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Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.

ABSTRACT: 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.

SUBMITTER: Quick NJ

PROVIDER: S-EPMC5374633 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.

Quick Nicola J NJ Isojunno Saana S Sadykova Dina D Bowers Matthew M Nowacek Douglas P DP Read Andrew J AJ

Scientific reports 20170331

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 digi ...[more]

PMID: 28361954

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Project description:Air-breathing marine predators that target sub-surface prey have to balance the energetic benefit of foraging against the time, energetic and physiological costs of diving. Here we use on-animal data loggers to assess whether such trade-offs can be revealed by the breathing rates (BR) and timing of breaths in long-finned pilot whales (Globicephela melas). We used the period immediately following foraging dives in particular, for which respiratory behavior can be expected to be optimized for gas exchange. Breath times and fluke strokes were detected using onboard sensors (pressure, 3-axis acceleration) attached to animals using suction cups. The number and timing of breaths were quantified in non-linear mixed models that incorporated serial correlation and individual as a random effect. We found that pilot whales increased their BR in the 5-10 min period prior to, and immediately following, dives that exceeded 31 m depth. While pre-dive BRs did not vary with dive duration, the initial post-dive BR was linearly correlated with duration of >2 min dives, with BR then declining exponentially. Apparent net diving costs were 1.7 (SE 0.2) breaths per min of diving (post-dive number of breaths, above pre-dive breathing rate unrelated to dive recovery). Every fluke stroke was estimated to cost 0.086 breaths, which amounted to 80-90% average contribution of locomotion to the net diving costs. After accounting for fluke stroke rate, individuals in the small body size class took a greater number of breaths per diving minute. Individuals reduced their breathing rate (from the rate expected by diving behavior) by 13-16% during playbacks of killer whale sounds and their first exposure to 1-2 kHz naval sonar, indicating similar responses to interspecific competitor/predator and anthropogenic sounds. Although we cannot rule out individuals increasing their per-breath O2 uptake to match metabolic demand, our results suggest that behavioral responses to experimental sound exposures were not associated with increased metabolic rates in a stress response, but metabolic rates instead appear to decrease. Our results support the hypothesis that maximal performance leads to predictable (optimized) breathing patterns, which combined with further physiological measurements could improve proxies of field metabolic rates and per-stroke energy costs from animal-borne behavior data.

Project description:BackgroundThere is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF.MethodsWe build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of plains zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution.ResultsIn the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection ("encamped" and "exploratory"). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening.ConclusionsThis method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use.

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Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.

Publications

Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.

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