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The use of accelerometers in wildlife tracking provides a fine-scale datasource for understanding animal behavior and decision-making. Current methodsin movement ecology focus on behavior as a driver of movement mechanisms. OurMarkov model is a flexible and efficient method for inference related toeffects on behavior that considers dependence between current and pastbehaviors. We applied this model to behavior data from six greaterwhite-fronted geese (Anser albifrons frontalis) during spring migration inmid-continent North America and considered likely drivers of behavior,including habitat, weather and time of day effects. We modeled the transitionsbetween flying, feeding, stationary and walking behavior states using afirst-order Bayesian Markov model. We introduced P\'olya-Gamma latent variablesfor automatic sampling of the covariate coefficients from the posteriordistribution and we calculated the odds ratios from the posterior samples. Ourmodel provides a unifying framework for including both acceleration and GlobalPositioning System data. We found significant differences in behavioraltransition rates among habitat types, diurnal behavior and behavioral changesdue to weather. Our model provides straightforward inference of behavioral timeallocation across used habitats, which is not amenable in activity budget orresource selection frameworks.

A Bayesian Markov model with P\'olya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications