Ocean and Polar Research

Korea Institute of Ocean Science & Technology (한국해양과학기술원)
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Comparing Surplus Production Models for Selecting Effective Stock Assessment Model: Analyzing Potential Yield of East Sea, Republic of Korea

효과적인 자원평가모델 선정을 위한 잉여생산량모델의 비교 분석: 동해 생태계의 잠재생산량 분석을 대상으로

  • Choi, Min-Je (Department of Marine & Fisheries Business and Economics, College of Fisheries Sciences, Pukyong National University) ;
  • Kim, Do-Hoon (Department of Marine & Fisheries Business and Economics, College of Fisheries Sciences, Pukyong National University)
  • 최민제 (국립부경대학교 수산과학대학 해양수산경영학과) ;
  • 김도훈 (국립부경대학교 수산과학대학 해양수산경영학과)
  • Received : 2019.06.13
  • Accepted : 2019.09.07
  • Published : 2019.09.30
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Abstract

This study sought to find which model is most appropriate for estimating potential yield in the East Sea, Republic of Korea. For comparison purposes, the Process-error model, ASPIC model, Maximum entropy model, Observation-error model, and Bayesian state-space model were applied using data from catch amounts and total efforts of the whole catchable fishes in the East Sea. Results showed that the Bayesian state-space model was estimated to be the most reliable among the models. Potential yield of catchable species was estimated to be 227,858 tons per year. In addition, it was analyzed that the amount of fishery resources in 2016 was about 63% of the biomass that enables a fish stock to deliver the maximum sustainable yield.

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References

  1. Kim DH (2013) Bayesian statistics using R and WinBUGS. Freedom Academy, Paju, 384 p
  2. Kim HA, Seo YI, Cha HK, Kang HJ, Zhang CI (2018) A study on the estimation of potential yield for Korean west coast fisheries using the holistic production method (HPM). J Korean Soc Fish Ocean Tech 54(1):38-53 https://doi.org/10.3796/KSFOT.2018.54.1.038
  3. Nam JO, Sim SH, Kwon OM (2015) Estimating optimal harvesting production of yellow croaker caught by multiple fisheries using hamiltonian method. J Fish Bus Admin 46(2):59-74 https://doi.org/10.12939/FBA.2015.46.2.059
  4. Park CS, Lee DW, Kim ZG, Kang YJ (2000) Stock assessment and management of the hairtail, Trichiurus lepturus Linnaeus in Korean waters. J Korean Soc Fish Res 3:29-38
  5. Lim JH (2018) A comparative study on the estimation methods for the potential yield in the Korean waters of the East Sea. Ph.D. Thesis, Pukyong National University, 114 p
  6. Lim JH, Seo YI, Zhang CI (2018) A comparative study on the estimation methods for the potential yield in the Korean waters of the East Sea. J Korean Soc Fish Ocean Tech 54(2):124-137 https://doi.org/10.3796/KSFOT.2018.54.2.124
  7. Zhang CI, Kim SA, Yoon SB (1992) Stock assessment and management implications of small yellow croker in Korean waters. Kor J Fish Aquat Sci 25(4):282-290
  8. KOSIS (2018) Fishery production survey. http://kosis.kr Accessed 22 Feb 2019
  9. Bolker B (2008) Ecological models and data in R. Princeton University Press, New Jersey, 396 p
  10. Boerema L, Gulland J (1973) Stock assessment of the Peruvian anchovy (Engraulis ringens) and management of the fishery. J Fish Board Can 30(12):2226-2235 https://doi.org/10.1139/f73-351
  11. Chaloupka M, Balazs G (2007) Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock. Ecol Mmodel 205:93-109 https://doi.org/10.1016/j.ecolmodel.2007.02.010
  12. Clarke R, Yoshimoto S, Pooley S (1992) A bioeconomic analysis of the Northwestern Hawaiian Islands lobster fishery. Mar Resour Econ 7(3):115-140 https://doi.org/10.1086/mre.7.3.42629029
  13. de Valpine P, Hilborn R (2005) State-space likelihoods for nonlinear fisheries time-series. Can J Fish Aquat Sci 62(9):1937-1952 https://doi.org/10.1139/f05-116
  14. FAO (2016) The state of world fisheries and aquaculture. http://www.fao.org/3/i9540en/i9540en.pdf Accessed 27 Feb 2019
  15. FAO (2018) FishStatJ. http://www.fao.org Accessed 27 Feb 2019
  16. Fox W (1970) An exponential surplus yield model for optimizing exploited fish populations. T Am Fish Soc 99(1):80-88 https://doi.org/10.1577/1548-8659(1970)99<80:AESMFO>2.0.CO;2
  17. Haddon M (2010) Modelling and quantitative methods in fisheries. CRC press, Florida, 465 p
  18. Hilborn R, Walters C (1992) Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Rev Fish Biol Fish 2(2):177-178 https://doi.org/10.1007/BF00042883
  19. ICCAT (2008) Report of the standing committee on research and statistics (SCRS). https://www.iccat.int/com2018/ENG/PLE_104_ENG.pdf Accessed 27 Feb 2019
  20. Kery M, Schaub M (2011) Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, Cambridge, 554 p
  21. Lunn D, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10(4):325-337 https://doi.org/10.1023/A:1008929526011
  22. McAllister M (2014) A generalized Bayesian surplus production stock assessment software (BSP2). Collect Vol Sci Pap ICCAT 70(4):1725-1757
  23. McAllister M, Ianelli J (1997) Bayesian stock assessment using catch-age data and the sampling-importance resampling algorithm. Can J Fish Aquat Sci 54(2):284-300 https://doi.org/10.1139/f96-285
  24. McAllister M, Pikitch E, Babcock E (2001) Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding. Can J Fish Aquat Sci 58(9):1871-1890 https://doi.org/10.1139/f01-114
  25. McAllister M, Pikitch E, Punt A, Hilborn R (1994) A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can J Fish Aquat Sci 51(12):2673-2687 https://doi.org/10.1139/f94-267
  26. Meyer R, Millar R (1999) Bayesian stock assessment using a state-space implementation of the delay difference model. Can J Fish Aquat Sci 56(1):37-52 https://doi.org/10.1139/f98-146
  27. Millar R, Meyer R (2000a) Bayesian state-space modeling of age-structured data: fitting a model is just the beginning. Can J Fish Aquat Sci 57(1):43-50 https://doi.org/10.1139/f99-169
  28. Millar R, Meyer R (2000b) Non?linear state space modelling of fisheries biomass dynamics by using Metropolis Hastings within Gibbs sampling. J Roy Stat Soc C-App 49(3):327-342 https://doi.org/10.1111/1467-9876.00195
  29. NIFS (2016) Study on the estimation of fishing power according to the development of fishing vessels and gears. National Institute of Fisheries and Science, Gijang, 114 p
  30. Polacheck T, Hilborn R, Punt A (1993) Fitting surplus production models: comparing methods and measuring uncertainty. Can J Fish Aquat Sci 50(12):2597-2607 https://doi.org/10.1139/f93-284
  31. Punt A (1990) Is $B_1$ = K an appropriate assumption when applying an observation error production-model estimator to catch-effort data? S Afr J Marine Sci 9(1):249-259 https://doi.org/10.2989/025776190784378925
  32. Punt A, Hilborn R (1997) Fisheries stock assessment and decision analysis: the Bayesian approach. Rev Fish Biol Fisher 7(1):35-63 https://doi.org/10.1023/A:1018419207494
  33. Pyo HD (2006) A comparative analysis of surplus production models and a maximum entropy model for estimating the anchovy's stock in Korea. Stud Edu Fishe Marine Sci 18(1):19-30
  34. Schaefer M (1954) Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Sr Imter Amer Trop T 1(2):23-56
  35. Schnute J (1977) Improved estimates from the Schaefer production model: theoretical considerations. J Fish Board Can 34(5):583-603 https://doi.org/10.1139/f77-094
  36. Seo YI, Hwang KS, Cha HK, Oh TY, Jo HS, Kim BY, Lee YW (2017) Change of relative fishing power index from technological development in the offshore large powered purse seine fishery. J Kor Soc Fish Technol 53(1):12-18 https://doi.org/10.3796/KSFT.2017.53.1.012
  37. Spiegelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS user manual, version 1.4. Medical Research Council Biostatistics Unit, Cambridge, 60 p
  38. Uhler R (1980) Least squares regression estimates of the Schaefer production model: some Monte Carlo simulation results. Can J Fish Aquat Sci 37(8):1284-1294 https://doi.org/10.1139/f80-164
  39. Winker H, Carvalho F, Kapur M (2018) JABBA: just another bayesian biomass assessment. Fisher Res 204:275-288 https://doi.org/10.1016/j.fishres.2018.03.010
  40. Zhang CI, Lee JB (2001) Stock assessment and management implications of horse mackerel (Trachurus japonicus) in Korean waters, based on the relationships between recruitment and the ocean environment. Prog Oceanogr 49:513-537 https://doi.org/10.1016/S0079-6611(01)00038-6
  41. Zhang CI, Kim S, Gunderson D, Marasco R, Lee JB, Park HW, Lee JH (2009) An ecosystem-based fisheries assessment approach for Korean fisheries. Fisher Res 100(1):26-41 https://doi.org/10.1016/j.fishres.2008.12.002