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Impact of the future coastal water temperature scenarios on the risk of potential growth of pathogenic Vibrio marine bacteria.


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Ferchichi, Habiba, St-Hilaire, André, Ouarda, Taha B. M. J. ORCID: https://orcid.org/0000-0002-0969-063X et Lévesque, Benoît (2021). Impact of the future coastal water temperature scenarios on the risk of potential growth of pathogenic Vibrio marine bacteria. Estuarine, Coastal and Shelf Science , vol. 250 . p. 107094. DOI: 10.1016/j.ecss.2020.107094.

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Vibrio (V), a genus of marine bacteria, are common inhabitants of warm coastal waters and estuaries. Vibrio includes V. parahaemolyticus and V. vulnificus species that can cause human infections through the consumption of contaminated shellfish (as bivalve molluscs). The growth of pathogenic Vibrio is related to ambient water temperature and seems to increase at 15 °C and over. The expansion of Vibrio infection outbreak is increasing worldwide due to the increase of the sea surface temperature as a result of ocean warming. Canada's coast is not an exception to this worldwide Vibrio spread. Faced with this issue, this study focuses on modelling the future potential Vibrio growth risk along the coasts of the St. Lawrence Gulf and Estuary, where the shellfish industry is well developed. This is done using the adequate machine learning model with explanatory variables that include air temperature and wind speed for predicting future water temperatures. Based on the predicted future water temperature scenarios and a threshold of 15 °C to determine the conditions favorable to the growth of Vibrio bacteria, we modelled the Vibrio growth risk indicator, i.e. the number of days exceeding the minimum temperature for Vibrio pathogenic growth (15 °C), in the horizon 2040–2100. Simulations show that the number of days, where the minimum temperature (15 °C) will be reached, will increase spatially and even seasonally and all the shellfish beds would meet the temperature condition for Vibrio growth regardless of the climate scenario (optimistic and pessimistic).

Type de document: Article
Mots-clés libres: coastal water temperature; vibrio bacteria; climate change; modelling; prediction; machine learning models; artificial neural networks; Random Forests
Centre: Centre Eau Terre Environnement
Date de dépôt: 24 févr. 2021 16:54
Dernière modification: 19 nov. 2022 05:00
URI: https://espace.inrs.ca/id/eprint/11350

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