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

• Main Authors: Schoombie, Stefan, Jeantet, Lorène, Sutton, Grace J., Dufourq, Emmanuel, Chimienti, Marianna, Pistorius, Pierre, Lowther, Andy, Oosthuizen, W. Chris
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: The Royal Society 2024
• Subjects: Ecology not elsewhere classified; Antarctic; Southern Ocean; Antarc*; Antarctic Krill; Antarctica; Euphausia superba; Pygoscelis antarctica
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.7227081; https://rs.figshare.com/collections/Supplementary_material_from_Identifying_prey_capture_events_of_a_free-ranging_marine_predator_using_bio-logger_data_and_deep_learning_/7227081

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 require 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 dataset (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 (CNN and V-Net). Although the CNN and V-Net architectures and input data pipelines ...