The abundance index used in a tuned virtual population analysis (VPA) is usually assumed to be proportional to actual abundance. However, the actual abundance and abundance index do not always have a linear relationship. Such nonlinearity can cause biases in abundance estimates as well as retrospective biases arising from systematic differences in abundance estimates when more data are successively added. Severe retrospective biases can damage the reliability of stock assessments. In this study, we use an approach to estimate an additional parameter that controls the nonlinearity in a tuned VPA. A performance test using simulated data revealed that the tuned VPA was able to accurately estimate the nonlinearity parameter and thus yielded less biased abundance estimates and smaller retrospective biases. We also found that estimating the nonlinearity parameters was effective even under other model misspecification scenarios, such as disregarding historical increases in catchability and time-varying natural mortality. Moreover, we applied this approach to some Japanese fish stocks and evaluated its validity. We found that estimating the nonlinearity parameters in the tuned VPA enhances the reliability of fisheries stock assessments.
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Brooks EN, Legault CM (2016) Retrospective forecasting—evaluating performance of stock projection for New England groundfish stocks. Can J Fish Aquat Sci 73:1–16
Butterworth DS (1981) The value of catch-statistics-based management techniques for heavily fished pelagic stocks with special reference to the recent decline of the southwest African pilchard stock. In: Haley K (ed) Applied operations research in fishing, NATO conference series II, vol 10. Plenum, London, pp 441–464
Chen Y, Jiao Y, Sun CL, Chen X (2008) Calibrating virtual population analysis for fisheries stock assessment. Aquat Living Resour 21:89–97
Chimura Y, Yamashita Y, Tanaka H, Funamoto T (2016) Stock assessment and evaluation for Sea of Japan stock of walleye pollock (fiscal year 2015). In: Marine fisheries stock assessment and evaluation for Japanese waters (fiscal year 2015/2016), Fisheries Agency and Fisheries Research Agency of Japan, pp 337–393 (In Japanese)
Deroba JJ (2014) Evaluating consequences of adjusting fish stock assessment estimates of biomass for retrospective patterns using Mohn’s rho. N Am J Fish Manage 34:380–390
Deroba JJ, Schueller AM (2013) Performance of stock assessments with misspecified age- and time-varying natural mortality. Fish Res 146:27–40
Dunn A, Harley SJ, Doonan IJ, Bull B (2000) Calculation and interpretation of catch-per-unit-effort (CPUE) indices. NZ Fish Assess Rep 2000/1. Ministry of Fisheries, Wellington, New Zealand
Fisheries Agency and Fisheries Research Agency of Japan (2016) Marine fisheries stock assessment and evaluation for Japanese waters (fiscal year 2015/2016) (In Japanese)
Harley SJ, Myers RA, Dunn A (2001) Is catch-per-unit-effort proportional to abundance? Can J Fish Aquat Sci 58:1760–1772
Hilborn R, Walters C (1992) Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Chapman and Hall, New York
ICES (2002) Report of the Working Group on Methods on Fish Stock Assessments. ICES Headquarters, Copenhagen, Denmark, 3–7 December 2001. ICES CM 2002/D:01
ICES (2007) Report of the Working Group on Methods on Fish Stock Assessments (WGMG). Woods Hole, USA, 13–22 March 2007. ICES CM 2007/RMC:04
ICES (2008) Report of the Working Group on Methods on Fish Stock Assessments (WGMG). Woods Hole, USA, 7–16 March 2008. ICES CM 2008/RMC:03
Ichinokawa M, Okamura H (2014) Review of stock evaluation methods using VPA for fishery stocks in Japan: implementation with R. Bull Jpn Soc Fish Oceanogr 78:104–113 (in Japanese with English abstract)
Lassen H, Medley P (2001) Virtual population analysis—a practical manual for stock assessment. FAO fisheries technical paper 400
Legault CM (2009) Report of the Retrospective Working Group. January 14-16, 2008, Woods Hole, Massachusetts, US Department of Commerce, Northeast Fish Sci Cent Ref Doc 09-01
Mohn R (1999) The retrospective problem in sequential population analysis: an investigation using cod fishery and simulated data. ICES J Mar Sci 56:473–488
Mori K, Hiyama Y (2014) Stock assessment and management for walleye pollock in Japan. Fish Sci 80:161–172
Nakayama Y, Hiramatsu K (2010) Evaluation of the reliability of VPA used for stock assessment for TAC species. Nippon Suisan Gakkaishi 76:1043–1047 (In Japanese with English abstract)
Okamura H, Yamashita Y, Ichinokawa M (2017) Ridge virtual population analysis to reduce the instability of fishing mortality in the terminal year. ICES J Mar Sci 74:2427–2436
Pope JG (1972) An investigation of the accuracy of virtual population using cohort analysis. Res Bull Inst Comm Northw Atlant Fish 9:65–74
Quin TJ, Deriso RB (1999) Quantitative fish dynamics. Oxford University Press, New York
R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Watanabe C, Kamimura Y, Yukami R, Akamine T, Kishida T (2016a) Stock assessment and evaluation for the Pacific stock of Japanese sardine (fiscal year 2015). In: Marine fisheries stock assessment and evaluation for Japanese waters (fiscal year 2015/2016), Fisheries Agency and Fisheries Research Agency of Japan, pp 15–47 (In Japanese)
Watanabe C, Yukami R, Kamimura Y, Akamine T, Watari S (2016b) Stock assessment and evaluation for the Pacific stock of jack mackerel (fiscal year 2015). In: Marine fisheries stock assessment and evaluation for Japanese waters (fiscal year 2015/2016), Fisheries Agency and Fisheries Research Agency of Japan, pp 85–113 (In Japanese)
Wilberg MJ, Thorson JT, Linton BC, Berkson J (2010) Incorporating time-varying catchability into population dynamic stock assessment models. Rev Fish Sci 18:7–24
Yoda M, Kurota T, Fukuwaka M (2016) Stock assessment and evaluation for the Tsushima warm current stock of jack mackerel (fiscal year 2015). In: Marine fisheries stock assessment and evaluation for Japanese waters (fiscal year 2015/2016), Fisheries Agency and Fisheries Research Agency of Japan, pp 114–145 (In Japanese)
We appreciate the valuable comments of Drs. Hiromu Zenitani, Shota Nishijima and Sho Furuichi on our manuscript. This study was supported by the Japan Science and Technology Agency, Core Research for Evolutional Science and Technology program. Finally, we thank Dr. Yutaka Kurita and two anonymous reviewers whose constructive comments improved our manuscript.
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Hashimoto, M., Okamura, H., Ichinokawa, M. et al. Impacts of the nonlinear relationship between abundance and its index in a tuned virtual population analysis. Fish Sci 84, 335–347 (2018). https://doi.org/10.1007/s12562-017-1159-0
- Maximum likelihood method
- Retrospective bias
- Stock assessment