Behavioural state identification from ultra‐high‐frequency movement data

Abstract

The hidden Markov model (HMM) is a central framework for identifying behavioural state changes from animal movement data. In this context, movement is typically represented as a sequence of observed metrics, such as step length and turning angle distributions, generated by an unobserved behavioural state process. Each hidden state corresponds to a distinct behavioural mode (e.g. foraging, resting), and transitions between states are governed by probabilistic rules. Typically, these models have been applied to data collected at relatively low frequency, for example one location every few minutes or hours, so that turning angle and step length distributions are, to some extent, an artefact of the data‐gathering frequency. However, with the growing availability of high‐frequency biologging data (often greater than 1 Hz), it is now possible to determine the precise places where an animal has turned, enabling behaviourally informed step length and turning angle distributions to be fed into HMMs. In this study, we introduce a fast and accurate method for identifying animal behavioural states from high‐frequency data (e.g. ≥1 Hz) within the HMM framework. We first use an existing algorithm to segment the animal's high‐frequency movement path into steps, where each ‘step’ is defined as the straight‐line trajectory between successive real turning points. We then develop a new HMM model for identifying behavioural states that accounts for steps that are of differing durations. We then extend our technique to allow state‐switching to be driven by environmental effects. To evaluate the accuracy of our method, compared with methods that use lower‐frequency data, we apply it to both simulated trajectories and high‐frequency data from free‐ranging goats (Capra aegagrus hircus). Our technique greatly improves the accuracy of inference compared with using step lengths and turning angles derived from fix‐to‐fix steps in lower‐frequency data. Indeed, we demonstrate that the latter can lead to quite notable inaccuracies, and be very sensitive to sampling frequency, in situations where our method achieves >95% accuracy in state identification.