Bayesian Estimation Of Heterogeneous Environments From Animal Movement Data

We describe a flexible class of stochastic models that aim to capture key features of realistic patterns of animal movements observed in radio-tracking and global positioning system telemetry studies. In the model, movements are represented as a diffusion-based process evolving differently in heterogeneous regions. In this article, we extend the process of inference for heterogeneous movement models to the case in which boundaries of habitat regions are unknown and need to be estimated. Data augmentation is used in reconstructing the partition of the heterogeneous environment. The augmentation helps to diminish the impact of uncertainty about when and where the animal crosses habitat boundaries, and allows the extraction of additional information from the given observations. The approach to inference is Bayesian, using Markov chain Monte Carlo methods, allowing us to estimate both the parameters of the diffusion processes and the unknown boundaries. The suggested methodology is illustrated on simulated data and applied to real movement data from a radio-tracking experiment on ibex. Some model checking and model choice issues are also discussed.