Improving the interpretability of the effects of environmental factors on abundance of fish stocks
Adequately assess the environmental effects on commercially exploited fish stocks is one of the main challenges faced by stock managers in their attempt to achieve ecosystemic management. In this study, we have developed a methodology which allows simulate the evolution of abundance or biomass of a...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/325560 |
| Acesso em linha: | http://hdl.handle.net/10261/325560 |
| Access Level: | acceso abierto |
| Palavra-chave: | Pesquerías Ecosystemic approach Centro Oceanográfico de Cádiz Computational simulation Time series ARIMA models Oceanic parameters Atmospheric parameters |
| Resumo: | Adequately assess the environmental effects on commercially exploited fish stocks is one of the main challenges faced by stock managers in their attempt to achieve ecosystemic management. In this study, we have developed a methodology which allows simulate the evolution of abundance or biomass of a fish population and calculate two indexes (the significant effect of the environmental parameter –EF- and the relative weight index –RW-) that improve the interpretability of the environmental effects on abundance of fish stocks. This methodology is based on the assumption that under the premise of environmental invariability the variation in the abundance of a population is linearly dependent on its abundance in the past. These indexes have been integrated into a decision support system namely SFish SubClass 1.1, specifically designed to disaggregate the effects of different environmental factors on a commercially exploited fish population. We have applied our model to the fishery of Pagellus bogaraveo in a complex environmental area (Strait of Gibraltar). The results show that the developed methodology provides plausible simulations. Finally, the RW and EF indexes indicated that the most influential variables were salinity, sea temperature and salinity flux. |
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