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Current Status of Forecasting Toxic Harmful Algae for the North-East Atlantic Shellfish Aquaculture Industry

期刊

FRONTIERS IN MARINE SCIENCE
卷 8, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmars.2021.666583

关键词

modeling; machine learning; toxins; phytoplankton; food production; short-term; regulation; early warning systems

资金

  1. Interreg Atlantic Area Programme Project PRIMROSE [EAPA_182/2016]
  2. project SNMB-MONITOR [Monitor16.02.01FEAMP0043]
  3. Portuguese Government, Operational Program (OP) March 2020, Portugal 2020
  4. European Union through the European Structural Funds and Investment Funds (FEEI)
  5. European Maritime and Fisheries Fund (EMFF)
  6. IPMA fellowship [IPMABCC201635]
  7. project EGRECOST CALIDAD - Control de Calidad de Aguas Cultivos Marinos (Departamento de Desarrollo Econimico e Infraestructuras del Gobierno Vasco)
  8. UKRI project CAMPUS [NE/R00675X/1]
  9. UKRI project OffAqua [BB/S004246/1]
  10. European H2020 project FutureMARES [869300]
  11. NERC [NE/R00675X/1] Funding Source: UKRI

向作者/读者索取更多资源

The shellfish aquaculture industry in the European Atlantic Arc is impacted by harmful algal blooms that produce biotoxins accumulating in shellfish flesh, affecting consumer health. Regulatory monitoring focuses on harmful cells and toxin concentrations, but there is a need for early warning systems for business planning. Web portals and operational Early Warning Systems (EWS) are being developed, incorporating environmental data parameters and varied forecasting approaches to mitigate HAB-generated harmful effects. Advanced EWS include satellite data and oceanographic modeling to predict HAB behavior, with traffic light indices for easy risk assessment and expert interpretation of multiple data streams for future risk assessment. Proof-of-concept EWS combine model information with in-situ data, sometimes using machine learning-based approaches.
Across the European Atlantic Arc (Scotland, Ireland, England, France, Spain, and Portugal) the shellfish aquaculture industry is dominated by the production of mussels, followed by oysters and clams. A range of spatially and temporally variable harmful algal bloom species (HABs) impact the industry through their production of biotoxins that accumulate and concentrate in shellfish flesh, which negatively impact the health of consumers through consumption. Regulatory monitoring of harmful cells in the water column and toxin concentrations within shellfish flesh are currently the main means of warning of elevated toxin events in bivalves, with harvesting being suspended when toxicity is elevated above EU regulatory limits. However, while such an approach is generally successful in safeguarding human health, it does not provide the early warning that is needed to support business planning and harvesting by the aquaculture industry. To address this issue, a proliferation of web portals have been developed to make monitoring data widely accessible. These systems are now transitioning from nowcasts to operational Early Warning Systems (EWS) to better mitigate against HAB-generated harmful effects. To achieve this, EWS are incorporating a range of environmental data parameters and developing varied forecasting approaches. For example, EWS are increasingly utilizing satellite data and the results of oceanographic modeling to identify and predict the behavior of HABs. Modeling demonstrates that some HABs can be advected significant distances before impacting aquaculture sites. Traffic light indices are being developed to provide users with an easily interpreted assessment of HAB and biotoxin risk, and expert interpretation of these multiple data streams is being used to assess risk into the future. Proof-of-concept EWS are being developed to combine model information with in situ data, in some cases using machine learning-based approaches. This article: (1) reviews HAB and biotoxin issues relevant to shellfish aquaculture in the European Atlantic Arc (Scotland, Ireland, England, France, Spain, and Portugal; (2) evaluates the current status of HAB events and EWS in the region; and (3) evaluates the potential of further improving these EWS though multi-disciplinary approaches combining heterogeneous sources of information.

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