基于多尺度地理加权回归模型的中西太平洋围网鲣渔获率环境影响机制研究

Environmental impact mechanism of skipjack tuna fishery in Western and Central Pacific Ocean based on Multi-scale Geographical Weighted Regression Model (MGWR)

  • 摘要: 鲣 (Katsuwonus pelamis) 是中西太平洋金枪鱼围网捕捞的重要资源,其资源分布受环境影响明显。为探索环境对鲣渔获率影响的空间异质性特征,利用中西太平洋渔业委员会 (Western and Central Pacific Fisheries Commission, WCPFC) 所公布的2005—2019年中西太平洋金枪鱼围网综合的1°×1°渔业及海洋环境数据,对标准化后的环境因子及渔获率选用多尺度地理加权回归 (Multi-scale Geographically Weighted Regression, MGWR) 方法进行研究。结果表明:1) 与传统广义加性模型 (Generalized Additive Model, GAM) 相比,考虑环境影响空间异质性问题的地理加权回归模型 (Geographically Weighted Regression, GWR) 和MGWR拟合优度 (R2) 有明显提升,校正后拟合优度 (Adjusted R2) 分别为0.273、0.846和0.871,且拟合结果的空间分布形态更符合真实情况。2) 各环境因子对鲣资源分布存在显著的空间非平稳性影响。各海洋环境因子对鲣渔获率分布影响的空间异质性程度 (各环境变量变异系数大小) 依次为水下55 m东西向海流速度 (Sea water X velocity at 55 m depth, U55)>海表面温度 (Sea surface temperature, SST)>净初级生产力 (Net primary productivity, NPP)>100 m盐度 (Sea water salinity at 100 m depth, S100)>55 m南北向海流速度 (Sea water Y velocity at 55 m depth, V55)。3) 各环境因子的影响存在明显尺度效应差异,NPP的作用尺度为44,其次为S100和U55 (均为48),SST的为54,V55为全局尺度。4) 总体上,S100、NPP、SST、V55和U55对鲣渔获率正向影响比例依次为73.5%、64.8%、66.8%、80.8%和32.3%;其中S100、NPP和SST对鲣渔获率空间分布的影响相似,具体表现为东西向差异,170°E以西主要为正向影响,170°E以东为负向影响;U55为负向影响为主的因子。

     

    Abstract: Katsuwonus pelamis is an important resource for tuna purse seine fishing in the Western and Central Pacific Ocean, and its resource distribution is significantly affected by environment. In order to explore the characteristics of spatial heterogeneity of environmental impact on tuna catch rate, we used the 1°×1° fishery and marine environmental data of the Western and Central Pacific Ocean tuna purse-seine published by the Western and Central Pacific Fisheries Commission (WCPFC) from 2005 to 2019, and investigated the standardized environmental factors and catch rates by using Multi-scale Geographically Weighted Regression (MGWR) method. The results show that: 1) Compared with the traditional Generalized Additive Model (GAM), the Geographically Weighted Regression (GWR) and MGWR with spatial heterogeneity of environmental impacts improved the fit performance significantly. 2) Significant spatial non-stationarity was found for each environmental factor on the distribution of tuna resources. The degree of spatial heterogeneity (The magnitude of the coefficient of variation) of each environmental factor on the distribution of tuna catch rate followed a descending order of Sea water X velocity at 55 m depth (U55) > Sea surface temperature (SST) > Net primary productivity (NPP) >Sea water salinity at 100 m depth (S100) > Sea water Y velocity at 55 m depth (V55). 3) The effects of the environmental factors were found to have significant scale effects. 4) Overall, the positive effects of S100, NPP, SST, V55 and U55 on the catch rate of tuna were 73.5%, 64.8%, 66.8%, 80.8% and 32.3%, respectively. The effects of S100, NPP and SST on the spatial distribution of bonito catch rate were similar, specifically in terms of east-west differences, with positive effects mainly west of 170°E and negative effects east of 170°E. U55 was the main factor with negative effects.

     

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