Data-rich but model-resistant: an evaluation of data-limited methods to manage fisheries with failed age-based stock assessments

Abstract

Age-based stock assessments are sometimes rejected by review panels due to large retrospective patterns. When this occurs, data-limited approaches are often used to set catch advice, under the assumption that these simpler methods will not be im-pacted by the problems causing retrospective patterns in the age-based assessment. This assumption has never been formally evaluated. Closed-loop simulations were conducted where a known source of error caused a retrospective pattern in an age-based assessment. Twelve data-limited methods, an ensemble of a subset of these methods, and a statistical catch-at-age model with retrospective adjustment were all evaluated to examine their ability to prevent overfishing and rebuild overfished stocks. Overall, none of the methods evaluated performed best across the scenarios. A number of methods performed consistently poorly, resulting in frequent and intense overfishing and low stock sizes. The retrospective adjusted statistical catch-at-age assessment performed better than a number of the alternatives explored. Thus, using a data-limited approach to set catch advice will not necessarily result in better performance than relying on the age-based assessment with a retrospective adjustment.