Bayesian Inference for Animal Space Use and Other Movement Metrics
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The analysis of animal movement and resource use has become a standard tool in the study of animal ecology. Telemetry devices have become quite sophisticated in terms of overall size and data collecting capacity. Statistical methods to analyze movement have responded, becoming ever more complex, often relying on state-space modeling. Estimation of movement metrics such as utilization distributions have not followed suit, relying primarily on kernel density estimation. Here we consider a method for making inference about space use that is free of all of the major problems associated with kernel density estimation of utilization distributions such as autocorrelation, irregular time gaps, and error in observed locations. Our proposed method is based on a data augmentation approach that defines use as a summary of the complete path of the animal which is only partially observed. We use a sample from the posterior distribution of the complete path to construct a posterior sample for the metric of interest. Three basic importance sampling based methods for sampling from the posterior distribution of the path are proposed and compared. We demonstrate the augmentation approach by estimating a spatial map of diving intensity for female northern fur seals in the Pribilof Islands, Alaska.
Key WordsAnimal movement Correlated random walk Movement metric Posterior distribution State-space model Utilization distribution
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