Death comes for us all: relating movement-integrated habitat selection and social behavior to human-associated and disease-related mortality among gray wolves.

Avoiding death affects biological processes, including behavior. Habitat selection, movement, and sociality are highly flexible behaviors that influence the mortality risks and subsequent fitness of individuals. In the Anthropocene, animals are experiencing increased risks from direct human causes and increased spread of infectious diseases. Using integrated step selection analysis, we tested how the habitat selection, movement, and social behaviors of gray wolves vary in the two months prior to death due to humans (being shot or trapped) or canine distemper virus (CDV). We further tested how those behaviors vary as a prelude to death. We studied populations of wolves that occurred under two different management schemes: a national park managed for conservation and a provincially managed multi-use area. Behaviors that changed prior to death were strongly related to how an animal eventually died. Wolves killed by humans moved slower than wolves that survived and selected to be nearer roads closer in time to their death. Wolves that died due to CDV moved progressively slower as they neared death and reduced their avoidance of wet habitats. All animals, regardless of dying or living, maintained selection to be near packmates across time, which seemingly contributed to disease dynamics in the packs infected with CDV. There were no noticeable differences in behavior between the two management areas. Overall, habitat selection, movement, and sociality interact to put individuals and groups at greater risks, influencing their cause-specific mortality.

Optimal prey switching: Predator foraging costs provide a mechanism for functional responses in multi-prey systems.

Foragers must balance the costs and gains inherent in the pursuit of their next meal. Classical functional response formulations describe consumption rates driven by prey density and are naive to predator foraging costs. Here, we integrated foraging costs into functional responses to add mechanism and precision to foundational ideas. Specifically, using a model system with a single predator and two prey, we express a functional response emerging from variable energy and time costs of each predation phase: searching, attacking, or consuming prey. The utility of our model is explored through a focused example where prey can exert variable influence on predator foraging costs through antipredator traits. Dissimilarity between prey in their foraging costs influence the energy gain rate of the predator through optimal prey switching. We found that a small subset of prey antipredator traits and density conditions generated a stabilizing Type III (sigmoidal) functional response-the pattern often thought to typify a generalist predator switching between prey species. The sigmoid functional response occurred for highly profitable prey only when the costly prey (1) were at a high density and (2) their antipredator traits increased energy or time costs following an encounter. We outline testable predictions regarding foraging costs from our model. We provide guidance on how to apply optimal foraging theory to empirical scenarios where predator foraging costs vary due to prey type, predator type, or environmental conditions. Our framework represents a synergy of foundational and contemporary theory across disciplines, facilitating the discovery of shared principles and context-dependent variation across varied predator-prey systems.

Wolf spatial behavior promotes encounters and kills of abundant prey.

Predators use different spatial tactics to track the prey on the landscape. Three hypotheses describe spatial tactics: prey abundance for prey that are aggregated in space; prey habitat for uniformly distributed prey; and prey catchability for prey that are difficult to catch and kill. The gray wolf (Canis lupus) is a generalist predator that likely employs more than one spatial hunting tactic to match their diverse prey with distinct distributions and behavior that are available. We conducted a study on 17 GPS collared wolves in 6 packs in Riding Mountain National Park, Manitoba, Canada where wolves prey on moose (Alces alces) and elk (Cervus canadensis). We evaluated wolf selection for prey density, habitat selection and catchability on the landscape through within-territory habitat selection analysis. We reveal support for both the prey habitat and prey catchability hypotheses. For moose, their primary prey, wolves employed a mixed habitat and catchability tactic. Wolves used spaces described by the intersection of moose habitat and moose catchability. Wolves selected for the catchability of elk, their secondary prey, but not elk habitat. Counter to our predictions, wolves avoided areas of moose and elk density, likely highlighting the ongoing space race between predator and prey. We illustrate that of the three hypotheses the primary driver was prey catchability, where the interplay of both prey habitat with catchability culminate in predator spatial behaviour in a multiprey system.

Individual differences in habitat selection mediate landscape level predictions of a functional response.

Predicting future space use by animals requires models that consider both habitat availability and individual differences in habitat selection. The functional response in habitat selection posits animals adjust their habitat selection to availability, but population-level responses to availability may differ from individual responses. Generalized functional response (GFR) models account for functional responses by including fixed effect interactions between habitat availability and selection. Population-level resource selection functions instead account for individual selection responses to availability with random effects. We compared predictive performance of both approaches using a functional response in elk (Cervus canadensis) selection for mixed forest in response to road proximity, and avoidance of roads in response to mixed forest availability. We also investigated how performance changed when individuals responded differently to availability from the rest of the population. Individual variation in road avoidance decreased performance of both models (random effects: ß = 0.69, 95% CI 0.47, 0.91; GFR: ß = 0.38, 95% CI 0.05, 0.71). Changes in individual road and forest availability affected performance of neither model, suggesting individual responses to availability different from the functional response mediated performance. We also found that overall, both models performed similarly for predicting mixed forest selection (F1, 58 = 0.14, p = 0.71) and road avoidance (F1, 58 = 0.28, p = 0.60). GFR estimates were slightly better, but its larger number of covariates produced greater variance than the random effects model. Given this bias-variance trade-off, we conclude that neither model performs better for future space use predictions.

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.