Predicting seasonal movements and distribution of the sperm whale using machine learning algorithms.

ABSTRACT: Implementation of effective conservation planning relies on a robust understanding of the spatiotemporal distribution of the target species. In the marine realm, this is even more challenging for species rarely seen at the sea surface due to their extreme diving behavior like the sperm whales. Our study aims at (a) investigating the seasonal movements, (b) predicting the potential distribution, and (c) assessing the diel vertical behavior of this species in the Mascarene Archipelago in the south-west Indian Ocean. Using 21 satellite tracks of sperm whales and eight environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales' potential distribution during the wet and dry season, separately. Fourteen of the whales remained in close proxi

SUBMITTER: Chambault P 

PROVIDER: S-EPMC7863674 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature