Volume 19 Issue 5
Oct.  2023
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ZHENG Haohao, YANG Xiaoming, ZHU Jiangfeng. Environmental impact mechanism of skipjack tuna fishery in Western and Central Pacific Ocean based on Multi-scale Geographical Weighted Regression Model (MGWR)[J]. South China Fisheries Science, 2023, 19(5): 1-10. DOI: 10.12131/20230014
Citation: ZHENG Haohao, YANG Xiaoming, ZHU Jiangfeng. Environmental impact mechanism of skipjack tuna fishery in Western and Central Pacific Ocean based on Multi-scale Geographical Weighted Regression Model (MGWR)[J]. South China Fisheries Science, 2023, 19(5): 1-10. DOI: 10.12131/20230014
shu

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

  • 1.

    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

  • 2.

    National Engineering Research Center for Oceanic Fisheries/Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China

  • 3.

    Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs/Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

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  • Received Date: February 08, 2023
  • Revised Date: May 05, 2023
  • Accepted Date: May 11, 2023
  • Available Online: May 18, 2023
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    • 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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