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One of the central interests of animal movement ecology is relating movementcharacteristics to behavioural characteristics. The traditional discrete-timestatistical tool for inferring unobserved behaviours from movement data is thehidden Markov model (HMM). While the HMM is an important and powerful tool,sometimes it is not flexible enough to appropriately fit the data. Data formarine animals often exhibit conditional autocorrelation, self-dependence ofthe step length process which cannot be explained solely by the behaviouralstate, which violates one of the main assumptions of the HMM. Using a grey sealtrack as an example, along with multiple simulation scenarios, we motivate anddevelop the conditionally autoregressive hidden Markov model (CarHMM), which isa generalization of the HMM designed specifically to handle conditionalautocorrelation.  In addition to introducing and examining the new CarHMM, we provideguidelines for all stages of an analysis using either an HMM or CarHMM. Theseinclude guidelines for pre-processing location data to obtain deflection anglesand step lengths, model selection, and model checking. In addition to thesepractical guidelines, we link estimated model parameters to biologicallymeaningful quantities such as activity budget and residency time. We alsoprovide interpretations of traditional "foraging" and "transiting" behavioursin the context of the new CarHMM parameters.

journal of agricultural, biological, and environmental statistics

The conditionally autoregressive hidden Markov model (CarHMM): Inferring  behavioural states from animal tracking data exhibiting conditional  autocorrelation