Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Van Elslander, Jonathan
Master of Science - MSc
University of British Columbia
Dietary niche variation is a key facet of an animal’s niche and can be a driver of spatial variation in behaviour, population dynamics, and sensitivity to anthropogenic threats. Spatial assessments of the variation of an animal’s dietary niche helps provide key baseline knowledge for the research and management of a species and are particularly important for species with large geographic ranges and highly variable niches. Grizzly bears (Ursus arctos) are a wide-ranging omnivorous mammal with enormous dietary flexibility and a species of concern in Canada. I estimated the proportion of vegetation, terrestrial meat, anadromous salmon, and non- anadromous kokanee salmon in the diet of over 1800 grizzly bears via stable isotope analyses of over 2500 guard hair samples collected across the province of British Columbia. Using these estimates, I created fine-scale maps of grizzly bear diet using a parametric generalized additive mixed effects model with spatial random fields. The results of these predictive models showed that spatial distribution of grizzly bear’s dietary niche in B.C. can be broadly categorized into coastal areas where bears are reliant on salmon, and interior areas where they are reliant on plant foods. Terrestrial meat sources and kokanee salmon provided important supplements to bear diet in certain regions, but nowhere were they as important to bear diet as plants or salmon. These results also showed that spatial variation in grizzly bear diet is not currently reflected in the boundaries of B.C.’s Grizzly Bear Population Units, which represents a major obstacle to effective management across the province.
Spatial Variation in Grizzly Bear Diet Across British Columbia by Jonathan Van Elslander B.Sc. (Honours), Simon Fraser University, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Jonathan Van Elslander, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Spatial Variation in Grizzly Bear Diet Across British Columbia submitted by Jonathan Van Elslander in partial fulfillment of the requirements of the degree of Master of Science . Dr. Mathieu Bourbonnais, Irving K. Barber Faculty of Science Supervisor Dr. Garth Mowat, Irving K. Barber Faculty of Science Supervisory Committee Member Ed Hornibrook, Irving K. Barber Faculty of Science Supervisory Committee Member Kaitlyn Gaynor, University of British Columbia (Vancouver) University Examiner Click or tap h ere t o ente r text. External Examiner iii Abstract Dietary niche variation is a key facet of an animal’s niche and can be a driver of spatial variation in behaviour, population dynamics, and sensitivity to anthropogenic threats. Spatial assessments of the variation of an animal’s dietary niche helps provide key baseline knowledge for the research and management of a species and are particularly important for species with large geographic ranges and highly variable niches. Grizzly bears (Ursus arctos) are a wide-ranging omnivorous mammal with enormous dietary flexibility and a species of concern in Canada. I estimated the proportion of vegetation, terrestrial meat, anadromous salmon, and non-anadromous kokanee salmon in the diet of over 1800 grizzly bears via stable isotope analyses of over 2500 guard hair samples collected across the province of British Columbia. Using these estimates, I created fine-scale maps of grizzly bear diet using a parametric generalized additive mixed effects model with spatial random fields. The results of these predictive models showed that spatial distribution of grizzly bear’s dietary niche in B.C. can be broadly categorized into coastal areas where bears are reliant on salmon, and interior areas where they are reliant on plant foods. Terrestrial meat sources and kokanee salmon provided important supplements to bear diet in certain regions, but nowhere were they as important to bear diet as plants or salmon. These results also showed that spatial variation in grizzly bear diet is not currently reflected in the boundaries of B.C.’s Grizzly Bear Population Units, which represents a major obstacle to effective management across the province. iv Lay Summary Grizzly bears are Species of Concern in Canada that eat an incredible range of foods, though the proportion of different foods in their diet varies. By measuring isotope values, the naturally occurring ratio of heavy to light molecules of carbon and nitrogen, in bear hairs and their foods, I estimated the proportion of sea-run salmon, terrestrial meat, kokanee salmon, and plants in the diet of over 1800 bears from across British Columbia. I then created maps of the proportion of each food in bear diet for the entire province, which show that grizzly bears in B.C. are generally either coastal bears reliant on salmon or interior bears reliant on plants. Terrestrial meat and kokanee were important foods in certain areas but are not as important as plants and salmon. These results give wildlife managers and ecologists a better understanding of the biology of grizzly bears across different regions in the province. v Preface This study was conceived by Dr. Garth Mowat at the Wildlife and Habitat Branch; BC Ministry of Forests and co-supervised by Dr. Mowat and Dr. Mathieu Bourbonnais. Funding was primarily supplied by the Ministry of Forests and supplemented by Mitacs Accelerate. All funding for the isotopic analysis of hair came from the Ministry of Forests. The research and data analysis, as well as writing, were completed by Jonathan Van Elslander and supervised by Dr. Bourbonnais and Dr. Mowat. Dr. Ed Hornibrook reviewed this study for clarity and content. All data analysis, including spatial analyses, were completed using R. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. One research paper was completed as a part of the work on this thesis but was not a part of the core research on grizzly bear diet and thus is not included in this document. It can be found here: Shardlow, T. F., Van Elslander, J., & Mowat, G. (2022). The influence of human disturbance on Pacific salmon (Oncorhynchus spp.) in the diet of American black bears (Ursus americanus) in two areas of coastal British Columbia, Canada. Canadian Journal of Zoology, cjz-2021-0198. https://doi.org/10.1139/cjz-2021-0198 vi Table of Contents Abstract .......................................................................................................................................................iii Lay Summary .............................................................................................................................................. iv Preface .......................................................................................................................................................... v Table of Contents ........................................................................................................................................ vi List of Tables .............................................................................................................................................viii List of Figures ............................................................................................................................................. ix List of Abbreviations ................................................................................................................................... x Acknowledgements ..................................................................................................................................... xi Chapter 1: Introduction .............................................................................................................................. 1 1.1 Diet Ecology............................................................................................................................... 1 1.2 Stable Isotope Analyses and Diet ............................................................................................... 3 1.3 Stable Isotope Mixing Models ................................................................................................... 9 1.3.1 Mixing Models and Diet Sources ...................................................................................... 10 1.3.2 Trophic Fractionation ........................................................................................................ 12 1.3.3 Concentration Dependence ................................................................................................ 16 1.4 Spatial Modelling and Diet Niche ............................................................................................ 18 1.5 Knowledge Gaps and Research Goals ..................................................................................... 22 Chapter 2: Fine Scale Spatial Variation in Grizzly Bear (Ursus arctos) dietary niche across British Columbia .................................................................................................................................................... 25 2.1 Introduction .............................................................................................................................. 25 2.2 Study Area ................................................................................................................................ 28 2.3 Data .......................................................................................................................................... 29 2.3.1 Grizzly Bear Data .............................................................................................................. 29 2.3.2 Spatial Data........................................................................................................................ 30 2.4 Methods .................................................................................................................................... 32 vii 2.4.1 Isotope Analysis ................................................................................................................ 32 2.4.2 Diet Estimation .................................................................................................................. 33 2.4.3 Spatial Analyses ................................................................................................................ 36 2.5 Results ...................................................................................................................................... 38 2.5.1 Diet Estimates .................................................................................................................... 38 2.5.2 Spatial Models ................................................................................................................... 38 2.6 Discussion ................................................................................................................................ 40 2.6.1 Grizzly Bear Dietary Niche ............................................................................................... 40 2.6.2 Predicting Grizzly Bear Diet ............................................................................................. 47 2.6.3 Methodological Considerations for Stable Isotope Analyses ............................................ 52 2.6.4 Management and Conservation Implications .................................................................... 55 2.7 Tables ....................................................................................................................................... 59 2.8 Figures ...................................................................................................................................... 64 Chapter 3: Conclusion ............................................................................................................................... 73 3.1 Main Conclusions..................................................................................................................... 73 3.2 Limitations and Considerations for Future Research ............................................................... 75 3.2.1 Stable Isotope Analysis ..................................................................................................... 75 3.2.2 Spatial Modelling of Bear Diet Ecology ........................................................................... 77 3.3 Contributions and Management Implications .......................................................................... 79 3.4 Future Opportunities ................................................................................................................ 81 Bibliography ............................................................................................................................................... 85 Appendix A. Supplementary Figures ..................................................................................................... 123 viii List of Tables Table 1. Eleven different models were run in MixSIAR, separated spatially based on available food sources and ecological boundaries. ...................................................................................... 59 Table 2. A range of spatial data were tested as potential parameters for spatial modelling.. ....... 60 Table 3. Dietary source isotope values and standard deviations, as well as fractionation factors for each diet source in each diet model. ........................................................................................ 62 Table 4. Top spatial models for each diet source.......................................................................... 63 ix List of Figures Figure 1. All bear hair samples included in this study. ................................................................. 64 Figure 2. New rasters generated for this study. Male (a) and female (b) bear skull size based on British Columbia's Compulsory Inspection Bear Data and available salmon biomass (c) from the Canadian Department of Fisheries and Oceans NuSEDs database. ............................................. 65 Figure 3. Different bear dietary zones. Each zone represents a stable isotope mixing model. Note that an eleventh model was run on one individual in the Okanagan Lake region (see Table 1). . 66 Figure 4. Kokanee salmon and terrestrial meat isotope value data sources. ................................. 67 Figure 5. Isospace figures showing the various methods for selecting carbon fractionation factors. ........................................................................................................................................... 68 Figure 6. MixSIAR estimates for the proportion of different food sources in the diets of grizzly bears across B.C. ........................................................................................................................... 69 Figure 7. Predicted maps of bear diet across British Columbia, with the existing Grizzly Bear Population Units overlayed. Note that a non-parametric model for kokanee in female bear diet did not converge, and thus is not included here. ........................................................................... 70 Figure 8. Scatterplots for each parameter in spatial diet models for both female (a.) and male (b.) grizzly bears. ................................................................................................................................. 71 Figure A 1. Sampled Isotope value (a.) and diet estimates (b.) by sampling date. ..................... 123 Figure A 2. Sampled isotope value (a.) and diet estimates (b.) by adjusted date. All bears sampled after September 30 or before June 30 (i.e., outside the period of guard hair growth) have been assigned a date of Sept 30. ................................................................................................. 124 Figure A 3. Predicted maps of bear diet across British Columbia, with B.C.'s Ministry of Forests’ existing Ecoprovinces overlayed. Note that a non-parametric model for kokanee in female bear diet did not converge, and thus is not included here. .................................................................. 125 x List of Abbreviations Abbreviation: Full Description: 13C Carbon-13 Isotope 15N Nitrogen-15 Isotope AIC Akaike Information Criterion BC British Columbia CI Compulsory Inspection Database CV Coefficient of Variation FWA Freshwater Atlas GBPU Grizzly Bear Population Unit GLMM Generalized Linear Mixed-Effect Model GWR Geographically Weighted Regression MAP Mean Annual Precipitation MWMT Mean Warmest Month Temperature NTEMS National Terrestrial Ecosystem Monitoring System NuSEDs New Salmon Escapement Database System SHM Summer Heat Moisture Index SIA Stable Isotope Analysis SIMM Stable Isotope Mixing Model SVC Spatially Varying Coefficient TD Temperature Difference TEF Trophic Enrichment Factor xi Acknowledgements First of all, I would like to extend an enormous thanks to Garth Mowat for not only spearheading this project but choosing to trust me with it. I can’t say how much I appreciate being brought into the grizzly bear program after not much more than a cold email. Thank you for the immeasurable guidance through every stage of this work and in my life beyond. A huge thank you is also due to Mathieu Bourbonnais for your advice through this entire project and for taking me and my project into your lab at a time when it was already very busy. Your expertise was a major boon to this project and I can’t imagine how much better it is for it. I would also like to thank everyone who shared data or knowledge with me for this work. Data from Megan Adams, Chris Darimont, Oliver Holt, Elena Jones, Diana Lafferty, Shelley Marshall, and Michelle McLellan helped the spatial resolution and gave this thesis much firmer ground to stand on. I’d particularly like to thank Kitasoo Xai’xais First Nation and Wuikinuxv First Nation for trusting me with their data, and to Mathieu, Chris, and Megan for facilitating this connection. Also, a big thank you to the Tŝilhqot’in National Government for sharing some of their hair and data with me as a part of the Chilko Lake project. I would also like to thank Tom Shardlow for sharing his work and data with me and helping me get acquainted with my methods. Thank you to my lab mates and other coworkers who, whether or not they provided direct comment on my research, are dear friends to me both academically and personally. I must also extend a big thank you to all the people who have ever taught me the skills that have made me an ecologist, from species identification to digging a nice soil pit to getting a truck unstuck. In particular I’d like to thank Ken Lertzman, Jordan Benner, Ian Giesbrecht, Andy Belicka, and Mackenzie Clarke for being such important people in my life in the woods. I would like to especially extend my unending gratitude to Laura Smit for everything you do for me, my work, and the grizzly bear program as a whole. Without your knowledge, passion, persistence, and emotional support this project and many like it would pale in comparison. I am unspeakably thankful to my friends and family who have supported me through my thesis. It does not escape me the patience and love you have put into supporting not only my work, but my passions, wanderings, meditations, sidetracks, lost causes. With your support both my work and my life have flowered over these years. 1 Chapter 1: Introduction 1.1 Diet Ecology While a species’ fundamental niche is the full range of possible environmental states that can support its existence (Hutchinson, 1957), its realized niche is the extant environmental state(s) in which it lives, a concept that considers limitations on its life history such as disparate resource availability and competition (Malanson et al. 1992). Defining the range of a species’ realized niche and its niche variation across space is key to understanding the variability and extent of the processes that affect its ecology (Brown et al. 1995; McGill et al. 2006; Elith and Leathwick, 2009). Niche evolution is inherently tied to environmental heterogeneity (Soberón and Peterson, 2011), and studying environmental gradients allows researchers to quantify niche variation (Broennimann et al. 2012) and reveal the underlying interactions that restrict it from its fundamental niche (McGill et al. 2006). Studying how different habitats can meet the niche requirements of a species and affect its niche variability is integral to understanding the species’ abundance (Brown et al. 1995), distribution (Pulliam, 2000; Saupe et al. 2012), competition dynamics (Chesson, 2000), and life histories (Kearney and Porter, 2009). Examining how environmental heterogeneity drives niche variation can also help build an understanding of how intraspecific variability can cause genetic changes in a population (Silver and Di Paolo, 2006), and potentially cause speciation (Rainey and Travisano, 1998). An animal’s diet is a fundamental part of its niche and the environmental dynamics driving its realized niche, and thus dietary knowledge can help describe the species spatial niche variation (Ingram et al. 2018). Life history strategies, competition, resource availability, or anthropogenic effects can all restrict a species’ diet, creating a realized dietary niche (Shipley et al. 2009), which may not match the fundamental nutrient requirements of the species (Costello et 2 al. 2016). These factors, when combined with fine scale spatiotemporal resource variability (Zerba and Collins, 1992), can create a mosaic of realized dietary niches that vary both between and within populations (e.g., Sinclair and Zeppelin, 2002; Bolnick et al. 2003). The evolution of new dietary niches can represent opportunities for dispersal and population connectivity (Clobert et al. 2004), and even speciation (De León et al. 2014). The importance of diet is particularly evident for brown bears, also known as grizzly bears (U. arctos), as the abundance, distribution, and competitive dynamics of their populations are related in part to the availability of food resources (Ben-David et al. 2004; Rode et al. 2006; Nielsen et al. 2010; Mowat et al. 2013), particularly salmon (Oncorhynchus spp.). Salmon is a key protein source with particular importance for individual fitness (Lafferty et al. 2015). The importance of protein has bidirectional implications, as protein sources create areas of increased competition (Rode et al. 2006), while increased protein in the diet can improve a bear’s fitness, as larger, dominant bears can exclude others from protein sources (Ben-David et al. 2004; Lafferty et al. 2015) and have greater reproductive success (Zedrosser et al. 2007). However, the dietary ecology of bears is further complicated by their omnivorous diets, which can vary immensely across space and time (Hilderbrand et al. 1996; Mowat and Heard, 2006). High protein diets are not necessary for healthy bears (Costello et al. 2016) and robust populations, as both individuals (Edwards et al. 2011) and whole populations (Mowat and Heard, 2006) that subsist on almost entirely plant-based diets are not uncommon. Establishing baseline knowledge on grizzly bears’ fundamental ecology and niche is key to their conservation and management, particularly their dietary spatial variation, given the influence of dietary and energy factors on their abundance and distribution (Nielsen et al. 2010; Mowat et al. 2013). Considering both the importance of diet and the difficulty of monitoring and 3 studying grizzly bears due to their large ranges, inaccessible habitats, and low population densities, spatial dietary research is key to efficient allocation of conservation resources. In particular, understanding nutritional requirements and associated spatial variability in diet resource availability is key to the recovery and sustainability of a healthy grizzly bear population (Coogan et al. 2019). Fine scale spatial modelling of grizzly bear diet can provide ecologists with a critical resource for quantifying the variability of bear niche across space and time and provide wildlife managers and conservationists with a valuable spatial resource for understanding the associations between grizzly bear diet and habitat in the face of changing species distributions and environmental gradients due to various anthropogenic factors, most notably climate change (Carscadden et al. 2020). 1.2 Stable Isotope Analyses and Diet Stable isotope analysis (SIA) is an increasingly popular method for studying animal diet that measures isotopic values – the ratios of heavy to light isotopes (most commonly 13C/12C and 15N/14N) – in samples from an organism using mass spectrometry (Jardine and Cunjak, 2005). Different organisms, tissues, taxa, and abiotic materials have distinct isotopic values, allowing researchers to quantify consumption and movement of these elements through an ecosystem, such as via the ingestion of plants or animals by consumers, by comparing isotope values (Kelly, 2000). By comparing isotope value in a consumer’s tissue to values of each dietary source, it is possible to estimate the contribution of each source to the diet (Ben-David and Flaherty, 2012). Stable isotope analyses have distinctive advantages over the scatological dietary methods that are common in mammalian ecology. Hair is both easier to collect and more persistent in the environment than scat (Hilderbrand et al. 1996; Mowat and Heard, 2006) reducing sampling 4 intensity and widening the temporal potential of studies. Diet analyses using prey remains found in scat are also biased by differences in food digestibility, often overestimating vegetation in the diet while underestimating easily digested foods like salmon (Pritchard and Robbins, 1990; Hilderbrand et al. 1996). Importantly, thanks to genetic analysis, hair can be assigned to individuals, allowing researchers to track individual diets over time and study within-population variability (Hilderbrand et al. 1996; Mowat and Heard, 2006). SIA is a well-established method for dietary research in ecology and conservation that allows researchers to study diet at an individual level and examine intraspecific differences in dietary niche, facilitating a better understanding niche variability and its drivers, including both bottom-up and top-down drivers (Ben-David and Flaherty, 2012). Bottom-up effects such as diet resource variation can form the foundation of individual dietary niche variability (Newsome et al. 2007; Service et al. 2019), though their effects are constrained by top-down factors, such as intraspecific competition (Snowberg et al. 2015), interspecific competition (Belant et al. 2006), anthropogenic impacts (Manlick and Pauli, 2020; Shardlow et al. 2022), or other spatial effects (Adams et al. 2017). Investigating how these factors affect individual diets within a population can provide the basis for understanding how a species interacts with its environment. This individual level focus is one of the main advantages of SIA and can elicit important information for population ecology and even speciation (Newsome et al. 2007). Using SIA to quantify the range of animals’ diets and to investigate diet variability across ecological processes and space gives researchers the ability to track spatial population differences in niche and subsequent divergence of life-history (Newsome et al. 2007). For example, Ben-David et al. (2004) and Adams et al. (2017) have used SIA to determine sex-specific diets for brown and black bears (U. americanus) and explain the causes of sexual dimorphism. Similarly, estimating 5 diet at the individual level allows researchers to build an understanding of how within-species and within-population dietary variation drives population dynamics (Newsome et al. 2007). SIA is commonly employed by conservationists and wildlife managers to better understand their focal species and inform conservation goals and methods (Newsome et al. 2007; Ben-David and Flaherty, 2012), including in spatial analyses that help track habitat connectivity and the efficacy of conservation planning. For example, Adams et al. (2017) determined that conservation areas did not protect key habitat for coastal bear populations that rely heavily on salmon. Urton and Hobson (2005) meanwhile found that wolves living in Prince Albert National Park, Saskatchewan had distinct isotopic values compared to wolves in the surrounding area, suggesting that the stability of the National Park wolves’ social and population dynamics affects their diet. Findings like these can build a better understanding of the range of spatial variation in diet can help wildlife managers and conservation planners assess how different anthropogenic and landscape effects are changing species’ ecology. Specifically, researchers can use SIA to track anthropogenic diet sources and environmental contaminants and study their influence on behavioural ecology. Comparing isotopic values spatiotemporally can elicit information on variable use of anthropogenic resources, including in relation to variability in bears’ use of other foods (Teunissen van Manen et al., 2014). Bears with outlying isotopic values can be identified as food-conditioned (Hopkins et al. 2014) and researchers have proposed that measuring carbon and nitrogen isotope values over time can be used to track responses to non-lethal intervention methods such as relocation (Bentzen et al., 2014). Additionally, using isotopic ratios from other elements such as sulphur can be used to determine to what extent animals are exploiting agricultural resources and anthropogenic transportation corridors (Hopkins et al. 2014). 6 SIA can also be used to consider environmental drivers of diet ecology and how they vary by scale. For example, SIA can be used to map diet at broad spatial scales to build an understand of diet variability across a species’ range (e.g., Mowat and Heard, 2006), or at fine scales to compare diet to local habitat characteristics (e.g., Quinn et al. 2017). Many drivers of dietary variability, such as resource density (Shardlow et al. 2022), resource diversity (Service et al. 2019), foraging habitat structure (Quinn et al. 2017), or environmental effects like topography (Darimont et al. 2009) and climate (DeSantis and Hedberg, 2016), are spatial in nature and have scale-dependent effects on diet. Studying how these drivers and their effects on diet vary spatially helps build an understanding of the variability of a realized dietary niche and what its fundamental drivers are across space. Furthermore, spatial diet knowledge from SIA can then be used to understand other landscape-scale processes, such as population connectivity and dispersal (Herzka, 2005). Across their spatial range, a grizzly bear’s diet is constrained by their physiological need to gain energy, store energy, and maximize mass gain, and bears balance these needs by adjusting their intake of protein, lipids, and carbohydrates across seasonal changes (Erlenbach et al. 2014; Costello et al. 2016). In captive environments, a diet consisting of approximately 17% digestible protein maximizes mass gain, while lipid heavy diets improve energy storage and carbohydrates are converted to energy the most efficiently (Erlenbach et al. 2014), though natural variability in available foods constrains nutrient intake for wild bears (Costello et al. 2016). For instance, brown bears tend to gain muscle mass via protein heavy diets in the spring, and store energy via lipid or carbohydrate heavy diets in the fall (Erlenbach et al. 2014; Costello et al. 2016). When captive bears are given the option, they prefer to reduce their protein intake by prioritizing energy storage by consuming lipids rather than carbohydrates, though they also 7 seek to maximize mass gain based on the relative availability of the two (Erlenbach et al. 2014). In western North America, protein and lipid intake are codependent likely because they are both in limited supply, while carbohydrate intake is independently and highly variable (Coogan et al. 2018). A highly flexible dietary niche allows bears to account for seasonal or spatial variation in protein- and fat-rich foods by supplementing their diet with carbohydrate-rich vegetation (Coogan et al. 2018), a factor with wide-ranging consequences both physiologically, and ecologically. Dietary macronutrient content varies among different food sources, and their availability, along with bears’ effort to maximize nutrient intake, has subsequent ecological effects. Where salmon is present, it is the most important source of protein for bears (Hilderbrand et al. 1999), including those living near salmon-bearing streams far from the coast (Belant et al. 2006). Bears that consume more protein – particularly in the form of salmon – are larger, reproduce at a higher rate, and have higher population densities (Hilderbrand et al. 1999; Belant et al. 2006; Costello et al. 2016; Mangipane et al. 2020). Interior bears, which on average consume less salmon than coastal bears, are generally smaller, have much larger home ranges (McLoughlin et al. 2000; Hatler et al. 2008), and occur at lower densities (Mowat et al. 2013); however, male bears tend to be larger and consume more meat than females across the species’ range (Hilderbrand et al. 1999; Mowat and Heard, 2006; Milakovic and Parker, 2013). Interior bears that rely almost entirely on vegetation can be smaller than more carnivorous bears (Hilderbrand et al. 1999), though Mowat and Heard (2006) did not find a strong relationship between terrestrial meat consumption and bear size. This may be because there are physiological drawbacks to low protein diets, such as reduced growth rates, and high protein diets, such as increased risk of diabetes and kidney disease (Erlenbach et al. 2014). 8 The benefits of salmon to bears are not limited to protein intake and mass gain – for instance, bears are often able to meet their protein needs in times of low meat availability by consuming insects or certain plants (Costello et al. 2016) – as salmon also represent a readily available sources of lipids (Hendry and Berg, 1999). When consuming salmon, many bears exhibit behaviour that indicates they prioritize lipid intake, such as consuming the lipid rich brains, eggs and skin while leaving the remaining carcass behind, particularly when salmon are more readily available (Gende et al. 2001). Bears are able to store large amounts of energy for hibernation from salmon, as most of the energy used in hibernation comes from lipids (Belant et al. 2006), thanks to the seasonal nature of salmon abundance, a dynamic that helps bears overcome foraging constrains on fruit intake (Welch et al. 1997). This diet-energy dynamic is important for bear populations, as body condition is linked to reproductive success (Lopez-Alfaro et al. 2013). There are further spatial implications to this relationship, as salmon lose the majority of their lipid content as they travel further up freshwater systems (Hendry and Berg, 1999), indicating that the macronutrient impact of salmon on bears is likely different for inland populations. Beyond macronutrient intake, salmon creates unique intraspecific ecological dynamics in bears across space: the aggregate nature of salmon runs creates high bear-density areas, where both competition (Ben-David et al. 2004; Rode et al. 2006), and human conflict (Artelle et al. 2016) can be higher. As a result, salmon foraging bears may experience spatiotemporal behavioural changes, such as shifts to nocturnal foraging due to human presence (MacHutchon et al. 1998) or competition (Klinka and Reimchen, 2002). Furthermore, female bears with cubs may avoid some fishing locations, during high use times or all together, to reduce the risk of infanticide from males (Ben-David et al. 2004). Measuring where bears are heavily reliant on 9 salmon helps clarify dietary niche variation across space and how it is connected to interspecific, intraspecific, and anthropogenic ecological factors (Adams et al. 2017). It is also of increasing conservation importance considering the widespread decline of salmon across Western North America (Levi et al. 2020). 1.3 Stable Isotope Mixing Models Researchers use stable isotope mixing models to estimate consumer diet proportions by comparing isotopic values from consumers to those of diet sources. The most basic models, linear mixing models, solve for K unknowns based on J isotope ratios, and thus are restricted to determining only J +1 = K diet sources (Ben-David and Schell, 2001). Linear mixing models can only incorporate single value parameters and estimate single values outputs (Moore and Semmens, 2008; Hopkins et al. 2017). Earlier studies using SIA for estimating grizzly bear diet used these models (e.g., Hilderbrand et al. 1996, Mowat and Heard, 2006). Recently, the use of stable isotopes has been greatly advanced through Bayesian statistical approaches for dietary mixing models (Hopkins and Ferguson, 2012; Parnell et al. 2013), which incorporate the range and uncertainty of model inputs to generate probability distributions of diet proportions (Parnell et al. 2013; Semmens et al. 2013). This allows for researchers to consider parameter uncertainty (Moore and Semmens, 2008) and to include prior knowledge such as relative prey availabilities into models (Yeakel et al. 2011). Bayesian models also produce probability distributions of diet source proportions, which can include multi-modal estimates (Semmens et al. 2013). Previously, the Bayesian model framework MixSIR was developed by Moore and Semmens (2008), while Stable Isotope Analysis in R (SIAR) was developed by Parnell et al. (2010). These models, along other Bayesian methods, were combined 10 into the MixSIAR model framework which allows users to incorporate residual error, fixed and random effects, continuous dietary covariates, and uncertainty in fractionation or concentration dependence values, among others (Stock et al. 2018a). Since their development, Bayesian models have become the primary dietary mixing models used in ecology (Parnell et al. 2013; Semmens et al. 2013), including use in grizzly bear research (e.g., Milakovic and Parker, 2013, Adams et al. 2017; Mowat et al. 2017). Though SIA can use many different tissues (Ben-David and Flaherty, 2012), wildlife researchers often focus stable isotope analyses and mixing models on hair, a methodological choice with several advantages. Hair is biologically inert, and thus the isotope ratios represent the diet of the animal at the time of hair synthesis (Schwertl et al. 2003), and retains its chemical make-up longer than soft tissue, allowing researchers to use hair from decades earlier. In bear research, diet analyses typically use guard hairs (Jones et al. 2006), which grow between June or July and October, allowing for the estimation of diet for that period of time, which makes up much of the time a bear is active during a year (Mowat et al. 2017). The stable nature of hair improves the ease of both tissue collection, by allowing for non-invasive sampling, and dietary estimation, by estimating diet for longer periods of time than soft tissue. 1.3.1 Mixing Models and Diet Sources When estimating diet using mixing models, the isotopic values of diet sources are as important to the results as the values of the consumer being studied (Phillips et al. 2014). Thus, strong prior knowledge of consumer diet is key to robust models, in particular understanding and including all possible diet sources (Phillips et al. 2014). Overlooking any isotopically distinct source can skew model output by incorrectly allocating diet proportions from the missing source to other sources (Parnell et al. 2013; Phillips et al. 2014). Contrastingly, including too many 11 isotopically similar sources limits the model’s ability to provide meaningful estimates (Phillips et al. 2005). It has been suggested dietary mixing models should strive to include as few dietary sources as possible without excluding isotopically distinct sources (Phillips et al. 2014), and researchers often reduce the number of sources by combining isotopically similar sources. This can be accomplished a priori by averaging source values (e.g., Rosing et al. 1998), or a posteriori by combining model output distributions (Stock et al. 2018a). If sources are isotopically similar and ecologically similar, such as salmon (Oncorhynchus spp.) and intertidal marine foods in bear diets, combining sources can greatly improve the ability of a model to produce meaningful estimates for both the combined sources and other foods (Phillips et al. 2005; Phillips et al. 2014). However, combining sources can reduce explanatory power of a mixing model, constraining what can be learned from results, particularly if one food source is of great interest to researchers (Mowat et al. 2023). When isotope values from diet sources collected in the field are not available, researchers select dietary isotopic values from the literature, requiring special attention to the taxonomy and ecological similarity of foods. Instead of considering all possible foods, bear research generally focuses on four or fewer key diet groups, typically anadromous salmon, terrestrial meat, and vegetation (e.g., Mowat and Heard, 2006; Adams et al. 2017). Based on spatial factors and interest in specific ecological phenomena, research may also include other foods or group foods in different ways. This can include assessing non-anadromous kokanee salmon (O. nerka – e.g. Mowat and Heard, 2006), individual species of ungulates (e.g. Milakovic and Parker, 2013), marine intertidal foods such as shellfish (e.g. Service et al. 2019), or masting foods like whitebark pine (Pinus albicaulis) seeds (e.g. Felicetti et al. 2003b) as their own distinct food 12 group. Though grouping dietary sources is integral to SIA in regard bear dietary ecology, unduly reducing the number or dietary sources can create isotopic uncertainty (Phillips et al. 2014), an effect magnified by the enormous range of organisms consumed by bears (Hamilton and Bunnell, 1987; McLellan and Hovey, 1995; MacHutchon, 2021). To navigate problems created by bears dietary breadth and heightened sampling intensity required to measure the source isotope values of different bear foods across space, researchers utilize prior knowledge or consumer-focused techniques when selecting source isotope values for mixing models. Vegetation is of particular concern given the strong spatial and ecological gradients in plants’ isotopic values (Dawson et al. 2002); Mowat and Heard (2006) calculated an endpoint for vegetation based on the isotope values of a population of bears known to consume almost entirely plants, a method that allowed them to overcome the uncertainty surrounding the contributions of different plant foods. However, within source variation extends to other diet source categories such as terrestrial meat, as bears often consume several species of mammals, including ungulates and rodents (MacHutchon, 2021). Thus, researchers creating a dietary source isotope value for terrestrial meat must consider which potential prey species are mostly likely to be abundant in bear diets (Mowat et al. 2017), spatial variability in the isotopic value of prey species (Hofman-Kamińska et al. 2018), and the propensity of bears to consume a single meat source for extended time periods (Mowat et al. 2023). Given these sources of uncertainty, researchers must therefore consider the spatial and environmental transferability of source isotopic values sources from the literature. 1.3.2 Trophic Fractionation Along with consumer and source isotope values, the third key parameter when estimating diet using stable isotope mixing models is the rate of trophic fractionation, also known as trophic 13 enrichment factors (TEFs) or discrimination rates (Ben-David and Flaherty, 2014), which account for the range of processes by which stable isotope ratios change between the diet source and the consumer (Caut et al. 2009). Fractionation can be due to physical processes, such as evaporation or condensation, or the enzymatic processes involved in digestion and nutrient assimilation that preferentially select for certain isotopes (Ben-David and Flaherty, 2014). To account for this change when estimating diet, researchers calculate fractionation factors for their focal consumer and diet sources (Caut et al. 2009). Though the accuracy of these rates is of great importance to accurate dietary estimates (Caut et al. 2009; Phillips, 2012; Parnell et al. 2013; Walter et al. 2014), the resource intensity of the controlled captive-feeding experiments required for their determination cause researchers to often use rates not specific to the consumer, tissue, or diet sources being studied (Caut et al. 2009). This represents a major source of uncertainty, one which is magnified for the study of large animals, as the requirements of captive feeding experiments for megafauna are immense (Rioux et al. 2020). Additionally, the constraints on captive feeding resources and the artificial diets fed to captive animals creates uncertainty in how transferable captive-feeding-study based rates are to nutrient assimilation in the wild. A variety of factors have been found to affect fractionation. Different consumer tissues are known to exhibit differential fractionation factors (Ben-David and Schell, 2001), as are different consumer taxa (Caut et al. 2009), and dietary sources (Walter et al. 2014). Rates also vary between consumed vegetation with different photosynthetic pathways (C4 vs C3 vs CAM plants) or in marine versus terrestrial ecosystems, when animals are experiencing water stress or live in xeric habitats, as well as with latitude and climate (Kelly, 2000; Balter et al. 2006). Additionally, several studies have found that the isotope values of the total or bulk diet influence fractionation factors (Felicetti et al. 2003b; Balter et al. 2006; Caut et al. 2009), although this is 14 not always the case (e.g., Robbins et al. 2005) and is criticized by some for lacking a theoretical framework (Phillips et al. 2014). While there have been several detailed reviews of fractionation (e.g., Kelly, 2000; Caut et al. 2009), the extent of the variability in factors is not well established, nor is there agreement on the chemical factors that influence the variation in fractionation (Phillips et al. 2014). Until recently, experimentally determined hair-specific fractionation factors for bears have been scarce (Rioux et al. 2020), though some have been measured in recent years via artificial captive diets. Some of the first rates specifically for bear hair include those from L'Hérault et al. (2018), who found grizzly bear fractionation factors of 2.10 ± 0.30‰ for ∆13C and 2.60 ± 0.65‰ for ∆15N, and Rioux et al. (2020), who found black bears rates of 5.15-6.35‰ for ∆13C and 4.48-5.44‰ for ∆15N. While the species-tissue specificity of these experiments should offer some confidence to bear ecologists, the variation in the results does not. Fractionation factors around 3‰ are common for carbon in large herbivores (Sponheimer et al. 2003), and carnivore rates are usually higher, reducing confidence in some of the measured rates (Caut et al. 2009; L'Hérault et al. 2018). Encouragingly, Ueda and Bell (2019) found no difference in isotope ratios in hair collected from different parts of a bear’s body. Despite these new findings, there continues to be several sources of uncertainty for bear-specific fractionation. Some studies have examined fractionation for blood, plasma, and other tissues (e.g., Hilderbrand et al. 1996; Felicetti et al. 2003b), while some have found no difference in rates across the tissues sampled. However, several reviews for a range of taxa recommend tissue specific rates and suggest diet to hair fractionation is usually higher than from diet to plasma (Caut et al. 2009; Kurle et al. 2014). Additionally, there remains uncertainty surrounding fractionation for omnivores. Voigt et al. (2008) found that the preferential assimilation of 13C in 15 bats was lower for herbivorous diets than for omnivorous diets, while Rode et al. (2016) found in bears that increasing lipid content in the diet can increase 13C fractionation, while increasing protein content increases 15N fractionation. There are fundamental physiological differences in carbon and nitrogen fractionation, and over the history of stable isotope analysis researchers have developed a good understanding of the processes that cause nitrogen fractionation. In particular, recent work has established a positive relationship between nitrogen fractionation and 15N nitrogen values in dietary sources (Florin et al. 2011; Van Daele et al. 2013). Researchers often use an equation first developed by Felicetti et al. (2003b) on bears to determine the fractionation rate of nitrogen (Δ15N = (0.88 × δ15N + 5.28) − δ15N) for each food source (e.g., Mowat and Heard, 2006; Van Daele et al. 2013). Similar equations were developed by Caut et al. (2009) for a range of species. The physiological and chemical constraints of carbon fractionation are much less well understood, complicating the selection of 13C fractionation factors for mixing models. Both Felicetti et al. (2003b) and Caut et al. (2009) developed equations for carbon fractionation using approaches similar to nitrogen, though this relationship is criticized by some researchers for lacking a theoretical framework (Phillips et al. 2014). To account for carbon fractionation in bears, some bear researchers have also used a “model species” approach, which calculates rates based on captive studies on rats and mice, which like bears are monogastric omnivores (e.g., Service et al. 2019). This approach involves using values based on studies on captive-fed rats or mice, and results from these studies support selecting different rates based on the protein content of the food (Hopkins et al. 2017; Hopkins et al. 2021; Jimbo et al. 2022). In fact, Hopkins et al. (2021) state that “it is no longer acceptable to apply the same standard discrimination factors to both plant and animal food sources when estimating the diets of free-ranging mammalian 16 omnivores.” Despite these results, some studies have found that their rates (2.4 ± 0.4‰ for plants, 0 ± 0.1‰ for meat), as well as the equations from Felicetti et al. (2003b) and Caut et al. (2009), are not always useful in producing realistic dietary estimates (Shardlow et al. 2022). Other approaches include using variable rates based on terrestrial versus marine environments (Mowat and Heard, 2006), and a parsimonious fixed rate of 3.7‰ from Fortin et al. (2007) and Van Daele et al. (2013). Several studies have sought to test a variety of approaches to fractionation in light of these uncertainties. Both Milakovic and Parker (2013) and Javornik et al. (2021) compared several discrimination factors in single bear populations and found that different values can have strong effects on results. Milakovic and Parker (2013) found fixed values often used in the literature (3.0 Δ15N and 2.0 for Δ13C) resulted in much higher meat consumption estimates compared to the source isotope value-fractionation approach described by Felicetti et al. (2003b). Javornik et al. (2021) compared discrimination factors based on bears, the “model species” (rats (Rattus norvegicus) from Kurle et al. 2014), the equation from Felicetti et al. (2003b), and the modelling software SIDER (Healy et al. 2018), and found that all four methods produced different results, including particularly disparate results from the model species approach. Other studies have determined that a mixed approach, using diet-source equations for nitrogen (Felicetti et al. 2003b), and a simple fixed rate for carbon (Fortin et al. 2007), resulted in mixing models with the most realistic results (Shardlow et al. 2022). 1.3.3 Concentration Dependence The nature of stable isotope mixing models inherently assumes that each diet source contributes equally to the measured isotope ratios of each element in the analyzed tissue. If one or more diet sources are either rich or poor in any element, dietary proportion estimates could be 17 biased (Phillips, 2012), creating “concentration effects” or “concentration dependence” that can significantly affect dietary modelling (Phillips, 2012). For bear diet, terrestrial plants can be much poorer in nitrogen (and have a much higher C:N ratios) than salmon or terrestrial meat (Phillips and Koch, 2002), and thus models that do not consider the concentration of C and N in the bulk diet may underestimate the proportion of plants and overestimate terrestrial meat and salmon in the diet (Phillips and Koch, 2002). Beyond chemical composition, concentration effects are increasingly complicated for omnivores, as the disparity in digestibility of plants and meat affect how much of each element consumed is assimilated into animal’s tissue (Robbins et al. 2002; Phillips, 2012). However, there remains uncertainty in the impact of elemental concentrations, and considering concentration effects does not necessarily require including them in mixing models. Phillips and Koch (2002), who initially asserted the importance of concentration effects in studying bear diet using SIA, used elemental concentrations from domestic fruit species that may not be applicable to wild vegetation consumed by those bears. Alternative – and more ecologically appropriate – methods for considering concentration dependence include sourcing concentration values from centralized databases (e.g., Hopkins et al. 2017) such as the USDA Nutrient Database (www.nal.usda.gov/fnic/foodcomp/search), or most preferably, measuring elemental concentrations of the foods in questions (e.g., Matsubayashi et al. 2015), which can be generated simultaneously with isotopic values during mass spectrometry. These uncertainties have driven some researchers to test concentration effects in their studies and subsequently remove them from their final models. For instance, both Deacy et al. (2018) and Service et al. (2019) found that models that included concentration dependence did not adequately fit their study system. 18 1.4 Spatial Modelling and Diet Niche While many recent studies have explored how dietary ecology changes with different ecological factors and in different populations, fewer studies have explicitly considered the effects of geographic space on diet. Considering spatial effects allows researchers to elicit greater understanding of the mechanisms that influence diet across a species range and across various scales. Historically, spatial analyses have commonly been used by landscape ecologists, geneticists, and conservation ecologists to assess animal habitat, movement, population connectivity, and a variety of other ecological factors (Fletcher and Fortin, 2018). One major facet of spatial ecology is predictive geographic modelling, which interpolates between sampled geographic points and can connect the response variable at these points to other measured environmental factors, allowing ecologists to explore how various animals’ environment, including environmental change over time, affect a species ecology (Guisan and Zimmermann, 2000). Stable isotope analyses have regularly been used to explore diet among spatially disparate habitats, but they are less commonly used to explore how diet shifts across space. SIA-focused literature reviews do not often explain how the method can be used for spatial analyses (e.g., Alp and Cucherousset, 2022; Ben-David and Flaherty, 2012; Phillips et al. 2014; Yeakel et al. 2015), or limit their discussion of space to the use of stable isotopes as spatial tracer elements without extending geographic considerations to diet (e.g., Crawford et al. 2008; Inger and Bearhop, 2008). However, while previously used dietary ecology methods, such as scatological analyses, are not well suited to spatial modelling, the individual-level-scope of stable isotope analyses (Post, 2002) create the opportunity to link diet and sampling locations to develop models of 19 dietary geographic variability. To be used in predictive mapping of diet, a population of animals must be sampled for SIA across the entire spatiotemporal range on which researchers wish to predict, while still controlling for fine scale temporal variation in diet through either repetitive sampling regimes or by using inert tissues, such as hair (Layman et al. 2012). While the use of SIA by bear ecologists is common, studies that have explicitly incorporated space are limited, despite well-known geographic factors which influence bear ecology and life history. For instance, bear size (Blanchard, 1987), population density (Mowat et al. 2013), home range size (McLoughlin et al. 2003), abundance (Apps et al. 2016), habitat selection (Nielsen et al. 2010) and dispersal (Peck et al. 2017) are all known to vary across space. In regard to diet, pseudo-spatial effects, such as the effects of habitat type (Quinn et al. 2017), competitive exclusion (Ben-David et al. 2004), spatially limited resources (Hamer and Pengelly, 2015), or body size (Schwartz et al. 2014) are often explored through stratified sampling regimes. However, explicit spatial analysis and predictive modelling of bear diet has been limited (e.g., Mowat and Heard, 2006; Adams et al. 2017). Previous spatial modelling of grizzly bear diet has been limited in scope or utilized non-parametric modelling approaches. For instance, Adams et al. (2017) focused on a single diet resource, while Mowat and Heard (2006) used a predictive method that interpolated between population geographic centres rather than individual sampling locations, a method that fails to account for spatial heterogeneity or autocorrelation among the locations of individual bears. Furthermore, while parametric spatial models, which link the response variable (here, diet) to a set of environmental variables at each individual location, are common in other fields (Van Der Meer, 2012), they have not been used to quantify and map bear diet. Using parametric models allows researchers to explore the links between the environment and the response (Fotheringham 20 and Sachdeva, 2022), helping to develop an understanding of the spatial process that affect diet. Additionally, by linking response variables to underlying environmental variables, parametric models are able to better control for spatial heterogeneity of the response (Basile and Mínguez, 2018), particularly in areas of low sampling density. A variety of environmental factors should be considered when modelling bear diet parametrically, particularly the distribution or abundance of food sources, which is commonly used for modelling wildlife distributions (e.g., Nielsen et al. 2010; Mowat et al. 2013; Kuzyk and Hatter, 2014) For bears, patterns in their consumption of salmon are known to often change with salmon abundance (Quinn et al. 2017), although the relationship may not be linear. There may also be a similar relationship between the availability of taxonomic groups of prey like ungulates and grizzly bear’s consumption of terrestrial meat. Ungulates are generally the most important source of terrestrial meat for grizzly bears (Milakovic and Parker, 2013) and their distributions have been used previously to model predator population densities (e.g., Kuzyk and Hatter, 2014). Additionally, there are a range of factors that may influence the consumption of plants by bears. Incorporating the availability of plant foods on the landscape for modelling diet is more difficult than meat due to the immense range of plant foods consumed by bears (Gunther et al. 2014). While some studies have found the availability of key plant foods to be correlated with reductions in bears’ intake of meat across ecosystems (Hopkins et al. 2017; Niedziałkowska et al. 2019), modelling based on the distributions of potentially hundreds of species of plants bears consume (MacHutchon, 2021) is unwieldly. However, measurements of plant productivity and diversity have been previously incorporated in studies of both grizzly bears’ habitat selection and population density (e.g., Denny et al. 2018). For instance, McLellan (2015) suggested that areas with more available plant food species could be attractive to bears and support larger bear 21 populations than areas with less plant diversity. Additionally, vegetative productivity may be a potential influence on diet, having been used in modelling bear population density distributions, and considering several spatial measures of plant biomass and productivity are correlated with grizzly bear density (Mowat et al. 2013; Lamb, 2019). Besides the availability of diet sources, other environmental variables are well known influences on bear ecology through habitat selection and population density (e.g., Ciarniello et al. 2007; Mowat et al. 2013), and may also affect diet. For instance, the influence of bottom-up environmental factors, such as land cover, on diet may be similar to their influence on habitat selection. Diet and habitat selection are often linked (McLellan, 2015) and the availability of key diet sources is known to vary across land cover classes (Denny et al. 2018) and time (Nielsen et al. 2010). Meanwhile, Larsen et al. (2019) found that changes to land cover from forestry had measurable effects on food availability for bears, both in terms of size of cutblocks and retention patches, and in the time since harvest. Topography and climate, which are often correlated (Mowat et al. 2013), are commonly used in wildlife models (e.g., Darimont et al. 2009; Mowat et al. 2013), and could potentially influence bear diet via a range of effects. For instance, Mowat et al. (2013) found that topography, a key driver of climate variability across British Columbia (Pojar et al. 1987), can be used to predict grizzly bear population densities across the province, while together topography and climate are known drivers of niche variability across species (John and Post, 2022). Grizzly bears diet is known to vary among ecosystems (Mowat and Heard, 2006), though the extent to which the underlying drivers of ecosystem type affect diet has not been studied. Additionally, other climactic variables such as continentality, seasonal temperature variation, and growing 22 season length can drive ecosystem structure through changes to plant productivity and forest disturbance regimes (Seidl et al. 2011) and may have subsequent effects on bear diet. To model ecological responses like diet, numerous spatial modelling approaches have been developed and used by wildlife ecologists, many of which are based on generalized linear mixed-effect models (GLMMs). Spatial GLMMs, which retain the basic structure of a non-spatial GLMM, can consider space by using both spatially varying coefficients (SVCs) and by including spatial patterns as random effects using Gaussian random fields (Anderson et al. 2022). The use of Gaussian random fields allows researchers to explicitly account for spatial effects and fit models to data with a variety of different statistical distributions in order to best represent the spatial covariance of the data (Anderson et al. 2022). Other spatial modelling approaches include geographically weighted regression (GWR), which uses SVCs but does not allow for random effects or non-normally distributed data (Lu et al. 2014), and spatial autoregressive models which consider spatial impact of covariates on the dependent variable to account for spatial autocorrelation (Kitchin and Thrift, 2009). Given the fine-scale heterogeneity of bear diet among individuals and potential broad-scale spatial autocorrelation among populations, spatial GLMMs provide researchers tools to fit models and help improve the predictive power in areas of poor sampling density. 1.5 Knowledge Gaps and Research Goals Diet represents a major part of an animal’s niche, and bear diet – which can vary from highly generalist to highly specialized (Mowat and Heard, 2006) – represents an exciting opportunity for the study of spatial variability in niche, particularly the variability of realized dietary niches. Bears have been successful in an extraordinary range of terrestrial ecosystems, and exhibit highly variable life histories, highlighting the ability of generalist omnivores to adapt 23 to new environments across space. Examining the spatial distribution of grizzly bears’ realized dietary niches represents an opportunity to study how environmental and spatial factors affect a species’ realized niche across a variety of scales. Quantifying the spatial variability in diet is key for understanding ecological dynamics like competition, dispersal, population density, population connectivity, and genetic diversity vary across space (Belant et al. 2006; Nielsen et al. 2010; Clobert et al. 2004; De León et al. 2014; Ingram et al. 2018), but despite the opportunities for studying spatial variability in realized dietary niche, few studies have attempted to track diet flexibility across broad spatial scales, particularly in brown bears. Geographic variability in grizzly bears’ diet is one of the most important factors in studying their ecology and managing populations of the species across their range. Diet is one of the key driving factors in bear population densities and recovery (Mowat and Heard, 2006; Mowat et al. 2013; Adams et al. 2017; Coogan et al. 2019), and while a baseline understanding of the spatial variability of bear diet has been established (Mowat and Heard, 2006, Adams et al. 2017), fine-scale maps of their diet across the entire province of British Columbia are lacking. Additionally, while there is a general understanding of the importance of key foods (Adams et al. 2017), and the variety of species consumed by brown bears (MacHutchon, 2021), no effort has previously been made to link spatial variation in their diet to underlying environmental variables. These knowledge gaps are of particular importance given changing vegetation dynamics (Hamann and Wang, 2006), ungulate species’ ecologies (Aikens et al. 2020), and the decline of key foods like salmon (Levi et al. 2020) from anthropogenic effects like climate change. Given the state of both dietary niche ecology and bear ecology, this thesis has several research goals: 24 1. Utilize parametric spatial models to predict and map the proportion of terrestrial meat, anadromous salmon, kokanee salmon, and vegetation in the diet of brown bears across British Columbia. 2. Asses how various environmental variables, including but not limited to climate, resource availability, ecosystem structure, and anthropogenic disturbance, are related to the diet of brown bears across British Columbia. 3. Assess the effectiveness, advantages, and drawbacks, of updated stable isotopic methods – including Bayesian models, different fractionation factors, and concentration dependence – and novel spatial modelling methods to the study of bear diet. 4. Generate fine-scale maps of grizzly bear diet and provide them to land and wildlife managers in order to inform management decisions related to grizzly bear strategic planning, landscape level habitat enhancement, and site level decisions such as major project development. By mapping bear diet, this research will refine our understanding of realized dietary niche variation and its environmental drivers, as well as provide key baseline knowledge for bear ecology and management. This thesis involves the application of Bayesian stable isotope mixing models and a geospatial GLMM to a large data set of over 1800 brown bears to create maps of bear diet for the entire province of British Columbia. This study represents an advancement in the use of SIMMs in bear research, a novel spatial approach to bear diet, and an innovative assessment of the dietary niche of a generalist species. Click or tap here to enter text. 25 Chapter 2: Fine Scale Spatial Variation in Grizzly Bear (Ursus arctos) dietary niche across British Columbia 2.1 Introduction A species’ fundamental niche is based on the full breadth of environmental states that enable its survival and represents a range of habitable conditions that are driven by both the species’ resource use and the availability of resources in its environment (Hutchinson, 1957). A species’ spatial range on the landscape is a “reflection” of those limiting environmental conditions, the constraints of which are also evident in the variability of the species’ realized niche – the range of ways the species meets its needs – within that range (Brown et al. 1996). In fact, environmental heterogeneity can interact with spatial population dynamics to create variation in a species’ realized niche across space (Pulliam, 2000). This dynamic can create a mosaic of realized niches within a species range (Pulliam, 2000), where variable life histories are driven by a variety of factors, including differential resource use, climate (Kearney and Porter, 2009), competition, dispersal dynamics, or other factors (Brown et al. 1996). This mosaic can then result in spatial variability in the species’ competitive and population dynamics (Bolnick et al. 2011). Species are constrained by their physiological requirements, which may limit their distribution (Kearney and Porter, 2009) and realized niche (Pulliam, 2000), though a species can achieve those requirements in multiple ways (e.g., Young et al. 2013). Considering niche variation at the individual level can elicit deeper implications of a species’ relationship with its environment, when compared to only considering the population or species level (Ingram et al. 2018). For instance, species with diverse resource use can have a broader effect on their environment (Ingram et al. 2018), be more able to disperse to new habitats (Clobert et al. 2004), 26 and more able to withstand environmental change (McCann et al. 1998). The evolution of new life history strategies and diverse niches can allow species to exploit novel resources and expand beyond their historical range (e.g., House Sparrows Passer domestics, Liebl and Martin, 2014). Diet is a key aspect of an animal’s niche and an important factor in understanding how a species functions across space (Ingram et al. 2018). Dietary niche variability is evident across space (e.g. Stellar sea lions Eumetopias jubatus, Sinclair and Zeppelin, 2002) and across habitat types or climates (e.g. Japanese macaques Macaca fuscata, Tsuji et al. 2015), but can also vary sympatrically based on other factors such as body size or sex (e.g. American alligators Alligator mississippiensis, Nifong et al. 2015). Within populations, diet can also vary due to more subtle individual variability within a population (Bolnick et al. 2003). This individual variation, defined by West-Eberhard (1989) as “phenotypic plasticity,” may be in response to spatiotemporal variability in prey availability (Zerba and Collins, 1992). This individual variation can then lead to the development of new realized niches within a population, or even speciation, as famously evident in the Galapagos Islands finches as first posited by Charles Darwin (De León et al. 2014). On shorter time scales, the nascent development of new diets within populations may constitute opportunity for a species to disperse (Clobert et al. 2004), including expanding its range to new areas, or creating population connectivity with allopatric populations. Grizzly bears (Ursus arctos) have highly variable diets across space (Mowat and Heard, 2006; Adams et al. 2017), and consume an enormous range of different foods (McLellan and Hovey, 1995; MacHutchon, 2021, Haroldson et al. 2020). Flexibility in their omnivorous diet has allowed them to use a wide range of habitats in circumpolar locations and influences their habitat selection spatiotemporally. While the species is a generalist consumer and has a highly variable niche, certain populations can be reliant on single food sources (e.g. Pacific salmon O. spp., 27 Belant et al. 2006), suggesting that some individuals may be spatially constrained by diet resource availability (Belant et al. 2006, Hilderbrand et al. 2018b). Previous spatial work on bear diet has mostly focused on fine scale within population variation (e.g. Bojarska and Selva, 2012; Milakovic and Parker, 2013), while the few broader scale predictive studies have used non-parametric spatial methods (e.g. Mowat and Heard, 2006; Adams et al. 2017) that did not consider the effects of the underlying environmental variables that may drive diet resource selection or availability. Given the importance of diet to grizzly bear abundance and distribution (Nielsen et al. 2010; Mowat et al. 2013) and progressing environmental change globally due to climate change and anthropogenic ecosystem use and destruction, understanding the extent to which diet and environment are linked is a major question in both grizzly bear ecology, and wildlife ecology more generally. Previous work on bear diet had been used to establish key baselines for the species’ management, though major conservation questions remain. In western North America, a range of realized dietary niches are evident, including diets heavily reliant on salmon on the pacific coast (Hilderbrand et al 1999), on ungulates in arctic and sub-arctic regions (Gau et al. 2002), and on vegetation in many inland temperate regions (Hobson et al. 2000, Mowat and Heard, 2006). Local studies have established key food sources and considered the ecological ramifications of diet on populations (e.g., Felicetti et al., 2003a; Ben-David et al. 2004), helping manage bear ecology as a part of broader management issues (e.g., Artelle et al. 2016; Lamb et al. 2017), but knowledge about the spatial variation of different diets is limited. Studies by Mowat and Heard (2006) and Adams et al. (2017) represent the widest ranging spatial assessments of diet, though were limited in scope, used non-parametric spatial methods, and focused on dietary breadth or a single food, rather than spatial variability in dietary niche or the relationship between diet and 28 environment across different scales. Fine-scale parametric models of grizzly bear diet would help identify the extent of spatial variation of different dietary niches across the province, knowledge that could be used in landscape planning, wildlife life population management, and in environmental reviews of the impact of major anthropogenic developments on bear populations. To explore the relationship between grizzly bears’ niche, life history, and their environment, we estimated the diet of over 1800 grizzly bears across the Canadian province of British Columbia (B.C.) using stable isotope mixing models that consider the carbon and nitrogen isotopic ratios in their guard hairs. B.C. has strong climatic and topographic heterogeneity and bears in the province have strong niche variability, creating opportunity for understanding a wider range of interactions between their niche and their environment (John and Post, 2022). Using these dietary estimates, we used parametric spatial models to test the influence of several environmental correlates – including diet resource availability, topography, habitat type, and climate, some as proxy measurements for food availability – on local bear diet, and to predict estimates of bear diet across the entire province. These results have major conservation implications for grizzly bears, as well as key implications for the niche variability of generalist species across different spatial scales. In particular, we examine how dietary niche’s variation across space and links to underlying environmental factors, as well as some wider implications for the population ecology of the species. 2.2 Study Area Our study area consists of the entirety of the Canadian province of British Columbia, an area of approximately 950,000 km2. British Columbia is an area with rugged topography and stark climatic changes including some of the wettest, driest, and warmest climates in all of Canada. Historically grizzly bear habitat included nearly the entire province, but the species is 29 considered extirpated from 10% of their former range, particularly valleys with high human population density in the southern portion of the province (Bellringer, 2017). Currently, management of grizzly bears in British Columbia is divided into more than 50 discrete units (Grizzly Bear Population Units – GBPUs), the boundaries of which are delineated based on logistical management concerns, such as following the boundaries of provincial wildlife management units (Mowat et al. 2013, Morgan et al. 2020), and thus may not represent ecologically relevant spatial knowledge about bear behaviour or population dynamics. 2.3 Data 2.3.1 Grizzly Bear Data We analyzed the stable isotope ratios of carbon and nitrogen in the hair of 1816 grizzly bears (967 males, 653 females, 16 sex unidentified) sampled from across B.C. (Figure 1); 593 were analyzed as a part of this study, while the remaining data were provided by other researchers (see Table 1). Much of the data from Mowat and Heard (2006) and Oliver Holt, as well as most of the bears analyzed as a part of this study, were taken from B.C.’s Compulsory Inspection database of human killed bears (hereafter, CI), a total of 944 bears (849 of these bears were killed legally by hunters, 75 were killed for animal control, and the remaining 20 were killed for other means). All data shared by other researchers had been previously analyzed for carbon and nitrogen stable isotope ratios. Seven bears were removed from the analysis due to unrealistically high isotope values for both elements, possibly due to feeding on anthropogenic food sources or long-distance dispersal. 145 bears were sampled in multiple locations and 114 in more than one year, while 72 had multiple hair samples from a single location and year, resulting in a total of 2557 isotopic values included in our stable isotope mixing models. 30 2.3.2 Spatial Data We used parametric spatial modelling to quantify relationships between a wide range of covariates and bear diet to predict diet across the province, including several parameters as proxies for food availability. Climate has known spatial relationships with bears’ use of salmon, terrestrial meat (Mowat and Heard, 2006), and vegetation (Gunther et al. 2014). We used several different climatic parameters based on climate normal data from Climate North America (Wang et al. 2016): mean annual precipitation (MAP) and two measurements to consider climate continentality: the temperature difference between mean warmest and mean coldest month temperature (TD or effective continentality) and the summer heat moisture index (SHM = mean warmest month temperature/[Mean Summer Precipitation/1000]). We also included elevation from a Digital Elevation Model for all of B.C. (Natural Resources Canada, 2017) to consider topographic effects on diet. Measurements of ecosystem structure are also important considerations in bear diet ecology. We used a range of terrestrial ecosystem layers (Table 2) from National Terrestrial Ecosystem Monitoring System (NTEMS), developed by the Canadian Forest Service (Hermosilla et al. 2016): two proxies for forest productivity: Forest Elevation Mean (canopy height; Matasci et al. 2018) and total above ground forest biomass (Matasci et al. 2018), as well as the percentage of first returns above 2m (canopy cover; Matasci et al. 2018). Together these parameters were considered as proxies for food availability. Forest productivity is related to more available plant foods, but key plant foods for bears are generally associated with more open sites (Stewart et al. 2013; Proctor et al. 2015). Thus, we used two spatial parameters together, canopy height or forest biomass (as proxies for productivity), and canopy openness, as a single proxy for plant food availability. 31 As fine-scale data on ungulate distributions in B.C. is poor, we considered two proxies for terrestrial meat availability. Ungulate population densities are generally thought to be higher in drier, more open forest types, a relationship that has strong support in B.C. (British Columbia Ministry of Water, Land and Resource Stewardship, 2022), and we therefore tested the influence of MAP and canopy openness on terrestrial meat in the diet. We also used species range data from the Mammal Diversity Database (2022) to develop an ungulate species count layer by summing the overlapping ranges of ungulate prey species. We focused on known, commonly available ungulate species, avoiding species with limited ranges or small populations (e.g., American Bison, Bison bison). The availability of salmon is one of the key considerations for many facets of bear ecology in B.C. (Adams et al. 2017; Service et al. 2019). We considered the effect of spawning salmon abundance on bear diet by creating a 1km resolution spatial layer for salmon biomass (Figure 2) by linking the New Salmon Escapement Database System (NuSEDs) (Fisheries and Oceans Canada, 2016) to the B.C. Freshwater Atlas (FWA, GeoBC, 2010). Salmon escapement counts for the five major species of anadromous salmon (O. gorbusha, O. keta, O. kisutch, O. nerka, O. tschawytscha) were converted to biomass in kilograms based on average spawner mass data by species from Service et al. (2019), then matched by water feature code to spatial features in the FWA. We then interpolated total salmon biomass for all five species, averaged over the span of our study (1994 to 2021) using a Stewart gravity model (Stewart, 1942) with a span of 50 km and a beta coefficient of 1.7. Due to the overwhelming influence of the lower Fraser River population estimates and potential sampling bias (as the population is sampled again in upstream reaches), we removed escapement data from the Lower Fraser. 32 Grizzly bear size and diet are thought to be linked, particularly in regard to salmon in bear diets (Mowat and Heard, 2006, Hilderbrand et al. 2018a; Cameron et al. 2020). To potentially improve the prediction of salmon in the diet of bears across B.C., we developed maps of grizzly bear size based on the data in the provincial CI database (Figure 2). We considered the skull length of all male bears in the database over the age of seven (n = 3451) and female bears above the age of 5 (n = 5871), ages by which they are thought to have reached their full size (Mowat and Heard, 2006). Using these data, we predicted male and female size for the province by interpolating skull length using a simple kriging process based on a Gaussian variogram model with the R package gstat (Pebesma, 2004). These size layers, as well as the salmon biomass layer, are publicly available on github.com/vanelslander-ecology. 2.4 Methods 2.4.1 Isotope Analysis For this study, hair from 593 bears was analyzed for 13C and 15N values at the University of Ottawa’s Ján Veizer Stable Isotope Laboratory, including duplicates to assess measurement error (n = 52). All hairs analyzed were full length grizzly bear guard hairs. Stable isotope analysis of bear guard hairs estimates diet between June or July and October, (Mowat et al. 2017), the hyperphagic period where bears put on the most weight. Thus, analyzing their isotopic values allows for the estimation of bear diet for approximately those months (Mowat et al. 2017). Hair collected prior to June 30 was assumed to be from the previous year; we checked for temporal influences at both an annual scale (Figure A1), and across the hair growth period (Figure A2). We assumed that there was no variability in measurements among the laboratories at which the hairs were analyzed. 33 2.4.2 Diet Estimation We estimated the proportion of vegetation, terrestrial meat, anadromous salmon, and non-anadromous kokanee salmon in individual bears’ diet from isotopic values using the Bayesian model framework MixSIAR (Stock et al. 2018a, Stock et al. 2018b) in R version 4.2.2 (R Core Team, 2022). For a detailed overview of Bayesian stable isotope mixing models see Parnell et al., 2013. Due to the broad spatial scale of this study, as well as the spatial variation in the isotopic value of different dietary sources across B.C., we divided the province into eleven different MixSIAR models (Figure 3; Table 1). The assigned spatial boundaries for each model were selected using the B.C. government’s Ecoprovinces and Ecoregions (Demarchi, 2011), and further subdivided using watershed boundaries and available fish range data. We sought to divide the province into as few models as possible, while still having each model follow the advice of Phillips et al. (2014): “the number of sources therefore should be kept as low as possible without excluding sources.” Ecological boundaries, the availability of different ungulate and fish prey, and the isotopic variability of diet sources formed the conceptual basis for each diet model. Some zones were created based on consistent ecological characteristics, such as the coastal temperate rainforest that stretches the entire length of the province. Interior diet zones were split based on ecological boundaries and food availability, such as the southeast corner of the province where there is a consistent dry, montane climate and no sizeable salmon runs. Using data from the literature (Figure 4; Table 3), we developed unique terrestrial meat and kokanee salmon isotope values for each of the models, selecting data that were ecologically and spatially relevant to the area, and, for terrestrial meat, focusing on species that were abundant enough to be important to bear diet in the area. 34 In addition to the different meat and kokanee baselines, we used generalized salmon and vegetation isotope values for the entire province. Due to the low spatial variability of isotopic values of anadromous salmon in the literature, we used a single generalized salmon isotope value from Shardlow et al. (2022) for all models that included salmon (δ13C‰ = -19.9 ± 0.24, δ15N‰ = 12.5 ± 0.31). Due to high uncertainty surrounding the spatial variability of available plant isotope values from the literature, we used a single plant isotopic baseline from Mowat and Heard (2006) for all models (δ13C‰ = -26.6 ± 2.0, δ15N‰ = -2.8 ± 3.0). This baseline was calculated based on bears known to be completely vegetarian and has been used in several subsequent studies (e.g., Lafferty et al. 2015; Adams et al. 2017). This method accommodates for the enormous variability in plants consumed by bears (Hamilton and Bunnell, 1987; McLellan and Hovey, 1995; MacHutchon, 2021), at the cost of local value specificity. Heavy to light isotope ratios generally increase in consumers relative to their diet, a process known as fractionation (Caut et al. 2009). To account for this, stable isotope mixing models adjust source isotope values by adjusting for fractionation, though these adjustment values can vary based on the consumer tissue sampled (Ben-David and Schell, 2001), consumer taxa (Caut et al. 2009), diet sources (Walter et al. 2014), and other ecological and physiological factors (Kelly, 2000). These rates are key to model accuracy and are often calculated using captive feeding studies (Phillips, 2012; Parnell et al. 2013). Selecting fractionation factors for bears is complicated by their diverse diets and a paucity of data due to the challenges of captive-feeding large animals. Despite this, the processes that cause nitrogen fractionation are well understood and bear researchers often use variable rates corresponding to the nitrogen isotope value of each food source (Van Daele et al. 2013). Because of established knowledge surrounding the physiological and empirical nature of nitrogen fractionation and its relationship 35 with the nitrogen isotope values of diet sources (Florin et al. 2011), we followed other studies (Mowat and Heard, 2006; Fortin et al. 2007; Van Daele et al. 2013, Mowat et al. 2017; Shardlow et al. 2022) by using an equation from Felicetti et al. (2003b) to determine the fractionation rate of nitrogen: Eq 1: Δ15N = (0.88 × δ15N + 5.28) − δ15N. Due to uncertainty surrounding the processes that influence carbon fractionation, we tested several different fractionation factors from the literature (Figure 5), including the source isotope value dependent equation from Felicetti et al. (2003b), as well as a similar equation from Caut et al. (2009). Due to the lack of data on bears, some studies have used fractionation factors based on captive studies on rats (e.g., Service et al. 2019), which are also monogastric omnivores, and some researchers have proposed fractionation rates based on the protein content of their food (Hopkins et al. 2017; Hopkins et al. 2021). Thus, we also tested variable fractionation factors of 2.7‰ for vegetation and 0‰ for all protein sources as proposed by Jimbo et al (2022). We also tested a fixed rate of 3.7‰ for all foods from Fortin et al. (2007) and Van Daele et al. (2013), as well as variable rates of 2‰ for freshwater and terrestrial foods and 1‰ (adjusted +1.5‰ for hair) for marine foods based on Mowat and Heard (2006). Only Fortin et al.’s (2007) fixed rate of 3.7‰ produced logical isospaces (Figure 5) and model results, and thus was used in all subsequent mixing model analyses. MixSIAR is a Bayesian isotope mixing model framework that allows a variety of inputs and uncertainties that affect model results (Stock et al. 2018a). We considered uncertainty for source isotope values and fractionation in all models, using the standard deviation of observations from the literature for all diet sources, and fixed distributions for all fractionation factors. Following Deacy et al. (2018), Service et al. (2019), and Shardlow et al (2022), we did 36 not consider elemental concentrations of food sources in our models. We used uninformative priors for all models and ran models for 100,000 or 300,000 iterations, evaluating model convergence using the Gelman–Rubin statistic (Brooks and Gelman, 1998). 2.4.3 Spatial Analyses We generated fine-scale predictive maps (1000 metre resolution) of the proportion of anadromous salmon, terrestrial meat, kokanee, and vegetation in the diet of male (967 individuals) and female (653 individuals) grizzly bears for all of B.C. using generalized additive mixed effects models with spatial random fields (Anderson et al. 2022). We used the mean diet source proportions as estimated by MixSIAR as the response for each spatial model. While MixSIAR does provide a probability distribution of all diet estimates, we were unable to find an efficient method for including these distributions as the response variable in spatial modelling. Additionally, while some studies have used the estimated median values of each diet source in their analyses (e.g., Adams et al. 2017) as they are less sensitive to skew, the median values do not sum to one and thus are not appropriate for our proportional analyses. In our spatial models, each spatiotemporally unique sampled bear was considered a single point, i.e. bears sampled across years, or in multiple locations, were included as multiple points. We included both individual and sampling year as categorical random effects, and thus removed 16 male samples and 13 female samples for which the year was unknown, resulting in models with n = 1600 for males and n = 883 for females. To model diet across space, we used the R package sdmTMB (Anderson et al. 2022). This model framework considers spatial correlation as a random effect using a Gaussian random field, which assumes these spatial random effects are drawn from a multivariate normal 37 distribution (Anderson et al. 2022). In our study, this spatial field was modelled as a random field using a triangulated mesh with a minimum cut-off distance of ten kilometres (Lindgren and Rue, 2015). We built and tested several models for each diet category (e.g., salmon, meat, kokanee, and vegetation) and sex separately. As we were interested in factors influencing the proportional abundance of the dietary component, models were fit using a beta distribution (Douma and Weedon, 2019). Additionally, because both zeroes and ones occurred commonly in the diet estimates, we transformed our data by the equation: Eq 2: PdietII = [PdietI (N – 1) + 1/2]/N Where PdietI is the original diet estimate, N is the total sample size, and PdietII is the adjusted diet estimate. This equation accounts for ones and zeros in the estimates and reduces skew (Smithson and Verkuilen, 2006). To allow for non-linear effects in the models, we used splines on parameters with potentially non-linear effects to “smooth” relationships (Wood, 2017). To avoid over-fitting, we set the dimensions of the basis of each smoothed term to a minimum (Wood, 2017). We developed a range of models based on a series of a priori hypotheses for each of the dietary models by sex (Table 2). First, we tested the influence of salmon biomass availability and our proxy measurements for plant and meat availability (ungulate species density, forest structure, and climate). Second, we considered broader correlative effects from climate, forest structure, and topography on diet. Finally, we considered possible relationships between bear size and diet, using our predicted male and female size maps. All numerical variables were centred and scaled. While we were able to estimate kokanee in the diet of bears using stable isotope mixing models, a lack of data for kokanee availability restricted us from parametrically 38 modelling kokanee in the diet across space. Instead, we predicted spatial distribution of kokanee in bear diet using non-parametric models. We compared models using the Akaike Information Criterion (AIC) and examined top models for uninformative parameters that may lead to over-fitting (Arnold, 2010). We also examined the residuals of the top models to examine their distribution and check for the presence of outliers. Finally, we selected one top model for the consumption of salmon, terrestrial meat, and vegetation for males and females respectively and created predicted raster layers of each diet source for the entire province at the 1 km resolution. 2.5 Results 2.5.1 Diet Estimates Consumption of all foods varied across the province though trends were evident. (Figure 6). Salmon consumption was high in coastal areas (males = 36.1 ± 40.0%, females = 11.8 ± 24.0%), while plant consumption was high across the province (males = 53.8 ± 35.7%, females = 79.7 ± 24.5%). Terrestrial meat consumption was generally low (males = 6.2 ± 8.0%, females = 5.1 ± 5.9%), as was kokanee consumption (males = 3.9 ± 6.8%, females = 3.5 ± 4.9%). Male consumption of salmon was more variable (CV = 0.90) than females (CV = 0.49), while female consumption of plants was more variable (CV = 3.2) than males (CV = 1.4). Variation in the consumption of both kokanee (female CV = 0.71, male CV = 0.56) and terrestrial meat (female CV = 0.86, male CV = 0.78) was similar between sexes. 2.5.2 Spatial Models Spatial models revealed highly spatial variability in dietary niche across the province, though there were differences across sex. Models showed males had increased breadth, as well as 39 stronger local concentrations, in the consumption of meat and salmon compared to females (Figure 7). For instance, salmon consumption declined from the coast inland, though areas of high salmon consumption extended further inland for males. There were several areas of high salmon consumption inland, particularly on the Skeena and Chilcotin River systems, and in the Horsefly, Quesnel and Bowron Lakes areas, though these patterns were stronger for males. Terrestrial meat consumption was highest in the northeast of the province but was also slightly higher along the coast than in much of the interior, again generally higher for males than females. Our models also showed higher areas of meat consumption in the drier ecosystems of the south-central portion of the province for males only. Plant consumption was uniformly high across the province, except for in high-salmon consumption areas along the coast. Finally, there were local concentrations of high kokanee consumption around the province, most notably in the Bridge River watershed, the Quesnel/Horsefly River area, and the Lakes region of the Columbia watershed in southeast B.C. Two predominant realized dietary niches were evident in our results. Coastal bears rely heavily on salmon, with predicted results showing proportions in excess of thirty percent for females and 50 percent for males across the entire coastal ecoprovinces of British Columbia (Figure A3). Reliance on salmon declined with distance from the coast though it extended further inland in certain regions. In all interior areas of the province vegetation makes up the majority of bear diet, more so for females. Nowhere in the province do our models predict diets based on predominantly terrestrial meat or kokanee salmon. Our top models showed slightly differential parameterization of male and female models (Table 4). The top model for male consumption of salmon included a negative term for elevation, and smoothed relationships for salmon biomass, mean annual precipitation (MAP), and predicted 40 male size (Figure 8). The top model for female salmon consumption included a negative term for elevation and a positive term for MAP, as well as smoothed terms for predicted female size and salmon biomass (Figure 8). Top models for terrestrial meat consumption included a smoothed term for elevation for both males and females, with the male model including a positive term for MAP and the female model included a negative term for the summer heat-moisture index (SHM). The top model for male plant consumption included a negative term for predicted male size, a positive term for elevation, and smoothed terms for salmon biomass and MAP. The top model for female plant consumption included a positive term for elevation, a negative term for MAP, and a smoothed term for salmon biomass. Other parameters, particularly forest ecosystem measurements (forest productivity, canopy openness), were not meaningful predictors of bear diet. The results of our spatial models showed a poor alignment of grizzly bear dietary niche and the boundaries of B.C.’s GBPUs (Figure 7). Many units contained obvious niche boundaries, particularly in coast-interior transitional areas where certain units overlap with stark transitions in diet from salmon-reliance to plant-reliance. Additionally, many units contained only fragments of important regional diet sources such as salmon in the Quesnel/Horsefly Lakes area. 2.6 Discussion 2.6.1 Grizzly Bear Dietary Niche Our analyses revealed two wide-spread dietary niches of grizzly bears in B.C. in the form of salmon-reliant diets and plant-reliant diets, a distinction with major implications for bear ecology. Due to both nutrient content and foraging ecology of different foods, there are major differences in physiological, environmental, and competitive factors between these realized 41 niches. Spatial variation in diet constrains bear physiology (Costello et al. 2016), influences bear behaviour (e.g., MacHutchon et al. 1998, Ben-David et al. 2004), and affects population abundance and distribution (Nielsen et al. 2010; Mowat et al. 2013). In mapping realized dietary niches, it is possible to infer important considerations about bear ecology at the population level. Grizzly bears’ dietary ecology is constrained by several physiological factors – particularly the need to meet basal energy needs through the summer, store energy via fat deposition in preparation for winter, and gain lean mass to increase their body size (Erlenbach et al. 2014; Costello et al. 2016) – and the strong disparity in dietary niche across British Columbia indicates that there are also realized physiological niches among grizzly bears in the province. Captive studies have shown consuming lipids is most efficient for fat gain, carbohydrates are converted to energy most efficiently, and consuming a diet consisting of approximately 17% digestible protein maximizes weight gain (Erlenbach et al. 2014), though high protein wild diets (above 17%) appear to generate greater growth than low protein diets (Coogan et al. 2018). In coastal areas of the province, where we found bears are heavily reliant on salmon and reach their largest size, salmon provide both excess protein and a readily available source of lipids (Hendry and Berg, 1999), with bears often modifying their salmon consumption by consuming only the lipid rich brains, eggs, and skin (Gende et al. 2001). Outside of salmon-reliant coastal areas, our results show diets in excess of fifty percent vegetation nearly province wide (and often much higher) for males, and even higher for females, while interior bears and plant-reliant bears are also some of the smallest in the province. Reliance on plant foods can constrain lean mass gain (Costello et al. 2016), indicating that bears in interior areas must utilize other dietary sources in order to reach larger sizes. 42 However, the relative importance of plants over terrestrial meat and kokanee salmon throughout the interior indicates that bears seek to meet physiological goals while minimizing foraging effort without prioritizing mass gain. Consumption of terrestrial meat, which has a higher associated foraging cost despite its benefits for mass gain (Mikkelsen et al. 2024), appears to be an opportunistic and supplemental part of bear diet in the interior, with our spatial models never predicting terrestrial meat in the diet in excess of 15%, instead of a fundamental food. Consumption of terrestrial meat is very likely not related to its availability on the landscape, as we found that bears in dry, continental forests of southern B.C., where ungulates are plentiful, to have some of the most plant-reliant diets in the province, suggesting that terrestrial meat is consumed opportunistically, rather than actively pursued. It should be stressed that our results do not indicate that terrestrial meat is not important to bear diets; terrestrial meat, including ants, is a key source of protein for bears, particularly in the spring when high-carbohydrate fruits are not present (Costello et al. 2016), and is key for adding lean mass when bears emerge from dens in the spring (Felicetti et al. 2003a, Hilderbrand et al. 1999). Through the summer and fall, terrestrial meat may represent a quick way to meet protein needs and allow more time for foraging for other foods that maximize winter energy storage. The physiological constraints of diet and foraging are evident in a bimodal concentration of terrestrial meat in the diet, as we found higher concentrations in high-latitude continental ecosystems and coastal areas than adjacent regions. Prior findings indicate that the shift to a meat-reliant dietary niche at high-latitudes maybe due to the poor availability of bears’ key plant foods, which renders bears unable to meet physiological needs on plant foods alone (Wiegand et al. 2008; Bojarska and Selva, 2012). Meanwhile coastal bears, which our results showed are generally the largest bears in the province, may have to increase their terrestrial meat intake to 43 meet their protein needs, as the ability of larger bears to gain and maintain mass is constrained when they are heavily reliant on plant foods (Costello et al. 2006). The seasonal (and inconsistent) nature of salmon runs means that large coastal bears likely need to supplement their protein intake in the spring and early summer with other meat sources before salmon arrive, or possibly even throughout the fall during periods of low salmon density or high intraspecific competition. These results are in keeping with previous work that has shown the importance of certain terrestrial meat sources, such as marmots and goats, to coastal salmon-reliant bears (Mowat et al. 2017). The spatial distribution of dietary niches and uptake of secondary foods indicates that food availability and ease of foraging are the primary drivers of dietary niche: bears rely first on readily available foods, while meeting their physiological needs with other, more foraging intensive, foods. Prior results from Mowat and Heard (2006) indicated that terrestrial meat may form the basis for a third major dietary niche for North American grizzly bears in high-latitude areas, likely based on caribou (Gau et al. 2002). While our SIA estimates include results over 30%, and as high as 60% terrestrial meat in the diet, these results are lower than findings by Mowat and Heard (2006) and results from spatial prediction never included terrestrial meat dietary proportions above 15%. Temporal factors may drive the difference between our results and Mowat and Heard’s, as the timing of our updated dataset coincides with a rapid decline of woodland caribou in Canada’s boreal forests (Festa-Bianchet et al. 2011; Hervieux et al. 2013), as well as declines in moose populations (Kuzyk et al. 2019). Regardless, the low spatial estimates for meat in the diet suggest that a third dietary niche, where bears orient their behaviour around meat consumption, is not present in B.C. However, we found the highest terrestrial meat consumption in the boreal areas of northeastern B.C., indicating that some of the 44 ecological processes that drive bears to a meat-reliant diet are likely at work in the province, a particularly important question for the region’s bear management given declining prey populations. British Columbia’s two major grizzly bear dietary niches also have important behavioural considerations at both the local and landscape level. The aggregate nature of salmon foraging is known to elicit noticeable behavioural changes in coastal bears, making bears more sensitive to human presence (MacHutchon et al. 1998; Artelle et al. 2016; Shardlow et al. 2022) and driving spatiotemporal intraspecific avoidance due to high levels of competition (Klinka and Reimchen, 2002), particularly among female bears who have to balance dietary needs with the risk of infanticide from male bears (Ben-David et al. 2004). Given these factors, the spatial distribution of these dietary niches may represent a strong difference in behaviour, as it is unclear if similar effects are present in areas where bears are reliant on aggregate patches of fruiting plants or other food sources. However, considering the overwhelming importance of plant foods to bear diet across the province (e.g., McLellan, 2015), it is possible that similar effects are present in areas with high dietary value regardless of dietary niche, though the lower spatial energy density among plant foods relative to salmon likely means these behavioural effects are weaker. At broader scales, these dietary niches represent potential barriers in spatiotemporal patterns in bear behaviour and movement driven by food availability. In coastal areas, bears track salmon both across the landscape and locally across spawning sites to maximize foraging success (Schindler et al. 2013; Deacy et al. 2018; Deacy et al. 2019). Analogously, grizzly bears in North America have been shown to track a “green wave” of spring plant food availability (Bowersock et al. 2023), while brown bear occurrence in Russia is correlated with fruit ripening in the late summer and fall (Belkin et al. 2022). Tracking bear movement, driven by food or otherwise, has 45 important ramifications for understanding population connectivity and dispersal (e.g., Peck et al. 2017), as well as for human-caused bear mortality (Parsons et al. 2023), a factor identified as a major threat to grizzly bears in B.C. (Bellringer, 2017). Given the results of our analyses, it is likely that diet-driven movement patterns are variable across B.C., and further research linking diet and bear movement could elicit key considerations for bear management. Our results show the shift from coastal salmon-reliant diets to plant-reliant diets in the interior is a stark spatial boundary along the length of the province, which raises important questions for bear populations at both the landscape level. Brown et al. (1996) indicate that niche variation can drive the “internal structure of a species’ range,” while major ecological barriers – including major glaciated mountain ranges, climatic and ecological gradients, and disparate plant productivity, plant availability, and salmon availability – seem to represent barriers to movement and genetic connectivity among bear populations (Mowat et al. 2005; Proctor et al. 2012). Work on other species has found that allopatric niche diversity can be a source of genetic variation (e.g., Maia‐Carvalho et al. 2018), though the extent to which this may affect bears has not been studied. If bears are not readily able to adapt to new dietary niches, or if there is inadequate movement between the two groups (Lafferty, 2015), this spatial barrier in diet could represent both a source of genetic diversity and a barrier to population connectivity for grizzly bears. While Adams et al. (2017) postulated that coastal areas may act as population sources for grizzly bears, the rapid shift in dietary niche shown by our results may represent a potential barrier to migration and genetic connectivity between coastal and interior areas, particularly if the availability of foods change in the future. In B.C., salmon populations are declining and becoming increasingly spatiotemporally homogenous (Price et al. 2021), something of particular importance given sex-specific differences in our dietary findings and in bears’ movement 46 ecology. Female bears tend to be more vulnerable to genetic fragmentation than males, while male dispersal migration may represent the only movement between small or isolated populations (Proctor et al. 2012; Sawaya et al. 2014). Further, Jimbo et al. (2022) found that female grizzly bears in Hokkaido, Japan tend to be more likely to retain their natal dietary preferences than male bears. If small, upstream salmon runs decline further, it is possible that restricted female movement begins to represent a widening gap between breeding populations of plant-reliant interior bears and salmon-reliant coastal bears. Our study found notable differences from the previous work by Mowat and Heard (2006) and Adams et al. (2017) on which it builds, particularly on B.C.’s south coast, where our models predicted much higher salmon consumption. All three studies have poor sampling coverage for bears in the area, likely due to low population densities, but our parametric spatial models explicitly considered the area’s high salmon availability and predicted high salmon consumption throughout the south coast. South coast grizzly bear populations are some of the most threatened in the province and are subject to intense anthropogenic ecosystem change, while the nearby North Cascade populations are “functionally extirpated” (Rine et al. 2020). While the North Cascade ecosystems have a mosaic of anthropogenic presence, more remote areas are not necessarily more developed than adjacent areas that still support grizzly bear populations. However, dams on the Skagit River system (Zimmerman et al. 2015) have blocked salmon from more intact habitats with less human presence, possibly contributing to population decline. Besides these anthropogenic effects, these ecosystems are functionally similar to areas further north where robust bear populations are heavily salmon reliant, suggesting that bear’s dietary need for salmon will be a key consideration in the recovery of these populations. 47 Our models also predicted lower average meat consumption across the province, particularly in the north, compared to Mowat and Heard (2006), though we did find high individual meat estimates that aligned with their results. Mowat and Heard (2006) found that populations of grizzly bears in boreal and arctic ecosystems may exhibit a third major dietary niche in the form of heavily meat reliant diets, likely based on caribou (Gau et al. 2002). The differences in our results may be due to spatial modelling differences, but also may be due to the temporal scale of our studies. Our dataset includes more than a decade of more recent bear isotope values and the lower meat in the diet could possibly be related to the rapid decline of woodland caribou in Canada’s boreal forests (Festa-Bianchet et al. 2011; Hervieux et al. 2013), as well as declines other species like moose (Kuzyk et al. 2019). Regardless, our results show the highest terrestrial meat consumption in the province is in the boreal areas of northeastern B.C., indicating that meat-reliant bear populations may extend into B.C., an important question for bear management given the decline of caribou. 2.6.2 Predicting Grizzly Bear Diet Using parametric spatial modelling allowed us to explore the links between the environment and the response (Fotheringham and Sachdeva, 2022), while controlling for spatial heterogeneity (Basile and Mínguez, 2018), and our results highlight the strong link between the two disparate dietary niches in B.C. In our study, plant and salmon consumption were best predicted by models with the same parameters but opposite effects, highlighting the prevalence and interdependence of these two dietary niches. Though the additive nature of dietary mixing models constrains the freedom of each diet source relative to each other, the strongly mirrored results for plants and salmon in the diet, relative to the independence of terrestrial meat in the 48 diet, highlights the environmental drivers of these two partnered dietary niches. In particular, available salmon biomass, bear size, climate, and elevation all predicted both salmon consumption and plant consumption in opposite ways. Available salmon biomass is a key parameter for modelling a wide range of responses for bears, including local diet (Service et al. 2019; Shardlow et al. 2022), stress (Bryan et al. 2014), and human-bear conflict (Artelle et al. 2016), and we found it was a key predictor for both plants and salmon in the diet at a provincial scale. While both Service et al. (2019) and Shardlow et al. (2022) did not find available salmon to be useful predictors for local black bear diet in fine-scale assessments of coastal populations (in both areas with and without competitive pressure from grizzly bears), our results showed at a provincial scale that grizzly bears ate more salmon when more salmon was available. However, for both males and females, the relationship between salmon biomass and salmon consumption plateaued noticeably, indicating that at high levels of salmon biomass, salmon consumption is dictated by other factors. Further, salmon consumption by female bears declined in areas with the highest available salmon biomass, indicating that these areas may represent less ideal foraging habitat for females, possibly due to competitive risks (Ben-David et al. 2004). Salmon biomass was also a negative and linear predictor for plant consumption, which, considering the plateaued effect on salmon consumption, indicates that bears in salmon-rich areas may be more readily switching to a third food source rather than relying on just plants and salmon, a conclusion strengthened by our high estimates for terrestrial meat in the diet of coastal bears. As expected, our models showed a strong relationship between bear size and diet, though the relationship between size and salmon consumption also plateaued (Figure 8). Understanding the drivers of grizzly bear size is complicated and influenced by a wide range of unresolved 49 factors, though it is well established that salmon-reliant bears are larger than bears with other dietary niches (Hilderbrand et al. 1999; Mowat and Heard, 2006). This could indicate that the size differences among the largest bears in B.C. may be due to factors other than salmon consumption, such as genetics, or temporal factors. To grow to immense size bears must consume large quantities of salmon every year and, as we only measured diet for one year per sampled bear, our methods cannot account for size variation due to inter-annual variability in salmon availability. It is possible that annual consistency in the quantity of large salmon returns, rather than their absolute abundance, drives the spatial variation in size among the province’s largest bears. Meanwhile the only parameter that was not mirrored in the plant model for both sexes was female size, which was not included in the top model for female plant consumption. Differential influences of body size on fitness – the underlying drivers of sexual dimorphism, as reproduction limits female body size while males must increase their size to improve competitive mating success (Bartareau et al. 2011) – likely drive sex-specific differences in diet given the importance of diet to bear’s ability to gain mass. Given that female foraging behaviour and diet are more likely to be constrained by intraspecific competition (e.g., Klinka and Reimchen, 2002; Ben-David et al. 2004), factors related to limits to reproduction such as the age at first breeding may limit the relationship between female size and diet across the province (Zedrosser et al. 2013, Van de Walle et al. 2021). Mean annual precipitation was an important predictor for all foods. Salmon consumption increased with precipitation, a relationship likely driven by the heavy rainfall in coastal areas where salmon is plentiful. For other foods, we hypothesized that drier and more continental climates may be associated with higher plant consumption (due to availability of key plant foods) and higher terrestrial meat consumption (due to high ungulate densities). While we found 50 evidence supporting the former, we found, contrary to our expectations, that terrestrial meat consumption was positively correlated with climatic moisture. Female meat consumption declined in areas with hotter, drier summer climate, while male meat consumption decreased with decreasing annual precipitation, although the slope of the male relationship was low. Drier ecosystems in B.C. have an abundance of high-value bear foods, including whitebark pine (Pinus albicaulis, Felicetti et al. 2003b), Canada buffaloberry (Shepherdia canadensis, Denny et al. 2018), and huckleberry (Vaccinium spp., McLellan, 2015; Prevéy et al. 2020), and we found these regions to have some of the highest proportions of plants in the diet. It is possible that the availability of key plant foods in dry ecosystems overshadows the availability of terrestrial meat, suggesting that meat consumption is opportunistic and driven by poor availability of other foods, a hypothesis strengthened from prior work in high-latitude ecosystems (Wiegand et al. 2008; Bojarska and Selva, 2012). Our results are consistent with past findings that males consume more meat than females (Mowat and Heard, 2006), and the relative strength of relationship between female meat consumption and their environment over males may indicate that this opportunistic behaviour is exhibited more often by dominant males. While we hypothesized that forest productivity, in the form of measurements of canopy openness and canopy height or forest biomass – proxies for plant productivity (Moles et al. 2009; Fischer et al. 2019) – would together be related to plant consumption (Stewart et al. 2013; Proctor et al. 2015), forest ecosystem factors did not influence bear diet across B.C. This may be a scale related issue: while more productive forests at a local scale may have more plant foods, at the provincial scale B.C.’s coastal temperate rainforest has enormous highest productivity but is not necessarily home to high densities of key foods like huckleberry (Prevéy et al. 2020). Similarly, canopy openness may attract bears (Colton et al. 2021) or affect plant food 51 distributions (e.g., Braid et al. 2016) at fine scales, but not necessarily drive dietary shifts; Mowat et al. (2013) speculated that increased plant food availability is simply increasing the density of bears, rather than changing diet. While the diversity of available ungulate prey was not a useful predictor for bear diet, we were unable to assess the diversity of other available foods, factors that may have key implications for dietary ecology, particularly at finer scales. Dietary diversity is a known driver of niche variability (Sánchez‐Hernández et al. 2021) and spatiotemporal diversity in the availability of both salmon and plants could influence the spatial distribution of the two major dietary niches in B.C. Service et al. (2019) found salmon prey diversity was a key predictor of salmon consumption by black bears, and it is possible that salmon prey diversity may be a factor in the transition from a salmon-reliant niche to a plant-reliant niche in coast-interior transition areas. There is considerable temporal variation in salmon spawning seasons (Kovach et al. 2015) and pink (O. gorbuscha) and chum salmon (O. keta) do not travel far inland (Groot and Margolis, 1991), reducing the spatiotemporal diversity of prey for inland bears. If the decline of pacific salmon further reduces upstream diversity, spatiotemporal gaps in seasonal foraging for bears may arise, potentially disrupting diets and creating a barrier in niche distribution. At similar fine-scales, spatiotemporal diversity in food availability requires bears to track available foods across the province (Deacy et al. 2018; Deacy et al. 2019; Bowersock et al. 2023) and likely promotes consumption of a food group across broader landscapes. A similar relationship between bear diet and seasonal availability in food is found in the emergent timing of certain key fruits, including saskatoon (Amelanchier alnifolia), buffaloberry, and huckleberry. Our study found some of the highest plant consumption in leeward ecosystems of southern B.C. where several major fruiting species are readily available. While one fruit species can be a major 52 influence on bear ecology (McLellan, 2015), future work would benefit from considering the spatial, seasonal, and biological diversity of available plant foods in bear diet. 2.6.3 Methodological Considerations for Stable Isotope Analyses This study represents one of the largest uses of stable isotope mixing models in grizzly bear research – and for wildlife in general – and as with any rapidly advancing method, several limitations are evident, perhaps most prominently carbon fractionation. Carbon specific fraction rates represent a major source of uncertainty for bear research, and although we and other researchers (e.g., Milakovic and Parker, 2013; Javornik et al. 2021) have assessed the use of several methods, the issue is far from settled. While we adopted Felicetti et al.’s (2003b) approach for nitrogen fractionation, a corresponding equation for carbon (as well as a similar equation from Caut et al. 2009) resulted in adjusted source isotope values that did not fit our data and did not provide reliable dietary estimates. While some studies have utilized carbon fractionation factors utilizing a “model species” approach based on other species of captive-fed monogastric omnivores, these values were also not useful to our analysis. Based on lab work with mice, Hopkins et al. (2021) state that “it is no longer acceptable to apply the same standard discrimination factors to both plant and animal food sources when estimating the diets of free-ranging mammalian omnivores” and suggest values as low as 0‰ for dietary meat. However, recent studies that fed bears meat-heavy (>50%) diets have found fractionation factors above 0‰ (L'Hérault et al. 2018; Rioux et al. 2020). Other studies have found rates around 3‰ are common for ungulates (Sponheimer et al. 2003) while meta-analyses have found fractionation factors in the order Carnivora are usually higher than that of other orders (Vanderklift and Ponsard, 2003; Dalerum and Angerbjörn, 2005; Caut et al. 2009), indicating that the rates for terrestrial meat and fish from Jimbo et al. (2022) and Hopkins 53 et al. (2021) may be too low. Using the 3.7‰ from Fortin et al. (2007) across food groups was not only the most parsimonious option, but also better aligned with measured fractionation factors in experimental studies while best fitting to our isospace, particularly for salmon. Though the isotopic values of diet sources are as important to the results as the values of the consumer being studied (Phillips et al. 2014), sources of uncertainty remain for grizzly bear research, particularly surrounding the plant isotope value. First, plant isotope values can be highly variable across space (Bowen, 2010), and ideally mixing models for populations with large spatial ranges would account for this variability by using locally specific isotope values. Second, the variation of plant isotope values within the range of species consumed by bears can be variable enough to affect model results (Shardlow et al. 2022). Given the enormous range of plants bears consume (MacHutchon, 2021), it would be extraordinarily difficult to methodically consider the relative contributions of all plant foods. Using the plant isotope value calculated based on vegetarian bears by Mowat and Heard (2006), we can account for the latter source of uncertainty, but not the former. In order to account for spatial variation, researchers would need to select and analyze vegetarian bears from each population, a task that would require considerable investment in studying local diet before undertaking a broad scale stable isotope analysis such as the one described here. A more efficient assessment of the spatial variation of plant isotope values could be undertaken by calculating plant isotopes based on the isotope values of local herbivores as demonstrated by Van Daele et al. (2013). They calculated baseline plant isotope values by subtracting species specific fractionation factors from measured isotope values in the hair of various ungulate species collected as a part of their study, allowing them to establish isotope values for both plants and terrestrial meat via the collection of ungulate hair. However, while bear researchers could use 54 this method to establish underlying plant isotope value variation across space, it does not account for a disparity in the plants consumed by bears versus ungulates. As with any study at such broad spatial scales, there remained uncertainty surrounding the variation of model inputs across our study area. Besides vegetation, the spatial variability of terrestrial meat isotope values was the most variable endpoint in our models, and uncertainty surrounding the values arises from multiple sources. The isospace for salmon-consuming-bears is often a triangle, but the use of the single 3.7‰ value for fractionation among sources often resulted in near linear space among vegetation, terrestrial meat, and salmon. Using the method recommended by Hopkins et al. (2021) and values similar to Jimbo et al. (2022) would have created a larger isospace to separate salmon and terrestrial meat but resulted in unrealistic model results for our highly precise salmon endpoint. Uncertainty surrounding terrestrial meat may have been more so due to the poor spatial coverage of isotope values we were able to source from the literature, as well as poor data availability on ungulate population densities and individual bear preference for different species of prey, than to fractionation factors. Isotope values for ungulates can vary across space (Hofman-Kaminska et al. 2018) and, with better data on ungulate population densities and range, future work could weigh terrestrial meat isotope values by ungulate species based on spatial differences in bear preference and relative availability. Isotopic data availability was much better for aquatic diet sources than for vegetation and terrestrial meat, though there remain uncertainties surrounding coastal bear diet. Though salmon values we found in the literature were highly precise, including across species, uncertainty for coastal bear diet arises from poor knowledge surrounding the use of marine foods besides salmon. The use of intertidal foods, including crustaceans, mollusks, and scavenged whales by 55 bears is well established (e.g., Adams et al. 2017; Service et al. 2019), but the availability of intertidal isotope values is poor. For a study of this scale, salmon represented a much more important contribution to bear diet, both in terms of the bulk diet fraction and in the management implications of our results. Given the potentially poor divergence in isotope values between salmon and other marine foods (Shardlow et al. 2022), including intertidal foods as an endpoint was not tenable. Future research on coastal bear diet would benefit from closer attention paid to intertidal food sources using isotopic and non-isotopic methods. Finally, using hair for estimating diet requires careful consideration of seasonality. Due to the timing of hair growth, bear isotope studies overlook spring diet (Mowat et al. 2017), and the estimates do not necessarily reflect seasonal dietary needs of the period they are able to quantify. Assuming, for example, that a bear is active for 8 months of the year, a bear that consumed a diet of 100% terrestrial meat during one month, but no meat throughout the rest of the year, would be estimated to have a diet of less than 15% meat, despite that meat represented all of its diet for a significant portion of time. Additionally, the terrestrial meat consumption estimates would in practice appear even lower given that bears tend to consume more food during the hyperphagic fall period than the spring. These sorts of patterns, in which bears optimize their macronutrient intake across the season by selecting different foods when they are available (Coogan et al. 2014), may not be reflected in annual isotopic studies, which can mask the importance of seasonally available foods. 2.6.4 Management and Conservation Implications Diet is a major bottom-up effect on grizzly bears’ ecology, and the species continues to be a species of concern in Canada (Canadian Wildlife Service, Environment Canada, 2016). Their diet has strong influence on their physiology (Costello et al. 2016), size (Mowat and 56 Heard, 2006), movement (Deacy et al. 2018; Deacy et al. 2019; Bowersock et al. 2023), and population abundance and distributions (Nielsen et al. 2010; Mowat et al. 2013). As a result, the dietary variation described here is a key consideration for the management of grizzly bear populations across B.C. To help augment grizzly bear management in the province, all of our maps of diet, as well as the maps we produced for bear size and salmon biomass, are publicly available on github.com/vanelslander-ecology. Key questions remain for bear diet with respect to the species’ management, given ecological changes over time; bear population densities (Bellringer, 2017), prey availability (e.g., Kuzyk et al. 2017; Price et al. 2021), ecosystem dynamics (e.g., Holmes et al. 2015; Parisien et al. 2023), and climate (Mahony et al. 2018) have all changed over the three-decade span of our study, and will continue to change going forward. If declining prey numbers have been of sufficient magnitude to drive changes in bear diet, it is possible that our results represent an overestimate of salmon or terrestrial meat in present or future diets. This an important consideration not only for bear management (as declining prey may represent a threat to population health), but also to the management of prey species, as consumption by bears may represent a significant part of the prey species’ total annual mortality and have considerable impact on how human caused mortality of the prey is managed (Van Daele et al. 2013). Additionally, there is uncertainty about the effects of climate-mediated changes to wildfire regimes that will affect the distribution of the key plant foods such as Vaccinium spp. (McLellan, 2015; Prevéy et al. 2020). Accelerating wildfire frequency and severity could result in an increase (from increased wildfire frequency) or decrease (from increased wildfire severity) in Vaccinium spp. abundance, either of which would have important effects on bear diet and ecology. 57 Our results show that the current GBPUs do not reflect spatial variation in the diet ecology of bears in B.C. (Figure 7), with current units often containing mixed areas of high-salmon reliance and plant-reliance, or areas of diverse influences of all food groups including kokanee and terrestrial meat. Given the disparate ecologies of bears across their dietary niches, the spatial ecology of these mixed-diet units greatly complicates the management of bears in any one unit. Our findings indicate that bear management in the province would benefit from changes to these units’ boundaries. Additionally, as diet is a key influence on bear behaviour in relation to top-down anthropogenic threats – including direct human conflict (Artelle et al. 2016; Lamb et al. 2017) and mortality threats from roadways and railways (Hopkins et al. 2014; Lamb et al. 2023; Proctor et al. 2023), one of the single most influential threats to bear populations (Bellringer, 2017) – there are key ramifications from our findings in managing these populations. Our results show changes to these units’ boundaries would help bear management in the province to best consider key ecological questions for different populations going forward. Updated grizzly bear management across the province and new GBPU boundaries should consider the spatial nature of diet in order to best classify bears into units with more straightforward management challenges. Our analyses of bear diet provide bear managers with key spatial life history information to inform population decisions around environmental change across B.C., such as the impact of salmon declines or large wildfires. For instance, these data can be used by land managers at fine scales to consider the potential impact of a major development on a local bear population or to inform habitat restoration decisions based on diet of the local bear population. Additionally, they could also be organized as a time series to help monitor changes in importance of key foods, such as salmon. At broad scales, these results represent essential baseline knowledge for bear 58 ecology and management, helping researchers and managers to study and manage their populations across ecosystems with a focus on the bears’ fundamental ecology. 59 2.7 Tables Click or tap here to enter text. Click or tap h ere t o ente r text. Table 1. Eleven different models were run in MixSIAR, separated spatially based on available food sources and ecological boundaries. Bear Zone Data Sources n Isotope Values n Bears Diet Sources Bowron Mowat and Heard, 2006 16 16 Plants + Salmon + Kokanee This Study 8 8 Coast Central Coast Bear Working Group, Kitasoo Xai’xais First Nation, 2018; Adams et al. 2017 405 52 Plants + Salmon + Terrestrial Meat Central Coast Bear Working Group, Wuikinuxv First Nation, 2018; Adams et al. 2017 287 99 McLellan et al. 2019 1 1 Mowat and Heard, 2006 111 110 Lafferty, 2015 83 83 This Study 70 66 North East Jones et al. 2006 20 2 Plants + Terrestrial Meat Mowat and Heard, 2006 52 51 Oliver Holt, Unpublished Data 4 4 This Study 46 42 North West Mowat and Heard, 2006 133 132 Plants + Salmon + Terrestrial Meat Lafferty, 2015 1 1 Oliver Holt, Unpublished Data 76 76 This Study 52 50 Okanagan Mowat and Heard, 2006 1 1 Plants + Terrestrial Meat + Kokanee Ootsa Mowat and Heard, 2006 15 15 Plants + Salmon + Terrestrial Meat + Kokanee Oliver Holt, Unpublished Data 1 1 Shelley Marshall, Dexter Hodder, Unpublished Data 29 20 This Study 2 1 South East Lafferty, Unpublished Data 232 181 Plants + Terrestrial Meat + Kokanee Mowat and Heard, 2006 72 69 This Study 269 258 Southern Boreal Jones et al. 2006 14 2 Plants + Salmon + Terrestrial Meat + Kokanee Mowat and Heard, 2006 76 73 Oliver Holt, Unpublished Data 26 26 Shelley Marshall, Dexter Hodder, Unpublished Data 43 35 This Study 44 39 Southern Interior McLellan et al. 2019 24 24 Plants + Salmon + Terrestrial Meat + Kokanee Mowat and Heard, 2006 4 4 This Study 28 26 Temperate Mountains Lafferty, Unpublished Data 4 3 Plants + Salmon + Terrestrial Meat + Kokanee Mowat and Heard, 2006 82 81 This Study 50 46 Williston Jones et al. 2006 59 5 Plants + Terrestrial Meat + Kokanee Mowat and Heard, 2006 43 41 Oliver Holt, Unpublished Data 11 11 This Study 63 54 60 Table 2. A range of spatial data were tested as potential parameters for spatially modelling diet. Parameter Data Response Diet Category Causative or Correlative Factor Hypothesis Citation Available Salmon Biomass Salmon Salmon Availability Bears may consume more salmon in areas with more available salmon biomass Service et al. 2019; Lincoln et al. 2020 - Forest productivity: canopy height OR - Forest productivity: forest biomass AND - Canopy cover Plants Forest Structure as a proxy for plant availability Key plant foods for bears are more plentiful in forests that are more productive AND have more open canopies, and thus bears may consume more vegetation there Mowat and Heard, 2006; McLellan, 2015; Adams et al. 2017 Ungulate Species Diversity Terrestrial Meat Ungulate species diversity as a proxy for meat availability Ungulate densities may be higher in areas with more ungulate species, and thus bears may consume more terrestrial meat there Hypothetical based on observations in certain regions (e.g., Yellowstone National Park, Wyoming) - Mean annual precipitation OR - summer heat moisture index Terrestrial Meat Climate as a proxy for meat availability Ungulate densities are generally negatively correlated with precipitation, thus bears may consume more meat in drier regions British Columbia Ministry of Water, Land and Resource Stewardship, 2022 - Seasonal temperature difference OR - summer heat moisture index Terrestrial Meat Climate as a proxy for meat availability Ungulate densities are thought to be higher in more continental climates, thus bears may consumer more meat as continentality increases British Columbia Ministry of Water, Land and Resource Stewardship, 2022 Canopy Cover Terrestrial Meat Forest Cover as a proxy for meat availability Ungulate densities tend to be higher in more open forest types, thus bears may consumer more meat as canopy cover declines Stewart et al. 2013; Mowat et al. 2013 Bear skull size Salmon Bear Size Bears that consume more salmon tend to be larger, thus bears likely consume more salmon where bears are larger Mowat and Heard, 2006; Cameron et al. 2020 61 Parameter Data Response Diet Category Causative or Correlative Factor Hypothesis Citation Mean annual precipitation Salmon Climate Salmon runs tend to be concentrated in high precipitation coastal areas, thus bears may consume more salmon in wetter areas Mowat and Heard, 2006 Digital Elevation Model Salmon Elevation Salmon runs are located in valley bottoms, thus bears likely consume more salmon at lower elevations Digital Elevation Model Plants Elevation Salmon runs are located in valley bottoms and quality bear foods are often associated with open, high elevation habitat, thus bears likely consume more plants at higher elevations Felicetti et al. 2003b; McLellan, 2015; Denny et al. 2018; Prevéy et al. 2020 62 Table 3. Dietary source isotope values and standard deviations, as well as fractionation factors for each diet source in each diet model. Diet Source Diet Model Zone δ13C‰ δ13C‰ S.D. δ15N‰ δ15N‰ S.D. n ∆13C‰ ∆15N‰ Data Sources Generalized Plants All -26.6 2.0 -2.8 3.0 44 3.7 5.6 a. Generalized Salmon All -19.9 0.2 12.5 0.3 338 3.7 3.8 b. Terrestrial Meat Coast -26.5 1.0 2.7 1.0 135 3.7 5.0 c. Terrestrial Meat North East -24.9 1.0 2.7 1.0 252 3.7 5.0 d. Terrestrial Meat Southern Boreal -25.1 1.0 3.1 1.0 182 3.7 4.9 e. Terrestrial Meat Southern Interior -26.1 1.0 3.3 1.0 137 3.7 4.9 f. Terrestrial Meat Temperate Mountains -25.7 1.0 3.1 1.0 136 3.7 4.9 g. Terrestrial Meat North West -24.6 1.0 2.5 1.0 321 3.7 5.0 h. Terrestrial Meat Williston -24.2 1.0 2.8 1.0 259 3.7 4.9 i. Kokanee Bowron -27.7 0.6 9.4 0.4 3 3.7 4.2 j. Kokanee Ootsa -24.7 0.7 9.0 2.2 8 3.7 4.2 k. Kokanee South East -24.7 0.9 7.5 1.4 24 3.7 4.4 j. Kokanee Southern Boreal -31.1 1.7 9.2 0.6 105 3.7 4.2 l. Kokanee Southern Interior -23.3 0.9 11.8 0.7 36 3.7 3.9 j. Kokanee Temperate Mountains -22.7 2.3 7.1 0.7 55 3.7 4.4 j. Kokanee Williston -32.2 1.4 9.2 0.5 116 3.7 4.2 m. Kokanee Okanagan -23.2 0.3 11.8 0.5 31 3.7 3.9 j. a. Mowat and Heard, 2006. b. Bilby et al. 1996; Hilderbrand et al. 1996; Ben-David et al. 1997; Jacoby et al. 1999; Chaloner et al. 2002; Satterfield and Finney, 2002; Ben-David et al. 2004; Tom Johnston pers. Comm. c. Ben-David et al. 2004; Darimont et al. 2007; Mowat et al. 2017 d. Milakovic and Parker, 2013; Oliver Holt, Unpublished Data e. Oliver Holt, Unpublished Data. f. Derbridge et al. 2012; Hobson et al. 2000; Stotyn, 2008; Oliver Holt, Unpublished Data. g. Derbridge et al. 2012; Hobson et al. 2000; Stotyn, 2008 h. Merkle et al. 2017; Milakovic and Parker, 2013; Mowat et al. 2017; Oliver Holt, Unpublished Data i. Milakovic and Parker, 2013; Oliver Holt, Unpublished Data j. Guiry et al. 2020 k. Guiry et al. 2020; Shelley Marshall, Dexter Hodder, Unpublished Data l. Azimuth Consulting Group Partnership et al. 2019; Coxson et al. 2018; Guiry et al. 2020; Shelley Marshall, Dexter Hodder, Unpublished Data; Tom Johnston, Unpublished Data. m. Azimuth Consulting Group Partnership et al. 2019; Coxson et al. 2018; Guiry et al. 2020 63 Table 4. Top spatial models for each diet source. Response Parameters Estimated Coefficient Coefficient S.E. Male Salmon Consumption Intercept -1.63 0.21 Elevation -0.26 0.04 Mean Annual Precipitation, Smoothed - - Predicted Male Size, Smoothed - - Salmon Biomass, Smoothed - - Female Salmon Consumption Intercept -3.9 0.16 Elevation -0.52 0.06 Mean Annual Precipitation 0.58 0.09 Predicted Female Size, Smoothed - - Salmon Biomass, Smoothed - - Male Meat Consumption Intercept -3.15 0.16 Elevation, Smoothed - - Mean Annual Precipitation 0.15 0.04 Female Meat Consumption Intercept -3.74 0.11 Summer Heat Moisture Index -0.54 0.06 Elevation, Smoothed - - Male Plant Consumption Intercept 0.41 0.07 Elevation 0.28 0.04 Predicted Male Size -0.59 0.06 Mean Annual Precipitation, Smoothed - - Salmon Biomass, Smoothed - - Female Plant Consumption Intercept 1.83 0.07 Elevation 0.5 0.05 Mean Annual Precipitation -0.6 0.07 Salmon Biomass, Smoothed - - 64 2.8 Figures Figure 1. The location of all bear hair samples included in this study. 65 Figure 2. New rasters generated for this study. Male (a) and female (b) bear skull size based on British Columbia's Compulsory Inspection Bear Data and available salmon biomass (c) from the Canadian Department of Fisheries and Oceans NuSEDs database. 66 Figure 3. Different bear dietary zones. Each zone represents a stable isotope mixing model. Note that an eleventh model was run on one individual in the Okanagan Lake region (see Table 1). 67 Figure 4. Kokanee salmon and terrestrial meat isotope value data sources. 68 Figure 5. Isospace figures showing the effect on dietary endpoints of various methods for selecting carbon fractionation factors. 69 Figure 6. MixSIAR estimates for the proportion of different food sources in the diets of grizzly bears across B.C. 70 Figure 7. Predicted maps of bear diet across British Columbia, with the existing Grizzly Bear Population Units overlayed. Note that a non-parametric model for kokanee in female bear diet did not converge, and thus is not included here. 71 Figure 8. Scatterplots for each parameter in spatial diet models for both female (a.) and male (b.) grizzly bears. 72 73 Chapter 3: Conclusion 3.1 Main Conclusions This study represents one of the broadest scale assessments of the dietary niche of grizzly bears to date and has key implications for bear ecology and management. Grizzly bears have enormous dietary flexibility (MacHutchon, 2021) that helps drive the sizeable variety of dietary niches that are present across their large range. The species also has prominent conservation concerns and a broad allure to the public, which combined with their niche variability and the recent development of advanced methods, make them a compelling subject for ecological research. Despite this, few attempts have been made at synthesizing a spatial understanding of their dietary variation, and fewer still have attempted to link environmental factors to their niche using parametric modelling. I assessed the dietary niche variation of grizzly bears across the province of British Columbia in two steps: first, by estimating the proportion of anadromous salmon, terrestrial meat, kokanee salmon, and plants in the diet of over 1800 individual bears from across the province using stable isotope analysis of over 2500 grizzly bear hair samples. And second, by utilizing those dietary estimates in parametric models to create fine-scale predictive maps of diet for the entire province and to better understand about the underlying environmental and biological drivers of bear diet. This assessment shows primarily that grizzly bears in British Columbia fall into one of two major dietary niches: a reliance on salmon or a reliance on plants. Though the geographic boundaries between these two dietary niches will require careful consideration in future work, the disparity between these two niches elicits important takeaways when managing and researching grizzly bear populations across the province. I found that salmon is the key diet 74 source for all coastal populations in British Columbia, and a major source for bears in some inland areas. In contrast, plant foods formed the vast bulk of the diet for nearly all other bear populations. While I also found that terrestrial meat and kokanee salmon play important parts in bear diets in certain regions of the province, nowhere were they as fundamental as salmon and plants. Parametric modelling showed that available anadromous salmon biomass is the key underlying variable driving the variation of bear diet in British Columbia, a finding that will have major implications for bear management going forward as salmon populations decline (Levi et al. 2020). Salmon abundance was the key parameter in models for both salmon and plant consumption for both sexes, and a more influential predictor than climatic variables, forest ecosystem measurements, or landcover class, indicating that in areas where salmon is abundant bears will choose it over other foods. Finally, these results show that the B.C.’s current Grizzly Bear Population Units (GBPUs) do not adequately reflect the dietary ecology of grizzly bears. The predictive dietary models routinely showed GBPUs that overlapped dietary niche boundaries or contained irregular spatial concentrations of key foods in the diet. For instance, many units along the coast-interior transition had strong shifts from heavily salmon based diets to heavily plant-based diets in the middle of the units, indicating that bears within those units are likely made up of two disparate populations with dramatically different ecologies. The GBPUs are the fundamental jurisdictions that delineate grizzly bear management across British Columbia, and their failure to reflect bear ecology represents a major barrier to effective management. 75 3.2 Limitations and Considerations for Future Research 3.2.1 Stable Isotope Analysis Though the use of stable isotope mixing models for diet ecology has been a popular method for years (Phillips, 2012), several limitations were evident in this study. First, the selection of diet-, tissue-, and species-specific trophic fractionation factors is an unsettled issue, particularly for carbon fractionation and for grizzly bears. Though there is physiological merit to several different methods, (e.g., Felicetti et al. 2003b; Hopkins et al. 2021), I found that the use of many of these methods for carbon fractionation produced nonsensical dietary estimates. In the end, a parsimonious approach using a single carbon fractionation value of 3.7‰ for all foods from Fortin et al. (2007) and Van Daele et al. (2013) produced the most realistic dietary estimates. Trophic fractionation factors represent perhaps the largest uncertainty in the use of stable isotope mixing models for bears, and thus bear research would benefit immensely from a systematic approach to studying fractionation. Though the physiological mechanisms behind nitrogen fractionation are well understood, the exact reasons for variable carbon fractionation at the cellular and molecular level are less well known, particularly for omnivorous diets (Kurle et al. 2014; Hopkins et al. 2021), and future work would benefit from increased microbiological understanding. Additionally, though there has been preliminary work using captive feeding experiments (e.g., Hilderbrand et al. 1996; L'Hérault et al. 2018; Rioux et al. 2020), there is no agreed upon baseline understanding of the quantitative variation in carbon fractionation factors in bears, nor the chemical causes of this variation, reflecting logistical barriers for feeding large animals. 76 Perhaps a better understanding of fractionation could be elicited from wild bears via the measurements of isotope values of bears in areas where researchers already have a very high degree of knowledge about diet. Most simply, researchers could measure isotope values in bears and their foods in areas where there is very little dietary variation. There is also opportunity to reverse engineer Bayesian methods that have recently become dominant in stable isotope mixing models; in areas with well understood diet variation, Bayesian models could be used to estimate trophic fractionation factors by inputting known dietary proportions (such as from scatological studies), diet source isotope values, bear isotope values, and using informative priors. For the study of grizzly bear ecology, the second major barrier to stable isotope mixing models is the accuracy of dietary source isotope values, particularly of plants and terrestrial meat. Given the enormous range of foods consumed by bears (MacHutchon, 2021) a broad scale approach to measuring diet isotope values is required. Terrestrial ecology as a whole would benefit immensely from wide-ranging measurements of vegetative isotope values, similar to isotopic monitoring work in oceanic ecosystems (Bowen, 2010). Central data repositories of vertebrate and vegetation isotope values are an excellent start, but a systematic sampling regime would augment the use of SIA in jurisdictions like B.C. For instance, there is no well-established measurements of isotopic variation in terrestrial vertebrates like ungulates in B.C., and a sampling regime that makes use of hunter kills, roadkill, and hair trapping could quickly create a province-wide baseline if there is adequate funding for the chemical analysis of hair. Finally, despite recent advancements, stable isotope analysis is a blunt tool for dietary assessments and is not able to elicit specific understanding about specific food sources or the seasonal nature of diet. Due to the nature of grizzly bear hair growth, these estimates do not reflect the important seasonal variation in bear diet and underestimate or omit spring terrestrial 77 meat sources like rodents and winter-killed ungulate carcasses (Mowat et al. 2017). When assessing grizzly bear diet, future research would benefit from a multi-pronged approach using other methods such as local knowledge and scatological assessments. This diverse approach to dietary studies would also help elicit more nuanced understanding of specific foods. Due to overlapping isotope values, SIA using carbon and nitrogen isotope values are not able to provide estimates for important plant foods like Whitebark pine seeds (Felicetti et al. 2003b) or the relative contribution of early season forbs and late season fruits (Shardlow et al. 2022). While this analysis is able to provide fine scale maps of the importance of major food groups, a more localized understanding of spatiotemporal variation requires additional dietary assessments to determine what the key foods are for bear populations. 3.2.2 Spatial Modelling of Bear Diet Ecology The availability of spatial data was a barrier to understanding some of the underlying drivers of bear diet. First, I was unable to test if ungulate population densities influence bear diet due to a paucity of spatial data for ungulates in British Columbia. Though several species of ungulates are actively monitored for hunting management, the province keeps no readily available centralized dataset of their distributions or densities at adequately fine scales. Better utilization of the data from the monitoring already in place would not only greatly contextualize ungulate distributions and benefit dietary modelling for grizzly bears and other predators but be an enormous boon to wildlife ecology across the province. Similarly, underwhelming data management also became evident for kokanee salmon in the province. Though kokanee occurrence data is publicly available, population estimates are exceedingly rare and occurrence data did not conform to local knowledge from biologists from around the province. Future 78 improvement of datasets surrounding these species would greatly augment both predator dietary studies and environmental monitoring as a whole. There is also little available data on the province wide distribution of key plant foods for bears. While the results of this study show the importance of plants broadly, specific foods like buffaloberry, huckleberries, or whitebark pine play major roles in grizzly bear ecology (e.g., Felicetti et al. 2003b; McLellan, 2015; Clarke, 2022). Nielsen et al. (2013) recommended more pointed identification of key resources, and future studies on resource distributions would greatly benefit assessments of bear diet, bear behaviour, and population ecology. Finally, this study did not explicitly consider changing resources over time. The decline of pacific salmon is a major ecological question (Beamish, 2022) and has enormous implications for bears (Levi et al. 2020), particularly in terms of spatial ecology as particularly acute declines are noticeable in certain ecosystems (e.g., Boulanger et al. 2004; Ainsworth et al. 2011). Additionally, ungulate populations (Festa-Bianchet et al. 2011; Hervieux et al. 2013; Kuzyk et al. 2019), vegetation dynamics (Prevéy et al. 2020), and forest disturbance dynamics (Gustafsson et al. 2021; Parisien et al. 2023) are also changing rapidly, each having potential ramifications for bear diet and possibly abundance. Given that dietary niche represents a major bottom-up influence on bear population dynamics, spatiotemporal modelling of how anthropogenic influences are changing salmon populations, ungulate populations, key plant resource distributions, and bear diet is needed to inform effective and proactive grizzly bear management. Considering the recent rapid advancement in the deterioration of ecosystems across North America and beyond due to anthropogenic arrogance and ignorance, studying how bears’ key dietary resources are changing over time is essential. 79 3.3 Contributions and Management Implications By mapping diet, this research has established a key baseline for the study and management of grizzly bears in British Columbia. Diet is one of the most important drivers of grizzly bear ecology, influencing their size (Mowat and Heard, 2006), movement (Deacy et al. 2018; Deacy et al. 2019; Bowersock et al. 2023), population distributions (Nielsen et al. 2010; Mowat et al. 2013), anthropogenic mortality (Hopkins et al. 2014; Artelle et al. 2016; Lamb et al. 2017; Lamb et al. 2023; Proctor et al. 2023), and competition (Belant et al. 2006; Lafferty, 2015). As a result, the maps produced here provide managers and researchers with key information about grizzly bear populations. For instance, predicted dietary proportions can reveal the importance of different foods, helping managers predict how and when bears will be moving on the landscape (Deacy et al. 2019; Bowersock et al. 2023), or could be combined with human presence data to predict potential patterns of human conflict sites (Artelle et al. 2016; Proctor et al. 2023). There are also other opportunities to incorporate this research into effective management going forward. Bear diet is an important question for cumulative effects modelling (Carlson et al. 2022) and these results help provide key information for environmental assessments across the entire province. Historically, resource development in British Columbia has progressed without adequate monitoring of cumulative effects as ecosystems and anthropogenic systems are harmed by more types of industry (Nitschke, 2008), and understanding the relative importance of different food sources could help guide landscape-level industrial planning. Additionally, these results could be used at finer scales to understand the potential effects of any single resource project on bear populations. For instance, coal mines could drive dietary changes in terrestrial 80 food sources for grizzly bears well beyond the lifetime of a mine (Cristescu et al. 2015) or have downstream effects on fish populations (Sergeant et al. 2022). The results of this study reveal a coastal bear population heavily reliant on salmon across the entire length of the province. It also shows salmon are an important food source extending into certain coast-interior transitional ecosystems, most notably the Skeena and Babine watersheds, and to a lesser extent in certain localized inland areas such as the Chilko Lake and Quesnel Lake areas. This study also confirms that coastal salmon-reliant bears are some of the largest bears in the province, though the relationship between size and salmon consumption plateaus, indicating that the size disparities of the largest bears in the province are dictated by factors beyond the temporal scale of this study, or potentially beyond diet entirely (such as via genetic effects). In contrast, these results show that plants were by far the most important food for bears throughout the interior of the province, indicating that kokanee salmon and terrestrial meat represent opportunistic rather than fundamental foods to B.C.’s bears. Additionally, areas with interior plant reliant bears were spatially overlapped with populations of smaller bears, strengthening a priori assumptions that salmon in the diet is a key driver of bear size across B.C. This study builds on work by Mowat and Heard (2006), and Adams et al. (2017), both of which used non-parametric interpolative models for creating maps of bear diet. The updated parametric models of this study revealed several interesting takeaways for bear ecology and management. First, available salmon biomass was a key predictor for not only salmon in the diet, but plants in the diet as well, indicating that salmon availability is the key mediating factor in bear diets across the province. Additionally, bear size helped to predict both plants and salmon in the diet but did not improve models for terrestrial meat. Elevation was included in nearly every predictive model, indicating that British Columbia’s rugged terrain is a potential mediating factor 81 in bear diets. For instance, salmon in the diet was negatively related with elevation for both sexes, indicating distance to spawning streams influences bear diet. Meanwhile, elevation was positively related to plant consumption and negatively related to terrestrial meat consumption for both sexes, indicating that valley bottoms may represent different dietary opportunities relative to upland areas. Finally, the parametric nature of these models strengthened predictions in areas of poor sampling density; for example, they predict that salmon was a key food source for the now threatened or extirpated populations of bears in the highly developed southwest portion of the province, which could help inform future initiatives for population recovery in areas like the North Cascades. 3.4 Future Opportunities There are several additional spatial parameters that could be used to explore interesting questions about grizzly bear diet in future work. First, we did not assess the influence of landscape pattern metrics (LPMs; McGarigal, 2014) on bear diet. LPMs are commonly used in bear ecology (Wang et al. 2010) and have previously elicited important information about grizzly bears’ dietary ecology, such as the favourability of disturbed and edge habitat (Stewart et al. 2013). Similarly, we did not consider forest disturbance, which has an important influence on grizzly bear habitat selection (Stewart et al. 2012) and is a mediator of food availability (Larsen et al. 2019), most notably the availability of key fruiting plants (Braid et al. 2016; Dawe et al. 2017). Including landscape metrics or disturbance histories may reveal additional important influences on grizzly bear diet at the landscape scale in B.C. The results from this study represent an important baseline for bear research in B.C. and present several opportunities for further investigating the influence of diet on grizzly bear ecology. For instance, salmon availability is known to influence bear behaviour, making bears 82 more competitive (Klinka and Reimchen, 2002; Ben-David et al. 2004) and more sensitive to human presence (MacHutchon et al. 1998; Artelle et al. 2016; Shardlow et al. 2022), effects likely driven by the aggregate nature of salmon runs. Thus, it is possible that similar portfolio effects are present in areas with highly productive fruiting or masting plants, though less research has focused on how aggregate fruit patches influence bears than salmon. Additionally, both plants and salmon availability drive bear movement across the landscape (Schindler et al. 2013; Costello et al. 2014; Deacy et al. 2018; Deacy et al. 2019; Belkin et al. 2022; Bowersock et al. 2023). The results of this study could be used in future research to assess how diet influences landscape level patterns in bear behaviour, competition, and movement. Perhaps most importantly to grizzly bear conservation, fine-scale dietary maps could be paired with human activity data to help identify areas where diet-influenced anthropogenic mortality are more likely (e.g., Artelle et al. 2016; Proctor et al. 2023). Coastal and interior populations of grizzly bears have long been known to have differential ecologies that may drive their population dynamics. Diet is an important influence on bear population density (Nielsen et al. 2010; Mowat et al. 2013) and future work should look to assess the interaction between the landscape level dietary patterns identified here and population density. Additionally, grizzly bear movement between coastal and interior populations may be affected by diet, as dispersal and diet are linked in sex-specific ways (Jimbo et al. 2020), and the differences between male and female movement (and the ability to adapt to new environments) can have important ramifications for the genetic connectivity of populations (Proctor et al. 2012; Sawaya et al. 2014). Allopatric niche diversity can be an important source of genetic variation (Maia‐Carvalho et al. 2018) and the results of this study help provide a framework for spatial assessments of how diet influences bear movement, population density, and genetic variation. 83 The results of this study have reinforced the overwhelming importance of plants and anadromous salmon in the diet of British Columbian grizzly bears, but important questions remain about the importance of kokanee salmon and terrestrial meat sources. Both food sources were identified as locally important supplements to grizzly bear diet and may represent ecologically significant influences on bear ecology in certain areas. In boreal and arctic ecosystems, terrestrial meat is the key food source for grizzly bears (Mowat and Heard, 2006, Bojarska and Selva, 2012), and the results of this study found individual diet fractions as high as sixty percent for meat and fifty percent for kokanee. Establishing to what extent these prey species influence bear behaviour, movement, and population dynamics is a major question for bear ecology in North America. Despite the known importance of diet, fundamental questions about diet remain for grizzly bear ecology. In the past, research has often focused on the nutritional ecology of bear diets (e.g., Erlenbach et al. 2014; Costello et al. 2016) and how diet influences bear size and growth (Felicetti et al. 2003a, Hilderbrand et al. 1999; Mowat and Heard, 2006). However, despite the nutritional value of foods like meat, some research suggests that optimal foraging strategies and the ease of access to foods may take precedence over its nutritional value (Wiegand et al. 2008; Bojarska and Selva, 2012). It is possible that the nutritional quality of different foods is only a secondary influence on bears’ dietary selection, and that food availability, or rather the ratio of nutritional value to availability, is the key driver of bears’ diets and physiology. For instance, both the densest bear populations and the largest individual bears occur in coastal areas with readily available salmon (Mowat and Heard, 2006; Mowat et al. 2013), and both population density and bear size seem to decrease from salmon reliant areas to plant reliant areas to terrestrial meat reliant areas, despite nuances in nutritional values among 84 those food groups. Given the links between and the importance of both diet and movement ecology, grizzly bear ecology would benefit from future research into the influence of aggregate versus dispersed distributions, and stationary versus mobile dynamics of key food resources on bear physiology, behaviour, and population dynamics. Finally, this study represents a key resource for the future of grizzly bear research and should be utilized in updating grizzly bear management in British Columbia, most notably the boundaries of the Grizzly Bear Population Units. The importance of diet to bear ecology, combined with the results of this study, show that the current GBPU boundaries may represent a barrier to effective local grizzly bear management. Ensuring scientific knowledge, including new findings, is incorporated into environmental management is key for the long-term health of species and ecosystems (McCleery et al. 2007; Raymond et al. 2010). Meanwhile, grizzly bear management in British Columbia is an issue for which ecological decisions are not always free from political influence (Bellringer, 2017). Using the scientific evidence on diet presented here to augment the boundaries of the current GBPUs such that they best reflect current ecological knowledge is both the logical next step in this research and a pressing issue for the management of bears in the province. In the near future, ecologists, managers, and conservationists should work to ensure that grizzly bear management is based first and foremost on the most up-to-date knowledge about the fundamental ecological factors that drive the species’ biology. Click or tap here to enter text. Click or tap here to enter text. 85 Bibliography Adams, M. S., Service, C. N., Bateman, A., Bourbonnais, M., Artelle, K. A., Nelson, T., Paquet, P. C., Levi, T., & Darimont, C. T. (2017). 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Sampled isotope value (a.) and diet estimates (b.) by adjusted date: because of the cumulative nature of isotope values in bear hair, all bears sampled after September 30 or before June 30 (i.e., outside the period of guard hair growth) have been assigned a date of Sept 30. 125 Figure A 3. Predicted maps of bear diet across British Columbia, with B.C.'s Ministry of Forests’ existing Ecoprovinces overlayed. Note that a non-parametric model for kokanee in female bear diet did not converge, and thus is not included here.
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bourbonnais, Mathieu
Spatial variation in grizzly bear diet across British Columbia
Text