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Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka
Author(s) -
AMARASINGHE U. S.,
DE SILVA S. S.,
NISSANKA C.
Publication year - 2002
Publication title -
fisheries management and ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 55
eISSN - 1365-2400
pISSN - 0969-997X
DOI - 10.1046/j.1365-2400.2002.00307.x
Subject(s) - watershed , environmental science , land cover , principal component analysis , ordination , land use , hydrology (agriculture) , sri lanka , drainage basin , shrub , water resource management , fishery , ecology , geography , statistics , mathematics , computer science , cartography , geology , tanzania , environmental planning , geotechnical engineering , machine learning , biology
Land‐use patterns in the catchment areas of Sri Lankan reservoirs, which were quantified using Geographical Information Systems (GIS), were used to develop quantitative models for yield prediction. The validity of these models was evaluated through the application to five reservoirs that were not used in the development of the models, and by comparing with the actual fish yield data of these reservoirs collected by an independent body. The robustness of the predictive models developed was tested by principal component analysis (PCA) on limnological characteristics, land‐use patterns of the catchments and fish yields. The predicted fish yields in five Sri Lankan reservoirs, using the empirical models based on the ratios of forest cover and/or shrub cover to reservoir capacity or reservoir area were in close agreement with the observed fish yields. The scores of PCA ordination of productivity‐related limnological parameters and those of land‐use patterns were linearly related to fish yields. The relationship between the PCA scores of limnological characteristics and land‐use types had the appropriate algebraic form, which substantiates the influence of the limnological factors and land‐use types on reservoir fish yields. It is suggested that the relatively high predictive power of the models developed on the basis of GIS methodologies can be used for more accurate assessment of reservoir fisheries. The study supports the importance and the need for an integrated management strategy for the whole watershed to enhance fish yields.