Yield predictive models for Sri Lankan reservoir fisheries

Abstract

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.

Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs

“…For the Sri Lankan reservoirs, the regression of the yield (kg/ha) against RLLF gave a higher coefficient of determination (r 2 = 0.81), than any of the yield predictive models by Nissanka et al (2000), based on multiple regressions on fishing intensity and various productivity related variables. In their analysis, the best fitted multiple regression for fish yield (r 2 = 0.69) consisted of the independent variables effort (boat days) and total phosphorous, nearly identical (r 2 = 0.68) with effort and the ratio between catchment area and reservoir capacity, and better than effort and MEI (r 2 = 0.56).…”

Analysis of artisanal fisheries in two reservoirs of the upper Paraná River basin (Southeastern Brazil)

“…The Barra Bonita Reservoir showed relatively high fish productivity, due to the exotic species O. niloticus, and is among the most productive in Brazil, with its productivity levels being comparable to reservoirs with similar characteristics, i.e., eutrophic reservoirs that contain the exotic species O. niloticus (Paiva et al, 1994;Alvares et al, 2000;Minte-Vera & Petrere Jr., 2000;Walter & Petrere Jr., 2007). High fish productivity has been noted in eutrophic reservoirs of other countries that contain introduced "tilapia" species (Averhoff, 1999;Nissanka et al, 2000;Sugunan, 2000;Jackson & Marmulla, 2001;Amarisinghe, 2002;Kester et al, 2007;Caraballo, 2009). Exotic species have become successfully established in eutrophic freshwater reservoir ecosystems around the world (Smith & Schindler, 2009;Wolos et al, 2009;La Porta et al, 2010;Morosawa, 2011).…”

Stocking models of Chinese mitten crab (Eriocheir japonica sinensis) in Yangtze lakes

“…In comparison to other inland fishery models, ours have the following characteristics: 1) Z SD / Z M , the driving variable, is easy to measure and can be conveniently used by producers. Contrarily, the variables in other models, such as standing crops of plankton or zoobenthos (Liang and Liu, 1995;Nissanka et al, 2000), are more difficult to obtain by ordinary users and, thus, have less practicability. 2) The optimal-stocking model is generated based on the driving variable, Z SD / Z M , during planting season, so that it provides practical references for juvenile stocking.…”

“…For several decades, such models have made great contributions to predict the production of fish with all kinds of diets or total fish production. Their x-variables range from physical, chemical and biological or their combination, e.g., the morpho-edaphic index (MEI, ratio of total dissolved solids in mg L − 1 and mean depth in m) (Ryder et al, 1974;Rempel and Colby, 1991), primary production and phytoplankton standing crop Liang and Liu, 1995;Nissanka et al, 2000), zoobenthos standing crop (Matuszek, 1978;Liang and Liu, 1995).…”