A General Discrete-time Modeling Framework for Animal Movement Using Multi-State Random Walks.

Brett T. McClintock, Ruth King, Len Thomas, Jason Matthiopoulos, Bernie J. McConnell and Juan M. Morales

University of St. Andrews and Universidad Nacional del Comahue (Argentina)


Recent developments in animal tracking technology have permitted the collection of detailed data on the movement paths of individuals from many species. However, methods for the analysis of these data have not developed at a similar pace. This is largely due to a lack of suitable candidate models, coupled with the technical difficulties of fitting such models to data. These difficulties are typically compounded by observation error in time and space, as well as missing (or intermittent) observations. Sophisticated state-space models of the underlying movement and observation process are therefore required to facilitate reliable inference. To better understand animal movements in heterogeneous landscapes, we propose that complex movement paths can be dissected into general movement strategies among which animals transition as they are affected by changes in their internal and external environment. We develop a suite 1 of mechanistic models based on biased and correlated random walks that include different behavioral states for directed (e.g., migration), exploratory (e.g., dispersal), and area-restricted (e.g., foraging) movements. Using this “tool-box” of nested model components, multi-state movement models may be custom-built for a wide variety of species applications that allow the simultaneous estimation of latent movement behavior states, state transition probabilities, locations of centers of attraction, and strengths of attraction to specific locations. The inclusion of memory or covariate information permits further investigation of specific hypotheses related to factors driving different types of movement behavior. Using reversible jump Markov chain Monte Carlo methods to facilitate Bayesian model selection and multi-model inference, we apply the proposed methodology to grey seal (Halichoerus grypus) movements in the North Sea. We use posterior model probabilities to provide evidence that seals transition among directed, area-restricted, correlated, and exploratory movements associated with haul- out, foraging, and other behaviors.