Journal of the Korean Society of Fisheries and Ocean Technology (수산해양기술연구)

The Korean Society of Fisheries and Ocean Technology (한국수산해양기술학회)
권호 표지
DOI QR코드

DOI QR Code

Comparative analysis of stock assessment models for analyzing potential yield of fishery resources in the West Sea, Korea

서해 어획대상 잠재생산량 추정을 위한 자원평가모델의 비교 분석

  • CHOI, Min-Je (Department of Marine & Fisheries Business and Economics, Graduate school, Pukyong National University) ;
  • KIM, Do-Hoon (Department of Marine & Fisheries Business and Economics, Pukyong National University) ;
  • CHOI, Ji-Hoon (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science)
  • 최민제 (부경대학교 대학원 해양수산경영학과) ;
  • 김도훈 (부경대학교 해양수산경영학과) ;
  • 최지훈 (국립수산과학원 연근해자원과)
  • Received : 2019.05.22
  • Accepted : 2019.07.30
  • Published : 2019.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study is aimed to compare stock assessment models depending on how the models fit to observed data. Process-error model, Observation-error model, and Bayesian state-space model for the Korean Western coast fisheries were applied for comparison. Analytical results show that there is the least error between the estimated CPUE and the observed CPUE with the Bayesian state-space model; consequently, results of the Bayesian state-space model are the most reliable. According to the Bayesian State-space model, potential yield of fishery resources in the West Sea of Korea is estimated to be 231,949 tons per year. However, the results show that the fishery resources of West Sea have been decreasing since 1967. In addition, the amounts of stock in 2013 are assessed to be only 36% of the stock biomass at MSY level. Therefore, policy efforts are needed to recover the fishery resources of West Sea of Korea.

Keywords

References

  1. Bolker BM. 2008. Ecological models and data in R. Princeton University Press, 233-242.
  2. Chaloupka M and Balazs G. 2007. Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock. Ecological modelling 205, 93-109. (DOI:10.1016/j.ecolmodel.2007.02.010).
  3. Clarke RP, Yoshimoto SS and Pooley SG. 1992. A bioeconomic analysis of the Northwestern Hawaiian Islands lobster fishery. Marine Resource Economics 7, 115-140. (DOI:10.1086/mre.7.3.42629029).
  4. de Valpine P and Hastings A. 2002. Fitting population models incorporating process noise and observation error. Ecological Monographs 72, 57-76. (DOI:10.1890/0012-9615(2002)072[0057:FPMIPN]2.0.CO;2).
  5. de Valpine P and Hilborn R. 2005. State-space likelihoods for nonlinear fisheries time-series. Canadian Journal of Fisheries and Aquatic Sciences 62, 1937-1952. (DOI:10.1139/f05-116).
  6. Fitzpatrick J. 1996. Technology and fisheries legislation. FAO Fisheries Technical Paper 350, 191-200.
  7. Fox WW. 1970. An exponential surplus-yield model for optimizing exploited fish populations. Transactions of the American Fisheries Society 99, 80-88. (DOI:10.1577/1548-8659(1970)99<80:AESMFO>2.0.CO;2).
  8. Gilks WR and Wild P. 1992. Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 337-348. (DOI:10.2307/2347565).
  9. Haddon M. 2010. Modelling and quantitative methods in fisheries. CRC press, 285-333.
  10. Hilborn R and Walters CJ. 1992. Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Reviews in Fish Biology and Fisheries 2, 177-178. (DOI:10.1007/BF00042883).
  11. Kim DH. 2013. Bayesian statistics using R and WinBUGS. Freedom Academy, 87-248.
  12. Kim HA, Seo YI, Cha HK, Kang HJ and 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 Technol 54, 38-53. (DOI:10.3796/KSFOT. 2018.54.1.038).
  13. Kwon YJ, Zhang CI, Pyo HD and Seo YI. 2013. Comparison of models for estimating surplus productions and methods for estimating their parameters. J Korean Soc Fish Ocean Technol 49, 18-28. (DOI:10.3796/KSFT.2013.49.1.018).
  14. Lunn DJ, Thomas A, Best N and Spiegelhalter D. 2000. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and computing 10, 325-337. (DOI:10.1023/A:1008929526011).
  15. McAllister MK, Pikitch EK and Babcock EA. 2001. Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding. Canadian Journal of Fisheries and Aquatic Sciences 58, 1871-1890. (DOI:10.1139/f01-114).
  16. McAllister MK and Ianelli JN. 1997. Bayesian stock assessment using catch-age data and the sampling-importance resampling algorithm. Canadian Journal of Fisheries and Aquatic Sciences 54, 284-300. https://doi.org/10.1139/f96-285
  17. Meyer R and Millar RB. 1999. BUGS in Bayesian stock assessments. Canadian Journal of Fisheries and Aquatic Sciences 56, 1078-1087. (DOI:10.1139/f99-043).
  18. Millar RB and Meyer R. 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics) 49, 327-342. (DOI:10.1111/1467-9876.00195).
  19. Neal RM. 1997. Markov chain Monte Carlo methods based onslicing' the density function. Department of Statistics, University of Toronto, Cananda, 1-27.
  20. Pella JJ and Tomlinson PK. 1969. A generalized stock production model. Inter-American Tropical Tuna Commission Bulletin 13, 416-497.
  21. Polacheck T, Hilborn R and Punt AE. 1993. Fitting surplus production models: comparing methods and measuring uncertainty. Canadian Journal of Fisheries and Aquatic Sciences 50, 2597-2607. (DOI:10.1139/f93-284).
  22. Prager MH. 1994. A suite of extensions to a nonequilibrium surplus-production model. Fish Bull 92, 374-389.
  23. Punt AE. 1990. Is B 1= K an appropriate assumption when applying an observation error production-model estimator to catch-effort data?. South African Journal of Marine Science 9, 249-259. (DOI:10.2989/025776190784378925).
  24. Pyo HD. 2002. Determining appropriate bioeconomic models for stock assessment of aquatic resources. The Journal of Fisheries Business Administration 33(2), 75-98.
  25. Robins CM, Wang YG and Die D. 1998. The impact of global positioning systems and plotters on fishing power in the northern prawn fishery, Australia. Canadian Journal of Fisheries and Aquatic Sciences 55, 1645-1651. (DOI: 10.1139/f98-037).
  26. Schaefer MB. 1954. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Inter-American Tropical Tuna Commission Bulletin 1, 23-56.
  27. Schnute J. 1977. Improved estimates from the Schaefer production model: theoretical considerations. Journal of the Fisheries Board of Canada 34, 583-603. (DOI:10.1139/f77-094).
  28. Spiegelhalter D, Thomas A, Best N, and Lunn D. 2003. WinBUGS user manual. 1-60.
  29. Winker H, Carvalho F and Kapur M. 2018. JABBA: Just Another Bayesian Biomass Assessment. Fisheries Research 204, 275-288. (DOI:10.1016/j.fishres.2018.03.010).
  30. Zhang CI, KIM SA and YOON SB. 1992. Stock assessment and management implications of small yellow croaker in Korean waters. Korean J Fish Aquat Sci 25, 282-290.