Continuous-time discrete-space models for animal movement

TL;DR

This paper introduces a computationally efficient continuous-time discrete-space model for animal movement that integrates location and directional drivers, enabling behavioral change analysis with standard statistical tools.

Contribution

The paper presents a novel CTDS model that simplifies animal movement analysis using GLM methods and incorporates behavioral variability with a varying-coefficient framework.

Findings

Applied to mountain lion data in Colorado

Demonstrated effective variable selection with group lasso

Enabled joint modeling of location and directional influences

Abstract

The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.