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ESTIMATION OF MAXIMUM SUSTAINABLE HARVEST LEVELS OF SEA CATFISHES IN SINDH, PAKISTAN.

Byline: M. Noman, Y. T. Mu, S. Abbas, M. Mohsin and A. Mehak

ABSTRACT

It is necessary to access fishery stock status before harvesting due to continued overexploitation of fishery resources. The aim of this study is to estimate maximum sustainable yield (MSY) of sea catfishes (Family: Ariidae) in Pakistani marine waters from Sindh coastline. Research method used is the statistical analysis of fishery output (FO) and fishery input (FI) data by using non-equilibrium surplus production models (SPMs). In Catch and Effort Data Analysis (CEDA) software, three SPMs viz. Fox model (F-M), Schaefer model (S-M) and Pella-Tomlinson model (PT-M) were used along with three error assumptions (EAs) viz. log (L-EA), log normal (LN-EA) and gamma (G-EA). Whereas, in A Stock Production Model Incorporating Covariates (ASPIC) computer package two SPMs viz. Fox (F-M) and Logistic (L-M) were employed for data analysis. For initial proportion (IP) 0.9, CEDA software computed MSY in range 12,000 - 16,000 t (metric ton).

F-M showed the highest goodness of fit (R2= 0.928) value. For the same IP value, ASPIC computer package estimated MSY in a range 14,000 - 20,000 t. Here, F-M again showed higher R2 (0.926), value. Estimated results suggest that, MSY range at Sindh coast is around 12,000 - 200,000 t. However, FO statistics indicate that this resource has consistently been overexploited. The conclusion of this study is that this fishery resource is overharvested and demand some concrete fishery management steps for its conservation in Pakistani marine waters.

Keywords: Sea catfishes, MSY, CEDA, ASPIC, Sindh, Pakistan.

INTRODUCTION

Exploitation of marine fishes demands studies focused on understanding the population of the exploited stock at a particular place and at a particular time (Sawant et. al., 2013). Therefore, the main objective of stock assessment of exploited commercially important fisheries stocks is to forecast that what is going to happen in terms of the value of the fishery output (FO), biomass levels i.e. sustainability and FO (Kilduff et. al., 2009). Stock assessments deliver significant scientific evidence essential for the conservation and management of fish stocks (NOAA, 2013). It also involves the use of different mathematical and statistical computations in order to make right forecast related to the reaction of the fish population to alternative management choice (Hilborn and Walters, 1992). Various models of fish populations are required to assess the stock status of important fisheries resources. These models are based on three basic categories of information viz. FO, abundance, and biology.

Therefore, the data employed for the stock assessment must be correct and appropriate to ensure the highest quality of assessment (NOAA, 2013). However, surplus production models i.e. SPMs (also known as biomass dynamic models) are extensively used in fisheries stock assessment and fisheries management because of their lesser data requirement than age-structured models (Schaefer, 1954; Pella and Tomlinson, 1969; Fox, 1970; Prager, 1994). Therefore, they are widely used to manage tropical fisheries where age estimation is difficult or almost impossible. They are also preferred because of their single production function to estimate all the crucial parameters such as growth, recruitment and mortality (Haddon, 2011). Sea catfishes are demersal in habitat and extensively distributed in the Indo-Pacific region.

They are distributed more preciously on the west and east coast of India to Bangladesh, Myanmar, Singapore, Malacca, Indonesia, Brunei Darussalam, Malaysia Bangladesh and Pakistan (Anderson and Gutreuter, 1983; Petrakis and Stergiou, 1995). They are bottom dwellers and generally found over inshore areas in depths up to 30 - 80 m. This bottom dwelling habit makes them a best target for bottom trawling. However, their population is considered to be under great threat due to their low fecundity and excessive destruction of eggs, juveniles and incubating males with the use of purse seines (Acero, 2002; Sawant et. al., 2013). Landings of sea catfishes follow ebb and tide pattern with some species ebbing and totally disappearing from the fishery catches at certain times, followed by resurfacing of the same, after a period. These observations calls for update studies to assess the population dynamics and recruitment of sea catfishes on regular basis.

It is very important to induce effective assumption related to revitalization of their fishery (Sawant et. al., 2013). This aspect is significant in the perspective of the multispecies fishery in Pakistani marine waters. Because here the impact of fishing is not only on particular target groups but also on associated species/groups caught a by catch. Maximum sustainable yield (MSY) and maximum economic yield (MEY) are the two reference points which are used to manage fishery resources around the world. Though, according to economists the concept of MEY sounds great (Gordon, 1954; Grafton et. al., 2007). However, the benefits of this reference point are not witnessed till now. It has been found that MSY reference point is the better reference point at which maximum economic utility of the fishery resources is generated (Christensen, 2010).

Previously, even though the biology and population dynamics of sea catfishes have been studied extensively worldwide (Conand et. al., 1995; Chakraborty et. al., 1997; Raje, 2003, 2006; Mazlan et. al., 2008; Sawant and Raje, 2009; Chu et. al., 2011; Farooq et. al., 2016) but there are no previous studies available to examine the stock status of sea catfishes in Pakistani marine waters. The present study is the first attempt to assess the maximum sustainable yield of sea catfishes in Pakistani marine waters.

MATERIALS AND METHODS

Data Procurement: The stock status of sea catfishes was evaluated by using available CE data (1997-2012) for this fishery resource from marine waters of Sindh, Pakistan. The published data was obtained from Marine Fisheries Department (MFD). FO and FI are in the form of tons (t) and the number of fishermen correspondingly.

Data Analysis: In this study, three SPMs i.e. F-M, S-M, and PT-M were used to evaluate the procured time series data of 16 years (1997-2012) of sea catfishes. These SPMs were explained by the three scientists named Fox, Schaefer and Pella-Tomlinson. S-M is the most commonly used model in stock assessment and is based on a logistic growth model.

dB/dt = rB(Ba - B) (Schaefer, 1954)

While F-M and PT-M are based on Gompertz growth equation and common production equation, correspondingly.

dB/dt = rB(1nBa - 1nB) (Fox, 1970)

dB/dt = rB(Bn-1a - Bn-1) (Pella and Tomlinson, 1969)

Where, B is for fish stock biomass, n reveals shape parameter, t donates for the time (year), B a represents carrying capacity (K) and r is the intrinsic rate of population growth. However, data was analyzed through the specified computer packages such as Catch and Effort Data Analysis (CEDA) and A Stock Production Model Incorporating Covariates (ASPIC) downloaded from MRAG website and NOAA Fisheries Toolbox respectively. The catch per unit effort (CPUE) is a significant RP to evaluate the stock status of commercially important fishery resources (Hoggarth et. al., 2006). Hence, the nominal CPUE is used here in this study to evaluate the stock status of sea catfishes.

CEDA (version 3.0.1): CEDA computer software is menu driven data fitting tool and has the ability to estimate customized parameters. CEDA employs a confidence interval (CI) of 95% through bootstrapping method for all the SPMs models i.e. F-M, S-M and PT-M with three EAs viz. L-EA, LN-EA and G-EA. It requires an input of IP (B1/K) which is a key indicator used to access fishery stock. For example, when the input value is one then it does assume that data is acquired from fully developed fishery stock. On the other hand, if the input value of IP is zero or close to zero then its mean that data is obtained from virgin fishery stock. Occasionally, initial biomass is fixed as B1 = C1/(qE1). In this mathematical equation, C is catch; q and E denote catch and catch ability respectively. Some programmers also use B1 equal to K. The coefficient of variation (CV) is predicted by using CIs.

Other key parameters expected by using CEDA are MSY (maximum sustainable yield), K (carrying capacity), q (catchability coefficient), r (intrinsic growth rate), Ryield (replacement yield) and final biomass.

ASPIC (version 5.0): ASPIC computer package also needs an input of IP. Nevertheless, in contrast to CEDA, it needs individual input files for each IP value. Two SPMs i.e. F-M (a special case of GENFIT) and L-M (also called Schaefer model) were employed by using this fishery software. FIT and BOT files, for both of the SPMs, were prepared to compute CV (coefficient of variation) for all IP values. FIT and BOT represent the program modes used in ASPIC and there exists a technical difference between them. The managerial parameters are estimated by ASPIC software during FIT mode while during BOT program mode, it uses bootstrapped CIs with many trials for the calculation of parameters. Therefore, the finishing time of BOT mode is much higher than FIT mode. 500 trails were done in order to calculate maximum sustainable yield (MSY).

Different Important parameters approximated by using this computer package include MSY, K, q, B1/K (starting biomass over carrying capacity), R (coefficient of determination), FMSY (fishing mortality rate at MSY), BMSY (stock biomass giving MSY). Sensitivity analysis were done for constant recruitment model. However, due to unpredictable results obtained we switched to non-equilibrium SPMs evaluation i.e. three production models. Within these models again sensitivity analysis was performed by using IP values. The results obtained for different parameters were further considered along with R2 values and visual inspection of the graphs for model selection and evaluation for depicting reliable results.

RESULTS

During the study period, the total capture production of sea catfishes form Pakistani marine waters at Sindh coastline was 370,149 t. The average FO remained 23,134 t year-1 while maximum and minimum FO quantity was examined in 1998 (44,272 t) and 2010 (13,100 t) respectively (Figure 1). Similarly, the average CPUE during the study period remained 0.247 year-1 while highest and lowest values of CPUE were observed during 2010 (0.122) and 1998 (0.522) i.e. second and fourteenth study years in that order (Figure 2). Recorded results by using CEDA and ASPIC were further calculated by considering four factors viz. maximum sustainable yield (MSY), the goodness of fit (R2), graphs between observed and expected FO and coefficient of variation (CV). Estimated MSY values were compared with data figures and very large or small MSY values were not treated.

Models were compared by keeping in view R2 values and visual examination of residual plots. The higher is the value of R2 the better is the fitting of the model. Results with appropriate CV values were acknowledged.

CEDA Estimates: CEDA exhibited sensitivity towards the input IP values as it created various output MSY figures for various IP inputs (Table 1). L-EA for CV values produced minimization failure (MF) for IP values of 0.2 and 0.4 which were obtained by using a special method called bootstrapping confidence limit method for both S-M and PT-M. Besides this, G-EA sometimes showed MF in all the SPMs used. Except for IP 0.9, for all the SPMs used along with their error assumptions either MSY or R2 value did not produce rational results. Evaluated parameters for IP 0.9 are given in Table 2. Computed values of MSY and their CV for F-M with L-EA were 12,042 t and 0.139, respectively.

For LN-EA their values remained 13,315 t and 0.111 in that order while for G-EA showed MF. Estimated MSY values for N-EA and LN-EA used in S-M and PT-M remained same as 14,515 t and 16,401 t respectively while calculated CV values for both of these models for both error assumptions were 0.214, 0.103 and 0.218, 0.109 correspondingly. The G-EA showed MF for both viz. S-M and PT-M. Calculated BMSY values are same for the both S-M and PT-M. This may be due to the convergence of PT-M at 0.5 of BMSY/K which implies the results of this model may not be considerable. However, for model evaluation full criteria used has already been described. For IP 0.9, R2 values computed by using L-EA and LN-EA F-M were 0.907, 0.928 respectively while G-EA produced MF (Table 2). R2 values for both the models i.e. S-M and PT-M with L-EA and LN-EA were same as 0.892 and 0.912 in that order while G-EA produced MF.

R2 (the goodness of fit) values are very important to consider as they tell us about the fitting of the model. Figure 2 shows the graphical demonstration of experiential and predicted annual FO values. From visual examination it can be acknowledged that for all the error assumptions used in F-M, observed and expected FO values are close to each other, however in detail, they change from each other. CEDA computed higher MSY values with lower IP values and vice versa.

ASPIC Estimates: In ASPIC, computed parameters for IP 0.9 are listed in Table 3. F-M showed better fit as its R2 value (0.926) was higher than the computed R2 value (0.908) for L-M. MSY along with their CV values for both i.e. F-M and L-M used in ASPIC were estimated at 14,040 t (0.076) and 19,670 t (0.139) in that order. For F-M, computed K, BMSY (fishing mortality rate at MSY) and FMSY (stock biomass giving MSY) remained 236,200 t, 86,900 t and 0.1615, respectively. Similarly, the K, B MSY, and FMSY values were remained 167500 t, 83740 and 0.2349 in that order for Logistic models. Table 4 reveals various factors estimated for IP 0.3 - 0.9. Like CEDA, ASPIC also showed sensitivity to IP values as it evaluated different output parameter values for different IP input. ASPIC computed smaller MSY for larger IP value and vice versa. However, parameters calculated by this software did not exhibit higher differences as compared to CEDA.

Computed MSY by ASPIC, for instance, ranged in 500,000 t - 2,550,000 t while for CEDA its most of estimated range was 14,000 t - 44,000 t. It means even though ASPIC is sensitive to IP values but its sensitivity is less than CEDA. In contrast to CEDA, ASPIC models showed lower R2 values indicating that less good fitting of the data. Estimated fishing mortality (F) and biomass (B) values of sea catfishes by using ASPIC are highlighted in Table 5. Calculated figures indicate that F has shown rising trend with the passage of time whereas B is decreasing. F/FMSY is increased and B/BMSY is decreased during the course of the study period. Both, F/FMSY and B/BMSY indicate overexploitation of the fishery stock.

Table 1. CEDA estimates of various parameters for sea catfishes from Sindh, Pakistan (IP = 0.1-0.9).

###Model

IP###F-M###S-M###PT-M

###L-EA###LN-EA###G-EA###L-EA###LN-EA###G-EA###L-EA###LN-EA###G-EA

0.1###1.25E+11###44,528###MF###352,807###108,327###MF###358,207###108,327###MF

###0.424###0.000###0.000###0.042###0.000###0.000###0.035###0.000###0.000

0.2###32,659###27,133###MF###MF###52,813###52,813###MF###52,813###52,813

###0.055###0.036###0.000###0.000###0.000###0.000###0.00###0.000###0.000

0.3###24,374###20,180###MF###203,505###38,076###38,072###203,505###38,076###38,072

###0.097###0.072###0.00###0.703###0.000###0.000###0.342###0.000###0.000

0.4###19,995###19,597###MF###MF###30,937###108,510###MF###30,937###108,510

###0.096###0.047###0.000###0.000###0.000###814.65###0.00###0.000###515.88

0.5###17,241###18,176###MF###26,827###27,127###26,835###26,827###27,127###26,835

###0.115###0.069###0.000###0.056###0.003###0.000###0.057###0.003###0.000

0.6###15,345###16,126###MF###23,306###23,608###25,373###23,306###23,608###25,373

###0.128###0.086###0.000###0.092###0.007###0.000###0.095###0.007###0.000

0.7###13,953###15,448###MF###19,463###22,381###MF###19,463###22,381###MF

###0.131###0.092###0.000###0.173###0.013###0.000###0.162###0.011###0.000

0.8###12,886###14,827###13,799###16,656###20,554###18,510###16,656###20,554###18,510

###0.142###0.106###0.113###0.184###0.024###0.174###0.211###0.021###0.179

0.9###12,042###13,315###MF###14,515###16,401###MF###14,515###16,401###MF

###0.139###0.111###0.000###0.214###0.103###0.00###0.218###0.109###0.000

Table 2. CEDA estimates of various parameters for sea catfishes from Sindh, Pakistan (IP = 0.9).

Model###MSY###CV###R2###K###q###r###RYield###B

F-M (L-EA)###120,42###0.139###0.907###279,256###2.36E-06###0.117###9,924###48,187

F-M (LN-EA)###13,315###0.111###0.928###256,145###2.64E-06###0.141###11,102###45,420

F-M (G-EA)###MF###MF###MF###MF###MF###MF###MF###MF

S-M (L-EA)###14,515###0.214###0.892###248,047###2.36E-06###0.234###7,640###38,668

S-M (LN-EA)###16,401###0.103###0.912###219,982###2.36E-06###0.298###9,017###36,191

S-M (G-EA)###MF###MF###MF###MF###MF###MF###MF###MF

PT-M (L-EA)###14,515###0.218###0.892###248,047###2.36E-06###0.234###7,640###38,668

PT-M (LN-EA)###16,401###0.109###0.912###219,982###2.36E-06###0.298###9,017###36,191

PT-M (G-EA)###MF###MF###MF###MF###MF###MF###MF###MF

Table 3. ASPIC estimates of various parameters for sea catfishes from Sindh, Pakistan (IP = 0.9).

Model###IP###MSY###CV###R2###K###BMSY###FMSY###q

F-M###0.9###14,040###0.076###0.926###236,200###86,900###0.162###2.92E-06

L-M###0.9###19,670###0.139###0.908###167,500###83,740###0.235###4.24E-06

Table 4. ASPIC estimates for sea catfishes from Sindh, Pakistan (IP = 0.3-0.9).

Model###IP###MSY###CV###R2###K###BMSY###FMSY###q

###0.3###27,630###0.066###0.921###345,800###127,200###0.217###6.02E-06

###0.4###22,600###0.079###0.923###314,400###115,700###0.195###4.93E-06

###0.5###19,730###0.089###0.923###285,800###105,100###0.188###4.36E-06

###0.6###17,640###0.078###0.924###268,200###98,650###0.179###3.87E-06

F-M###0.7###16,090###0.071###0.925###255,300###93,910###0.171###3.48E-06

###0.8###14,950###0.085###0.925###244,500###89,940###0.166###3.18E-06

###0.9###14,040###0.076###0.926###236,200###86,900###0.162###2.92E-06

###0.3###43,580###0.017###0.907###225,000###112,500###0.388###7.85E-06

###0.4###36,350###0.018###0.914###172,400###86,210###0.422###8.18E-06

###0.5###32,390###0.026###0.912###142,200###71,110###0.456###8.49E-06

###0.6###28,520###0.044###0.907###141,000###70,500###0.405###7.41E-06

L-M###0.7###25,010###0.078###0.905###148,700###74,340###0.337###6.11E-06

###0.8###22,150###0.105###0.906###157,300###78,630###0.282###5.09E-06

###0.9###19,670###0.139###0.908###167,500###83,740###0.235###4.24E-06

Table 5. ASPIC software estimates of fishing mortality (F) and biomass (B) (IP = 0.9) (1997-2012).

###Model

Year###F-M###L-M

###F###B###F/FMSY###B/BMSY###F###B###F/FMSY###B/BMSY

1997###0.212###212,700###1.311###2.447###0.305###150,700###1.299###1.799

1998###0.278###177,600###1.720###2.043###0.412###121,700###1.756###1.454

1999###0.303###143,300###1.873###1.649###0.461###95,390###1.962###1.139

2000###0.248###116,700###1.533###1.343###0.379###75,860###1.612###0.906

2001###0.267###103,200###1.653###1.187###0.403###67,970###1.717###0.812

2002###0.243###91,220###1.506###1.050###0.361###60,680###1.536###0.725

2003###0.278###83,970###1.722###0.966###0.404###57,360###1.721###0.685

2004###0.304###75,780###1.884###0.872###0.438###52,510###1.866###0.627

2005###0.262###67,790###1.622###0.780###0.372###47,140###1.585###0.563

2006###0.299###64,120###1.854###0.738###0.418###45,640###1.782###0.545

2007###0.300###59,070###1.858###0.680###0.416###42,470###1.769###0.507

2008###0.369###55,050###2.287###0.634###0.515###39,950###2.194###0.477

2009###0.295###48,640###1.829###0.560###0.419###34,410###1.783###0.411

2010###0.282###46,870###1.748###0.539###0.402###32,950###1.713###0.394

2011###0.305###45,970###1.888###0.529###0.440###32,180###1.874###0.384

2012###0.340###44,270###2.104###0.510###0.509###30,370###2.168###0.363

DISCUSSION

This study reveals that sea catfishes are overexploited in Pakistani marine waters. This overharvesting of fishery resources confers many disadvantages to the society such as it results in the competitive harvesting of the fishery resources. Moreover, private owner tries to maximize their profits without considering the damage caused by overfishing. In order to increase profit more and more FO is done which may lead fishery stock to the depletion stage. Thus, it is of utmost importance to access the commercial fishery stock regularly to ensure the sustainable economic contribution of fishery resources (Clark, 1973). As aforementioned, overexploitation may lead to fishery stock depletion. This depletion is actually an outcome of an attempt to increase profit gain beyond the capacity of the fishery stock (Clark, 1973). In the beginning of fishery, fishermen get more profit with lower FI.

The incentives created by fishery at this stage attracts more and more fishermen to join fishery however a stage comes when profit goes negative. At this stage, BCUR becomes lower than BMSY and for more FI lesser FO is obtained. Thus, fishing does not remain economic. In order to make fishing beneficial again, fishery stock building is usually recommended. But, there is a problem associated with fishery stock rebuilding. Actually, for slow growing fish species fishery stock rebuilding can take dozens of years. Therefore, usually fishers oppose the idea of fishery stock rebuilding (Grafton et. al., 2007).Moreover, during the process of rebuilding, fishers have to pay the transition costs for fishery stock rebuilding. However, if these transition costs are compensated for fishers then they may be agreed upon the idea of rebuilding. Transition costs may be supplemented by some incentives which may include harvesting rights given individually or at community level (Hilborn, 2007).

Such incentives may motivate fishers for fishery stock rebuilding (Grafton, 1995). Overcapacity management also proves very effective for fishery stock rebuilding. If fishery stock is harvested at sustainable level it would automatically start to rebuild itself. In Pakistan, overcapacity management has received very little attention in the past. First national fisheries policy in Pakistan was announced in 2007 viz. "The National Policy and Strategy for Fisheries and Aquaculture Development in Pakistan". This policy legislatively monitors FO and FI matters in Pakistan (GoP, 2007). Though, this policy has some framework to monitor these FO and FI matters however ground reality proves that outcome of this policy is not witnessed. It has been found that the number of trawlers operating in Sindh is more than double than the recommended ones (Schmidt, 2014). Thus, this ineffective control on overcapacity has led to overexploitation of fishery resources as this study shows.

This study uses famous fishery SPMs which have already been used for the stock status of fishery resources in Pakistan. (Memon et. al., 2015; Siyal et. al., 2013; Kalhoro et. al., 2013; Panhwar et. al., 2012; Kalhoro et. al., 2014; Panhwar and Liu, 2013). Actually, these SPMs possess many advantages over the other routine used in the science of fishery management. Such as, these models use simple data related to FI and FO. In addition to this, these models estimated simultaneously exclusive parameters of the fishery stock. Besides, the computed parameter such as q indicate the fishery stock status. Other main computed fishery parameters include F, FMSY, B and BMSY. It is necessary to mention that these SPMs rely on some assumption which may not be met in nature.

These assumptions include non-existence of immigration or emigration in the fishery stock, age composition of the fishery stock has no relation with r, q does not change with the passage of time, there exists single fishery stock and natural with fishing mortality occur at the same time (Ewald and Wang, 2010; Hoggarth et. al., 2006). Moreover, these models do not use data related to the age-structure of the population and confer the risk of uncertainty in their estimation. Nevertheless, these models are frequently used in the science of fishery management and are very effective tools which tell us about the status of the fishery stock upon which fishery management advice is made (Musick and Bonfil, 2005).

Conclusion: The computed value of MSY for IP 0.9, ranged from 12,000 t - 16,000 t and 14,000 t - 20,000 t for CEDA and ASPIC correspondingly. Thus, CEDA remained more conservative in terms of MSY calculation as compared to ASPIC. Higher values of R2 for ASPIC reveal that its results are more reliable. Since the computed MSY range by F-M and L-M overlaps thus by considering the results of both the software and applying pulse fishing rule we recommend that the MSY target reference pint (TRP) range of sea catfishes is as 14,000 t - 16,000 t in Pakistani marine waters. Capture production of 16500 t or more must be considered as a limiting reference point (LRP). By comparing computed MSY values with recorded data (Table 1) and considering F/FMSY and B/BMSY, it can be concluded that in the past, this fishery resource has constantly been overexploited.

The stock of sea catfishes is declining with the passage of time due to overfishing. Thus, instantaneous steps with appropriate forecast with rightful execution are direly required to conserve this fishery resource for future.

Acknowledgement: The first author is grateful to Chinese Scholarship Council (CSC) for funding his PhD. Degree. This work is supported by the special research fund of China Agriculture Research System (CARS-48).

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