Using the American Lobster Settlements Index and Environmental Indicators in Fishery Forecasting and Stock Assessment

Richard Wahle - University of Maine / School of Marine Sciences


The proposal describes a 5-year plan to evaluate to utility of the American Lobster Index (ALSI) coupled with key environmental indicators as a tool for stock assessment and forecasting. ALSI is a monitoring program of coastal lobster nurseries throughout New England and Atlantic Canada. Founded in 1989, the time series has already proven to a useful indicator of the health of the lobster resource. The present effort aims to comprehensively assess the forecasting power of the index for stocks and substocks over a considerable portion of the species range. ALSI data will improve forecasting capability because the fishery is recruitment driven and there is currently no available information about the strength of recruitment.  

The project is a partnership among the University of Maine, Gulf of Maine Research Institute and NOAA’s Northeast Fisheries Science Center.  It is currently supported by NOAA for two years to evaluate the first four objectives: (1) Assess spawner-to-settler relationships along with key oceanographic/atmospheric indicators; (2) Evaluate cohort fate over first years of benthic life; (3) Forecast fishery recruitment, and (4) Make stock level data available for use in the stock assessment model.  In effect, these objectives take the important and ambitious step of closing the lobster life cycle by evaluating the spawner-to-fishery-recruit relationship at stock-wide and sub-stock spatial scale

Work during years 3-5 is contingent on additional funding.  In those years we propose to (1) Expand the spatial domain of the model development, (2) Work with stock assessment  on the development of harvest control rules based on ALSI, and (3) Evaluate model predictions under different climate change scenarios.

The research is particularly relevant to CINAR’s goal to conduct research that identifies linkages among fisheries, climate change and ecosystem health; to transition monitoring tools into predictive models; and to distinguish human impacts on marine resources from naturally induced variability.