Perception and Memory in Animal Movement

Terrestrial landscapes change on spatial and temporal scales that are relevant for the movement of large-bodied vertebrates. Ecological observations and datasets derived from efforts to track the movements of wild animals (e.g., using GPS-satellite collars) have presented new opportunities to use mathematical approaches for the study of stochastic processes. Examples include applications of semi-variograms, which identify multiple movement modes and solve the sampling rate problem for tracking data, and autocorrelated kernel density estimators, which provide robust approaches for delineating animal ranges. Using several empirical datasets about vertebrate migrations, I will outline the critical role that food resources play in determining variation in migration distance among populations and in driving changes in migration distance over time within populations.  Continuous-time, continuous-space stochastic processes, which can be characterized in terms of a population's critical spatial and temporal scales of autocorrelation, provide one useful mathematical framework for the analysis of animal movement trajectories.  This framework lends itself naturally to a variety of extensions that are useful in ecological applications relying on movement tracks, including the delineation of animal home ranges (and shifts in range) and probabilistic path reconstruction.  

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