Quantifying Spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phyto-plankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009-2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France. A spatiotemporal statistical modelling framework was developed within the Generalized Additive Model (GAM) framework to quantify long-term HAB risks for different bivalve shellfish species across each region, capturing seasonal variations, and spatiotemporal interactions. In all regions spatial functions were most important for predicting seasonal HAB risk, offering the potential to inform optimal siting of new shellfish op-erations and safe harvesting periods for businesses. A 10-fold cross-validation experiment was carried out for each region, to test the models' ability to predict toxin risk at harvesting locations for which data were withheld from the model. Performance was assessed by comparing ranked predicted and observed mean toxin levels at each site within each region: the correlation of ranks was 0.78 for Northern France, 0.64 for Western Scotland, and 0.34 for South-West England, indicating our approach has promise for predicting unknown HAB risk, depending on the region and suitability of training data.

Automated summary

Harmful algal blooms (HABs) have devastating effects on marine life, causing intoxication and asphyxiation, and costing at least $8bn/yr globally. We developed a new modeling approach using historical HAB monitoring data to predict Dinophysis toxin concentrations in bivalve shellfish species in Western Scotland, South-West England, and Northern France. Our spatiotemporal statistical modeling framework captured seasonal variations and interactions, with spatial functions being the most important for predicting HAB risk. The models showed promise in predicting unknown HAB risk, dependent on the region and availability of training data.