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Article

Stock Assessment of Marine Elasmobranchs (Sharks and Rays) in the Bay of Bengal, Bangladesh

by
Dwipika Gope
1,†,
Md. Mostafa Shamsuzzaman
1,
Md. Shahidul Islam
1,
Tanni Sarkar
1,
Alaka Shah Roy
2,
Mohammad Mojibul Hoque Mozumder
3,* and
Partho Protim Barman
1,*,†
1
Department of Coastal and Marine Fisheries, Sylhet Agricultural University, Sylhet 3100, Bangladesh
2
Department of Fisheries Genetics and Breeding, Habiganj Agricultural University, Habiganj 3300, Bangladesh
3
Fisheries and Environmental Management Group, Helsinki Institute of Sustainability Science (HELSUS), Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Mar. Sci. Eng. 2025, 13(6), 1126; https://doi.org/10.3390/jmse13061126
Submission received: 6 May 2025 / Revised: 29 May 2025 / Accepted: 3 June 2025 / Published: 4 June 2025
(This article belongs to the Section Marine Biology)
Browse Figures
Versions Notes

Abstract

The Bay of Bengal (BoB) is a global hub for marine elasmobranchs, particularly sharks and rays. These apex predators maintain and structure the balanced marine ecosystem and food webs. Marine elasmobranchs in Bangladesh are under-researched and under-managed, and face threats such as habitat degradation, global warming, pollution, illegal fishing, and overexploitation. This study aimed to evaluate the stock status of marine elasmobranches in the Bay of Bengal (BoB), Bangladesh. This research used catch and effort (CE) data for a period of 21 years (2002–2022). Both the Monte Carlo CMSY and BSM models were applied to assess biomass, exploitation rates, and sustainable yields. The BSM estimated a maximum carrying capacity (k) of 134,000 mt, which is larger than the CMSY estimate of 119,000 mt. The estimated intrinsic annual growth (r) from CMSY was 0.282. The MSY values ranged from 5110 mt (BSM) to 8420 mt (CMSY), with BSM indicating overexploitation, as the 2022 catch (7017 mt) exceeded the BSM-derived MSY. Both models suggested depleted and overfishing stock conditions, with B/BMSY ratios < 1.0 and F/FMSY ratios > 1.0. Effective management is crucial to prevent overfishing and ensure sustainable practices. Elasmobranch catches must be kept below the BSM-estimated maximum sustainable yield (MSY) of 5110 metric tons with fishing pressure maintained at or below F/FMSY = 1.0. It is vital to regulate illegal and unlicensed fishing activities. Because of the aggregation of CE data, the results should be interpreted cautiously and never serve as a substitute for species-level assessments.

1. Introduction

Marine elasmobranchs, comprising sharks and rays, are cartilaginous megafauna, belonging to the subclass Elasmobranchii [1]. They are found in aquatic environments, particularly in coastal regions extending from the intertidal zone to the continental shelf [2]. Elasmobranch fisheries are positioned as tertiary predators within the marine trophic levels and contribute significantly to maintaining a balanced marine ecosystem [3,4,5]. However, their biological characteristics—low fecundity, sluggish growth pattern, delay maturity, and an enduring life cycle—make them more vulnerable to population decline [3,6]. Globally, they face severe decline because of overfishing, unsustainable fishing practices, habitat degradation, and climate change [7,8].
Approximately 25% of elasmobranchs are vulnerable to extinction, whereas 47% are poorly studied because of a lack of information [9]. Additionally, 6.6% were classified as data-deficient, 19.8% as least concern, 15% as near threatened, 25.6% as vulnerable, 22.7% as endangered, and 10.3% as critically endangered (Figure 1) [7]. This scenario is more critical in Southeast Asian countries like Bangladesh because of the high market value and unregulated trade of elasmobranch products (e.g., shark fins), with intense fishing pressure, unsustainable fishing practices, and limited stock assessment efforts further accelerating global and local population decline [10,11,12,13].
The Bay of Bengal (BoB) is the single most productive marine ecosystem in Bangladesh, supporting 475 species of fishes, including diverse elasmobranchs [14]. Sharks and rays are the major contributors of marine elasmobranchs, comprising 88 species under 20 families reported in the BoB, Bangladesh [15]. Sharks and rays contribute 0.28% of the total marine production as target species or bycatch in both artisanal and industrial fishing, accounting for 8.77% and 91.20% of catches in Bangladesh, respectively [14]. Fishing practices range from artisanal to industrial in nature and are multispecies in nature. Set-bag nets, gill nets, longlines, trawl nets, and other items are non-selective gears operated from non-mechanized boats and mechanized boats/trawlers for fishing, resulting in a higher rate of bycatch of sharks and rays and other endangered species in Bangladesh [16]. Furthermore, Bangladesh is considered a hub of shark fin trade in the Southeast Asian region, which supports the economy and livelihood of coastal people of Bangladesh [1,15].
Despite their socioeconomic and ecological importance, limited data availability, improper monitoring, and the lack of an effective regulatory framework have hindered sustainable conservation and management efforts for sharks and/or rays [17]. Moreover, the absence of species-specific catch records results in the aggregated landing of sharks and rays together as total elasmobranch catch, highlighting the data-limited nature of elasmobranch fishery in Bangladesh [14,18]. Most of the current knowledge on marine elasmobranchs in Bangladesh is limited to anecdotal landing records, taxonomic survey, growth, trade, mortality, and unregulated fishing data [1,16,18,19,20,21,22], where stock assessment remains scarce, highlighting the research gap.
Stock assessments provide critical insight into the health and sustainability of fisheries. However, traditional assessment methods are often impractical for data-limited fisheries such as elasmobranchs [23,24,25,26,27,28,29]. Advanced models, such as the Monte Carlo Maximum Sustainable Yield (CMSY) and the Bayesian State-Space Schaefer Production Model (BSM), have been developed to evaluate the stock status of data-limited fisheries using the time series of catch-effort (CE) data [27]. These models estimate key population parameters, including exploitation, stock status, and maximum sustainable yield (MSY), which are critical for guiding fisheries regulatory authorities. Therefore, this research aimed to apply the CMSY and BSM models to evaluate the marine elasmobranch (sharks and rays) stocks in the BoB, Bangladesh, using 21 years (2002–2022) of CE data. By addressing key knowledge gaps, this study aimed to provide a preliminary evaluation of exploitation trends, rather than species-specific management advice. Given the persistent data limitations in elasmobranch fisheries, the CMSY and BSM approaches align with global best practices for data-poor assessments. The outcome of this research will inform broader conservation advocacy and justify the need for improved taxonomic monitoring to promote local and regional efforts to conserve, restore, and sustain elasmobranch fisheries.

2. Materials and Methods

2.1. Data Collection and Sources

This research was performed based on the elasmobranch fisheries data from the (BoB), Bangladesh (Figure 2). The BoB is the northern tip of the large Indian Ocean water, lying between 5–22° N and 80–90° E, where Bangladesh is positioned at the northern border of the BoB [30]. There are four major fishing grounds in the BoB, Bangladesh, namely, the South Patches, Swatch of No Ground, Middle Ground, and South of South Patches [31]. Marine elasmobranchs (sharks and rays) are captured from these four fishing grounds and mainly landed in the Chittagong and Cox’s Bazar landing centers, with a few landing in the Patuakhali region [14].
Set-bag nets, gill nets, longlines, trawl nets, and other gears are operated from non-mechanized and mechanized vessels for elasmobranch fishing in Bangladesh [14,16]. There are no species-specific catch records due to the aggregated and combined landing records of sharks and rays, which are thus considered as total elasmobranch catch. The fishing effort is recorded as the number of gears operated. Twenty-one years (2002–2022) of effort and catch data were collected from the Statistical Yearbook compiled by Fisheries Research Survey System, published by the Department of Fisheries (DoF) under the Ministry of Fisheries and Livestock (MoFL) of the Government of the People’s Republic of Bangladesh (Figure 3). Annual catch data were recorded in metric tons (mt), and effort data were recorded as the number of total gear (g) per year (/yr). Finally, the Catch Per Unit Effort (CPUE) was measured as metric ton per gear per year (mt/g/yr) (Figure 4).

2.2. CMSY and BSM Models

This research applied a Surplus Production Model (SPM)-based Catch Maximum Sustainable Yield (CMSY) method for data analysis. The CMSY method is suitable for data-limited fisheries and requires simple catch and effort (CE) data as inputs [27,28]. A Monte Carlo Markov Chain (MCMC) approach, along with the Bayesian Schaefer Production Model (BSM), was combined with the CMSY method [27]. The CMSY analysis provides critical population parameters, and BSM provides fisheries management information. The CMSY and BSM estimate the exploitation pattern, biomass, MSY, and associated biological reference points (BRPs) for the marine elasmobranchs. The Monte Carlo MCMC approach estimates the intrinsic population growth (r) and carrying capacity (k) as well. The logarithmic scale representation of most robust r-k pairs depicted a positive growth pattern in a triangle. The CMSY identified the most likely r (maximum growth rate) at the apex of the triangle. The BSM model derived r, k, and MSY from Equation (1). Additionally, a separate equation accounted for the reduced recruitment when the stock biomass dropped below a quarter of the carrying capacity (k) (Equation (2)).
B t + 1 = B t + r 1 − B t k B t − C t
B t + 1 = B t + 4 B t k r 1 − B t k B t − C t | B t k < 0.25
In Equations (1) and (2), Bt indicates the current biomass at time t, Ct indicates the catch of biomass at time t, and B(t+1) is the biomass exploited in year t + 1.
The resilience of marine elasmobranch was very low, as observed from the FishBase [32] with a prior range of r values between 0.015 and 0.1 (Table 1). There are three assumptions that the prior k is: (1) maximum sustainable fishing mortality (FMSY) depends on fisheries productivity; (2) unexploited stock size (k) must be larger than the largest catch in the time series data; and (3) maximum catch should be a larger fraction of carrying capacity (k) with significantly reduced stocks as opposed to moderately reduced ones. The default estimation of the relative biomass of the start year (2002) and end year (2022) in the time series was provided by potential biomass ranges based on the expected depletion level in Table 2.
The constant r values in Table 1 were converted into central values of previous densities in BSM. Additionally, the catchability coefficient q (Equation (3)) was used to relate the stock biomass abundance index. The variables in this equation are q for data-limited fisheries, Bt for biomass in year t, and CPUEt for mean CPUE in year t.
CPUEt = qBt
The R-code (CMSY_0_7q. R), developed by Froese et al. [27], was downloaded and applied to run the CMSY analysis. In addition, the JAGS package was used to analyze the distribution of the sample probability [33]. The following equation (Equation (4)) was used to assist in the mixing of Gibbs samples between the annual biomass and unexploited biomass:
Pt = Bt/k.
CMSY analysis provided information on MSY, BMSY, and FMSY. Further, the biomass producing MSY (BMSY) was calculated as 0.5k [23,34] and the fishing mortality producing MSY (FMSY) was calculated as 0.5r [24,35,36], and MSY was calculated as rk/4 for the BSM analysis. Table 3 shows the prior ranges for the inputs of the B, k, q, and r parameters. The relative intermediate biomass was calculated to be 0.2–0.6 in 2012; the initial prior B/k range was 0.2–0.6; and the final prior B/k range was 0.4–0.8. Subsequently, the prior range was 0.05–0.5 for r, 2.55 × 10−7 to 1.61 × 10−6 for q, and 24,900–1,496,000 for k (Table 3).

3. Results

3.1. Population Characteristics

The marine elasmobranch catch series data in Figure 3 show a tendency for gradual upgrading in both catch and effort. The annual catch fluctuated significantly between 3373 mt (2020) and 8228 mt (2021), with an average catch of 4899 mt. The effort measured in gears per year (g/yr) remained relatively consistent, averaging 163,507 g/yr, but showed notable variations, particularly in 2009 (81,622 gears) and 2011 (192,222 gears). The CPUE, an indicator of fishery efficiency, averaged 0.031 mt/g/yr, with values varying from 0.019 in 2020 to 0.057 in 2010 (Figure 4). These variations highlight the dynamic nature of the fishery, which is influenced by changes in catch volume, fishing effort, and efficiency over the two-decade period. Gill net fishing contributes a higher portion of elasmobranch fishing (64.16%) compared to set-bag nets (2.42%), long lines (0.03%), trawl net (2.03%), and other gear (22.55%). Both mechanized and non-mechanized fishing vessels have been employed for elasmobranch (shark and ray) fishing in the BoB, Bangladesh.

3.2. Stock Status

Table 4 and Table 5 show the CMSY method and BSM model-derived BRPs and stock information concerning shark and ray fisheries in Bangladesh. A larger carrying capacity (k) was estimated from the BSM model (134,000 mt) than the CMSY model (119,000 mt). The BSM model showed a narrow range of k at a 95% confidence interval compared to CMSY. The BSM-estimated catchability coefficient (q) was 5.52 × 10−7, with the lower and upper bounds (qlow and qhigh) ranging from 3.68 × 10−7 to 8.28 × 10−7. The CMSY model suggested a more significant intrinsic population growth rate (r) of 0.282 per year, suggesting that the population might grow by more than 20% annually. At a 95% confidence level, the CMSY model simulated a broader range of MSY values than the BSM model (Table 4). The CMSY and BSM models produced a higher k value than the estimated MSY value. The smallest MSY from BSM was 5110 mt, whereas CMSY estimated an MSY of 8420 mt. BSM models estimated lower MSY values than the actual catch in 2022 (7017 mt), suggesting that the marine elasmobranch fishery in the BoB is currently in an overexploited status.
BSM assessed the catch (in mt) curve in 5A, total biomass (B/BMSY) in 5B, exploitation (F/FMSY) in 5C, and correlation of exploitation (F/FMSY) and total biomass (B/BMSY) in the 5D panel of Figure 5. Different shades indicate the different levels of confidence intervals (CIs) in 5D panels, such as the 50% CI demonstrated by the light gray color shade, the 80% CI indicated by the gray color shade, and the 95% CI displayed by the dark gray color shade, respectively. The BSM model indicated lower biomass (43,300 mt) values than CMSY (59,500 mt), and a higher BMSY was assessed from the BSM model. Both models indicated a smaller biomass than the BMSY, which indicates that the stock was depleted. In addition, the B/BMSY values from CMSY and BSM were <1.0 (Table 5), indicating overfished biomass where the biomass is not sufficient to produce MSY. The BSM-estimated fishing mortality was F (0.12/yr) > FMSY (0.08), and the CMSY-estimated fishing mortality was F (0.14/yr) > FMSY (0.09). Both models demonstrate evidence of ongoing overfishing. The exploitation ratio (F/FMSY) values from CMSY were 1.56 and 1.58 from BSM (Table 5).
Figure 6 shows the comprehensive display of data and results from the CMSY and BSM models. Panel 6A shows the annual catch data by the black curve line and the smoothed data by the blue curve line. Here, the lowest and highest catch point is highlighted with a red dot, which is essential for estimating prior biomass. Panel 6B shows the number of viable log r-k pairs, where the light gray shade indicates the likely r-k values and the dark gray shade indicates the well-match r-k for catch and prior values. The best practical r-k values are indicated in the central point of the blue cross, and the details are further explored in Panel 6C. Again, the blue curve in Panel 6D shows the estimation of the biomass median trajectory in CMSY, with dotted lines depicting the 2.5th and 97.5th percentiles. The ranges for prior biomass are depicted by three vertical purple lines; dashed horizontal lines display BMSY; and dotted lines indicate 0.5BMSY as a proxy for the reduced recruitment border. In Panel 6E, the blue curve indicates the exploitation ratio (F/FMSY), with dotted lines depicting the 2.5th and 97.5th percentiles range. Pannel 5F displays the Schaefer equilibrium curve of MSY relative to B/k < 0.25 to estimate the reduced recruitment for small stock. The blue curve indicates the biomass prediction for the first data year (square shape) to the last data year (triangle shape).
The BSM and CMSY findings confirm the overfishing and overexploitation status of the marine elasmobranch (shark and ray) fishery in the BoB, Bangladesh (Figure 5 and Figure 6 and Table 4 and Table 5).

4. Discussion

4.1. Suitability of CMSY and BSM Model

Stock assessment provides critical information for fisheries regulatory authorities to develop efficient management plans and actions. Unfortunately, conventional practices require large data sets for stock assessment, which limits their usage. For example, traditional stock assessment models require age-structured data, which is not feasible due to data unavailability [27,37]. In data-limited fisheries contexts, particularly in developing regions where species-specific catch, biological, and effort data are scarce or absent, the application of data-poor assessment methods becomes essential to initiate fisheries stock assessment [38]. Thus, scientists and experts use various statistical models and methodologies to assess biological reference points and stock information of data-limited fisheries. The CMSY and BSM methods are widely recognized for their applicability in such scenarios, as they enable the estimation of stock status and reference points [27]. This research applied the CMSY and BSM models for the analysis of 21 years (2002–2022) of CE data for marine elasmobranchs, particularly sharks and rays, in Bangladesh. Both models can produce information on the exploitation, biomass, and stock status of data-limited contexts like elasmobranch fishery in Bangladesh [27,28].
While we fully acknowledge the ecological limitations of aggregating diverse elasmobranch taxa—each with distinct life-history traits and conservation risks—this approach was necessitated by the absence of species-specific catch records in national reporting systems. The CMSY and BSM can still provide coarse, first-order assessments in such cases, especially to flag potentially overexploited stocks and guide further data collection [39,40].
The goal of applying these methods was not to derive precise or species-targeted management advice, but rather to produce a preliminary, diagnostic evaluation of exploitation trends that could inform broader conservation advocacy and justify the need for improved taxonomic monitoring.
Given the chronic data limitations in many tropical and subtropical elasmobranch fisheries, including in Bangladesh, such an application is consistent with global best practices for data-poor fisheries management. However, this study emphasizes that the outputs of this assessment should be interpreted cautiously and should not be used in isolation for species-specific policy decisions.
The CMSY run, based on Surplus Production Models (SPMs), which are considered versatile, can be applied in various contexts, occasionally yielding superior BRPs compared to age-structured models, while also effectively evaluating BRPs from multispecies data [26,28,35,38]. The CMSY assessed the population parameters from catch, while BSM utilized the effort data to suggest probabilistic and more realistic outputs for recent years by considering the observation errors and processing uncertainties [27]. The BSM is the more conservative model, with lower biomass and high uncertainty levels, supporting the comparative study on the CMSY and BSM outputs in tropical waters [39]. Additionally, integrating effort data into the BSM model strengthens the understanding of increasing trends of fishing pressure on marine elasmobranchs in Bangladesh [14,18].
The CMSY and BSM-estimated MSY are best suited for low-to-high-resilience fisheries and exhibit some inefficiencies when used for very-low-resilience or less-captured fisheries [27,40]. Marine elasmobranchs (sharks and rays) had a low resilience value, indicating that the CMSY and BSM models were suitable for assessing their BRPs. Therefore, the CMSY and BSM models may be selected as viable alternative methods for assessing the stock of sharks and rays and other data-limited fisheries in Bangladesh.

4.2. Population Parameters

This study used the CMSY and BSM models to assess the stock status of shark and ray fisheries in Bangladesh. Fishery experts and policymakers recognize that the k, q, r, and MSY are vital for achieving fishery sustainability. Instead, k and r were the necessary pre-knowledge for the CMSY and BSM models, which may be obtained from FishBase resilience data [32]. While CMSY produced smaller k values, BSM produced greater values; however, the range of k values produced by both models was nearly the same. Compared to the CMSY model, the BSM (5.52 × 10−7) model yielded a higher catchability coefficient (q). The q indicates the relationship between fishing mortality and effort and is essential for stock assessment, particularly where catch and effort data are prior inputs for the analysis [27]. Fishing efforts may be influenced by seasonal variations and regional weather [41]. Additional important population parameters are known as the intrinsic growth rate (r), which helps to study the population dynamics and recruitment capacity of a stock [2,42]. An r value of 0.1 indicates that the population can grow by 10% during a given time. In contrast, the r value strongly correlates with fisheries resilience, which is linked to natural mortality [27,41,43]. The ranges of r (0.15 year−1 to 0.20 year−1) in this study indicated that only 15 to 20% of the population could contribute to recruitment in the next year’s population, supporting that marine elasmobranchs have a very low recruitment rate in nature because of their slow growth rate [3,6]. The resilience value was categorized as high (0.6–1.5), medium (0.2–0.8), low (0.05–0.50), and very low (0.015–0.1), and, accordingly, the marine elasmobranchs are considered to have low resilience because their populations take more than 15 months to double in size [27].

4.3. Maximum Sustainable Yield (MSY)

MSY is the target reference point (TRP) and one of the necessary BRPs for assessing fishery status [28,44,45]. Based on the BSM-derived management reference of MSY at 5110 mt, which is less than the catch of 7017 mt in 2022, Bangladesh’s marine elasmobranch (sharks and rays) fisheries are overfished. Both models projected an MSY between 5110 and 8420 mt. When the observed catch is near or equal to the MSY value and a further increase in capture is not practical, the stock can be considered entirely exploited [25]. A higher catch with a lower MSY indicates overexploitation, while a higher MSY with a lower observed catch indicates under-exploitation [41]. While the goal of fisheries management is to keep fish populations at their maximum sustainable level or full exploitation, overexploitation of the species may result if fishing pressure increases during that period [46]. Maximum sustainable yield is the recommended total allowable catch (TAC) to ensure the sustainability of a data-limited fishery [46,47]. This means that annual captures should not surpass the 5110 mt of MSY determined by the BSM. Bangladeshi fishery managers are encouraged to use TACs despite their frequent use in affluent countries [48,49].

4.4. Exploitation and Stock Status

B/BMSY was developed as a framework to evaluate the status of fisheries [50]. The lowest predictable stock sizes are often used as proxies for calculating B/BMSY and F/FMSY values. However, the thresholds for B/BMSY vary among regulatory organizations. For instance, the FAO and UN define a fishery as underfished when B/BMSY falls below 0.8, whereas the USA sets a more conservative threshold at 0.5 [41]. In this analysis, B/BMSY values below 1.0 indicate that the stock biomass is overfished. A significant reduction in B compared to BMSY indicates that the fishery may no longer be capable of producing the MSY, underscoring the risks of overexploitation. Figure 5B,C provide evidence of reduced biomass and overexploitation, confirming that the fishery is overfished, with a low growth rate, further increasing its susceptibility [1,16].
When F exceeds FMSY, resulting in F/FMSY values above 1.0, overfishing becomes evident, posing additional threats to the stock [27]. Overfishing can disrupt ecosystems, heighten the risk of species extinction, and destabilize food webs [51,52]. Numerous studies have documented the overexploitation of elasmobranch fisheries globally, such as severe exploitation in the northern Adriatic Sea [46], significant pressures on Madagascar’s fisheries [53], decline in elasmobranch populations in the Saudi Arabian Red Sea [54], and overexploitation of sharks and rays in Indonesia [55]. Meanwhile, other studies emphasized the widespread depletion of elasmobranch stocks [13,56,57,58]. Similar findings were also confirmed in the Indian Ocean, aligning with findings from the Bay of Bengal [3,6,8]. In Bangladeshi waters, different studies noted sustained overfishing over decades [20,21,22,59].

4.5. Limitation and Scope Interpretation

Elasmobranchs encompass a functionally and taxonomically diverse assemblage of species with specific life histories, ecologies, and conservation statuses. The absence of species-specific data and unregulated reporting of artisanal fishing are the major challenges of this study. Thus, data on marine elasmobranchs in Bangladesh mainly aggregate broad and multi-taxonomic data, ranging from large-sized sharks to small-sized rays, making it hard to study species-level stock dynamics [58]. This study used available mixed-species (sharks and rays) CE data to assess the stock status of marine elasmobranchs in the BoB, Bangladesh. Pooling all elasmobranchs in a single group for stock assessment has significant ecological and interpretive limitations. Thus, the findings should be interpreted as a preliminary indicator of potential overexploitation rather than the species-specific stock status.
The reliability of CMSY and BSM models is inherently constrained by constant catchability and effort efficiency assumptions that may not hold in the context of evolving fishing practices, gear modifications, and policy interventions [34]. Additionally, the CMSY and BSM models assume SPM dynamics that may oversimplify the complex life history of marine elasmobranchs with low productive outputs. Considering the challenges, both CMSY and BSM are carefully applied for data analysis.
The CMSY and BSM are not applied for their robustness in stock assessment, but rather for their capability to draw attention to the exploitation risk and initiate dialogue for the conservation of elasmobranchs in the BoB, Bangladesh. However, meaningful fisheries management requires genus- or species-level data; this research does not replace the need for finer-scale assessments. Therefore, this research should be considered as a scoping-level analysis that underscores the urgency for improved taxonomical resolution of elasmobranch data collection. This research strongly recommends follow-up efforts involving species composition surveys, Probability Susceptibility Analysis (PSA), and Ecological Risk Assessment (ERA) to identify vulnerable taxa and inform targeted conservation actions. Until such data are available, this study provides a foundational step to justify investments in monitoring and capacity building.

5. Conclusions

This study provides baseline information on marine elasmobranch (shark-ray) fisheries in Bangladesh. The analysis of 21 years of CE data revealed alarming trends of increasing effort and catch, leading to overfishing. The results from both CMSY and BSM models consistently indicate that the marine elasmobranch stocks in the BoB were depleted, with current biomass levels below the biomass required for MSY. Fishing mortality rates exceeding sustainable levels further emphasize the overexploitation status. While the CMSY and BSM are widely used in data-limited contexts, their use here, applied at such a high taxonomic aggregation, should be interpreted cautiously and should never serve as a substitute for species-level assessments. Thus, the findings should not be interpreted as management-ready values, as they do not account for species-level biological variations.
However, this research emphasizes the urgent need for effective conservation strategies. To ensure the sustainability of marine elasmobranchs, species-specific management, such as catch limits, particular enclosures, and trade regulations, should be emphasized. It is also important to improve the collection of species-specific catch and length data at landing sites for future assessment. Integrating length data and genetic data can improve the ecological understanding of marine elasmobranchs. Finally, incorporating socioeconomic data and fishers’ behavior into bioeconomic models could help design more effective and socially acceptable management interventions.

Author Contributions

D.G.: Data collection, data analysis, and draft writing; M.M.S.: Reviewing and editing; M.S.I.: Editing and reviewing; T.S.: Writing and reviewing; A.S.R.: Draft writing, reviewing; M.M.H.M.: Editing, reviewing; P.P.B.: Conceptualization, data collection, methodology, data analysis, writing, editing, and reviewing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Sylhet Agricultural University Research System (SAURES) and the University Grants Commission (UGC) of Bangladesh under grant number 3257103, as part of a three-year project at Sylhet Agricultural University.

Data Availability Statement

Data will be available from the corresponding author upon request.

Acknowledgments

The authors wish to thank the Department of Fisheries, Government of Bangladesh, for enabling this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Infographic of Marine Elasmobranch (Source: Author, 2024).
Figure 1. Infographic of Marine Elasmobranch (Source: Author, 2024).
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Figure 2. Map showing different depths and positions of four fishing grounds in the BoB, Bangladesh (Source: Chowdhury, 2014, [31]).
Figure 2. Map showing different depths and positions of four fishing grounds in the BoB, Bangladesh (Source: Chowdhury, 2014, [31]).
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Figure 3. Time series (2002–2022) of catch and effort data of shark and ray fishery in the BoB, Bangladesh.
Figure 3. Time series (2002–2022) of catch and effort data of shark and ray fishery in the BoB, Bangladesh.
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Figure 4. Time series (2002–2022) of CPUE data of shark and ray fishery in the BoB, Bangladesh.
Figure 4. Time series (2002–2022) of CPUE data of shark and ray fishery in the BoB, Bangladesh.
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Figure 5. Management information of sharks and rays in the BoB, Bangladesh, during the period 2002–2022 from the BSM model.
Figure 5. Management information of sharks and rays in the BoB, Bangladesh, during the period 2002–2022 from the BSM model.
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Figure 6. Shows the CMSY assessment for the marine elasmobranch (shark and ray) fishery in the BoB, Bangladesh, during the study period (2002–2022). (A) depicts the catch data series trend, with the blue curve indicating the smoothed data, and red points indicating the high and low catches. (B,C) display the findings of the robust r-k pair. (D) indicates the biomass median trajectory with the blue curve from CMSY. (E) shows the exploitation ratio (F/FMSY) in the blue curve. (F) displays the Schaefer equilibrium curve for MSY.
Figure 6. Shows the CMSY assessment for the marine elasmobranch (shark and ray) fishery in the BoB, Bangladesh, during the study period (2002–2022). (A) depicts the catch data series trend, with the blue curve indicating the smoothed data, and red points indicating the high and low catches. (B,C) display the findings of the robust r-k pair. (D) indicates the biomass median trajectory with the blue curve from CMSY. (E) shows the exploitation ratio (F/FMSY) in the blue curve. (F) displays the Schaefer equilibrium curve for MSY.
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Table 1. Input ranges for prior r.
Table 1. Input ranges for prior r.
r RangesResilience
0.6–1.5High
0.2–0.8Medium
0.05–0.5Low
0.015–0.1Very low
Table 2. Input ranges for prior B/k.
Table 2. Input ranges for prior B/k.
B/kPrior Biomass
0.5–0.9High
0.2–0.6Medium
0.01–0.4Low
Table 3. The inputs for prior biomass, r, q, and k.
Table 3. The inputs for prior biomass, r, q, and k.
Prior InputsValue Ranges
Initial biomass (relative)0.2–0.6
Intermeediate biomass (relative)0.2–0.6 (in 2012)
Final biomass (relative)0.4–0.8
r0.05–0.5
q2.55 × 10−7–1.61 × 10−6
k24,900–1,496,000
Table 4. CMSY and BSM-estimated BRPs for shark and ray fisheries (with 95% confidence interval) from the BoB, Bangladesh.
Table 4. CMSY and BSM-estimated BRPs for shark and ray fisheries (with 95% confidence interval) from the BoB, Bangladesh.
ModelsParameters
k (mt)r (/yr)MSY (mt)
CMSY119,000
(41,300–344,000)		0.282
(0.163–0.487)		8420
(3080–23,000)
BSM134,000
(84,400–213,000)		0.152
(0.089–0.260)		5110
(3530–7390)
Table 5. CMSY and BSM-estimated stock information for shark and ray fisheries from the BoB, Bangladesh.
Table 5. CMSY and BSM-estimated stock information for shark and ray fisheries from the BoB, Bangladesh.
F (/yr)FMSY (/y)B (mt)BMSY (mt)F/FMSYB/BMSYStock Health
CMSY0.140.0959,50064,1001.560.93Overfished
BSM0.120.0843,30067,0001.580.65Overfished
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Gope, D.; Shamsuzzaman, M.M.; Islam, M.S.; Sarkar, T.; Roy, A.S.; Mozumder, M.M.H.; Barman, P.P. Stock Assessment of Marine Elasmobranchs (Sharks and Rays) in the Bay of Bengal, Bangladesh. J. Mar. Sci. Eng. 2025, 13, 1126. https://doi.org/10.3390/jmse13061126

AMA Style

Gope D, Shamsuzzaman MM, Islam MS, Sarkar T, Roy AS, Mozumder MMH, Barman PP. Stock Assessment of Marine Elasmobranchs (Sharks and Rays) in the Bay of Bengal, Bangladesh. Journal of Marine Science and Engineering. 2025; 13(6):1126. https://doi.org/10.3390/jmse13061126

Chicago/Turabian Style

Gope, Dwipika, Md. Mostafa Shamsuzzaman, Md. Shahidul Islam, Tanni Sarkar, Alaka Shah Roy, Mohammad Mojibul Hoque Mozumder, and Partho Protim Barman. 2025. "Stock Assessment of Marine Elasmobranchs (Sharks and Rays) in the Bay of Bengal, Bangladesh" Journal of Marine Science and Engineering 13, no. 6: 1126. https://doi.org/10.3390/jmse13061126

APA Style

Gope, D., Shamsuzzaman, M. M., Islam, M. S., Sarkar, T., Roy, A. S., Mozumder, M. M. H., & Barman, P. P. (2025). Stock Assessment of Marine Elasmobranchs (Sharks and Rays) in the Bay of Bengal, Bangladesh. Journal of Marine Science and Engineering, 13(6), 1126. https://doi.org/10.3390/jmse13061126

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