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Stock assessment and management of blackthroat seaperch Doederleinia seaperch using Bayesian state-space model

베이지안 State-space 모델을 이용한 눈볼대 자원평가 및 관리방안

  • CHOI, Ji Hoon (Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • KIM, Do Hoon (Department of Marine & Fisheries Business and Economics, Pukyong National University) ;
  • CHOI, Min-Je (Department of Marine & Fisheries Business and Economics, Pukyong National University) ;
  • KANG, Hee Joong (Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • SEO, Young Il (Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • LEE, Jae Bong (Fisheries Resources Research Division, National Institute of Fisheries Science)
  • 최지훈 (국립수산과학원 연근해자원과) ;
  • 김도훈 (부경대학교 해양수산경영학과) ;
  • 최민제 (부경대학교 해양수산경영학과) ;
  • 강희중 (국립수산과학원 연근해자원과) ;
  • 서영일 (국립수산과학원 연근해자원과) ;
  • 이재봉 (국립수산과학원 연근해자원과)
  • Received : 2019.03.26
  • Accepted : 2019.05.20
  • Published : 2019.05.31

Abstract

This study is aimed to take a stock assessment of blackthroat seaperch Doederleinia seaperch regarding the fishing effort of large-powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery. For the assessment, the state-space model was implemented and the standardized catch per unit effort (CPUE) of large powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery which is necessary for the model was estimated with generalized linear model (GLM). The model was adequate for stock assessment because its r-square value was 0.99 and root mean square error (RMSE) value was 0.003. According to the model with 95% confidence interval, maximum sustainable yield (MSY) of Blackthroat seaperch is from 2,634 to 6,765 ton and carrying capacity (K) is between 33,180 and 62,820. Also, the catchability coefficient (q) is between 2.14E-06 and 3.95E-06 and intrinsic growth rate (r) is between 0.31 and 0.72.

Keywords

References

  1. Bolker BM. 2008. Ecological models and data in R. Princeton University Press, 233-242.
  2. Valpine P and Hilborn R. 2005. State-space likelihoods for nonlinear fisheries time-series. Canadian Journal of Fisheries and Aquatic Sciences 62(9), 1937-1952. https://doi.org/10.1139/f05-116
  3. Fisheries information service. 2018. Retrieved from http://fips.go.kr. Accessed 25 Nov 2018.
  4. Fox and WW. 1970. An exponential surplus-yield model for optimizing exploited fish populations. Transactions of the American Fisheries Society 99(1), 80-88. https://doi.org/10.1577/1548-8659(1970)99<80:AESMFO>2.0.CO;2
  5. Gavaris S. 1980. Use of a multiplicative model to estimate catch rate and effort from commercial data. Can J Fish Aquat Sci 37(12), 2272-2275. https://doi.org/10.1139/f80-273
  6. Gilks WR and Wild P. 1992. Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 337-348.
  7. Haddon M. 2010. Modelling and quantitative methods in fisheries. CRC press, 285-333.
  8. Hilborn R and Walters CJ. 1992. Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Reviews in Fish Biology and Fisheries 2(2), 177-178. https://doi.org/10.1007/BF00042883
  9. Kim DH. 2013. Bayesian statistics using R and WinBUGS. Freedom Academy, 87-248.
  10. 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). Journal of the Korean Society of Fisheries and Ocean Technology 54(1), 38-53. (DOI:10.3796/KSFOT.2018.54.1.038).
  11. Lunn DJ, Thomas A, Best N, and Spiegelhalter D. 2000. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and computing 10(4), 325-337. https://doi.org/10.1023/A:1008929526011
  12. 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(9), 1871-1890. https://doi.org/10.1139/f01-114
  13. Meyer R and Millar RB. 1999. BUGS in Bayesian stock assessments. Canadian Journal of Fisheries and Aquatic Sciences 56(6), 1078-1087. https://doi.org/10.1139/f99-043
  14. 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(3), 327-342. https://doi.org/10.1111/1467-9876.00195
  15. NIFS (National Institute of Fisheries Science), 2017 Ecology and Fishing Ground of Fisheries Resources in Korean Waters, Busan, Korea, 77-86.
  16. Neal RM. 1997. Markov chain Monte Carlo methods based onslicing' the density function. Department of Statistics, University of Toronto, Cananda, 1-27.
  17. Pella, JJ and Tomlinson PK. 1969. A generalized stock production model. Inter-American Tropical Tuna Commission Bulletin 13(3), 416-497.
  18. Prager, MH. 1994. A suite of extensions to a nonequilibrium surplus-production model. Fish. Bull. 92, 374-389.
  19. Quinn TJ, Deriso RB. 1999, Quantitative fish dynamics. University of Oxford, 1-560.
  20. 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(7), 1645-1651. https://doi.org/10.1139/f98-037
  21. Schaefer and 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(2), 23-56.
  22. Sim SH and Nam JO. 2015. A stock assessment of YellowCroaker using Bioeconomic Model: a case of single species and multiple fisheries. Ocean Polar Res 37(2), 161-177. (DOI:10.4217/OPR.2015.37.2.161).
  23. Spiegelhalter, D, Thomas A, Best N, and Lunn D. 2003. WinBUGS user manual. 1-60.
  24. Winker, H, Carvalho F, and Kapur M. 2018. JABBA: Just Another Bayesian Biomass Assessment. Fisheries Research 204, 275-288. https://doi.org/10.1016/j.fishres.2018.03.010