Abstract
Projection models are commonly used to evaluate the impacts of fishing. However, previously developed projection tools were not suitable for China’s fisheries as they are either overly complex and data-demanding or too simple to reflect the realistic management measures. Herein, an intermediate-complexity projection model was developed that could adequately describe fish population dynamics and account for management measures including mesh size limits, summer closure, and spatial closure. A two-patch operating model was outlined for the projection model and applied to the heavily depleted but commercially important small yellow croaker (Larimichthys polyactis) fishery in the Haizhou Bay, China, as a case study. The model was calibrated to realistically capture the fisheries dynamics with hindcasting. Three simulation scenarios featuring different fishing intensities based on status quo and maximum sustainable yield (MSY) were proposed and evaluated with projections. Stochastic projections were additionally performed to investigate the influence of uncertainty associated with recruitment strengths and the implementation of control targets. It was found that fishing at FMSY level could effectively rebuild the depleted stock biomass, while the stock collapsed rapidly in the status quo scenario. Uncertainty in recruitment and implementation could result in variabilities in management effects; but they did not much alter the management effects of the FMSY scenario. These results indicate that the lack of science-based control targets in fishing mortality or catch limits has hindered the achievement of sustainable fisheries in China. Overall, the presented work highlights that the developed projection model can promote the understanding of the possible consequences of fishing under uncertainty and is applicable to other fisheries in China.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anderson C M, Krigbaum M J, Arostegui M C, et al. 2019. How commercial fishing effort is managed. Fish and Fisheries, 20(2): 268–285, doi: https://doi.org/10.1111/faf.12339
Brodziak J, Rago P, Conser R. 1998. A general approach for making short-term stochastic projections from an age-structured fisheries assessment model. In: Fishery Stock Assessment Models. Fairbanks, AK, USA: University of Alaska, 933–1012
Cao Ling, Chen Yong, Dong Shuanglin, et al. 2017. Opportunity for marine fisheries reform in China. Proceedings of the National Academy of Sciences of the United States of America, 114(3): 435–442, doi: https://doi.org/10.1073/pnas.1616583114
Carruthers T R, Hordyk A R. 2019. Using management strategy evaluation to establish indicators of changing fisheries. Canadian Journal of Fisheries and Aquatic Sciences, 76(9): 1653–1668, doi: https://doi.org/10.1139/cjfas-2018-0223
Chen Ning, Zhang Chongliang, Sun Ming, et al. 2018. The impact of natural mortality variations on the performance of management procedures for Spanish mackerel (Scomberomorus niphonius) in the Yellow Sea, China. Acta Oceanologica Sinica, 37(8): 21–30, doi: https://doi.org/10.1007/s13131-018-1234-0
Coro G, Large S, Magliozzi C, et al. 2016. Analysing and forecasting fisheries time series: purse seine in Indian Ocean as a case study. ICES Journal of Marine Science, 73(10): 2552–2571, doi: https://doi.org/10.1093/icesjms/fsw131
Costello C, Rassweiler A, Siegel D, et al. 2010. The value of spatial information in MPA network design. Proceedings of the National Academy of Sciences of the United States of America, 107(43): 18294–18299, doi: https://doi.org/10.1073/pnas.0908057107
De Oliveira J A A, Kell L T, Punt A E, et al. 2008. Managing without best predictions: The management strategy evaluation framework. In: Payne A, Cotter J, Potter T, eds. Advances in Fisheries Science: 50 Years on from Beverton and Holt. Oxford, UK: Wiley-Blackwell, 104–134
Dunn D C, Boustany A M, Halpin P N. 2011. Spatio-temporal management of fisheries to reduce by-catch and increase fishing selectivity. Fish and Fisheries, 12(1): 110–119, doi: https://doi.org/10.1111/j.1467-2979.2010.00388.x
Goethel D R, Legault C M, Cadrin S X. 2015. Testing the performance of a spatially explicit tag-integrated stock assessment model of yellowtail flounder (Limanda ferruginea) through simulation analysis. Canadian Journal of Fisheries and Aquatic Sciences, 72(4): 582–601, doi: https://doi.org/10.1139/cjfas-2014-0244
Goethel D R, Lucey S M, Berger A M, et al. 2019. Recent advances in management strategy evaluation: Introduction to the special issue “Under pressure: Addressing fisheries challenges with management strategy evaluation”. Canadian Journal of Fisheries and Aquatic Sciences, 76(10): 1689–1696, doi: https://doi.org/10.1139/cjfas-2019-0084
Guan Lisha, Chen Yong, Boenish R, et al. 2020. Improving data-limited stock assessment with sporadic stock index information in stock reduction analysis. Canadian Journal of Fisheries and Aquatic Sciences, 77(5): 857–868, doi: https://doi.org/10.1139/cjfas-2018-0500
Han Dongyan, Chen Yong, Zhang Chongliang, et al. 2017. Evaluating impacts of intensive shellfish aquaculture on a semi-closed marine ecosystem. Ecological Modelling, 359: 193–200, doi: https://doi.org/10.1016/j.ecolmodel.2017.05.024
Hilborn R, Amoroso R O, Anderson C M, et al. 2020. Effective fisheries management instrumental in improving fish stock status. Proceedings of the National Academy of Sciences of the United States of America, 117(4): 2218–2224, doi: https://doi.org/10.1073/pnas.1909726116
Holland D S. 2010. Management strategy evaluation and management procedures: tools for rebuilding and sustaining fisheries. OECD Food, Agriculture and Fisheries Working Papers, No. 25. Paris, France: OECD Publishing
Hoos L A, Buckel J A, Boyd J B, et al. 2019. Fisheries management in the face of uncertainty: Designing time-area closures that are effective under multiple spatial patterns of fishing effort displacement in an estuarine gill net fishery. PLoS ONE, 14(1): e0211103, doi: https://doi.org/10.1371/journal.pone.0211103
Kuparinen A, Keith D M, Hutchings J A. 2014. Increased environmentally driven recruitment variability decreases resilience to fishing and increases uncertainty of recovery. ICES Journal of Marine Science, 71(6): 1507–1514, doi: https://doi.org/10.1093/icesjms/fsu021
Lee Q, Lee A, Liu Zunlei, et al. 2020. Life history changes and fisheries assessment performance: a case study for small yellow croaker. ICES Journal of Marine Science, 77(2): 645–654, doi: https://doi.org/10.1093/icesjms/fsz232
Li Yunzhou, Sun Ming, Zhang Chongliang, et al. 2020. Evaluating fisheries conservation strategies in the socio-ecological system: A grid-based dynamic model to link spatial conservation prioritization tools with tactical fisheries management. PLoS ONE, 15(4): e0230946, doi: https://doi.org/10.1371/journal.pone.0230946
Li Yunzhou, Zhang Chongliang, Xue Ying, et al. 2019. Developing a marine protected area network with multiple objectives in China. Aquatic Conservation: Marine and Freshwater Ecosystems, 29(6): 952–963, doi: https://doi.org/10.1002/aqc.3076
Liang Cui, Xian Weiwei, Pauly D. 2018. Impacts of ocean warming on China’s fisheries catches: An application of ‘mean temperature of the catch’ concept. Frontiers in Marine Science, 5: 26, doi: https://doi.org/10.3389/fmars.2018.00026
Liu Qun, Xu Binduo, Ye Zhenjiang, et al. 2012. Growth and mortality of small yellow croaker (Larimichthys polyactis) inhabiting Haizhou bay of China. Journal of Ocean University of China, 11(4): 557–561, doi: https://doi.org/10.1007/s11802-012-2099-z
Liu Zunlei, Yuan Xingwei, Yang Linlin, et al. 2015. Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea. Chinese Journal of Applied Ecology (in Chinese), 26(2): 588–600
Ma Shuyang, Liu Yang, Li Jianchao, et al. 2019. Climate-induced long-term variations in ecosystem structure and atmosphere-ocean-ecosystem processes in the Yellow Sea and East China Sea. Progress in Oceanography, 175: 183–197, doi: https://doi.org/10.1016/j.pocean.2019.04.008
Matson S E, Taylor I G, Gertseva V V, et al. 2017. Novel catch projection model for a commercial groundfish catch shares fishery. Ecological Modelling, 349: 51–61, doi: https://doi.org/10.1016/j.ecolmodel.2017.01.023
Memarzadeh M, Britten G L, Worm B, et al. 2019. Rebuilding global fisheries under uncertainty. Proceedings of the National Academy of Sciences of the United States of America, 116(32): 15985–15990, doi: https://doi.org/10.1073/pnas.1902657116
Punt A E, A’mar T, Bond N A, et al. 2014. Fisheries management under climate and environmental uncertainty: control rules and performance simulation. ICES Journal of Marine Science, 71(8): 2208–2220, doi: https://doi.org/10.1093/icesjms/fst057
Punt A E, Butterworth D S, de Moor C L, et al. 2016. Management strategy evaluation: best practices. Fish and Fisheries, 17(2): 303–334, doi: https://doi.org/10.1111/faf.12104
Punt A E, Donovan G P. 2007. Developing management procedures that are robust to uncertainty: Lessons from the International Whaling Commission. ICES Journal of Marine Science, 64: 603–612, doi: https://doi.org/10.1093/icesjms/fsm035
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing
Rice J C, Richards L J. 1996. A framework for reducing implementation uncertainty in fisheries management. North American Journal of Fisheries Management, 16(3): 488–494, doi: https://doi.org/10.1577/1548-8675(1996)016<0488:AFFRIU>2.3.CO;2
Sethi S A. 2010. Risk management for fisheries. Fish and Fisheries, 11(4): 341–365, doi: https://doi.org/10.1111/j.1467-2979.2010.00363.x
Sethi G, Costello C, Fisher A, et al. 2005. Fishery management under multiple uncertainty. Journal of Environmental Economics and Management, 50(2): 300–318, doi: https://doi.org/10.1016/j.jeem.2004.11.005
Sguotti C, Otto S A, Frelat R, et al. 2019. Catastrophic dynamics limit Atlantic cod recovery. Proceedings of the Royal Society B: Biological Sciences, 286(1898): 20182877, doi: https://doi.org/10.1098/rspb.2018.2877
Shan Xiujuan, Li Xiansen, Yang Tao, et al. 2017. Biological responses of small yellow croaker (Larimichthys polyactis) to multiple stressors: a case study in the Yellow Sea, China. Acta Oceanologica Sinica, 36(10): 39–47, doi: https://doi.org/10.1007/s13131-017-1091-2
Shen Gongming, Heino M. 2014. An overview of marine fisheries management in China. Marine Policy, 44: 265–272, doi: https://doi.org/10.1016/j.marpol.2013.09.012
Stefansson G, Rosenberg A A. 2005. Combining control measures for more effective management of fisheries under uncertainty: Quotas, effort limitation and protected areas. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1453): 133–146, doi: https://doi.org/10.1098/rstb.2004.1579
Stow C A, Jolliff J, McGillicuddy D J Jr, et al. 2009. Skill assessment for coupled biological/physical models of marine systems. Journal of Marine Systems, 76(1–2): 4–15, doi: https://doi.org/10.1016/j.jmarsys.2008.03.011
Su Shu, Tang Yi, Chang Bowen, et al. 2020. Evolution of marine fisheries management in China from 1949 to 2019: How did China get here and where does China go next?. Fish and Fisheries, 21(2): 435–452, doi: https://doi.org/10.1111/faf.12439
Sun Ming, Li Yunzhou, Ren Yiping, et al. 2019. Developing and evaluating a management strategy evaluation framework for the Gulf of Maine cod (Gadus morhua). Ecological Modelling, 404: 27–35, doi: https://doi.org/10.1016/j.ecolmodel.2019.04.007
Sun Ming, Li Yunzhou, Ren Yiping, et al. 2020. Rebuilding depleted fisheries towards BMSY under uncertainty: harvest control rules outperform combined management measures. ICES Journal of Marine Science, 1–15
Sun Ming, Zhang Chongliang, Chen Yong, et al. 2018a. Assessing the sensitivity of data-limited methods (DLMs) to the estimation of life-history parameters from length-frequency data. Canadian Journal of Fisheries and Aquatic Sciences, 75(10): 1563–1572, doi: https://doi.org/10.1139/cjfas-2017-0325
Sun Ming, Zhang Chongliang, Li Yunzhou, et al. 2018b. Management strategy evaluation of fishery stocks in Haizhou Bay based on Data-Limited Methods. Journal of Fisheries of China (in Chinese), 42(10): 1661–1669
Worm B. 2016. Averting a global fisheries disaster. Proceedings of the National Academy of Sciences of the United States of America, 113(18): 4895–4897, doi: https://doi.org/10.1073/pnas.1604008113
Xing Lei, Zhang Chongliang, Chen Yong, et al. 2017. An individual-based model for simulating the ecosystem dynamics of Jiaozhou Bay, China. Ecological Modelling, 360: 120–131, doi: https://doi.org/10.1016/j.ecolmodel.2017.06.010
Xu Binduo, Zhang Chongliang, Xue Ying, et al. 2015. Optimization of sampling effort for a fishery-independent survey with multiple goals. Environmental Monitoring and Assessment, 187: 252, doi: https://doi.org/10.1007/s10661-015-4483-9
Zhang Chongliang, Chen Yong, Ren Yiping. 2016. An evaluation of implementing long-term MSY in ecosystem-based fisheries management: Incorporating trophic interaction, bycatch and uncertainty. Fisheries Research, 174: 179–189, doi: https://doi.org/10.1016/j.fishres.2015.10.007
Zhang Chi, Ye Zhenjiang, Wan Rong, et al. 2014. Investigating the population structure of small yellow croaker (Larimichthys polyactis) using internal and external features of otoliths. Fisheries Research, 153: 41–47, doi: https://doi.org/10.1016/j.fishres.2013.12.012
Zhang Kui, Zhang Jun, Xu Youwei, et al. 2018. Application of a catch-based method for stock assessment of three important fisheries in the East China Sea. Acta Oceanologica Sinica, 37(2): 102–109, doi: https://doi.org/10.1007/s13131-018-1173-9
Zhong Xiaming, Zhang Hu, Tang Jianhua, et al. 2011. Temporal and spatial distribution of Larimichthys polyactis Bleeker resources in offshore areas of Jiangsu Province. Journal of Fisheries of China (in Chinese), 35(2): 238–246
Acknowledgements
The authors thank Jessica Chen for polishing the English writing as a native speaker.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
The Fund of the China Scholarship Council under contract Nos 201806330043 and 201806330042; the Marine Science and Technology Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018SDKJ0501-2; the National Key Research and Development Program of China under contract Nos 2018YFD0900904 and 2018YFD0900906.
Supplementary information
Rights and permissions
About this article
Cite this article
Sun, M., Li, Y., Ren, Y. et al. Developing an intermediate-complexity projection model for China’s fisheries: A case study of small yellow croaker (Larimichthys polyactis) in the Haizhou Bay, China. Acta Oceanol. Sin. 40, 108–118 (2021). https://doi.org/10.1007/s13131-021-1793-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13131-021-1793-3