Foraging behaviour and habitat use during chick-rearing in the Australian endemic black-faced cormorant (Phalacrocorax fuscescens).

Despite its wide distribution, relatively little is known of the foraging ecology and habitat use of the black-faced cormorant (Phalacrocorax fuscescens), an Australian endemic seabird. Such information is urgently required in view of the rapid oceanic warming of south-eastern Australia, the stronghold of the species. The present study used a combination of opportunistically collected regurgitates and GPS/dive behaviour data loggers to investigate diet, foraging behaviour and habitat-use of black-faced cormorants during four chick-rearing periods (2020-2023) on Notch Island, northern Bass Strait. Observed prey species were almost exclusively benthic (95%), which is consistent with the predominantly benthic diving behaviour recorded. Males foraged at deeper depths than females (median depth males: 18 m; median depth females: 8 m), presumably due to a greater physiological diving capacity derived from their larger body size. This difference in dive depths was associated with sexual segregation of foraging locations, with females predominantly frequenting shallower areas closer to the coastline. These findings have strong implications for the management of the species, as impacts of environmental change may disproportionally affect the foraging range of one sex and, thereby, reproductive success.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning.

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.