Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China | |
Li Qiuhua; Shang Lihai; Gao Tingjing; Zhang Lei; Ou Teng; Huang Guojia; Chen Chuan; Li Cunxiong | |
2014 | |
Source Publication | Acta Ecologica Sinica
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Volume | 34Issue:1Pages:72-78 |
Abstract | The relationships between chlorophyll-a, phytoplankton abundance and 20 chemical, physical and biological water quality variables were studied by using principal component scores (PCs) in stepwise linear regression analysis (SLR) to simulate chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China. Score values obtained by PC scores were used as independent variables in multiple linear regression models. The following models were used to simulate chlorophyll-a and abundance of Cyanobacteria, Chlorophyta, Bacillariophyta, and Pyrrophyta respectively: chlorophyll-a1 = 10.501 + 1.390 (score 1) (P < 0.01), chlorophyll-a 2 = 10.501 + 1.102 (score 1)−0.877 (score 2) (P < 0.05), log10(Cyanobacteria) = 1.277−0.726 (score 2) (P < 0.05), log10 (Chlorophyta) = 3.927−0.150 (score 2) (P < 0.01), log10 (Bacillariophyta) = 4.872−0.131 (score 4) (P < 0.01) and log10(Pyrrophyta) = 2.463 + 0.578 (score 1) (P < 0.05). The models could be used to simulate chlorophyll-a and phytoplankton abundance levels successfully, and revealed that DO, WD, Tem, TD, pH, NH4–N and TSS were the most important factors regulating the composition of chlorophyll-a and Pyrrophyta abundance. ORP, Cl−, SO42-, TN were the main factors affecting Chlorophyta and Cyanobacteria abundance. F− and Ca2+ were the main factors influencing the Bacillariophyta abundance. |
Keyword | Chlorophyll-a Phytoplankton Principal Component Scores Stepwise Linear Regression Analysis Reservoir |
Indexed By | SCI |
Language | 英语 |
Document Type | 期刊论文 |
Identifier | http://ir.gyig.ac.cn/handle/42920512-1/9378 |
Collection | 环境地球化学国家重点实验室 |
Affiliation | 1.Key Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guiyang 550001, PR China 2.State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, PR China |
Recommended Citation GB/T 7714 | Li Qiuhua;Shang Lihai;Gao Tingjing;Zhang Lei;Ou Teng;Huang Guojia;Chen Chuan;Li Cunxiong. Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China[J]. Acta Ecologica Sinica,2014,34(1):72-78. |
APA | Li Qiuhua;Shang Lihai;Gao Tingjing;Zhang Lei;Ou Teng;Huang Guojia;Chen Chuan;Li Cunxiong.(2014).Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China.Acta Ecologica Sinica,34(1),72-78. |
MLA | Li Qiuhua;Shang Lihai;Gao Tingjing;Zhang Lei;Ou Teng;Huang Guojia;Chen Chuan;Li Cunxiong."Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China".Acta Ecologica Sinica 34.1(2014):72-78. |
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