Robust state-space modeling of animal movement data

Remotely sensed tracking data collected on animal movement is vastly underutilized due to a lack of statistical tools for appropriate analysis. Features of such data that make analysis particularly challenging include the presence of estimation errors that are non-Gaussian and vary in time, observations that occur irregularly in time, and complexity in the underlying behavioral processes. We develop a state-space framework that simultaneously deals with these features and demonstrate our method by analyzing three seal pathway data sets. We show how known information regarding error distributions can be used to improve inference of the underlying process(es) and demonstrate that our framework provides a powerful and flexible method for fitting different behavioral models to tracking data.