Modeling Animal Trajectories with Discrete Covariates using Stochastic Differential Equations and Neural Networks with Remotely Sensed data

Music City Center 

Recent advances in tracking technology have made animal trajectory data increasingly common, offering new opportunities to address scientific questions in ecology. Statistical models for such data often leverage remotely sensed information on individual locations over time. Among these, mechanistic models such as the Langevin model are widely used, as they characterize how species use space and respond to environmental conditions while incorporating underlying movement dynamics. Notably, the advection term in the Langevin model is typically defined via spatial derivatives of continuous covariates to ensure differentiability. However, many ecological questions involve discrete covariates with spatial discontinuities. In this talk, we explore the use of neural networks to address the challenges posed by such discontinuities, which are problematic for standard mechanistic models. We apply our method to improve understanding of animal behavior and better capture the effects of discrete covariates on movement of an important invasive species.