Annual occurrence of paralytic shellfish poison (PSP) across the coast of Maine poses a challenge for fishery managers who implement regional harvesting closures to protect human health. Similarly, shellfish growers and harvesters also must make decisions to operate their businesses in the face of these closures. Two process-based predictive models exist that deliver seasonal and weekly Alexandrium catenella bloom potential forecasts. A more recently developed machine-learning model predicts the probabilistic risk of PSP accumulation in shellfish at a site-specific, weekly timescale. The latter model was developed with shellfish industry members and managers, to produce the most usable forecast possible. Through two seasons of delivering predictions in an experimental mode, the forecast has achieved high accuracy and received positive feedback from its users. Both the process-based and machine learning models provide important, but different insights for their users. While the process-based models capture a suite of environmental conditions that may lead to blooms of A. catenella, high cell concentrations cannot always predict spikes in toxicity. On the other hand, the machine learning model excels at predicting toxicity at a finer (weekly) timescale, but loses skill with longer lead times (> two weeks). Combining the two model types is being explored and will be discussed.

Primary Presenter: Johnathan Evanilla, Bigelow Laboratory for Ocean Sciences (jevanilla@bigelow.org)

Authors:

Johnathan Evanilla, Bigelow Laboratory for Ocean Sciences  (jevanilla@bigelow.org)

Nicholas Record, Bigelow Laboratory for Ocean Sciences  (nrecord@bigelow.org)

Benjamin Tupper, Bigelow Laboratory for Ocean Sciences  (btupper@bigelow.org)

Bryant Lewis, Maine Department of Marine Resources  (Bryant.J.Lewis@maine.gov)

David Miller, Maine Department of Marine Resources  (David.W.Miller@maine.gov)

Kohl Kanwit, Maine Department of Marine Resources  (Kohl.Kanwit@maine.gov)

Craig Burnell, Bigelow Laboratory for Ocean Sciences  (cburnell@bigelow.org)

Stephen Archer, Bigelow Laboratory for Ocean Sciences  (sarcher@bigelow.org)