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
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...
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Published in | Acta ecologica Sinica Vol. 34; no. 1; pp. 72 - 78 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.02.2014
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 1872-2032 1872-2032 |
DOI: | 10.1016/j.chnaes.2013.11.009 |