The use of machine learning to detect a foraging behaviour in whale sharks

Dec 1

Written By Karina Busto

The use of machine learning to detect a foraging behaviour in whale sharks: A new tool in conservation

Darren Whitehead, Felipe Galván-Magaña, James Ketchum, Mauricio Hoyos, Rogelio González-Armas, Francesca Pancaldi, Damien Olivier

Journal of Fish Biology (2020)

DOI: https://doi.org/10.1111/jfb.14589

Abstract

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 8 sharks were monitored with tri‐axial accelerometers and their foraging behaviours were visually observed. Our results highlighted 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.

Keywords: Acceleration data logger, Biologging tool, Endangered species, Shark conservation

Karina Busto

The use of machine learning to detect a foraging behaviour in whale sharks

The use of machine learning to detect a foraging behaviour in whale sharks: A new tool in conservation

Darren Whitehead, Felipe Galván-Magaña, James Ketchum, Mauricio Hoyos, Rogelio González-Armas, Francesca Pancaldi, Damien Olivier

Journal of Fish Biology (2020)

Abstract

Evidence of interactions between white sharks and large squids in Guadalupe Island, Mexico

Habitat suitability of Rhincodon typus in three localities of the Gulf of California