Volume 19 Issue 4
Aug.  2023
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WANG Xiao, LIU Wenjun, ZHANG Jian. Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model[J]. South China Fisheries Science, 2023, 19(4): 10-20. DOI: 10.12131/20230007
Citation: WANG Xiao, LIU Wenjun, ZHANG Jian. Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model[J]. South China Fisheries Science, 2023, 19(4): 10-20. DOI: 10.12131/20230007
shu

Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model

  • 1.

    Shanghai Kai Chuang Deep Sea Fisheries Co., Ltd., Shanghai 200082, China

  • 2.

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

  • 3.

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

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  • Received Date: January 14, 2023
  • Revised Date: February 18, 2023
  • Accepted Date: March 01, 2023
  • Available Online: March 07, 2023
    Graphical Abstract
    • Abstract
  • As a highly migratory pelagic fish, yellowfin tuna (Thunnus albacares) has high ecological and economic value. The Western and Central Pacific Ocean (WCPO) is the sea area with the highest tuna production of all oceans. In order to understand and predict the response of yellowfin tuna to climate change at different life stages in WCPO, we used the catch data of yellowfin tuna in purse seining and pelagic longlining and Oceanic Niño index (ONI) data from 1990 to 2020 in the WCPO to validate the applicability of general ARIMA (Autoregressive integrated moving average) model and dynamic ARIMA model, so as to explore the influence of the ONI on the interannual CPUE (Catch per unit effort) of yellowfin tuna. The results show that: 1) General ARIMA models could be used for long-term fitting of annual CPUE of yellowfin tuna in the WCPO, taking full account of the variability characteristics of annual CPUE of yellowfin tuna. 2) Compared with the general ARIMA model, the dynamic ARIMA model provided a better fit and a higher correlation between the fitted and true values, as well as smaller mean absolute and root mean square errors. 3) The influence of the ONI on the annual CPUE of yellowfin tuna differed between the northern and southern equatorial regions of the WCPO, with the ONI being a more critical factor and a better model fit relatively north of the equator. 4) The ONI had different impacts on the annual CPUE of yellowfin tuna in different fisheries in the WCPO, with a 1–2 years' lag in the ONI for the yellowfin tuna longline fishery in the WCPO, and a faster impact on the purse seine fishery during strong El Niño and strong La Niña events, without a lag.
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