Dataset Information

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 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.

SUBMITTER: Chambault P

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

REPOSITORIES: biostudies-literature

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

Chambault Philippine P Fossette Sabrina S Heide-Jørgensen Mads Peter MP Jouannet Daniel D Vély Michel M

Ecology and evolution 20210112 3

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 ...[more]

PMID: 33598142

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Project description:In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of microbiome information taken at weeks 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting microbiome information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of the microbiome on feed efficiency as well as carcass and meat quality should be investigated.

Dataset Information

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

Publications

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

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