中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性

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。

Hot spot analysis and spatial heterogeneity of skipjack tuna (Katsuwonus pelamis) purse seine resources in the western and central Pacific Ocean

The western and central Pacific Ocean is one of the most productive purse seine skipjack tuna (Katsuwonus pelamis) fisheries in the world. To understand the structural characteristics of skipjack tuna resources in the western and central Pacific Ocean, based on a Chinese fleet purse seine skipjack tuna catch and effort data from 2005-2009, the data were summarized by year and by yearly average for 0.5° × 0.5° areas, and the spatial autocorrelation and spatial heterogeneity of skipjack tuna resources were calculated by using a spatial statistical method. The hot spot areas of skipjack tuna resources were analyzed with marine environment factors (sea surface temperature and chlorophyll concentration) to determine the reasons for hot spot formation. The methods and conclusions are as follows. (1) Through conventional statistics calculations we obtained the skewness value, Kurtosis number, coefficient of variation, and global spatial autocorrelation Geary c index of skipjack tuna resources, and found that the density of skipjack tuna resources was low in most of the western and central Pacific, but high density areas could be found in some "hot spot". Fish resources exhibited spatial distribution differences and showed strong concentration distribution spatial patterns. As a whole, there was medium spatial autocorrelation. (2) Through hotspot analysis, we found that local spatial autocorrelation of skipjack tuna resources was strong. There was more than one "hot spot" and "cold spot" that were statistically significant. (3) When studying the spatial heterogeneity and directional variation of skipjack tuna resources by using geostatistical methods, we obtained the semivariograms parameters and best-fitting semivariogram models. The best-fitting semivariogram model was the sphere model. The average spatial correlation distance (the geostatistical range) was about 1000 km. Approximately 50% variation was explained by spatial autocorrelation, and the rate of spatial autocorrelation was moderate. In directional variation, the structure of the south-north direction was stronger than that of the east-west direction. The rate of structure variation was 67% in the south-north direction and 49% in the east-west direction. This conclusion is consistent with marine environmental structure in which spatial distribution has better regularity and structure in the south-north direction compared with the east-west direction. The fractal dimension value of different directions was from 1.876 to 1.9; therefore, spatial autocorrelation was weak. (4) This paper regards skipjack tuna resource hotspot areas as regional fishing grounds. Respectively mapped fishing grounds overlapped with sea surface temperature and chlorophyll concentration. The fishing grounds of skipjack tuna resources in the western and central Pacific Ocean were divided into two different local fishing grounds: cold-tongue and warm-pool ground. The cold-tongue fishing ground, which is caused by the Middle East Pacific equatorial upwelling, provided more abundant primary productivity in the frontal zone, and hence more food for fish. The warm-pool fishing ground is nearer to the mainland and islands where coastal upwelling system can provide primary productivity. (5) Finally, we compared the method of hot spot analysis with that of fishing ground gravity and habitat suitability index, and identified advantages and disadvantages of those methods. Future studies will involve using the space residual model to solve spatial autocorrelation problems.