An analysis of pilot whale vocalization activity using hidden Markov models
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Vocalizations of cetaceans form a key component of their social interactions. Such vocalization activity is driven by the behavioral states of the whales, which are not directly observable, so that latent-state models are natural candidates for modeling empirical data on vocalizations. In this paper, we use hidden Markov models to analyze calling activity of long-finned pilot whales (Globicephala melas) recorded over three years in the Vestfjord basin off Lofoten, Norway. Baseline models are used to motivate the use of three states, while more complex models are fit to study the influence of covariates on the state-switching dynamics. Our analysis demonstrates the potential usefulness of hidden Markov models in concisely yet accurately describing the stochastic patterns found in animal communication data, thereby providing a framework for drawing meaningful biological inference.
Popov , V M , Langrock , R , De Ruiter , S L & Visser , F 2017 , ' An analysis of pilot whale vocalization activity using hidden Markov models ' , Journal of the Acoustical Society of America , vol. 141 , no. 1 , pp. 159-171 . https://doi.org/10.1121/1.4973624
Journal of the Acoustical Society of America
© 2017, Acoustical Society of America. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at asa.scitation.org / https://doi.org/10.1121/1.4973624
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