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Schoombie S, Jeantet L, Chimienti M, Sutton GJ, Pistorius PA, Dufourq E, Lowther AD, Oosthuizen WC. Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240271. [PMID: 39100157 PMCID: PMC11296051 DOI: 10.1098/rsos.240271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 08/06/2024]
Abstract
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.
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Affiliation(s)
- Stefan Schoombie
- Department of Statistical Sciences, Centre for Statistics in Ecology, Environment and Conservation (SEEC), University of Cape Town, Cape Town7701, South Africa
- National Institute for Theoretical and Computational Sciences, South Africa
| | - Lorène Jeantet
- African Institute for Mathematical Sciences, Cape Town7945, South Africa
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch7602, South Africa
| | - Marianna Chimienti
- Centre D’Études Biologiques de Chizé, UMR7372 CNRS-La Rochelle, Villiers-en-Bois, France
| | - Grace J. Sutton
- Department of Environment & Genetics, and Research Centre for Future Landscapes, La Trobe University, Melbourne, VIC3086, Australia
| | - Pierre A. Pistorius
- Marine Apex Predator Research Unit, Department of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Gqeberha6031, South Africa
| | - Emmanuel Dufourq
- African Institute for Mathematical Sciences, Cape Town7945, South Africa
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch7602, South Africa
- African Institute for Mathematical Sciences, Research and Innovation Centre, Kigali, Rwanda
| | | | - W. Chris Oosthuizen
- Department of Statistical Sciences, Centre for Statistics in Ecology, Environment and Conservation (SEEC), University of Cape Town, Cape Town7701, South Africa
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