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Predicting seasonal  movements and distribution of the sperm whale using machine learning algorithms            
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  • Philippine Chambault,
  • Sabrina Fossette,
  • Mads Peter Heide-Jørgensen,
  • Daniel Jouannet,
  • Michel Vély
Philippine Chambault
Greenland Institute of Natural Resources Climate Research Centre

Corresponding Author:[email protected]

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Sabrina Fossette
Biodiversity and Conservation Science, Department of Biodiversity
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Mads Peter Heide-Jørgensen
Greenland Institute of Natural Resources
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Daniel Jouannet
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Michel Vély
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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 species rarely seen at the sea surface due to their extreme diving behaviour like the sperm whales. Our study aims at (i) investigating the seasonal movements, (ii) predicting the potential distribution and (iii) assessing the diel vertical behaviour of this species in the Mascarene Archipelago in the Southwest Indian Ocean. Using 21 satellite tracks of sperm whales and 8 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 8 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 behaviour, likely following the vertical migrations of squids. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which aregional IUCN assessment are 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.
29 Jul 2020Submitted to Ecology and Evolution
04 Aug 2020Submission Checks Completed
04 Aug 2020Assigned to Editor
06 Aug 2020Reviewer(s) Assigned
18 Nov 2020Review(s) Completed, Editorial Evaluation Pending
23 Nov 2020Editorial Decision: Revise Minor
27 Nov 20201st Revision Received
30 Nov 2020Submission Checks Completed
30 Nov 2020Assigned to Editor
30 Nov 2020Review(s) Completed, Editorial Evaluation Pending
30 Nov 2020Reviewer(s) Assigned
10 Dec 2020Editorial Decision: Accept