Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China
Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China
复制标题
松嫩平原湖泊硅藻-总磷转移函数的建立
DOI：
10.1007/s00343-019-9223-5
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发表时间：
2019-12
影响因子：
1.6
通讯作者：
HUI Hongkuan
中科院分区：
文献类型：
--
作者：
SUI Fengyang;ZANG Shuying;FAN Yawen;LU Xinxin;HUI Hongkuan
为了解中国东北松嫩平原湖泊中浮游硅藻与环境变量之间的关系，我们对27个湖泊的71个采样点的水质和浮游硅藻进行了调查，并在此基础上进行了典范对应分析（Canonic后面可能还有其他内容未给出完整）
To understand the relationship between planktonic diatoms and environmental variables in the lakes on the Songnen Plain, northeast (NE) China, we investigated water quality and planktonic diatoms from 71 sampling points in 27 lakes, based on which Canonical Correspondence Analysis (CCA) was conducted. The result show that planktonic diatoms displayed certain responses to environment gradients, and the total phosphorus (TP) explained the maximum variation of planktonic diatom species among the 15 environmental variables, suggesting that TP was the most important and significant environmental parameter affecting the distribution of diatom species. In addition, we established a diatom-total phosphorus transfer (DTPT) function, of which component 5 of the weighted averaging partial least squares regression (WAPLS) was chosen to and compared with a series of weighted average regression (WA) models and WA-PLS models. According to the jackknifing statistical test, the component 5 of WA-PLS models provided a lower root-mean-square error prediction (RMSEP=0.202) and a higher correlation coefficient between observation and prediction (R2_Jack=0.759). After deletion of three outliers, the root-mean-square error prediction of the DTPT function was reduced (RMSEP=0.169) while the correlation coefficient between observation and prediction was increased (R2_Jack=0.823). Therefore, this DTPT function performed better than other regional TP models in the world. However, it remains demanding for expanding the background dataset to improve the prediction ability of the model.
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影响因子：
2.7
作者：
S. Fritz;J. Kingston;D. Engstrom
通讯作者：
S. Fritz;J. Kingston;D. Engstrom
影响因子：
0.9
作者：
M. Witak;I. Hernández‐Almeida;M. Grosjean;W. Tylmann
通讯作者：
M. Witak;I. Hernández‐Almeida;M. Grosjean;W. Tylmann
影响因子：
2.1
作者：
Battarbee, RW;Grytnes, JA;Lami, A
通讯作者：
Lami, A
DOI：
--
发表时间：
2013
期刊：
Ecological Science
影响因子：
--
作者：
Huang Ying-ya
通讯作者：
Huang Ying-ya
影响因子：
4.8
作者：
C. Braak
通讯作者：
C. Braak