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

• Published in: Journal of oceanology and limnology Vol. 42; no. 3; pp. 972 - 987
• Main Authors: Lian, Peng, Gao, Le
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.05.2024; Springer Nature B.V; University of Chinese Academy of Sciences,Beijing 100049,China; 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
• Subjects: advection; Artificial intelligence; data collection; domain; Earth and Environmental Science; Earth Sciences; El Nino; El Nino phenomena; Enthalpy; Explainable artificial intelligence; Fisheries; Fishery data; Fishing; Fishing areas; Geographical distribution; heat; Heat content; High frequency; Information flow; Migratory species; Mixed layer; Mixed layer depth; Oceanography; Pacific Ocean; Purse seines; Research Paper; Sea surface; Sea surface temperature; Spatial analysis; Surface temperature; surface water temperature; Temperature differences; Temperature gradients; Thunnus obesus; Tuna; Pacific Ocean; information flow; bigeye tuna; central-Pacific El Niño (CPEN); ocean heat content; Argo; explainable artificial intelligence (XAI); central-Pacific El Nino(CPEN); explainable artificial intelligence(XAI)
• ISSN: 2096-5508; 2523-3521
• DOI: 10.1007/s00343-023-3051-3

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.