Lobster Settlement Forecasting in the Gulf of Maine

            Lobster (Homarus americanus) is the most lucrative coastal fishery in the Gulf of Maine comprising more than 80% of the total commercial landing value of all species in the state of Maine. During the past decade, regional landings were more than double the previous long-term mean with variations in larval transport, survival, and settlement among the possible contributing factors.

            This project transitions research models of circulation, lobster larval development, transport and settlement into operational use by the current fisheries recruitment and stock assessment models used by New England managers. The foundation of this work is a multi-year time series of lobster settlement field data and an operational numerical circulation model, currently run for the Gulf of Maine Ocean Observation System. We have developed both statistical and numerical models that link larval transport and post-larval settlement to oceanic and atmospheric conditions.  We have recently coupled the circulation model with an individual based model of lobster larvae development and behavior. Results provide the background for forecasting time and space patterns of settlement.  In parallel, we have an individual-based lobster simulator to model recruitment into the fishery from settlers, and a Bayesian size-structured lobster stock assessment model. Both are developed and run by Chen at U.Maine in collaboration with the Atlantic States Marine Fisheries Commission for lobster management in the northeastern USA. Merged, these models will allow us to forecast the fishery recruitment from settlement.  The forecast system will be developed in consultation with three primary fisheries management agencies, the Atlantic States Marine Fisheries Commission, NEFSC, and the State of Maine Department of Marine Resources.  Contacts are Dr. Larry Jacobson and Dr. Carl Wilson.  Satellite data improve two key aspects to the forecast system, building a presently missing direct link to the variable oceanic environment into the fisheries management models. Real-time satellite SST fields are assimilated into the circulation model.  We will improve both the temporal component as well as the cloud screening capability by merging MODIS SST into the input. At present, the model is forced by forecast wind products that are known to be relatively smooth. The model will be modified to accept forcing by high resolution real-time satellite wind fields, providing more realistic output fields.