PS 66-104
Evaluating the accuracy of data-poor stock assessment methods in the Southeast United States
Many fish stocks in the Southeast do not have sufficient data to allow for traditional stock assessments. These ‘data-poor’ stocks lack or have unreliable information concerning catch time-series, stock size, or life history parameters. Such data are the primary information sources for traditional stock assessments. Without this information it is difficult to conduct stock assessments and determine annual catch limits (ACLs) and other reference points legally required for every fished stock by the Magnuson-Stevens Act. While data-poor stocks are present around the United States, 75% of stocks and stock complexes assessed for ACLs in the Southeast are stocks that have only catch history data. Alternative data-poor methods to calculate ACLs in such cases exist, but their effectiveness is still subject to question. Two of the more common data-poor methods, DCAC and DB-SRA, have been applied to data-rich stocks on the West Coast and found to be relatively accurate at estimating Maximum Sustainable Yield in comparison to the more traditional stock synthesis assessment method. Accuracy ranged from 70% to 85% for DCAC and 80% to 155% for DB-SRA for the west coast studies, but no similar work has been done on stocks in the Southeast. This study compares ACLs and biological reference point outputs from the Southeast Data, Assessment, and Review (SEDAR) stock assessments to those of data-poor methods for the same stocks in order to determine the accuracy of the data-poor estimates when compared to the SEDAR estimates for fisheries in the Southeast. It will determine which, if any, simplified assessment methods are appropriate for use on a per-species basis and reveal which methods are best suited to set ACLs for data-poor fish stocks in the Southeast.
Results/Conclusions
Preliminary results indicate that certain methods are not appropriate for stocks in the Southeast. DB-SRA, which requires full catch histories of fisheries, is not appropriate for several of the SEDAR-assessed stocks being considered as they lack a full time series of catch. The ORCS process, while theoretically applicable to all stocks, is not practical for many stocks in the Southeast due to the high levels of uncertainty associated with the assumptions required, which can have the effect of compounding the unreliability of reference points for stocks with already unreliable input data. DCAC is the most promising of the methods, however, summary statistics are still in preparation for comparisons of ACLs produced by data-poor methods to those produced by the SEDAR assessments.