Research Article

Length-Based Fishery Status of Indo-Pacific King Mackerel (Scomberomorus guttatus) in the Iranian Part of Persian Gulf and Oman Sea Waters

Seyed Ahmadreza Hashemi1*, Mastooreh Doustdar2, Rahimeh Rahmati3 and Asadullah Ali Muhammad4

1Offshore Fisheries Research Centre, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Chabahar, Iran; 2Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran; 3Caspian Sea Ecology Research Centre, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Sari, Iran; 4Fisheries and Coastal Development Department Government of Balochistan, Pakistan.

Received | December 14, 2024; Accepted | April 23, 2025; Published | June 17, 2025

*Correspondence | Seyed Ahmadreza Hashemi, Offshore Fisheries Research Centre, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Chabahar, Iran; Emai: [email protected]

Citation | Hashemi, S.A., M. Doustdar, R. Rahmati and A.A. Muhammad. 2025. Length-based fishery status of Indo-pacific king mackerel (Scomberomorus guttatus) in the Iranian part of Persian gulf and Oman sea waters. Sarhad Journal of Agriculture, 41(2): 918-927.

DOI | https://dx.doi.org/10.17582/journal.sja/2025/41.2.918.927

Keywords | Indo-Pacific king mackerel, Population dynamics, Persian Gulf, Oman sea

Copyright: 2025 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Sustainable marine exploitation and development depends critically on the proportion of stocks at (BSLs) to those at BULs. In 1974, BULs were around 10%, rising to approximately 33% by 2016. Conversely, BSLs were 90% in 1974 but declined to about 63% by 2020. The greatest BUL ratios are initiate in the Mediterranean, Black Sea, Southeast Pacific, and Southwest Atlantic oceans. Thus 57% of tuna stocks are regarded as viable, and around 43% are considered as physiologically unsustainable. Approximately 9% of the world’s catch comprises tuna and similar species (FAO, 2024). Resource conservation is a widely accepted idea and a crucial requirement for a long-term preservation of all resources connected to water. Fisheries managers assess the appropriate and authorized use of these resources while concentrating on guaranteeing sufficient and safe food from natural sources (Ganga and Pillia, 2000).

The Persian Gulf and Oman Sea, with their unique ecological conditions, harbor a diverse array of aquatic animals that offer sustenance, work, and other economic opportunities for the inhabitants. Iran employs almost 120,000 fishermen, whose primary occupation is fishing. And an important factor in the creation of jobs in coastal communities has been fishing. Eco, as well as commercial endeavors for operations after harvest (Taghavimotlagh et al., 2008). In 2022, the total catch from the Persian Gulf and Oman Sea was approximately 718,000 tons. Of this, large pelagic species contributed 344,677 tons, with tuna and tuna-like species making up around 288,735 tons. The catch mainly consisted of tropical tuna (42.8% or 123,343 tons), neritic tuna (45.2% or 130,576 tons), and billfish species (12% or 34,816 tons) (IFO, 2024). Between 2018 and 2022, Indonesia, India, and Iran accounted for nearly 85% of IOTC (2023).

Tuna is classified under the order Perciformes and the family Scombridae, which includes 52 species across 15 genera. The genus Scomberomorus comprises 18 species, with two species inhabiting the Persian Gulf and the Oman Sea (Kaymaram et al., 2009). Tropical and subtropical areas of the western Pacific and Indian seas are home to the epipelagic migratory Indo-Pacific king mackerel. It lives in coastal waters up to 200 meters deep, usually about 90 meters, and occasionally makes its way into murky estuaries. Although it is typically approximately 40 cm long, this species may grow up to 76 cm in length and weigh up to 5 kg. It is often found in small schools. (Collette and Nauen, 1983; Froese and Pauly, 2017). The S. guttatus is a significant species in tuna fisheries across tropical and subtropical seas, with global catches nearing 50,000 tons (FAO, 2024) and about 45,000 tons in the Indian Ocean (IOTC, 2022).

In the Indian Ocean, gillnets capture the majority of Indo-Pacific King mackerel (about 66%), with fisheries in Indonesia and India accounting for over two-thirds of the total, with Iran also reporting significant catches (IOTC, 2022). The variations in the distribution of these nomadic species depend on differences in their life history, migration patterns, and habitats affected by unstable environmental conditions such as temperature, patterns of ocean currents and circulations, and food access (Keymaram et al., 2009; Keymaram et al., 2013; Hashemi et al., 2020).

Very little is known about the assessment of this fish, regardless of its economic importance. This study aims to provide fundamental information on the biological traits and population dynamics of the Indo-Pacific king mackerel, as well as to provide suitable management and exploitation guidelines for this species in the Iranian seas of the Persian Gulf and Oman Sea. Additionally, it offers a first-ever preliminary evaluation of S. guttatus biomass in this region.

Materials and Methods

Between August 2020 and February 2023, samples of Indo-Pacific king mackerel (S. guttatus) were collected from three touchdown sites: Bushehr, Bandar Abbas, and Chabahar, to investigate population trends and exploitation ratios. The ports of Bushehr (50˚ 82’E, 28˚ 91’N), Bandar Abbas (56˚ 28’E, 27˚ 19’N), and Chabahar (60˚ 64’E, 25˚ 29’N) were chosen for sampling based on commercial fishing conditions in the Iranian Persian Gulf and Oman Sea. Gill nets with a mesh size that was roughly 140 mm were used (Figure 1).

Length frequency distribution

Fish arriving monthly at the ports of Bushehr, Bandar Abbas, and Chabahar were sampled. From August 2020 to February 2023, random samples of commercial catches from these harbors were taken. Biometric analysis, including length and weight measurements, was conducted on the specimens. A biometric ruler measured the fork length with 1 cm precision.

 

Biometry

Biometric information was acquired, such as weight and length. Accurate measurements of fork length to 1 cm and wet weight to 0.1 grams were made using a biometric ruler. Wi is the overall weight, which is measured in grams, Li is the length of the fork, which is measured in centimeters, an is a constant coefficient, and b is the exponent included inside the equation (Wi = a × Li^b). Through the use of the equation, significant disparities were discovered between the estimated value of b derived from the equation and the value of b equal to three for an Indo-Pacific king mackerel that exhibited comparable development. As an example, the letter (b) represents the slope, the letter (s.dx) represents the standard deviation of the natural logarithm of length, the letter (s.dy) represents the standard deviation of the natural logarithm of weight, the letter (r2) represents the coefficient of determination, and the letter (n) refers to the sample size (Zar, 2010). This is the formula that is employed:

t = [(s.dx)/ (s.dy)] × [(lb-3l)/ (√ (l-r2)] × [√ (n-2)]

Growth studies

Through the utilization of the TropFishR software and the ELEFAN optimization model, Mildenberger et al. (2017) conducted an evaluation of the growth rate and the (L∞) value. With the help of the equation (Log (-t0) = - 0.3922 - 0.2752 Log L∞ - 1.038 Log K), which was applied to the empirical Pauly equation (Froese and Binohlan, 2000), the ideal value of (t0) was determined. In order to determine the connection between the growth factor (K) and the infinite length (L∞), the equation Τ’ = Log (K) + 2 Log (L∞) was utilized.

Mortality estimate

The natural mortality rate (M) was calculated using the following formula: M = 4.118 multiplied by K raised to the power of 0.73 multiplied by L∞ raised to the power of -0.33. In this equation, K represents the von Bertalanffy growth rate parameter, while L∞ represents the asymptotic length of the fish, measured in centimeters. A calculation of total mortality (Z) was performed using the catch curve data that had been converted to length. For the purpose of determining fishing mortality (F), the equation F = Z - M was utilized, where M stands for natural mortality. For the purpose of calculating the exploitation rate (E), which is defined as the ratio of fishing mortality to overall mortality (Sparre and Venema, 1998), the equation E = F / Z was utilized. More specifically, the equation tmax = t0 + 3 / K was utilized in order to determine the maximum life expectancy (Froese and Pauly, 2017).

Length-based reference point

According to research, this species’ maturity length (Lmat) can reach 40 cm in the northern Oman Sea near Iran (Keymaram et al., 2013). The ideal fishing length (Lopt) is calculated as Lopt = L∞ * (3 / (3 + M/K)), based on Froese and Binohlan’s (2000) studies.

Fishery assessment

The exploitation rate (U) was calculated using the formula U = F (1 - e^-z) / z. To determine the total biomass of the stock at the beginning of the year, the formula B0 =Y/U was applied (Nurulamin et al., 2000), where Y represents the annual average catch of approximately 10,000 tons for this species in 2023 (IFO, 2024). Additionally, to evaluate the annual average of the standing stock, the formula Bt = Y / F was utilized (Nurulamin et al., 2000).

Yield per recruit and biomass per recruit

For the purpose of computing the relative yield per recruit, which is represented by the variable, the fishing mortality coefficient or the exploitation rate was utilized. As stated by Y’/R. Gayanilo et al. (2003), the letters (E) represent the exploitation coefficient, the letters (U) represent the exploitation rate, the letters (M) represent the natural mortality coefficient, the letters (F) represent the fishing mortality coefficient, and the letters (Lc) refer to the (Lc50) equation. To further elaborate, the accompanying procedure was utilized in order to calculate the relative biomass per recruit, which is denoted by the symbol B’ / Rp.

Y’ / R = EU M/K ( -3 U / (1 + m) + 3 U2 / (1 + 2m) + U3 / (1 + 3m ) U = 1 - (LC / L∞) ; M = (1 - E) / ( M / K) = ( K / Z) ; E = F / Z B’ / R= Y’ / R / F

Length-based SPR assessment methodology (LB-SPR)

In their 2018 publication, Carruthers and Hordyk present a comprehensive explanation of the fundamental components that make up the length-based SPR (LB-SPR) evaluation approach that has just been deployed and is currently being employed in the Oman Sea. There are five essential input parameters that are required for the LB-SPR framework. Obtaining accurate information about the latter might be difficult in the absence of trustworthy length and age statistics; however, it is commonly assumed to be somewhere around 10%. According to Carruthers and Hordyk (2018), the LB-SPR investigation was carried out with the assistance of the LB-SPR software tool. SPR= Total Egg Production (Fished)/ Total Egg Production (Unfished). The data was analyzed using the TropFishR package, Excel application, R software (R Core Team, 2022), and R studio (2023.03.1-446).

C-MSY (CMSY) method (Monte-Carlo algorithm)

The C-MSY and Graham-Shaefer models share characteristics and both use catch time data to estimate maximum sustainable yield and fisheries reference points. The CMSY requires prior distributions on r and k, as well as biomass at the beginning (Froese et al., 2017). Subsequent biomass is generated using a Schaefer equation (Martell and Froese, 2013).

By + 1 = By + rBy (1- By / k) es1 - Ct es2

This methodology uses formulas to calculate instantaneous population growth (r) and carrying capacity (K) based on depletion (d) and resource saturation (S). The maximum sustainable yield (MSY) is calculated from MSY= rk/4, and BMSY= K/2. A tentative range for r is set based on the sustainability of stocks, with highly sustainable stocks assigned r values between 0.6 and 1.5. Population growth rates were calculated using the irf formula, which involves the inverse range coefficient and 3/r high– r low (Froese et al., 2018).

To achieve the desired model sampling, the prior process error variance should be 0.2 and the explanatory error must be 0.1 according to Froese et al. (2018). Exploit records for the first and last year are used, and the previous preliminary relative biomass ranges from 0.1 to 0.4, while the most recent relative biomass ranges from 0.2 to 0.65. The initial median relative biomass in 2005 was 0.5 to 0.9 over a year. See Martell and Froese (2013) and Froese et al. (2018) for CMSY details.

Results

During this research, researchers analyzed a total of 6,094 specimens of Indo-Pacific king mackerel in the laboratory. The fork lengths of these fish varied between 25 cm and 82 cm, with a mean length of 46 ± 17 cm and an average weight of 961±366 grams. The fork length measurements were categorized into intervals of 4 cm each, with the highest occurrence noted in the 41 to 45 cm range, comprising 1,238 specimens, which represents about 20% of the overall sample size (Figure 2).

 

The correlation between length and weight for all samples was represented by the equation W= 0.015 × CL^2.89 (n = 609, R² = 0.85, with a 95% confidence interval for b ranging from 2.81 to 2.96) (see Figure 3). Results from the Student’s t-test revealed no significant differences between the estimated b value and b = 3 at the 0.05 significance level (p > 0.05). This finding indicates an isometric growth pattern for this species in the Iranian segment of the Persian Gulf and the Oman Sea (Figure 3).

 

The population dynamics indicators for S. guttatus were determined as follows: the fork length at infinity (Linf) is 83 cm, (K) stands at 0.5 per year, and the growth performance index (Φ) is 3.54. There is a difference between the natural mortality rate (M) of 0.58 per year and the fishing mortality rate (F) of 2.25 per year, resulting in a total mortality rate (Z) of 2.83±0.03 (with a 95% confidence interval of 2.80-2.86). The exploitation coefficient (E) is calculated to be 0.8 (Figure 4). Moreover, the optimal fishing length (Lopt) for this species has been estimated at 60 cm.

 

These values were derived by comparing the ratios of production to recruitment. The maximum sustainable yield (EMSY) was found to have an exploitation ratio of 0.54, according to the findings of the study. Whereas the fishing mortality for the maximum sustainable yield (FMSY) was determined to be 0.75. This indicates that F/Fmsy > 1 (as depicted in Figure 5), which is considered unfavorable. The research results suggest that the maximum lifespan of S. guttatus is roughly 6 years.

 

Fishery assessment

At the beginning of the year, the exploitation rate (U) was assessed at 0.7, while the total stock amount, denoted as B0, was recorded at 13,333 tons. Meanwhile, the estimated average standing stock, referred to as Bt, was estimated at 4,444 tons. Additionally, the specific parameter for the Spawning Potential Ratio (SPR) was determined to be 0.21 per year, as demonstrated in Figure 6. The anticipated 95% confidence intervals for evaluations of spawning potential, relative fishing pressure, and selectivity can be found beneath the estimates presented.

 

C-MSY (CMSY) method

The average values for the instantaneous growth rate of the population (r) and the carrying capacity (K, measured in thousands of tons) were derived using the Catch Maximum Sustainable Yield (CMSY) models, were results of 0.56 (ranging from 0.40 to 0.78) and 147 (with a range of 96 to 225), respectively. For S. guttatus in the Iranian areas of the Persian Gulf and Oman Sea, the following metrics were evaluated: the maximum sustainable yield (MSY, in thousands of tons), the proportion of current plant matter to biomass at MSY (B/BMSY), the ratio of electricity fishing mortality to the fishing mortality rate at MSY (F/FMSY), and a saturation ratio (S = B/K = 0.5 B/BMSY). All of these metrics were evaluated in accordance with the following criteria: As shown in Figure 7, the saturation ratio is 0.67, with 11 (ranging from 9 to 15), 1.37 (ranging from 0.83 to 1.60), 0.67 (ranging from 0.57 and 1.11), and a saturation ratio of 0.67.

 

Discussion

The S. guttatus a significant large pelagic fish, holds substantial economic value in southern Iran. Its catch has notably risen in recent years in the Iranian waters of the Persian Gulf and Oman Sea, particularly in the Sistan and Baluchistan province. More than 10,000 tons of this fish have been caught in the Southern Waters of Iran (IFO, 2024).

In the field of fisheries biology, establishing length-weight relationships is essential, particularly when precise length measurements are available. These relationships facilitate estimates of fish weight and biomass, serve as metrics for the health of fish populations, and enable comparisons of growth trends among different species in various habitats (Williams, 2000; Bobori et al., 2010).

Table 1 compares this species biological indicators with those of previous research conducted globally. The infinite length of S. guttatus seems to be greater in the Persian Gulf and Oman Sea regions than it is in Bangladesh, Pakistan, India, and Indonesia. The biological distinctions between each region have an impact on these variations in limitless length and growing rate (King, 2007). Natural selection and biodiversity affect reproductive properties, physical characteristics, population density, and genetic diversity, leading to a variety of adaption techniques during the course of an organism’s life. Environmental factors and the quantity and quality of food sources are usually responsible for the differences in size and growth rates between locations (Bartulovic et al., 2004). Fish growth is also significantly influenced by other factors such as age, gender, seasonal and annual fluctuations, feeding habits, physiological conditions, food availability, and reproductive cycles (Lalèyè, 2006).

 

Table 1: Comparison of the biological traits of S. guttatus with global studies.

References	Region	L∞ (cm)	K (yr -1)	to*	Φ'*	M	F	Z	E
Devaraj, 1981	India	127	0.18	-0.63	3.46	-	-	-	-
Ghosh et al., 2009	India	61	1.4	-0.09	3.71	1.79	2.92	4.71	0.62
Rashid et al., 201	Bangladesh	73	0.6	-0.21	3.50	0.99	0.8	1.79	0.45
Ahmed et al., 2016	Pakistan	55	1.08	-0.12	3.51	-	-	-	-
Mahesh et al., 2017	India	71	0.23	-0.47	3.14	0.54	0.49	1.03	0.58
Noegroho et al., 2018	Indonesia	68	0.37	-0.35	3.23	0.71	1.39	2.1	0.48
Barua and Liu, 2023	Bengal Gulf	117	0.42	-0.26	3.75	0.46	0.72	1.18	0.61
Present study, 2023	Persian Gulf and Oman Sea (Iran)	83	0.5	-0.2	3.54	0.58	2.25	2.83	0.8

L∞ = asymptotic length, K = growth coefficient, t₀ = hypothetical age at zero length, Φ’ = growth performance index, M = natural mortality rate, F = fishing mortality rate, Z = total mortality rate, E = exploitation rate, * = estimated.

 

The correlation between growth rate and infinite length leads to the comparison of Φʹ values in the growth curve. A growth curve with fluctuating growth rates over time and across sizes is the result of this relationship. Furthermore, the limitless length and growth rate can be affected by changes in latitude and biological conditions. These variations include different Φ’ values, which can also alter over time within the same region due to changing environmental circumstances (King, 2007). In addition, this species’ fishing death rate was higher than its natural mortality rate. By having a fishing fatality to maximum sustainable yield ratio (F/FMSY) that was greater than one, Arrizabalaga et al. (2012) demonstrated that there was an excessive amount of fishing. In addition, both the exploitation rate and the exploitation coefficient were greater than 0.5, which indicates that the fisheries produced in excess of what would be considered ideal. According to Sparre and Venema (1998) and King (2007), in order to prevent overfishing, the exploitation coefficient and exploitation rate should ideally remain below 0.5, and the mortality rate experienced by fishermen should not exceed the mortality rate experienced by native fish.

In various regions around the globe (see Table 1), reports indicate that natural mortality, fishing mortality, total mortality, and the exploitation coefficient vary within the following yearly ranges: natural mortality from 0.46 to 1.79, fishing mortality between 0.49 and 2.92, total mortality spanning from 1.03 to 4.71, and the exploitation coefficient ranging from 0.45 to 0.80 (Devaraj, 1981; Ghosh et al., 2009; Rashid et al., 2011; Ahmed et al., 2016; Mahesh et al., 2017; Noegroho et al., 2018; Barua and Liu, 2023). This study compared to previous studies in the Persian Gulf and Oman Sea region showed higher fishing mortality, total mortality and exploitation coefficient which indicated an increase in the capture fisheries of this species in recent years (Table 1). The key factors influencing the pressure on fish stocks include (1) the volume of fish caught and harvested, and (2) environmental conditions that impact survival and access to fishery resources (Mateus and Estupinan, 2002). To effectively lower the exploitation rate, it is advisable to decrease fishing activities and limit the issuance of fishing permits, thereby reducing the number of entrants into the fishing community (Jenning et al., 2000). The results reveal that the longest lifespan of S. guttatus is around six years, as determined by the formula tmax = t0 + 3 / K (Froese and Pauly, 2017). By applying the standards set by the American Fisheries Society (Cheung et al., 2004) and contrasting the findings of the present study with these criteria, it was concluded that this species faces a medium risk of extinction (see Table 1)

Our findings show that the B/B0 and LBSPR indices are over 0.3 and 0.2, respectively. According to these indicators, fish populations are heading toward overfishing, and the species’ stock status is average. A good stock condition is indicated by values that are greater than 0.6 (0.6 = B/B0), while values that are less than 0.2 (0.2 ≈ B/B0) indicate that reserves have been depleted. The LBSPR index is a tool that offers estimations of the SPR. Values that fall between the range of 0.2 to 0.6 are used to indicate an average stock condition (B/B0 = 0.2-0.6) (Froese et al., 2018; Zhai et al., 2020; Hashemi et al., 2020, 2021; Hashemi and Doustdar, 2022). Furthermore, it is of the utmost importance to highlight the 95% confidence limits that are supplied by LBSPR. These bounds demonstrate the extraordinarily low error that is connected with these calculations (Carruthers and Hordyk, 2018).

A 2022 research by Hashemi and Doustdar concluded that the Indo-Pacific king mackerel stock is presently completely exploited, that the ratio of fishing effort to exploitation is at its maximum, and that neither should be increased. The CMSY approach used data collected for this species in the southern seas of Iran from 1997 to 2019 and reported a Maximum Sustainable Yield (MSY) of 1,000 tons. The F/FMSY, B/BMSY, and S ratios, according to Hashemi and Doustdar (2022), were 8.44 (range between 4.65 and 15.30), 0.94 (range between 0.83 and 1.11), 1.36 (range between 0.83 and 1.59), and 0.68 (range between 0.4 and 0.78), respectively.

Another study by Haghi and Ghanbarzadeh’s (2022), evaluated the sustainable harvesting levels and reference points for S. guttatus in Iran’s southern Persian Gulf waters by looking at catch data from 1997 to 2019. According to their findings, the maximum sustainable yield (MSY) was determined to be 7,040 tons using the Bayesian Surplus Model (BSM) and 9,100 tons using the Catch Maximum Sustainable Yield (CMSY) technique. These figures contrast with our own estimate of approximately 11000 tons. The discrepancies in these estimates may stem from differing assumptions regarding the stock’s resilience and overall condition. Furthermore, Martell and Froese (2013) have suggested that reliable MSY estimates can often be derived using data- poor methodologies, albeit with a certain degree of error margin. Stock status assessment for Indo-Pacific king mackerel in the Indian Ocean used catch-only methods techniques as Catch-MSY and OCOM in 2018, and the yield target (MSY) which was similar for both models considered (45,022, Catch-MSY and 45,632 OCOM). The FMSY and BMSY (1000 t) for this species in the Indian Ocean was reported 0.52 (0.40–0.69) and 66 (45.9–107), respectively (IOTC, 2022). Ultimately, the authors suggested that if all relevant countries under Regional Fisheries Management Organizations (RFMOs) cooperate in gathering biological data as well as information on catch and fishing effort for shared stocks, the outcomes will yield more accurate and effective management strategies.

ConclusionS and Recommendations

An exploitation coefficient (E) of greater than 0.5, an F/FMSY ratio of less than one, Bt/B0 index greater than 0.3, all suggest that this species is not in favorable stock conditions and that fish populations are trending toward overfishing. According to the calculated parameters (MSY, B/BMSY and S ratio index) Indo-Pacific king mackerel species is full fishing (full exploitation) status. Consequently, targeted actions should not be implemented to increase catch levels and fishing effort.

Acknowledgments

We would like to thank Dr. Bahmani, the head of the Iranian Fisheries Science Research Institute (IFSRI), and the experts at the Offshore Fisheries Research Center (Chabahar) for their invaluae assistance with this project.

Novelty Statement

The aim of this research is to provide fundamental insights into the biological parameters and population dynamics of the Indo-Pacific king mackerel, as well as to establish proper management and exploitation guidelines for this species in the Iranian waters of the Persian Gulf and Oman Sea. Additionally, it offers an initial estimate of the biomass of S. guttatus in this region, a task not previously undertaken.

Author’s Contribution

Seyed Ahmadreza Hashemi: Conceptualization, validation, investigation, analysis, writing original draft.

Mastooreh Doustdar: Conceptualization, validation, analysis, writing review and editing.

Rahimeh Rahmati and Asadullah Ali Muhammad: Validation, investigation, writing review and editing.

Wajiha Shaikh: Edited and finalized the manuscript.

Funding

No funding was received for conducting this study.

Ethical approval

Approval of all authors have been obtained. The animal study was reviewed and approved by Iranian Fisheries Science Research Institute.

Data availability

All of the data that supports the conclusions of this study may be found inside the paper itself as well as the supplementary resources that accompany it.

Consent of publication

The Research Paper has not been published previously nor is being considered for publication.

Conflict of interests

The authors have declared no conflict of interest.

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