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The use of machine learning to detect foraging behaviour in whale sharks: a new tool in conservation
Author(s) -
Whitehead Darren A.,
Magaña Felipe G.,
Ketchum James T.,
Hoyos Edgar M.,
Armas Rogelio G.,
Pancaldi Francesca,
Olivier Damien
Publication year - 2021
Publication title -
journal of fish biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 115
eISSN - 1095-8649
pISSN - 0022-1112
DOI - 10.1111/jfb.14589
Subject(s) - foraging , whale , biology , accelerometer , fishery , cetacea , random forest , machine learning , artificial intelligence , ecology , computer science , operating system
In this study we present the first attempt at modelling the feeding behaviour of whale sharks using a machine learning analytical method. A total of eight sharks were monitored with tri‐axial accelerometers and their foraging behaviours were visually observed. Our results highlight that the random forest model is a valid and robust approach to predict the feeding behaviour of the whale shark. In conclusion this novel approach exposes the practicality of this method to serve as a conservation tool and the capability it offers in monitoring potential disturbances of the species.