Fisheries and Aquatic Sciences

The Korean Society of Fisheries and Aquatic Science (한국수산과학회)
권호 표지
DOI QR코드

DOI QR Code

A Bayesian state-space production model for Korean chub mackerel (Scomber japonicus) stock

  • Jung, Yuri (College of Fisheries Sciences, Pukyong National University) ;
  • Seo, Young Il (East Sea Fisheries Research Institute, National Institute of Fisheries Science) ;
  • Hyun, Saang-Yoon (College of Fisheries Sciences, Pukyong National University)
  • Received : 2021.02.03
  • Accepted : 2021.03.24
  • Published : 2021.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

Keywords

Acknowledgement

The National Institute of Fisheries Science provided the CPUE data, and Statistics Korea offered fishery yield data. Handling editor, Dr. Yong-Woo Lee's advice improved our manuscript.

References

  1. Aeberhard WH, Flemming JM, Nielsen A. Review of statespace models for fisheries science. Annu Rev Stat Appl. 2018;5:215-35. https://doi.org/10.1146/annurev-statistics-031017-100427
  2. Carruthers TR, McAllister MK, Taylor NG. Spatial surplus production modeling of Atlantic tunas and billfish. Ecol Appl. 2011;21:2734-55. https://doi.org/10.1890/10-2026.1
  3. Castro Hernandez JJ, Ortega Santana AT. Synopsis of biological data on the chub marckerel (Scomber japonicus Houttuyn, 1782).Rome: Food and Agriculture Organization of the United Nations; 2000.
  4. Chaloupka M, Balazs G. Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock. Ecol Model. 2007;205:93-109. https://doi.org/10.1016/j.ecolmodel.2007.02.010
  5. Cho J, Lee JS, Nam J. A study on estimating the fishery optimal production by using a bio-economic model. Busan: Korea Maritime Institute; 2009.
  6. Choi YM, Zhang CI, Kim YS, Baik CI, Park YC. Ecological characteristics and biomass of chub mackerel, Scomber japonicus Houttuyn in Korean waters. Korean Soc Fish Resour. 2004a ;7:79-89.
  7. Choi YM, Zhang CI, Lee JB, Kim JY, Cha HK. Stock assessment and management implications of chub mackerel, Scomber japonicus in Korean waters. Korean Soc Fish Resour. 2004b;6:90-100.
  8. Clarke RP, Yoshimoto SS, Pooley SG. A bioeconomic analysis of the northwestern Hawaiian Islands lobster fishery. Mar Resour Econ. 1992;7:115-40. https://doi.org/10.1086/mre.7.3.42629029
  9. de Valpine P. Frequentist analysis of hierarchical models for population dynamics and demographic data. J Ornithol. 2012;152:393-408. https://doi.org/10.1007/s10336-010-0642-5
  10. Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A, Maunder MN, et al. AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw. 2012;27:233-49. https://doi.org/10.1080/10556788.2011.597854
  11. Fox WW. An exponential surplus-yield model for optimizing exploited fish populations. Trans Am Fish Soc. 1970;99:80-8. https://doi.org/10.1577/1548-8659(1970)99<80:AESMFO>2.0.CO;2
  12. Haddon M. Modelling and quantitative methods in fisheries. 2nd ed. Boca Raton, FL: CRC Press; 2011.
  13. Hilborn R. Pretty good yield and exploited fishes. Mar Policy. 2010;34:193-6. https://doi.org/10.1016/j.marpol.2009.04.013
  14. Hilborn R, Walters CJ. Quantitative fisheries stock assessment. Boston, MA: Springer; 1992.
  15. Hiyama Y, Yoda M, Ohshimo S. Stock size fluctuations in chub mackerel (Scomber japonicus) in the East China sea and the Japan/East sea. Fish Oceanogr. 2002;11:347-53. https://doi.org/10.1046/j.1365-2419.2002.00217.x
  16. Jeong M, Nam J. Estimation of fishery resource rebuilding and economic effects on coastal gill-net fishery as a result of Korean vessel buy-back program. Ocean Polar Res. 2017;39:221-32. https://doi.org/10.4217/OPR.2017.39.3.221
  17. Kim HA, Seo YI, Cha HK, Kang HJ, Zhang CI. A study on the estimation of potential yield for Korean west coast fisheries using the holistic production method (HPM). J Korean Soc Fish Technol. 2018;54:38-53. https://doi.org/10.3796/KSFOT.2018.54.1.038
  18. Korean Ministry of Ocean and Fisheries. Management plan for total allowable catch for 11 species in 2017 [Internet]. 2017 [cited 2019 Mar 28]. URL http://www.mof.go.kr/article/view.do?menuKey=376&boardKey=10&articleKey=14439
  19. Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: automatic differentiation and Laplace approximation. J Stat Softw. 2016;70:1-21.
  20. Kwon Y, Zhang CI, Pyo HD, Seo YI. Comparison of models for estimating surplus productions and methods for estimating their parameters. J Korean Soc Fish Technol. 2013;49:18-28. https://doi.org/10.3796/KSFT.2013.49.1.018
  21. Lee HN, Kim HS. Variation of fisheries conditions of mackerel (Scomber japonicus) fishing ground for large purse seine fisheries. Bull Korean Soc Fish Technol. 2011;47:108-17. https://doi.org/10.3796/KSFT.2011.47.2.108
  22. McAllister MK, Pikitch EK, Babcock EA. 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. 2001;58:1871-90. https://doi.org/10.1139/f01-114
  23. McAllister MK, Pikitch EK, Punt AE, Hilborn R. A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can J Fish Aquat Sci. 1994;51:2673-87. https://doi.org/10.1139/f94-267
  24. Meyer R, Millar RB. BUGS in Bayesian stock assessments. Can J Fish Aquat Sci. 1999;56:1078-87. https://doi.org/10.1139/f99-043
  25. Millar RB, Meyer R. Bayesian state-space modeling of age-structured data: fitting a model is just the beginning. Can J Fish Aquat Sci. 2000a;57:43-50. https://doi.org/10.1139/f99-169
  26. Millar RB, Meyer R. Bayesian stock assessment using a nonlinear state-space model. Can J Fish Aquat Sci. 1999;56:37-52. https://doi.org/10.1139/f98-146
  27. Millar RB, Meyer R. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. J R Stat Soc Ser C Appl Stat. 2000b;49:327-42. https://doi.org/10.1111/1467-9876.00195
  28. Musick JA, Bonfil R. Management techniques for elasmobranch fisheries. Rome: Food and Agriculture Organization of the United Nations; 2005.
  29. National Research Council. Improving fish stock assessments. Washington, DC: National Academies Press; 1998.
  30. Pedersen MW, Berg CW. A stochastic surplus production model in continuous time. Fish Fish. 2017;18:226-43. https://doi.org/10.1111/faf.12174
  31. Plummer M, Best N, Cowles K, Vines K. CODA: convergence diagnosis and output analysis for MCMC. R News. 2006;6:7-11.
  32. Prager MH. A suite of extensions to a nonequilibrium surplus-production model. Fish Bull. 1994;92:374-89.
  33. Prager MH. User's guide for ASPIC suite, version 7: a stock-production model incorporating covariates and auxiliary programs. Portland, OR: Prager Consulting; 2016.
  34. Punt A. Extending production models to include process error in the population dynamics. Can J Fish Aquat Sci. 2003;60:1217-28. https://doi.org/10.1139/f03-105
  35. Quinn TJ, Deriso RB. Quantitative fish dynamics. Oxford: Oxford University Press; 1999.
  36. Ricker WE. Computation and interpretation of biological statistics of fish populations. Bull Fish Res Board Can. 1975;1-382.
  37. Schaefer MB. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Inter-Am Trop Tuna Comm. 1954;1:23-56.
  38. Schnute J. Improved estimates from the Schaefer production model: theoretical considerations. J Fish Res Board Can. 1977;34:583-603. https://doi.org/10.1139/f77-094
  39. Skaug H, Fournier D. Random effects in AD model builder, ADMB-RE user guide 11.2. admb-project.org; 2017.
  40. Walters CJ, Hilborn R. Adaptive control of fishing systems. J Fish Res Board Can. 1976;33:145-59. https://doi.org/10.1139/f76-017
  41. Walters CJ, Martell SJD. Fisheries ecology and management. Princeton, NJ: Princeton University Press; 2004.
  42. Wang Y, Zheng J, Yu C. Stock assessment of chub mackerel (Scomber japonicus) in the central East China Sea based on length data. J Mar Biol Assoc UK. 2014;94:211-17. https://doi.org/10.1017/S0025315413001434
  43. Winker H, Carvalho F, Kapur M. JABBA: just another Bayesian biomass assessment. Fish Res. 2018;204:275-88. https://doi.org/10.1016/j.fishres.2018.03.010
  44. Yoshimoto SS, Clarke RP. Comparing dynamic versions of the Schaefer and Fox production models and their application to lobster fisheries. Can J Fish Aquat Sci. 1993;50:181-89. https://doi.org/10.1139/f93-020