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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
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Ying Jian1, 3, Yunlei Zhang1, 3, Yehui Song1, 3, Chongliang Zhang1, 3, Yupeng Ji1, 3, Yiping Ren1, 2, 3, *
Haiyang Xuebao | 2022, 44(7) : 103 - 111
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Haiyang Xuebao | 2022, 44(7): 103-111
• Article •
Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters
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Ying Jian1, 3, Yunlei Zhang1, 3, Yehui Song1, 3, Chongliang Zhang1, 3, Yupeng Ji1, 3, Yiping Ren1, 2, 3, *
Affiliations
  • 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
Published: 2022-07-01 doi: 10.12284/hyxb2022146
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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
Ying Jian, Yunlei Zhang, Yehui Song, Chongliang Zhang, Yupeng Ji, Yiping Ren. 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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Article Info
doi: 10.12284/hyxb2022146
  • Receive Date：2021-12-21
  • Online Date：2026-02-01
  • Published：2022-07-01
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History
  • Received：2021-12-21
  • Revised：2022-03-23
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Affiliations
    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
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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