渤海鳀鱼产卵场关键影响因素识别及变迁预测

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Abstract To identify the key impact factors and predict the future distribution of the spawning ground in the Bohai Sea, the geographically weighted regression (GWR) method was used to analyze the relationship between the anchovy eggs' distribution and the environmental factors. Based on the GWR model of anchovy spawning ground distribution established in this study, the future anchovy spawning ground in the Bohai Sea were predicted by considering the sea surface temperature and salinity variation trend. The results showed that the sea surface temperature, silicate concentration, salinity and water depth average regression coefficients are 1.296、-1.133、-0.374 and 0.521, which are the key factors influencing the anchovy spawning ground distribution. Under future scenario of the sea surface temperature and salinity, the area of the anchovy spawning ground will shrink in the future, the maximum percentage of shrinking area is to 47 now, especially in the northeast of the Bohai Bay. The distribution of the spawning ground will also change with the appearing of the new aggregation of the spawning ground in the Liaodong Bay. The GWR method can be used to identify the spatial non-stationarity of the variables. The results from the GWR model of the anchovy spawning ground distribution could provide scientific bases for the comprehensive ecological management of the Bohai Sea.

Key words： geographically weighted regression anchovy spawning ground prediction Bohai Sea

Received: 25 October 2019

PACS:	X145
	X174

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	Articles by authors
	YU Jin-zhen
	ZHANG Yan-wei
	BIAN Xiao-dong
	CHEN Yun-long
	ZHANG Xue-qing

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YU Jin-zhen,ZHANG Yan-wei,BIAN Xiao-dong等. Key impact factor identification and future distribution prediction of the anchovy spawning ground in the Bohai Sea[J]. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(5): 2214-2221.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2020/V40/I5/2214

[1]	李晓炜,赵建民,刘辉,等.渤黄海渔业资源三场一通道现状、问题及优化管理政策[J]. 海洋湖沼通报, 2018,164(5):149-159. Li X W, Zhao J M, Liu H, et al. Status, problems and optimized management of spawning, feeding, overwintering grounds and migration route of marine fishery resources in Bohai Sea and Yellow Sea[J]. Transactions of Oceanology and Limnology, 2018,164(5):149-159.
[2]	卞晓东,万瑞景,金显仕,等.近30年渤海鱼类种群早期补充群体群聚特性和结构更替[J]. 渔业科学进展, 2018,39(2):1-15. Bian X D, Wang R J, Jin X S, et al. Ichthyoplankton succession and assemblage structure in the Bohai Sea during the past 30years since the 1980s[J]. Progress in Fishery Sciences, 2018,39(2):1-15.
[3]	李翘楚,邹琰,张少春,等.山东省环渤海区域主要鱼类资源变化的研究[J]. 水产科学, 2015,34(10):647-651. Li Q C, Zou Y, Zhang S C, et al. Changes in main fish stocks in Bohai Sea around Shandong Province[J]. Fisheries Science, 2015,34(10):647-651.
[4]	覃文忠,王建梅,刘妙龙.地理加权回归分析空间数据的空间非平稳性[J]. 辽宁师范大学学报(自然科学版), 2005,28(4):476-479. Qin W Z, Wang J M, Liu M L. Spatial nonstationarity of geographically weighted regression analysis of spatial data[J]. Journal of Liaoning Normal University (Natural Science Edition), 2005, 28(4):476-479.
[5]	Fotheringham A S, Brunsdon C, Charlton M. Geographically weighted regression:The analysis of spatially varying relationships[M]. New York:John Wiley and Sons, 2002.
[6]	吴玉鸣,李建霞.基于地理加权回归模型的省域工业全要素生产率分析[J]. 经济地理, 2006,26(5):748-752. Wu Y M, Li J X. Analysis of China's provincial industrial total factor productivity based on geographical weighted regression model[J]. Economic Geography, 2006,26(5):748-752.
[7]	陈辉,厉青,张玉环,等.基于地理加权模型的我国冬季PM2.5遥感估算方法研究[J]. 环境科学学报, 2016,36(6):2142-2151. Chen H, Li Q, Zhang Y H, el al. Estimations of PM2.5 concentrations based on the method of geographically weighted regression[J]. Acta Scientiae Circumstantiae, 2016,36(6):2142-2151.
[8]	张海涛,杨顺华.平原丘陵过渡带土壤有机碳空间分布及环境影响[J]. 中国环境科学, 2015,35(12):3728-3736. Zhang H T, Yang S H. The spatial variability of soil organic carbon in plain-hills transition belt and its environmental impact[J]. China Environmental Science, 2015,35(12):3728-3736.
[9]	Sheehan K R, Michael P Strage, Stuart A Welsh. Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams[J]. Environmental Modeling & Assessment, 2013,18(2):209-219.
[10]	Windle M J S, Rose G A, Devillers R, et al. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR):an example from the Northwest Atlantic[J]. ICES Journal of Marine Science, 2009,67:145-154.
[11]	Tseng C T, Su N J, Sun C L, et al. Spatial and temporal variability of the Pacific saury (Cololabis saira) distribution in the northwestern Pacific Ocean[J]. ICES Journal of Marine Science, 2013,70(5):991-999.
[12]	陈广威,陈吕凤,朱国平,等.南乔治亚岛冬季南极磷虾渔场时空分布及其驱动因子[J]. 生态学杂志, 2017,36(10):2803-2810. Chen G W, Chen L F, Zhu G P, et al. Spatial-temporal distribution of fishing ground for Antarctic krill fishery in the South Georgia Island during the austral winter and its drivers[J]. Chinese Journal of Ecology, 2017,36(10):2803-2810.
[13]	Sadorus L L, Mantua N J, Essington T, et al. Distribution patterns of Pacific halibut (Hippoglossus stenolepis) in relation to environmental variables along the continental shelf waters of the US West Coast and southern British Columbia[J]. Fisheries Oceanography, 2014,23(3):225-241.
[14]	Liu C, Wan R, Jiao Y, et al. Exploring non-stationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie[J]. Marine and Freshwater Research, 2017,68(2):270-281.
[15]	赵杨,张学庆,卞晓东.基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数[J]. 应用生态学报, 2018,29(1):293-299. Zhao Y, Zhang X Q, Bian X D. Habitat suitability index of larval Japanese Halfbeak (Hyporhamphus sajori) in Bohai Sea based on geographically weighted regression[J]. Chinese Journal of Applied Ecology, 2018,29(1):293-299.
[16]	单秀娟,陈云龙,金显仕.气候变化对长江口和黄河口渔业生态系统健康的潜在影响[J]. 渔业科学进展, 2017,38(2):1-7. Shan X J, Chen Y L, Jin X S. Projecting fishery ecosystem health under climate change scenarios:Yangtze River Estuary and Yellow River Estuary[J]. Progress in Fishery Sciences, 2017,38(2):1-7.
[17]	刘尊雷,袁兴伟,杨林林,等.气候变化对东海北部外海越冬场渔业群落格局的影响[J]. 应用生态学报, 2015,26(3):901-911. Liu Z L, Yuan X W, Yang L L, et al.Effect of climate change on the fisheries community pattern in the overwintering ground of open waters of northern East China Sea[J]. Chinese Journal of Applied Ecology, 2015,26(3):901-911.
[18]	刘红红.气候变化对海洋渔业的影响与对策研究[J]. 现代农业科技, 2019,10:244-247. Liu H H. Impacts of climate change on marine fisheries and its Countermeasures[J]. Modern Agricultural Science and Technology, 2019,10:244-247.
[19]	Cai R S, Tan H J, Qi Q H. Impacts of and adaptation to inter-decadal marine climate change in coastal China seas[J]. International Journal of Climatology, 2016,36(11):3770-3780.
[20]	宋春阳,张守文,姜华,等.CMIP5模式对中国近海海表温度的模拟及预估[J]. 海洋学报, 2016,38(10):1-11. Song C Y, Zhang S W, Jiang H, et al. Evaluation and projection of SST in the China seas from CMIP5[J]. Acta Oceanologica Sinica, 2016,38(10):1-11.
[21]	谭红建,蔡榕硕,颜秀花.基于IPCC-CMIP5预估21世纪中国近海海表温度变化[J]. 应用海洋学学报, 2016,35(4):451-458. Tan H J, Cai R S, Yan X H. Projected 21st century sea surface temperature over offshore China based on IPCC-CMIP5 models[J]. Journal of Applied Oceanography, 2016,35(4):451-458.
[22]	谭红建,蔡榕硕,颜秀花.基于CMIP5预估21世纪中国近海海洋环境变化[J]. 应用海洋学学报, 2018,37(2):4-13. Tan H J, Cai R S, Yan X H. Projecting changes of marine environment in coastal China Seas over 21st century based on CMIP5 models[J]. Journal of Applied Oceanography, 2018,37(2):4-13.
[23]	Lin C, Su J, Xu B, et al. Long-term variation of temperature and salinity of the Bohai Sea and their influence on its ecosystem[J]. Progress in Oceanography, 2001,49(1):7-19.
[24]	李向心.基于个体发育的黄渤海鳀鱼种群动态模型研究[D]. 青岛:中国海洋大学, 2007. Li X X. Study on individual-based model of anchovy population dynamics in the Huanghai Sea and Bohai Sea[D]. Qingdao:Ocean University of China, 2007.
[25]	严威.异方差和多重共线性的影响——模拟蒙特卡洛[J]. 北方经贸, 2014,20(8):26-27. Yan W. The effect of heteroscedasticity and multicollinearity——a monte Carlo Simulation[J]. Northern Economy and Trade, 2014, 20(8):26-27.
[26]	刘明.一类新的多重共线性检验方法[J]. 统计与信息论坛, 2012, 27(10):14-16. Liu M. A new diagnosis approach of multicollinearity[J]. Statistic and Information Forum, 2012,27(10):14-16.
[27]	Song L, Zhou Y. Developing an integrated habitat index for bigeye tuna (Thunnus obesus) in the Indian Ocean based on longline fisheries data[J]. Fisheries Research, 2010,105(2):0-74.