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

• Published in: Frontiers in Marine Science
• Main Authors: Jose A. Fernandes-Salvador, Keith Davidson, Marc Sourisseau, Marta Revilla, Wiebke Schmidt, Dave Clarke, Peter I. Miller, Paola Arce, Raúl Fernández, Luz Maman, Alexandra Silva, Callum Whyte, Maria Mateo, Patricia Neira, Marcos Mateus, Manuel Ruiz-Villarreal, Luis Ferrer, Joe Silke
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2021
• Subjects: modeling; machine learning; toxins; phytoplankton; food production; short-term; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; North East Atlantic
• Online Access: https://doi.org/10.3389/fmars.2021.666583; https://doaj.org/article/0585f7c454b8453e93d8254333e9a57c

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 ...