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 th...
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ftdatacite:10.5061/dryad.bnzs7h482 2023-12-03T10:30:46+01:00 Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... Chambault, Philippine Fossette, Sabrina Heide-Jørgensen, Mads Peter Jouannet, Daniel 2020 https://dx.doi.org/10.5061/dryad.bnzs7h482 https://datadryad.org/stash/dataset/doi:10.5061/dryad.bnzs7h482 en eng Dryad Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 cetaceans Diving behaviour dataset Dataset 2020 ftdatacite https://doi.org/10.5061/dryad.bnzs7h482 2023-11-03T11:08:28Z 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 ... Dataset Sperm whale DataCite Metadata Store (German National Library of Science and Technology) Indian |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
cetaceans Diving behaviour |
spellingShingle |
cetaceans Diving behaviour Chambault, Philippine Fossette, Sabrina Heide-Jørgensen, Mads Peter Jouannet, Daniel Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
topic_facet |
cetaceans Diving behaviour |
description |
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 ... |
format |
Dataset |
author |
Chambault, Philippine Fossette, Sabrina Heide-Jørgensen, Mads Peter Jouannet, Daniel |
author_facet |
Chambault, Philippine Fossette, Sabrina Heide-Jørgensen, Mads Peter Jouannet, Daniel |
author_sort |
Chambault, Philippine |
title |
Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
title_short |
Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
title_full |
Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
title_fullStr |
Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
title_full_unstemmed |
Using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
title_sort |
using machine learning models to predict the distribution of a cryptic marine species: the sperm whale ... |
publisher |
Dryad |
publishDate |
2020 |
url |
https://dx.doi.org/10.5061/dryad.bnzs7h482 https://datadryad.org/stash/dataset/doi:10.5061/dryad.bnzs7h482 |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Sperm whale |
genre_facet |
Sperm whale |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.bnzs7h482 |
_version_ |
1784256803678191616 |