Stock assessment prioritization in the Azores : procedures, current challenges and recommendations

• Autor(a) principal: Santos, Régis
• Data de Publicação: 2020
• Outros Autores: Leal, Wendell Medeiros, Pinho, Mário Rui
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.3/5851
• topic: Fishery Resources; Management; Sustainable Development Goals; Azores

To implement the Marine Strategy Framework Directive (MSFD) of the European Union (EU) in order to achieve the Sustainable Development Goals (SDGs) of the United Nations (UN) regarding the biological sustainability of marine fisheries, it is fundamental to apply a framework for prioritizing stocks. This process helps the regional managers to make the best use of data and resources for management. The present study describes and applies a standard framework for prioritization of stock assessment in the Azores. The current state of the selected stocks is identified and the main issues and gaps for assessment are presented and discussed. A total of 138 species were landed in the region during the period 2009-2019. Twenty-two (18 fishes, 2 molluscs and 2 crustaceans) were selected as priority stocks according to the Food and Agriculture Organization of the United Nations (FAO) and International Council for the Exploration of the Sea (ICES) criteria. Most of these showed a decreasing trend in their abundances. Only four stocks are currently assessed using data-limited approaches: Pagellus bogaraveo, Aphanopus carbo, Raja clavata, and Trachurus picturatus. No biological reference points are defined and stock and exploitation status relative to Maximum sustainable yield (MSY) are not assessed. The main issues identified were the lack of information regarding catches and population structure and validated analytical methods. Future studies should evaluate which methods for assessment may be suitable for each stock and identify what additional data are needed to improve the analyses.