A Multi-State Conditional Logistic Regression Model for the Analysis of Animal Movement

TL;DR

This paper introduces a multi-state conditional logistic regression model for animal movement analysis, enhancing the traditional Step Selection Function to distinguish between different behavioral states and interpret habitat preferences.

Contribution

It develops a novel multi-state SSF model that incorporates hidden states and proves its equivalence to a random walk, enabling better understanding of animal movement behaviors.

Findings

Successfully applied to GPS-collared bison in Canada

Differentiates foraging and traveling behaviors

Reveals habitat-driven movement patterns

Abstract

A multi-state version of an animal movement analysis method based on conditional logistic regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the equivalence between the new model and a random walk model on the plane. This equivalence allows one to use both pure movement and local discrete choice behaviors in identifying the model's hidden states. The new method is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada. The multi-state SSF successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.