General animal movement and migration models using multi-state random walks
Speaker: Brett McClintock (CREEM)
Recent developments in animal tracking technology have permitted the collection of detailed movement paths from individuals of many species. Despite this rapidly increasing wealth of information, model development for the analysis of complex movement data has not kept pace with these technological advancements. To better understand complicated animal movements in heterogeneous landscapes, we propose that complex movement paths can be dissected into a few general movement strategies among which animals transition as they are affected by changes in the internal and external environment. We develop a suite of discrete-time individual animal movement models based on biased and correlated random walks that include different behavioural states for migration, exploratory, and resident movements. Models may then be “custom-built” for a wide variety of species applications, thereby allowing the simultaneous estimation of movement behaviour states, state transition probabilities, locations of migration or resident centres of attraction, and the strength of attraction to specific locations. The inclusion of memory or covariate information in the modeling of state transition probabilities permits further investigation of specific factors related to different types of movement. Using Markov chain Monte Carlo methods to facilitate Bayesian inference, we apply the proposed methodology to grey seal movements among haul-out and foraging locations in eastern Scotland.