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

Abstract

O_LIUnderstanding 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. While there are a variety of methods that make behavioral inferences from biotelemetry data, some features of these methods (e.g., analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioral states. C_LIO_LITo address some of the limitations of existing behavioral state estimation models, we introduce a non-parametric Bayesian framework called the mixed-membership method for movement (M4), which is available within the open-source bayesmove R package. This framework can analyze multiple data streams (e.g., step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviors more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioral change point analysis (BCPA) and segclust2d), one machine learning method (expectation-maximization binary clustering (EMbC)), and one type of state-space model (hidden Markov model (HMM)). We also illustrated this Bayesian framework using movements of juvenile snail kites (Rostrhamus sociabilis) in Florida, USA. C_LIO_LIThe Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behavior than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g., truncated normal, beta, uniform). 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. C_LIO_LIOur non-parametric Bayesian framework estimated behavioral states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology. C_LI