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

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 nonacoustic 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 nonforaging deep dives) for southern California Cuvier's beaked whales.