  In this study, explainable machine learning techniques are applied to predict
the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful
algal blooms. By analysing a newly created 28-year dataset containing records
of toxic phytoplankton in mussel farming areas and toxin concentrations in
mussels (Mytilus galloprovincialis), we train and evaluate the performance of
ML models to accurately predict diarrhetic shellfish poisoning (DSP) events.
The random forest model provided the best prediction of positive toxicity
results based on the F1 score. Explainability methods such as permutation
importance and SHAP identified key species (Dinophysis fortii and D. caudata)
and environmental factors (salinity, river discharge and precipitation) as the
best predictors of DSP outbreaks. These findings are important for improving
early warning systems and supporting sustainable aquaculture practices.
