The effectiveness of Bayesian state-spacemodels for estimating behavioural states frommovement paths

• Main Authors: Hawthorne L. Beyer, Juanm. Morales
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Morales; Alces alces
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.662.8024; http://dennismurray.ca/pdf/Beyer+et+al.+2013+MEE.pdf

1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes fromwhich pat-terns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of twomovement models to quantify how behavioural state movement charac-teristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states frommovement paths. 3. Classification accuracywas contingent upon the degree of separation between the distributions that character-ize the states (e.g. step length and turn angle distributions) and the relative frequency of the behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0%when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was un-correlated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distribu-tions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data. Key-words: classification accuracy, correlated random walk, global positioning system, mechanis-tic movementmodel, telemetry