A multistateLangevin diffusion for inferring behavior‐specific habitat selection and utilization distributions

Abstract

The identification of important habitat and the behavior(s) associated with it is critical to conservation and place‐based management decisions. Behavior also links life‐history requirements and habitat use, which are key to understanding why animals use certain habitats. Animal population studies often use tracking data to quantify space use and habitat selection, but they typically either ignore movement behavior (e.g., foraging, migrating, nesting) or adopt a two‐stage approach that can induce bias and fail to propagate uncertainty. We develop a habitat‐driven Langevin diffusion for animals that exhibit distinct movement behavior states, thereby providing a novel single‐stage statistical method for inferring behavior‐specific habitat selection and utilization distributions in continuous time. Practitioners can customize, fit, assess, and simulate our integrated model using the provided R package. Simulation experiments demonstrated that the model worked well under a range of sampling scenarios as long as observations were of sufficient temporal resolution. Our simulations also demonstrated the importance of accounting for different behaviors and the misleading inferences that can result when these are ignored. We provide case studies using plains zebra ( Equus quagga) and Steller sea lion ( Eumetopias jubatus) telemetry data. In the zebra example, our model identified distinct “encamped” and “exploratory” states, where the encamped state was characterized by strong selection for grassland and avoidance of other vegetation types, which may represent selection for foraging resources. In the sea lion example, our model identified distinct movement behavior modes typically associated with this marine central‐place forager and, unlike previous analyses, found foraging‐type movements to be associated with steeper offshore slopes characteristic of the continental shelf, submarine canyons, and seamounts that are believed to enhance prey concentrations. This is the first single‐stage approach for inferring behavior‐specific habitat selection and utilization distributions from tracking data that can be readily implemented with user‐friendly software. As certain behaviors are often more relevant to specific conservation or management objectives, practitioners can use our model to help inform the identification and prioritization of important habitats. Moreover, by linking individual‐level movement behaviors to population‐level spatial processes, the multistate Langevin diffusion can advance inferences at the intersection of population, movement, and landscape ecology.