黄海中南部越冬鳀空间格局的年际变化
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摘要
探索式空间数据分析(ESDA)方法能揭示复杂的空间现象,诊断鱼类种群的动态空间模式,是深入了解和把握黄海中南部越冬鳀空间格局和演化规律的有效手段之一。本文基于ESDA方法,结合GIS技术,利用2002～2015年的底拖网调查数据和遥感海表温度,分析了冬季黄海中南部鳀的空间格局及动态变化,并探讨了其空间格局与水温的关系。结果表明,2002～2015年,大多数年份鳀资源密度水平相似的站位呈空间集聚分布,但空间集聚程度无年间变动规律;资源密度的高值聚集区主要分布在调查区域的中东部水域,而低值聚集区则主要分布在西部沿岸区域和北部区域。与水温关系研究表明,鳀资源密度高值聚集区分布在黄海暖流暖水舌及以东区域,而低值聚集区主要分布在冷暖水交汇区域;高高类型聚集区资源密度重心纬度与适宜等温线平均纬度变化趋势基本一致,表明高高类型聚集区在纬度方向上的移动受海水温度变化的影响。
Exploratory spatial data analysis( ESDA) can reveal complex spatial phenomenon and identify dynamic spatial pattern of fish population. It is a powerful tool to help us understand the spatial pattern and change of wintering anchovy( Engraulis japonicus) in central and southern Yellow Sea. On the basis of ESDA and GIS,this study investigated the spatial pattern and dynamic variations of wintering anchovy and its relationship with water temperature,using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea,during 2002-2015.The results indicated that the stations with similar stock density of anchovy aggregated in most years,but there wasn't change rule about the aggregating degree between years. The aggregation areas with high stock density were located in the central and eastern waters,and the aggregation areas with low stock density were located in the coastal and northern areas. The relationship between spatial pattern of wintering anchovy and water temperature showed that the highhigh areas of stock density gather at the tongue of Yellow Sea warm current and its east regions,and the low-low areas of stock density are concentrated between the converging areas of cold water and warm water. There was a good correspondence between the latitude center of high-high areas of wintering abundance and the mean latitude of the preferred temperature isotherms,suggesting that the latitudinal distribution of high-high aggregation areas is affected by water temperature.
Exploratory spatial data analysis( ESDA) can reveal complex spatial phenomenon and identify dynamic spatial pattern of fish population. It is a powerful tool to help us understand the spatial pattern and change of wintering anchovy( Engraulis japonicus) in central and southern Yellow Sea. On the basis of ESDA and GIS,this study investigated the spatial pattern and dynamic variations of wintering anchovy and its relationship with water temperature,using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea,during 2002-2015.The results indicated that the stations with similar stock density of anchovy aggregated in most years,but there wasn't change rule about the aggregating degree between years. The aggregation areas with high stock density were located in the central and eastern waters,and the aggregation areas with low stock density were located in the coastal and northern areas. The relationship between spatial pattern of wintering anchovy and water temperature showed that the highhigh areas of stock density gather at the tongue of Yellow Sea warm current and its east regions,and the low-low areas of stock density are concentrated between the converging areas of cold water and warm water. There was a good correspondence between the latitude center of high-high areas of wintering abundance and the mean latitude of the preferred temperature isotherms,suggesting that the latitudinal distribution of high-high aggregation areas is affected by water temperature.
引文
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[23]朱德山,IVERSEN A.黄、东海鳀鱼及其他经济鱼类资源声学评估的调查研究[J].海洋水产研究,1990(11):1-142,封3.
[24]陈聚法,马绍赛,王文兰.秋冬季黄东海区影响鳀鱼行动分布的三大水系变化特征[J].海洋水产研究,1997,18(1):54-59.
[25]陈新军.渔业资源与渔场学[M].北京:海洋出版社,2004.
[2] ANSELIN L,SRIDHARAN S,GHOLSTON S. Using exploratoryspatial data analysis to leverage social indicator databases:thediscovery of interesting patterns[J]. Social Indicators Research,2007,82(2):287-309.
[3]MORAN P. Notes on continuous stochastic phenomena[J]. Bi-ometrika,1950,37(1/2):17-23.
[4]JUMARS P A. Spatial autocorrelation with RUM(Remote Under-water Manipulator):vertical and horizontal structure of a bathyalbenthic community[J]. Deep Sea Research,1978,25(7):589-604.
[5]BENZIE J A H. The detection of spatial variation in widespreadmarine species:methods and bias in the analysis of populationstructure in the crown of thorns starfish(Echinodermata:Aster-oidea)[J]. Hydrobiologia,2000,420:1-14.
[6]YE X Y,WU L. Analyzing the dynamics of homicide patterns inChicago:ESDA and spatial panel approaches[J]. Applied Geog-raphy,2011,31(2):800-807.
[7]潘竟虎,张佳龙,张勇.甘肃省区域经济空间差异的ESDA-GIS分析[J].西北师范大学学报:自然科学版,2006,42(6):83-87,91.
[8]俞振宁,吴次芳.基于ESDA-GWR的浙江省土地城镇化空间特征及影响因素分析[J].中国土地科学,2016,30(3):29-36.
[9]杨奇勇,杨劲松,余世鹏,等.不同尺度下耕地土壤Cr含量的空间自相关性分析[J].应用与环境生物学报,2011,17(3):393-397.
[10]冯永玖,陈新军,杨铭霞,等.基于ESDA的西北太平洋柔鱼资源空间热点区域及其变动研究[J].生态学报,2014,34(7):1841-1850
[11]牛明香,李显森,赵庚星.黄海中南部越冬鳀鱼空间分布及其与水温年际变化的关系[J].应用生态学报,2012,23(2):552-558.
[12]金显仕,赵宪勇,孟田湘,等.黄、渤海生物资源与栖息环境[M].北京:科学出版社,2005.
[13]邱炳文,王钦敏,陈崇成,等.福建省土地利用多尺度空间自相关分析[J].自然资源学报,2007,22(2):311-321.
[14]CLIFF A D,ORD J K. Spatial processes:models and applica-tions[M]. London:Pion,1981:17.
[15]ANSELIN L. Local indicators of spatial association-LISA[J].Geographical Analysis,1995,27(2):93-115.
[16]苏奋振.海洋渔业资源时空动态研究[D].北京:中国科学院地理科学与资源研究所,2001,80-90
[17]李小恕,李继龙,贾静.基于GIS的东黄海渔场影响因子分析[J].湛江海洋大学学报,2005,25(4):44-48.
[18]赵宪勇.黄海鳀鱼种群动力学特征及其资源可持续利用[D].青岛:中国海洋大学,2006.
[19]陈云龙.黄海鳀鱼种群特征的年际变化及越冬群体的气候变化情景分析[D].青岛:中国海洋大学,2014
[20]周甦芳,樊伟,崔雪森,等.环境因子对东海区帆式张网主要渔获物渔获量影响[J].应用生态学报,2004,15(9):1637-1640.
[21]TOJO N,KRUSE G H,FUNK F C. Migration dynamics of pacificherring(Clupea pallasii)and response to spring environmentalvariability in the southeastern Bering Sea[J]. Deep Sea Re-search Part II:Topic Studies in Oceanography,2007,54(23/24/25/26):2832-2848.
[22]BRANDER K. Impacts of climate change on fisheries[J]. Jour-nal of Marine Systems,2010,79(3/4):389-402.
[23]朱德山,IVERSEN A.黄、东海鳀鱼及其他经济鱼类资源声学评估的调查研究[J].海洋水产研究,1990(11):1-142,封3.
[24]陈聚法,马绍赛,王文兰.秋冬季黄东海区影响鳀鱼行动分布的三大水系变化特征[J].海洋水产研究,1997,18(1):54-59.
[25]陈新军.渔业资源与渔场学[M].北京:海洋出版社,2004.