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A cephalopod fishery GIS for the Northeast Atlantic: development and application

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Abstract:

Cephalopod stocks are of increasing economic importance. Cephalopod fisheries show marked inter-annual fluctuations unrelated to fishery landings and effort. Their population dynamics, particularly recruitment, are thought to be strongly susceptible to changes in environmental conditions. This arises in part from the short life cycle, resulting in poor buffering of the population against changing conditions. These characteristics make traditional approaches to stock assessment and fishery management inappropriate. GIS offers a tool to improve understanding of spatio-temporal trends in abundance and facilitate rational management. A cephalopod fishery geographical information system for Northeast Atlantic waters (CFGIS-NEA) was developed. The system covers the area from 28.0° W to 11.0° E, and 34.5° N to 65.5° N. It was designed for investigating cephalopod resource dynamics in relation to environmental variation. It is based on Unix Arc/Info, and PC ArcView, combined with the statistical software package S-PLUS and supported by a database in Microsoft Access. Environmental data (e.g. sea surface temperature and salinity, sea bottom temperature and salinity, and bathymetric data), cephalopod fishery, survey and biological data, from a variety of sources, were integrated in the GIS as coverages, grids, shapefiles, and tables. Special functions were developed for data integration, data conversion, query, visualisation, analysis and management. User-friendly interfaces were developed allowing relatively inexperienced users to operate the system. The spatial and temporal distribution patterns of cephalopod abundance by species, the spatial and temporal relationships between cephalopod abundance and environmental factors, and the spatial and temporal patterns of cephalopod fishing activity were analysed using a combination of visual (qualitative) and quantitative methods. Predictive empirical models, such as GAMs (generalized additive models), were developed for modelling cephalopod abundance using environmental variables.

Document Type: Research Article

DOI: https://doi.org/10.1080/13658810110074500

Publication date: 2001-12-01

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