Multi-Dimensional Integral Fractional Ornstein-Uhlenbeck Process Model for Animal Movement

Oregon Convention Center 

Predicting animal trajectories poses a significant challenge due to the intricate nature of their behaviors, unpredictable environmental elements, individual differences, and the scarcity of precise movement data. Further complexities arise from factors such as migration, hunting, reproduction, and social interactions, making precise trajectory prediction challenging. Various models in the literature attempt to investigate animal telemetry by either modeling the velocity or position, or both concurrently using Gaussian processes. In this work, we consider multi-dimensional trajectories with respect to longitude, latitude and altitude. Our approach involves modeling the velocity of each dimension as a fractional Ornstein-Uhlenbeck (fOU) process, where correlation is induced from an associated multi-dimensional fractional Brownian motion. We propose fast simulation and prediction algorithms, and present the feasibility of maximum likelihood estimation. The applicability of our model for animal movement is presented through simulation studies and by modeling the trajectory of bats in Germany.

Animal tracking

fractional Brownian motion

Gaussian process simulations

telemetry data

trajectory prediction 

Section on Statistics and the Environment