  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.
