Identifying latent behavioral states in animal movement with non-parametric Bayesian methods

Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape (or seascape). While there are a variety of methods that make behavioral inference from biotelemetry data, some features of these methods (e.g., analysis of a single data stream, use of parametric distributions) may result in the misclassification of behavioral states. We address some of the limitations of segmentation and state-space models (SSMs) in our non-parametric Bayesian framework, available within the open-source R package bayesmove. This framework can analyze multiple data streams, which may capture complex behaviors more successfully than a single data stream. Additionally, parametric distributions are not used in our framework since they may poorly characterize the underlying true distributions. We tested our Bayesian framework using simulated trajectories and compared model performance against a representative segmentation method (behavioral change point analysis; BCPA) and one type of SSM, a hidden Markov model (HMM). We also illustrated this Bayesian framework using movements of juvenile snail kites (Rostrhamus sociabilis) in Florida, USA. The Bayesian framework estimated breakpoints more accurately than BCPA for tracks of different lengths, albeit at a slower computational speed. Likewise, the Bayesian framework provided more accurate estimates of behavior than the HMM when simulations were generated from atypical distributions. This framework also performed up to three times faster than the HMM when run in a similar fashion. Three behavioral states were estimated from snail kite movements, which were labeled as ‘encamped’, ‘area-restricted search’, and ‘transit’. Changes in these behaviors over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. Our framework estimated behavioral states with comparable or superior accuracy compared to BCPA and HMM when step lengths and turning angles of simulations were generated from atypical distributions. Since empirical data can be complex and do not necessarily conform to parametric distributions, methods (such as our Bayesian framework) that can flexibly classify latent behavioral states will become increasingly important to address questions in movement ecology.