Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method

作者：Joshua A. Cullen, Caroline Poli, Robert J. Fletcher, Denis Valle · 发表于：Methods in Ecology and Evolution · 年份：2021 · DOI：10.1111/2041-210x.13745 · 被引用次数：28 · 研究领域：Avian ecology and behavior、Wildlife Ecology and Conservation、Marine animal studies overview

Abstract Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural 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 behavioural states. To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed‐membership method for movement (M4), which is available within the open‐source bayesmove R package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural 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. The Bayesian framework estimated breakpoints more accurately than the other segmentatio...