Editorial: Management and monitoring of deep-sea fisheries and stocks

Abstract

The management and monitoring of deep-sea fisheries and stocks is challenging due to the diversity of life histories in exploited stocks and sparseness of biological knowledge about certain species. Moreover, these issues are often combined with a relative shortage of suitable data. The FAO defines deep-sea fisheries as those exploiting species that can only sustain low exploitation rates (FAO 2009). In the European Union (EU) deep-sea fisheries are covered by council regulation No. 2347/2002 on “establishing specific access requirements and associated conditions applicable to fishing for deepsea stocks", which lists 24 species, including many deepwater sharks. Other definitions are used in other jurisdictions around the world. The papers presented in this special issue address a number of questions relevant to assess deepwater stocks and moving towards an ecosystem approach to the management of European deepwater fisheries. The underlying research was carried out in the EU-funded Deepfishman project, “Management and Monitoring of Deep-sea Fisheries and Stocks” (Grant agreement No. 227390), which was conducted from 2009 to 2012. These papers complement a number of recently published studies on deepwater stocks and fisheries, many of which were part of the same project. A recent review of deepwater stocks and their management worldwide concluded that, although deepwater fisheries in different regions might exploit the same species, case-specific monitoring and management strategies are often necessary (Large et al. 2013). Furthermore, Edwards et al. (2012) suggested that, although a range of approaches exist for deepwater stock assessment, from very simple to more sophisticated and informative, some methodological developments are still required. A recent example of such a development is the statistical catch-at-age model, which was used to assess the recruitment strength of the beaked redfish, Sebastes mentella in the Barents Sea (Planque et al. 2012). Another statistical catch-atage model was developed to provide estimates of total mortality and abundance of blue ling, Molva dypterygia, to the west of the British Isles (Trenkel et al. 2012). This model makes stock assessments possible using only fishery-dependent data