Diving Behavior of Sei Whales<i>Balaenoptera borealis</i>Relative to the Vertical Distribution of Their Potential Prey

Ishii, Motomu; Murase, Hiroto; Fukuda, Yoshiaki; Sawada, Kouichi; Sasakura, Toyoki; Tamura, Tsutomu; Bando, T.; Matsuoka, Koji; Shinohara, Atsushi; Nakatsuka, Sayaka; Katsumata, Nobuhiro; Okazaki, Makoto; Miyashita, Kazushi; Mitani, Yoko

doi:10.3106/041.042.0403

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…In the oceanic North Pacific, sei whale foraging dives were shallower at night in response to vertical migration of their prey species towards the surface, but they continued to undertake foraging dives throughout the daytime [ 46 ]. Consequently, if sei whales are assumed to maintain foraging dives throughout the day, then the distributions predicted from visual survey data collected during daylight hours around the Falkland Islands are likely to broadly reflect those of their prey species.…”

Section: Discussionmentioning

confidence: 99%

Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

Baines

Weir

2020

PLoS ONE

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Species distribution models (SDMs) are valuable tools for describing the occurrence of species and predicting suitable habitats. This study used generalized additive models (GAMs) and MaxEnt models to predict the relative densities of four cetacean species (sei whale Balaeanoptera borealis, southern right whale Eubalaena australis, Peale’s dolphin Lagenorhynchus australis, and Commerson’s dolphin Cephalorhynchus commersonii) in neritic waters (≤100 m depth) around the Falkland Islands, using boat survey data collected over three seasons (2017–2019). The model predictor variables (PVs) included remotely sensed environmental variables (sea surface temperature, SST, and chlorophyll-a concentration) and static geographical variables (e.g. water depth, distance to shore, slope). The GAM results explained 35 to 41% of the total deviance for sei whale, combined sei whales and unidentified large baleen whales, and Commerson’s dolphins, but only 17% of the deviance for Peale’s dolphins. The MaxEnt models for all species had low to moderate discriminatory power. The relative density of sei whales increased with SST in both models, and their predicted distribution was widespread across the inner shelf which is consistent with the use of Falklands’ waters as a coastal summer feeding ground. Peale’s dolphins and Commerson’s dolphins were largely sympatric across the study area. However, the relative densities of Commerson’s dolphins were generally predicted to be higher in nearshore, semi-enclosed, waters compared with Peale’s dolphins, suggesting some habitat partitioning. The models for southern right whales performed poorly and the results were not considered meaningful, perhaps due to this species exhibiting fewer strong habitat preferences around the Falklands. The modelling results are applicable to marine spatial planning to identify where the occurrence of cetacean species and anthropogenic activities may most overlap. Additionally, the results can inform the process of delineating a potential Key Biodiversity Area for sei whales in the Falkland Islands.

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Section: Discussionmentioning

confidence: 99%

Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

Baines

Weir

2020

PLoS ONE

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“…We compiled maximum dive depth information [as an index of physiological diving capacities (15)] on as many diving marine mammals in the North Pacific as could be found in the published literature (15,(32)(33)(34)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68) based on the species lists from two previous studies (15,60). We made our list based on two criteria: (i) We prioritized to use the maximum dive depth data from animal-borne electronic archival tags and (ii) we used the values in the previous lists (15,60) even if the values are not from animal-borne electronic archival tags (but from other source such as radio tags) in the case where we could not find any updated reports by animal-borne electronic archival tags.…”

Section: Obtaining Maximum Dive Depth Information Of Marine Mammals I...mentioning

confidence: 99%

Forced into an ecological corner: Round-the-clock deep foraging on small prey by elephant seals

et al. 2021

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Small mesopelagic fishes dominate the world’s total fish biomass, yet their ecological importance as prey for large marine animals is poorly understood. To reveal the little-known ecosystem dynamics, we identified prey, measured feeding events, and quantified the daily energy balance of 48 deep-diving elephant seals throughout their oceanic migrations by leveraging innovative technologies: animal-borne smart accelerometers and video cameras. Seals only attained positive energy balance after feeding 1000 to 2000 times per day on small fishes, which required continuous deep diving (80 to 100% of each day). Interspecies allometry suggests that female elephant seals have exceptional diving abilities relative to their body size, enabling them to exploit a unique foraging niche on small but abundant mesopelagic fish. This unique foraging niche requires extreme round-the-clock deep diving, limiting the behavioral plasticity of elephant seals to a changing mesopelagic ecosystem.

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“…These interactions are increasingly being measured with bio-logging techniques, where biological data are recorded remotely from devices attached to animals. This approach has allowed researchers to answer questions on everything from hunting tactics of puma (Williams et al, 2014) to energy expenditure in cormorants (Gómez Laich et al, 2011) and diving behaviour in whales (Ishii et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

High accuracy at low frequency: detailed behavioural classification from accelerometer data

Tatler

Cassey

Prowse

2018

Journal of Experimental Biology

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Accelerometers are a valuable tool for studying animal behaviour and physiology where direct observation is unfeasible. However, giving biological meaning to multivariate acceleration data is challenging. Here, we describe a method that reliably classifies a large number of behaviours using tri-axial accelerometer data collected at the low sampling frequency of 1 Hz, using the dingo (Canis dingo) as an example. We used out-of-sample validation to compare the predictive performance of four commonly used classification models (random forest, k-nearest neighbour, support vector machine, and naïve Bayes). We tested the importance of predictor variable selection and moving window size for the classification of each behaviour and overall model performance. Random forests produced the highest out-of-sample classification accuracy, with our best-performing model predicting 14 behaviours with a mean accuracy of 87%. We also investigated the relationship between overall dynamic body acceleration (ODBA) and the activity level of each behaviour, given the increasing use of ODBA in ecophysiology as a proxy for energy expenditure. ODBA values for our four 'high activity' behaviours were significantly greater than all other behaviours, with an overall positive trend between ODBA and intensity of movement. We show that a random forest model of relatively low complexity can mitigate some major challenges associated with establishing meaningful ecological conclusions from acceleration data. Our approach has broad applicability to free-ranging terrestrial quadrupeds of comparable size. Our use of a low sampling frequency shows potential for deploying accelerometers over extended time periods, enabling the capture of invaluable behavioural and physiological data across different ontogenies.

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Diving Behavior of Sei WhalesBalaenoptera borealisRelative to the Vertical Distribution of Their Potential Prey

Cited by 15 publications

References 29 publications

Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

Forced into an ecological corner: Round-the-clock deep foraging on small prey by elephant seals

High accuracy at low frequency: detailed behavioural classification from accelerometer data

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