The effect of climate oscillations on skipjack tuna (Katsuwonus pelamis) in the Indian Ocean

被引:3
作者
Liu, Shigang [1 ]
Zhang, Liyan [1 ,2 ]
Wang, Rui [1 ]
Song, Puqing [1 ]
Miao, Xing [1 ]
Li, Hai [1 ]
Li, Yuan [1 ]
Lin, Longshan [1 ]
机构
[1] Minist Nat Resources, Inst Oceanog 3, Xiamen, Peoples R China
[2] Fujian Inst Oceanog, Xiamen, Peoples R China
关键词
Climatic oscillation; Indian Ocean; Gradient forest analysis; Generalized additive model; Skipjack tuna; THUNNUS-ALBACARES; VARIABILITY; POPULATION; FISHERIES; DYNAMICS; IMPACTS;
D O I
10.1016/j.jmarsys.2023.103939
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Skipjack tuna (Katsuwonus pelamis) (SKJ) is one of the most commercially important marine fish species distributed throughout the world's tropical and subtropical oceans. The Indian Ocean is a main fishing ground for SKJ, whose exploitation rate is just below the maximum sustainable yield. Therefore, SKJ stocks may potentially be significantly affected by climate change. In this study, climatic oscillation indices related to the Indian Ocean, including the Indian Ocean Dipole (IOD), the mean water temperature anomaly in the Eastern Indian Ocean (EIO) and Western Indian Ocean (WIO), the Madden-Julian Oscillation Index at 80 degrees E (MJO80) and the Arctic Oscillation Index (AOI), were associated with SKJ catch per unit effort (CPUE). This study aimed to understand the effect of these five climatic oscillations on SKJ, with the goal of optimizing the utilization of skipjack tuna in the Indian Ocean. We combined gradient forest analysis (GFA) and generalized additive models (GAMs) to evaluate the importance of each climatic index with a 0-5 year lag on the impact of SKJ CPUE and to establish an optimal prediction model. The GFA results show that MJO80 is the most important climatic index influencing SKJ CPUE, followed by MJO80_1, AOI_2, IOD_4, WIO_2, etc. The best GAM model includes MJO80, AOI_2, and WIO_2, which could be related to the recruitment and larval survival of SKJ by influencing water temperature. Meanwhile, there is a significant negative correlation between SST and SKJ CPUE in the tropical regions of the western and central Indian Ocean. Our results suggest that climate oscillations have a 0-2 year lag effect on the SKJ fishery in the Indian Ocean, which can be used to predict resource changes in SKJ over the next 2 years.
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页数:9
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