Data-rich but model-resistant: an evaluation of data-limited methods to manage fisheries with failed age-based stock assessments
| Autor: | Christopher M. Legault, John Wiedenmann, Jonathan J. Deroba, Gavin Fay, Timothy J. Miller, Elizabeth N. Brooks, Richard J. Bell, Joseph A. Langan, Jamie M. Cournane, Andrew W. Jones, Brandon Muffley |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Canadian Journal of Fisheries and Aquatic Sciences. 80:27-42 |
| ISSN: | 1205-7533 0706-652X |
| DOI: | 10.1139/cjfas-2022-0045 |
| Popis: | 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 impacted 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. |
| Databáze: | OpenAIRE |
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