g. “aerial”, “ground survey”, “pellet survey”, etc.) into the one broad category of “survey” (Table S4.2.4). 142 Table S4.2. 1: The metrics used as response variables to test the impact of linear features within the reviewed literature, and the number of analyses for each of these identified metric. Dependent Metric Examples Number of Analyses caribou-predator co-occurrence Caribou-predator co-occurrence Caribou-predator encounter Potential wolf-caribou encounter Probability of being predated Proportion killed 39 caribou-prey co-occurrence Caribou-moose co-occurrence 16 density Abundance Calf density 30 fecundity Calving rate Recruitment Fecundity 3 lambda Population growth rate 1 movement Any metric studying caribou crossing lfs Number of crossings Crossing probability Crossing days, date, frequency Permeability Residency time # any metric on predator movement Predator tortuosity Predator movement rate Predator movement Predator speed Seasonal wolf movement rate Seasonal grizzly bear movement rate Step length 75 space use Winter habitat selection Population-level habitat selection Home-range habitat selection Fine-scale habitat selection Broad-scale habitat selection Calving site habitat selection Calving location-annual home-range habitat selection Calving location- calving home-range habitat selection Calving location -forest stand habitat selection Preference 416 143 Dependent Metric Examples Number of Analyses Home range Annual home range size Seasonal home range size Use Probability of use Subpopulations vs historic range Occupancy Occurrence Distribution Sightings Number of sightings Sighting rates Sighting rate change Denning season wolf habitat selection Rendezvous season wolf habitat selection Nomadic season wolf habitat selection Spring female grizzly bear habitat selection Summer female grizzly bear habitat selection Fall female grizzly bear habitat selection Spring male grizzly bear habitat selection Summer male grizzly bear habitat selection Fall male grizzly bear habitat selection Spring male grizzly bear habitat selection Habitat selection - denning Habitat selection - nomadic Habitat selection – rendezvous (any predator) occurrence Observation of predator Probability of predator observation Lichen biomass Annual home-range habitat selection size Predator foraging efficiency Calving range fidelity stress Psychological stress Nutritional stress 11 survival Adult survival 29 144 Dependent Metric Examples Number of Analyses Calf survival Adult mortality [mortality -> survival (changed main & interacting impacts-inverse)] Calf mortality [mortality -> survival (changed main & interacting impacts-inverse)] Quantity of disturbance length of linear feature linear feature density percent disturbed percent yearly additional disturbance percent area disturbed 14 145 Table S4.2. 2: The metrics used as explanatory variables to quantify linear features and test the impact of linear features within the reviewed literature, and the number of analyses for each of these identified metrics. Metric Examples Number of Analyses amount disturbed percent disturbed proportion disturbed habitat proportion disturbed area disturbed proposed road route proportion disturbed percent habitat loss <- "habitat loss" 4 density 229 distance to 211 human activity traffic density traffic presence/absence of protected areas isolated vs not-isolated areas disturbed area vs. undisturbed areas 14 LF restoration Before/after simulated restoration 32 LF use occurrence proportional use on vs. off crossing occurrence step length presence habitat loss 58 other ISD spacing above vs. underground permeability moose density 5 time 2002-2008 1992-2002 Before-After 21 zone of influence 0-100m buffer 100-250m buffer 250-500m buffer 36 146 There were 10 analyses that did not have explanatory variables to quantify linear features (labeled NA). These analyses are from Mumma et al. 2018, and were included despite each analysis not having an explicit LF focus because the explanatory metrics used (prey density or co-occurrence) were a function of the prior analyses that explicitly included linear features in the same study. These studies were also labeled NA for category of linear feature used. Table S4.2. 3: The linear feature classes used to test the impact of linear features within the reviewed literature, and the number of analyses for each linear feature class. Category of Linear Feature Number of Analyses linear features 98 natural linear features 11 pipelines 25 roads 259 seismic lines 217 Table S4.2. 4: The data types used to test the impact of linear features within the reviewed literature, and the number of analyses for each datatype. Data type Number of Analyses camera trap 25 collars 493 simulation 40 surveys 62 147 Appendix S4.3: General Results Of the 54 peer-reviewed manuscripts, 35 had study areas in western North America, 13 in eastern North America, and 6 in Scandinavia. Within North America, many papers studied boreal woodland caribou (32), some studied barren-ground caribou (10) whereas only a few studied mountain caribou (6) or reindeer (6). Specifically, 5 of the 6 reviewed manuscripts exclusively studied mountain caribou in British Columbia. Of the grey literature we reviewed, 4 reports or theses focused on mountain caribou in British Columbia. One of our interests was to detail the ways in which linear features impact were studied as well as how these impacts affected caribou. The majority of the studies focused on metrics of altered space use, thus it is unsurprising that the 80% of analyses used collars to measure these impacts. Of the 10 consolidated independent linear feature metrics, “density” and “distance to” were used the most. Together, these metrics encompassed 73% of metrics of linear feature disturbances in this review (Figure S4.3.1). Effects of linear features on caribou and their community We focused our analysis on the four groups that encompassed the most commonly-used dependent metrics in the evaluated manuscripts: caribou-predator co-occurrence, demographics, movement and space use (Figure S4.3.1). These four metrics correspond to two overall mechanisms of hypothesized linear feature-mediated caribou decline depicted in the conceptual diagram, namely predation (pathways “a”, “c”, “f” and “p”) apparent competition (pathways “j” and “l”; Figure 4.1 in main text). The impact of linear features on non-caribou prey densities can indirectly influence caribou through feedbacks in predator density. With these processes in mind, we translated the reported effects on predators and prey to inferred impacts on caribou through the mechanisms of predation and apparent competition depicted in the pathways of the conceptual diagram. We inferred a negative impact on caribou from a positive impact on prey or predator metrics, and 148 visa-versa. As mentioned previously, there were analyses that use “co-occurrence” as a dependent metric and thus when the impacts of the linear features were accounted for on both species, one co-occurrence analysis became two separate analyses. We only counted the impact on caribou for these analyses to avoid doubling the impact of linear features; treating a shifted co-occurrence pattern to a positive or negative impact on caribou already infers the effects of apparent competition and predation, thus we did not have to convert the reported effects from predators or prey in the same manner as other metrics. Caribou generally avoided areas that prey or predator species selected, further compounding the effect of linear features by altered prey and predator space use (Figure S4.3.1). The adverse effects of linear features on caribou movement notably increases when predator movement and space use are accounted for; this impact alludes to the increase in predator search rate and attack success as a result of linear feature-mediated facilitation of predator movement. 149 Figure S4.3. 1: Impacts linear features on caribou, moose and predator species, by dependent metric. (A) The direct impacts of linear features on caribou, moose and a broad “predator” species, which encompasses the direct impacts of linear features on wolves, black bears and grizzly bears. (B) The direct impacts of linear features on the three most-studied predator species (in relation to caribou and linear features) in the reviewed papers are differentiated by dependent metrics used to study the impact. (C) The inferred impacts of linear features on caribou due to apparent competition and predation from the presence of prey (moose, deer, etc.) and predator species (wolves and bears), in addition to the direct impacts of linear features on caribou, separated by ecotype (barren-ground (B-G), boreal, mountain and reindeer). 150 Appendix S4.4: List of literature reviewed Peer-reviewed literature reviewed: Apps, Clayton D.; McLellan, Bruce N. (2006). Factors influencing the dispersion and fragmentation of endangered mountain caribou populations. Biological Conservation, 130(1), 10.1016/j.biocon.2005.12.004 Apps, Clayton D.; McLellan, Bruce N.; Kinley, Trevor A.; Serrouya, Robert; Seip, Dale R.; Wittmer, Heiko U. (2013). Spatial factors related to mortality and population decline of endangered mountain caribou. Journal Of Wildlife Management, 77(7), 10.1002/jwmg.601 *Barber, Quinn E.; Parisien, Marc-Andre; Whitman, Ellen; Stralberg, Diana; Johnson, Chris J.; St-Laurent, Martin-Hugues; DeLancey, Evan R.; Price, David T.; Arseneault, Dominique; Wang, Xianli; Flannigan, Mike D. (2018). Potential impacts of climate change on the habitat of boreal woodland caribou. Ecosphere, 9(10), 10.1002/ecs2.2472 Beguin, Julien; McIntire, Eliot J. B.; Fortin, Daniel; Cumming, Steven G.; Raulier, Frederic; Racine, Pierre; Dussault, Claude. (2013). Explaining Geographic Gradients in Winter Selection of Landscapes by Boreal Caribou with Implications under Global Changes in Eastern Canada. Plos One, 8(10), 10.1371/journal.pone.0078510 Beyer, Hawthorne L.; Gurarie, Eliezer; Boerger, Luca; Panzacchi, Manuela; Basille, Mathieu; Herfindal, Ivar; Van Moorter, Bram; Lele, Subhash R.; Matthiopoulos, Jason. (2016). You shall not pass!': quantifying barrier permeability and proximity avoidance by animals. Journal Of Animal Ecology, 85(1), 10.1111/1365-2656.12275 151 Boan, Julee J.; Malcolm, Jay R.; McLaren, Brian E.. (2014). Forest overstorey and age as habitat? Detecting the indirect and direct effects of predators in defining habitat in a harvested boreal landscape. Forest Ecology and Management, 326, 10.1016/j.foreco.2014.03.052 Boulanger, John; Poole, Kim G.; Gunn, Anne; Wierzchowski, Jack. (2012). Estimating the zone of influence of industrial developments on wildlife: a migratory caribou Rangifer tarandus groenlandicus and diamond mine case study. Wildlife Biology, 18(2), 10.2981/11-045 Bowman, Jeff; Ray, Justina C.; Magoun, Audrey J.; Johnson, Devin S.; Dawson, F. Neil. (2010). Roads, logging, and the large-mammal community of an eastern Canadian boreal forest. Canadian Journal Of Zoology-Revue Canadienne De Zoologie, 88(5), 10.1139/Z10-019 Brown, WK; Hall, WK; Linton, LR; Huenefeld, RE; Shipley, LA. (2000). Repellency of three compounds to caribou. 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Stockdale, C., Barber, Q., & Parisien, M.-A. (2018). Wildfire Risk to Caribou Conservation Projects in Northeastern Alberta. Stotyn, S. A. (2008). Ecological Interactions of Mountain Caribou, Wolves and Moose in the North Columbia Mountains, British Columbia. University of Alberta. 174 Sutherland, G., McNay, R. S., & Serrouya, R. (2016). Feasibility of Some Direct Management Options to Recover Populations of Boreal Caribou. Szkorupa, T. (2001). 2000/2001 Progress report on caribou research in west central Alberta. Thiessen, C. (2010). Horn River Basin Moose Inventory January / February 2010. Vors, L. S. (2013). Caribou in Canada: Ecology and Policy [University of Alberta]. https://doi.org/10.1227/00006123-197907010-00116 Williamson-Ehlers, E. P. (2002). Impacts of industrial development on the distribution and movement ecology of wolves (Canis lupus) and woodland caribou (Rangifer tarandus caribou) in the South Peace Region of British Columbia. University of Northern British Columbia. 175 Appendix S4.5: Mechanistic Pathway Frequency Table Table S4.5. 1: The number of analyses addressing each mechanistic pathway identified in the Conceptual Diagram (Box 1). Pathway Label Number of Analyses Percent of Analyses o 243 39.2 a 198 31.9 j 50 8.0 1 25 4.0 c 18 2.9 o/j/l 16 2.6 p 10 1.6 f 9 1.4 h 8 1.3 o/a/r 8 1.3 g 6 1.0 g/n/m 6 1.0 n 5 .8 i 4 .6 e 3 .5 p/h 3 .5 4 2 .3 d 2 .3 b 1 .2 k/m 1 .2 o & l 1 .2 o & r 1 .2 176 CHAPTER 5: UNINTENDED CONSEQUENCES: DOES THE REDUCTION OF AN APEX PREDATOR RELEASE DEMOGRAPHIC AND BEHAVIORAL CONSTRAINTS ON LARGE-MAMMAL PREY? Introduction Predation is a fundamental ecological process that shapes the structure and function of ecosystems (Errington, 1946; Gese & Knowlton, 2001; Glasser, 1979). Predators can limit prey distribution and abundance (Holt & Barfield, 2009; Krebs et al., 1995), and influence behaviors such as habitat selection and foraging strategies (Brown, 1999; Mumma et al., 2017; Sih, 1984). The effects of predators may cascade through the food web, impacting vegetation dynamics and other species (Schmitz et al., 1997; Terborgh & Estes, 2010). However, in addition to these top-down forces, bottom-up effects arise from resource availability, where food and habitat dictate population dynamics and drive changes from the base of the food web upwards (Hunter & Price, 1992; Oksanen et al., 1981; Sinclair & Krebs, 2002). These top-down and bottom-up forces do not operate in isolation, rather, they interact in complex ways that vary across space and time (Messier, 1994; Sinclair & Krebs, 2002; Wang et al., 2009). For instance, the impact of predators on prey populations may be heightened during periods where the prey’s resources are scarce, whereas abundant resources for prey could dampen the role of predation (Boyce et al., 1999; Melis et al., 2009; Tveraa et al., 2003). The interplay between these forces can determine ecosystem stability, with variations in predator and resource dynamics potentially leading to significant shifts in community composition and function (Messier, 1994; Schmitz et al., 1997; Terborgh & Estes, 2010). Understanding how systems respond to changes in predation pressure is essential, especially as human activities and climate change increasingly disrupt these dynamics (Estes et al., 2011). Prey respond to predation through a variety of mechanisms that collectively shape their population dynamics and behavior. Numerically, prey populations may decline as a result of 177 increased mortality from predation (Holling, 1959b), but prey may experience increase reproductive output to compensate for mortalities (Boyce et al., 1999; Tveraa et al., 2003). Functionally, prey can alter their behavior in response to predator presence, modifying their use of space or activity times to minimize encounters with predators (Brown, 1999; Peacor & Werner, 2001; Sih, 1984). These behavioral adaptations often occur through predator-sensitive foraging, where prey balance the need to acquire resources with the risk of predation (Pierce, 1988; Sinclair & Arcese, 1995). The balancing act between foraging and risk aversion can result in prey using less optimal habitats, altering their feeding times, or reducing overall activity levels to minimize exposure to predators, impacting survival and reproduction and so feeding back into population-level responses (Brown, 1999; Schmitz et al., 1997; Sheriff et al., 2020). Additionally, these responses can have broader implications by influencing species interactions, resource competition, and ultimately, ecosystem structure and function (Schmitz et al., 1997). Understanding the pathways in which predators influence prey distribution, abundance, and behavior is crucial for predicting how prey populations will respond to changes in predator dynamics, particularly in the context of conservation efforts aimed at restoring predator-prey interactions (Alston et al., 2019; Choquenot & Parkes, 2001; Serrouya et al., 2011). In systems where predators are the primary limitation to prey populations, reducing predation pressure through predator control can have significant impacts by “releasing” behavior and demographic limitations (Clark & Hebblewhite, 2021; Connolly, 1978). Predator reductions aim to alleviate pressures on prey species that are in decline, potentially leading to increased prey populations and so, recovery. While this approach can be beneficial in supporting targeted prey populations (Connolly, 1978), the broader ecological repercussions of such interventions can be challenging to anticipate. Non-target species and overall community dynamics can also be affected (Stantial et al., 2021; Trewby et al., 2008). The relative roles of predation, along with other limitations such as habitat alteration and climate change, introduce uncertainty into these outcomes (Clark & Hebblewhite, 2021; Doherty & Ritchie, 2017). Furthermore, variation in prey 178 traits, including size and catchability, can influence predator-prey interactions (Prokopenko et al., 2023), and so how effectively predator reductions achieve their intended goals. In western Canada, predator reduction initiatives have been employed as a management intervention to support declining caribou (Rangifer tarandus) populations (Hayes et al., 2003; McNay et al., 2022). Caribou face high predation, particularly by wolves, lowering adult female survival and recruitment (Boutin et al., 2010; Wittmer, Sinclair, et al., 2005). Predation by wolves is unsustainably high in many areas as a result of disturbance-mediated apparent competition, whereby forest harvest results in increased forage availability for ungulates such as moose (Alces alces) and white-tailed deer (Odocoileus virginianus) which results in increased wolf abundance (Latham, Latham, McCutchen, et al., 2011; Neufeld et al., 2021; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Additionally, the creation of linear features, cutlines through the forest to facilitate human travel, also facilitates wolf hunting efficiency and use of caribou refugia (DeMars & Boutin, 2017; Dickie et al., 2017, 2022a). While recovery goals aimed at achieving self-supporting caribou populations incorporate the need for large-scale habitat protection and recovery, reducing wolf populations through intensive management programs have improved survival and recruitment, providing a critical tool to maintain caribou populations while habitat recovery and protection actions take hold (Hayes et al., 2003; Hervieux et al., 2014; McNay et al., 2022). However, there is a need to understand the broader implications of predator reductions for the ecological community. In addition to the applied intent of such interventions, predator reductions offer a unique opportunity to study prey responses to changes in predation pressure, enhancing our understanding of how prey species adapt to fluctuations in predator populations and informing future conservation strategies. In 2017, the Government of Alberta expanded their wolf reduction efforts into boreal herds of northeastern Alberta. In this region, the effect of wolf reductions on caribou populations remains unknown, as is the response of other ungulate prey. Here, we evaluated the response 179 of the ungulate prey of wolves, white-tailed deer, moose, and caribou, to predator reductions, hypothesizing that these species will experience demographic and behavioral release as a result of reduced top-down limitation and risk-sensitive foraging (Figure 5.1). We predict that the density and occurrence of these species will increase in the wolf reduction area over time relative to reference areas. Behaviorally, we predict that prey will alter their space use in the wolf reduction area, leading to increased use of open habitats and high-forage areas in the wolf reduction area over time. Finally, because these species would no longer need to avoid times of high wolf activity to reduce encounters, we predict that these species will shift their temporal activity over time in the wolf reduction area relative to reference areas, such that overlap would increase over time. 180 Figure 5. 1: A general example of predicted changes in the demography and behavior of ungulate prey over time in response to wolf reductions relative to reference areas. Predictions display both a reference area with low habitat alteration (low reference) and a reference area with habitat alteration more similar to the wolf reduction area (reference). Demographic responses include both density and distribution over time, contrasting the three areas. We Density Low Reference Wolf Reduction Reference Time Distribution Time Temporal Overlap Behavioral Response: Space Demographic Response Behavioral Response: Time Year of Study Habitat Productivity Canopy Cover Low Reference Wolf Reduction Reference Low Reference Wolf Reduction Reference 181 predict that prey density and distribution will increase over time in areas with wolf reductions, whereas density will remain constant over time in both the reference areas. Behavioral responses are separated into space, (i.e., the use of habitats that provide more security or resources), and time, (i.e., temporal overlap with high predator activity periods). We predict that prey will transition from using areas with higher cover (i.e., security) and lower productivity (i.e., food resources) to areas with low cover and high productivity over time in the wolf reduction area, but remain constant over time in the two reference areas. Finally, we predict that prey will increase their use of high wolf activity periods over time in the wolf reduction area, resulting in increased temporal overlap. In contrast, we predict that temporal overlap in the low reference and reference areas will remain constant over time. Methods Study Design To test these hypotheses, we evaluated the demographic and behavioral responses of white-tailed deer, moose, and caribou to wolf reductions using a multi-faceted approach. We first assessed the demographic response by estimating species density and distribution (indexed as the proportion of cameras occupied by each species) over time. Subsequently, we analyzed behavioral responses by comparing space use and temporal activity patterns over time, contrasting areas with and without wolf reductions. According to predator-sensitive foraging theory, prey species are expected to adjust their spatial and temporal patterns in response to the presence or absence of predators (Lima & Bednekoff, 1999; Lima & Dill, 1990). Therefore, we investigated shifts in the use of habitats that offer varying degrees of security and forage availability, contrasting the wolf reduction area with reference areas. Additionally, we examined changes in temporal activity overlap between each ungulate species and periods of peak wolf activity over time, focusing on differences between wolf reduction and reference areas. 182 To measure indicators of demography and behavior of multiple ungulate species we deployed clusters of camera traps in areas with and without wolf reduction programs (Figure 5.2). In 2017 the Government of Alberta began a wolf reduction program in the Cold Lake and East Side Athabasca River (ESAR) caribou ranges. We placed camera trap clusters across these two ranges, in addition to two reference caribou ranges; i) the West Side Athabasca River (WSAR) range, which represents a highly altered region similar to that of the wolf reduction area (hereafter termed the “reference” area), and ii) the Saskatchewan boreal plains herd, which represents an area with low anthropogenic habitat alteration, and in turn low wolf densities (Serrouya et al., 2021; hereafter termed the “low reference” area). See Dickie et al. (2024) for additional details about the study area. 183 Figure 5. 2: Study area map depicting camera cluster deployments in relation to caribou ranges with wolf reductions (Cold Lake and East Side Athabasca River) and reference areas with similar habitat alteration (West Side Athabasca River; “reference”) and low habitat alteration (Saskatchewan Boreal Plains; “low reference”). Camera Data To monitor ungulates across the study area, we deployed clusters of camera traps, with 25 cameras per cluster, across approximately 55,375 km² within the ESAR, Cold Lake, WSAR, and western portion of the Saskatchewan Boreal Plains herd. Camera cluster placement was strategically designed to reflect multiple objectives (see Dickie et al., 2024), maximizing 184 variation in habitat alteration along an east-west gradient and capturing climatic variation across a northwest-southeast axis. In 2017, we deployed three clusters in ESAR and three in WSAR, selecting these locations to compare areas with similar landscape characteristics but different wolf reduction efforts. In 2018, we deployed three clusters in Saskatchewan and one additional cluster in Cold Lake, followed by further clusters in Saskatchewan in 2019 and 2021. In 2020, imminent forest harvesting prompted the relocation of the southern most cluster in WSAR. Data collection continued year-round until January 2022, at which point cameras were removed from Saskatchewan, and the servicing schedule in ESAR, WSAR, and Cold Lake was adjusted from annual to biennial servicing after 2022. We used Reconyx Hyperfire2 and Recoynx PC900 camera models (Holmen, WI, USA), which captured both motion-triggered images and time-lapse photos every two hours. Specific camera trigger settings followed guidelines from the Alberta Biodiversity Monitoring Institute (2019). Camera clusters straddled roads for ease of access, with roads typically bisecting the shorter edge of each cluster and running parallel to the longest edge. Within each cluster, we used a random location generator to determine individual camera sites, ensuring a minimum distance of 1 km between cameras. Is there an increase in density of ungulates in the wolf reductions area over time, relative to the reference areas? Estimating annual trends in density We estimated density of each species in each cluster and year using Time In front of Camera (TIFC) as per Becker et al. (2022; see Dickie et al., 2024 for additional details). Years were defined as April 16 to April 15 of the following year, roughly reflecting the seasons and the application of wolf reductions which typically January through March each year. We estimated the annual trend for each species in each cluster by modelling density as a function of cluster and the interaction between cluster and year as a continuous variable, such 185 that the slope for each cluster-year combination represents the estimated change in density over time in that cluster. We included the number of camera traps operating in that cluster and year, scaled between 0 and 1, as a weighting to reflect greater confidence in estimates derived from more camera traps operating within a cluster. Environmental Covariates To assess the response of ungulate densities and distribution to wolf reductions, it is important to account for the influence of underlying habitat characteristics that may interactively impact changes in density over time (Seip, 2022). Habitat characteristics, such as forage availability, climate, and anthropogenic alterations, can directly influence the survival and reproduction of ungulate species, thereby interacting with the effects of predator reductions (Tveraa et al., 2003). Therefore, we estimated several key landscape characteristics across the study area to evaluate their interactive effects on ungulate density and distribution. First, we estimated habitat productivity as an indicator of forage availability. Habitat productivity reflects the availability of food resources for herbivores, and increased forage availability can lead to improved body condition, higher reproductive success, and lower mortality rates (Dickie et al., 2022a; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021; Villamuelas et al., 2016). We estimated habitat productivity using the change in the Enhanced Vegetation Index (ΔEVI), a satellite-derived measure of deciduous vegetation (Villamuelas et al., 2016). As in Dickie et al., (2024), we extracted median EVI values during the leaf-on period (July 1–August1), and the leaf-off period before snow accumulated (September 1–October 1) using MODIS Terra Vegetation Indices 16-Day Global data (500 m resolution). We calculated ΔEVI by subtracting the leaf-off median EVI from the leaf-on median EVI, and eliminated pixels covered by clouds or shadows by only retaining pixels classified as “VI produced, good quality” and removing those covered by water, following Hansen et al. (2013). 186 Secondly, we quantified indicators of climate, which are well-documented to influence ungulate survival and reproductive capacity (Delguidice et al., 2002; Michel et al., 2018). For example, severe winter conditions can increase energy expenditure, thereby limiting survival and reproductive success. Including climate indicators in the analysis allows for the examination of how climate variability may modulate population responses to wolf reductions. We conducted a multiple factor analysis (MFA) to reduce climate variables expected to impact ungulate demographic rates while accounting for sampling variables across years at the same location, hence representing medium-term “climate” dimensions for each cluster (see also Dickie et al., 2024). However, DAYMET Version 4 (Thornton et al., 2022) were not available through the final years of this study. We therefore extracted monthly climate variables from ClimateNA (Wang et al., 2016). For each cell we calculated the maximum mean temperatures (°C), mean temperatures, and the sum of degree-days above 18°C from July to September (i.e., summer), the sum and mean precipitation (mm) from April to September (spring and summer), as well as the minimum mean temperatures, mean temperatures, sum and mean of the precipitation as snow (mm) sum of the degree-days below 0°C and the sum of the number of frost-free days from October through March (i.e., the snow season). For each camera cluster and each year, we then extracted the mean value for each metric, and reduced these variables to two dimensions. Following the MFA, climate Dimension 1 explained 45.9% of the variance in the climate variables, and included high quality of representation (cos2> 0.6) by the mean temperature and sum of degree-days above 18°C during summer, as well as the number of frost free days and sum of the degree-days below 0°C in the snow season. The number of frost-free days in the snow season contributed most to Dimension 1. Dimension 1 can be interpreted roughly as warmer summers and winters with more degree days below 0, but more frost-free days (Appendix S5.1). Dimension 2 explained 22.1% of the variation, and included high quality of representation (cos2>0.4) by the sum and mean precipitation during spring and summer. The 187 sum and mean precipitation from April to September, as well as the minimum mean temperature and sum and mean of the precipitation as snow during the snow season contributed most to Dimension 2. The direction of these relationships varied across years, making climate Dimension 2 difficult to interpret, although Dimension 2 can be considered a metric of precipitation (Appendix S5.1). Climate Dimension 1 is related to longitude, with areas farther West having higher values. In contrast, climate Dimension 2 is related to latitude, with areas farther North having higher values. In addition to natural landscape features, we also considered anthropogenic factors that could affect the density and behavior of the ungulate species as well as their predators. We estimated the percent habitat alteration within each study area, as the conversion of old-growth forest to early seral vegetation through human land-use can enhance forage availability by promoting the growth of early successional plant species favored by ungulates (Best et al., 2024; Finnegan et al., 2018). While habitat alteration can increase food resources, it may also introduce other ecological pressures, such as increased predation risk or competition. We calculated the mean percent area altered by human land-use in each cluster using Saskatchewan and Alberta's Footprint Inventories (Alberta Biodiversity Monitoring Institute, 2018; Government of Saskatchewan, 2021) circa 2018. We also assessed the length of linear features (e.g., roads, seismic lines) within each cluster. Linear features increase predator foraging efficiency by providing travel corridors that facilitate hunting (Dickie et al., 2017). These features can therefore alter predator-prey dynamics, potentially undermining the intended effects of predator reductions by enabling predators to exploit prey populations more effectively even after population reductions. To quantify the availability of linear features, we calculated the total length of linear features in each 50km2 camera cluster area. We used linear features delineated by Environment and Climate Change Canada (Environment and Climate Change Canada, 2017). These data are based off Landsat imagery at a 1:50,000 scale. While these data underestimate linear features in Alberta (Dickie, Bampfylde, Cody, et al., 2023), no high-188 resolution linear feature data exists within Saskatchewan, so we chose a consistent dataset across the study region. Evaluating predictors of annual trends We modelled annual trends as a function of treatment (low reference, reference, and wolf reduction) and environmental covariates (habitat productivity, climate dimensions 1 and 2, overall percent altered habitat, and length of linear features). We set the wolf reduction treatment as the reference category in our analyses to facilitate comparing the wolf reduction treatment against the two reference treatments. We used a model selection framework to choose the model with the lowest Akaike’s Information Criterion, corrected for small sample sizes (AICc; Akaike, 1974), as the most supported model for each species, contrasting the univariate effects of each as well as the additive effect of treatment along with each of the environmental covariates. We included a null model with an intercept only as a model candidate. We included the inverse variance of the estimated annual trend as a weighting in each model. Is there an increase in distribution of ungulates in the wolf reduction area over time, relative to the reference areas? Estimating annual trends in distribution We estimated the annual trend in distribution for each species in each cluster by modelling the proportion of cameras in which the species was detected at least once (hereafter termed “proportion occupied”) as a function of cluster and the interaction between cluster and year as a continuous variable, such that the slope for each cluster-year combination is the estimated change in proportion occupied over time in that cluster. Again, we included the number of camera traps operating in that cluster and year, scaled between 0 and 1, as a weighting. 189 Evaluating predictors of annual trends We modelled the annual trend in proportion occupied as a function of treatment (low reference, reference, and wolf reduction) and environmental covariates (habitat productivity, indicators of climate, percent habitat alteration, and the length of linear features; as described above). We again set the wolf reduction treatment as the reference category used AICc to select the most supported model. We included a null model with an intercept only as a model candidate. We included the inverse variance of the estimated annual trend in proportion occupied as a weighting in each model. Do ungulates change their use of habitats that provide security and food resources in response to wolf reductions? We quantified the intensity of use of a site by calculating the minimum number of individuals detected in each biweekly period, hereafter termed the “total count”. If multiple individuals of the same species were identified within an image, each individual counted as a unique detection. We indexed security using canopy cover, with the idea that dense canopies provide vegetation cover to reduce detection probability and provide increase chance of escape in comparison to open habitats (Brown, 1999; Frey et al., 2018; Pierce, 1988). We measured the percent canopy cover within the immediate area surrounding the camera trap location by extracting canopy cover from Landsat data with a 30m pixel size (Sexton et al., 2013). We again indexed habitat productivity using ΔEVI. However, because we were interested in the area immediately surrounding the camera, we estimated ΔEVI using Landsat data processed with the ‘LandsatTS’ package (Berner et al., 2023). We first imported Landsat data and reformatting it for analysis, removing observations with excessive geometric errors, cloud cover, or unsuitable solar zenith angles, and to filter out snow and water. We computed EVI for each day of the year for each camera location, calibrating satellite observations using polynomial regression to correct for temporal inconsistencies. We fit phenological curves to 190 reflect vegetation growth and senescence throughout the year. We calculated ΔEVI for each camera and year by subtracting the minimum EVI (representing senescence) from the maximum EVI (representing peak green up). While canopy cover reflects vegetation structure, serving as an indicator of habitat complexity and security, ΔEVI measures vegetation greenness or deciduousness, accounting for variations in photosynthetic activity independent of the structural consistency provided by evergreens. Given that these metrics are only weakly correlated (R² = 0.31; Appendix 5.2), they capture distinct aspects of habitat quality, minimizing concerns about redundancy. For each species we modelled the biweekly total count as a function of treatment area using a General Additive Mixed Model (GAMM), with a smoothing spline for ΔEVI by treatment area and canopy cover per treatment area. We included the interaction between canopy cover and ΔEVI with the year of study as a continuous variable using a tensor spline to test if the use of cover and productive habitat changed over time in each area. We included a cyclical spline for biweekly period by treatment area, and a random intercept for cluster. We z-standardized each response variable to place them on the same scale centered at their means. Finally, we used the proportion of the biweekly period in which the camera operated as a weighting to give additional confidence to periods that were fully monitored, and used a negative binomial link. We used the package ‘mgcv’ (Wood, 2023) in R, and visualized the model outputs using package ‘mgcViz’ (Fasiolo, 2023). Do ungulates shift their temporal activity in response to wolf reductions? First, we explored the temporal activity of wolves to identify times when wolves are typically active by fitting kernel density functions with package ‘Overlap’ (Ridout & Linkie, 2009). Because the number of wolf detections in each cluster and year were low (Appendix 5.3), we pooled all wolf data to estimate a “combined” kernel density estimate as an index of active wolf periods. Wolves were typically active during dusk and into the evening, and to a lesser extent 191 during dawn (Figure 5.3). Visually, wolf temporal activity differed marginally between the treatment areas, but given low detections of wolves, we combined observations across all areas and years. Figure 5. 3: Temporal activity of wolves monitored using camera traps from 2017 through 2023 in an area with wolf reduction, a reference area with high habitat alteration similar to the wolf reduction area (Reference) and a reference area with low habitat alteration (Low Reference). We fit kernel density functions to each ungulate prey species (white-tailed deer, moose, and caribou) for each year and cluster to evaluate their temporal activity patterns. We estimated the coefficient of overlap between the ungulate prey species’ temporal activity kernel densities in each cluster and year with the combined kernel density estimate for wolves to quantify each ungulate species’ temporal activity overlap with active wolf periods using the compareCkern function with package ‘activity’ (Rowcliffe, 2023). We tested if the temporal overlap between each ungulate prey species and the wolf activity period differed between the treatment areas, and if there was a change over time in the wolf reduction area relative to the reference areas. For each ungulate species we used a 192 generalized linear mixed model with a Beta distribution to model overlap as a function of treatment, year (as a continuous variable), and their interaction as fixed effects, while accounting for variation across clusters by including a random intercept for cluster. We included the number of observations of that ungulate in that cluster and year as a weighting. Results Is there an increase in density of ungulates in the wolf reduction area over time, relative to the reference areas? The top model explaining annual trends in deer density (Figure 5.4) included the additive effect of treatment and linear features (Table 5.1). On average, changes in deer density over time were highest in the low reference area and lowest in the wolf reduction area (Table 5.2, Figure 5.5). Furthermore, deer densities increased over time where there were more linear features (Table 5.2). In the wolf reduction area and reference area deer densities were predicted to decrease over time when the length of linear features was low, but increase in areas with high linear feature length (Figure 5.5). In the low reference area, deer were predicted to increase over time across the range of linear feature length (Figure 5.5) The top model explaining annual trends in moose density (Figure 5.4) included the additive effect of treatment and habitat alteration (Table 5.1). On average, moose increased over time in the wolf reduction area, though relative to the wolf reduction area there was a higher change in moose density over time in the reference area (Table 5.2). On average moose density decreased over time in the low reference area (Table 5.2). The predicted change in moose density over time was lower in areas with higher habitat alteration (Figure 5.5). The top model explaining annual trends in caribou density (Figure 5.4) included the additive effect of treatment and habitat productivity (Table 5.1). Changes in caribou density over time was highest in the reference area and lowest in the low reference area (Table 5.2). When 193 habitat productivity was low, caribou densities increased over time in both the reference and wolf reduction areas, but when productivity was high caribou densities decreased over time in all three areas (Table 5.2; Figure 5.5). Figure 5. 4: Density of moose, white-tailed deer, and caribou in the wolf reduction (WR) area and reference areas (Ref) over time. Wolf reductions implementation began in 2017. Colors represent camera cluster replicates in each treatment. Note the y-axis scale varies by species. 194 Table 5. 1: Comparing model support using Akaike’s Information Criterion, corrected for small sample sizes (AICc), for the effect of wolf reductions (treatment, such that wolf reductions is contrasted against two reference areas) and environmental covariates (climate Dimension 1 and 2, habitat productivity measured using the change in the Enhanced Vegetation Index, percent habitat alteration, the length of linear features (km)) as well as the additive effect of treatment and environmental covariates on the change in density of white-tailed deer, moose, and caribou over time. Model White-tailed Deer Moose Caribou df AICc df AICc df AICc Null 2 -583 2 -210 2 -37893 Treatment 4 -695 4 -470 4 -39628 Dimension 1 3 -663 3 -229 3 -37897 Dimension 2 3 -690 3 -247 3 -37982 Habitat Productivity 3 -781 3 -207 3 -39269 Habitat Alteration 3 -603 3 -227 3 -38750 Linear Feature 3 -670 3 -230 3 -37917 Treatment + Dimension 1 5 -789 5 -473 5 -41494 Treatment + Dimension 2 5 -1079 5 -466 5 -39745 Treatment + Habitat Productivity 5 -916 5 -467 5 -42600 Treatment + Habitat Alteration 5 -694 5 -487 5 -41532 Treatment + Linear Feature 5 -1208 5 -465 5 -40006 195 Table 5. 2: The effect of treatment (low reference, reference, and wolf reductions; such that wolf reductions is the intercept) and environmental covariates on the change in white-tailed deer, moose, and caribou densities (animals/km2) over time. The environmental covariate included varies per species, reflecting the most supported model from Table 5.1. Species Variable Estimate SE P value White-tailed Deer Intercept -0.375 0.019 <0.001 Low Reference 0.519 0.02 <0.001 Reference 0.086 0.01 <0.001 Length (km) 0.007 <0.001 <0.001 Moose Intercept 0.122 0.016 <0.001 Low Reference -0.428 0.026 <0.001 Reference 0.052 0.016 <0.001 Alteration (%) -0.007 0.002 <0.001 Caribou Intercept 0.063 0.001 <0.001 Low Reference -0.033 <0.001 <0.001 Reference 0.009 <0.001 <0.001 Productivity <0.001 <0.001 <0.001 196 Figure 5. 5: Predicted average annual change in white-tailed deer, moose, and caribou density (animals/km2) across the study period in the low reference, reference, and wolf reductions areas, as a function of environmental covariates. The environmental covariate included varies per species, reflecting the most supported model from Table 5.1. Is there an increase in distribution of ungulates in the wolf reduction area over time, relative to the reference areas? The top model explaining annual trends in deer distribution (Figure 5.6) included treatment and climate dimension 1 (Table 5.3). Relative to the wolf reduction area, there was an increase in the proportion of cameras occupied over time more so in the low reference area, and less so in the reference area (Table 5.4). Additionally, the proportion of cameras occupied by deer increased over time more strongly in areas with lower Dimension 1. The top model explaining annual trends in moose distribution (Figure 5.6) included treatment and habitat productivity (Table 5.3). Relative to the wolf reduction area, the proportion of cameras occupied by moose decreased more strongly over time in the reference and low reference areas (Table 5.4). There was also an increase in the proportion of cameras occupied by moose over time in areas with higher productivity. The top model explaining annual trends in caribou distribution (Figure 5.6) included treatment and linear features (Table 5.3). Relative to the wolf reduction area, the proportion of 197 cameras occupied by caribou over time was higher in the reference and lower in the low reference areas. Additionally, the proportion of cameras occupied by cameras over time was higher in areas with more linear features (Table 5.4). 198 Table 5. 3: Comparing model support using Akaike’s Information Criterion, corrected for small sample sizes (AICc),for the effect of wolf reductions (treatment, such that wolf reductions is contrasted against two reference areas) and environmental covariates (climate Dimension 1 and 2, habitat productivity measured using the change in the Enhanced Vegetation Index, percent habitat alteration, the length of linear features (km)) as well as the additive effect of treatment and environmental covariates on the change in distribution, as measured by the proportion of cameras occupied, of white-tailed deer, moose, and caribou over time. Model White-tailed Deer Moose Caribou df AICc df AICc df AICc Null 2 -1820 2 -2229 2 -310996 Treatment 4 -2345 4 -2289 4 -312817 Dimension 1 3 -1909 3 -2261 3 -313335 Dimension 2 3 -2048 3 -2239 3 -320090 Habitat Productivity 3 -1818 3 -2343 3 -311140 Habitat Alteration 3 -1835 3 -2227 3 -311003 Linear Feature 3 -1906 3 -2324 3 -316980 Treatment + Dimension 1 5 -2422 5 -2308 5 -324016 Treatment + Dimension 2 5 -2377 5 -2284 5 -323561 Treatment + Habitat Productivity 5 -2339 5 -2374 5 -312816 Treatment + Habitat Alteration 5 -2363 5 -2326 5 -313548 Treatment + Linear Feature 5 -2390 5 -2358 5 -334927 199 Table 5. 4: The effect of treatment (low reference, reference, and wolf reductions; such that wolf reductions is the intercept) and environmental covariates on changes in white-tailed deer, moose, and caribou distribution over time, as measured by the proportion of cameras occupied in each cluster-year combination. The environmental covariate included varies per species, reflecting the most supported model from Table 5.3. Species Variable Estimate SE P value White-tailed Deer Intercept 0.012 0.004 0.003 Low Reference 0.28 0.011 <0.001 Reference -0.049 0.006 <0.001 Dimension 1 -0.014 0.002 <0.001 Moose Intercept -0.04 0.015 0.009 Low Reference -0.035 0.012 0.005 Reference -0.037 0.006 <0.001 Habitat Productivity <0.001 <0.001 <0.001 Caribou Intercept -0.027 <0.001 <0.001 Low Reference -0.028 <0.001 <0.001 Reference 0.007 <0.001 <0.001 Linear Features (km) <0.001 <0.001 <0.001 200 Figure 5. 6: Proportion of camera traps in which each species (moose, white-tailed deer, and caribou) was detected for camera trap clusters in the wolf reduction (WR) area, reference area (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. Do ungulates change their use of habitats that provide security and food resources in response to wolf reductions? In the low reference area white-tailed deer were more often observed in areas with high canopy cover as well as areas of either high or low productivity regardless of the year of study (Figure 5.7). In the reference area white-tailed deer were more often observed in areas with low cover in earlier years of the study, but in areas with higher cover and high productivity in later years of the study. In the wolf reduction area white-tailed deer were more often observed in areas with low cover in earlier years of the study, but in areas with higher cover in later years of the study; 201 use of high productivity areas was highest in the middle of the study, but relatively similar in the beginning and end of the study. In the low reference area, moose were more often observed in areas with intermediate canopy cover regardless of the year of the study, though use of high canopy areas became less frequent later in the study (Figure 5.8). Moose most often used highly productive habitat at the beginning of the study, and this effect diminished over time. In the reference area, moose were observed consistently relative to cover and at intermediate habitat productivity in early years, but throughout the study moose became observed more frequently in areas with high cover and intermediate to high productivity. In the wolf reduction area moose were more frequently observed in areas of high cover and high productivity early in the study. The effect of productivity decreased over time, whereas moose were more frequently observed in areas of intermediate cover over time. In the low reference area, caribou were most often observed in areas with low canopy cover and productivity at the beginning of the study, and these effects diminished throughout the study (Figure 5.9). In the reference area caribou were most often observed in areas with low cover at the beginning of the study, whereas productivity did not have a strong effect on frequency of use. As the study progressed caribou were more often observed in areas of high productivity and the effect of cover diminished. In the wolf reduction area, caribou were more frequently observed in areas with low cover and productivity at the beginning of the study. The effect of habitat productivity remained relatively consistent throughout the study, whereas caribou were more often observed in areas of intermediate canopy cover as the study progressed. 202 Figure 5. 7: The predicted biweekly total count of white-tailed deer as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. 203 Figure 5. 8: The predicted biweekly total count of moose as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. 204 Figure 5. 9: The predicted biweekly total count of caribou as a function of habitat productivity and canopy cover throughout the study period (2017 through 2022) in each treatment area (low reference, reference, and wolf reduction). Contour lines with predicted biweekly total counts in text are presented. Darker greens represent lower estimates and brighter yellows representing higher estimates to support visualization of the effect of cover and productivity over time within each panel, though colour scales are not consistent across panels. The total number of deer, moose, and caribou detected tended to increase throughout the spring and into summer, then decline through fall into winter (Figure 5.10). The increase was stronger in the low reference area than the reference area for deer, but the opposite for moose and caribou: there was a larger increase in detections through the spring/summer in the reference area than the low reference area. The observed increase was highest in the wolf reduction area for both deer and moose. Caribou detections in the wolf reduction area were consistent through the year, and were also consistently higher in the wolf reduction area than the reference and low reference areas. 205 Figure 5. 10: The predicted biweekly total count of white-tailed deer, moose, and caribou as a function of the biweekly period in each treatment area (low reference, reference, and wolf reduction). Note the y-axis differs between panels. Do ungulates shift their temporal activity in response to wolf reductions? Temporal activity patterns of white-tailed deer were similar across the treatment areas (Figure 5.11). Deer tended to have relatively consistent activity throughout the day, with a small peak in the morning (Figure 5.11). In contrast, moose tended to more diurnal in the wolf reduction and reference areas, but active during daylight hours in the low reference area. Caribou displayed diurnal activity patterns in both of the reference areas, but were more consistently active throughout the day in the wolf reduction area (Figure 5.11). In all cases, ungulates tended to be less active during the evening peak wolf activity period. Temporal overlap with wolf activity periods was typically higher for white-tailed deer than for moose and caribou (Figure 5.12). Overlap between wolves and white-tailed deer was relatively consistent across the treatment areas on average, though there was a tendency for overlap to increase over time in the two reference areas but not in the wolf reduction area 206 (Table 5.5). Both moose and caribou overlap with wolves tended to increase over time in the wolf reduction area; this effect was stronger in the low reference area and weaker in the high alteration reference area (Table 5.5). Figure 5. 11: Temporal activity patterns of wolves and their ungulate prey (white-tailed deer, moose, and caribou). Temporal activity was quantified using camera traps from 2017 through 2023 in an area with wolf reduction, a reference area with high habitat alteration similar to the wolf reduction area (Reference) and a reference area with low habitat alteration (Low Reference). Grey shading represents overlap between wolves and their prey. 207 Figure 5. 12: Temporal overlap between ungulate prey (white-tailed deer, moose, and caribou) and high wolf activity periods in the wolf reduction (WR) area, a low habitat alteration reference (Low Ref) area, and high-habitat alteration reference (Ref) area. Overlap was calculated for each camera cluster and year combination. Boxplots display the interquartile range (IQR), with the line inside the box representing the median and the whiskers extend to the smallest and largest values within 1.5 times the IQR. The boxplots are overlaid with each sample point, colored by the number of observations in which the overlap value was calculated. 208 Table 5. 5: The effect of wolf reductions over time on the temporal overlap between white-tailed deer, moose, and caribou with high wolf activity periods relative to a low anthropogenic alteration reference area and a reference area with similar alteration. Species Variable Estimate SE P Value White-tailed Deer Intercept -0.735 0.442 0.096 Reference -0.292 0.625 0.640 Low Reference 0.104 0.593 0.860 Year -0.001 0.001 0.123 Reference : Year 0.082 0.001 <0.001 Low Reference : Year 0.042 0.002 <0.001 Moose Intercept -1.140 0.292 <0.001 Reference -1.018 0.393 0.010 Low Reference 0.302 0.413 0.466 Year 0.068 0.002 <0.001 Reference : Year 0.142 0.007 <0.001 Low Reference : Year -0.021 0.003 <0.001 Caribou Intercept -0.981 0.395 0.013 Reference -1.907 0.605 0.002 Low Reference 0.053 0.604 0.931 Year 0.005 0.003 0.037 Reference : Year 0.243 0.009 <0.001 Low Reference : Year -0.160 0.004 <0.001 209 Discussion We evaluated the response of ungulate prey—white-tailed deer, moose, and caribou—to predator reductions, with the hypothesis that these species would exhibit both demographic and behavioral release. We did not detect demographic release in response to wolf reductions. While the density of each species increased over time in the wolf reduction area to some degree, the effect was mediated by environmental covariates (metrics of habitat alteration or productivity), and populations increases were also observed in the reference area(s). Moose distribution did, however, expand over time within the wolf reduction area, suggesting some level of demographic release. We also found little evidence that ungulates shifted their spatial or temporal activity patterns in response to wolf reductions. Overall, these results highlight the complex and species-specific nature of prey responses to predator management, with implications for understanding the cascading effects of predators on prey populations. The lack of observed demographic responses to wolf reductions in this study underscores the complexity of prey population dynamics and the potential influence of compensatory mechanisms. While wolf predation is a significant mortality factor for many ungulate species, such as caribou, and that moose and caribou populations are linked to wolf density across the boreal (Bergerud & Elliot, 1986; Hayes et al., 2003; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021), compensatory mortality may result in muted demographic changes (Tveraa et al., 2003). Bottom-up limitations, such as habitat productivity and environmental constraints like winter severity, likely play a critical role in shaping prey populations (Abrams, 1993; Hunter & Price, 1992; Sinclair & Krebs, 2002). For instance, white-tailed deer, which are at the edge of their northern range in this region, are likely constrained more by harsh winter conditions than by predation alone, with over-winter survival acting as a primary limiter of population growth (Carstensen et al., 2009; Dawe & Boutin, 2016; Dickie et al., 2024). White-tailed deer density increased notably in one cluster in the wolf reduction area that was characterized by intermediate environmental conditions relative to the other sampling 210 areas, suggesting that there may be an interaction between top-down and bottom-up factors that should be further investigated. For moose, a spatial release was evident through the expansion of their distribution within the wolf reduction area, yet this did not correspond with increased density, indicating a potential decoupling between distribution and density or density-dependent dispersal (Brown, 1984; Shoo et al., 2005, 2005). The ecological significance of this distributional change remains unclear without a concurrent increase in density or a better understanding of the underlying mechanisms (Brown, 1984). The complexity of these interactions highlights the importance of considering the broader ecological context when assessing the outcomes of predator management (Clark & Hebblewhite, 2021). Contrary to the prediction that wolf reductions would prompt a shift from secure to more productive habitats, no such response was detected across the species evaluated. While moose in the wolf reduction area did reduce their use of high canopy cover over time, indicating a potential relaxation of security-driven behavior (Brown, 1999; DeMars et al., 2016; Schmitz et al., 1997), similar trends were observed in the reference area, and crucially, this change did not correspond with an increased use of more productive habitats. In addition to changing spatial behavior to mediate risk, species can change their temporal activity (Lima & Bednekoff, 1999; Ross et al., 2013; Roth & Lima, 2007; J. A. Smith et al., 2019). All three species tended to be inactive during the night, which is when wolves were most active. Temporal overlap with wolf activity increased in the wolf reduction area over time for both moose and caribou, as predicted if these species were no longer constrained to avoid these periods of time, though this pattern was also observed in the reference areas. These findings indicate that temporal activity may also be intertwined with other ecological variables (Lima & Bednekoff, 1999; Van Scoyoc et al., 2023). The absence of clear changes in habitat use and temporal activity over time underscores the complexity of behavioral responses following predator removal, pointing to the multifaceted nature of habitat selection. For example, subtler behaviors, such as changes in vigilance or group size, could be occurring, without prompting dramatic shifts in space use and activity 211 patterns (Brown, 1999; Lima & Bednekoff, 1999; Wikenros et al., 2015). Understanding behavioral responses is essential, as they may at least in part explain the lack of demographic responses observed across species, highlighting the importance of behavioral plasticity in mediating population-level outcomes (Hawkes, 2009). As a corollary to evaluating trends in habitat use over time using biweekly observations, we observed a larger increase in the frequency of observations of deer and moose through the spring and summer months in the wolf reduction area relative to the reference areas. These results could indicate that the wolf reduction area experienced a larger birth pulse, and early survival of young was higher in areas with wolf reductions (Brown & Conover, 2011; Clark & Hebblewhite, 2021; Krebs et al., 1986; Sinclair & Krebs, 2002). However, the frequency of observations declined through fall, such that over-winter observations were similar across all treatments. This convergence in the frequency of observations suggests that compensatory mortalities may be occurring (Clark & Hebblewhite, 2021; Errington, 1946; Sinclair & Krebs, 2002), corroborating the lack of observed changes densities measured across a full year. The gradual decline in biweekly observations through the fall could also indicate compensatory predation by other predators – for example black bears are abundant in this system (Zager & Beecham, 2006), though food limitation resulting in starvation (Tveraa et al., 2003) or emigration out of the study area are potential alternative mechanisms. Assessing changes in reproductive metrics to evaluate if reproduction increased in the wolf reduction area over time compared to the reference area would be valuable. Future research could use camera trap data to estimate fawn:doe or calf:cow ratios to evaluate reproduction effects more directly. While our study is strengthened by extensive monitoring over multiple reference areas, it is limited by the absence of monitoring data prior to wolf reductions. We focused on evaluating differences in trends over time to partially account for potential differences between the wolf reduction and reference areas, but a before-after control-impact design would provide stronger inference (Clark & Hebblewhite, 2021; Treves et al., 2019). Additionally, there may be a lag in 212 population recovery, with the seven years of monitoring being insufficient to detect subtler changes. Long-term studies are often required to capture delayed population responses, particularly in systems where multiple factors, such as predation, habitat quality, and climate, interact to influence population dynamics (Clark & Hebblewhite, 2021). Additionally, long-term studies allow for the observation of gradual ecological shifts and the persistence of effects over time. Further, while camera trap data are ideal for providing information on a variety of species, they may lack the precision needed to detect subtle changes in distribution, density, and behavior over the short to medium term (Burton et al., 2015). High resolution, species-specific data such as Global Positioning System collars, abundance surveys, or survival and recruitment surveys could offer deeper insights into the demographic and behavioral responses to predator reductions. Understanding the broader implications of predator reductions on community dynamics and ecosystem processes remains a priority. It is also possible that the lack of observed response to wolf reductions reflects rapid wolf ingress into areas following annual removal efforts, thereby reducing the duration of reduced predation pressure necessary for prey populations to respond (Boertje et al., 1996; Seip, 2022). Ingress from surrounding areas into the wolf reduction area may also contribute to reduced wolf densities in the reference areas if individuals or packs emigrated from the reference areas into the newly opened home ranges in the reduction area (Adams et al., 2008; Schmidt et al., 2017). Furthermore, insufficient removal efforts, both in terms of intensity (i.e., the number of wolves removed per area) and the spatial extent of removals, may have contributed to the lack of observed changes (Clark & Hebblewhite, 2021). The timing of wolf removals (January-March) relative to key life history stages of prey species, such as calving or fawning in May and June, may also be crucial; predator reductions that coincide with vulnerable periods are more likely to effectively enhance survival and recruitment (Clark & Hebblewhite, 2021). We were unable to evaluate the influence of reduction intensity or extent on responses, and this remains a priority for future longer-term studies. Understanding the interplay between 213 removal intensity, extent, and timing is essential for evaluating the efficacy of predator management strategies and their potential to influence prey populations. Overall, the observed responses to wolf reduction (or lack thereof) highlight the intricate and multifaceted nature of prey dynamics within predator-prey systems. Responses to predation operate through a web of interconnected demographic, behavioral, and physiological processes, each modulated by species-specific traits and ecological contexts (Hunter & Price, 1992; Peacor & Werner, 2001; Sinclair & Krebs, 2002). The degree to which prey populations are limited by top-down pressures, such as predation, versus bottom-up factors like habitat quality, climate, or harvest regimes, can vary markedly between species and regions (Sinclair & Krebs, 2002). 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Ursus, 17(2), 95–108. https://doi.org/10.2192/1537-6176(2006)17[95:TROABB]2.0.CO;2 225 Appendix 5.1 Using Multi Factor Analysis to quantify climate dimensions Figure 5.1. 1: The correlation between quantitative variables and dimensions estimated using Multi Factor Analysis, colored by variable groups. 226 Appendix 5.2 The relationship between indices of forage availability and security: Figure S5.2. 1: The relationship between habitat productivity (ΔEVI), and index of forage availability, and percent canopy cover, an index of security. Each point represents a camera trap location in a given year, and points are colored by cluster. Pearson’s Correlation R2 = -0.31. 227 Appendix 5.3 Using camera traps to estimate wolf temporal activity: One of the benefits of camera traps is that they, in theory, provide information on anything that can be detected visually. Cameras traps are therefore often used to study large-mammal communities across time and space (Steenweg et al., 2017). However, studies on cryptic species or species that are at low abundances continue to be plagued by low detection rates given the area sampled by cameras is small. Camera traps therefore lend themselves well for studying trends across space and time for species that are relatively detectable or are relatively abundant, particularly if other methods for observing these species are logistically difficult, cost-prohibitive, or highly invasive. In some cases, detections can be increased through the use of targeted sampling (e.g., on trails or other highly used habitat types) or by using lure or scents to attract animals. Various analytical approaches have different study design and requirements or considerations, necessitating careful consideration for deployment protocols and study design given the questions being asked and analytical approaches being considered. In our study system, camera traps were ideal for studying white-tailed deer and moose response to a wolf reduction program; deer and moose are large mammals that are relatively abundant across the landscape. We also included woodland caribou, though they are at lower abundances and so they are likely better studied using detailed and targeted sampling methods. We chose, however, to not include the response of wolves to wolf reductions, which may seem counter-intuitive. Because density and distribution were two key response variables of interest, we deployed camera traps in a clustered random design to match the requirements of Time in Front of Camera (Becker et al., 2022). The trade-off for this design is that the number of detections is likely lower than had we used targeted sampling for feature types used for movement, such as game trails. While the number of detections using random sampling was sufficient for white-tailed deer, moose, and caribou, the number of wolves detected was low; an unsurprising result given their relatively low abundances on the landscape (Serrouya et al., 228 2021). Additionally, because wolves are social and live in packs, observations of wolves were also highly variable. Estimated wolf densities were highly sensitive to a few observations (Figure S5.3.1). The proportion of cameras in which the species was detected somewhat smoothed over some of this sensitivity (Figure S5.3.2). However, the minimum number of wolves detected per day monitored was consistently low over the course of the year, and similarly sensitive to only a few observations in a particular area or year (Figure S5.3.3). As such, we did not evaluate the response of wolf density, distribution, or space-use to wolf reductions. We chose to use the camera trap data to index temporal activity patterns of wolves. To evaluate temporal activity patterns, we extracted the solar time of day of each photo of a wolf individual, and used the time of day to build a kernel density. The number of wolf photos in each year and each camera cluster tended to be low (Table S5.3.1), reflecting their low abundances (Figure S5.3.1) and biweekly hit rates (Figure S5.3.3). We therefore combined all data when evaluating temporal use, making the assumption that wolf activity periods were constant across time and space. However, it is important to note that the majority of the data used to estimate wolf temporal activity were extracted from two camera clusters (AUB and CHR), each from one year (2020 and 2019, respectively; Table S5.3.1). 229 Figure S5.3. 1: Density of wolves in the wolf reduction (WR) area, reference areas (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. Figure S5.3. 2: Density of wolves in the wolf reduction (WR) area, reference areas (Ref), and low-alteration reference area (Low Ref) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. 230 Figure S5.3. 3: The minimum number of wolves counted per day monitored (i.e. the total count) using camera traps in the wolf reduction (Wolf-Reduction) area, reference areas (Reference), and low-alteration reference area (Low-Reference) over time. Wolf reductions began implementation in 2017. Colors represent camera cluster replicates in each treatment. Wolf reductions occur from approximately the end of December to the end of March; the dashed vertical line represents March 30 in each year. 231 Table S5.3. 1: The number of photos used to evaluate temporal activity patterns of wolves in the low reference area, reference area, and wolf reduction area over time. The number of photos is calculated per camera cluster. Treatment Cluster Year 2017 2018 2019 2020 2021 2022 2023 Low Reference AUB - - 57 1467 85 - - DEW 5 36 6 1 - - - LRN 0 16 12 6 0 - - MAL - 0 0 11 0 0 - MCC 2 3 0 0 3 - - Reference CAL 16 13 10 - - - - MAC 3 8 13 2 0 2 2 PEL - - - 10 0 21 3 WAB 0 0 0 0 0 12 0 Wolf Reduction CHR 0 1 638 41 24 30 1 FMM 3 3 79 10 1 6 0 LID - 0 10 0 4 33 24 LLB 25 25 2 6 7 8 0 232 CHAPTER 6: INTERVENTION-FORWARD ADAPTIVE MANAGEMENT IN THE FACE OF EXTINCTION The Anthropocene has spurred the onset of the Earth's 6th mass extinction (Jaureguiberry et al., 2022), resulting in global calls to accelerate efforts to restore ecosystems (United Nations Environment Agency, 2019) and the newly negotiated Kunming-Montreal Global Biodiversity Framework (United Nations, 2022). Conservation leaders emphasize identifying and addressing underlying mechanisms of population decline to avoid misplaced efforts, limited recovery success, and degraded public trust (Ford et al., 2021; Kear & Berger, 1980). Identifying the mechanisms of decline is challenging under scenarios with concurring threats of habitat loss, disease, climate change, and overexploitation of populations, particularly when threats are interacting and cumulative. The challenges to addressing complex mechanisms of decline have prompted calls for removing all probable threats (or agents) of decline, eloquently paraphrased by Krebs (2009): “Several suspected agents of decline may have to be removed at once... It is better to save the species than to achieve scientific purity.” This philosophy is most effectively applied within a framework of adaptive management (AM), where, to the degree possible, management interventions such as habitat restoration, predator/competitor control, or population augmentation are treated as experiments (Walters & Holling, 1990). AM has the potential to restore ecosystems and gain scientific insights to provide information that guides future efforts. However, as identified by Månsson and colleagues (Månsson et al., 2022), AM has been hindered by socio-political constraints, perceived costs, and ecological complexity (Månsson et al., 2022; Westgate et al., 2013). Given the urgency to halt and reverse the loss of biodiversity, we suggest a new approach to maximize the likelihood of recovery while maintaining the scientific benefits of an experimental approach to management: applying all plausible interventions at the onset of significant population decline, followed by strategically removing interventions to facilitate learning (Figure 6.1). 233 In their constructive and thought-provoking review, Månsson and colleagues (Månsson et al., 2022) outline a number of solutions to overcome barriers associated with implementing AM. While these solutions are likely to improve the effectiveness of AM, for some species there is further need to hasten recovery efforts beyond which conventional AM can typically deliver. As Månsson and colleagues (Månsson et al., 2022) state “In the face of large-scale climate and land use change, AM’s stepwise learning may not keep pace with environmental changes.”. Under ideal circumstances, AM applies management interventions over time (or space) to create a positive demographic response (Idealized Adaptive Management; Figure 6.1). In addition to improving the status of the focal population, contrasting demographic responses across treatments can increase scientific understanding. However, and as Månsson and colleagues (Månsson et al., 2022) point out, it is common for species to continue declining in spite of the application of AM. These persistent declines occur because the appropriate intervention(s) are not applied, the intervention is not applied quickly or intensely enough, or the population is experiencing an extinction vortex (Realized AM; Figure 6.1). Realized AM can still provide valuable lessons if implemented with scientific rigor, but the process often fails to recover populations. For species undergoing rapid declines, the need for immediate action limits opportunities for the careful planning needed to achieve rigor and engagement across stakeholders (Månsson et al., 2022; Westgate et al., 2013). Building off of the foundational idea of removing all probable threats to maximize recovery potential (Caughley, 1994), we propose an intervention-forward AM framework in which: 1) all feasible interventions are applied at the onset of significant decline; and 2) only after populations have demonstrated a sufficient demographic response are interventions strategically removed to inform learning (Intervention-forward Adaptive Management; Figure 6.1). When multiple interventions are used in tandem, strategic removal of interventions can inform a scientific understanding of ecological processes, such as resource limitation, predation, or competition to increase applicability of learnings to other management problems. Elements of 234 Intervention-forward AM have been used in isolation, such as the relaxing of hunting quotas as species recovery ensues (Linnell et al., 2010), or the application of multiple management interventions in the face of scientific uncertainty (Serrouya et al., 2019). Intervention-forward AM uses these established practices, but in a novel sequencing that accelerates recovery under urgent and uncertain conditions. Intervention-forward AM is likely most useful in cases of urgency, high uncertainty, and/or complex mechanisms of decline. Intervention-forward AM, much like Idealized AM, should begin with a conceptual model of the mechanisms leading to the species’ decline to guide clear objectives, timelines, and decision points on when and which interventions are removed (Walters & Holling, 1990; Westgate et al., 2013). Examples of these criteria include the degree to which the intervention is presumed to be tied to the hypothesized mechanism(s) of decline, the time scales over which they are presumed to occur, and financial, social, or political constraints. While the front-loading of interventions may require higher financial and social investment in the short-term, the higher likelihood of successful recovery may reduce the cost of recovery in the long term relative to Realized and Idealized AM. We offer this “Intervention-forward” AM approach as an addition to the myriad of solutions Månsson and colleagues (Månsson et al., 2022) outline to improve implementation of AM, with the explicit goal of maximizing conservation success while maintaining opportunities to learn while doing. 235 Figure 6. 1: Comparison of Idealized Adaptive Management (AM), Realized AM, and the newly proposed Intervention-forward AM. Under Idealized AM, management interventions (solid line; left Y axis) are implemented one at a time to facilitate learnings, and a positive demographic response (dashed line; right Y axis) occurs. Under Realized AM, management interventions are implemented (also ideally one at a time, though independent implementation is not always achieved), yet the demographic response continues to decline. 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Biological Conservation, 158, 128–139. https://doi.org/10.1016/j.biocon.2012.08.016 238 CHAPTER 7: CONCLUSIONS The difficulty of quantifying the mechanistic pathways in which various factors impact populations, and predicting the relative contribution of those pathways, is emphasized by their interactive effects and how they change over time and space. In this dissertation, I investigated how habitat alteration, climate, and management interventions influenced various components of the predator-prey interactions that are implicated in the decline of caribou. Specifically, I evaluated the relative influence of habitat alteration and climate on invading white-tailed deer populations (Chapter 2), how resource density and the ability to exploit those resources (i.e., resource exploitation efficiency) interact to influence wolf home ranges (Chapter 3), and I synthesized existing knowledge on the effects of linear features on caribou, focussing on the mechanisms that link this stressor to caribou populations (Chapter 4). Additionally, I evaluated how predator reduction programs influenced the abundance, distribution, and behavior of prey species (Chapter 5). Finally, recognizing the complexities inherent in ecological processes, I proposed a novel adaptive management framework designed to maximize the likelihood of recovering species in precipitous declines while maintaining the scientific benefits of an experimental approach to management (Chapter 6). By integrating the findings across my chapters, I highlight how the intricate relationships within food webs can inform more targeted and effective management strategies. Human land-use is a primary driver of global biodiversity declines (Newbold et al., 2015; Tilman et al., 1994), principally through habitat loss and modification, which disrupts the ecological processes that sustain populations. As natural landscapes are increasingly transformed for agriculture, urban development, and resource extraction, the abundance and behavior of species are altered in ways that undermine ecosystem function (Dirzo et al., 2014). Linear features, such as roads and seismic lines, exacerbate the impacts of large-scale land conversion by altering movement patterns, thereby influencing predator-prey dynamics (Fahrig & Rytwinski, 2009; Trombulak & Frissell, 2000). The proliferation of linear features, coupled with 239 more extensive habitat conversion, fundamentally alters the structure and function of ecosystems, diminishing their capacity to support native species and exacerbating the cascading effects of predation and other ecological interactions. Climate change compounds these challenges by driving widespread shifts in species distribution, behavior, and ecological interactions. As temperatures rise, many species are shifting their ranges poleward or to higher elevations, seeking suitable climates (Osland et al., 2021; Tingley et al., 2012). This redistribution of species often leads to novel interactions, with potential consequences for native species (Smith et al., 2021). For example, the northward expansion of red foxes into Arctic regions threatens the persistence of the less competitive native arctic fox (Elmhagen et al., 2017). These shifts can also occur through more complex mechanisms. For example, altered precipitation regimes and temperature fluctuations are disrupting the timing of biological events, leading to phenological mismatches that pose significant risks to survival, reproduction, and long-term population viability (Durant et al., 2007; Miller-Rushing et al., 2010; Post et al., 2008). In my study system, I found that reduced winter severity supports higher abundances of white-tailed deer (Dickie et al., 2024). Invading white-tailed deer may represent a direct competitor to caribou or moose, though the primary mechanism in which this novel species is expected to disrupt native predator-prey interactions associated with caribou declines is through apparent competition whereby deer support higher abundances of wolves that subsequently predate caribou (Latham, Latham, McCutchen, et al., 2011). In addition to the direct effects of climate change on wildlife populations, climate change is altering habitat productivity on a global scale, with some regions experiencing greening due to increased temperatures and carbon dioxide fertilization, while others are facing browning due to drought and heat stress (Wang et al., 2011; Zhao & Running, 2010; Zhu et al., 2016). These shifts in productivity have profound implications for species, influencing food availability and quality, that flow through the food web (Abrams, 1993; Oksanen et al., 1981). In my study 240 system, I found that habitat productivity mediates the impact of winter severity on white-tailed deer abundance, such that deer densities were less impacted by winter severity in areas with higher habitat productivity (Dickie et al., 2024). In addition, the effect of linear features on wolf home range size was mediated by habitat productivity, such that in areas with high habitat productivity, home ranges were small regardless of linear feature density (Dickie et al., 2022b). Combining these two results suggest that if the boreal forests become more productive under a changing climate (Price et al., 2013), the abundance of wolves will continue to grow and exceed targets associated with self-sustaining caribou (Bergerud & Elliot, 1986; Environment and Climate Change Canada, 2020; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). The interplay between direct and indirect climate-driven changes, and how they interact with other stressors, further complicates conservation efforts under changing environmental conditions. The interaction between these stressors - human-induced habitat alteration, climate change, and climate-driven productivity changes - creates complex and often synergistic pathways that may reshape ecosystems in ways that challenge traditional conservation strategies. Understanding and addressing these multifaceted impacts is crucial for mitigating biodiversity loss in the face of ongoing environmental change. The challenges of uncovering mechanistic pathways in ecology In many respects, the decline of woodland caribou presents a somewhat straightforward system for examining the relative influence of limiting factors. Extensive scientific research has established that predation is the primary limiting factor for many woodland caribou populations (Boutin et al., 2010; Rettie & Messier, 1998; Seip, 1992). While other factors, such as food limitation, insect harassment , and disease do play a role (Finnegan et al., 2016; Schaefer et al., 2016), they are often considered secondary or interacting influences. Given the importance of predation, considerable research has gone into investigating what has perturbed predator-prey 241 interactions such that woodland caribou are unable to sustain predation. These disturbances typically stem from habitat alteration or climate change, and emerge through pathways associated with changes in the behavior and abundance of wolves and their primary prey species (Dickie, Love, et al., 2023; Neufeld et al., 2021; Serrouya, Dickie, Lamb, Van Oort, Kelly, DeMars, et al., 2021). Unlike more complex ecosystems, which involve a multitude of interacting species and factors, the woodland caribou system is characterized by a limited number of key players, making it a useful case for exploring the nuances of ecological interactions and their management. In other respects, the woodland caribou system is fraught with complexities that make it challenging to unravel the pathways leading to their decline. Caribou populations are largely decoupled from their primary limiting factor—predation—and the ecological pathways influencing their decline are often multi-step and indirect. These multi-step pathways make it difficult not only to quantify the relative effects of various stressors but also to identify the specific mechanisms through which these stressors operate. In Chapter 4, I evaluated existing scientific knowledge along each of the steps along the mechanistic pathways from linear features to caribou declines, with the ultimate goal of identifying indicators to support management decisions. Even focussing on only one stressor, linear features, there was an intricate web in which changes in behavior and abundance cascades through the food web to ultimately impact the species of interest. Despite a large body of research investigating the decline of woodland caribou, many of the intermediate steps linking an ultimate stressor (linear features) to caribou declines via proximate stressor(s) lacked sufficient examination in which to quantify their relative influence (Dickie, Love, et al., 2023). Indeed, disentangling the relative role of various pathways linking one particular stressor to species declines is complex enough, without even yet considering interacting effects of additional stressors. 242 How do we proceed given the unpredictable nature of nature? The complexity of tracing multistep pathways complicates management efforts aimed at effectively reducing predation in a timely manner. To navigate ecological complexities and uncertainties, adaptive management is often considered as the gold standard for "learning while doing" (Walters & Holling, 1990; Westgate et al., 2013). Adaptive management allows for systematic experimentation and iterative learning, enabling managers to adjust strategies based on observed outcomes. However, in the case of woodland caribou, the pace of decline is too rapid to afford the luxury of testing single factors over extended periods before acting. While there have been instances, such as with southern mountain caribou (Lamb et al., 2024), where management actions across multiple populations and years provided some insights post hoc, these are exceptions rather than the norm. To address this urgent need, I proposed an alternative approach to adaptive management—Intervention-Forward Adaptive Management (IFAM; Chapter 6)—which emphasizes a more precautionary strategy of "act now, learn as you undo" (Dickie, Ford, et al., 2023). While IFAM offers a pragmatic solution, its implementation may still be constrained by various factors (Månsson et al., 2022). The caribou system is not only biologically complex, but also entangled in social, political, and ethical considerations that cannot be overlooked (Johnson et al., 2022; Lamb et al., 2023). Caribou live at low densities across vast landscapes, making localized management interventions insufficient—effective management must be large-scale and coordinated across regions (Dickie, Bampfylde, Habib, et al., 2023; Environment Canada, 2011; Serrouya et al., 2020). Moreover, caribou habitat overlaps with areas rich in resources that are valuable to humans (Hebblewhite, 2017), creating a delicate balance between environmental conservation and human economic interests. This overlap raises ethical questions about what constitutes sustainable environmental management and resource use. It also prompts reflection on the morality of conservation decisions, especially when interventions 243 designed to protect one species occur at the expense of another, particularly when human activities are the root cause of such conflicts. In the field of ecology, we are often reminded that the outcomes of ecological processes depend on a multitude of factors, which can be frustrating for those aiming to manage or restore ecosystems. For example, Chapter 4 highlights that context matters for how species respond to linear features (Dickie, Love, et al., 2023). These contexts can be intrinsic, for example driven by sex or age class (Peacor & Werner, 2001), extrinsic, for example driven by interacting environmental conditions (Crain et al., 2008; Mantyka-Pringle et al., 2013; Mumma et al., 2020), or can even be individual plasticity (Avgar et al., 2013; Courbin et al., 2017). Yet, it is precisely this variability that offers a pathway to more tailored and effective management interventions. By understanding the contexts in which specific mechanistic pathways are likely to operate, we can better predict how different stressors will interact and influence ecological outcomes. To demonstrate, I use a simplified version of the caribou system as an example, first illustrating hypothesized mechanisms in which stressors influences caribou populations (Figure 7.1). In this example, climate and habitat alteration exert impacts through multiple mechanisms that flow through vegetation, primary consumers (ungulates), secondary consumers (bears and wolves), and ultimately to caribou. I then contrast hypothetical populations that vary by their environmental context, driven by underlying differences in the productivity of the system, the dominant habitat alteration types, and their climates (Figure 7.2). 244 Figure 7. 1: A simplified visualization of how stressors flow through mechanistic pathways to influence caribou. Climate, which varies both across time and space, influences vegetation structure and composition, which in turn influences the abundance, distribution and abundance of white-tailed deer and moose. In this simplified example, climate change results in increased habitat productivity, and so provides increased food quantity and quality for moose and deer. Climate can also directly impact the abundance, distribution, and behavior of white-tailed deer and moose. In this example, severe winters reduce over-winter survival of deer. In addition to climate impacts, human land-use is a stressor linked to caribou. Forest harvest impacts vegetation structure and composition, converting mature forests to early seral vegetation, thereby increasing food availability to moose and deer. Additionally, the creation of linear features impacts the behavior of wolves, increasing their hunting efficiency which in turn also supports higher wolf abundances. The impacts of these stressors then cascade through the food web, leading to increased predation on caribou. Note that direct impacts of each stressor on caribou are not depicted here to focus on the indirect pathways. Beavers and black bears are depicted for reference, though their responses to these stressors, and cascading influence to 245 caribou is less well documented and further excluded from the hypothetical examples. In addition, climate change can indirectly influence vegetation structure and composition by changing the severity of wildfires, though these pathways are also excluded from the hypothetical examples. Figure 7. 2: Depiction of how the relative influence of mechanistic pathways leading from stressors to caribou declines can vary depending on environmental context. Dark solid lines represent relatively strong pathways, whereas light dashed lines represent relatively weak pathways. In productive systems where forest harvest is a predominant habitat disturbance, the conversion of mature forest to early seral vegetation is expected to support increased white-tailed deer and moose densities (Rempel et al., 1997; Serrouya et al., 2011). Here, pathways linking forest harvest to caribou declines are key - changes in vegetation structure and 246 composition lead to an increase in moose and deer abundance and a corresponding increase in wolf abundance. In this case, habitat protection and/or restoration to reduce the creation of food subsidies to moose and deer may be effective management interventions. In contrast, in unproductive systems where oil and gas exploration is a predominant habitat disturbance, the creation of linear features is expected to increase wolf hunting efficacy and use of caribou refugia (DeMars & Boutin, 2017; Dickie et al., 2017). Here, pathways linking linear features to caribou declines through increased wolf hunting efficiency and wolf abundance may be more important. Given this, restoration of linear features can be used to reduce wolf hunting efficiency, which should in turn reduce predation on caribou. However, if this system is also characterized by mild winters that support abundant white-tailed deer (Dawe & Boutin, 2016; Dickie et al., 2024), pathways linking climate, white-tailed deer, wolf abundance, and caribou declines are important. In this case, habitat restoration may be less effective in isolation; additional management interventions to reduce white-tailed deer or wolf abundance may be necessary. By clarifying the ecological context, management interventions can better target mechanisms of decline. In the face of uncertainties surrounding the relative effect(s) and strength(s) of each of the mechanisms, the precautionary principal should prevail under an IFAM approach to the degree possible. Navigating uncertainties in ecological processes often force us to confront the uncomfortable reality that there are no simple solutions—every decision involves trade-offs that ripple through the ecosystem. This reality ties into a broader truth in ecology: while science often seeks to identify consistent patterns and processes, in practice, these “truths” are frequently obscured by the variability and context-dependence of natural systems, the “it depends”. 247 Lessons learned along the way Following the publication of Chapter 2 (Dickie et al., 2024), a rebuttal was submitted to Global Change Biology, critiquing the analytical decisions used to evaluate the relative effects of habitat alteration and climate on white-tailed deer densities (Barnas et al., 2024). It comes to no surprise that the process of scientific inquiry is faced with a myriad of analytical decisions, and that these decisions can alter the strength, or even direction, of outcomes (Silberzahn et al., 2018). Strong, consistent impacts should be relatively robust to analytical decisions, but subtler responses may be more at risk to being masked or misspecified based on analytical decisions. It is often the hope that sound study designs, coupled with defensible and best-practice analytical decisions, can overcome this difficulty. Open access data and coding can support this process by providing opportunities for careful peer review and re-analysis. Indeed, science is meant to be an iterative process, such that the process of replication builds up a more vigorous body of work. The process of responding to the critique prompted me to carefully think about how results are communicated, and how often results can be taken one step further to illustrate the implications of our findings. By doing so, we can better understand how sensitive our findings may be to our analytical decisions. Barnas et al. (2024) suggested that our results were sensitive to the data transformation method used; their claim underscored the importance of clearly depicting and interpreting interactions. Furthermore, while we used an a priori hypothesis testing framework that suggested climate had approximately twice the effect on deer densities as did habitat alteration, Barnas et al (2024) re-analysed our data finding that a simplified model without interactions, which they suggest is more appropriate for the sample size of the study, resulted in an equal impact of climate and habitat alteration. 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Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ford, Adam
Evaluating the mechanisms linking anthropogenic habitat alteration to caribou declines in western Canada
Text