As an economically critical pelagic migratory species,yellowfin tuna(Thunnus albacores,YFT)is very sensible to physical and environmental conditions,such as sea surface temperature(SST),ocean heat content(OHC),and the...As an economically critical pelagic migratory species,yellowfin tuna(Thunnus albacores,YFT)is very sensible to physical and environmental conditions,such as sea surface temperature(SST),ocean heat content(OHC),and the mixed layer depth(MLD).We investigated the impact of SST,OHC,and MLD on fluctuations of YFT catch in the western/eastern Indian Ocean using the long time series of 63-year environmental and YFT datasets.We found that the impact of SST on YFT was heavily overestimated in the past,and MLD plays a more critical role in the YFT catch fluctuation.When the MLD deepens(>34.8 m),SST was more influential in predicting the catches of YFT than OHC in the western Indian Ocean,and OHC was more critical to YFT than SST in the eastern Indian Ocean.However,when the MLD shallows(<34.8 m),MLD was more vital to predict the catch per unit effort(CPUE)of YFT than SST/OHC in the western.After 2000,there was an asynchronous pattern of YFT CPUE induced by higher frequency variations and ocean hiatus of SST/OHC signals in the western and eastern Indian Oceans basins.The impact of the subsurface hiatus may induce the decrease of YFT in the eastern Indian Ocean.The above findings clarified a non-stationary relationship between the environmental factors and catches of YFT and provided new insights into variations in YFT abundance.展开更多
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 recognize...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 4indexes.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.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.42090044,42376175,U2006211)the Marine S&T Fund of Laoshan Laboratory(Qingdao)(No.LSKJ202204302)。
文摘As an economically critical pelagic migratory species,yellowfin tuna(Thunnus albacores,YFT)is very sensible to physical and environmental conditions,such as sea surface temperature(SST),ocean heat content(OHC),and the mixed layer depth(MLD).We investigated the impact of SST,OHC,and MLD on fluctuations of YFT catch in the western/eastern Indian Ocean using the long time series of 63-year environmental and YFT datasets.We found that the impact of SST on YFT was heavily overestimated in the past,and MLD plays a more critical role in the YFT catch fluctuation.When the MLD deepens(>34.8 m),SST was more influential in predicting the catches of YFT than OHC in the western Indian Ocean,and OHC was more critical to YFT than SST in the eastern Indian Ocean.However,when the MLD shallows(<34.8 m),MLD was more vital to predict the catch per unit effort(CPUE)of YFT than SST/OHC in the western.After 2000,there was an asynchronous pattern of YFT CPUE induced by higher frequency variations and ocean hiatus of SST/OHC signals in the western and eastern Indian Oceans basins.The impact of the subsurface hiatus may induce the decrease of YFT in the eastern Indian Ocean.The above findings clarified a non-stationary relationship between the environmental factors and catches of YFT and provided new insights into variations in YFT abundance.
基金Supported by the Marine S&T Fund of Laoshan Laboratory(Qingdao)(No.LSKJ202204302)the National Natural Science Foundation of China(Nos.42090044,42376175,U2006211)。
文摘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 4indexes.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.
