Tropical reservoirs are primarily constructed for irrigation, generation of hydroelectricity and water supply schemes. Development of inland fisheries is a secondary use of most reservoirs. In Sri Lanka, most reservoirs are scattered in the rural areas of the country so that investigation of the fisheries of individual reservoirs with a view to developing management plans is prohibitive. The present study was instigated to explore the possibilities of developing suitable yield predictive models, which can be used in developing management strategies for the Sri Lankan reservoirs. The study was carried out in 11 perennial reservoirs of Sri Lanka. Basic limnological parameters (conductivity, dissolved phosphorus, total phosphorus, chlorophyll a [chl a] content and alkalinity) were determined in each of these reservoirs. Daily data on fish catch and fishing effort were collected in each reservoir. Data on catchment areas (CA), reservoir area (RA) and reservoir capacity (RC) were obtained from the irrigation and survey departments. It is evident that chl a is positively influenced by nutrients (dissolved phosphorus and total phosphorus), morphoedaphic indices derived as alkalinity to mean depth (MEIa) and conductivity to mean depth (MEIc) ratios and CA/RC ratios. MEIa and MEIc are also positively influenced by CA/RC ratios. All these morphological and edaphic parameters were found to positively influence fish yield in reservoirs. As fishing intensity (FI) is also a major determinant of fish yields, fish yield was better accounted by multiple regression models in which FI and individual morphological and edaphic parameters were used as independent variables. Of these multiple regression relationships, the best predictive power for fish yield (Y in kg ha−1 yr−1) was found by Y=18.9+6.78 FI+0.0073 CA/RC where FI is expressed as boat‐days ha−1 yr−1 and CA and RC are in km2 and km3, respectively. In this relationship, FI and CA/RC account for about 68% of the variation in fish yield.

The Negombo estuary brush park fishery in Sri Lanka was investigated from June 1998 to March 1999. The mean fish yield (all species) in the fishery was 12.46 t ha−1 yr−1. Fin fish species formed about 91% of the yield and the rest was formed by penaeid shrimps and crabs. Fish yield and the twig density in the brush park exhibited a second order polynomial relationship indicating a minimum yield at an intermediate value of twig density in the brush parks. The relationship between fish yield and duration of implantation of brush parks showed an optimal period of 30–40 days for high fish yields. Income levels of fishermen, availability of construction material and suitable sites for implantation of brush parks are some of the factors determining size and number of brush parks per fisherman and duration of installation. Indigenous knowledge within the fishing community about the effect of salinity variations, twig density and mangrove species used on the harvests greatly contribute to effective operation of this fishing practice. Cultivation of mangroves to obtain twigs and branches is a unique feature in this estuary. Indigenous knowledge on mangroves has led to the adoption of sound silvicultural practices. Although cultivating mono‐specific mangrove stands may not increase diversity of mangrove forests, it reduces denudation of naturally occurring mangrove forests because of brush park construction, and retains habitats for other organisms. Mangrove management plans in the Negombo estuary should therefore be viewed and treated in an integrated manner that takes into account both resource and social components.

Use of geographical information systems (GIS) in inland fisheries has hitherto been essentially restricted to site evaluation for aquaculture development and assessment of limnological changes in time and space in individual water bodies. The present GIS study was conducted on the land‐use pattern of the catchments of nine reservoirs in Sri Lanka, for which detailed fishery data, viz. yield, fishing intensity, landing size of major constituent species, together with selected limnological data such as conductivity and chlorophyll‐a, were available. Potential statistical relationships (linear, curvilinear, exponential and second‐order polynomial) of fish yield (FY, in kg ha−1 yr−1) to different land‐use patterns, such as forest cover (FC, in km2) and shrub‐land (SL, in km2), either singly, or in combination, and/or the ratio of each land type to reservoir area (RA in km2) and reservoir capacity (RC in km3), were explored. Highly significant relationships were evident between FY to the ratio of SL and/or FC+SL to RA and/or RC. Similarly, the above land‐use types to RA and RC ratios were significantly related to limnological features of the reservoirs. The relationships of FY to various parameters obtained in this study were much better correlated than those relationships of FY to limnological and biological parameters used in yield prediction in tropical and temperate lacustrine waters previously.

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.

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