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Article

Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh

by
Mohammed Shahidul Alam
1,2,
Qun Liu
1,*,
Petra Schneider
3,
Mohammad Mojibul Hoque Mozumder
4,
Mohammad Muslem Uddin
5,
Md. Mostafa Monwar
6,
Md. Enamul Hoque
5 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
Department of Oceanography, University of Chittagong, Chattogram 4331, Bangladesh
6
Institute of Marine Science, 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), 201; https://doi.org/10.3390/jmse10020201
Submission received: 24 December 2021 / Revised: 25 January 2022 / Accepted: 27 January 2022 / Published: 1 February 2022
(This article belongs to the Special Issue Marine Fisheries Management)
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Versions Notes

Abstract

:
The two economically important shrimp species in Bangladesh are the tiger shrimp, Penaeus monodon, and the brown shrimp, Metapenaeus monoceros. However, a continuous decline in the landing of these species from the industrial trawling made it critical to assess their stock biomass status to explore their response to the present degree of removal. Given the minimum data requirement and robustness, this study employed the depletion-based stock reduction analysis (DB-SRA) to assess these fisheries rigorously. For the industrial fishing zone (beyond the 40 m depth in the EEZ of Bangladesh), the estimated historic mean carrying capacity (K) was 5015 metric tons for the Penaeus monodon and 35,871 metric tons for Metapenaeus monoceros. The estimated overfishing limits (OFL), which were much smaller than the reported catches throughout the time series, indicate the overfishing status of these fisheries. As a result, the estimated biomass for the reference year (B2020) for both species was lower than BMSY, indicating that these fisheries are not producing MSY. Therefore, for the rebuilding and sustainable management of these stocks, this study recommended a catch limit of 100 metric tons for P. monodon and 750 metric tons for M. monoceros for the next ten years from biomass projections.

1. Introduction

Marine fisheries are critical to the economic and well-being of coastal communities in terms of food, employment, income, and livelihoods [1,2]. However, in today’s world, it is increasingly recognized that a significant number of marine fish stocks have already been depleted, particularly in developing countries, due to their ever-growing population, increased demand, over-exploitation, and/or insufficient fisheries management ability [3,4,5,6]. Given these situations, to sustain these resources and rebuild the depleted stocks, science-based stock assessments that will provide necessary information for effective management policies are urgent [7,8,9,10,11,12]. Furthermore, stock assessments provide the basis to evaluate the effectiveness of existing management policies for sustainable harvesting or rebuilding a depleted fish stock [13,14].
In Bangladesh, industrial trawling beyond 40 m depth in the exclusive economic zone (EEZ) for shrimp and demersal fishes significantly contributes to the country’s total marine landings [15,16], with shrimp playing a pivotal role. In 2019, shrimp contributed approximately 7% (42,749 metric tons) of total marine landings [17]. Despite the fact that 37 shrimp species have been reported from Bangladesh’s maritime waters, the most economically important shrimp species include tiger shrimp (Penaeus monodon), brown shrimp (Metapenaeus monoceros), and white shrimp (Penaeus indicus) [17,18,19,20,21]. However, in terms of commercial importance, the tiger shrimp, P. monodon, outperform others [22]. With the growing number of tiger shrimp hatcheries along the coast of Bangladesh, extensive collections of postlarvae (PL) and broodstock (matured shrimp) from the wild have made this fishery vulnerable [23]. On the other hand, the brown shrimp is a major contributor to the shrimp trawling landing, and in 2019, it was accounted for over half of the total shrimp landing [24]. Even though the number of shrimp trawlers has remained constant for nearly two decades, a continuous decline in total shrimp landings since the beginning of the fishery was observed, i.e., 12% lower than the previous year in 2019 [24]. Given their significant economic importance, the sustainability of these fisheries must be ensured through appropriate management strategies based on scientific advice following a complete and formal stock assessment. But a traditional stock assessment method requires detailed information on the population, time-series of catch data, mortality, age structure, stock-recruitment relationships, catch per unit effort, and other life-history parameters [25,26]. Unfortunately, like most of the world’s fish stocks, the marine fisheries of Bangladesh lack these data and are classified as data-poor fisheries. Though several data-poor stock assessment methods based on surplus production models (SPMs) have been developed [26,27,28,29,30,31,32,33], the primary indices of abundance essential for these methods as input parameters are based on catch and effort data [34,35]. However, these indices can be misleading in a multi-gear and multi-species fishery (i.e., marine fisheries of Bangladesh) if a proper standardization technique for the catch and effort data is not employed [36]. Studies that assessed shrimp populations using catch and effort data from the marine water of Bangladesh reported the over-exploitation and depleted stock biomass [23,37,38,39,40,41,42,43]. Unfortunately, these studies used the effort data presented in the “Yearbook of Fisheries Statistics of Bangladesh” [17] without any evidence of standardization, which could be affected the estimation of fisheries reference points. Furthermore, they mainly focused on estimating maximum sustainable yield (MSY), but other reference points, such as the overfishing limit (OFL) and information on population trends, remained ignored, which are indispensable for fish stock management [44].
To minimize the data requirement, Walters et al. (2006) [45] proposed an alternative based on a stock reduction analysis (SRA) that predicts the historical abundance required to sustain the observed fishery catches without extinction based on life-history parameters (primarily the intrinsic population growth rate r, carrying capacity K, and natural mortality rate M) rather than effort data. Dick and MacCall (2011) [46] later developed the depletion-based stock reduction analysis (DB-SRA) by introducing a depletion rate (the ratio between present biomass and the initial carrying capacity of the stock) in the procedures of SRA. However, this method requires an actual time series of catch data and basic life-history parameters of the exploited stock. Given the availability of some life-history parameters of P. monodon and M. monocereos in literature and time-series of catch data, the objective of this study was to evaluate the stock status of these two shrimp stocks using the DB-SRA method to determine the reasonable yield and management reference points including overfishing limit (OFL). Because of its robustness, this method is approved by the Pacific Fisheries Management Council (PFMC) for the assessment of data-poor fish stocks and recommended for Only Reliable Catch Stocks (ORCS) by the national oceanic and atmospheric administration (NOAA) [47,48]. Based on the results, this study also ran biomass projections for both species to identify the catch limit that will actively rebuild the stock biomass and defined the limit as the total allowable catch (TAC) for the respective species for the next ten years.

2. Materials and Methods

2.1. Study Area and Data Source

Bangladesh’s marine industrial fishing zone defines the area beyond 40 m depth in the EEZ (Figure 1), and the fishing activities in this area are mainly characterized by demersal trawling for fish and shrimp. Shrimp trawlers ranged in length from 20.5 to 44.5 m, had outriggers, and operated 2–4 nets at once with modern shrimp trawl nets. The cod-end mesh sizes of trawl nets range from 4.5–6 cm and head rope lengths 15–35 m. [49,50]. These trawlers typically have a gross tonnage capacity of 150–250 metric tons and a main engine power range of 500–900 BHP. Although trawl fishing in Bangladesh was introduced in 1972, the commercially based demersal trawling for shrimp has been in full swing since 1986 [22]. Therefore, from 1987 to 2019, 33 years of time-series catch data of two commercially important shrimp species, P. monodon and M. monoceros, were collected from the catch logbook data sheets of the Marine Fisheries Office and Fisheries Resources Survey System’s (FRSS) publications [24]. The catches are expressed in metric tons.

2.2. The DB-SRA Model

The DB-SRA model [46] uses a surplus production model in the form of
Bt = Bt−1 + P(Bt−a) − Ct-1
where Bt and Ct are the biomass and catch at time t, P(Bt−a) is the latent annual production based on the initial biomass a (median age at sexual maturity) years earlier. In the DB-SRA model, annual production follows a Pella-Tomlinson-Fletcher production model [51] that allows flexible specification of peak latent productivity and MSY, which is
P(Bt−a) = γm(Bt−a/K) − γm(Bt−a/K)n
where n is the Pella-Tomlinson shape parameter that defines the magnitude of the maximum productivity of the stock concerning its carrying capacity (K) based on the following assumptions:
  • when n = 2, the biomass at maximum sustainable yield (BMSY) equals K/2;
  • when n < 2, BMSY is less than half of K; and
  • when n > 2, BMSY is higher than half of K [52].
From n, the parameter γ can be calculated as:
γ = n(n/n−1)/n−1
m denotes the stock’s maximum sustainable yield (MSY) and is a function of n, K, and exploitation at maximum sustainable yield (UMSY)
m = Kn(n/n−1)UMSY
The DB-SRA model starts its analysis with a set of four Monte Carlo-drawn life history-based input parameters [46] and projects the population forward based on the number of removals each time, equaling the observed time series of catches with some uncertainty in those catches [46,52]. The life history- parameters that are used as input parameters in the model run are nature mortality (M), the ratio of fishing mortality that is required to produce MSY to M (FMSY/M), the relative biomass at maximum latent productivity (BMSY/K), and the relative biomass depletion level (Bt/K) for a specific recent year t. t doesn’t need to be the final year in the time series, and this study assigned t to the following year of the final year of the time series (2020) and defined it as the reference year.
Based on the observed catches, the model estimates the stock’s virgin population size required to prevent the extinction of the population and end up at some fraction of K by year t. The DB-SRA model proceeds iteratively, and the iterations that end in extinction are rejected, while iterations that allow the population to survive till the end of the time series are accepted. Therefore, final distributions of K, MSY, BMSY, UMSY, and a harvest control rule, overfishing limit (OFL), can be estimated from the distributions of the accepted parameter sets.

2.3. Estimation of Input Parameters

The initial carrying capacity (K) for the model run was assumed to be ten times the maximum catch in the time series data.
Due to the unavailability of age at sexual maturity and the difference in estimated length at sexual maturity (Lm) in different studies [53,54,55,56,57,58], this study used the following empirical equation proposed by Froese and Binohlan (2000) [59] to estimate the length at maturity (Lm) of both shrimp species from the asymptotic length (L∞):
log Lm = 0.8979(log L∞) − 0.0782
The following equation derived from the von Bertalanffy (1938) [60] growth formula was then used to estimate the age at maturity (tm) from growth coefficient (K), L∞, and estimated Lm:
tm = t0 − (1/K)ln[1 − (Lm/L∞)]
Based on the estimated values of L∞ and K by Mustafa et al. (2006) [61], this study calculated the age at sexual maturity for P. monodon to be 0.91 years and 0.63 years for M. monoceros. Therefore, the age of maturity for both species was set to 1 year for the model run.
The reported maximum age for P. monodon was three years [62] and 2.5 years for M. monoceros [63]. Therefore, natural mortality (M) for both species was estimated using the following equation proposed by Hoenig’s (1983) [64];
ln(M) = 1.44 − 0.982 × ln(maximum age)
The M for P. monodon was calculated to be 1.43 year−1 and 1.70 year−1 for M. monoceros, assumed a log-normal distribution with a standard deviation (sd) of 0.4, and bounded from 0.001 to 3. The upper bound was set to cover other estimates reported in the literature for both species [61,65]. According to the rule of thumb, in a sustainably managed fishery, the FMSY/M ratio should equal 1; therefore, this value was used in the base model run. However, Walters and Martel (2004) [66] suggested that the FMSY/M ratio should be 0.8, and this study used this value in the model sensitivity runs (see description of sensitivity runs below). Based on the assumption that the target biomass (BMSY/K) is 40% of unfished biomass (0.4 K), the input value for BMSY/K was set to 0.4 for the base model run. Because of their relatively fast sexual maturity, shrimp species show high productivity, which results in a low value for BMSY/K [52] and, thus, the input value of BMSY/K was decreased to 0.25 for sensitivity analysis [52], assuming a beta distribution with sd = 0.05 and bounded from 0.05 to 0.95 (Table 1). The time-series data showed a significant decrease in catches, and therefore, this study assumed a high level of depletion and set Bt/K at 0.30 following a beta distribution with sd = 0.1 and bounded by 0.01 and 0.99. The relative biomass to K (B1/K) in the first year when the fisheries began was set to 1 (assuming the initial biomass was equal to the carrying capacity), assuming that B1/K) equals the mean and sd = 0.1 and bounded from 0.01 to 1 (Table 1).

2.4. Sensitivity Analysis of DB-SRA

A sensitivity analysis was carried out to investigate the ability of the DB-SRA model to predict the true value for each reference point (MSY, BMSY, Brefer, UMSY, FMSY, OFL, and K) with varying levels of input parameters, and thus to investigate uncertainty in those parameters. Table 1 describes the input settings for the sensitivity run. For each sensitivity run, the value of one input parameter was modified at a time while the remaining values remained unchanged.
After the analysis of the DB-SRA model, this study ran biomass projections with different yearly catch limits and DB-SRA results using the dlproj function of R statistical software [67] described by Nelson (2013) [68] to obtain a positive trend in stock biomass. The key inputs for this function are
  • dlobj: the output object from DB-SRA.
  • projyears: the number of projection years (10 years).
  • projtype: 2 (user-specified catch)
  • projcatch: projected catches (different catch limits including MSY and OFL were used).

3. Results

3.1. Landing Trend

The industrial landing data for P. monodon and M. monoceros was first recorded in the statistical yearbook of Bangladesh in 1987. Therefore, a 33-years (1987 to 2019) review of yearly landings from the industrial trawlers observed a similar declining trend for both species (Figure 2).
The maximum catch for P. monodon was in 1988, while for M. monoceros in 2014. Catches in the final year were 58 and 50 percent lower than catches in the first year of the data set for both species. Despite a sudden increase in M. monoceros capture in 2013 and 2014, it began to decline again in 2015.

3.2. Stock Analysis Based on DB-SRA Model Results

The distributions of input parameter values from accepted and rejected model runs were significantly overlapped (Figure 3).
For all parameters, including FMSY/M, BMSY/K, Bt/K, and M, the distributions of accepted and rejected model runs were highly diverged, with accepted runs moving towards the lower values for both species.
The DB-SRA model estimated the fisheries reference points from the accepted model runs that allowed both shrimp populations to survive for the modeled period (Figure 4). The estimated mean carrying capacity (K) for the P. monodon population was 5015 metric tons and 35,871 metric tons for M. monoceros, before the beginning of the fishery. Thus, the fishing reduced the population biomass by more than 70 percent of the initial biomass for both species (assuming that K = initial biomass). In addition, the estimated OFL values, which were much smaller than the reported catches throughout the time series, indicate the overfishing status of these fisheries from the start of the time series data (Table 2, Figure 2).
The estimated mean MSY for tiger shrimp is 203 metric tons and 1408 metric tons for brown shrimp, but this level was exceeded in nearly all years until 1996 (Figure 5), and as a consequence, population biomass fell below the mean estimate of BMSY of 2062 metric tons for P. monodon and 15,140 metric tons for M. monoceros from 1996 (Figure 6), (Table 2).
The model results show that these fisheries are inherently overfished, with a yearly exploitation rate much greater than the mean UMSY estimates of 0.10 for both species. In 2019, the biomass of both species reached its minimum, and for the reference year (2020), the estimated biomass indicates that these two fisheries are not currently able to produce MSY (B2020 < BMSY) (Table 2 & Figure 6).
The DB-SRA model showed robustness in model sensitivity runs, producing similar results with different input parameter values across diverse scenarios. The outputs from the sensitivity tests mostly overlapped the base model’s output; however, in some instances, heterogeneity in models’ outputs were observed (Figure 7). The predicted mean MSY values for both species were more or less steady in all sensitivity runs and varied from 3 to 12% for P. monodon and 1 to 17% for M. monoceros from the base model run. Similarly, mean estimates of BMSY varied only 2 to 8% from the baseline model with one significant deviance (25% from the base model) when input parameter BMSY/K was reduced to 0.25 but did not significantly affect other reference points estimation. Therefore, like the basic model run, the mean estimates of BMSY from the sensitivity runs have remained much higher than the biomass in the reference year (B2020).
Different values (50–500 metric tons for P. monodon and 500–1500 metric tons for M. monoceros), including the estimated MSY and OFL from the base model run, were considered for the biomass projection. However, 100 metric tons for P. monodon and 750 metric tons for M. monoceros as yearly landing limits resulted in continuous biomass growth, reversing the declining trend of stock biomass (Figure 8). With these annual capture limits, the biomass of these two species will be yielded 61 percent (2293 metric tons) and 52 percent (14,512 metric tons) growth after ten years, respectively (Figure 8).

4. Discussion

With the number of trawlers nearly constant over time, the captures with a continuous fall for both species could be a sign of stock biomass depletion (Figure 1). Therefore, this study used a robust assessment approach to evaluate the stocks’ current biomass status with other fisheries reference points. Due to the lack of species-specific total marine landing data for shrimp, only the catch data from commercial landings from 1987 to 2019 was used in this study. Most traditional stock assessment methods require catch and effort data in combination; however, in Bangladesh, effort statistics from the capture fisheries are incomplete and misreported. As a result, using these data may result in perplexing findings. Even though these two shrimp species have enormous importance, the stock assessment of these species from Bangladesh marine water is lacking in international literature, with only two published studies identified during this study, one for each species [23,43]. Those two studies, like this study, also used catch data from industrial trawl landings but were based on catch per unit effort (CPUE) data. Given the limitations in CPUE data in multi-species and multi-gears fisheries, this study employed a stock reduction analysis (DB-SRA) that is well suited to analyzing stocks that exhibit a monotonic decline in abundance [46] over the historical harvest period and can provide useful information on current stock status from catch history despite a lack of current abundance knowledge. For the best possible evaluation of the stock status, the DB-SRA method requires the complete history of the catches from the beginning of the fisheries [46]. Though offshore fish trawling in Bangladesh began in 1972 [19], industrial shrimp trawling beyond the 40 m depth was started in 1981 with only eight shrimp trawlers and gained traction in 1986 when the number of shrimp trawlers increased to 36 [22]. Therefore, the historical catch data used in this study from 1987 was assumed to be the complete capture history for these fisheries.
With a high degree of removal, where catches were much higher than the OFL for every year since the start of the fisheries, the biomass of the stocks has already been depleted. But, more importantly, the ongoing depletion of stock biomass exacerbated the problem to the point where these fisheries were unable to produce MSY in the following year of the data set (2020), with biomass less than the required biomass to produce MSY (BMSY).
Estimating MSY is critical for policymakers when developing a sustainable harvest strategy [69], and DB-SRA can reduce uncertainty in estimating this quantity [46]. However, there was a large discrepancy observed in the estimates of MSY between the present and previous studies of Barua (2020 and 2021) [23,43]. The estimated MSY by Barua (2020 and 2021) [23,43] was much higher than in the present study (Table 3). Though the 95% confidence intervals of his estimates of BMSY for P. monodon overlapped the range of this study’s estimates, for M. monoceros, it was much lower than the present study. Similarly, the estimated mean biomass for M. monoceros for the reference year 2017 is significantly lower than this study’s estimate (Table 3). The estimates of K also revealed a similar disparity with a very low value estimated by Barua (2020) [43] for M. monoceros (Table 3). He also showed that the stock status of M. monoceros was healthy in terms of BMSY, despite a steep decline in capture. In the stock evaluation of a multi-gear and multi-species fishery with the methods that require effort data, efforts standardization is essential to get a reliable result. However, Barua (2020 and 2021) [23,43] used the same effort data to analyze both species, which could be resulting in erroneous estimates of the reference points. Using the DB-SRA model, this study did not need to use the effort data to analyze the stocks. Nevertheless, the results of this study and those of Barua (2021) [23] demonstrate that the harvest of P. monodon far surpassed the sustainable level and is responsible for the depleted biomass.
Given the high level of pollution in Bangladesh’s marine water [70,71], the natural mortality of shrimp can be considered higher than the value used in the base model analysis. Demersal trawling for fish and shrimp began in 1981 with a few trawlers in Bangladesh’s EEZ [19]. Thus, considering the stock biomass equal to unfished biomass (B1/K = 1) in 1987 is not safe. The rapid decline in catch could be attributed to a rapid decline in stock biomass, indicating a high level of depletion and lowering the value of Bt/K. Again, short-lived shrimp species can exhibit high productivity, producing MSY even with lightly depleted biomass, resulting in a much lower value for BMSY/K used in this study. Again, many scientists suggested that FMSY should not be greater than 80% of natural mortality to ensure the stock’s long-term viability [45]. Therefore, given all those uncertainties in input parameters, alternative values to address all the issues mentioned above were used for the DB-SRA model’s sensitivity analysis.
Since the values of the different estimated reference points varied with different input parameters, the overall results showed a similar pattern, the depleted biomass for both stocks, due to historic overexploitation. Therefore, in DB-SRA analysis, the results have little possibility of deviating if the true distribution of M is greater than the base model run, the true productivity of both species is significantly higher, or FMSY/M is significantly lower than the values used in the base model runs. Wetzel and Punt (2011) [72] reported a high sensitiveness of the DB-SRA model to the Bt/K. Although this study found a significant influence of changing Bt/K on OFL and B2020, the impact was very little on MSY and BMSY, similar to Sweka et al. (2018) [52]. Since the consistency of estimated reference points across all sensitivity runs of the model is desired for managers when determining what management plan should be taken to restore stocks biomass, the consistent findings of this study across all scenarios showed the rationality and effectiveness of the DB-SRA model for the assessment of data-poor fisheries of Bangladesh.
Based on the results of the DB-SRA model, it is possible to conclude that the stocks’ biomass of these two shrimp species in the offshore (beyond 40 m depth) water of Bangladesh are depleted, and hence stock rebuilding strategies need to be adopted. To get the stocks’ biomass above the BMSY, it is likely that a trawling ban should be imposed. However, given the high resilience of the species (population doubling time < 15 months) [73] and increased demand and unemployment problem, this study conducted projections using different catch limits to ensure the consistent growth of the stocks’ biomass. Based on the projections, an annual catch of 100 metric tons of P. monodon and 750 metric tons of M. monoceros will shift the biomass trend from down to upwards, and with these catch limits, biomass will exceed the BMSY level in 2030. Therefore, this study recommends those catch limits as the total allowable catches (TACs) to sustain both shrimp populations in the marine water of Bangladesh that are assigned for industrial fishing for the next ten years. After ten years, another rigorous stock assessment should be carried out using the same methodology and set the OFLs as TACs for another ten years. The OFL is a strong tool that can ensure the sustainability of the stock biomass with optimum yield.

5. Conclusions

Due to the unavailability of reliable effort data, this study conducted a stock assessment of two shrimp species, P. monodon, and M. monoceros, using the depletion-based stock reduction analysis (DB-SRA) with catch and life-history data to get robust results. Depending on the outputs, this study concluded that due to the historic overexploitation, stocks’ biomass of both shrimp species had been depleted in Bangladesh’s industrial fishing zone (beyond 40 m depth) and that action to rebuild the stock biomass is urgent. Therefore, this study recommended annual catch limits of 100 metric tons for P. monodon and 750 metric tons for M. monoceres. These catches will actively rebuild and raise the stocks’ biomass above the BMSY within the next ten years. After successfully implementing this recommendation, a further rigorous stock assessment should be carried out after ten years to evaluate the impact of implemented management policy and adopt new strategies to manage these fisheries in the future sustainably.

Author Contributions

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

Funding

This work is supported by the special research fund of the 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 their sponsorship during his doctoral degree.

Conflicts of Interest

The authors declare no conflict of interest.

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Jmse 10 00201 g001 550
Figure 1. Map showing the Bay of Bengal coast of Bangladesh. The area beyond the black dotted line (40 m depth contour) is the industrial fishing zone. Shaded regions indicate the four major fishing grounds, i.e., South Patches, South of South Patches, Middle Ground, and Swatch of No Ground.
Figure 1. Map showing the Bay of Bengal coast of Bangladesh. The area beyond the black dotted line (40 m depth contour) is the industrial fishing zone. Shaded regions indicate the four major fishing grounds, i.e., South Patches, South of South Patches, Middle Ground, and Swatch of No Ground.
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Figure 2. Catches of Penaeus monodon and Metapenaeus monoceros in metric tons from 1987–2019 from industrial trawling in the EEZ of Bangladesh.
Figure 2. Catches of Penaeus monodon and Metapenaeus monoceros in metric tons from 1987–2019 from industrial trawling in the EEZ of Bangladesh.
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Jmse 10 00201 g003 550
Figure 3. Input parameters distributions from accepted (black) and rejected (white) model runs.
Figure 3. Input parameters distributions from accepted (black) and rejected (white) model runs.
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Figure 4. Estimated reference points by the DB-SRA model from accepted (black) and rejected (white) model runs.
Figure 4. Estimated reference points by the DB-SRA model from accepted (black) and rejected (white) model runs.
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Figure 5. Historical catches of Penaeus monodon and Metapenaeus monoceros with MSY estimates.
Figure 5. Historical catches of Penaeus monodon and Metapenaeus monoceros with MSY estimates.
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Figure 6. Biomass projections for Penaeus monodon and Metapeneaus monoceros from accepted (black) and rejected (white) model runs.
Figure 6. Biomass projections for Penaeus monodon and Metapeneaus monoceros from accepted (black) and rejected (white) model runs.
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Figure 7. Sensitivity analysis for both Penaeus monodon and Metapenaeus monoceros. The scenarios are 1. The base model outputs; 2. M increases to 2; 3. FMSY/M decreases to 0.8; 4. BMSY/K decreases to 0.25; 5. Bt/K decreases to 0.2; 6. B1/K decreases to 0.8.
Figure 7. Sensitivity analysis for both Penaeus monodon and Metapenaeus monoceros. The scenarios are 1. The base model outputs; 2. M increases to 2; 3. FMSY/M decreases to 0.8; 4. BMSY/K decreases to 0.25; 5. Bt/K decreases to 0.2; 6. B1/K decreases to 0.8.
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Figure 8. Biomass projections for both Penaeus monodon and Metapenaeus monoceros in metric tons (MT). Dots indicate the BMSY for the respective years.
Figure 8. Biomass projections for both Penaeus monodon and Metapenaeus monoceros in metric tons (MT). Dots indicate the BMSY for the respective years.
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Table
Table 1. Input distribution for the DB-SRA analysis base model and sensitivity runs.
Table 1. Input distribution for the DB-SRA analysis base model and sensitivity runs.
ParametersBase Model DistributionSensitivity Perturbation
Initial KTen times of maximum catch.------
Age at maturityOne year.------
MLognormal, low = 0.001, up = 3, mean = 1.43 for Penaeus monodon and 1.71 for Metapenaeus monoceros, sd = 0.4.Increase to 2.
B1/KNone, low = 0.01, up = 1, mean = 1, sd = 0.1).Decrease to 0.80.
Bt/KBeta, low = 0.01, up = 0.99, mean = 0.3, sd = 0.1, refyr = max(final year of time-series) + 1).Decrease to 0.2.
FMSY/MLognormal, low = 0.1, up = 2, mean = 1, sd = 0.2.Decrease to 0.80.
BMSY/KBeta, low = 0.05, up = 0.95, mean = 0.4, sd = 0.05.Decrease to 0.25.
Table
Table 2. Parameters estimate from DB-SRA model with 95% CI.
Table 2. Parameters estimate from DB-SRA model with 95% CI.
ParametersP. monodonM. monoceros
MeanLCI(2.5%)UCI (97.5%)MeanLCI (2.5%)UCI (97.5%)
MSY203166250140811551715
BMSY20621451269415,14010,79519,320
FMSY0.130.080.230.120.070.20
UMSY0.100.070.160.100.060.15
OFL146532799123341871
B2020142962624589470420017,097
K50153635580835,87126,19240,750
Note: LCI and UCL indicate lower and upper confidence intervals.
Table
Table 3. The mean of the estimated reference points for Penaeus monodon and Metapenaeus monoceros by Barua et al. (2020 and 2021) [23,43] and the present study with 95% confidence intervals.
Table 3. The mean of the estimated reference points for Penaeus monodon and Metapenaeus monoceros by Barua et al. (2020 and 2021) [23,43] and the present study with 95% confidence intervals.
Species NameKMSYBMSYB2020 *FMSYReference
Penaeus
monodon		4720
(3350–6650)		527
(388–717)		2360
(1670–3320)		1250
(885–1550)		0.22
(0.16–0.31		[23]
5015
(3635–5808)		203
(166–250)		2062
(1451–2694)		1429
(626–2458)		0.13
(0.08–0.23)		Present
Metapenaeus monoceros10,000
(8380–12,200)		3090
(2920–3260)		5060
(4990–6110)		5960
(4760–6830)		0.61
(0.51–0.73)		[43]
35871
(26,192–40,750)		1408
(1155–1715)		15140
(10,795–19,320)		9470
(4200–17,097)		0.12
(0.07–0.20)		Present
* B2020 stands for biomass in the reference year of 2020.
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Alam, M.S.; Liu, Q.; Schneider, P.; Mozumder, M.M.H.; Uddin, M.M.; Monwar, M.M.; Hoque, M.E.; Barua, S. Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh. J. Mar. Sci. Eng. 2022, 10, 201. https://doi.org/10.3390/jmse10020201

AMA Style

Alam MS, Liu Q, Schneider P, Mozumder MMH, Uddin MM, Monwar MM, Hoque ME, Barua S. Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh. Journal of Marine Science and Engineering. 2022; 10(2):201. https://doi.org/10.3390/jmse10020201

Chicago/Turabian Style

Alam, Mohammed Shahidul, Qun Liu, Petra Schneider, Mohammad Mojibul Hoque Mozumder, Mohammad Muslem Uddin, Md. Mostafa Monwar, Md. Enamul Hoque, and Suman Barua. 2022. "Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh" Journal of Marine Science and Engineering 10, no. 2: 201. https://doi.org/10.3390/jmse10020201

APA Style

Alam, M. S., Liu, Q., Schneider, P., Mozumder, M. M. H., Uddin, M. M., Monwar, M. M., Hoque, M. E., & Barua, S. (2022). Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh. Journal of Marine Science and Engineering, 10(2), 201. https://doi.org/10.3390/jmse10020201

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