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Abstract:
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Advances in animal telemetry data collection techniques have served as a catalyst for the creation of statistical methodology for analyzing animal movement data. Such data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal-environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying quality. While many contemporary statistical approaches for inferring movement behavior are specified in discrete time, we develop a flexible continuous-time stochastic differential equation framework that is amenable to reduced-rank second-order covariance parameterizations. We demonstrate how the associated first-order basis functions can be constructed to mimic behavioral characteristics in realistic Lagrangian movement processes. Our approach allows for Bayesian inference for multiple individual trajectories simultaneously and is feasible for large telemetry data sets.
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