Volume 20 Issue 4
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LI Dongxu, ZOU Xiaorong, ZHOU Shuting. Spatio-temporal distribution of Thunnus albacares CPUE and its relationship with environmental factors in central Pacific Ocean[J]. South China Fisheries Science, 2024, 20(4): 68-76. DOI: 10.12131/20240047
Citation: LI Dongxu, ZOU Xiaorong, ZHOU Shuting. Spatio-temporal distribution of Thunnus albacares CPUE and its relationship with environmental factors in central Pacific Ocean[J]. South China Fisheries Science, 2024, 20(4): 68-76. DOI: 10.12131/20240047
shu

Spatio-temporal distribution of Thunnus albacares CPUE and its relationship with environmental factors in central Pacific Ocean

  • 1.

    College of Marine Living Resource Sciences and Management , 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/Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

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  • Received Date: March 07, 2024
  • Revised Date: April 25, 2024
  • Accepted Date: May 10, 2024
  • Available Online: May 16, 2024
    Graphical Abstract
    • Abstract

  • Yellowfin tuna (Thunnus albacares) is one of the primary target species in China's tuna longline fishing industry. In order to improve fishing efficiency and promote sustainable use of resources, based on the longline production data of T. albacares in central Pacific Ocean from 2018 to 2022, combining with the environmental factors, such as temperature at different water layers, concentration of chlorophyll a, and salinity of sea surface (SSS), we analyzed the spatio-temporal distribution of the catch perunit of effort (CPUE) of T. albacares in that sea area and its relationship with environmental factors by constructing two models (GAM and nodeGAM). The results show that the peak season of T. albacares in the central Pacific Ocean was from May to July, and the CPUE was mainly distributed in the southern equatorial Pacific, with the highest tuna catches near 5°S. Compared with GAM [Bias explained rate 29.4%, Mean-square error (MSE) 0.149], the nodeGAM showed significantly better goodness-of-fit with a bias explained rate of 49.4% (68.02% enhancement), and the MSE reduced to 0.103 (Decrease of 30.87%). Both models indicate that T0 ≥27 °C, T100 of 15−20 °C, SSS of 34.2‰−35.2‰, and CHL-a concentration of 0.1−0.25 mg·m−3 were the suitable environmental ranges for T. albacares. Compared with GAM, nodeGAM has a better fitting effect and explanatory ability. According to the effect curves of the model output, nodeGAM can better reflect the nonlinear relationship between CPUE and environmental factors.

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