Maximum sustainable yield estimates of Ladypees, Sillago sihama (Forsskål), fishery in Pakistan using the ASPIC and CEDA packages
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Using surplus production model packages of ASPIC (a stock-production model incorporating covariates) and CEDA (Catch effort data analysis), we analyzed the catch and effort data of Sillago sihama fishery in Pakistan. ASPIC estimates the parameters of MSY (maximum sustainable yield), F msy (fishing mortality), q (catchability coefficient), K (carrying capacity or unexploited biomass) and B1/K (maximum sustainable yield over initial biomass). The estimated non-bootstrapped value of MSY based on logistic was 598 t and that based on the Fox model was 415 t, which showed that the Fox model estimation was more conservative than that with the logistic model. The R 2 with the logistic model (0.702) is larger than that with the Fox model (0.541), which indicates a better fit. The coefficient of variation (cv) of the estimated MSY was about 0.3, except for a larger value 88.87 and a smaller value of 0.173. In contrast to the ASPIC results, the R 2 with the Fox model (0.651–0.692) was larger than that with the Schaefer model (0.435–0.567), indicating a better fit. The key parameters of CEDA are: MSY, K, q, and r (intrinsic growth), and the three error assumptions in using the models are normal, log normal and gamma. Parameter estimates from the Schaefer and Pella-Tomlinson models were similar. The MSY estimations from the above two models were 398 t, 549 t and 398 t for normal, log-normal and gamma error distributions, respectively. The MSY estimates from the Fox model were 381 t, 366 t and 366 t for the above three error assumptions, respectively. The Fox model estimates were smaller than those for the Schaefer and the Pella-Tomlinson models. In the light of the MSY estimations of 415 t from ASPIC for the Fox model and 381 t from CEDA for the Fox model, MSY for S. sihama is about 400 t. As the catch in 2003 was 401 t, we would suggest the fishery should be kept at the current level. Production models used here depend on the assumption that CPUE (catch per unit effort) data used in the study can reliably quantify temporal variability in population abundance, hence the modeling results would be wrong if such an assumption is not met. Because the reliability of this CPUE data in indexing fish population abundance is unknown, we should be cautious with the interpretation and use of the derived population and management parameters.
Key wordsPakistan Sillago sihama ASPIC CEDA surplus production models
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- FAO, 2009. Fishery and Aquaculture Country Profile. FAO’s Fisheries Department, Rome, Italy, 1–18.Google Scholar
- Hoggarth, D. D., Abeyasekera, S., Arthur, R. I., Beddington, J. R., Burn, R. W., Halls, A. S., Kirkwood, G. P., McAllister, M., Medley, P., Mees, C. C., Parkes, G. B., Pilling, G. M., Wakeford, R. C., and Welcomme, R. L., 2006. Stock Assessment for Fishery Management. FAO Fisheries Technical Paper No. 487. Food and Agriculture Organization of the United Nations, Rome, Italy, 261pp.Google Scholar
- McKay, R. J., 1992. FAO species catalogue. Sillaginid Fishes of the World. (Family Sillaginidae). An Annotated and Illustrated Catalogue of the Sillago, Smelt or Indo-Pacific Whiting Species Known to Date. FAO Fisheries Synopsis, 14(125): 87pp.Google Scholar
- Musick, J. A., and Bonfil, R., 2004. Elasmobranch Fisheries Management Techniques. Asia-Pacific Economic Cooperation (APEC) Fisheries Working Group, Singapore, 133–164.Google Scholar
- Pitcher, T. J., and Hart, P. J. B., 1982. Fisheries ecology. Kluwer Academic Publishers, the Netherlands, 47pp.Google Scholar
- Prager, M. H., 1994. A suite of extensions to a nonequilibrium surplus-production model. Fishery Bulletin, 92: 374–389.Google Scholar
- Prager, M. H., 2005. A stock-Production model incorporating covariates (version 5) and auxiliary programs. CCFHR (NOAA) Miami laboratory document MIA-92/93-55, Beaufort Laboratory Document BL-2004-01.Google Scholar
- Quinn II, T. J., and Deriso, R. B., 1999. Quantitative Fish Dynamics. Oxford University Press, New York, USA, 542pp.Google Scholar
- Ricker, W. E., 1975. Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada, 191: 382pp.Google Scholar
- Weber, M., and de Beaufort, L. F., 1931. The Fishes of the Indo-Australian Archipelago. Perciformes (continued). Brill, Leiden, Nederland, 6: 477pp.Google Scholar