A CONTINUOUS-TIME SEMI-MARKOV MODEL FOR ANIMAL MOVEMENT IN A DYNAMIC ENVIRONMENT

Abstract

We consider an extension to discrete-space, continuous-time models for animal movement that have previously been presented in the literature. The extension from a continuous-time Markov formulation to a continuous-time semi-Markov formulation allows for the inclusion of temporally dynamic habitat conditions as well as temporally changing movement responses by animals to that environment. We show that, with only a little additional con-sideration, the Poisson likelihood calculation for the Markov version can still be used within the multiple imputation framework commonly employed for analysis of telemetry data. In addition, we consider a Bayesian model selection methodology within the imputation framework. The model selection method uses a Laplace approximation to the posterior model probability to provide a computationally feasible approach. The full methodology is then used to analyze movements of 15 weaned northern fur seal (Callorhinus ursi-nus) pups with respect to surface winds, geostrophic currents and sea surface temperature. The highest posterior model probabilities belonged to those models containing only winds and current; SST was not a significant factor for modeling their movement.