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Acta Oceanologica Sinica

, Volume 37, Issue 8, pp 21–30 | Cite as

The impact of natural mortality variations on the performance of management procedures for Spanish mackerel (Scomberomorus niphonius) in the Yellow Sea, China

  • Ning Chen
  • Chongliang Zhang
  • Ming Sun
  • Binduo Xu
  • Ying Xue
  • Yiping Ren
  • Yong Chen
Article
  • 17 Downloads

Abstract

Natural mortality rate (M) is one of the essential parameters in fishery stock assessment, however, the estimation of M is commonly rough and the changes of M due to natural and anthropogenic impacts have long been ignored. The simplification of M estimation and the influence of M variations on the assessment and management of fisheries stocks have been less well understood. This study evaluated the impacts of the changes in natural mortality of Spanish mackerel (Scomberomorus niphonius) on their management strategies with data-limited methods. We tested the performances of a variety of management procedures (MPs) with the variations of M in mackerel stock using diverse estimation methods. The results of management strategies evaluation showed that four management procedures DCAC, SPMSY, curE75 and minlenLopt1 were more robust to the changes of M than others; however, their performance were substantially influenced by the significant decrease of M from the 1970s to 2017. Relative population biomass (measure as the probability of B>0.5BMSY) increased significantly with the decrease of M, whereas the possibility of overfishing showed remarkable variations across MPs. The decrease of M had minor effects on the long-term yield of curE75 and minlenLopt1, and reduced the fluctuation of yield (measure as the probability of AAVY<15%) for DCAC, SPMSY. In general, the different methods for M estimation showed minor effects on the performance of MPs, whereas the temporal changes of M showed substantial influences. Considering the fishery status of Spanish mackerel in China, we recommended that curE75 has the best trade-off between fishery resources exploitation and conservation, and we also proposed the potentials and issues in their implementations.

Key words

fishery management uncertainty management strategy evaluation (MSE) data limited method DLMtool 

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Notes

Acknowledgements

The authors thank Ocean University of China for their generous modeling support, ideas and perspectives. The authors also thank the other members of the Ecosystem Assessment and Evaluation Laboratory for their help and the East China Sea Fisheries Research Institute for their data support.

References

  1. Brodziak J, Ianelli J, Lorenzen K, et al. 2011. Estimating natural mortality in stock assessment applications. NOAA Technical Memorandum NMFS–F/SPO–119. Washington, DC: US Department of Commerce, 38Google Scholar
  2. Butterworth D S. 2007. Why a management procedure approach? Some positives and negatives. ICES Journal of Marine Science, 64(4): 613–617CrossRefGoogle Scholar
  3. Butterworth D S, Punt A E. 1999. Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science, 56(6): 985–998CrossRefGoogle Scholar
  4. Carruthers T R, Hordyk A R. 2016. DLMtool: data–limited methods toolkit. https://cran.r–project.org/web/packages/DLMtool/index. html [2016–12–27/2017–03–11]Google Scholar
  5. Carruthers T R, Kell L T, Butterworth D D S, et al. 2015. Performance review of simple management procedures. ICES Journal of Marine Science, 73(2): 464–482CrossRefGoogle Scholar
  6. Carruthers T R, Walters C J, McAllister M K. 2012. Evaluating methods that classify fisheries stock status using only fisheries catch data. Fisheries Research, 119–120: 66–79CrossRefGoogle Scholar
  7. Cheng Qingtai, Wei Baoshan. 1987. Systematic Synopsis of Chinese Fishes (in Chinese). Beijing: Science PressGoogle Scholar
  8. Costello C, Ovando D, Hilborn R, et al. 2012. Status and solutions for the world’s unassessed fisheries. Science, 338(6106): 517–520CrossRefGoogle Scholar
  9. Deroba J J, Schueller A M. 2013. Performance of stock assessments with misspecified age–and time–varying natural mortality. Fisheries Research, 146: 27–40CrossRefGoogle Scholar
  10. Dutil J D, Lambert Y. 2000. Natural mortality from poor condition in Atlantic cod (Gadus morhua). Canadian Journal of Fisheries and Aquatic Sciences, 57(4): 826–836CrossRefGoogle Scholar
  11. Fromentin J M, Bonhommeau S, Arrizabalaga H, et al. 2014. The spectre of uncertainty in management of exploited fish stocks: the illustrative case of Atlantic Bluefin tuna. Marine Policy, 47: 8–14CrossRefGoogle Scholar
  12. Gaertner D. 2015. Indirect estimates of natural mortality rates for Atlantic skipjack (Katsuwonus pelamis), using life history parameters. Collect Vol Sci Pap ICCAT, 71(1): 189–204Google Scholar
  13. Gayanilo F C Jr, Soriano M, Pauly D. 1988. A draft guide to the complete ELEFAN. In: ICLARM Software 2. Manila, Philippines: International Center for Living Aquatic Resources ManagementGoogle Scholar
  14. Geromont H F, Butterworth D S. 2015. Generic management procedures for data–poor fisheries: forecasting with few data. ICES Journal of Marine Science, 72(1): 251–261CrossRefGoogle Scholar
  15. Gulland J A. 1971. The Fish Resources of the Ocean. West Byfleet, UK: Fishing News BooksGoogle Scholar
  16. Hamel O S. 2015. A method for calculating a meta–analytical prior for the natural mortality rate using multiple life history correlates. ICES Journal of Marine Science, 72(1): 62–69CrossRefGoogle Scholar
  17. Hoenig J M. 1983. Empirical use of longevity data to estimate mortality rates. Fish Bull, 82: 898–903Google Scholar
  18. Honey K T, Moxley J H, Fujita R M. 2010. From rags to fishes: datapoor methods for fishery managers. Managing Data–Poor Fisheries: Case Studies, Models & Solutions, 1: 159–184Google Scholar
  19. Hordyk A, Ono K, Sainsbury K, et al. 2015. Some explorations of the life history ratios to describe length composition, spawningper–recruit, and the spawning potential ratio. ICES Journal of Marine Science, 72(1): 204–216CrossRefGoogle Scholar
  20. Jiang Yiqian, Fan Yannan, Zheng Chunjing, et al. 2016. The effect of temperature on embryonic development of Scomberomorus niphonius. Journal of Zhejiang Ocean University (Natural Science), 35(4): 271–275CrossRefGoogle Scholar
  21. Johnson K F, Monnahan C C, McGilliard C R, et al. 2015. Time–varying natural mortality in fisheries stock assessment models: identifying a default approach. ICES Journal of Marine Science, 72(1): 137–150CrossRefGoogle Scholar
  22. Kenchington T J. 2014. Natural mortality estimators for informationlimited fisheries. Fish and Fisheries, 15(4): 533–562CrossRefGoogle Scholar
  23. Kokkalis A, Eikeset A M, Thygesen U H, et al. 2017. Estimating uncertainty of data limited stock assessments. ICES Journal of Marine Science, 74(1): 69–77Google Scholar
  24. Lee H H, Maunder M N, Piner K R, et al. 2011. Estimating natural mortality within a fisheries stock assessment model: an evaluation using simulation analysis based on twelve stock assessments. Fisheries Research, 109(1): 89–94CrossRefGoogle Scholar
  25. Lin Qun, Wang Jun, Yuan Wei, et al. 2016. Effects of fishing and environmental change on the ecosystem of the Bohai Sea. Journal of Fishery Sciences of China, 23(3): 619–629Google Scholar
  26. Liu Chanxin, Zhang Xu, Yang Kaiwen. 1982. Studies on the growth of Spanish mackerel, Scomberomorus niphonius in the Huanghai sea and Bohai sea. Oceanologia et Limnologia Sinica, 13(2): 170–178Google Scholar
  27. MacCall A D. 2009. Depletion–corrected average catch: a simple formula for estimating sustainable yields in data–poor situations. ICES Journal of Marine Science, 66(10): 2267–2271CrossRefGoogle Scholar
  28. Martell S, Froese R. 2013. A simple method for estimating MSY from catch and resilience. Fish and Fisheries, 14(4): 504–514CrossRefGoogle Scholar
  29. Maunder M N. 2014. Management strategy evaluation (MSE) implementation in stock synthesis: application to pacific Bluefin tuna. IATTC Stock Assessment Report 15. La Jolla: Inter–American Tropical Tuna Commission, 100–117Google Scholar
  30. Pauly D. 1980. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. ICES Journal of Marine Science, 39(2): 175–192CrossRefGoogle Scholar
  31. Powers J E. 2014. Age–specific natural mortality rates in stock assessments: size–based vs. density–dependent. ICES Journal of Marine Science, 71(7): 1629–1637CrossRefGoogle Scholar
  32. Prince J, Hordyk A, Valencia S R, et al. 2015. Revisiting the concept of Beverton–Holt life–history invariants with the aim of informing data–poor fisheries assessment. ICES Journal of Marine Science, 72(1): 194–203CrossRefGoogle Scholar
  33. Qiu Shengyao, Ye Maozhong. 1996. Studies on the reproductive biology of Scomberomorus niphonius in the Yellow sea and Bohai sea. Oceanologia et Limnologia Sinica, 27(5): 463–470Google Scholar
  34. Quiroz J C, Wiff R, Caneco B. 2010. Incorporating uncertainty into estimation of natural mortality for two species of Rajidae fished in Chile. Fisheries Research, 102(3): 297–304CrossRefGoogle Scholar
  35. R Development Core Team. 2016. R: a language and environment for statistical computing. Vienna, Austria: The R Foundation for Statistical Computing. http://www.R–project.org [2016–06–21/2016–09–25]Google Scholar
  36. Shui Bonian, Han Zhiqiang, Gao Tianxiang, et al. 2009. Mitochondrial DNA variation in the East China sea and Yellow sea populations of Japanese Spanish mackerel scomberomorus niphonius. Fisheries Science, 75(3): 593–600CrossRefGoogle Scholar
  37. Song Chao, Wang Yutan, Liu Zunlei, et al. 2016. Relationship between environmental factors and distribution of Scomberomorus niphonius eggs, larvae, and juveniles in Xiangshan Bay. Journal of Fishery Sciences of China, 23(5): 1197–1204Google Scholar
  38. Suda M, Akamine T, Kishida T. 2005. Influence of environment factors, interspecific–relationships and fishing mortality on the s t o c k f l u c t u a t i o n o f t h e J a p a n e s e s a r d i n e, S a r d i n o p s melanostictus, off the Pacific coast of japan. Fisheries Research, 76(3): 368–378CrossRefGoogle Scholar
  39. Sun Benxiao. 2009. The current situation and conservation of Scomberomorus niphonius in Yellow sea and Bohai bay (in Chinese)[dissertation]. Beijing: Chinese Academy of Agricultural SciencesGoogle Scholar
  40. Sun Jiting, Lu Kun. 2016. Effect evaluation and implementation adjustment of “Double Control” system of Chinese marine fishing. Fujian Tribune, (11): 49–55Google Scholar
  41. The Ministry of Agriculture Fishery and Fishery Administration. 2016. Chinese Fisheries Statistical Yearbook (in Chinese). Beijing: China Agriculture PressGoogle Scholar
  42. Then A Y, Hoenig J M, Hall N G, et al. 2015. Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species. ICES Journal of Marine Science, 72(1): 82–92CrossRefGoogle Scholar
  43. Uriarte A, Ibaibarriaga L, Pawlowski L, et al. 2016. Assessing natural mortality of bay of biscay anchovy from survey population and biomass estimates. Canadian Journal of Fisheries and Aquatic Sciences, 73(2): 216–234CrossRefGoogle Scholar
  44. Whitlock R E, McAllister M K, Block B A. 2012. Estimating fishing and natural mortality rates for Pacific Bluefin tuna (Thunnus orientalis) using electronic tagging data. Fisheries Research, 119–120: 115–127CrossRefGoogle Scholar
  45. Windsland K. 2015. Total and natural mortality of red king crab (Paralithodes camtschaticus) in Norwegian waters: catch–curve analysis and indirect estimation methods. ICES Journal of Marine Science, 72(2): 642–650CrossRefGoogle Scholar
  46. Yan Liping, Liu Zunlei, Zhang Hui, et al. 2014. On the evolution of biological characteristics and resources of small yellow croaker. Marine Fisheries, 36(6): 481–488Google Scholar
  47. You Zongbo. 2014. The selectivity of the big mesh gillnet for Scomberomorus niphonius (in Chinese) [dissertation]. Shanghai: Shanghai Ocean UniversityGoogle Scholar
  48. Zheng Yuanjia, Li Jiansheng, Zhang Qiyong, et al. 2014. Research progresses of resource biology of important marine pelagic food fishes in China. Journal of Fisheries of China, 38(1): 149–160Google Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ning Chen
    • 1
  • Chongliang Zhang
    • 1
  • Ming Sun
    • 1
  • Binduo Xu
    • 1
  • Ying Xue
    • 1
  • Yiping Ren
    • 1
    • 2
  • Yong Chen
    • 3
  1. 1.College of FisheriesOcean University of ChinaQingdaoChina
  2. 2.Laboratory for Marine Fisheries Science and Food Production ProcessesQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.School of Marine SciencesUniversity of MaineOronoUSA

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