  Continuous-time models have been developed to capture features of animal
movement across temporal scales. In particular, one popular model is the
continuous-time correlated random walk, in which the velocity of an animal is
formulated as an Ornstein-Uhlenbeck process, to capture the autocorrelation in
the speed and direction of its movement. In telemetry analyses, discrete-time
state-switching models (such as hidden Markov models) have been increasingly
popular to identify behavioural phases from animal tracking data. We propose a
multistate formulation of the continuous-time correlated random walk, with an
underlying Markov process used as a proxy for the animal's behavioural state
process. We present a Markov chain Monte Carlo algorithm to carry out Bayesian
inference for this multistate continuous-time model. Posterior samples of the
hidden state sequence, of the state transition rates, and of the
state-dependent movement parameters can be obtained. We investigate the
performance of the method in a simulation study, and we illustrate its use in a
case study of grey seal (Halichoerus grypus) tracking data. The method we
present makes use of the state-space model formulation of the continuous-time
correlated random walk, and can accommodate irregular sampling frequency and
measurement error. It will facilitate the use of continuous-time models to
estimate movement characteristics and infer behavioural states from animal
telemetry data.
