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The study of animal movement is challenging because it is a process modulatedby many factors acting at different spatial and temporal scales. Several modelshave been proposed which differ primarily in the temporal conceptualization,namely continuous and discrete time formulations. Naturally, animal movementoccurs in continuous time but we tend to observe it at fixed time intervals. Toaccount for the temporal mismatch between observations and movement decisions,we used a state-space model where movement decisions (steps and turns) are madein continuous time. The movement process is then observed at regular timeintervals. As the likelihood function of this state-space model turned out tobe complex to calculate yet simulating data is straightforward, we conductinference using a few variations of Approximate Bayesian Computation (ABC). Weexplore the applicability of these methods as a function of the discrepancybetween the temporal scale of the observations and that of the movement processin a simulation study. We demonstrate the application of this model to a realtrajectory of a sheep that was reconstructed in high resolution usinginformation from magnetometer and GPS devices. Our results suggest thataccurate estimates can be obtained when the observations are less than 5 timesthe average time between changes in movement direction. The state-space modelused here allowed us to connect the scales of the observations and movementdecisions in an intuitive and easy to interpret way. Our findings underscorethe idea that the time scale at which animal movement decisions are made needsto be considered when designing data collection protocols, and that sometimeshigh-frequency data may not be necessary to have good estimates of certainmovement processes.

Approximate Bayesian inference for a "steps and turns" continuous-time random walk observed at regular time intervals