A General Hidden State Random Walk Model for Animal Movement

TL;DR

This paper introduces a versatile hidden state random walk model that captures animal movement influenced by environmental features, using an EM algorithm for fitting, demonstrated on caribou movement data.

Contribution

It presents a novel general model incorporating multiple environmental targets into animal movement analysis, with an EM algorithm for parameter estimation.

Findings

Model successfully captures animal movement behavior.

Application to caribou data demonstrates effectiveness.

Allows inclusion of multiple environmental influences.

Abstract

In this paper, we propose a general hidden state random walk model to describe the movement of an animal that takes into account movement taxis with respect to features of the environment. A circular-linear process models the direction and distance between two consecutive localizations of the animal. A hidden process structure accounts for the animal's change in movement behavior. The originality of the proposed approach is that several environmental targets can be included in the directional model. An EM algorithm is devised to fit this model and an application to the analysis of the movement of caribou in Canada's boreal forest is presented