Background

Since the 1970s, fluctuations have been observed in the species composition of the world capture fisheries due to overexploitation (Rodhouse, 2001). Fisheries resources have been overexploited, because of increasing seafood demand for domestic consumption and export, which has resulted in an increase of FC and capacity utilization all around the world (Hilborn and Walters, 1992). Along with other aquatic fauna, cephalopods have also been frequently targeted in order to increase their contribution in the marine capture fisheries (Chen, 1996). Now, they are contributing significantly to sustain and develop the world’s capture fisheries production. Like the world, in Pakistan, cephalopods have historically contributed significantly in the marine capture production. However, the decline is witnessed in the capture production of cephalopods, after 2000, from Pakistani marine waters advocates to evaluate the stock status of this fisheries resource.

According to FAO (2015), Pakistani marine cephalopod fauna is comprised of cuttlefish, squids, and octopuses belonging to twenty-eight species and three orders. All of them are commercially important. Most of the cephalopod capture production is from Sindh coast of Pakistan. This is the main coast of Pakistan and more than two-third capture production of molluscs is from this coast. Class cephalopoda in Pakistani marine waters is mainly represented by octopuses, squids, and cuttlefish species. Thirteen species of squids belonging to eight families are reported form Pakistani marine waters. The names of these eight families are ancistrocheiridae, octopoteuthidae, cranchiidae, loliginidae, onychoteuthidae, chtenopterygidae, ommastrephidae and thysanoteuthidae. Squid species dwelling Pakistani marine waters include Ancistrocheirus lesueurii, Taningia danae, Liocranchia reinhardti, Loliolus hardwickei, Sepioteuthis lessoniana, Uroteuthis edulis, Uroteuthis duvaucelii, Uroteuthis singhalensis, Onychoteuthis banksia, Chtenopteryx sicula, Ornithoteuthis volatilis, Sthenoteuthis oualaniensis and Thysanoteuthis rhombus. These squid species have same common name and are locally known as “mayyah, sisi mayyah, shishi mayya” in Sindhi language while in Balochi language it is known as “mus” or “mayyah”.

Octopuses are the members of the order octopoda. Octopuses dwelling Pakistani marine waters belong to two families viz. octopodidae and argonautidae. Among six reported species of octopuses from Pakistani marine waters, four species belong to the former family while two species belong to the latter family. The local name of every octopus species belonging to this family is “Pichankara” and “Panchranga” in the Sindhi language whereas in Balochi language known as “mus”. Moreover, all of these species are fished through spears, lured hooks and bottom trawls. Octopuses found in Pakistani marine waters are Amphioctopus aegina, Amphioctopus marginatus, Octopus cyanea, Octopus cf. vulgaris, Argonauta argo and Argonauta hians (FAO, 2015). Cuttlefishes are the members of the family sepiidae. Nine species of cuttlefishes are reported form Pakistani marine waters. All of these species are locally known as “myyah” or “dimiri” in the Sindhi language while in Balochi language these are called “mus”. These cuttlefish species are caught from bottom trawling. Cuttlefishes caught from Pakistani marine waters include Sepia Arabica, Sepia omani, Sepia kobiensis, Sepia pharaonic, Sepia prashadi, Sepia ramani, Sepia stellifera, Sepia trygonina and Sepiella inermis (FAO, 2015).

Several studies have been conducted in Pakistan in order to assess the stock status of various fisheries resources, other than molluscs (Panhwar and Liu, 2013; Siyal et al., 2013; Kalhoro et al., 2014; Memon et al., 2015; Mohsin et al., 2016). All of these studies were based on the similar surplus production models (SPMs) used in our study. No scientific study evaluates the stock status of cephalopods, even molluscs, except Soomro et al. (2015). Thus, this study is the first attempt to know the stock status of class cephalopoda, a major landed molluscan class in Pakistan.

1 Materials and Methods

Catch-effort statistics of 11 years, 1999-2009, related to class cephalopoda from Sindh, Pakistan (Figure 1) were analyzed by using SPMs through CEDA and ASPIC software. Published data of this fishery resource was obtained from the statistical book viz. Handbook of Fisheries Statistics of Pakistan complied by Marine Fisheries Department, Pakistan. The catch is in tons (t) whereas effort is in the number of fishermen.

1.1 Data analysis

In total three SPMs or biomass dynamic models were used in this study. SPMs were applied through computer packages viz. CEDA (MRAG, 2016) and ASPIC (NOAA, 2016). CEDA and ASPIC stand for catch and effort data analysis and a stock production model incorporating covariates respectively. Three versions of SPMs were used which are proposed by three different scientists viz. Fox (FM), Schaefer (SM) and Pella-Tomlinson (PTM).

Gompertz growth equation and generalized production equation are the basis of Fox and Pella-Tomlinson models and are represented as follow:

On the other hand, logistic growth model of population forms the basis of Schaefer model and is expressed as follow:

Where, n, B, B_∞, r and t represent shape parameter, fish stock biomass, carrying capacity, population increase and the time (year).

1.2 Catch-effort data analysis-CEDA (version 3.0.1)

CEDA computer package is operated manually to estimate fishery parameters. It uses error assumptions method by employing 95% confidence interval level for all the SPMs. Three error assumptions (EAs) were used through this computer application viz. normal (NEA), log normal (LNEA) and gamma error assumption (GEA). In order to compute fishery parameters initial proportion (IP) is estimated. IP is estimated by dividing initial catch with the maximum catch in the data series. Key parameters estimated through CEDA include MSY (maximum sustainable yield), q (catchability coefficient), K, B (biomass) and r (population increase).

1.3 A stock-production model incorporating covariates-ASPIC (version 5.0)

Unlike CEDA, two SPMs were used in ASPIC viz. Fox (FM) and Logistic models (LM). For each of these models BOT and FIT files were prepared. These files correspond to the different program modes for the estimation of managerial parameters. Separate IP files were prepared for the estimation of various parameters through ASPIC. Important parameters computed by using ASPIC include MSY, R² (coefficient of determination), K, F_MSY (fishing mortality rate at MSY), q and B_MSY (stock biomass giving MSY).

2 Results

Data figures show that capture production of class cephalopda has considerably decreased during the study period. In 1999, the capture production was 9,981 t, which gradually declined and reached at 5,713 t in 2009. The maximum and the minimum capture production was observed in 1999 (9,981 t) and 2009 (5,713 t) i.e. in the first and last year of the study. The average landed mass of this fisheries resource was 6,525 t y^-1 during the study period (Figure 1). In contrast to increase in fishing effort, CPUE has decreased significantly starting from 0.117 in 1999 to 0.054 in 2009. The average value of CPUE during the study period remained 0.068 y^-1 (Figure 2).

2.1 CEDA estimates

MSY estimates, for IP 0.1-1, along with their CV values are given in Table 1. CEDA showed sensitivity towards IP values which means it produced different values of MSY for different IP values. This computer package computed the higher values of MSY for the lower IP values and vice versa. Mostly, GEA produced MF in all the SPMs used viz. FM, SM and PTM. Output graphs, representing observed catch and expected catch, obtained by using CEDA for IP 1 are presented in Figure 3. From visual inspection all the graphs look alike, however, in detail differ from each other.

CEDA estimates for various parameters by using IP 1 are listed in Table 2. FM estimated MSY as, for all the EAs viz. NEA, LNEA and GEA, 4,598 t, 4,701 t and 4,643 t respectively whereas their CV values remained 0.041, 0.050 and 0.042 in that order. For this model calculated values of R² and B_CUR for all the EAs remained as 0.958, 0.939, 0.949 and 16,301 t, 15,214 t, 15,892 t correspondingly. Calculated values of K and B_MSY, for all the EAs, were 46,470 t, 43,979 t, 45,474 t and 17,095 t, 16,179 t, 16,729 t respectively. SM and PTM, for all the EAs, estimated same MSY values i.e. 4,723 t, 4,878 t and 4,815 t in that order. However, their calculated CV values differed from each other.

For SM, CV estimates, for all the EAs, were 0.062, 0.094 and 0.064 respectively and for PTM their values remained 0.063, 0.104 and 0.066 in that order. Values of R² remained same for both the models by using all the EAs i.e. 0.944, 0.920 and 0.933 respectively. Like MSY, some other parameters such as B_CUR, K and B_MSY showed same parameter estimates for both of these models. The estimates of B_CUR and K were 13,600 t, 12,695 t, 13,096 t and 40,786 t, 38,339 t, 39,345 t correspondingly. The output values of B_MSY for all the EAs were 20,393 t, 19,170 t and 19,673 t respectively.

2.2 ASPIC estimates

ASPIC estimates by using IP 1 are presented in Table 3. FM estimated MSY, along with the value of CV, as 4,780 t (0.032). Same parameter estimate for LM was 5,231 t (0.046). Former model computed parameters K, F_MSY, B_MSY and R² as 39,100 t, 0.332, 14,380 t and 0.977 respectively. LM estimated same parameters as 29,130 t, 0.359, 14,560 t and 0.970 correspondingly.

Table 4 shows estimates of various parameters by using IP 0.1 to 1. Like CEDA, ASPIC software computed the higher MSY values for the lower IP inputs and vice versa. For example, by using IP 0.1 in FM, the MSY estimate was 13,890 t while for IP 1 the MSY estimate was 4,780 t. Likewise, other parameters viz. K and B_MSY showed the same pattern i.e. the lower is the IP value the higher is the computed value of the parameter and vice versa. Values of R² did not show much variation with the change of the IP values and more or less remained almost same. LM followed the same pattern of parameter estimation for all the parameters. However, overall this model showed generally lower values of R² as compared to FM.

Estimated parameters of F, B_CUR, F/F_MSY and B_CUR/B_MSY for both the models are listed in Table 5. For FM, F has shown increasing trend with the passage of time. In contrast to F, B_CUR is showing decreasing trend. This model has shown that F/F_MSY is increasing whereas B_CUR/B_MSY is decreasing swiftly. Same parameters computed by LM showed same trend as computed by the FM. Such as, F has increased from 0.402 (1999) to 0.571 (2009) whereas, B_CUR has decreased from 29,130 t (1999) to 10,530 t (2009). Similarly, F/F_MSY is following rising trend while B_CUR/B_MSY is declining with the passage of time, which indicates that this resource is decreasing continuously.

3 Discussion

Statistical software, such as CEDA and ASPIC used in this study, compute parameters and give us the figures. These figures serve as reference points. Scientists use these figure and make management strategies. In other words, reference points are, in fact, the signposts which reflect the condition of the fishery stock. In the fisheries management science, commonly three reference points are used viz. F_MSY, B_MSY and MSY. Among all the reference points, MSY is the most commonly used reference point. When estimated MSY is above the observed catch, in this case the fishery stock is assumed to be flourishing. On the other hand, if computed MSY is lower than the observed catch, it indicates the fishery stock is overexploited. Moreover, if both the estimated MSY and observed catch are same the fishery stock is assumed to be in a stable state i.e. neither increasing nor decreasing (Hoggarth et al., 2006). Identifying an appropriate MSY is very important. If MSY is underestimated, it would result in economic loss. On the other hand, if MSY is overestimated, it would deplete the fishery stock (Rosenberg et al., 1993; Gabriel and Mace, 1999).

Fishery management is basically a step by step procedure which begins from data collection and ends with the formulation of policies (FAO, 1997; Die, 2002). As aforesaid, scientists evaluate fishery status on the basis of parameters estimated by statistical software. There are two types of reference points which are used for the purpose of fishery management. First are those points which fishery policy makers and managers strive to achieve in order to manage fisheries and hence are called target reference points. On the other hand, the points which are avoided are referred to as limiting reference points (Caddy and Mahon, 1995; Cochrane, 2002). For instance, in this study, the cephalopod fisheries resource is found to be overexploited and on the basis of obtained results it is suggested that the target reference point for the harvest of this fisheries resource in Pakistani marine waters is around 4,900-5,000 t. Moreover, it is also recommended that harvest beyond 5,200 t may be considered as limiting reference point because at this point fishery will start to suffer from overexploitation. 

CEDA and ASPIC computer packages used in this study have several advantages over the other statistical routines. Such as, CEDA (version 3.0.1) software is easy to use in Windows environment. Effort data series can be constructed by various means such as manual entry of the data or copy and paste from the data source. Computed results can be handled according to the user’s choice such as graphs and data can be saved, reloaded or printed at a later stage. These features make this computer package an ideal data fitting tool that has the ability to fit various models. Similarly, ASPIC computer package (version 5) confers several features, which make it an idea statistical tool for fisheries stock assessment. ASPIC software is user-friendly and files can be uploaded by drag and drop method. Input data series may be as long as 250 years. This software has the ability to fit logistic as well as generalized production models. Data of different kinds can be analyzed by using this computer package such as abundance data, catch statistics and B approximation or indices.

As this study has shown that cephalopod fisheries resource is overexploited in Pakistani marine waters. This overexploitation of the fishery resource has many problems. The biggest threat is the extinction of the fishery resource because fishermen try to catch more and more fish in order to maximize their profit (Clark, 1973). When fishery starts somewhere, the resources are plentiful and fishermen catch more fish with little effort thus attract more fishers to join fishery. When effort continue to rise, the catch start to decline until a stage come when the profit of the fishing fleets goes negative (Grafton et al., 2007). Thus reviving the fishery resource at this stage becomes very necessary. For this, the best option is fishery stock rebuilding. However, there are problems associated with the idea of fishery stock rebuilding. Such as, fishery stock building of fast growing fishes is not difficult but of slow growing fishes it is very difficult because it takes a long time to grow for these fishes. In addition to this, stakeholders usually do not comply with the idea of fishery stock rebuilding (Hilborn, 2007). However, the idea of fishery stock rebuilding can be accomplished if attracting incentives are offered to the stakeholders during the period of fishery stock rebuilding (Grafton, 1995).

In this study the computed MSY is below the observed catch which indicated overexploitation of cephalopod fishery in Pakistani marine waters. Pattern of computed CPUE is also speak about the fishery stock status. In this study as we have witnessed decreased CPUE with the passage of time which also clearly indicate the overexploitation of this fishery resource in Pakistani marine waters.

While collecting published data for this study a data limit situation was encountered. Such as, data related to diverse individual molluscan landed species is not available. Therefore, our analysis remained confined to major class. Moreover, more length of data series was not available. In addition to this, SPMs and software i.e. CEDA and ASPIC used in this study do have some issues with them. For SPMs, drawbacks are related with the changes of catchability i.e. q and effort distribution over various spatial areas with respect to time (Hilborn and Walters, 1992). In addition to this, some of the fisheries parameters such as K, q, and r are rigorously interrelated and cannot be quantified without reliable catch statistics. However, it must not be envisaged that reliability of the analysis is weakened rather these are famous fishery tools, which are frequently used in fisheries management around the globe (Musick and Bonfil, 2005).

4 Conclusion

Obtained results show that class cephalopoda fishery resource is overexploited. CEDA software computed MSY in a range between 4,600-4,900 t. On the other hand, ASPIC range for MSY estimation remained between 4,800-5,200 t for this fishery resource. It is vivid that MSY range computed by both the computer packages i.e. CEDA and ASPIC overlap each other. CEDA showed conservation in MSY estimation as compared to ASPIC. Moreover, ASPIC results showed higher R² values. Considering the MSY estimates it is recommended that target reference point for MSY should be around 4,900-5,000 t. Harvest of this fisheries resource beyond 5,200 t should be considered as limiting reference point.

Author’s contributions

This research work is a part of the Doctoral study of the first author MM. MM wrote manuscript and did statistical analysis. MYT supervised this project, provided access and training to the software and edited various sections. MMS and AMM complied statistical data, helped in their technical narration and constructed figure. They also helped to write various sections of the manuscript.

Acknowledgement

The first author is thankful to the China Scholarship Council (CSC) for funding his PhD degree. This study was supported by China Agriculture Research System (CARS-48). Authors are also thankful to Marine Fisheries Department, Pakistan for data procurement.

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