The refined ORCS approach: A catch-based method for estimating stock status and catch limits for data-poor fish stocks

Abstract The ‘Only Reliable Catch Stocks’ (ORCS) Working Group approach to data-poor fisheries stock status and catch limit estimation has been used by U.S. fisheries managers but has yet to be fully evaluated. The ORCS approach estimates stock status using a fourteen question ‘Table of Attributes’ and the overfishing limit by multiplying a historical catch statistic by a scalar based on the estimated status. We evaluated the performance of the approach by applying it to 193 stocks with data-rich stock assessments and comparing its predictions of stock status with the assessment model estimates. The approach classified all but three stocks as fully exploited indicating that it is a poor predictor of status and should not be used by managers. We refined the original ORCS approach by: (1) developing a more predictive model of stock status using boosted classification trees and (2) identifying the historical catch statistics and scalars that best estimate overfishing limits using assessment model data. The refined ORCS approach correctly classified 74% of all stocks and 62% of overexploited stocks in a training dataset and 74% of all stocks and 50% of overexploited stocks in an independent test dataset. The refined approach performed better than six other widely used catch-only methods. However, the overfishing limits estimated by the refined approach would further deplete overexploited stocks without the use of conservative catch scalars to buffer against classification uncertainty. Conservative catch scalars can reduce the probability of overfishing below 50%, the U.S. legal maximum, but with concomitant increases in the probability and magnitude of underfishing. The refined ORCS approach may therefore be useful when other methods are not possible or appropriate and some risk of underfishing is acceptable.