Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales
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Diving behaviour of short-finned pilot whales is often described by two states; deep foraging and shallow, non-foraging dives. However, this simple classification system ignores much of the variation that occurs during subsurface periods. We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the transitions between states in short-finned pilot whales. We used three parameters (number of buzzes, maximum dive depth and duration) measured in 259 dives by digital acoustic recording tags (DTAGs) deployed on 20 individual whales off Cape Hatteras, North Carolina, USA. The HMM identified a four-state model as the best descriptor of diving behaviour. The state-dependent distributions for the diving parameters showed variation between states, indicative of different diving behaviours. Transition probabilities were considerably higher for state persistence than state switching, indicating that dive types occurred in bouts. Our results indicate that subsurface behaviour in short-finned pilot whales is more complex than a simple dichotomy of deep and shallow diving states, and labelling all subsurface behaviour as deep dives or shallow dives discounts a significant amount of important variation. We discuss potential drivers of these patterns, including variation in foraging success, prey availability and selection, bathymetry, physiological constraints and socially mediated behaviour.
Quick , N J , Isojunno , S , Sadykova , D , Bowers , M , Nowacek , D P & Read , A J 2017 , ' Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales ' Scientific Reports , vol. 7 , 45765 . DOI: 10.1038/srep45765
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DescriptionThis work was supported by award RC-2154 from the Strategic Environmental Research and Development Program and funding from the Naval Facilities Engineering Command Atlantic and NOAA Fisheries, Southeast Region. DS was supported by the United States Office of Naval Research grant N00014-12-1-0204, under the project entitled Multi-study Ocean acoustics Human effects Analysis (MOCHA).
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