ASSESSING THE APPLICABILITY OF ENVIRONMENTAL INDICATORS FOR IMPROVING THE FISHERIES ASSESSMENT OF THE ALBACORE (THUNNUS ALALUNGA) UNDER THE A4A APPROACH
In this study we explore the potential for improving the stock assessment of Mediterranean Albacore by integrating environmental indicators. For this purpose we developed a catch at age model within the A4A stock assessment approach. The input data was similar to that used in the official SCRS stock...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/326574 |
| Acceso en línea: | http://hdl.handle.net/10261/326574 |
| Access Level: | acceso abierto |
| Palabra clave: | Centro Oceanográfico de Málaga Albacore Pesquerías environmental indicators Balearic Sea Mediterranean stock assessment operational fisheries oceanography fish fisheries indicators catch/effort length frequency |
| Sumario: | In this study we explore the potential for improving the stock assessment of Mediterranean Albacore by integrating environmental indicators. For this purpose we developed a catch at age model within the A4A stock assessment approach. The input data was similar to that used in the official SCRS stock assessment in 2017 but with an updated larval index. The environmental indicator provides information on the interannual variability of the sea surface temperature in the Balearic Sea during the spawning season, and it is included in the “Environmental pressure” component of the Ecosystem Report Card. The indicator is included in the assessment model in different ways, as index of the class age 0, as vector for the Stock/recruitment model, and as productivity value in other stock recruitment models (Ricker, Beverton-Holt). The results showed that incorporating the environmental variability indicators provide a better stock assessment fits (AIC, BIC), and also show the need for more advanced techniques to test stock assessment performance when testing the inclusion of environmental variability |
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