Spatial distribution and species composition of small pelagic fishes in the Gulf of California

Lanz, Edgar; Nevárez-Martínez, Manuel O.; López-Martínez, Juana; Dworak, Juan A.
June 2008
Revista de Biología Tropical;jun2008, Vol. 56 Issue 2, p575
Academic Journal
Traditional regionalization methods in fisheries based on provinces or major fishing areas, includes large and arbitrary grids in which basic statistics or inferences on distribution or abundance are made. We describe a method for regionalization and analysis of fishing activities for small pelagic fisheries in the Gulf of California based on spatial patterns of landing and catch data in a Geographic Information System (GIS) environment. A fisheries database from logbooks with spatial attributes from October 2002 to June 2007 was analyzed. Landings and catching data were transformed to a Weighted Region Index (WRI) by using fuzzy logic operators. The WRI revealed fishing action centers characterized by areas with the highest WRI values, and a hierarchy for the relative importance of the regions was established. Guaymas, Desemboque de Caborca, Isla Patos, and Bahía San Rafael they were the most prominent ones. An analysis of the relative frequency of species composition showed that the Pacific sardine had an over 80 % abundance in the midriff islands, and remained as the most important in the upper gulf regions, while in the central part of the gulf, relative abundances of Pacific sardine and Northern anchovy were more balanced. Relative abundance of mackerel was significantly larger around Isla Patos than in any other place. Guaymas had the largest relative composition of Northern anchovy and the lowest values for Pacific sardine. Desemboque de Caborca showed the largest homogeneity in species relative composition. It is important to highlight that this results come from in situ data, while the results previously reported come from landing statistics by port. Therefore, the present method acknowledges the spatial differences of species by regions, additional to the traditional time series analysis.


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