Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna

Peng LIAN; Le GAO

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Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna

Aquaculture and Fisheries | Updated：2024-10-14

- Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna
- Journal of Oceanology and Limnology Vol. 42, Issue 3, Pages: 972-987(2024)
- Affiliations：
  
  1.Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  2.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  gaole@qdio.ac.cn
- Funds：
- DOI：
  CLC：
- Received：24 March 2023，
  
  Accepted：19 April 2023，
  
  Online First：28 June 2023，
  
  Published：01 May 2024
- Accepted：
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LIAN Peng,GAO Le.Research Paper Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna[J].Journal of Oceanology and Limnology,2024,42(03):972-987.
DOI：

LIAN Peng,GAO Le.Research Paper Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna[J].Journal of Oceanology and Limnology,2024,42(03):972-987. DOI：

摘要

Abstract

Bigeye tuna

Thunnus

obesus

is an important migratory species that forages deeply

and El Niño events highly influence its distribution in the eastern Pacific Ocean. While sea surface temperature is widely recognized as the main factor affecting bigeye tuna (BET) distribution during El Niño events

the roles of different types of El Niño and subsurface oceanic signals

such as ocean heat content and mixed layer depth

remain unclear. We conducted A spatial-temporal analysis to investigate the relationship among BET distribution

El Niño events

and the underlying oceanic signals to address this knowledge gap. We used monthly purse seine fisheries data of BET in the eastern tropical Pacific Ocean (ETPO) from 1994 to 2012 and extracted the central-Pacific El Niño (CPEN) indices based on Niño 3 and Niño 4 indexes. Furthermore

we employed Explainable Artificial Intelligence (XAI) models to identify the main patterns and feature importance of the six environmental variables and used information flow analysis to determine the causality between the selected factors and BET distribution. Finally

we analyzed Argo datasets to calculate the vertical

horizontal

and zonal mean temperature differences during CPEN and normal years to clarify the oceanic thermodynamic structure differences between the two types of years. Our findings reveal that BET distribution during the CPEN years is mainly driven by advection feedback of subsurface warmer thermal signals and vertically warmer habitats in the CPEN domain area

especially in high-yield fishing areas. The high frequency of CPEN events will likely lead to the westward shift of fisheries centers.

关键词

Keywords

references

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