Evaluating the individuality of animal-habitat relationships.

Examining the ways in which animals use habitat and select resources to satisfy their life history requirements has important implications for ecology, evolution, and conservation. The advent of radio-tracking in the mid-20th century greatly expanded the scope of animal-habitat modeling. Thereafter, it became common practice to aggregate telemetry data collected on a number of tagged individuals and fit one model describing resource selection at the population level. This convention, however, runs the risk of masking important individuality in the nature of associations between animals and their environment. Here, we investigated the importance of individual variation in animal-habitat relationships via the study of a highly gregarious species. We modeled elk (Cervus elaphus) location data, collected from Global Positioning System (GPS) collars, using Bayesian discrete choice resource selection function (RSF) models. Using a high-performance computing cluster, we batch-processed these models at the level of each individual elk (n = 88) and evaluated the output with respect to: (a) the composition of parameters in the most supported models, (b) the estimates of the parameters featured in the global models, and (c) spatial maps of the predicted relative probabilities of use. We detected considerable individual variation across all three metrics. For instance, the most supported models varied with respect to parameter composition with a range of seven to 17 and an average of 14.4 parameters per individual elk. The estimates of the parameters featured in the global models also varied greatly across individual elk with little conformity detected across age or sex classes. Finally, spatial mapping illustrated stark differences in the predicted relative probabilities of use across individual elk. Our analysis identifies that animal-habitat relationships, even among the most gregarious of species, can be highly variable. We discuss the implications of our results for ecology and present some guiding principles for the development of RSF models at the individual-animal level.

The many faces of fear: a synthesis of the methodological variation in characterizing predation risk.

Predators affect prey by killing them directly (lethal effects) and by inducing costly antipredator behaviours in living prey (risk effects). Risk effects can strongly influence prey populations and cascade through trophic systems. A prerequisite for assessing risk effects is characterizing the spatiotemporal variation in predation risk. Risk effects research has experienced rapid growth in the last several decades. However, preliminary assessments of the resultant literature suggest that researchers characterize predation risk using a variety of techniques. The implications of this methodological variation for inference and comparability among studies have not been well recognized or formally synthesized. We couple a literature survey with a hierarchical framework, developed from established theory, to quantify the methodological variation in characterizing risk using carnivore-ungulate systems as a case study. Via this process, we documented 244 metrics of risk from 141 studies falling into at least 13 distinct subcategories within three broader categories. Both empirical and theoretical work suggest risk and its effects on prey constitute a complex, multi-dimensional process with expressions varying by spatiotemporal scale. Our survey suggests this multi-scale complexity is reflected in the literature as a whole but often underappreciated in any given study, which complicates comparability among studies and leads to an overemphasis on documenting the presence of risk effects rather than their mechanisms or scale of influence. We suggest risk metrics be placed in a more concrete conceptual framework to clarify inference surrounding risk effects and their cascading effects throughout ecosystems. We recommend studies (i) take a multi-scale approach to characterizing risk; (ii) explicitly consider 'true' predation risk (probability of predation per unit time); and (iii) use risk metrics that facilitate comparison among studies and the evaluation of multiple competing hypotheses. Addressing the pressing questions in risk effects research, including how, to what extent and on what scale they occur, requires leveraging the advantages of the many methods available to characterize risk while minimizing the confusion caused by variability in their application.