Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach

Stoner, O. , Economou, T., Torres, R., Ashton, I. and Brown, A. R. (2023) Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach. Harmful Algae, 121, 102363. (doi: 10.1016/j.hal.2022.102363)

Abstract

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 phytoplankton 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 operations 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.

• Item Type: Articles
• Additional Information: The authors gratefully acknowledge funding from the European Maritime and Fisheries Fund (ENG3103); Turing Pilot Research Grant (260320) and IIB Open Innovation Project Fund (115717). T. Economou was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 856612 and the Cyprus Government.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stoner, Dr Oliver
• Authors: Stoner, O., Economou, T., Torres, R., Ashton, I., and Brown, A. R.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Harmful Algae
• Publisher: Elsevier
• ISSN: 1568-9883
• ISSN (Online): 1878-1470
• Published Online: 06 December 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Harmful Algae 121: 102363
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5281/zenodo.7119036