Spatio-temporal dynamic of abundance index of Neon flying squid in relation to environmental variables in the Northwest Pacific Ocean using BP neural network

Wang Yameng; Wang Jintao; Chen Xinjun; Lei Lin

doi:10.12284/hyxb2021106

Volume 43 Issue 6

Jun. 2021

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Article Navigation > Haiyang Xuebao > 2021 > 43(6): 81-89

Wang Yameng，Wang Jintao，Chen Xinjun, et al. Spatio-temporal dynamic of abundance index of Neon flying squid in relation to environmental variables in the Northwest Pacific Ocean using BP neural network[J]. Haiyang Xuebao，2021, 43(6)：81–89 doi: 10.12284/hyxb2021106

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Spatio-temporal dynamic of abundance index of Neon flying squid in relation to environmental variables in the Northwest Pacific Ocean using BP neural network

doi: 10.12284/hyxb2021106

Wang Yameng^1
,,
Wang Jintao^{1, 2, 3, 4, 5
,
,},
Chen Xinjun^{1, 2, 3, 4, 5},
Lei Lin^{1, 2, 3, 4, 5}

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture, Shanghai 201306, China
3.
National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
4.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China
5.
Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, Shanghai 201306, China

Received Date: 2019-11-18
Rev Recd Date: 2020-04-23

Available Online: 2021-05-08

Publish Date: 2021-06-30

Abstract

Abstract

Ommastrephes bartramii is an economically important species for Chinese squid jigging fleet. Understanding the spatio-temporal distribution on fishing ground is crucial to the sustainable utilization of fish resources. The study constructed BP (back propagation) neural network models with different scenarios to speculate the dynamics of O. bartramii abundance based on fishery data in the months of July to October during 2000 to 2015. The model with year, month, longitude, latitude, sea surface temperature (SST), and sea surface salinity (SSS) as independent variables, 8 neurons in hidden layers, had the smallest mean square error, and thus selected as optimal model. The results showed that the significant fluctuation in CPUE between years, the local abundance was low and scattered in July and October, whereas was high and concentrated at 41.5°−43.5°N, 155°−160°E in August and September. Environmental factors, including SST and SSS affect the spatio-temporal distribution of local abundance, and should be considered in stock assessment and management.
- Northwest Pacific,
- back propagation neural network,
- spatial-temporal change,
- standardization

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References(38)

References

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