Research Article

Stock Assessment of Kawakawa (Euthynnus affinis Cantor, 1849) in the Persian Gulf and Oman Sea (Iranian Southern Waters), Using Data Limited Approach

Seyed Ahmadreza Hashemi1, Mastooreh Doustdar2, Asadullah Ali Muhammad3* and Reza Abbaspour Naderi4

1Offshore Fisheries Research Center, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Chabahar, Iran; 2Iranian Fisheries Science and Research Institute, Agricultural Research Education and Extension Organization, Tehran, Iran; 3Fisheries and Coastal Development Department Government of Balochistan, Pakistan; 4Iran Fisheries Organization (IFO) Tehran, Iran.

Received | June 10, 2024; Accepted | August 06, 2024; Published | October 16, 2024

*Correspondence | Asadullah Ali Muhammad, Fisheries and Coastal Development Department Government of Balochistan, Pakistan; Email: [email protected]

Citation | Hashemi, S.A., M. Doustdar, A.A. Muhammad and R.A. Naderi. 2024. Stock assessment of Kawakawa (Euthynnus affinis Cantor, 1849) in the Persian gulf and Oman Sea (Iranian southern waters), using data-limited approach. Sarhad Journal of Agriculture, 40(4): 1233-1240.

DOI | https://dx.doi.org/10.17582/journal.sja/2024/40.4.1233.1240

Keywords | Euthynnus affinis, Population biology, Assessment, Persian gulf, Oman sea, Data approached

Copyright: 2024 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

Fisheries science helps people make decisions about how to manage fish. This advice might have predictions about how much fish can be caught at different levels of fishing, and it usually includes an estimate of how much effort is needed to catch the most fish without harming the population (King, 2007). To keep fishing at a level that won’t harm fish populations, it is imperative to find a balance between how many fish are caught and how many are born and grow. It’s important to understand the biology of fish populations so to catch maximum fish as possible without hurting the environment or the fish population (Jenning et al., 2000).

Signs of overfishing of key fish species and other aquatic resources have been prominent in recent years. The proportion of fish caught at the biologically sustainable level (BSL) and biologically sustainable level (BUL) was about 90% in 1974 and about 67% in 2016 (FAO, 2018). Total fish capture in Iran reached nearly 800,000 tonnes, of which 720,000 tonnes (more than 90%) came from southern Iranian waters (IFO, 2023).

Today, length-based models such as length-based integrated mixed effects (LIME), length-based spawning potential ratio (LBSPR), and length-based Bayesian models (LBB) and catch-based methods such as maximum sustainable catch (Catch-MSY), Depletion-Based Resource Loss Analysis (DBSRA), Simple Resource Synthesis (SSS), Catch-MSY (CMSY), and Length-based models used in data-poor fisheries globally (known as “data-poor” or “data-limited” fisheries) (Wetzel and Punt, 2015).

Kawakawa (Euthynnus affinis) is a type of tuna fish that travels around the top layer of the ocean. It can be found in many warm waters in the Indo-Pacific area. This species lives in the western Pacific Ocean, from Malaysia to Mainland China, Taiwan, and southern Japan (Yesaki, 1989, 1994; TaghaviMotlagh et al., 2009; Hashemi et al., 2010, Hashemi, 2020). Kawakawa is a type of fish that is often found in shallow waters, and is usually caught in set nets. It can also be caught in gill nets, long lines, and small purse seines, but not as often (Yasaki, 1989, 1994). Between 1997 and 2022, the average amount of Kawakawa caught each year was about 2240 tons (IFO, 2023).

Although kawakawa is an economically significant fish, little is known about its evaluation; several authors have studied different parts of the fish’s biology. It appears that the overall capture of kawakawa has risen significantly over the previous two decades. Different aspects of biological work of kawakawa have been done by different authors (Talebzadeh, 1997; Darvishi et al., 2003; TaghaviMotlagh et al., 2009; Hashemi et al., 2010; Hashemi, 2020) however, there hasn’t been much study done in Iran on the species’ stock assessment. Within this framework, the primary goal of this research is to offer biological benchmarks and additional population dynamics data needed for Euthynnus affinis management.

Materials and Methods

A 26-year record of tuna catches (mostly in metric tons) in the Persian Gulf and the Sea of Oman (Iran) (Figure 1) is compiled by an Iranian fishing company (1997 to 2022).

 

Catch-MSY method (SIR algorithm)

Catch-MSY method based on resource reduction analysis (Martell and Froese, 2013), and the dynamic state is based on the Schaefer production model (Martell and Froese, 2013).

Bt = λ0k expνt t = 1

Bt +1 = [Bt + rBt (1- Bt / k) – Ct] expνt t > 1,

Bt, Ct, and K represent biomass, catch, and capacity respectively. The method error follows a lognormal structure and νt is independent, with a normal distribution of mean 0 and variance σ2. The initial depletion level λ0 is typically assumed to be 1 for basic stock. Martell and Froese (2013) assigned depletion level ranges for initial and current levels based on their analysis of 98 stocks in the RAM database (Ricard et al., 2012). In year one, B/k (λ01, λ02) was 0.5-0.9 for max catch <0.5 and 0.3-0.6 otherwise. In the last year, B/k (λ1, λ2) was 0.01-0.4 for max catch <0.5 and 0.3-0.7 otherwise.

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. 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 approach computes carrying capacity (K) based on resource saturation (S) and depletion (d) using formulae to determine instantaneous population growth (r). 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., 2017).

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., 2017). 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. Martell and Froese (2013) and Froese et al. (2017) for CMSY details.

Optimized catch only method (OCOM)

Zhou et al. (2016) developed an optimized catch-only method that uses time series data without prior distribution knowledge. The approach employs an infinite dictionary for both r and K, where 0 < K < ∞ and 0 < r < ∞. The maximum K is bounded by r = 0 and the maximum r is bounded by the smallest possible K due to negative correlation between the parameters. The approach aims to find viable ranges for r and K, with the most likely value of r among any combination on the curve. It excludes improbable results through trials, resulting in a posterior-focused catch-based technique for estimating biological reference points (Zhou et al., 2016).

This method optimizes final biomass to estimate valid combinations of r and K values for a fixed depletion rate. 500 simulations are run without additional constraints on valid pairs. Unviable trajectories are removed. The Max K = 50 * max (C) and Min K = max (C). Initial K population is set logarithmically between these values for higher density of lower K. The depletion phase ranges from 0.05 to 0.8 in 0.05 increments while r values vary from 0.1 to 2. Biomass dynamics model is run with constraints Bt ≤ K, Bt > 0, and B > C.

Zhou-boosted regression tree models (Zhou-BRT)

Use Zhou-BRT model to estimate saturation (B/K) and B/BMSY time series from catch time series. Zhou-BRT is trained on RAM Legacy database and estimates saturation based on 56 catch statistics. The pre- and post-peak catch, recent subseries, and entire catch time series are all fitted using linear regression coefficients. Values from two BRT models with decreased bias correction (8 and 38 predictors) are used to forecast saturation. B/BMSY is estimated to be twice the saturation, or twice the score of Saturation (S). The upper and lower 95% confidence intervals correspond to the high and low values, respectively. Using the zBRT saturation estimate can provide a depletion prior for advanced depletion analysis, as catch-MSY, DCAC (Dick and MacCall, 2011), DCAC (MacCall, 2009), and catch-MSY. Zhou et al. (2018) suggest that this can be a useful tool. In 2018, they used the zBRT method to create a catch-only stock decline analysis and determine preceding 12-month depletion.

Stochastic surplus production model in continuous-time (SPiCT)

The SPiCT model is a type of model that is based on the Pella-Tomlinson model. It allows for different shapes of the production curve, not just a symmetrical one. You can find more information about the SPiCT model and its different options in the book written by Pedersen and Berg (2017). The model has a basic structure that can include errors in the process and what is observed, as well as models that assume there are no errors in what is observed. SPiCT thinks that the numbers of fish caught might have mistaken, and it also tries to figure out how much error there is in estimating how many fish are left in the water (Mildenberger et al., 2020; Cai et al., 2023). The SPiCT can use some general guesses about the production curve and observation errors, or it can analyze the data without any guesses (Biais, 2022). Utilizing the R package spict v.1.2.8, which can be found at https://github.com/DTUAqua/spict. Statistical analyses were performed with R software (R Core Team, 2022), R studio (2022.12.0), SPSS (26) software package and a significance level of 0.05 was adopted.

Results and Discussion

The amounts (1000 t) of trend catch of Kawakawa species showed in Figure 2 and it has a positive and significant correlation with time (sig. 0.95 (0.89-0.98), P<0.05). The average catch (min-max) of this period was 22.4 (7-41), respectively, and the average catch was significantly increase for the twenty-six years (Figure 2).

 

The software used information about how many fish were caught each year and how fast the fish grow to start making models of the fish populations (initial growth 0.2-0.8 per year). It used a method called Catch-MSY, CMSY, OCOM, ZBRT, SPiCT, and Monte Carlo simulation. The starting amount of living matter was between 0. 5 to 0.9, and the ending amount was between 0. 2 to 0.6. The results from running the Monte Carlo simulation 10,000 times are shown in Table 1. The average values (95% confidence interval) of B/BMSY, F/Fmsy and maximum sustainable yield (MSY) and saturation (S=B/K=0.5B/BMSY) ratio are shown in Table 1.

Average (maximum-minimum) of maximum sustainable yield (MSY), biomass of MSY (BMSY) and current fishing mortality to fishing mortality rate of MSY (F/Fmsy) saturation (S = B/K) ratio based on Catch-MSY, CMSY, OCOM, ZBRT, SPiCT model was estimated and the mean reference points show significant difference by statistical tests (P < 0.05), but saturation (S= B/K) ratio have no significant difference (P > 0.05).

A strong and positive correlation has between the B/BMSY values in Catch-MSY, CMSY, OCOM models (P<0.05), and negative correlation with B/BMSY value in SPiCT model (Figure 4). The range of the current biomass to the biomass of MSY (B/BMSY), the proportion between MSY’s fishing fatality rate and current fishing mortality (F/Fmsy) and maximum sustainable yield (MSY) and saturation (S=B/K) ratio in different method present in Table 1 and Figure 3.

 

Table 1: Comparison of different indices of data-limited approach for Kawakawa in the Persian Gulf and Oman Sea. The range of the maximum sustainable yield (MSY), current biomass to the biomass of MSY (B/BMSY), the ratio of the current fishing mortality to fishing mortality rate of MSY (F/Fmsy) and saturation (S=B/K) ratio in different method.

Indices/models	Catch-MSY Mean (max-min)	CMSY Mean (max-min)	OCOM Mean (max-min)	ZBRT Mean (max-min)	SPiCT Mean (max-min)	Mean
MSY (1000 tonnes)	36 (16-66)	27 (24-31)	31 (27-35)	-	33 (18-60)	31±0.4
B/Bmsy	1.55(1.1-1.7)	1.40(0.8-1.97)	1.40(0.5-1.99)	1.29(0.6-1.97)	0.80(0.5-2)	1.28±0.2
F/Fmsy	-	0.28(0.2-0.39)	0.35(0.2-0.45)	-	0.94(0.3-1.2)	0.34±0.06
S=B/K	0.54	0.42	0.44	0.65	0.59	0.55±0.11

 

Table 2: Definition of fish stock status, based on indexes in the final year of a time series.

B/B0=B/K=S	B/BMSY	F/FMSY	Depletion	Stock status
≥ 0.6	≥ 1	<1	Very low	Healthy stock/under fished stock
< 0.2	0.5 – 1.0	<1	Very strong	Recovering stock
0.2 – 0.4	< 0.5	<1	Strong	Stock outside of safe biological limits
0.4 – 0.6	0.5 – 1.0	>1	Medium	Fully /overfished stock
0.2 – 0.4	0.2 – 0.5	>1	Strong	Stock outside of safe biological limits
< 0.2	< 0.2	>1	Very strong	Severely depleted stock/ Collapsed

 

 

This species has moderate flexibility (r= 0.2-0.8) in terms of intrinsic population growth rate, which affects its ability to withstand fishing pressure and recover from declining fish stocks. The intrinsic population growth rate is a critical modeling and fishing management parameter. Different flexibility classifications are assigned based on the rate values: high flexibility (r = 0.6-1.5), moderate flexibility (r = 0.2-1.0), low flexibility (r = 0.05-0.5), and very low flexibility (r < 0.015-0.1) (Martell and Froese, 2013; Froese et al., 2018; Zhou et al., 2016).

There is a strong correlation between intrinsic population growth (r) and other life history parameters, especially natural mortality (M). The quality model gives r = 1.73M for bony fish and r = 0.76M for elasmobranchs (Zhou et al., 2016). In addition, Froese et al. (2018) found that the intrinsic population growth rate (r) is approximately twice the maximum fish mortality rate (Fmsy) of the maximum sustainable catch, twice the natural mortality rate (M), 3 times the growth rate coefficient of the von Bertalanffy curve (K), 3 divided by the generation time (tgen) and nine divided through the most age (tmax).

In the present analysis, mean of amount of current biomass to biomass of maximum sustainable yield (B/BMSY) indicated that Kawakawa species is in full exploitation condition (Table 2) for catching fisheries in the Persian Gulf and Oman Sea (Iran). In addition, current level of F/Fmsy calculated as increasing trend and towards complete exploitation situation (Arrizabalaga et al., 2012). Fisheries are rated using the B/BMSY metric, with three categories: B/BMSY ≥1/2 is underexploited, 0.8-1.2 is fully exploited, 0.2-0.8 is overexploited, and <0.2 indicates collapse (Branch et al., 2011; Anderson et al., 2012).

Mean of S= B/K= B/B0 ratio (biomass relative to carrying capacity) show that this species had medium depletion or healthy stock in the Persian Gulf and Oman Sea (Iran). Based on available resources, stocks with B/K between 0.2-0.6 are fully exploited and those with B/K over 0.6 are lightly exploited.

Exploitation and population biomass impact population growth and sustainable yield biomass (Zhou et al., 2016) and our result indicating that don’t need to increase fishing efforts for Kawakawa species in the Persian Gulf and Oman Sea. Rebuilding overexploited fishery stocks takes 2-3 times a species’ life span (FAO, 2018).

All in all, the ZBRT method calculated more saturation (B/K) than the others, but the differences between these methods were not significant. It is suggested to use average of methods for less error in calculations. Estimating stock status is a first step and not a guarantee for management (Free et al., 2020). Using careful effort-based regulations and catch methods can improve B/BMSY status and reduce overfishing risks. However, this approach may result in lower yield compared to more precise determination of status (Walsh et al., 2018).

We suggest using catch-only methods as a temporary measure until more reliable options are available. Various COMs have been created to estimate stock status under data limitations; however, these models make simplifying assumptions that increase the chances of bias and uncertainty in their estimates. Using catch-only models can lead to inaccurate and biased stock status estimates, which can impede effective control efforts (Ovando et al., 2022).

FAO (1993) says that the reference points (RPs) used in fishery management, such as maximum sustainable yield, are mainly helpful for assessing individual fish populations and not very useful for highly migratory fish like tunas. Many different places catch tuna as they migrate, but they only do it for a short time each year.

Conclusions and Recommendations

According to the terms and situation mentioned conditions assessed, and considering the sustainable fishing requirements, it is not possible to increase the catch of this species in the south of the country. Results of last year in this study (mean of B/BMSY=0.94±0.16) showed that the exploitation ratio in Kawakawa stock is full fishing (full exploitation) and don’t increase in exploitation ratio and fishing effort are proposed.

Acknowledgments

We are thankful to Prof. Bahmani, Manager, Iranian Fisheries Science Research Institute (IFSRI) and also very grateful to the experts of the Offshore Fisheries Research Center (OFRC, Chabahar) for helping in the project work.

Novelty Statement

Stock assessment delivers important evidence to know the actual valuation of the fish in the specific area and the growth and development of fish species and can be beneficial for emerging actual management plans for justifiable exploitation of the fish population.

Author’s Contribution

Seyed Ahmed Reza Hashemi: Performed the experiment, conceptualization, execution and drafting of the manuscript.

Mastooreh Doustdar: Helped in writing and editing.

Asadullah Ali Muhammad: Helped in literature, format setting, and proofreading.

Reza Abbaspour Naderi: Helped in writing and editing.

Conflict of interest

The authors have declared no conflict of interest.

References

Anderson, S.C., T.A. Branch, D. Ricard and H.K. Lotze. 2012. Assessing global marine fishery status with a revised dynamic catch-based method and stock-assessment reference points. J. Mar. Sci., 1(2): 20-26.

Arrizabalaga, H., M. Murua and J. Majkowski. 2012. Global status of tuna stocks: summary sheets. Rev. Invest. Mar., AZTI-Tecnalia, 19(8): 645-676.

Biais, G., 2022. SPiCT runs for the northeast Atlantic porbeagle. Working document ICES Wkelasmo workshop.

Branch, T.A., O.P. Jensen, D. Ricard, Y. Ye and R. Hilborn. 2011. Contrasting global trends in marine fishery status obtained from catches and from stock assessments. Conserv. Biol., 25(1): 777–786. https://doi.org/10.1111/j.1523-1739.2011.01687.x

Cai, K., R. Kindong, Q. Ma and S. Tian. 2023. Stock assessment of chub mackerel (Scomber japonicus) in the northwest pacific using a multi-model approach. Fishes, 8: 80. https://doi.org/10.3390/fishes8020080

Darvishi, M., S. Behzadi and A. Salarpour. 2003. Survey population dynamic kawakawa (Euthynnus affinis) in Hormozgan Coastal waters (Persian Gulf and Oman sea). Pajouhesh and Sazandegy J., 60: 84-89.

Dick, E.J. and A.D. MacCall. 2011. Depletion-based stock reduction analysis: A catch-based method for determining sustainable yields for data-poor fish stocks. Fish. Res., 110: 331–341. https://doi.org/10.1016/j.fishres.2011.05.007

FAO, 1993. Reference points for fishery management: Their potential application to straddling and highly migratory resources. FAO Fish. Circ, 864: 52.

FAO, 2018. The state of world fisheries and aquaculture. 2018 Meeting the sustainable development goals. Rome. Licenses: CC BY-NC-SA 3.0 IGO. 227 P.

Free, C.M., O.P. Jensen, S. Anderson, N.L. Gutierrez, K., Kleisner, C. Longo, C. Minto, G. ChatoOsio and J. Walsh. 2020. Blood from a stone: Performance of catch-only methods in estimating stock biomass status. Fish. Res., 223: 1–10. https://doi.org/10.1016/j.fishres.2019.105452

Froese, R., N. Demirel, G. Coro, K.M. Kleisner and H. Winker. 2017. Estimating fisheries reference points from catch and resilience. Fish Fisheries, 18(3): 506-526. https://doi.org/10.1111/faf.12190

Froese, R., H. Winker, G. Coro, N. Demirel, A.C. Tsikliras, D. Dimarchopoulou and N. Matz-Lück. 2018. Status and rebuilding of European fisheries. Mar. Policy, 93: 159-170. https://doi.org/10.1016/j.marpol.2018.04.018

Hashemi, A., 2020. Demographic characteristics of Kawakawa (Euthynnus affinis) in Persian Gulf and Oman Sea (Hormozgan Province). J. Mar. Fishes, 4(4): 34-45.

Hashemi, S. S.A. TaghaviMotlagh and P. Kochinin. 2010. Population dynamics and assessment of Kawakawa (Euthynnus affinis) in the coasts of Hormozgan province. Iran. J. Fisheries Sci., 1(4): 84-99.

IFO, 2023. Iran fisheries organization (IFO). Bureau of Statistics; Yearbook of Fisheries Statistics. pp. 25.

Jenning, S., M. Kasier and J. Reynold. 2000. Marine fisheries ecology. Black well Science. pp. 391.

King, M., 2007. Fisheries biology, assessment and management. Fishing News Book, London, pp. 342. https://doi.org/10.1002/9781118688038

MacCall, A.D., 2009. Depletion-corrected average catch: A simple formula for estimating sustainable yields in data-poor situations. ICES J. Mar. Sci., 66(10): 2267-2271. https://doi.org/10.1093/icesjms/fsp209

Martell, S., and R. Froese. 2013. A simple method for estimating MSY from catch and resilience. Fish Fisheries, 14(4): 504–514. https://doi.org/10.1111/j.1467-2979.2012.00485.x

Mildenberger, T.K., C.W. Berg, M.W. Pedersen, A. Kokkalis and J.R. Nielsen. 2020. Time-variant productivity in biomass dynamic models on seasonal and long-term scales. ICES J. Mar. Sci., 77(1): 174-187. https://doi.org/10.1093/icesjms/fsz154

Ovando, D., C.M. Free, O.P. Jensen and R. Hilborn. 2022. A history and evaluation of catch-only stock assessment models. Fish Fisheries, 23: 616–630. https://doi.org/10.1111/faf.12637

Pedersen, M.W. and C.W. Berg. 2017. A stochastic surplus production model in continuous time. Fish and Fisheries, 18: 226-243.

R Core Team, 2022. R: A language and environment for statistical computing [online]. R Foundation for Statistical Computing, Vienna. Available from https://www.R-project.org.

Ricard, D., C. Minto, O.P. Jensen and J.K. Baum. 2012. Examining the knowledge base and status of commercially exploited marine species with the RAM legacy stock assessment database. Fish Fisheries, 13(4): 380-398. https://doi.org/10.1111/j.1467-2979.2011.00435.x

TaghaviMotlagh, S.A., S.A. Hashemi and P. Kochanian. 2009. Population biology and assessment of Kawakawa (Euthynnusaffinis) in Coastal Waters of the Persian Gulf and Sea of Oman (Hormozgan Province). Iran. J. Fish. Sci., 9(2): 315-326.

Talebzadeh, A., 1997. Survy stocks of five species scomdride in Hormozgan Costal waters (Persan Gulf and Oman sea). Institue Res. Oman Sea. pp. 155.

Walsh, J.C., C. Minto, E. Jardim, S.C. Anderson, O.P. Jensen, J. Afflerbach, M. DickeyCollas, K.M. Kleisner, C. Longo, G.C. Osio, E.R. Selig, J.T. Thorson, M.B. Rudd, K.J. apacostas, J.N. Kittinger, A.A. Rosenberg and A.B. Cooper. 2018. Trade-offs for data-limited fisheries when using harvest strategies based on catch-only models. Fish Fish. 4: e4570–17. https://doi.org/10.1111/faf.12316

Wetzel, C.R. and A.E. Punt. 2015. Evaluating the performance of data-moderate and catch-only assessment methods for U.S. west coast groundfish. Fisheries Res., 171: 170–187. https://doi.org/10.1016/j.fishres.2015.06.005

Yesaki, M., 1989. Estimates of age and growth of kawakawa (E. affinis), longtail tuna (Thunnus tonggol) and frigate tuna (Auxis thazard) from the Gulf Thailand based on length data. Indo-pac. Tuna Dev.Mgt. Programme, IPTP/89/GEN/17: 94-108.

Yesaki, M., 1994. A review of the biology and fisheries of kawakawa (Euthynnus affinis) in the Indo-Pacific region. Interactions of Pacific tuna fisheries, vol. 2: Papers on biology and fisheries, processing of the first FAO expert consultation on interactions of Pacific tuna fisheries, pp. 3-11.

Zhou, S., Z. Chen, C.M. Dichmont, A.N. Ellis, M. Haddon, A.E. Punt, A.D.M. Smith, D.C. Smith and Y. Ye. 2016. Catch-based methods for data-poor fisheries. Report to FAO. CSIRO, Brisbane, Australia. pp. 74.

Zhou, S., A.E. Punt, A.D.M. Smith, Y. Ye, M. Haddon, C.M. Dichmont and D.C. Smith. 2018. An optimized catch-only assessment method for data poor fisheries. ICES J. Mar. Sci., 75(3): 964–976. https://doi.org/10.1093/icesjms/fsx226