Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird

Abstract

Movement has important consequences for individual and population-level processes, but methods are only starting to become available for quantifying fine-scale movement paths of smaller animals. New techniques for inferring behavioral states and their relation to social and environmental factors provide a powerful way to test the influence of such factors on individuals. One such technique that has recently gained popularity is the use of hidden Markov models, which link time series of movement variables and the underlying behavioral states of individuals. We used hidden Markov models to evaluate behavioral states and their relation to environmental, seasonal, and social factors in the cooperatively breeding red-cockaded woodpecker ( Picoides borealis ) while accounting for individual heterogeneity with discrete random effects. We identified 2 distinct behavioral states, resting and foraging, which were related to covariates in our models. Using this approach, we concluded that woodpecker step lengths tended to be longest in winter, larger groups of woodpeckers tended to spend less time foraging and more time resting when compared with smaller groups, and woodpeckers foraged more and rested less when in higher-quality habitat. Our results demonstrate the impact that social and environmental factors can have on movement in a social species and, thus, reinforce the importance of including these factors in animal movement studies. The extensions of basic hidden Markov models considered here may prove valuable in forthcoming studies that involve high-resolution tracking to understand behavior of birds and other small animals.