The utility of a Bayesian Markov model with Pólya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications
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The use of accelerometers in wildlife tracking provides a fine-scale data source for understanding animal behavior and decision-making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. The Bayesian Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this model to behavior data from six greater white-fronted geese (Anser albifrons frontalis) during spring migration in mid-continent North America and considered likely drivers of behavior, including habitat, weather, and time of day effects. We modeled the transition between flying, feeding, stationary, and walking behavior states using a first-order Bayesian Markov model. We introduced Pólya-Gamma latent variables for automatic sampling of the covariate coefficients from the posterior distribution and we calculated the odds ratios from the posterior samples. The model provided a unifying framework for including both acceleration and Global Positioning System data. We found significant pairwise differences between transitions across habitat types, confirmed diurnal behavior and behavioral changes due to weather. Our model provided straightforward inference of behavioral time allocation across used habitats which is not amenable in activity budget or resource selection frameworks.
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