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Mechanistic modelling of animal movement is often formulated in discrete timedespite problems with scale invariance, such as handling irregularly timedobservations. A natural solution is to formulate in continuous time, yet uptakeof this has been slow. This lack of implementation is often excused by adifficulty in interpretation. Here we aim to bolster usage by developing acontinuous-time model with interpretable parameters, similar to those ofpopular discrete-time models that use turning angles and step lengths. Movementis defined by a joint bearing and speed process, with parameters dependent on acontinuous-time behavioural switching process, creating a flexible class ofmovement models.  Methodology is presented for Markov chain Monte Carlo inference givenirregular observations, involving augmenting observed locations with areconstruction of the underlying movement process. This is applied to wellknown GPS data from elk (\emph{Cervus elaphus}), which have previously beenmodelled in discrete time. We demonstrate the interpretable nature of thecontinuous-time model, finding clear differences in behaviour over time andinsights into short term behaviour that could not have been obtained indiscrete time.

Bayesian inference for multistate `step and turn' animal movement in continuous time