Ribogospod. nauka Ukr., 2018; 3(45): 16-27
DOI: 10.15407/fsu2018.03.016
УДК 639.2.052.2(575.2) (04)
Fish yield prediction in water bodies of the Kyrgyz Republic
M. Sarieva,
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, Food and Agriculture Organization of the United Nations, GCP/KYR/012/FIN Project, Bishkek Kyrgyz Republic
M. Aliiaskarov,
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, Department of Pastures, Livestock and Fisheries of the Ministry of Agriculture, Food Industry and Melioration of the Kyrgyz Republic, Bishkek, Kyrgyz Republic
B. Dzhenbaev,
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, National Academy of Science of the Kyrgyz Republic, Bishkek, Kyrgyz Republic
A. Sergek uulu,
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, National Academy of Science of the Kyrgyz Republic, Bishkek, Kyrgyz Republic
Purpose. The development of a Fish Yield Prediction Model for lakes and water reservoirs of the Kyrgyz Republic based on easily measured physico-chemical and biological indicators (limnological parameters) for the effective management and sustainable use of fish resources.
Methodology. Water samples for the analysis were taken once at each season (winter, spring, summer, autumn) during 2014 - 2015. The sampling included the collection of the following parameters: water temperature, acid-base balance of water (pH), electrical conductivity, dissolved oxygen, total dissolved solids (TDS), total alkalinity, nitrates, chlorophyll a. The samples were taken in surface layer of water at a depth of 0.5 m into 250 ml sterile chemical bottles. Chlorophyll "a" concentration was measured by a spectrophotometric method in laboratory conditions, according to a method described by Stirling H.P. The total alkalinity was determined by titration of sodium tetraborate solution (reverse titration), nitrates were determined by a photometric method with the Griss reagent after reduction in the cadmium reducing agent. The measurement of rapidly changing parameters, such as dissolved oxygen concentration (О2) was carried out by portable field equipment “OAKTON” DO 110, acid-base balance (pH) – “OAKTON” PH 11, measurement of water temperature, electrical conductivity, total dissolved solids (TDS) was carried out with the device "Orion Star"A 322. All measurements were taken directly in water areas.
Findings. According to data analysis, a number of indices, such as the morpho-edaphic index (MEI), primary productivity and biomass of phytoplankton, are strong indicators of fish yield, both in temperate and tropical lakes and reservoirs. It was identified that with an increase in the morpho-edaphic index, fish yield per unit area increases. Based on the results of our analysis, a Fish yield prediction model in six water bodies of the Kyrgyz Republic was developed, which has the following form:
Y = 0.1154 х MEIc 1.1628;
Y = 0.5613 х MEIa 1.1786;
where: Y- fish yield, kg/ha/year;
MEIc - Morpho-edaphic Index of Conductivity;
MEIa - Morpho-edaphic Index of Alkalinity.
Originality. This is the first research on the empirical Model development of fish yield prediction for lakes and water reservoirs in Central Asian region. The proposed Fish Yield Prediction Model, developed on the basis of MEI, using the statistical method of analysis, has invaluable practical utility to understand the fisheries potential of lakes and water reservoirs in the region, where such environmental conditions prevail.
Practical value: If reliable data on fish stock assessment in water bodies are unavailable, the proposed model will be used as one of the tools in the management of fish stocks.
Key words.
limnological parameters, morpho-edaphic index (MEI), empirical model of fish yield prediction, commercial fishery.
REFERENCES
1. Shiklomanov, I. A. (1990). Global water resources. Nature and resources, 26, 34-43.
2. Nikitin, A. A. (1976). Acclimatization and artificial reproduction of whitefishes in the reservoirs of Kyrgyzstan. Frunze.
3. Berg, L. S. (1948). Freshwater fish of the USSR and neighboring countries. Vol. 1. Leningrad, Publishing House of the Academy of Sciences.
4. Goncharov, A. I. (1964). Fisheries development of reservoirs of Kirghizia. Frunze.
5. Konurbayev, A. O., Kustareva, L. A., Alpiev, M. N., Kabataev, D. T., & Konurbava, E. S. (2005). Conditions of ichthyofauna of the fish industry and management in the Issyk-Kul lake. Bishkek.
6. Hadrian, P. (1985). Chemical and Biological Methods of Water Analysis for Aquaculture. Stirling : Institute of Aquaculture University of Stirling.
7. Guidelines for the chemical analysis of surface waters. (1977). Leningrad: Gidrometeoizdat.
8. Ryder, R. A. (1965). A method for estimating the potential fish production of north-temperate lakes. Transactions of American Fisheries Society, 94, 214-218. https://doi.org/10.1577/1548-8659(1965)94[214:AMFETP]2.0.CO;2
9. Henderson, H. F., & Welcomme, R. L. (1974). The Relationship of Yield to Morpho edaphic Index and Numbers of Fishermen in African Inland Waters. CIFA Occasional, 1.
10. Oglesby, R. T. (1977). Relationship of fish yield to lake phytoplankton standing crop, production and morphoedaphic factors. Journal of Fisheries Research Board of Canada, 34, 2271-2279. https://doi.org/10.1139/f77-305
11. Moreau, J., & De Silva, S. S. (1991). Predictive Fish Yield Models for Lakes and Reservoirs of the Philippines, Sri Lanka and Thailand. FAO Fisheries Technical, 319.
12. Nissanka, C., Amarasinghe, U. S., & De Silva, S. S. (2000). Yield Predictive Models for the Sri Lankan Reservoir Fishery. Fisheries Management and Ecology, 7, 425-436. https://doi.org/10.1046/j.1365-2400.2000.00212.x
13. Amarasinghe, U. S., De Silva S.S., & Nissanka C. (2002). Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka. Fisheries Management and Ecology, 9, 293-302. https://doi.org/10.1046/j.1365-2400.2002.00307.x
14. Kitaev, S. P. (1984). Ecological bases of bioproductivity of lakes of different natural zones. Moscow: USSR Academy of Sciences, Zoological Institute.
15. Wetzel, R. G. (2001). Limnology. 3rd edn. London: Academic Press.
16. Annual Reports of the Department of Pastures, Livestock and Fisheries under the Ministry of Agriculture and Food of the Kyrgyz Republic for 2009-2014.