Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of <i>Sillago sihama</i> in the Shandong coastal waters

Jian Ying; Zhang Yunlei; Song Yehui; Zhang Chongliang; Ji Yupeng; Ren Yiping

doi:10.12284/hyxb2022146

Volume 44 Issue 7

Jul. 2022

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Jian Ying，Zhang Yunlei，Song Yehui, et al. Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters[J]. Haiyang Xuebao，2022, 44(7)：103–111 doi: 10.12284/hyxb2022146

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Jian Ying，Zhang Yunlei，Song Yehui, et al. Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters[J]. Haiyang Xuebao，2022, 44(7)：103–111 doi: 10.12284/hyxb2022146

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Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters

doi: 10.12284/hyxb2022146

Jian Ying^{1, 3
,},
Zhang Yunlei^{1, 3},
Song Yehui^{1, 3},
Zhang Chongliang^{1, 3},
Ji Yupeng^{1, 3},
Ren Yiping^{1, 2, 3
,
,}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China

Received Date: 2021-12-21
Rev Recd Date: 2022-03-23

Available Online: 2022-07-01

Publish Date: 2022-07-01

Abstract

Abstract

Sillago sihama is an important fishery species in China and plays an important role in the marine ecosystem of the Yellow Sea. Species distribution models can be used to predict its distribution by establishing the relationships between its abundance and environmental factors. However, due to high mobility of the marine animals, the relationship between their distribution and environmental factors is often nonlinear and variable with spatial locations. Based on data collected from bottom trawl survey in the Shandong coastal waters in autumn of 2016, both generalized additive model (GAM) and geographically weighted regression (GWR) model were used to analyze nonlinear and spatial nonstationary relationships between distribution of the species and environmental factors, and results from the two models were compared. Results from the GAM indicated that the main environmental factors were depth, sea bottom temperature and salinity, and depth had the largest deviance explained (23.50%). GWR model results showed that there were spatial non-stationary relationships between distribution of the species and depth and sea bottom temperature. GWR model results indicated a negative correlation between depth and biomass of the species, and a positive correlation between sea bottom temperature and biomass of species. Regarding performance of the models, GWR model showed advantages over GAM in identifying influencing factors and predicting distribution, and GWR model was recommended for use in similar applications.
- Sillago sihama,
- geographically weighted regression model,
- generalized additive model,
- spatial nonstationarity,
- spatial distribution

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References(39)

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