Estimating encounter-habitat relationships with scale-integrated resource selection functions.

Encounters between animals occur when animals are close in space and time. Encounters are important in many ecological processes including sociality, predation and disease transmission. Despite this, there is little theory regarding the spatial distribution of encounters and no formal framework to relate environmental characteristics to encounters. The probability of encounter could be estimated with resource selection functions (RSFs) by comparing locations where encounters occurred to available locations where they may have occurred, but this estimate is complicated by the hierarchical nature of habitat selection. We developed a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, we related encounter probabilities to landscape resources. The RSFs describe habitat associations at four scales, home ranges within the study area, areas of overlap within home ranges, locations within areas of overlap, and encounters compared to other locations, which can be combined into a single scale-integrated RSF. We apply this method to intraspecific encounter data from two species: white-tailed deer (Odocoileus virginianus) and elk (Cervus elaphus) and interspecific encounter data from a two-species system of caribou (Rangifer tarandus) and coyote (Canis latrans). Our method produced scale-integrated RSFs that represented the relative probability of encounter. The predicted spatial distribution of encounters obtained based on this scale-integrated approach produced distributions that more accurately predicted novel encounters than a naïve approach or any individual scale alone. Our results highlight the importance of accounting for the conditional nature of habitat selection in estimating the habitat associations of animal encounters as opposed to 'naïve' comparisons of encounter locations with general availability. This method has direct relevance for testing hypotheses about the relationship between habitat and social or predator-prey behaviour and generating spatial predictions of encounters. Such spatial predictions may be vital for understanding the distribution of encounters driving disease transmission, predation rates and other population and community-level processes.

Density-dependent changes in elk resource selection over successional time scales following forest disturbance.

There is an increasing need to understand how animals respond to modifications of their habitat following landscape-scale disturbances such as wildfire or timber harvest. Such disturbances can promote increased use by herbivores due to changes in plant community structure that improve forage conditions, but can also cause avoidance if other habitat functions provided by cover are substantially reduced or eliminated. Quantifying the total effects of these disturbances, however, is challenging because they may not fully be apparent unless observed at successional timescales. Further, the effects of disturbances that improve habitat quality may be density dependent, such that the benefits are (1) less valuable to high-density populations because the per-capita benefits are reduced when shared among more users or, alternatively, (2) more valuable to animals living in high densities because resources may be more depleted from the greater intraspecific competition. We used 30 years of telemetry data on elk occurring at two distinct population densities to quantify changes in space use at diel, monthly, and successional timescales following timber harvest. Elk selected logged areas at night only, with selection strongest during midsummer, and peak selection occurring 14 years post harvest, but persisting for 26-33 years. This pattern of increased selection at night following a reduction in overhead canopy cover is consistent with elk exploiting improved nutritional conditions for foraging. The magnitude of selection for logged areas was 73% higher for elk at low population density, consistent with predictions from the ideal free distribution. Yet elk avoided these same areas during daytime for up to 28 years post logging and instead selected untreated forest, suggesting a role for cover to meet other life history requirements. Our results demonstrate that while landscape-scale disturbances can lead to increased selection by large herbivores and suggest that the improvement in foraging conditions can persist over short-term successional timescales, the magnitude of the benefits may not be equal across population densities. Further, the enduring avoidance of logging treatments during the daytime indicates a need for structurally intact forests and suggests that a mosaic of forest patches of varying successional stages and structural completeness is likely to be the most beneficial to large herbivores.

Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.

The influence of vegetation structure on sleeping site selection by chimpanzees (Pan troglodytes troglodytes).

Sleep is an important aspect of great ape life; these animals build sleeping platforms every night. In a community of chimpanzees, each subgroup selects a sleeping site where each individual builds a sleeping platform, mostly on a tree. Previous studies have measured the heights of sleeping platforms and sleeping trees to test the predation avoidance and thermoregulation hypotheses of sleeping site selection. However, it remains unclear how components of vegetation structure (vertical and horizontal) together determine the selection of sleeping sites by chimpanzees. Using botanical inventories around sleeping sites in a tropical rainforest of Cameroon, we found that chimpanzees preferentially sleep in trees measuring 40-50 cm in diameter. Regarding height, on average, sleeping trees measured 26 m and sleeping platforms were built at 16 m. To build sleeping platforms, chimpanzees preferred four tree species, which represent less than 3% of tree species in the study area. We demonstrate that the variation in abundance of tree species and the vertical and horizontal structure of the vegetation drive chimpanzee sleeping site selection. It was previously thought that preference for vegetation types was the driver of sleeping site selection in chimpanzees. However, results from this study indicate that the importance of vegetation types in sleeping site selection depends on their botanical characteristics including the variation in tree size, the abundance of all trees, the abundance of sleeping trees, and the occurrence of preferred sleeping tree species, which predict sleeping site selection. The height and diameter of trees are considered by chimpanzees when selecting a particular tree for sleeping and when selecting a site with a specific vertical structure. In addition to tree height, the abundance of smaller neighboring trees may also play a role in the chimpanzee antipredation strategy. Our results demonstrate that chimpanzees consider several vegetation parameters to establish sleeping sites.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Accounting for animal movement improves vaccination strategies against wildlife disease in heterogeneous landscapes.

Oral baiting is used to deliver vaccines to wildlife to prevent, control, and eliminate infectious diseases. A central challenge is how to spatially distribute baits to maximize encounters by target animal populations, particularly in urban and suburban areas where wildlife such as raccoons (Procyon lotor) are abundant and baits are delivered along roads. Methods from movement ecology that quantify movement and habitat selection could help to optimize baiting strategies by more effectively targeting wildlife populations across space. We developed a spatially explicit, individual-based model of raccoon movement and oral rabies vaccine seroconversion to examine whether and when baiting strategies that match raccoon movement patterns perform better than currently used baiting strategies in an oral rabies vaccination zone in greater Burlington, Vermont, USA. Habitat selection patterns estimated from locally radio-collared raccoons were used to parameterize movement simulations. We then used our simulations to estimate raccoon population rabies seroprevalence under currently used baiting strategies (actual baiting) relative to habitat selection-based baiting strategies (habitat baiting). We conducted simulations on the Burlington landscape and artificial landscapes that varied in heterogeneity relative to Burlington in the proportion and patch size of preferred habitats. We found that the benefits of habitat baiting strongly depended on the magnitude and variability of raccoon habitat selection and the degree of landscape heterogeneity within the baiting area. Habitat baiting improved seroprevalence over actual baiting for raccoons characterized as habitat specialists but not for raccoons that displayed weak habitat selection similar to radiocollared individuals, except when baits were delivered off roads where preferred habitat coverage and complexity was more pronounced. In contrast, in artificial landscapes with either more strongly juxtaposed favored habitats and/or higher proportions of favored habitats, habitat baiting performed better than actual baiting, even when raccoons displayed weak habitat preferences and where baiting was constrained to roads. Our results suggest that habitat selection-based baiting could increase raccoon population seroprevalence in urban-suburban areas, where practical, given the heterogeneity and availability of preferred habitat types in those areas. Our novel simulation approach provides a flexible framework to test alternative baiting strategies in multiclass landscapes to optimize bait-distribution strategies.

Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem.

Wildlife migrations provide important ecosystem services, but they are declining. Within the Greater Yellowstone Ecosystem (GYE), some elk Cervus canadensis herds are losing migratory tendencies, which may increase spatiotemporal overlap between elk and livestock (domestic bison Bison bison and cattle Bos taurus), potentially exacerbating pathogen transmission risk. We combined disease, movement, demographic and environmental data from eight elk herds in the GYE to examine the differential risk of brucellosis transmission (through aborted foetuses) from migrant and resident elk to livestock. For both migrants and residents, we found that transmission risk from elk to livestock occurred almost exclusively on private ranchlands as opposed to state or federal grazing allotments. Weather variability affected the estimated distribution of spillover risk from migrant elk to livestock, with a 7%-12% increase in migrant abortions on private ranchlands during years with heavier snowfall. In contrast, weather variability did not affect spillover risk from resident elk. Migrant elk were responsible for the majority (68%) of disease spillover risk to livestock because they occurred in greater numbers than resident elk. On a per-capita basis, however, our analyses suggested that resident elk disproportionately contributed to spillover risk. In five of seven herds, we estimated that the per-capita spillover risk was greater from residents than from migrants. Averaged across herds, an individual resident elk was 23% more likely than an individual migrant elk to abort on private ranchlands. Our results demonstrate links between migration behaviour, spillover risk and environmental variability, and highlight the utility of integrating models of pathogen transmission and host movement to generate new insights about the role of migration in disease spillover risk. Furthermore, they add to the accumulating body of evidence across taxa that suggests that migrants and residents should be considered separately during investigations of wildlife disease ecology. Finally, our findings have applied implications for elk and brucellosis in the GYE. They suggest that managers should prioritize actions that maintain spatial separation of elk and livestock on private ranchlands during years when snowpack persists into the risk period.

A 'How to' guide for interpreting parameters in habitat-selection analyses.

Habitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modelling of both movement and habitat-selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat-selection analyses. Furthermore, we provide a 'how to' guide illustrating the steps required to implement integrated step-selection analyses using the amt package By providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users.

Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology.

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host-pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

Sex-Specific Elk Resource Selection during the Anthrax Risk Period.

Anthrax, caused by the spore-forming bacterium Bacillus anthracis, is a zoonosis affecting animals and humans globally. In the United States, anthrax outbreaks occur in wildlife and livestock, with frequent outbreaks in native and exotic wildlife species in Texas, livestock outbreaks in the Dakotas, and sporadic mixed outbreaks in Montana. Understanding where pathogen and host habitat selection overlap is essential for anthrax management. Resource selection and habitat use of ungulates may be sex-specific and lead to differential anthrax exposure risks across the landscape for males and females. We evaluated female elk (Cervus canadensis) resource selection in the same study areas as male elk in a previous anthrax risk study to identify risk of anthrax transmission to females and compare transmission risk between females and males. We developed a generalized linear mixed-effect model to estimate resource selection for female elk in southwest Montana during the June to August anthrax transmission risk period. We then predicted habitat selection of female and male elk across the study area and compared selection with the distribution of anthrax risk to identify spatial distributions of potential anthrax exposure for the male and female elk. Female and male elk selected different resources during the anthrax risk period, which resulted in different anthrax exposure areas for females and males. The sex-specific resource selection and habitat use could infer different areas of risk for anthrax transmission, which can improve anthrax and wildlife management and have important public health and economic implications.

Personality differences in the selection of dynamic refugia have demographic consequences for a winter-adapted bird.

For overwintering species, individuals' ability to find refugia from inclement weather and predators probably confers strong fitness benefits. How animals use their environment can be mediated by their personality (e.g. risk-taking), but does personality mediate how overwintering species select refugia? Snow cover is a dynamic winter characteristic that can influence crypsis or provide below-the-snow refugia. We explored how wintering ruffed grouse (Bonasa umbellus) selected snow roosting sites, a behaviour that reduces stress and cold exposure. We linked selection for approximately 700 roosts with survival of 42 grouse, and showed that grouse generally selected deeper snow and warmer areas. Grouse found in shallow snow were less likely to survive winter. However, individuals that selected deep snow improved their survival, suggesting that demographic consequences of selecting winter refugia are mediated by differences in personality. Our study provides a crucial, and seldom addressed, link between personality in resource selection and resulting demographic consequences.

Inference in MCMC step selection models.

Habitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady-state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady-state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady-state distributions in movement ecology, leading to a step selection model with an explicit steady-state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection-free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from a zebra.

Habitat predicts local prevalence of migratory behaviour in an alpine ungulate.

The resource hierarchy hypothesis predicts that the most important factors limiting a species' distribution act at the coarsest spatial scales. However, resource selection behaviour affords mobile organisms the opportunity to adopt a range of tactics for navigating spatial trade-offs between competing biotic and abiotic constraints. Throughout the animal kingdom, partial migration (where some individuals migrate, and others remain resident year round) offers a pervasive example of such behavioural polymorphism. Identifying the differences between these behaviours is therefore central to understanding the conditions (habitat) needed to sustain migrant and resident populations. Here we test an extension of the resource hierarchy hypothesis. We hypothesized that rather than responding to a single limiting factor, migration and residency represent contrasting scale-specific approaches to managing trade-offs between forage, predation risk and severe winter conditions. Furthermore, we predicted that the distribution of habitat selected by migrants and residents is predictive of the local prevalence of migratory behaviour. To test these hypotheses, we quantified migratory status- (resident/migrant) and season-specific (winter/summer) differences in resource selection by eight populations of federally endangered Sierra Nevada bighorn sheep Ovis canadensis sierrae across three spatial scales: population range, individual range and within individual range. We then integrated these spatial predictions to produce separate spatial predictions of migrant and resident winter habitat. As predicted, model selection provided strong evidence for the importance of status-specific differences in resource selection. Residents showed stronger coarse-scale selection for terrain associated with predator avoidance and stronger fine-scale selection for greenness, while in migrants this pattern was reversed. Availability of migrant habitat predicted the local prevalence of migration (top model pseudo R2 of .87). Our ability to respond to global declines of migratory species depends on improving our understanding of the conditions required to maintain migratory behaviour. Through explicitly contrasting migrant and resident behaviour, our results illustrate seasonal differences in migrant and resident habitat and how these two behaviours represent responses to different limiting conditions. Our analyses provides a novel empirical basis for assessing the local prevalence of migratory behaviour across large landscapes.

Leveraging multidimensional heterogeneity in resource selection to define movement tactics of animals.

Increasing interest in the complexity, variation and drivers of movement-related behaviours promise new insight into fundamental components of ecology. Resolving the multidimensionality of spatially explicit behaviour remains a challenge for investigating tactics and their relation to niche construction, but high-resolution movement data are providing unprecedented understanding of the diversity of spatially explicit behaviours. We introduce a framework for investigating individual variation in movement-defined resource selection that integrates the behavioural and ecological niche concepts. We apply it to long-term tracking data of 115 African elephants (Loxodonta africana), illustrating how a behavioural hypervolume can be defined based on differences between individuals and their ecological settings, and applied to explore population heterogeneity. While normative movement behaviour is frequently used to characterise population behaviour, we demonstrate the value of leveraging heterogeneity in the behaviour to gain greater insight into population structure and the mechanisms driving space-use tactics.

Linking resource selection and step selection models for habitat preferences in animals.

The two dominant approaches for the analysis of species-habitat associations in animals have been shown to reach divergent conclusions. Models fitted from the viewpoint of an individual (step selection functions), once scaled up, do not agree with models fitted from a population viewpoint (resource selection functions [RSFs]). We explain this fundamental incompatibility, and propose a solution by introducing to the animal movement field a novel use for the well-known family of Markov chain Monte Carlo (MCMC) algorithms. By design, the step selection rules of MCMC lead to a steady-state distribution that coincides with a given underlying function: the target distribution. We therefore propose an analogy between the movements of an animal and the movements of an MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilization distribution. We introduce a rejection-free MCMC algorithm, the local Gibbs sampler, that better resembles real animal movement, and discuss the wide range of biological assumptions that it can accommodate. We illustrate our method with simulations on a known utilization distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler give rise to the correct RSF. Using simulated data, we demonstrate how this framework can be used to estimate resource selection and movement parameters.

Accounting for phenology in the analysis of animal movement.

The analysis of animal tracking data provides important scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (eg, dens or kill sites), or to higher dimensional subspaces (eg, rivers or lakes). Features may be relatively static in time (eg, coastlines or home-range centers), or may be dynamic (eg, sea ice extent or areas of high-quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the year mean that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two-stage procedure for approximate Bayesian inference that allows us to analyze over 300 000 observed locations of 186 polar bears from 2012 to 2016. We use our model to estimate a spatial boundary of interest to wildlife managers that separates two subpopulations of polar bears from the Beaufort and Chukchi seas.

Functional responses in habitat selection: clarifying hypotheses and interpretations.

A fundamental challenge in habitat ecology and management is understanding the mechanisms generating animal distributions. Studies of habitat selection provide a lens into such mechanisms, but are often limited by unrealistic assumptions. For example, most studies assume that habitat selection is constant with respect to the availability of resources, such that habitat use remains proportional to availability. To the contrary, a growing body of work has shown the fallacy of this assumption, indicating that animals modify their behavior depending on the context at broader scales. This has been termed a functional response in habitat selection. Furthermore, a diversity of methods is employed to model functional responses in habitat selection, with little attention to how methodology might affect scientific and conservation conclusions. Here, we first review the conceptual and statistical foundations of methods currently used to model functional responses and clarify the ecological tests evaluated within each approach. We then use a combination of simulated and empirical data sets to evaluate the similarities and differences among approaches. Importantly, we identified multiple statistical issues with the most widely applied approaches to understand functional responses, including: (1) a complex and important role of random- or individual-level intercepts in adjusting individual-level regression coefficients as resource availability changes and (2) a sensitivity of results to poorly informed individual-level coefficients estimated for animals with low availability of a given resource. Consequently, we provide guidance on applying approaches that are insensitive to these issues with the goal of advancing our understanding of animal habitat ecology and management. Finally, we characterize the management implications of assuming similarity between the current approaches to model functional responses with two empirical data sets of federally threatened species: Canada lynx (Lynx canadensis) in the United States and woodland caribou (Rangifer tarandus caribou) in Canada. Collectively, our assessment helps clarify the similarities and differences among current approaches and, therefore, assists the integration of functional responses into the mainstream of habitat ecology and management.

Abiotic conditions mediate intraguild interactions between mammalian carnivores.

Intraguild (IG) interactions are common among mammalian carnivores, can include intraguild predation (IGP) and interspecific killing (IK), and are often asymmetrical, where a larger more dominant species (IGpredator ) kills a smaller one (IGprey ). According to ecological theory, the potential for an IGpredator and IGprey to coexist depends on whether the direct consumptive benefits for the IGpredator are substantial (IGP) or insignificant (IK), the extent to which the IGprey is the superior exploitative competitor on shared prey resources, and overall ecosystem productivity. We used resource selection models and spatially explicit age and harvest data for two closely related mesopredators that engage in IG interactions, American martens (Martes americana; IGprey ) and fishers (Pekania pennanti; IGpredator ), to identify drivers of distributions, delineate areas of sympatry and allopatry, and explore the role of an apex predator (coyote; Canis latrans) on these interactions. Model selection revealed that fisher use of this landscape was strongly influenced by late winter abiotic conditions, but other bottom-up (forest composition) and top-down (coyote abundance) factors also influenced their distribution. Overall, fisher probability of use was higher where late winter temperatures were warmer, snowpack was deeper, and measures of productivity were greater. Martens were constrained to areas of the landscape where the probability of fisher use, coyote abundance, and productivity were low and selected for forest conditions that presumably maximized prey availability. Marten age data indicated an increased proportion of juveniles outside of the predicted area of sympatry, suggesting that few animals survived >1.5 years in this area that supported higher densities of fishers and coyotes. Consistent with asymmetrical IG interaction theory, the IGpredator (fishers and, to a lesser degree, coyotes) competitively excluded the IGprey (martens) from more productive, milder temperature habitats, whereas IGpredators and IGprey coexisted in low productivity environments, where a combination of abiotic and biotic conditions enabled the IGprey to be the superior exploitative competitor.

Projecting the future of an alpine ungulate under climate change scenarios.

Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key "early warning signs" about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37-year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate-driven effects influence mountain goat populations, we developed an age-structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70-year time window (2015-2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., "thermoneutral") summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%-86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate-linked bottom-up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence.

Niche similarities among introduced and native mountain ungulates.

The niche concept provides a strong foundation for theoretical and applied research among a broad range of disciplines. When two ecologically similar species are sympatric, theory predicts they will occupy distinct ecological niches to reduce competition. Capitalizing on the increasing availability of spatial data, we built from single species habitat suitability models to a multispecies evaluation of the niche partitioning hypothesis with sympatric mountain ungulates: native bighorn sheep (BHS; Ovis canadensis) and introduced mountain goats (MTG; Oreamnos americanus) in the northeast Greater Yellowstone Area. We characterized seasonal niches using two-stage resource selection functions with a used-available design and descriptive summaries of the niche attributes associated with used GPS locations. We evaluated seasonal similarity in niche space according to confidence interval overlap of model coefficients and similarity in geographic space by comparing model predicted values with Schoener's D metric. Our sample contained 37,962 summer locations from 53 individuals (BHS = 31, MTG = 22), and 79,984 winter locations from 57 individuals (BHS = 35, MTG = 22). Slope was the most influential niche component for both species and seasons, and showed the strongest evidence of niche partitioning. Bighorn sheep occurred on steeper slopes than mountain goats in summer and mountain goats occurred on steeper slopes in winter. The pattern of differential selection among species was less prevalent for the remaining covariates, indicating similarity in niche space. Model predictions in geographic space showed broad seasonal similarity (summer D = 0.88, winter D = 0.87), as did niche characterizations from used GPS locations. The striking similarities in seasonal niches suggest that introduced mountain goats will continue to increase their spatial overlap with native bighorn. Our results suggest that reducing densities of mountain goats in hunted areas where they are sympatric with bighorn sheep and impeding their expansion may reduce the possibility of competition and disease transfer. Additional studies that specifically investigate partitioning at finer scales and along dietary or temporal niche axes will help to inform an adaptive management approach.