Improved seasonal prediction of harmful algal blooms in Lake Erie using large-scale climate indices

A machine learning approach based on nutrient loading observations and physical large scale climate indices improves early seasonal prediction of harmful algal bloom activity between July and October in Lake Erie, which can help local fisheries management. Harmful Algal Blooms lead to multi-billion-dollar losses in the United States due to shellfish closures, fish mortalities, and reluctance to consume seafood. Therefore, an improved early seasonal prediction of harmful algal blooms severity is important. Conventional methods for harmful algal blooms prediction using nutrient loading as the primary driver have been found to be less accurate during extreme bloom years. Here we show that a machine learning approach using observed nutrient loading, and large-scale climate indices can improve the harmful algal blooms prediction in Lake Erie. Moreover, the seasonal prediction of harmful algal blooms can be completed by early June, before the expected peak in harmful algal bloom activity from July to October. This improved early seasonal prediction can provide timely information to policymakers for adopting proper planning and mitigation strategies such as restrictions in harvesting and help in monitoring toxins in shellfish to keep contaminated products off the market.

Automated summary

A machine learning approach using nutrient loading observations and physical large scale climate indices improves early seasonal prediction of harmful algal bloom activity in Lake Erie between July and October, which can assist in local fisheries management.