Prediction of Alexandrium and Dinophysis algal blooms and shellfish contamination in French Mediterranean Lagoons using decision trees and linear regression: a result of 10 years of sanitary monitoring
Résumé
French Mediterranean lagoons are frequently subject to shellfish contamination by Diarrheic Shellfish Toxins (DSTs) and Paralytic Shellfish Toxins (PSTs). To predict the effect of various environmental factors (temperature, salinity and turbidity) on the abundance of the major toxins producing genera, Dinophysis and Alexandrium, and the link with shellfish contamination, we analysed a 10-year dataset collected from 2010 to 2019 in two major shellfish farming lagoons, Thau and Leucate, using two methods: decision trees and Zero Inflated Negative Binomial (ZINB) linear regression models. Analysis of these decision trees revealed that the highest risk of Dinophysis bloom events occurred at temperature <16.3°C and salinity <27.8, and of Alexandrium at temperature ranging from 10.4 to 21.5°C and salinity >39.2. The highest risk of shellfish contaminations by DSTs and PSTs occurred during the set of conditions associated with high risk of bloom events. Linear regression prediction enables us to understand whether temperature and salinity influence the presence of Alexandrium and affect its abundance. However, Dinophysis linear regression could not be validated due to overdispersion issues. This work demonstrates the tools which could help sanitary management of shellfish rearing areas.