Aurélien Nicosia

This paper introduces a framework using Hidden Markov Models with Step Selection Functions to infer behavioural states from movement and environmental data, showing improved accuracy in identifying ecologically meaningful states.

The paper introduces sequential predictive e-diagnostics for evaluating hidden Markov models of animal movement, showing their calibration and sensitivity to model misspecifications in simulations and a case study.