基于Voronoi图与空间自相关的西北太平洋柔鱼资源空间聚集特征分析

冯永玖; 杨铭霞; 陈新军

doi:10.3969/j.issn.0253-4193.2014.12.007

基于Voronoi图与空间自相关的西北太平洋柔鱼资源空间聚集特征分析

doi: 10.3969/j.issn.0253-4193.2014.12.007

1.
上海海洋大学海洋科学学院, 上海 201306;大洋渔业资源可持续开发省部共建教育部重点实验室, 上海 201306;国家远洋渔业工程技术研究中心, 上海 201306;远洋渔业协同创新中心, 上海 201306
2.
上海海洋大学海洋科学学院, 上海 201306

基金项目: 国家自然科学基金(41276156,41406146);上海市自然科学基金面上项目(13ZR1419300);教育部高等学校博士学科点专项科研基金新教师类项目(20123104120002);上海市一流学科水产学(A类)。

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出版历程
- 收稿日期: 2014-01-02
- 修回日期: 2014-03-27

Aanlyzing spatial aggregation of Ommastrephes bartramii in the Northwest Pacific Ocean based on Voronoi diagram and spatial autocorrelation

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

摘要

摘要: 以西北太平洋柔鱼Ommastrephes bartramii为例,基于2007—2010年中国鱿钓船的生产统计原始点位数据,利用Voronoi图和空间自相关方法,评估柔鱼资源的全局空间模式、局部空间聚集特征,并以空间可视化方式呈现。在渔业资源及空间分析中,高产值聚集的海域称为空间热点,而低产值聚集的海域称为空间冷点。研究表明,全局自相关统计量Moran's I和General G均指示了西北太平洋柔鱼资源的聚集分布状态。局部空间自相关显示,2007和2009年均具有2个热点和1个冷点区域,2008年具有1个热点和1个冷点区域,2010年具有1个热点和2个冷点区域,这些热冷点呈南北向或东西向分布态势。热冷点格局的叠加图显示,研究区内存在1个强热点、1个弱热点和1个强冷点,其中弱热点覆盖的区域在4年间表现为热点和冷点的交互变动。对7—11月平均海表温度和叶绿素a浓度的分析显示,热点和冷点均为中心渔场,热冷点形成的温度条件无显著差异;热冷点形成的叶绿素a浓度范围为0.2~1.1 mg/m³,其中冷点区域的浓度相对较高。
- 空间热点 /
- Voronoi图 /
- 空间自相关 /
- 柔鱼 /
- 西北太平洋
Abstract: An integrated method of Voronoi diagram and spatial autocorrelation was used to explore global spatial pattern, local spatial hot spot and its variation of fishery resources abundance of Ommastrephes bartramii in the Northwest Pacific Ocean. The O. bartramii within the boundaries from 38°N to 45°N and 150°E to 160°E from 2007 to 2010 in the Northwest Pacific Ocean was selected as the research subjects, based on the original fishing data of each fishing boat of China. Using an ArcGIS environment, the spatial aggregation patterns of O. bartramii were revealed by using the global spatial autocorrelation statistics of both Moran's I and General G, as well as mapped both spatially and visually. In the fields of fisheries resources and spatial analysis, sea areas with clustered high productivity are hot spots, whereas sea areas with clustered low productivity are cold spots. The local spatial autocorrelation statistics show that, there were 2 hot spots and 1 cold spot in both 2007 and 2009, and there were 1 hot spot and 1 cold spot in 2008, while there were 1 hot spot and 2 cold spot in 2010. These hot or cold spots were distributed along either a north-south or an east-west axis. An overlay map of the four years of hot/cold spots demonstrates that there was 1 strong hot spot, 1 weak hot spot and 1 strong cold spot across the study area. The strong hot/cold spots were always the same spots for each year, while the weak hot spot was changed its state between a hot spot and a cold spot. An analysis of the variation of spatial hot spots based on monthly mean (July to November) sea surface temperature (SST) and monthly mean (July to November) chlorophyll-a concentration (Chl a) demonstrated that, both hot spots and cold spots are central fishing grounds. There is not obvious difference of SST between the hot and cold spots, while the hot and cold spots were observed in the areas with 0.2 to 1.1 mg/m³ Chl a concentration but the Chl a concentration of a cold spot is larger than that of a hot spot.
- spatial hot spot /
- Voronoi diagram /
- spatial autocorrelation /
- Ommastrephes bartramii /
- Northwestern Pacific Ocean

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参考文献(17)

Nishida T, Chen D G. Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data[J]. Fisheries Research, 2004, 70(2/3): 265-274.

Petitgas P. Geostatistics in fisheries survey design and stock assessment: models, variances and applications[J]. Fish and Fisheries, 2001, 2(3): 231-249.

Rivoirard J, Simmonds J, Foote K G, et al. Geostatistics for Estimating Fish Abundance[M]. New York: Wiley Blackwell, 2000.

杜云艳, 周成虎, 邵全琴, 等. 东海区海洋渔业资源环境的空间聚类分析[J]. 高技术通讯, 2002(1): 91-95.

苏奋振, 张甲, 杜云艳, 等. 东海区中上层鱼类资源的时空分异[J]. 自然资源学报, 2004, 19(5): 591-596.

杨铭霞, 陈新军, 冯永玖, 等. 中小尺度下西北太平洋柔鱼资源丰度的空间变异[J]. 生态学报, 2013, 33(20): 6427-6435.

牛明香. 基于海洋遥感和GIS的黄海鳀鱼种群时空动态及对海洋环境因子的响应[D]. 济南: 山东农业大学, 2012.

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.

杨晓明, 戴小杰, 田思泉, 等. 中西太平洋鲣鱼围网渔业资源的空间热点和空间异质性分析[J]. 生态学报, 2014, 34(13): 3771-3778.

陈新军, 田思泉, 陈勇, 等. 西北太平洋柔鱼渔业生物学[M]. 北京: 科学出版社, 2011.

Longley P A, Goodchild M, Maguire D J, et al. Geographic Information Systems and Science[M]. 3rd ed. New York: Wiley, 2009.

Tian S Q, Chen Y, Chen X J, et al. Impacts of spatial scales of fisheries and environmental data on catch per unit effort standardization[J]. Marine and Freshwater Research, 2009, 60(12): 1273-1284.

Mitchell A. The ESRI Guide to GIS Analysis (Volume 2)[M].Redlands, CA: ESRI Press, 2005.

Goodchild M F. Spatial Autocorrelation(Catmog 47)[M]. Nowich,UK:Geo Books,1986.

Griffith D. Spatial Autocorrelation: A Primer. Resource Publications in Geography[M]. Washington, D.C: Association of American Geographers, 1987.

Getis A, Ord J K. The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 1992, 24(3):189-206.

Wang W Y, Zhou C H, Shao Q Q, et al. Remote sensing of sea surface temperature and chlorophyll-a: Implications for squid fisheries in the north-west pacific ocean[J]. International Journal of Remote Sensing, 2010, 31(17/18): 4515-4530.

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