The results of an investigation into the relationship between surface sediment subfossil chi- ronomid distribution and water quality are presented. Data from 30 lakes in the middle and lower reaches of the Yangtze Riv...The results of an investigation into the relationship between surface sediment subfossil chi- ronomid distribution and water quality are presented. Data from 30 lakes in the middle and lower reaches of the Yangtze River indicate that the nutrient gradi- ent was the major factor affecting the distribution of chironomids across these sites. Canonical corre- spondence analysis (CCA) revealed that of 12 sum- mer water environmental variables, total Phosphorus was most important, accounting for 20.1% of the variance in the chironomid data. This was significant enough to allow the development of quantitative in- ference models. A TP inference model was devel- oped using weighted averaging (WA), partial least squares (PLS) and weighted averaging partial least squares (WA-PLS). An optimal two-component WA-PLS model provided a high jack-knifed coeffi- cient of prediction for conductivity r 2jack = 0.76, with a low root mean squared error of prediction (RMSEPjack = 0.13). Using this model it is possible to produce long-term quantitative records of past water quality for lacustrine sites across the middle and lower reaches of the Yangtze River, which has important implications for future lake management and eco- logical restoration.展开更多
基金This study was supported by the National Natural Science Foundation of China （Grant No. 40402015） the State Key Basic Research and Development Plan of China （Grant No. 2004CB720205）the Knowledge Innovation Project of the CAS （Grant No. KZCX1-SW-12）.
文摘The results of an investigation into the relationship between surface sediment subfossil chi- ronomid distribution and water quality are presented. Data from 30 lakes in the middle and lower reaches of the Yangtze River indicate that the nutrient gradi- ent was the major factor affecting the distribution of chironomids across these sites. Canonical corre- spondence analysis (CCA) revealed that of 12 sum- mer water environmental variables, total Phosphorus was most important, accounting for 20.1% of the variance in the chironomid data. This was significant enough to allow the development of quantitative in- ference models. A TP inference model was devel- oped using weighted averaging (WA), partial least squares (PLS) and weighted averaging partial least squares (WA-PLS). An optimal two-component WA-PLS model provided a high jack-knifed coeffi- cient of prediction for conductivity r 2jack = 0.76, with a low root mean squared error of prediction (RMSEPjack = 0.13). Using this model it is possible to produce long-term quantitative records of past water quality for lacustrine sites across the middle and lower reaches of the Yangtze River, which has important implications for future lake management and eco- logical restoration.
