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

• Published in: Ecology and Evolution
• Main Authors: Chambault, Philippine, Fossette, Sabrina, Heide‐Jørgensen, Mads Peter, Jouannet, Daniel, Vély, Michel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Sperm whale
• Online Access: http://dx.doi.org/10.1002/ece3.7154; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.7154; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.7154

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 proximity to Mauritius, while a migratory pattern was evidenced with a synchronized departure for eight females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity of the whales for sea surface height during the wet season and for bottom temperature during the dry season. A more dispersed distribution was predicted during the wet season, whereas a more restricted distribution to Mauritius and Reunion waters was found during the dry season, probably related to the breeding period. A diel pattern was observed in the diving behavior, likely following the vertical migration of squids. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which a regional IUCN assessment is still missing in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide highly reproductible tools to support dynamic ocean management.