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This study is the first to describe maximum sustainable yield (MSY) of fish and shellfish resources of Pakistan. MSY of a fish species ladypees, Sillago sihama and shellfish spiny lobster, Panulirus sp were analyzed. The computer packages of a stock-surplus production model incorporating covariates (ASPIC) and catch and effort data analysis (CEDA) used here were based on the non-equilibrium assumption of the state of the stocks.
ASPIC estimates the parameters of MSY (maximum sustainable yield), Fmsy (fishing mortality at MSY), q (catchability coefficient), K (carrying capacity or unexploited biomass) and B1/K (starting biomass carrying capaaty).
The key parameters of CEDA are: MSY, K, q, and r (intrinsic growth), and the three error assumptions in the models are normal, log normal and gamma.
The time series catch and effort data were applied to estimate MSY for Sillago sihama fishery. The ASPIC estimate of the logistic model was 598 mt (metric tones) and that based on Fox model was 415 mt, which showed that the Fox model estimation was more conservative than that with the logistic model. The R2 with 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 0.88 and a smaller value of 0.173.
In contrast to the ASPIC results, the CEDA estimates of R2 with Fox model (0.651-0.692) was larger than that with the Schaefer model (0.435-0.567), indicating a better fit. Parameter estimates from the Schaefer and Pella-Tomlinson models were identical. The MSY estimations from the above two models were 398 mt, 549 mt and 398 mt for normal, log-normal and gamma error distributions respectively. The MSY estimates from Fox model were 381 mt, 366 mt and 366 mt for the above three error assumptions respectively. Fox model estimates were smaller than those for Schaefer and Pella-Tomlinson models. In the light of the MSY estimations of 415 mt from ASPIC for Fox model and 381 mt from CEDA for Fox model, MSY for S. sihama is calculated to be about 400 mt. As the catch in 2003 was 401 mt, we would suggest the fishery is sustainable and 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.
A comparison of logistic and generalized surplus-production models were made and applied on the time series catch and effort data of ladypees, Sillago sihama to investigate the performances of two closely related estimators. The logistic model estimates of such as MSY were more reasonable than that of the generalized estimator. However, the R2 in CPUE estimates in generalized model were more precise than the logistic. Simulation analyses were carried out on the S. sihama like simulated fishery. The estimated and observed abundance index (AI) showed that they were close for the logistic model, but different for the generalized production model. Standardized residuals were about distributed around 0.0 for logistic model, but had a weak increasing trend for the generalized model. Statistical outliers were seen in logistic model in 1989 and 1993 whereas in generalized in 1981 and 1999. Simulated results reveal that the logistic estimates were close to the true value for the low CV but largely dispersed for high CV, in contrast the generalized model estimates were loose for all the CV levels. The estimated production model curve shape parameter φ was not reasonable at all the white noise levels. When the noise level increased the R2 values in catch per unit effort decreased. Therefore. we would like to conclude that the logistic model appeared more reasonable than the generalized production model on the basis of ladypees fishery tested in this study.
The estimation of MSY of spiny lobster Panulirus sp fishery in Pakistan was made using CEDA computer programme. The MSY outputs of three models of Fox, Schaefer and Pella-Tomlinson are: 828 mt, 970 mt and 970 mt respectively. The outputs of error assumption of normal and log normal are 983mt (R2=0.57) & 950 mt (R2=0.53) in Schaefer and Pella-Tomlinson respectively. MSY outputs of normal error assumption of Fox are 817mt (R2= 0.56). All the gamma error assumptions are (790 mt) similar. The coefficient of variation (cv) of the estimated MSY was about 0.7 and the larger value (1.0) whereas lowest (0.5) were recorded. The Fox model output are more conservative hence best fits may be close to the annual average landings of the spiny lobster fishery in Pakistan which is 480 metric tones.
The age and growth information of fish is important for assessment of the dynamics, planning, and management in fisheries. The research presented here stems from an attempt to use the parameters of different growth models to compare the growth characteristics of different fishes in different environments. Also the goodness of fit of different models and the ability of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in model selection were analysis in this thesis.
The mean size at age data of 10 fish species were fitted by the five growth models e.g. of von Bertalanffy, Gompertz, Allometric, logistic and Polynomial and the growth parameters were eshmated by the maximum likelihood method. The best model was selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The growth of most species can be best described by the von Bertalanffy and Allometric growth models. The results showed that both AIC and BIC have their advantages in the testing of significance of the difference between the functions of models. As for the demersal fishes (e.g. snappers and groupers), BIC is better than AIC in selecting the best growth model.
As a part of this dissertation population biology and stock assessment of kelee shad, Hilsa kelee was estimated using the computer programme of FiSAT (FAO, ICLARM, fisheries stock assessment. The growth parameters were estimated with von Bertalanffy growth equation Lt=23.10(1-exp(-0.94(t+0.18))) cm. The length-at-first capture Lc=10.88 cm was estimated. The estimated parameters of total mortality (Z), natural mortality (M) and fishing mortality (F) were 2.08 yr-1, 1.78 yr-1, and 0.30 yr-1 respectively. Biomass per recruitment (B/R) and yield per recruitment (Y/R) were estimated 0.87 and 0.031 respectively. The annual exploitation rate was U=0.12. The exploitation ratio at MSY or Emax=0.73 and fishing mortality at maximum sustainable yield Fmax=1.52; biological reference point Fopt=0.89 yr-1 and Flimit=1.18 yr-1. From the results obtained here it is interpretable that the natural mortality was higher than fishing mortality in Hilsa kelee, indicating that the state of the stock is sustainable. Therefore, we conclude that the standing stock should be kept at the current level.
ASPIC estimates the parameters of MSY (maximum sustainable yield), Fmsy (fishing mortality at MSY), q (catchability coefficient), K (carrying capacity or unexploited biomass) and B1/K (starting biomass carrying capaaty).
The key parameters of CEDA are: MSY, K, q, and r (intrinsic growth), and the three error assumptions in the models are normal, log normal and gamma.
The time series catch and effort data were applied to estimate MSY for Sillago sihama fishery. The ASPIC estimate of the logistic model was 598 mt (metric tones) and that based on Fox model was 415 mt, which showed that the Fox model estimation was more conservative than that with the logistic model. The R2 with 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 0.88 and a smaller value of 0.173.
In contrast to the ASPIC results, the CEDA estimates of R2 with Fox model (0.651-0.692) was larger than that with the Schaefer model (0.435-0.567), indicating a better fit. Parameter estimates from the Schaefer and Pella-Tomlinson models were identical. The MSY estimations from the above two models were 398 mt, 549 mt and 398 mt for normal, log-normal and gamma error distributions respectively. The MSY estimates from Fox model were 381 mt, 366 mt and 366 mt for the above three error assumptions respectively. Fox model estimates were smaller than those for Schaefer and Pella-Tomlinson models. In the light of the MSY estimations of 415 mt from ASPIC for Fox model and 381 mt from CEDA for Fox model, MSY for S. sihama is calculated to be about 400 mt. As the catch in 2003 was 401 mt, we would suggest the fishery is sustainable and 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.
A comparison of logistic and generalized surplus-production models were made and applied on the time series catch and effort data of ladypees, Sillago sihama to investigate the performances of two closely related estimators. The logistic model estimates of such as MSY were more reasonable than that of the generalized estimator. However, the R2 in CPUE estimates in generalized model were more precise than the logistic. Simulation analyses were carried out on the S. sihama like simulated fishery. The estimated and observed abundance index (AI) showed that they were close for the logistic model, but different for the generalized production model. Standardized residuals were about distributed around 0.0 for logistic model, but had a weak increasing trend for the generalized model. Statistical outliers were seen in logistic model in 1989 and 1993 whereas in generalized in 1981 and 1999. Simulated results reveal that the logistic estimates were close to the true value for the low CV but largely dispersed for high CV, in contrast the generalized model estimates were loose for all the CV levels. The estimated production model curve shape parameter φ was not reasonable at all the white noise levels. When the noise level increased the R2 values in catch per unit effort decreased. Therefore. we would like to conclude that the logistic model appeared more reasonable than the generalized production model on the basis of ladypees fishery tested in this study.
The estimation of MSY of spiny lobster Panulirus sp fishery in Pakistan was made using CEDA computer programme. The MSY outputs of three models of Fox, Schaefer and Pella-Tomlinson are: 828 mt, 970 mt and 970 mt respectively. The outputs of error assumption of normal and log normal are 983mt (R2=0.57) & 950 mt (R2=0.53) in Schaefer and Pella-Tomlinson respectively. MSY outputs of normal error assumption of Fox are 817mt (R2= 0.56). All the gamma error assumptions are (790 mt) similar. The coefficient of variation (cv) of the estimated MSY was about 0.7 and the larger value (1.0) whereas lowest (0.5) were recorded. The Fox model output are more conservative hence best fits may be close to the annual average landings of the spiny lobster fishery in Pakistan which is 480 metric tones.
The age and growth information of fish is important for assessment of the dynamics, planning, and management in fisheries. The research presented here stems from an attempt to use the parameters of different growth models to compare the growth characteristics of different fishes in different environments. Also the goodness of fit of different models and the ability of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in model selection were analysis in this thesis.
The mean size at age data of 10 fish species were fitted by the five growth models e.g. of von Bertalanffy, Gompertz, Allometric, logistic and Polynomial and the growth parameters were eshmated by the maximum likelihood method. The best model was selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The growth of most species can be best described by the von Bertalanffy and Allometric growth models. The results showed that both AIC and BIC have their advantages in the testing of significance of the difference between the functions of models. As for the demersal fishes (e.g. snappers and groupers), BIC is better than AIC in selecting the best growth model.
As a part of this dissertation population biology and stock assessment of kelee shad, Hilsa kelee was estimated using the computer programme of FiSAT (FAO, ICLARM, fisheries stock assessment. The growth parameters were estimated with von Bertalanffy growth equation Lt=23.10(1-exp(-0.94(t+0.18))) cm. The length-at-first capture Lc=10.88 cm was estimated. The estimated parameters of total mortality (Z), natural mortality (M) and fishing mortality (F) were 2.08 yr-1, 1.78 yr-1, and 0.30 yr-1 respectively. Biomass per recruitment (B/R) and yield per recruitment (Y/R) were estimated 0.87 and 0.031 respectively. The annual exploitation rate was U=0.12. The exploitation ratio at MSY or Emax=0.73 and fishing mortality at maximum sustainable yield Fmax=1.52; biological reference point Fopt=0.89 yr-1 and Flimit=1.18 yr-1. From the results obtained here it is interpretable that the natural mortality was higher than fishing mortality in Hilsa kelee, indicating that the state of the stock is sustainable. Therefore, we conclude that the standing stock should be kept at the current level.