中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性

发布时间：2019-04-02 18:59

【摘要】：中西太平洋是世界鲣鱼围网主要作业水域。基于我国渔船2005—2009年的中西太平洋鲣鱼围网生产数据,运用空间统计方法对该水域鲣鱼资源的空间自相关性和空间异质性特征进行分析,并结合海洋环境特征分析资源分布的热点区域。(1)通过常规统计学计算获得鲣鱼资源的偏态Sk、峰态数Ku、变异值Cv、s2/m和全局空间自相关Geary c系数,发现中西太平洋鲣鱼资源总体上是以低密度区域为主,高密度区域较少;鱼类资源密度值差异较大,资源表现出强烈集聚分布,总体的空间自相关性中等偏弱。(2)通过局部空间自相关的热点分析方法计算,发现局部空间自相关性较强,存在多个在统计学上通过显著性检验的资源热点和冷点。(3)通过地统计方法研究鲣鱼资源的空间变异性特征和方向变异时,空间自相关类型上最优模型是球形模型,鲣鱼资源密度各向同性,最大相关距离1000km左右。发现空间自相关引起的差异占整个差异的50%左右,为中等强度变异;在方向性变异上,主要体现在南北向上,其该向上结构性误差占67%,而东西向结构性误差占49%。这一结果和海洋环境的南北向上结构性远好于东西向结构性有关;从各方向的分维数看,数值介于1.876—1.9之间,数值较大,空间自相关较弱。(4)以资源热点区域作为区域性渔场,结合海洋温度和叶绿素场海洋环境特征,将中西太平洋鲣鱼资源分为3个不同的局部渔场,即2个暖池渔场,1个冷舌渔场。冷舌渔场由中东太平洋赤道上升流引起,在锋面地带提供了较为丰富的初级生产力,便于鱼类获得丰富的食物;暖池渔场靠近岛屿和陆地区域,近岸上升流系统提供了丰富的初级生产力。(5)将热点分析和渔场重心方法及栖息地指数的优缺点做了对比,建议以后采用空间残差模型深入研究空间自相关问题。
[Abstract]:The Western and Central Pacific Ocean is the main operating area of skipjack seine in the world. Based on the seine production data of Chinese fishing vessels in the Western and Central Pacific Ocean from 2005 to 2009, the spatial autocorrelation and spatial heterogeneity of skipjack resources in the waters were analyzed by using the spatial statistical method. Combined with the characteristics of marine environment, the hot spots of resource distribution are analyzed. (1) the skewed Sk, peak number Cv,s2/m and the global spatial autocorrelation Geary c coefficient of skipjack fish resources are obtained by conventional statistical calculation, and the Ku, variation value and global spatial autocorrelation coefficient of skipjack fish resources are obtained. It is found that the skipjack resources in the Western and Central Pacific are dominated by low-density regions and less high-density regions. The density values of fish resources vary greatly, the resources show strong agglomeration distribution, and the overall spatial autocorrelation is moderate and weak. (2) the local spatial autocorrelation is found to be strong by the hot spot analysis method of local spatial autocorrelation, and it is found that the spatial autocorrelation of fish resources is stronger than that of local spatial autocorrelation. There are many hot spots and cold spots of resources which have passed the significance test statistically. (3) when the spatial variability and direction variation of skipjack fish resources are studied by geostatistical method, the optimal model of spatial autocorrelation type is spherical model. The density of skipjack is isotropic and the maximum correlation distance is about 1000km. It was found that the difference caused by spatial autocorrelation accounted for about 50% of the total difference, which was medium intensity variation, and the directional variation was mainly reflected in the north-south upward structural error, which accounted for 67% of the structural error, and the east-west structural error accounted for 49%. This result is related to the fact that the South-North-up structure of the marine environment is much better than the East-West structure; From the fractal dimension of each direction, the numerical value is between 1.876 and 1.9, the value is large, and the spatial autocorrelation is weak. (4) the hot spot area of resources is used as the regional fishing ground, combined with the ocean temperature and the marine environmental characteristics of chlorophyll field. The skipjack resources in the central and western Pacific Ocean were divided into three different local fishing grounds, that is, 2 warm-pool fisheries and 1 cold-tongued fishing ground. The cold tongue fishing ground is caused by the equatorial upwelling in the Middle East Pacific Ocean, which provides abundant primary productivity in the front zone and facilitates fish to get rich food. Warm pool fishing grounds are close to islands and land areas, and inshore upwelling systems provide abundant primary productivity. (5) Hot spot analysis is compared with the methods of fishing ground gravity center and habitat index, and their advantages and disadvantages are compared. It is suggested that the spatial residual model should be used to study the spatial autocorrelation problem in the future.
【作者单位】：大洋渔业资源可持续开发省部共建教育部重点实验室;上海海洋大学海洋科学学院;国家远洋渔业工程技术中心;
【基金】：国家“863”计划项目(2007AA092202) 国家自然科学基金项目(41006106) 教育部高等学校博士学科点专项科研基金新教师基金项目(20093104120005) 上海市青年科技启明星计划项目(11QA1403000) 上海市重点学科建设项目(S30702) 上海市教委创新项目(09YZ275)共同资助
【分类号】：S931.1

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