MATERIALS AND METHODS

Fishery statistical data

Effort, number of fishing boats; EEZ, exclusive economic zone; Total catch in metric tons.

Annual catch and effort data were analyzed by different surplus production models which are Schaefer, Fox and Pella-Tomlinson which were available into two computer program CEDA (catch effort data analysis) (Hoggarth et al., 2006) and ASPIC (a surplus production models incorporating covariates) (Prager, 2005)

dB / dt = rB(B∞ - B) (Schaefer, 1954)

Later work of Fox (1970) put forward a Gompertz growth equation, and another work from Pella and Tomlinson (1969) reported a generalized production equation:

dB / dt = rB(InB∞ - InB) (Fox, 1970)

dB / dt = rB(B∞n-1 - Bn-1) (Pella and Tomlinson, 1969)

Where, B is fish stock biomass, t is time (year), B∞, is carrying capacity, r is intrinsic rate of population increase and n is the shape parameter.

Bt+1 = Bt + rBt (B∞ - Bt) - Ct

Ct = qEtBt

Where, C is catch, q is catchability, E is the fishing efforts. Fishing mortality can then be calculated as:

F = qE

CEDA (catch and effort data analysis)

CEDA (Hoggarth et al., 2006) is computer base programming which is built on non-equilibrium surplus production models Schaefer, Fox and Pela and Tomlinson with three error assumptions (normal, lognormal and gamma) with output parameters were: MSY (maximum sustainable yield), K (carrying capacity), q (catchability coefficient), r (intrinsic growth rate), Ryield (replacement yield) and final biomass. The non-equilibrium surplus productions models were mostly used and it was assumed and fishery stock is not in equilibrium state because ecological, environmental and fishing efforts factors also affect the fishery stock and fishery stock will not in equilibrium state in that case non-equilibrium surplus production models were frequently used for stock assessment for better fishery management.

ASPIC (a surplus production models incorporating covariates)

ASPIC is non-equilibrium surplus production model and have two models; Logistic (Schaefer) and Fox model, ASPIC output parameters are: MSY, q, K, ration of the starting biomass over carrying capacity B1/K coefficient of determination (R2), coefficient of variation (CV), stock biomass in giving MSY (BMSY), and fishing mortality rate at MSY (FMSY). The initial proportion (IP) of B1/K (Starting biomass over carrying capacity) is input values by users, it was assumed that when IP is close to zero, this indicates that the data are from virgin population, and if IP is close to one it means the data starts from fully developed state (Prager, 2005).

RESULTS

CEDA

CEDA (catch and effort data analysis) computer software package shows sensitive with different IP values, different IP values and results are showing in Table II, gamma error assumptions of those three models mostly gives minimization failure (MF) it also shows that the values obtained from Schaefer (1954) and Pella and Tomlinson (1969) with all those error assumptions (normal, lognormal and gamma) were same. To estimate MSY value in present study IP value 0.9 were used because the starting catch was roughly 90% of the maximum catch which were calculate first year catch divided by maximum catch of those years, the estimated value from calculate IP (0.9) from Fox with three error assumptions (normal, lognormal and gamma) were 3378 R2 (R2 =0.590), 3360 (R2 =0.582), 3369 (R2 =0.586), respectively (Table III, Fig. 2), whereas, the obtained values from Schaefer (1954) and Pella and Tomlinson (1969) with two error assumptions (normal and lognormal) were 2878 (R2=0.587), 3035 (R2=0.578) and gamma were minimization failure (MF) in both models.

ASPIC

Output values from both Fox and logistic model are showing in Table IV using different IP values from 0.2-1, shows that ASPIC package is not sensitive with using different IP values. The MSY value from IP value 0.9 was obtained to set the level of fishery status because the initial yield was about 90% of the maximum catch. The estimated MSY value from Fox model were 3652 (R2 =0.8) with 0.183 coefficient of variation (CV) (Table V), overall values from 0.2-1 IP value estimated in fox models were range from 3650-3658 with R2 = 0.8, which shows the accuracy of data and best fit of the model showing the goodness of fit. Whereas, the MSY value from calculate IP (0.9) from logistic model were 2962 (R2 =0.799) with 0.199 CV (Table V), overall MSY values using

CEDA, catch and effort data analysis; IP, initial proportion; MF, minimization failure; MSY, maximum sustainable yield.

For abbreviations, see Table II.

IP, initial proportion; MSY, maximum sustainable yield; B1/K, starting biomass; K, carrying capacity; q, catchability coefficient; FMSY, fishing mortality rate at MSY; R2, coefficient of determination; CV, coefficient of variation, BMSY, stock biomass in giving MSY.

For abbreviations, see Table IV.

different IP values (0.2-1) from logistic model were 2151-5270 Table IV. The confidential interval is also easy to estimate using bootstrapping method which provides 95% confidential interval and overall values were lower than 0.2.

DISCUSSION

Catch and efforts data is mostly used to calculate MSY for better stock assessment for any fishery where catch should be yearly or monthly and efforts must be in number of fishing boats, number of fishermen engaged in that time period or number of fishing hours in that fishing time, in present study the yearly catch and number of fishing boats were used to estimate the MSY value for crab fishery from Pakistani waters, Northern Arabian Sea. The surplus production models are much more helpful to estimate the MSY and optimal level of efforts that produces the MSY and is always considered as target biological reference point (BRP) from which sustainable fishery management goals can be achieved (Hilborn and Walters, 1992). Earlier studies were based on equilibrium production models but stocks are seldom in equilibrium state due to some biological, environmental factors and unmanaged fishing mortality which effects the population in this case non-equilibrium surplus production models are frequently used to know the state of stock. The surplus production models can give idea about the stock because they do not require and environmental data (Quinn and Deriso, 1999) The surplus production models were frequently used for fishery management since last decades and recently were also used for Pakistani waters (Kalhoro et al., 2013, 2015; Soomro et al., 2015a, b; Memon et al., 2015a, b).

The overall results from CEDA and ASPIC surplus production models were almost equal or same using different IP values, the concept of the MSY is if MSY estimated value is greater than recent catch then it shows that the fish stock is in safe condition, but when the estimated MSY value equals to annual catch it may be assumed that fish population in in sustainable state, however, if the estimated MSY value from different surplus production models is smaller than annual catch it indicates that the stock of fishery is over-exploitation state. In the present study using different surplus production models results shows that the estimated MSY values (Tables II, III, IV, V) is smaller than annual catch. Table I shows that the stock of swimming crab fishery from Pakistani waters was in overexploitation state. In the light of present study, we may suggest that take some management steps to reduce the fishing efforts for crab fishery, protect the nursery grounds of the crab fishery to maintain the stock of crab fishery from Pakistani waters.

CONCLUSION

Maximum sustainable yield (MSY) estimated from both computer packages CEDA and ASPIC results were close and give value smaller than annual catch of the swimming crab fishery which indicates that the stock of crab fishery from Pakistani waters is in overexploitation state. We may suggest that fishery managers take some management steps like reduce the fishing efforts, declare Marine Protected Area at nursery grounds of fin and shellfish, generally due to freshwater inflow from Indus River creates rich mangrove ecosystem from Sindh area so it is considered to be best nursery grounds for fish and shellfish, monitoring the trawl mesh size, ban period during the breeding season of crab fishery to sustain the crab fishery stock from Pakistani waters, Northern Arabian Sea.

Acknowledgments

Present study is supported by research project awarded to Prof. Dr. DanLing Tang: 1) Key project, “Marine phytoplankton size classes and related ecological factors respond to typhoons - based on remote sensed and in situ data” awarded by National Natural Sciences Foundation of China (41430968); 2) Collaborative Innovation Center for 21st-Century Maritime Silk Road Studies, Guangzhou, China (2015HS05). First author also thankful to China Postdoctoral fellowship program by South China Sea Institute of Oceanology, Chinese Academy of Sciences (158563) and China Science and Technology Exchange Center, Ministry of Science and Technology for awarding Talented Young Scientist Award (PAK-16-021) recommended by Ministry of Science and Technology Pakistan.

Statement of conflict of interest

The authors have declared no conflict of interest.

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