Using accelerometry tags to quantify gray whale foraging behavior

Funding: Natural Sciences and Engineering Research Council of Canada; Office of Naval Research Marine Mammals and Biology Program, Grant/Award Number: N00014-20-1-2760;UBC Institute for the Oceans and Fisheries Cosmos International Graduate Travel Award; Oregon State University Marine Mammal Institu...

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Bibliographic Details
Published in:Marine Mammal Science
Main Authors: Colson, Kate, Pirotta, Enrico, New, Leslie, Cade, David E., Calambokidis, John, Bierlich, KC, Bird, Clara N., Fernandez Ajó, Alejandro, Hildebrand, Lisa, Trites, Andrew W., Torres, Leigh G.
Other Authors: University of St Andrews.School of Biology, University of St Andrews.Centre for Research into Ecological & Environmental Modelling
Format: Article in Journal/Newspaper
Language:English
Published: 2024
Subjects:
Accelerometry
Biologging
Classification and regression trees
Foraging
Hidden Markov models
PCFG gray whale
QH301 Biology
DAS
QH301
Canada
Pacific
baleen whale
Online Access:https://hdl.handle.net/10023/30998
https://doi.org/10.1111/mms.13210
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Summary:Funding: Natural Sciences and Engineering Research Council of Canada; Office of Naval Research Marine Mammals and Biology Program, Grant/Award Number: N00014-20-1-2760;UBC Institute for the Oceans and Fisheries Cosmos International Graduate Travel Award; Oregon State University Marine Mammal Institute Gray Whale License Plate Fund. High-resolution tri-axial accelerometry biologging tags have quantitatively described behaviors in baleen whale species that forage using lunges and continuous ram filtration. However, detailed quantitative descriptions of foraging behaviors do not exist for gray whales, a unique baleen whale species that primarily uses benthic suction feeding with a rolling component. We deployed suction cup biologging tags on Pacific Coast Feeding Group (PCFG) gray whales to quantify foraging behavior at the broad state (dive) and foraging tactic (roll event) scales. Hidden Markov models were used to describe three distinct states using turn angle, dive duration, pseudotrack tortuosity, and presence of roll events that can be interpreted as forage, search, and transit behavior. Classification and Regression Tree models best described foraging tactics (headstands, benthic digs, and side swims) using median pitch, depth to total length ratio, and absolute value of the median roll. On average, PCFG gray whales spent more time searching and performed more left-rolled foraging tactics at shallower depths at night compared to during the day, potentially to track prey above them in the water column. Describing foraging behavior in PCFG gray whales enables examination of links between behavioral budgets, energetics, and the physiological impact of threats facing this group. Peer reviewed

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