2 Sindh Fisheries Department, Hyderabad 71000, Sindh, Pakistan;
3 Marine Fisheries Department, Fish Harbor west wharf, Karachi, 74000, Pakistan;
4 Department of Fresh Water Biology and Fisheries, University of Sindh, Jamshoro 76080 Sindh, Pakistan;
5 Department of Economics, Management and Quantitative Methods Via Conservatorio, 7, Universita Degli Studi Di Milano, 20122, Milan, Italy
Pakistan is endowed with fishery resources that have a huge potential for economic development. The country has a long coastline, which is ideal for the development of the fisheries. In the coastal areas of the provinces of Sindh and Balochistan, about 90% of households rely on fishing and other fishing-related activities(Siddiqi, 1992). Marine fishery is important in Pakistan; it has great importance for promoting national food security by reducing the pressure on the dem and from mutton, beef and poultry. The marine fisheries sector of Pakistan accounts for about 57% in terms of total aquatic production and export earning, it was 1.1% of GDP in 2006(FAO, 2009).
The coast of Pakistan is about 1 120 km(FAO, 2009)long, stretching from the Indian border in the east to the Iranian border in the West(Fig. 1). Pakistani waters are home to about 250 commercially important demersal fish species(Bianchi, 1985). The exclusive economic zone(EEZ)of Pakistan consists of about 240 000 km², with an additional continental shelf area of about 50 270 km². As such; the total sea area of Pakistan is more than 30% of the l and area(FAO, 2009).
The Sparidae family is represented by about 147 species in 38 genera(Eschmeyer and Fong, 2015)in the oceans of the world. In Pakistan, the Sparidae consists of 14 species in 8 genera(Siddiqui et al., 2014). The genus Argyropsis represented in Pakistani waters by only one species, Argyrops spinifer(Forsskål, 1775). This species is commonly known as the King Soldier Bream, while locally called “Dh and ” in Sindhi or “Sorro” in Balochi, (Bianchi, 1985). A. spiniferis also known as fry pan bream. A. spinifer is mainly exploited in Pakistani waters by bottom trawls, gill nets, h and lines and long lines. Sexually ripe specimens are common in December and April, while juveniles up to 3 cm are common in July along the Balochistan coast. The king soldier bream is distributed throughout the Western Indian Ocean extending eastward to the Indo-Malayan archipelago and northern Australia. It inhabits a wide range of bottom types to 100 m depth, Young fish occur in very shallow waters of sheltered bays; larger individuals in deeper water(Sommer et al., 1996). It has a maximum length of 70 cm but is more commonly of length around 30 cm(Bauchot and Smith, 1984).
Some studies have been done on its biology(e.g. Druzhinin, 1975 in Yemen; Edwards et al., 1985 in Yemen; El-Sayed and Abdel-Bary, 1995 in Qatari waters; Al-Mamry et al., 2009 in Omani waters; AlMamry et al., 2011 in the Arabian Sea, Oman; Yoon et al., 2013 in the Sea of Oman; Ghanbarzadeh et al., 2014 in the Persian Gulf, Iran). Recently some work on the estimation of MSY of some species of fish has been done in Pakistan and China(Panhwar et al., 2012a, b; Kalhoro et al., 2013; Siyal et al., 2013; Wang and Liu 2013; Memon et al., 2015), but there is no work available for the estimation of MSY of A. spiniferin Pakistani waters. Therefore, the present work is to provide a preliminary stock assessment of A.spiniferin Pakistan. This may help to draw up the required fisheries regulations to sustain this fish resource. 2 MATERIAL AND METHOD2.1 Data
The catch and effort data of King soldier bream(Argyrops spinifer)during the period from 1985 to 2009(25 years)were obtained from the h and book of Fisheries Statistics of Pakistan compiled by Marine Fisheries Department(MFD), Karachi(Table 1). Fishing effort is presented by the number of fishing boats, and the annual total catch is presented in the form of weight(metric ton).2.2 Biomass dynamics models(BDMs)
Fishery statistics on A. spiniferin the 25-year period 1985–2009 were collected and analyzed by CEDA(catch effort data analysis)(Hoggarth et al., 2006) and ASPIC(surplus production models incorporating covariates)(Prager, 2005).
The BDMs are also called surplus production models(SPMs)which have three versions, respectively by Schaefer, Fox, and Pella Tomlinson. The most commonly used model is Schaefer model which is built on a logistic population growth model:
Next Fox proposed an analysis based on the Gompertz growth equation,
Finally, Pella and Tomlinson proposed the use of a generalized production equation,
where Bis the fish biomass, tis time, B ∞ is the carrying capacity, r is the intrinsic rate of population increase.
The discrete form of population dynamics with, for example, the Schaefer model is:
where C is catch, qis catchability, E is fishing effort.
Fishing mortality can then be calculated as F= qE. 2.3 Catch and effort data analysis(Hoggarth et al., 2006), version 3.0.1(CEDA)
CEDA(v. 3.0.1)is a computer software package, which is built on non-equilibrium surplus production models, including those of Fox(1970), Schaefer(1954) and Pella and Tomlinson(1969), with the normal, log-normal, and gamma error assumptions. The CEDA package calculates the following key parameters: K(carrying capacity), MSY(maximum sustainable yield), q(catchability coefficient), r(intrinsic growth rate), Ryield(replacement yield), and final biomass, whereas the CV(coefficient of variation)of the estimated MSY values can be calculated from the output confidence intervals. 2.4 The surplus production model incorporating covariates version 5.0(Prager, 2005)(ASPIC)
This package consists of two surplus production models Logistic(Schaefer) and Fox(a special case of GENFIT). The main output parameters are: MSY, q, K, ratio of starting biomass over carrying capacity(B1/ K), coefficient of determination(R2), coefficient of variation(CV), stock biomass giving MSY(B MSY), and fishing mortality rate at MSY(F MSY).
The initial proportion(IP)of B1/ K(starting biomass over carrying capacity)is dem and ed from the user. When IP is close to zero, this indicates that the data are from virgin population, and if the IP is close to one means that the data starts from a fully developed state. IP is an indicator that shows how data started. 3 RESULT 3.1 CEDA result
Results produced by the CEDA package are sensitive to the input IP values Table 2. There are some minimization failures for the gamma error assumption. Table 3 showed the CEDA results with initial proportion IP of 0.8. The estimated values of MSY with CV(coefficient of variation)from the Fox model and two types of error assumption(normal and log normal)were 1 692.08 t(CV=0.136) and 1 694.09 t(CV=0.110)respectively, and the estimated values of MSY with CV for Schaefer and Pella Tomlinson with the two error assumptions were 2 390.95 t(CV=0.040), and 2 380.06 t(CV=0.005)respectively. Both values of MSY, i.e. from Schaefer and Pella Tomlinson, were the same.(the Gamma error assumption showed a minimization failure here). The estimated MSY values from the Schaefer and Pella Tomlinson models were higher than the Fox model estimates. Estimated and observed catches areshown in Fig. 2, when using IP=0.8, catches observed by Fox and Schaefer models were near to estimated catches.3.2 ASPIC result
Results from the non-equilibrium models of Logistic and Fox applying the ASPIC computer package are shown in Table 4. The initial proportion(IP)was 0.8, because the initial yield was about 80 percent of the optimum capture. The estimated values of MSY from Fox and logistic with CV(coefficient of variation)were 1 498 t(CV=0.122) and 2 488 t(CV=0.033)respectively. The fishing mortality rate at MSY(F MSY) and stock biomass giving MSY(B MSY)from the Fox model was 0.099 and 14 980 and from the Logistic model was 0.036 and 6 847 correspondingly, while the estimated values of K(carrying capacity)in the Fox model were higher than in the Logistic model. Table 5 shows ASPIC results for the A. spinifer fishery in Pakistan with IP ranging from 0.1 to 0.9, which illustrates that the estimates are largely not sensitive to the IP values.4 DISCUSSION
CPUE is considered as an indicator of biomass. The catch and CPUE statistics are essential in the study of stock assessment, as they form the basic inputs for the surplus production models(Mehanna and El-Gammal, 2007). Surplus production models can estimate the maximum sustainable yield(MSY) and the optimal level of effort that produces the MSY(Hilborn and Walters, 1992).
MSY is often considered as a target biological reference point(BRP)by which sustainable exploitation goal can be achieved(Hilborn and Walters, 1992). BRPs are defined as the level of fishing mortality or of the biomass, which can permit a long-term sustainable exploitation of the stocks, with the best possible catch(Cadima, 2003).
Generally when surplus production of any stock is greater than catch, which indicates an increasing trend in population size; when catch equals the surplus production the catch is sustainable and population size remains constant. However, if catch is greater than surplus production the population size will decline. Biomass dynamic models are also called Surplus Production Models. These are among the simplest models used for fisheries stock assessment(Hilborn and Walters, 1992). Surplus production models were developed to determine the optimum fishing effort and MSY. The basic information required is the record of catch per unit effort CPUE data and the l and ed catch(Mehanna and El-Gammal, 2007), which do not need any age structure data(Haddon, 2011). 4.1 CEDA
The average catch of A. spiniferfrom Pakistani waters during the 25-year period 1985–2009 was 2 500 t. The contribution of the A. spinifer fishery in the total marine production of the Pakistan until 2007 was about 0.6%. Table 1 shows that the A. spiniferfishery was decreasing because the observed catch in 1985 was 2 944 t while in 2009 it was only 2 100 t. In Table 2, the CV(coefficient of variation)values were obtained by using the bootstrapping confidence-limit method. In the Fox model using CEDA package when the IP values ranged from 0.1 to 0.5, the MSY estimates were higher than the recent catch and when IP ranged from 0.6 to 0.9 the estimated MSY values were smaller than the recent catch. When applying IP=0.8, with error assumptions normal and lognormal the Fox model produced R2=0.572 and R2=0.606, whereas the R2values from Schaefer and Pella Tomlinson models were R2=0.563 and R2=0.605 respectively in Table 3. The estimated R2values from CEDA show a good fit to the data. 4.2 ASPIC
The IPs(initial proportion)values ranging from 0.1 to 0.9 showed in Table 5 that the fishery of A. spiniferin Pakistani waters had been overexploited. This computer package ASPIC does not show sensitivity to initial proportion(IP)as compared to CEDA package, because the estimated population parameter values from both models for IP values ranging from 0.1 to 0.9 appeared almost the same. The Fox and Logistic models produced R2=0.917 and R2=0.897 respectively, which were higher than those from the CEDA package and showed excellent fitting to the data. 5 CONCLUSION
The estimated values of MSY using CEDA were about 1 700–2 400 t and the values from ASPIC were 1 500–2 500 t. The estimates output by the CEDA and ASPIC packages indicated that the stock is overfished and needs some effective management to reduce the fishing effort of the species in Pakistani waters.
We used here the two computer software CEDA and ASPIC, which were developed by the fishery scientists from UK and USA. Even though they are based on the same theory, they have some technical differences. Because each result may have some sort of uncertainty, therefore the use of these two different software packages may be helpful to minimize uncertainty in the obtained results. 6 ACKNOWLEDGEMENT
The first author acknowledges the Chinese Scholarship Council(CSC)for funding his PhD. Degree
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