Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale

Implementation of effective conservation planning relies on a robust understanding of the spatio-temporal distribution of the target species. In the marine realm, this is even more challenging for cryptic species with extreme diving behaviour like the sperm whales. Our study aims at investigating the movements and predicting suitable habitat maps for this species in the Mascarene Archipelago in the South-West Indian Ocean. Using 21 satellite tracks of sperm whale and 8 environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales’ 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 8 females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity 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. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which IUCN regional assessments are still lacking in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide concrete tools to support dynamic ocean management.