Assessment of the Fish Stock Status of the Spangled Emperor Lethrinus nebulosus Along the Coast of Balochistan, Pakistan

Abstract

The sustainable exploitation of fishery resources in Pakistan was assessed using the catch-based Monte Carlo method (CMSY) and the length-based Bayesian biomass (LBB) method to evaluate the data-limited fishery of the Spangled Emperor, Lethrinus nebulosus. CMSY relies on catch data, resilience parameters, and quantitative stock status metrics, while LBB exclusively uses length–frequency (LF) data for stock assessments. This study utilized twenty-two years of catch–effort and LF data from 7230 fish along the Balochistan coastline in Pakistan. The study revealed that the relative biomass of the exploited stock, with a B/B_MSY ratio of 0.557, indicates significant depletion. The relative exploitation rate (F/F_MSY = 2.47) confirms that the stock is being severely overfished. The discrepancy between the optimal length at first capture (L_{c_opt} = 43.1 cm) and the length at first capture (L_c = 38.8 cm) further proves the overexploitation of L. nebulosus. The convergence of findings from both methodologies strengthens the reliability of stock status estimates. By integrating diverse data types and analytical frameworks, this study provides valuable insights into the sustainability of L. nebulosus populations. This dual approach not only underscores the importance of varied data sources but also informs management strategies for effective fisheries conservation, contributing to a deeper understanding of resource dynamics along the Balochistan coast of Pakistan.

1. Introduction

Unsustainable fishing practices are depleting marine resources and driving biodiversity loss and habitat degradation, which threaten global food security and coastal livelihoods [1,2]. Overfishing amplifies the effects of other stressors, such as pollution and climate change, further destabilizing marine ecosystems [3]. These combined pressures are pushing oceans toward irreversible ecological decline, undermining their capacity to support human populations and marine life [4,5]. Stock assessment studies provide vital information essential for effective fisheries policymaking, playing a crucial role in the sustainable management of fishery resources. To evaluate the status of fish stocks, scientists have developed biological reference points (BRPs) using a range of statistical methods [6,7]. Accurate fish stock assessments are critical for understanding the health of fish populations and predicting the impact of management strategies on stock dynamics [8]. The implementation of science-based management practices, combined with regulatory enforcement and community involvement, is essential for ensuring the long-term sustainability of fishery resources [9,10]. While data limitations often pose challenges, methods for assessing stock status in data-limited contexts have advanced significantly in recent years. These approaches enable reliable inferences about stock conditions, even without comprehensive data, and are increasingly used to monitor multiple stocks in developing countries [11].

Pakistani capture fisheries are vital for food security, employment, and foreign exchange, contributing approximately 80% to the country’s total fish production. This sector includes 250 demersal fish species, 50 small pelagic species, 15 medium-sized pelagic species, and 20 large pelagic species. Additionally, Pakistan’s marine waters are home to 15 shrimp species, 12 cephalopod species, and 5 lobster species, all of which are of commercial significance [12,13]. The coastal waters of Balochistan, which stretch 734 km and represent 76.2% of the country’s total coastline, are rich in marine biodiversity and natural resources [14]. However, despite this ecological wealth, marine fishery data for the region are unreliable due to challenges such as bycatch, illegal fishing, and the exclusion of small-scale fisheries [15,16]. The dominant fishing methods in Pakistan’s marine sector include gillnets and trawls, primarily targeting demersal species like croakers, snappers, and groupers [17]. These practices are increasingly contributing to overexploitation, exacerbated by excess fishing capacity, weak governance, and widespread illegal, unreported, and unregulated (IUU) fishing activities [18,19]. The lack of effective regulatory measures and enforcement has led to the severe depletion or collapse of several key fish stocks [20,21].

Lethrinus nebulosus [22], commonly known as the Spangled Emperor and locally called Gaddir in Balochistan, is a highly valuable fish species, both for commercial and recreational fisheries, found across the Indo-Pacific region. It inhabits marine and brackish waters, mostly associated with reefs [23]. The FAO region 51 recognizes 20 species in the Lethrinidae family, with L. nebulosus being one of the most notable [24]. This species can grow up to 87 cm in length, reaches sexual maturity at 45 cm, and can live up to 28 years [25]. As benthic feeders, the L. nebulosus primarily consumes small fish and invertebrates, including mollusks and crabs [26]. Despite its significant commercial value, there has been a lack of comprehensive analysis regarding the current state of the species in the region. Previous studies reported that this fish has been overfished since 1994, based on catch–effort methods [27,28,29,30,31,32,33,34,35]. A number of studies have been conducted on the stock assessment of different fish species from Pakistani waters using different methodologies [36,37,38,39,40,41,42,43,44,45,46]. However, only a limited percent of commercially harvested stocks has been investigated, primarily due to a lack of data and limited expertise in Pakistan, which is similar to many other developing countries in the world. In response to the current circumstances, data-limited approaches have recently been developed to address these concerns.

A surplus production model-based Monte Carlo approach, such as CMSY, assesses stock biomass and exploitation rates by integrating catch time series and species-specific resilience parameters [47]. Similarly, the length-based Bayesian (LBB) method employs LF data to estimate key population parameters, such as natural mortality (M), growth (G), and responses to exploitation [48,49]. In the present study, both CMSY and LBB were applied to evaluate the status of L. nebulosus stocks in Pakistan. These methodologies provided valuable insights into stock health, exploitation rates, and sustainability. By combining these approaches, the aim is to inform evidence-based management practices, ensuring the long-term viability and protection of L. nebulosus, a commercially important species along the Balochistan coastline.

2. Materials and Methods

2.1. Area of Study

The Balochistan coastal region is situated in the northern part of the Arabian Sea bordering Iran. Gwadar is a stunning harbor city on the western coast of Pakistan, holding significant importance for the province of Balochistan and situated around 635 km along the Makran coastal highway. This city has immense importance for the province of Balochistan [50]. The Makran coast is endowed with a number of breathtaking beaches, including Gwadar, Ormara, Pasni, and Somiani. The research region comprises Gwadar and the nearby landing sites of Ormara, Pasni, and Jiwani as its principal towns and fishing ports. The economic hub of the coast of Balochistan is the Gwadar deep-water harbor. (Figure 1).

2.2. Data Source

Data were collected from 2000 to 2017 through the Marine Fisheries Department (MFD), Government of Pakistan (Statistical Year Book of Pakistan). Additional catch-and-effort data from 2018 to 2022 were obtained from the Balochistan Fisheries Department, Government of Balochistan, and the Marine Fisheries Department, Government of Pakistan. The catch was measured in tons, while the effort refers to the number of fishing boats used during that specific time period.

2.3. Sample Collection

Length–frequency data were systematically collected from commercial fishing vessels operating within Pakistan’s exclusive economic zone (EEZ) at the Gwadar fish harbor and nearby landing sites, including Ormara, Pasni, and Jiwani, from January to December 2022. There was a total of 7230 fish, and species were randomly sampled bi-weekly at the landing site throughout the study period. To ensure an adequate representation across all length classes, samples were taken randomly by collecting 10–30% of each well-mixed heap (10% during peak landings and 30% during lower landings). Specimens were identified taxonomically using a standardized identification sheet and field guide [51], ensuring accuracy in species classification.

2.4. Method for Estimating CMSY and BSM

The CMSY and BSM methodologies were employed to estimate maximum sustainable yield (MSY), relative biomass (B/B_MSY), and relative exploitation rates (F/F_MSY) utilizing time series data on catch and effort as well as species resilience. Key parameters of intrinsic growth rate (r) and carrying capacity (K) were derived [52]. The population biomass dynamic model is given below:

B_t+1 = B_t + r (1 − B_t/K)B_t − C_t

(1)

The biomass in a year (t + 1) (B_t+1) is modeled as a function of the current biomass (B_t), catch (C_t), carrying capacity (K), and intrinsic growth rate (r). For severely depleted stocks, where biomass falls below ${\displaystyle \frac{1}{4}}K$, a linear decline in surplus production is integrated into the predictive framework, enhancing the accuracy of biomass forecasts under critical conditions [46].

$$B_{t+1}=B_t+{4B}_{t+1}/Kr(1-B_t/K)B_t-C_t,if{B}_t/K<0.25$$

(2)

2.5. Prior Biomass Ranges

The prior ranges of relative biomass at the beginning and end of the time series, as well as an intermediate year, are estimated using the method’s default rules, which categorize biomass into three ranges: high (0.5–0.9), medium (0.2–0.6), and low (0.01–0.4) (

Table 1. Suggested prior biomass ranges of L. nebulosus along Balochistan coast from 2000 to 2022.

Stock	Bstart/K	Bint/K	Bend/K
L. nebulosus	0.2–0.6	0.5–0.9	0.2–0.6

) [25]. This framework allows for a structured assessment of biomass dynamics over time and serves more accurate management decisions. Resilience for L. nebulosus is “Medium” by Fishbase [25], which has the prior range of (0.2–06).

The catchability coefficient q is derived from the catch-per-unit-effort (CPUE) data and biomass:

$${CPUE}_t=q{B}_t$$

(3)

The dynamic of the Schaefer model can then be shown as below [46]:

$${CPUE}_{\mathrm{t}+1}={CPUE}_{\mathrm{t}}+r\left(1-{\displaystyle \frac{{CPUE}_t}{qK}}\right){CPUE}_{\mathrm{t}}-{qC}_{\mathrm{t}}$$

(4)

2.6. Method for Estimating Length-Based Bayesian Biomass (LBB)

The LBB estimation was carried out using the Bayesian Gibbs sampler software JAGS [53,54] with the statistical language R [55]. The LBB approach uses representative length–frequency data from one or more fisheries and is useful for species that grow continuously, such as the most commercially significant tropical fish species [53]. This method allows for the estimation of key life history parameters such as mean length at first capture (L_c), asymptotic length (L_∞), relative fishing mortality (F/M), and relative natural mortality (M/k) from size composition data [25,53]. The ratio of B/B_MSY is used to estimate stock status, with B/B_MSY < 0.8 indicating overexploitation, 0.8 ≤ B/B_MSY ≤ 1.2 indicating mean full exploitation, and B/B_MSY > 1.2 reflecting a healthy stock [56].

Table 2. Prior information on L. nebulosus in Pakistani marine waters for LBB method.

Scientific Name	Common Name	Min Length (cm)	Max Length (cm)	Numbers	Linf Prior (cm)	Z/k Prior	M/k Prior	F/k Prior	Lc Prior (cm)	Alpha Prior
Lethrinus nebulosus	Spangled Emperor	28	58	7230	57.9	0.783	1.5	0.3	38.8	16.6

shows the prior information for the species in this study. The major LBB equations are below. The [57] growth function models the growth of organisms over time, describing how length or weight increases asymptotically as a function of age, with the equation

$$L_t=L_{\infty }[1-exp(-k(t-t_0\left)\right)]$$

(5)

where L_t is the length at age t, L_∞ is the asymptotic length, k is the growth coefficient, and t₀ is the hypothetical age at zero length.

LBB utilizes the catch curve in numbers, as outlined by [53] to estimate key parameters of optimum length for maximum yield (L_opt) and the optimum length at first capture (L_{c_opt}), as shown below [58,59]:

$$L_{opt}=L_{\infty }({\displaystyle \frac{3}{3+\frac{M}{k}}})$$

(6)

$$L_{c\_op\mathrm{t}}=({\displaystyle \frac{{\mathrm{L}}_{\infty }(2+\frac{3F}{M})}{1+\frac{F}{M}(3+\frac{M}{k})}})$$

(7)

3. Results

3.1. Stock Status from Catch and Resilience

Table 3. The L. nebulosus estimates for r, K, and MSY in CMSY and BSM.

BRPs	r/yr	K/mt	MSY/mt
CMSY	0.436	17,900	1920
BSM	0.375	20,000	1870

represents the output from the CMSY and BSM models to assess the biological reference points of L. nebulosus fisheries for the r, K, and MSY from the Balochistan coast, Pakistan. The estimated MSY using CMSY was 1920, while in using BSM, this was 1870. BSM estimates a higher carrying capacity and lower r values compared to the CMSY model (Table 3).

3.2. Approaches of CMSY and BSM for Fishery Reference Points (BRPs)

Biological reference points by BSM for the L. nebulosus fishery in the study region are shown in

Table 4. Results based on BSM model analysis of L. nebulosus from Balochistan coast, Pakistan.

Parameters	Value	95% CI
FMSY	0.187	0.115–0.305
MSY	1870	1550–2260
BMSY	9980	6.54–15.2
Last year biomass	5570	3540–8220
Relative biomass B/BMSY	0.557	0.355–0.824
Last year fishing mortality	0.459	0.311–0.721
Relative exploitation rate F/FMSY	2.47	1.54–5.57

. The B_MSY (9980 t) was higher than the latest Biomass (5570 t) (B/B_MSY = 0.557), and F_MSY was lower than the last fishing mortality F (F/F_MSY = 2.47), which indicated the overexploited condition of this fishery along the coast of Balochistan (Figure 2, Figure 3 and Figure 4).

3.3. Dynamics Response of LBB

In this study, the maximum length of L. nebulosus reached 56 cm. The biomass ratio (B/B₀ = 0.66) is very low, projecting that its standing biomass has declined significantly. Although, the stock is in a fully exploited (B/B_MSY = 0.85) condition. Figure 5 illustrates the calculated results of the analysis, providing insights into the stock’s dynamics.

Table 5. Bayesian biomass estimates for L. nebulosus species based on the length–frequency data in 2022.

Scientific Name	Lmean/Lopt	Lc/Lc-opt	L95th/Linf	B/B0	B/BMSY	F/M	F/k	Z/k	Stock Status
L. nebulosus	0.93	0.90	0.99	0.66	0.85	2	2.8	4.14	Overfished

shows that the L. nebulosus is negatively affected by massive fishing pressure F/M = 2 with lower standing stock biomass B/B_MSY = 0.85 (smaller than the reference point value of 1.0), indicating the overfished condition of this fishery. The L_c/L_c-opt = 0.90 showed that small and juvenile fishing is a clear feature of this fish stock and the absence of large fish is indicated by the L_mean/L_opt = 0. 93 and L₉5th/L_inf = 0.99 shown in this study (Figure 5).

4. Discussion

Biological reference points, such as stock biomass and maximum sustainable yield (MSY), are fundamental in fisheries management and conservation [60]. The accurate assessment of fish stock status requires comprehensive data on catch, growth, natural mortality, and exploitation rates. In this study, we applied two distinct methodological approaches to estimate critical biological reference points for L. nebulosus along the Balochistan coastline, Pakistan. This integrated approach enabled precise calculations of MSY and other key metrics, providing a robust framework for evaluating stock health and informing sustainable management strategies. The results of this analysis are important for ensuring the long-term viability and sustainable exploitation of this commercially important species in Pakistan’s waters.

4.1. Stock Status Analysis by CMSY

The CMSY approach was employed to estimate key fishery reference points (r, K, MSY, B/B_MSY, and F/F_MSY) for assessing the Spangled Emperor (Lethrinus nebulosus) fishery along the Balochistan coast, Pakistan. The analysis revealed that the B₂₀₂₂/B_MSY ratio was below 1, indicating the overexploitation of the stock (Table 4). The BSM analysis (Figure 2) further demonstrated that both the F/F_MSY and stock size of L. nebulosus have been declining since 2014 in this region. The biomass of Spangled Emperor L. nebulosus fell below the reference point (B/B_MSY = 0.557), reflecting a concerning downward trend. The Kobe plot (Figure 4) highlighted this shift from the “green” safe zone to the “red” highly exploited region.

4.2. Stock Status Analysis by Bayesian Biomass Based on Length–Frequency (LBB)

The length-based Bayesian (LBB) approach employs Bayesian Markov Chain Monte Carlo (MCMC) methods to analyze data from commercial fish and invertebrate populations [40]. The calculated F/M ratio of 2 further corroborates the overfished condition, while the F/k and Z/k ratios are 2.8 and 4.14, respectively. This discrepancy underscores the increasing prevalence of overfishing and suggests that targeting larger fish would be beneficial. Furthermore, implementing measures to reduce fishing pressure and increasing gear mesh sizes may enhance the recovery potential of the Spangled Emperor L. nebulosus population along the coast of Balochistan (Figure 5).

4.3. Notable Outcomes from Previous Studies

From 2009 to 2015, the Marine Fisheries Department of Pakistan, in collaboration with the FAO, conducted extensive fish stock assessments. The estimated report indicated that large fishing fleets and unsustainable practices have led to a “fishing for catastrophe” scenario, with principal fish populations in Pakistani waters experiencing declines of 60–90% [61]. The stock status of the Spangled Emperor (L. nebulosus) along the coast of Balochistan, Pakistan, has been evaluated for the first time by this study. This fish was previously shown to be overfished from 1994 to 2019 with different studies by [26,27,28,29,30,31,32,33,34] in the UAE, Iran, Western Australia, India, and Saudi Arabia (

Table 6. Comparative studies of L. nebulosus comparing the present findings with other previous research studies worldwide.

Region	Assessment Method	Assessed Year	Assessment Results	References
India Cochin	FiSAT model	1994	Overfished E = 0.94	[27]
United Arab Emirates	Beverton and Holt yield per recruitment model	2006	Overfished E = 0.64	[28]
Iran Persian Gulf and Oman Sea	FiSAT model	2010	Overfished E = 0.50	[29]
Western Australia	Von Bretalanffy Growth model	2011	Overfished F = 1.5	[30]
India Toothukudi	FiSAT model	2012	Overfished Z = 1.15	[31]
India Toothukudi Coast	FiSAT model	2012	Overfished E = 0.54	[32]
Saudi Arabia	FiSAT model	2017	Overfished E = 0.59	[33]
United Arab Emirates	Osteometrical methods	2018	Overfished R2 = 0.98	[34]
India Tamil Nadu	FiSAT model	2019	Overfished E = 0.54	[35]
Pakistan Balochistan Coast	CMSY and LBB	2022	Overfished F/FMSY = 2.47 F/M = 2	Present study

).

5. Conclusions

The length-based Bayesian (LBB) model offers a flexible framework for complex systems, accounting for uncertainty and hidden variables. It provides probability distributions for parameter estimates, making it a robust tool for fisheries stock assessments. However, its implementation is challenging, as it is highly sensitive to prior assumptions, and requires specialized knowledge for proper interpretation. In contrast, the CMSY approach, while simpler, is particularly useful for rapid assessments with limited data, such as catch time series. It is quick to compute and facilitates the estimation of MSY, making it a practical tool for fishery management. However, CMSY relies on simplified assumptions of stock dynamics, which may not always be applicable, and is sensitive to the accuracy of catch data. This study utilized both methodologies to assess the stock status of L. nebulosus along the Balochistan coast, Pakistan. The biomass analysis indicated that the standing stock biomass of L. nebulosus is below sustainable levels, with B/B_MSY = 0.85 from the LBB model and B/B_MSY = 0.557 from the CMSY. These findings suggest that the stock has been overexploited, with a significant depletion of both younger and older fish cohorts contributing to the collapse. Both methods consistently highlighted the critically overfished state of the population, raising serious concerns regarding its sustainability. Given the alarming status of L. nebulosus, immediate management interventions are essential. To prevent further stock depletion, it is crucial to reduce fishing mortality rates for this species. Implementing a reduction in fleet size and fishing hours by 30–40% to decrease fishing pressure on the L. nebulosus stock allows for recovery and sustainable growth. Applying Virtual Marine Surveillance (VMS) systems plays a crucial role in monitoring fishing activities along the Baluchistan coastline, ensuring compliance with regulations and preventing illegal, unreported, and unregulated (IUU) fishing. Moreover, establishing no-fishing zones along critical breeding areas on the Balochistan coastline to promote stock regeneration and protect juvenile Spangled Emperor populations is another potential strategy. Furthermore, the development of effective management plans and the enforcement of conservation regulations are urgently needed to prevent the collapse of this important fishery resource. Such actions will be critical for ensuring the long-term sustainability of the Spangled Emperor (L. nebulosus) fishery, supporting both ecological balance and the livelihoods of local communities along the Balochistan coast.