Evaluating the Causal Role of Environmental Data in Shellfish Biotoxin Contamination on the Portuguese Coast

Shellfish accumulation of marine biotoxins at levels unsafe for human consumption may severely impact their harvesting and farming, which has been grown worldwide in response to the growing demand for nutritious food and protein sources. In Southern European countries, diarrhetic shellfish poisoning (DSP) toxins are the most abundant and frequent toxins derived from algal blooms, affecting shellfish production yearly. Therefore, it is essential to understand the natural phenomenon of DSP toxins accumulation in shellfish and the meteorological and biological parameters that may regulate and influence its occurrence. In this work, we studied the relationship between the time series of several meteorological and biological variables and the time series of the concentration of DSP toxins in mussels on the Portuguese coast, using the Pearson’s correlation coefficient, time series regression modeling, Granger causality, and dynamic Bayesian networks using the MAESTRO tool. The results show that, for the models tested, the mean sea surface and air temperature time series with a one, two, or three-week lag can be valuable candidate predictors for forecasting the DSP concentration in mussels. Overall, this proof-of-concept study emphasizes the importance of statistical learning methodologies for analyzing time series environmental data and illustrates the importance of several variables in predicting DSP biotoxins concentration, which can help the shellfish production sector mitigate the negative impacts of DSP biotoxins accumulation.