A multi-state conditional logistic regression model for the analysis of animal movement
Aurélien Nicosia, Thierry Duchesne, Louis-Paul Rivest, Daniel Fortin
The Annals of Applied Statistics · 2017 · Volume 11 · Issue 3
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 relationship between the new model, called HMM-SSF, and a random walk model on the plane. This relationship allows one to use both movement characteristics and local discrete choice behaviors when identifying the model’s hidden states. The new HMM-SSF is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada, where it 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.
Keywords
animal movement, biased correlated random walk, conditional logistic regression, GPS, Hidden Markov model, Step Selection Function
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