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Article

Length-Based Stock Assessment for the Data-Poor Bombay Duck Fishery from the Northern Bay of Bengal Coast, Bangladesh

by
Mohammed Shahidul Alam
1,2,
Qun Liu
1,*,
Petra Schneider
3,
Mohammad Mojibul Hoque Mozumder
4,
Mohammad Zahedur Rahman Chowdhury
5,
Mohammad Muslem Uddin
6,
Md. Mostafa Monwar
5,
Md. Enamul Hoque
6 and
Suman Barua
1,7
1
College of Fisheries, Ocean University of China, Qingdao 266003, China
2
Department of Fisheries, University of Chittagong, Chattogram 4331, Bangladesh
3
Department for Water, Environment, Civil Engineering and Safety, University of Applied Sciences Magdeburg-Stendal, Breitscheidstraße 2, D-39114 Magdeburg, Germany
4
Fisheries and Environmental Management Group, Helsinki Institute of Sustainability Science (HELSUS), Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
5
Institute of Marine Science, University of Chittagong, Chattogram 4331, Bangladesh
6
Department of Oceanography, University of Chittagong, Chattogram 4331, Bangladesh
7
Department of Fisheries, Ministry of Fisheries and Livestock, Dhaka 1215, Bangladesh
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(2), 213; https://doi.org/10.3390/jmse10020213
Submission received: 11 December 2021 / Revised: 21 January 2022 / Accepted: 1 February 2022 / Published: 5 February 2022
(This article belongs to the Section Marine Biology)
Browse Figures
Versions Notes

Abstract

:
The Bombay duck (Harpadon nehereus) forms the second-largest single-species marine fishery in Bangladesh and therefore has a significant impact on the local economy, providing employment, food, and nutrition to thousands of people. Despite the immense importance, this fishery has never been a priority for the relevant regulatory authorities. To enhance the sustainability of this fishery, an effective management policy based on the scientific evaluation of the current stock’s status is urgently required. Therefore, this study used three methodological approaches (traditional size structure-based stock assessment to reveal current exploitation status; the length-based spawning potential ratio (LB-SPR) to evaluate the stock’s spawning biomass; and Froese’s sustainability indicators for sustainable fishing) to conduct a thorough assessment of the Bombay duck stock to establish basic standards for the sustainable management of this fishery. The results revealed that this fishery is presently suffering from overexploitation and the stock’s spawning biomass (SPR = 8%) is below the limit reference point because of the juvenile-oriented fishing behavior of the fishery. Based on the outputs, this study recommended the mesh size regulation of the set bag nets (SBNs) (5 cm mesh size for the codend) to ensure not to catch immature fishes with a length equal to or smaller than 17.95 cm; and reduce the existing number of SBNs by half to reduce fishing pressure on the stock to ensure the sustainability of this fishery.

1. Introduction

The Bombay duck (Harpadon nehereus), or “Lottiya” as it is known locally, is a member of the Synodontidae family, an aggressive predator with cannibalistic feeding behavior [1,2] that lives in the Indo–Pacific tropical areas and shows a wide range of distribution from Somalia to Papua New Guinea, in the north to Japan, and in the south to Indonesia [3]. It spends most of its life in deepwater offshore and migrates to river deltas forming large schools for feeding during the monsoon season [3]. This species has an extended breeding season and produces six brood batches each year [4,5]. Bombay duck forms Bangladesh’s second-largest commercial single-species fishery, accounting for around 10.32% of the country’s total marine landing (68,101 metric tons) and more than 35% of the global production (of Bombay duck) in 2019 [6,7]. Therefore, this fishery grew into a significant social and economic sector, providing employment, food, and nutrition to thousands of people. Despite its enormous social and economic importance, this fishery is suffering from different human-induced activities such as overfishing [8]. As a result, extensive monitoring of this fishery through stock assessment is required to develop long-term effective management strategies to ensure sustainability [9,10,11,12,13,14]. In the literature, there are only four studies on Bombay duck stock assessment [15,16,17,18] from the marine water of Bangladesh. Based on length-frequency data, these studies reported the over-exploitation status of this fishery. However, there is no data on Bombay duck’s stock spawning biomass in Bangladesh, and previous studies did not include this issue in their analysis, which is critical for understanding the stock status. Without knowing the stock biomass status, excessive and indiscriminate exploitation, there exists the risk that production increases but results in a long-term decline and subsequently creates a risk of stock collapse [19].
Similar to most of the world’s fish stocks, the Bombay duck fishery is also small-scale and lacks substantial data sets (i.e., catch at age, catch per unit effort, life-history parameters, etc.). Therefore, it is difficult to assess this fishery with conventional catch-based stock assessment methods that can produce the most robust estimates for the formulation of management strategies [20,21,22,23,24]. On the other hand, collecting length data is generally straightforward and inexpensive and is one of the most prevalent types of data available to fisheries researchers to assess the data-poor fisheries [25]. Thus, given the simplicity of length data collection, many length-based stock assessment methods have been developed during the last few decades for science-based fisheries management [22,26,27,28,29,30,31,32,33]. Although length-based methods are not as robust as catch-based methods, they can still produce less biased and reliable estimates [28]. Hence, if fisheries representative length-composition data are available, length-based methods can be used to take initial management actions in a data-limited fishery [28]. Therefore, this study aims to assess the Bombay duck stock status along the coast of Chattogram and Cox’s Bazar district based on length composition data using three different methodological approaches. We used the traditional size structure-based stock assessment method to estimate yield-based reference points, such as Fmax (maximum fishing mortality for optimum yield) and F0.1 (Fishing mortality at which the marginal gain in yield per recruit falls to an arbitrary 10% of that at F = 0.) following the growth parameter estimation. But these reference points do not consider reproductive characteristics and the impact of exploitation on the stock spawning biomass [34]. As a result, we used another method, length-based spawning potential ratio (LB-SPR), to evaluate the impact of fishing on the reproductive biomass of the stock. Finally, Froese’s sustainability indicators were used to distinguish the selectivity pattern and stock spawning biomass status. This study concludes by highlighting the management challenges identified by methodologies and viable recommendations on incorporating them into harvest control rules.

2. Materials and Methods

2.1. Study Area

Chattogram and Cox’s Bazar district make up about 245 km of Bangladesh’s 710 km long, diverse coastline, covering the country’s south-eastern region. For the Bombay duck stock assessment, length data were collected from 13 local landing centers along the coast of Chattogram and Cox’s Bazar district based on the availability of the species throughout the year (Figure 1). The major fishing crafts in this fishery are non-mechanized and mechanized boats with various storage capacities. Set bag nets (SBNs), also known as behundi jal in the local communities, are the primary fishing gears used in Bombay duck catch, accounting for more than 95 percent of total landings [6] (Figure 2). Therefore, we collected the length data for this study from landing associated with SBNs. These nets have mesh sizes ranging from 2 to 10 cm at the mouth and 0.2 to 5 cm in the codend. Seine nets (mesh size: 0.5 to 6 cm), gill nets (mesh size: 2 to 10 cm), and trawl nets (codend mesh size: 1 to 5 cm) are also used by the fishermen for Bombay duck fishing.

2.2. Data Collection

For sample collection, we selected all known landing sites along the coasts of Cox’s Bazar and Chattogram district. During the sampling days, samples were collected at the landing centers from the landings of all boats that used set bag nets (SBNs) and fished in different depths within the 40 m depth range (small-scale fishing area). Sampling was performed monthly from January to December 2018. A total of 5921 Bombay duck (both sexes) samples were collected for length and weight data. Fishers gather their fish in separate piles at the landing centers, and 10% of each well-mixed pile was collected randomly during the sampling days. Between 30–100% of the landed fishes were collected from the respective landing center during the low landing. The total length of all collected samples was measured to the nearest 0.1 cm using a digital slide caliper and weighed to the nearest 0.1 g using a digital balance. In some instances, data collection was hampered due to adverse weather conditions. Data was collected as soon as the bad weather was over in those cases.
A total of 34 years of catch and effort data (1986–2019) (associated with SBNs) of this fishery were extracted from the Yearbook of Fisheries Statistics of Bangladesh (1986–2019) [6] published by the Fisheries Resources Survey System (FRSS), Department of Fisheries (DoF), Bangladesh to analyze the landing trends of this species.

2.3. Length-Weight Relationship

The parameters of the length–weight relationship (‘a’ and ‘b’) were estimated from the following formula proposed by Le Cren (1951) [35]:
W = aLb
where W is body weight (g), L is the total length (cm), a is the intercept, and b is the slope.

2.4. Stock Assessment Indicators

For a rigorous stock assessment of Bombay duck, first, we used a newly developed R package, ‘TropFishR,’ based on FAO traditional stock assessment methods [36,37] to estimate the fishery’s biological characteristics (growth and mortality) and exploitation and selectivity.
Secondly, we used another R package, LB-SPR (Length-based spawning potential ratio), developed by Hordyk et al. (2015) [38] to estimate the spawning potential ratio (SPR) as a function of the relative fishing mortality (F/M). The SPR is the natural or unfished reproductive proportion that remains in the stock under the present degree of exploitation [39]. This well-established biological reference point has been carefully tested in the literature to determine the influence of present fishing pressure on the stock’s reproductive capacity [38,39,40]. For the sustainability of a fishery, 40% SPR is regarded as the target reference point and 20% as the limit reference point [41].
Finally, we used Froese’s (2004) length-based sustainability indicators to assess our length composition data in relation to length-reference points [9,26]. These indicators give the information required to develop a practical management approach that will aid in avoiding growth and recruitment overfishing [26].

2.4.1. Growth Parameters Estimation

The ‘TropFishR’ [36] package used the following seasonalized von Bertalanffy growth formula to estimate the von Bertalanffy (1938) [42] growth parameters (K, L∞, and t0), using Electronic Length Frequency Analysis (ELEFAN) procedure for the fitting process [43,44,45,46]:
Lt = L∞ × (1 − e − (K(t − t0) + S(t) − S(t0)))
where Lt is the length at age t, L∞ is the asymptotic length in cm, K is the growth coefficient in year−1, t0 is the theoretical age of a fish at which its length is zero.
In the tropical area, the winter–summer temperature differences are too small, and it is not possible to detect the seasonal growth oscillations in fish visually [3]. Therefore, we did not study the seasonal growth of Bombay duck, and thus, “+S(t) − S(t0)” in the above equation was set to zero.
The growth performance index (Φ′) was then calculated from the following formula proposed by Pauly and Munro (1984) [44]:
Φ′ = log (K) + 2 × log (L∞)
‘TropFishR’ requires priors for L∞ and K, and the initial value for L∞ was calculated from the following formula proposed by Pauly (1984) [47]:
L∞ = Lmax/0.95
where Lmax is the observed maximum length of the fish.
Then we set the initial seed value for L∞ as L∞ ± 20% and the K range between 0.01 and 2. The parameter tanchor in ELEFAN represents the day when the von Bertalanffy growth curve crosses length = 0 for a certain cohort [47]. The range of initial seed values for tanchor was 0 to 1 [36]. Finally, a suitable moving average (MA = 7) was set on a trial and error basis using different MA values and the rule of thumb described by Taylor and Mildenberger (2017) [36].

2.4.2. Fishing Mortality and Exploitation

‘TropFishR’ procedures produced a linearized length-converted catch curve from the length data and estimated growth parameters, and therefore, the total instantaneous mortality rate (Z) was estimated from the slope of the regression line of the descending part of the catch curve [36]. Following the Z estimation, the natural mortality (M) was estimated from the following formula proposed by Then et al. (2015) [48]:
M = 4.118 × K0.73 × L∞−0.33
Current fishing mortality (F) and exploitation rates were then estimated using the following formulas:
F = Z − M
E = F/Z
The current exploitation rate (E) was then compared to the threshold level of 0.5 proposed by Gulland (1971) [49]. The estimated fishing mortality (F) and exploitation rate (E) was also compared to the following reference points obtained from Beverton & Holt’s (1956) [50] Yield per Recruit (YPR) model:
(a)
fishing mortality and exploitation at maximum yield per recruit (Fmax and Emax),
(b)
fishing mortality and exploitation that reduces the population to 50% of unfished spawning biomass (F0.5 and E0.5),
(c)
fishing mortality and exploitation reduces the marginal gain in yield per recruit to an arbitrary 10% of that at F = 0 (F0.1 and E0.1),

2.4.3. Length-Based Spawning Potential Ratio (LB-SPR)

LB-SPR assessment estimates the spawning potential ratio (SPR) that is thoroughly benchmarked in the literature and has a solid theoretical foundation, and has been widely tested [23,28,39,41,51,52,53,54]. The model assumes that the length structure and the SPR of an exploited stock are a function of relative fishing mortality (F/M) and two life history ratios, M/K and Lm/L∞, where Lm is the length at which 50% of a length class is mature [41]. Therefore, in addition to the length distribution data, the LB-SPR model requires the following input parameters: (i) the M/K ratio; (ii) the asymptotic length (L∞); (iii) the variability of length-at-age (CVL∞); which is usually assumed to be around 10%; and (iv) length at which 50% (L50%) and 95% (L95%) of a fish population are mature [41]. The assessment model uses maximum likelihood methods to estimate the length of the first capture of 50% (SL50%) and 95% (SL95%) of the population and relative fishing mortality (F/M), which are then used to compute the SPR [33,41].
The equilibrium-based LB-SPR model, unlike many other length-based stock assessment methods, is based on the following assumptions: (i) asymptotic selectivity; (ii) the von Bertalanffy equation accurately describes the growth; (iii) length-at-age is normally distributed; (iv) natural mortality rates are constant throughout adult age groups; and (v) constant growth rate across the cohorts of a stock [41].
This study used the estimated L∞, M, and K values by ‘TropFishR’ as input parameters for LB-SPR. The length at 50% sexual maturity (L50%) was then estimated from the following empirical equation proposed by Froese and Binohlan (2000) [55];
logL50 = 0.8979logL∞ − 0.0782
The length at 95% sexual maturity (L95%) was estimated from the equation proposed by Prince et al. (2015) [41].
L95 = 1.1 × L50,
The analysis was conducted using the LB-SPR R package [56], available at: https://CRAN.R-project.org/package=LB-SPR (access date: 10 June 2021.).

2.4.4. Length-Based Indicators

For sustainable fishing and to avoid growth and recruitment overfishing, Froese (2004) proposed three simple length-based indicators denoted as Pmat, Popt, and Pmega [26].
Pmat and Popt refer to the percentage of mature and optimally sized fish present in the catch with 100% as the target, and Pmega refers to the percentage of mega-spawners, fish with a length greater than optimum length (Lopt) plus 10% of Lopt (≥1.1Lopt) with not more than 20% as the target. According to Froese (2004) [26], for the fishery’s sustainability and optimum biological yield, the targeted length classes should be within the range of Lopt ± 10% of Lopt. Therefore, these indicators can be calculated as:
Pmat = percentage of fish with a body length greater than the length at sexual maturity (Lm).
Popt = percentage of fish between 0.9 × Lopt and 1.1 × Lopt [26].
where, log(Lopt) = 1.053 × log(Lm) − 0.0565 [55].
Pmega = percentage of fish greater than optimum length plus 10% of optimum length (≥1.1Lopt) [26,57].

3. Results

3.1. Landing and Fishing Gears

In Bangladesh, there is no record readily available of the history of landings of Bombay duck (Harpadon nehereus), but it was first recorded in the fisheries statistics in 1986 [6] and was 21,811 metric tons (mt). Between 1986 and 2000, the average landing was 19,605 metric tons, rising to 48,898 metric tons between 2001 and 2019, making the landing of Bombay duck Bangladesh’s second-highest reported landing for marine capture fisheries (Figure 3).
Data on fishing gears showed that between 1986 and 2019, the number of set bag nets increased almost four times (Figure 3). In Figure 3, in the time series of effort data, the same number of SBNs (12,615) were recorded for 14 years (from 1986 to 1999), with a sudden increase to 66,927 in 2000. Again, from 2001 to 2009, the number of SBNs remained constant, at 50,083 per year. Therefore, it can be concluded that these data were not recorded properly, and thus, they are not suitable for any conventional stock assessment that requires catch per unit effort (CPUE) data.

3.2. Length Distributions

A total of 5921 individuals of the Bombay duck were measured for total length (TL) during the 12 months of this study (Table 1). The length–frequency distribution was found to be unimodal with one clear peak, and the modal size ranges from 11 to 12 cm, with around 40% of the fish samples being less than 12 cm (Figure 4). There was no spatial and temporal size difference observed during the sampling period.

3.3. Length–Weight Relationship

The length-weight parameters, a and b, were obtained from total length (TL) and weight (W) data using Equation (5), and the relationship was W = 0.0046L2.812 (R2 = 0.75) (Figure 5). These values were used as input parameters for Yield per Recruit analysis in TropFishR.

3.4. Growth Parameters and Mortality

3.4.1. Growth Parameters

The mean predicted von Bertalanffy growth parameters based on Equation (3) were 30.47 cm TL for L∞ and 0.86 year−1 for growth coefficient K (Table 2 & Figure 6A), implying that Bombay duck can grow up to a maximum of 30.47 cm TL with a moderately high growth rate if not caught by existing gear. The ELEFAN procedure within the ‘TropFishR’ calculated the mean growth performance index as 2.91 with a high goodness of fit value (Rn) of 0.202 (Table 2).

3.4.2. Mortality and Exploitation

Based on a linearized length-converted catch curve, the estimated total mortality rate (Z) by ‘TropFishR’ is 3.06 year−1. The natural mortality (M) was then estimated as 1.199 year−1 using the method described by Then et al. (2015) [48]. Finally, fishing mortality (Fcurr) was calculated as 1.86 year−1 by subtracting M from Z. Table 3 summarizes the estimated biological reference points of fishing mortality and exploitation rate. Figure 6B–D show the graphical outputs of the catch curve and YPR model. The estimated present fishing mortality (Fcurr) is much higher than the optimum biological fishing mortality Fmax (1.41 year−1) and F0.1 (0.85 year−1). The present exploitation rate (Ecurr) is 22% above the threshold of Eopt = 0.5. The length at first capture (Lc) was estimated as 9.69 cm, implying that this length class has a 50% and length class 12.36 cm has a 95% probability of being captured when the total mortality (Z) is 3.06 year−1 under the present pattern of gear selectivity. The outputs also indicate that the maximum fishing mortality (Fmax) and exploitation (Emax) for optimum yield should be maintained at 1.41 year−1 and 0.46 year−1, respectively. Up to 50% of the stock’s biomass might be obtained at a rate of F0.5 = 0.59 year−1 and E0.5 = 0.19 year−1. On the other hand, the F0.1 and E0.1 values should be kept at 0.85 year−1 and 0.28 year−1 for the biologically optimal yield.

3.5. Length-Based Spawning Potential Ratio (SPR)

3.5.1. Estimation of Life History Ratio (LHR) and Size of Maturity

For the LB-SPR assessment, estimates of the two LHR (L∞ and M/K) are required. From the outputs of ‘TropFishR’ analysis, we used the L∞, K, and M values and calculated the M/K ratio. From Equations (8) and (9), we estimated the size at which 50% (L50%) and 95% (L95%) maturity occur (Table 4).

3.5.2. Model Fitting to the Length Distribution Data

The estimated mean length for the collected length-frequency data is 12.59 (±4) cm. The estimated growth curve by LB-SPR fits well with the distribution of our data (Figure 7A) and is skewed towards small fishes’ length classes.

3.5.3. Length Selectivity and Maturity

From the automated procedure of LB-SPR in the R studio [58], the mean estimates of 50% (SL50%) and 95% (SL95%) selectivity are 10.01 cm and 14.02 cm, respectively, indicating fishing with small mesh nets (Table 5). The mean estimate of F/M is 2.03, more than two times higher than the threshold F/M = 1. The ogive curve of selectivity and maturity shows that the length at maturity is higher than at first capture (Figure 7B).

3.5.4. Spawning Potential Ratio (SPR)

Assuming that the size composition data used in this study are in a steady-state [38,39], the LB-SPR method estimated the SPR for the year 2019 at 8%, which is well below the limit reference point (LRP) of 20% (Figure 7C and Table 5).

3.5.5. Existing vs. Expected Size Composition at the Target SPR (0.40)

Figure 7D shows the length classes that should be targeted to maintain the threshold level (SPR = 0.40) in red. However, the currently targeted length classes by the fishery’s selectivity are significantly lower than those expected size classes, indicating that this fishery is characterized mainly by juvenile fishing.

3.6. Results from Catch Composition Analysis

The analysis of catch composition data shows that 84.97% of fish in the catch composition were immature, while 15.03% and 12.40% of fish were mature (Pmat) and optimally sized (Popt), respectively. The number of old and larger fishes, termed mega-spawners (Pmega), in the catch was significantly lower (4.84%) (Table 6). For the optimum yield, the selectivity of the Bombay duck fishery should target the length classes (Lopt ± 10% of Lopt) between 16.43 cm and 20.30 cm (Figure 8).

4. Discussion

Despite the fact that the Bombay duck fishery is critical to coastal communities in terms of income, employment, and food security, this fishery has never been a priority for the relevant regulatory authorities [59]. As a result, high-quality data are scarce for a thorough stock assessment using data-intensive catch-based methods that can give policymakers the information they need to develop plans for the long-term management of this fishery. In Bangladesh, data availability is minimal, and the only available data are the landing statistics with misreported, incomplete, and limited effort data (Figure 3), which made it challenging to use these data to assess any stock status. Given this, we focused on length-based stock assessment methods and collected data from all known landing centers along the coasts of Chattogram and Cox’s Bazar district and from all boats that used SBNs for Bombay duck catching to ensure that the collected length data are fishery representative. Since it is impossible to understand any stock’s status depending on a single biological reference point [60], we combined three methodological approaches for the best estimates of this fishery that require minimum data input (length data only). These methods are best suited for fishes with moderate to high growth rates and can produce reliable estimates from only one year’s data [36,38,61]. The results of our assessment and their implications for the long-term sustainability of the Bombay duck fishery are discussed in the following paragraphs.

4.1. Trends in Landing

The trends in Bombay duck landings show a significant increase in the reported catches while the numbers of set beg nets, the coarse indicator of fishing effort for this fishery, also increased by more than fourfold between 1986 to 2019. According to Ullah et al. (2014) [62], these landing data are under-reported and do not contain illegal, unregulated, and unreported (IUU) data. They reported that the reconstructed catch landing was 157% greater than the reported landing by DoF in 2010 and that the Bombay duck was the most important subsistence species, accounting for approximately 12% of the overall subsistence catch. The exact number of gears was recorded for several years in the effort data of DoF, which is unreasonable given that the rise in Bangladesh’s coastline population shows no signs of slowing down. Therefore, scientists should use these catch and effort data carefully for the stock assessment of any species (i.e., incorporation of linear interpolation for effort data or use assessment methods that do not require effort data) to obtain accurate information for the sustainable management of the marine fisheries resources of Bangladesh.

4.2. Growth, Mortality, and Exploitation

Life history parameters are the basic inputs in analyzing a fish stock status to provide effective management recommendations. As a result, high precision in growth parameter estimation is essential for a reliable stock assessment [63]. ‘TropFishR’ followed the conventional stock assessment procedures from length data with a few extra steps to generate more robust and precise estimates. Our findings show wide disparities with the previous estimates of growth parameters even though we used wider search range input for the bootstrapped ELEFAN analysis to cover those estimates (Table 7). The estimated L∞ was higher than the estimation of Mustafa et al. (1998) [16] and lower than that estimated by Sarker et al. (2017) [18].
Similarly, the estimated growth coefficient (K) value is significantly lower than the previous two estimates. In this study, we collected length data covering the entire coastline of the Chattogram and Cox’s Bazar district of Bangladesh. In comparison, Sarker et al. (2017) [18] collected samples from 35 landing centers from different districts of Bangladesh, and Mustafa et al. (1998) [16] only collected data from a relatively small area (Kutubdia channel). On the other hand, we collected data only from SBNs, but Sarker et al. (2017) [18] collected data from different gears. This may be the reason for the disparity because gear selectivity and temporal and spatial variation in sample collection significantly impact the size distribution of catch data and affect the visualization of cohort progression in the ELEFAN analysis, resulting in different growth parameters estimates [60].
Bombay duck is considered one of the most demanded marine species in the local market [18,62]. Due to the increased demand, there has been an expansion of unregulated fishing activity over the past few decades [14]. For the sustainability of a fishery, it is essential to keep the stock healthy, leaving enough fish in the stock to reproduce [26]. But previous studies show that this fishery has been suffering from overexploitation historically [16,18]. Our study also revealed that existing fishing mortality is more than 50% higher than natural mortality, and the exploitation rate is significantly higher than the threshold (E = 0.5). Due to overfishing and overexploitation, older and larger fishes that are highly important for their high fecundity rate that reduces the chance of stock-recruitment failure become scarce, and the small and immature fishes become dominant in the catch composition.
When Lc > Lm, the fishing gear gives up catching immature fishes to allow them to mature and reproduce at least once in their lifetime, which will compensate for the adverse impact of overfishing and therefore ensure the sustainability of a fish stock by maintaining the reproductive stocks in good health [26,69]. Bapet et al. (1951) [70] reported that the Bombay duck reaches sexual maturity at a length higher than 20 cm. But our estimated Lm (17.95 cm) from L∞ is smaller than this value. Though it is difficult to draw a conclusion depending on one observation, it seems likely that overfishing is partially responsible for the early maturation of fish species [71]. The present and previous Lc estimates were below the Lm, and around 85% of our sample was immature. Therefore, it is logical to comment that the present gear selectivity of the fishery that is determined by the mesh size of the nets (SBNs) used by the fishers is predominantly directed at young and immature fish, causing growth overfishing. Again, a smaller length at which 95% of fishes are likely to be captured indicated the absence of mature, old, and larger fishes in the stock, which is a sign of recruitment overfishing. From the outputs of ‘TropFishR,’ it is clear that this fishery is at risk and will soon collapse if the current fishing pattern continues.

4.3. Diagnosis of the Stock Based on LB-SPR and Catch Proportion Analysis

Spawning potential ratio (SPR) is a well-established biological reference point and powerful tool for assessing the impact of current fishing pressure on the stock’s reproductive potential [38,39,40]. Determining this ratio (SPR) is critical for developing an effective fish stock management strategy. This study used the LB-SPR method developed by Hordyk et al. (2015) [39] to evaluate the Bombay duck stock spawning biomass. For the estimation of SPR, LB-SPR methods showed its robustness, especially species with M/K > 0.53 and length data composition with uni- or bimodal distribution [38,39].
Since the LB-SPR method is highly sensitive to input parameters (L∞, K, M, L50, and L95) and previously estimated values are highly variable, we used the estimates produced by the ‘TropFishR’ in this study as input parameters for LB-SPR analysis. We estimated the life history ratio (LHR) M/K (1.40) from the M and K, which indicated that Bombay ducks mature and reach their maximum length at a moderately high growth rate. The LB-SPR revealed that the Bombay duck fishery in the Chattogram and Cox’s Bazar district of Bangladesh is being heavily exploited (F/M = 2.03), and thereby the reproductive potential left in the stock is very low (SPR = 0.08). When there is a high frequency of small and immature fishes in the catch composition and few mature or larger fishes, a low SPR value is produced. Due to the historical catches of larvae, juveniles, adults, and brood fishes indiscriminately, the availability of targeted length classes for maintaining the threshold level of SPR (0.40) is scarce (Figure 7D), a clear indication of the growth and recruitment overfishing status of the fishery. The validity of the LB-SPR approach largely depends on the idea that the length data represent the exploited stock and that if the sample is biased by any length class, the output will be biased as well [32,72]. We were careful not to leave any length group out of our samples during sampling. Therefore, we are confident that the representation of every size group from the sampling area was ensured.
Following the Froese (2004) [26] sustainability indicators, we further calculated the percentage of mature fish (Pmat), optimally larger fishes (Popt), and mega-spawners (Pmega) in our catch composition. To avoid growth and recruitment overfishing, the gear selectivity of the fishery should only target the optimally sized fishes [26]. But the major portion of our catch composition were immature and juvenile fishes, and more importantly, the presence of mega-spawners was critically low. Furthermore, the fisherman employed set bag nets with a fine mesh size, ensuring nothing was left behind in the fishing area. From this perspective, it is reasonable to assume that a large number of adult fish have already been removed from the stock and that very few fish get the chance to reach sexual maturity and reproduce before being caught. This result corresponds to the LB-SPR findings that there are very few mature fish in the stock to reproduce. Mustafa et al. (1998) reported the preponderance of small and immature fish in their samples using the similar data collection protocol (from set bag nets and same location) [16]. Therefore, it may be argued that the Bombay duck fishery has been over-exploited, and juveniles have been caught since the early 1990s.

4.4. Management Recommendations

In this study, we used three different approaches to analyze the present stock condition of Bombay duck in the Chattogram and Cox’s Bazar coast of Bangladesh. All methods returned similar results that this species is currently suffering from growth and recruitment overfishing with a high exploitation rate. As a result, it is urgent to take management measures to protect the juvenile through mesh size regulation of nets. According to the “Marine Fisheries Act, 2020” (previously “Marine fisheries ordinance, 1983”), fishing with an SBN with a mesh size smaller than 4.5 cm at codend is prohibited. This mesh size would not catch fish smaller than 17.30 cm. However, the reality is different due to the lack of effective monitoring (the bulk of small fish in the catch composition). We observed the illegal use of nets (mesh size smaller than the allowable size) and fishing during the ban periods. Therefore, a formal monitoring mechanism should be established for regular monitoring and evaluation of the management process.
Since SBN with mesh size 4.5 cm at codend would catch Bombay duck smaller than their length at sexual maturity (17.95 cm), this study recommends a 5 cm mesh size for the codend of SBN to allow all immature fish to mature and reproduce at least once before being caught. Fishing mortality is proportional to fishing effort [32]; therefore, the existing number of SBN should be reduced to its half to bring back the fishing mortality to the threshold level F = M. Finally, there are no alternatives to licensing. Anybody who wants to go fishing with an SBN should have a license. This is a tried-and-true approach for managing a fish stock that is currently over-fished.

5. Conclusions

This study employed three different methodological approaches to evaluate the stock status of Bombay duck in Chattogram and Cox’s Bazar coast of Bangladesh. Outputs from all three methods indicate that this fishery is in an overfished state and the stock’s reproductive potential is alarmingly low; therefore, this study came up with the following recommendations:
  • Not to allow the catching of fish with a length equal to or smaller than 17.95 cm;
  • Reduce fishing mortality by controlling the fishing efforts;
  • Ensure that all SBNs operating in Bangladesh’s coastal waters are licensed;
  • Establish a systematic monitoring framework to guarantee that existing laws and regulations are followed.

Author Contributions

M.S.A.: conceptualization, data collection, methodology, data analysis, visualization, writing, editing and reviewing, Q.L.: conceptualization, reviewing and editing, P.S.: editing, reviewing and funding, M.M.H.M.: editing, reviewing. M.Z.R.C.: data analysis, visualization, M.M.U.: editing, reviewing, M.M.M.: data collection, editing, reviewing, M.E.H.: data collection, editing, reviewing, S.B.: Data collection, editing, reviewing. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the special research fund of Ocean University of China (201562030).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The Datasets generated during this study are available from the corresponding author on reasonable request.

Acknowledgments

The first author would like to express his sincere gratitude to the Chinese Scholarship Council (CSC) and SOA (State Oceanic Administration) for the sponsorship during his doctoral degree.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of the Bay of Bengal along the south-eastern coast of Bangladesh showing the sampling sites and fish landing centers.
Figure 1. Map of the Bay of Bengal along the south-eastern coast of Bangladesh showing the sampling sites and fish landing centers.
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Figure 2. Set bag net, the predominant gear used by the fishers to catch Bombay duck.
Figure 2. Set bag net, the predominant gear used by the fishers to catch Bombay duck.
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Figure 3. Trends in annual landing and number of set bag nets for Bombay duck in marine water of Bangladesh from 1986 to 2019. Here, green dots indicate the landing in metric tons, and red dots indicate the number of set bag nets.
Figure 3. Trends in annual landing and number of set bag nets for Bombay duck in marine water of Bangladesh from 1986 to 2019. Here, green dots indicate the landing in metric tons, and red dots indicate the number of set bag nets.
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Figure 4. The bar chart shows the length–frequency, and the curve shows the unimodal distribution of the size data of the Bombay duck based on the data collected during this study (from January to December 2018).
Figure 4. The bar chart shows the length–frequency, and the curve shows the unimodal distribution of the size data of the Bombay duck based on the data collected during this study (from January to December 2018).
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Figure 5. Length-weight relationship of the Bombay duck from the marine water of Bangladesh.
Figure 5. Length-weight relationship of the Bombay duck from the marine water of Bangladesh.
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Figure 6. The outputs of ‘TropFishR’ showing: (A) the von Bertalanffy growth curve and length-frequency distribution of the Bombay duck (L∞ = 30.47 cm, K = 0.86 year−1); (B) linearized length-converted catch curve to estimate total mortality (Z = 3.06) and the selectivity function of the catch curve estimated as length at first capture (Lc) of 9.69 cm; (C) yield and biomass per recruit analysis when Lc = 9.69 cm—the green, yellow and red dashed lines represent F0.5 = 0.59 year−1, Fmax = 1.41 year−1, and F0.1 = 0.85 year−1, respectively; (D) relative YPR in response to different fishing mortality and different length at first capture (Lc).
Figure 6. The outputs of ‘TropFishR’ showing: (A) the von Bertalanffy growth curve and length-frequency distribution of the Bombay duck (L∞ = 30.47 cm, K = 0.86 year−1); (B) linearized length-converted catch curve to estimate total mortality (Z = 3.06) and the selectivity function of the catch curve estimated as length at first capture (Lc) of 9.69 cm; (C) yield and biomass per recruit analysis when Lc = 9.69 cm—the green, yellow and red dashed lines represent F0.5 = 0.59 year−1, Fmax = 1.41 year−1, and F0.1 = 0.85 year−1, respectively; (D) relative YPR in response to different fishing mortality and different length at first capture (Lc).
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Figure 7. The outputs of the LB-SPR model, showing: (A) length–frequency distribution of observed fished population, and the solid black line show the predicted fished size composition from the fitted LB-SPR model; (B) maturity and selectivity curve from the fitted LB-SPR model when L50% = 17.95 cm and L95% = 19.75 cm; (C) shows the distribution of mean selectivity parameters (SL50% and SL95%), fishing mortality to natural mortality (F/M), and spawning potential ratio; (D) observed size data against an expected size composition at a target SPR (0.4).
Figure 7. The outputs of the LB-SPR model, showing: (A) length–frequency distribution of observed fished population, and the solid black line show the predicted fished size composition from the fitted LB-SPR model; (B) maturity and selectivity curve from the fitted LB-SPR model when L50% = 17.95 cm and L95% = 19.75 cm; (C) shows the distribution of mean selectivity parameters (SL50% and SL95%), fishing mortality to natural mortality (F/M), and spawning potential ratio; (D) observed size data against an expected size composition at a target SPR (0.4).
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Figure 8. Length frequency distribution of the Bombay duck showing the L∞, Lopt, and Lopt ± 10% of Lopt.
Figure 8. Length frequency distribution of the Bombay duck showing the L∞, Lopt, and Lopt ± 10% of Lopt.
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Table
Table 1. The number of samples collected monthly from January to December 2018.
Table 1. The number of samples collected monthly from January to December 2018.
MonthJanFebMarAprMayJunJulAugSepOctNovDec
Number of samples650593447405594726387379399671355315
Table
Table 2. Growth parameters of Bombay duck.
Table 2. Growth parameters of Bombay duck.
ParametersValue
Asymptotic Length (L∞)30.47 cm
Growth Coefficient (K)0.86 year−1
t_anchor0.42
Growth Performance Index (Φ′)2.91
Rn value0.202
Note: t_anchor indicates the yearly repeating growth curves cross length equal to zero on 1 May, and Rn is the goodness of fit of model estimation.
Table
Table 3. Mortality and exploitation rate estimated by ‘TropFishR’.
Table 3. Mortality and exploitation rate estimated by ‘TropFishR’.
ParmetersValue
Natural Mortality (M)1.199 year−1
Total Mortality (Z)3.06 year−1
Fishing Mortality (Fcurr)1.86 year−1
Fmax1.41 year−1
F0.10.85 year−1
F0.50.59 year−1
Current Exploitation (Ecurr)0.61
Emax0.46
E0.10.28
E0.50.19
Length at First Capture (Lc)9.69 cm
Length at 95% capture12.36 cm
Table
Table 4. Input parameters for LB-SPR analysis.
Table 4. Input parameters for LB-SPR analysis.
ParametersValue
L∞30.47 cm
K0.86 year−1
M1.199 year−1
M/K1.40
Length at 50% maturity (L50%)17.95 cm
Length at 95% maturity (L95%)19.75 cm
Table
Table 5. Outputs form LB-SPR analysis with 95% confidence interval.
Table 5. Outputs form LB-SPR analysis with 95% confidence interval.
PrametersBestLCIUCI
SL50%10.01 cm9.8310.21
SL95%14.02 cm13.7314.39
F/M2.03 1.192.15
SPR0.080.070.09
Note: LCI = Lower confidence interval; UCL = Upper confidence interval
Table
Table 6. The estimated values of Forese (2004) [26] length-based indicators.
Table 6. The estimated values of Forese (2004) [26] length-based indicators.
ParametersValueComments
Lopt18.37 cmLength class at which maximum yield can be obtained.
Pmat15.03%Percentage of mature fish.
Popt12.40%Percentage of optimally sized fish.
Pmega4.84%Percentage of mega-spawners.
Table
Table 7. Growth parameters, mortality, and exploitation of Bombay duck in different countries in the world.
Table 7. Growth parameters, mortality, and exploitation of Bombay duck in different countries in the world.
CountryL∞ (cm)KΦMFEEmaxLcYearReference
Bangladesh 24.481.502.952.463.270.570.506.751995–1996[16]
45.051.303.421.862.580.580.5817.502013–2014[18]
30.470.862.911.1991.860.610.469.692018Present study
India43.40.813.181.303.030.700.38212003–2005[64]
35.390.863.031.521.730.530.403.422003–2006[65]
36.60.983.121.641.680.510.6616.34-[66]
Pakistan29.400.612.601.280.520.29--2013–2014[67]
China26.90.94 1.581.690.52 18.072015[68]
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Alam, M.S.; Liu, Q.; Schneider, P.; Mozumder, M.M.H.; Chowdhury, M.Z.R.; Uddin, M.M.; Monwar, M.M.; Hoque, M.E.; Barua, S. Length-Based Stock Assessment for the Data-Poor Bombay Duck Fishery from the Northern Bay of Bengal Coast, Bangladesh. J. Mar. Sci. Eng. 2022, 10, 213. https://doi.org/10.3390/jmse10020213

AMA Style

Alam MS, Liu Q, Schneider P, Mozumder MMH, Chowdhury MZR, Uddin MM, Monwar MM, Hoque ME, Barua S. Length-Based Stock Assessment for the Data-Poor Bombay Duck Fishery from the Northern Bay of Bengal Coast, Bangladesh. Journal of Marine Science and Engineering. 2022; 10(2):213. https://doi.org/10.3390/jmse10020213

Chicago/Turabian Style

Alam, Mohammed Shahidul, Qun Liu, Petra Schneider, Mohammad Mojibul Hoque Mozumder, Mohammad Zahedur Rahman Chowdhury, Mohammad Muslem Uddin, Md. Mostafa Monwar, Md. Enamul Hoque, and Suman Barua. 2022. "Length-Based Stock Assessment for the Data-Poor Bombay Duck Fishery from the Northern Bay of Bengal Coast, Bangladesh" Journal of Marine Science and Engineering 10, no. 2: 213. https://doi.org/10.3390/jmse10020213

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