<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yong Liu</style></author><author><style face="normal" font="default" size="100%">Guo, Huaicheng</style></author><author><style face="normal" font="default" size="100%">Yang, Pingjian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis</style></title><secondary-title><style face="normal" font="default" size="100%">ECOLOGICAL MODELLING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB 24</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">221</style></volume><pages><style face="normal" font="default" size="100%">681-688</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues &gt; 1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values. with R(2) = 0.80. For SEM, Chl a and eight variables were used: NH(4)-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu. (C) 2009 Elsevier B.V. All rights reserved.</style></abstract><custom7><style face="normal" font="default" size="100%">000274844500013</style></custom7></record></records></xml>