A new family of Gaussian processes for modeling animal movement: application to bat telemetry data
Modeling animal movement is essential for addressing various ecological and biological questions. However, developing an effective predictive model for animal movement is a challenging task. In this paper, we introduce a new family of Gaussian processes, derived from the limiting fluctuations of the rescaled occupation-time process of certain branching particle systems, and study its applicability to real animal movement data. We examine two subfamilies and show that these processes exhibit long-range dependence and covariance functions with logarithmic asymptotic growth. For the exponential subfamily used in the applied analysis, the process is also non-stationary and not intrinsically stationary on compact time intervals. These properties are relevant when dealing with animal trajectories that exhibit strong memory. Finally, we illustrate the practical applicability of the proposed model by analyzing bat movement data.