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Marine Ecology Progress Series

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MEPS 692:195-208 (2022)  -  DOI:

Cuvier’s beaked whale foraging dives identified via machine learning using depth and triaxial acceleration

David A. Sweeney1, Gregory S. Schorr1,*, Erin A. Falcone1, Brenda K. Rone1, Russel D. Andrews1, Shannon N. Coates1, Stephanie L. Watwood2, Stacy L. DeRuiter3, Mark P. Johnson4, David J. Moretti2

1Marine Ecology and Telemetry Research, Seabeck, WA 98380, USA
2Naval Undersea Warfare Center, Newport, RI 02841, USA
3Department of Mathematics and Statistics, Calvin University, Grand Rapids, MI 49546, USA
4Aarhus Institute of Advanced Studies, Aarhus University, 8000 Aarhus C, Denmark
*Corresponding author:

ABSTRACT: Knowledge of Cuvier’s beaked whale Ziphius cavirostris behavior has expanded through the utilization of animal-borne tags. However, many tag types do not record sound—thus preventing echolocation click detections to identify foraging—or have short deployments that sample a limited range of behaviors. As the quantity of such non-acoustic tag data grows, so too does the need for robust methods of detecting foraging from non-acoustic data. We used 692 dives from 5 sound-recording tags on Cuvier’s beaked whales in southern California, USA, to develop extreme gradient boosting tree models to detect foraging based on 1 Hz depth and 16 Hz triaxial acceleration data. We performed repeated 10-fold cross validation using classification accuracy to tune 500 models with randomly partitioned training and testing datasets. An average of 99.9 and 99.2% of training and testing dataset dives, respectively, were correctly classified across the 500 models. Dives without associated sound recordings (n = 2069 from 7 whales including 4 non-acoustic tags) were classified via a model that maximized training information using dive depth and duration, ascent and descent rates, bottom-phase average vertical speed, and roll circular variance during dive descents and bottom phases. Of all long, deep dives (conventionally assumed to include foraging), 2.4% were classified as non-foraging dives, while 0.3% of short, shallow dives were classified as foraging dives. Results confirm that conventional depth and/or duration classifiers provide reasonable estimates of longer-term foraging patterns. However, additional variables previously listed enhance foraging detections for unusual dives (notably non-foraging deep dives) for southern California Cuvier’s beaked whales.

KEY WORDS: Cuvier’s beaked whale · Ziphius cavirostris · Foraging · Diving · Machine learning · Tagging

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Cite this article as: Sweeney DA, Schorr GS, Falcone EA, Rone BK and others (2022) Cuvier’s beaked whale foraging dives identified via machine learning using depth and triaxial acceleration. Mar Ecol Prog Ser 692:195-208.

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