A HAB warning system for shellfish harvesting in Portugal

Area of Science:

• Marine Biology
• Oceanography
• Aquaculture Science

Background:

• Sustainable shellfish aquaculture requires accurate monitoring and prediction of harmful algal blooms (HABs).
• Existing systems often lack the integration of real-time data and predictive modeling for effective HAB management.

Purpose of the Study:

• To develop and assess a prototype early warning system for HABs and shellfish closures in Portugal.
• To evaluate the system's forecasting accuracy using historical monitoring and oceanographic data.

Main Methods:

• Weekly monitoring of HAB species and toxin concentrations in shellfish.
• Utilizing ocean circulation forecasts from an operational oceanographic model.
• Assessing forecast accuracy through verification of predicted versus actual events.

Main Results:

• The system achieved a forecast accuracy of 85% for verified correct forecasts.
• False negatives (unforeseen events) were 12%, and false positives (predicted but non-occurring events) were 3%.
• Prediction accuracy showed variations, particularly during initial implementation and autumn-winter months.

Conclusions:

• The integration of field data, remote sensing, and operational models enhances HAB prediction accuracy.
• The developed system provides a valuable tool for managing shellfish safety and aquaculture sustainability.
• Continuous refinement and validation are essential for optimizing the early warning system's performance.