The Langevin diffusion as a continuous-time model of animal movement and habitat selection

TL;DR

This paper introduces a continuous-time Langevin diffusion model for animal movement that links short-term movement decisions to long-term habitat use, enabling habitat preference inference from tracking data.

Contribution

The paper presents a novel Langevin diffusion-based model that explicitly connects animal movement with habitat selection, allowing analysis of irregular telemetry data.

Findings

Model accurately estimates habitat preference from simulated data.

Method performs well with irregular sampling intervals.

Application to sea lion data demonstrates practical utility.

Abstract

1. The utilisation distribution describes the relative probability of use of a spatial unit by an animal. It is natural to think of it as the long-term consequence of the animal's short-term movement decisions: it is the accumulation of small displacements which, over time, gives rise to global patterns of space use. However, most utilisation distribution models either ignore the underlying movement, assuming the independenceof observed locations, or are based on simplistic Brownian motion movement rules. 2. We introduce a new continuous-time model of animal movement, based on the Langevin diffusion. This stochastic process has an explicit stationary distribution, conceptually analogous to the idea of the utilisation distribution, and thus provides an intuitive framework to integrate movement and space use. We model the stationary (utilisation) distribution with a resource selection function to link the movement to spatial covariates, and allow inference into habitat selection. 3. Standard approximation techniques can be used to derive the pseudo-likelihood of the Langevin diffusion movement model, and to estimate habitat preference and movement parameters from tracking data. We investigate the performance of the method on simulated data, and discuss its sensitivity to the time scale of the sampling. We present an example of its application to tracking data of Stellar sea lions (Eumetopiasjubatus). 4. Due to its continuous-time formulation, this method can be applied to irregular telemetry data. It provides a rigorous framework to estimate long-term habitat selection from correlated movement data.