The impact of environmental factors on stock-recruitment relationship of spotted mackerel-based on Bayesian model averaging method

Zhang Chang; Chen Xinjun

doi:10.3969/j.issn.0253-4193.2019.02.009

Article Contents

Article Navigation > Haiyang Xuebao > 2019 > 41(2): 99-106

Zhang Chang, Chen Xinjun. The impact of environmental factors on stock-recruitment relationship of spotted mackerel-based on Bayesian model averaging method[J]. Haiyang Xuebao, 2019, 41(2): 99-106. doi: 10.3969/j.issn.0253-4193.2019.02.009

Citation:

Zhang Chang, Chen Xinjun. The impact of environmental factors on stock-recruitment relationship of spotted mackerel-based on Bayesian model averaging method[J]. Haiyang Xuebao, 2019, 41(2): 99-106. doi: 10.3969/j.issn.0253-4193.2019.02.009

Citation:

PDF( 2156 KB)

The impact of environmental factors on stock-recruitment relationship of spotted mackerel-based on Bayesian model averaging method

doi: 10.3969/j.issn.0253-4193.2019.02.009

Zhang Chang¹,
Chen Xinjun^2
,

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology(Qingdao), Qingdao 266237, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Ocean Fisheries Exploitation, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

Received Date: 2018-01-21
Rev Recd Date: 2018-05-17

Abstract

Abstract

Spotted mackerel (Scomber australasicus) is an important pelagic and economic species in northwest Pacific. The life cycle is relatively short, and the stock resources is obviously affected by the recruitment. It is important to know the recruitment of spotted mackerel for the stock resources and sustainable utilization. The data include 6 factors which are sea surface temperature of first spawning ground (SST1), sea surface temperature of second spawning ground(SST2), sea surface temperature of feeding ground (SST3), tidal range (TR), Pacific decadal oscillation(PDO) and spawning stock biomass(SSB), all factors and recruitment are used to build the forecasting model. The Bayesian model averaging (BMA) not only give us the importance of the factors in explaining the recruitment, but also can predict the recruitment. The result shows the spawning stock biomass is most important factor for recruitment with long and stable explanatory ability, the second is SST3, which may be due to the fact that supplemental groups lived longer in the feeding grounds and the temperature of the feeding grounds had a greater impact on the growth and survival of larvae or eggs. Meanwhile, PDO, TR, SST2 and SST1 are also important for recruitment models. The forecast, based on BMA which takes the advantages of each model into consideration, is better than single models, and can be used for the recruitment prediction of spotted mackerel.
- Scomber australasicus,
- the Pacific stock,
- Bayesian model averaging,
- feeding ground,
- spawning ground

FullText(HTML)

References(37)

References

Collette B, Acero A, Canales R, et al. Scomber australasicus. The IUCN red list of threatened species[EB/OL].[2017-10-12]. http://www.iucnredlist.org/details/170329/0.

Yukami R, Ohshimo S, Yoda M, et al. Estimation of the spawning grounds of chub mackerel Scomber japonicus and spotted mackerel Scomber australasicus in the East China Sea based on catch statistics and biometric data[J]. Fisheries Science, 2009, 75(1):167-174.

程娇. 西北太平洋两种鲐属鱼类的分子系统地理学研究[D]. 青岛:中国海洋大学, 2013. Cheng Jiao. Molecular phylogeography of Two Scomber species in northwestern Pacific[D]. Qingdao:Ocean University of China, 2013.

Ryuga U, Watanabe C, Uemura Y, et al. Stock assessment and evaluation for the Pacific stock of spotted mackerel[R]. Japan:the National Research Institute of Fisheries Agency, 2015:16-20.

严利平, 李建生, 唐敏, 等. 应用体长结构VPA评估东海群系澳洲鲐资源量[J]. 中国水产科学, 2007, 14(Z1):97-102. Yan Liping, Li Jiansheng, Tang Min, et al. Assessment of Scomber australasicus biomass in the East China Sea community by Length-Structure VPA[J]. Journal of Fishery Sciences of China, 2007, 14(Z1):97-102.

詹秉义. 渔业资源评估[M]. 北京:中国农业出版社, 1995. Zhan Bingyi. Fish Stock Assessment[M]. Beijing:Agricultural Sciences in China, 1995.

Mackenzie B R, Horbowy J, Köster F W. Incorporating environmental variability in stock assessment:predicting recruitment, spawner biomass, and landings of sprat (Sprattus sprattus) in the Baltic Sea[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2007, 65(7):1334-1341.

郑芳, 刘群, 王艳君. 环境因子对黄海鳀鱼亲体-补充量关系影响的初步研究[J]. 南方水产, 2008, 4(2):15-20. Zheng Fang, Liu Qun, Wang Yanjun. Study of impacts of environmental factors on stock and recruitment relationship of the anchovy stock in the Yellow Sea[J]. South China Fisheries Science, 2008, 4(2):15-20.

张辉, 袁兴伟, 程家骅. 东海区小黄鱼繁殖模型优化选择及其管理应用研究[J]. 中国水产科学, 2010, 17(6):1300-1308. Zhang Hui, Yuan Xingwei, Cheng Jiahua. Optimizing selection and application of reproduction model of small yellow croaker in the East China Sea[J]. Journal of Fishery Sciences of China, 2010, 17(6):1300-1308.

Draper D. Assessment and propagation of model uncertainty[J]. Journal of the Royal Statistical Society:Series B (Methodological), 1995, 57(1):45-97.

Hodges J S. Uncertainty, policy analysis and statistics[J]. Statistical Science, 1987, 2(3):259-275.

Graefe A, Küchenhoff H, Stierle V, et al. Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems[C]//Combining forecasts:evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems. Glasgow:European Consortium for Political Research General Conference University of Glasgow, 2014:1-12.

Brodziak J, Legault C M. Model averaging to estimate rebuilding targets for overfished stocks[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2005, 62(3):544-562.

Posada D, Buckley T R. Model selection and model averaging in phylogenetics:advantages of akaike information criterion and Bayesian approaches over likelihood ratio tests[J]. Systematic Biology, 2004, 53(5):793-808.

Jacobson T, Karlsson S. Finding good predictors for inflation:a Bayesian model averaging approach[J]. Journal of Forecasting, 2002, 23(7):479-496.

Ryuga U, Watanabe C, Uemura Y, et al. Stock assessment and evaluation for the Pacific stock of spotted mackerel[R]. Japan:the National Research Institute of Fisheries Agency, 2016:4-13.

NOAA. VPA/ADAPT Version 3.0 reference manual[CP/OL].[2014-08-03]. http://nft.nefsc.noaa.gov/VPA.html.

陈新军, 曹杰, 田思泉, 等. 表温和黑潮年间变化对西北太平洋柔鱼渔场分布的影响[J]. 大连水产学院学报, 2010, 25(2):119-126. Chen Xinjun, Cao Jie, Tian Siquan, et al. Effect of inter-annual change in sea surface water temperature and Kuroshio on fishing ground of squid Ommastrephes bartramii in the Northwest Pacific[J]. Journal of Dalian Fisheries University, 2010, 25(2):119-126.

Espino M. The Jack mackerel Trachurus murphyi and the environmental macro-scale variables[J]. Revista Peruana de Biología, 2013, 20(1):9-20.

Megrey B A, Hare J A, Stockhausen W T, et al. A cross-ecosystem comparison of spatial and temporal patterns of covariation in the recruitment of functionally analogous fish stocks[J]. Progress in Oceanography, 2009, 81(1/4):63-92.

吕俊梅, 琚建华, 张庆云, 等. 太平洋海温场两种不同时间尺度气候模态的分析[J]. 海洋学报, 2005, 27(5):30-37. Lv Junmei, Ju Jianhua, Zhang Qingyun, et al. The analysis of two climate patterns on different time scales in Pacific sea temperature fields[J]. Haiyang Xuebao, 2005, 27(5):30-37.

王亮. 贝叶斯模型平均方法研究综述与展望[J]. 技术经济与管理研究, 2016(3):19-23. Wang Liang. Overview and prospect of Bayesian model averaging[J]. Technoeconomics & Management Research, 2016(3):19-23.

Zeugner S. Bayesian Model Averaging with BMS. R-Package BMS[CP/OL].[2011-05-05]. http://bms.zeugner.eu.

Amini S, Parmeter C F. Bayesian model averaging in R[J]. Journal of Economic and Social Measurement, 2011, 36(4):253-287.

石琳, 刘洋. 贝叶斯模型平均法在流域组合预报中的应用[J]. 水利科技与经济, 2014, 20(12):62-67. Shi Lin, Liu Yang. Applications the BMA method in watershed combination forecast[J]. Water Conservancy Science and Technology and Economy, 2014, 20(12):62-67.

Wright J H. Forecasting U.S. inflation by Bayesian model averaging[J]. Journal of Forecasting, 2003, 28(2):131-144.

刘尊雷, 袁兴伟, 杨林林, 等. 亲体量和环境对东海小黄鱼补充成功率的影响[J]. 应用生态学报, 2015, 26(2):588-600. Liu Zunlei, Yuan Xingwei, Yang Linlin, et al. Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea[J]. Chinese Journal of Applied Ecology, 2015, 26(2):588-600.

李建生, 严利平, 程家骅. 2006年夏秋季东海群系澳洲鲐数量分布特征[J]. 海洋渔业, 2008, 30(1):49-55. Li Jiansheng, Yan Liping, Cheng Jiahua. On the quantitative distribution characteristics of Scomber australasicus colony in the East China Sea during summer and autumn 2006[J]. Marine Fisheries, 2008, 30(1):49-55.

Tanoue T, Kurata Y, Tokudome Y. On the spawning-season of the mackerel, Pneumatophorus tapeinocephalus, Bleeker in three different regions, East China Sea, Satsunan and Izu[J]. Nihon Suisan Gakkaishi, 1960, 26(3):277-283.

梁思梅. 台湾北部海域春季鲭鱼仔鱼之日龄与成长[D]. 台北:国立台湾大学, 2004. Liang Simei. Age and growth of spring Scomber larvae in the northern waters of Taiwan[D]. Taipei:National Taiwan University, 2004.

Takahashi M, Watanabe Y. Developmental and growth rates of Japanese anchovy Engraulis japonicus during metamorphosis in the Kuroshio-Oyashio transitional waters[J]. Marine Ecology Progress Series, 2004, 282:253-260.

Sinclair M, Tremblay M J. Timing of spawning of atlantic herring (Clupea harengus harengus) populations and the match-mismatch theory[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1984, 41(7):1055-1065.

Kamimura Y, Takahashi M, Yamashita N, et al. Larval and juvenile growth of chub mackerel Scomber japonicus in relation to recruitment in the western North Pacific[J]. Fisheries Science, 2015, 81(3):505-513.

Sassa C, Tsukamoto Y. Distribution and growth of Scomber japonicus and S. australasicus larvae in the southern East China Sea in response to oceanographic conditions[J]. Marine Ecology Progress Series, 2010, 419:185-199.

王闪闪. 黑潮、厄尔尼诺-南方涛动和太平洋年代际涛动的相互联系及对气候影晌的研究[D]. 兰州:兰州大学, 2015. Wang Shanshan. The interaction of the Kuroshio, PDO and ENSO and their impacts on climate[D]. Lanzhou:Lanzhou University, 2015.

Zwolinski J P, Demer D A. Environmental and parental control of Pacific sardine (Sardinops sagax) recruitment[J]. ICES Journal of Marine Science, 2014, 71(8):2198-2207.

余为. 西北太平洋柔鱼冬春生群对气候与环境变化的响应机制研究[D]. 上海:上海海洋大学, 2016. Yu Wei. Response mechanism of winter-spring cohort of neon flying squid to the climatic and environmental variability in the Northwest Pacific Ocean[D]. Shanghai:Shanghai Ocean University, 2016.

Relative Articles

Supplements(0)

Cited By

Proportional views