An analysis of pilot whale vocalization activity using hidden Markov models

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...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America
Main Authors: Popov, Valentin Mina, Langrock, Roland, De Ruiter, Stacy Lynn, Visser, Fleur
Other Authors: Office of Naval Research, University of St Andrews. Statistics, University of St Andrews. Centre for Research into Ecological & Environmental Modelling
Format: Article in Journal/Newspaper
Language:English
Published: 2017
Subjects:
GC Oceanography
QH301 Biology
QA Mathematics
QL Zoology
NDAS
GC
QH301
QA
QL
Lofoten
Norway
Vestfjord
Online Access:https://hdl.handle.net/10023/11194
https://doi.org/10.1121/1.4973624
Description
Summary: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. Peer reviewed

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