Markov modeling for a satellite tag data record of whale diving behavior
Joshua Hewitt, Nicola J. Quick, Alan E. Gelfand, Robert S. Schick

TL;DR
This paper develops a hierarchical Bayesian hidden Markov model to analyze satellite telemetry data of whale diving behavior, revealing behavioral changes in response to sonar exposure, including shortened intervals between dives and avoidance of deep dives.
Contribution
It introduces a novel HMM approach with movement types and covariates for analyzing discretized dive data, improving behavioral response detection in marine mammals.
Findings
Whales shorten intervals between deep dives after sonar exposure.
Whales tend to avoid deep dives following sound exposure.
Model effectively captures baseline and altered diving behaviors.
Abstract
Cuvier's beaked whales (Ziphius cavirostris) are the deepest diving marine mammal, consistently diving to depths exceeding 1,000m for durations longer than an hour, making them difficult animals to study. They are important to study because they are sensitive to disturbances from naval sonar. Satellite-linked telemetry devices provide up to 14-day long records of dive behavior. However, the time series of depths is discretized to coarse bins due to bandwidth limitations. We analyze telemetry data from beaked whales that were exposed to moderate levels of sonar within controlled exposure experiments (CEEs) to study behavioral responses to sound exposure. We model the data as a hidden Markov model (HMM) over the time series of discrete depth bins, introducing partially observed movement types and recent diving activity covariates to model marginal non-stationarity. Movement types provide…
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Taxonomy
TopicsMarine animal studies overview · Marine and fisheries research · Marine Bivalve and Aquaculture Studies
