

FOLLOWUS
1.Key Laboratory of Remote Sensing Monitoring of Geographic Environment, College of Heilongjiang Province, Harbin Normal University, Harbin 150025, China
2.Key Laboratory of Biodiversity of Aquatic Organisms, Harbin Normal University, Harbin 150025, China
3.Qilu Normal University, Jinan 250200, China
ZANG Shuying, zsy6311@163.com
FAN Yawen, fanyaw@163.com
收稿:2019-09-07,
录用:2019-10-11,
网络首发:2020-10-18,
纸质出版:2020-11
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Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China[J]. 海洋湖沼学报(英文), 2020,38(6):1771-1786.
Fengyang SUI, Shuying ZANG, Yawen FAN, et al. Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China[J]. Journal of Oceanology and Limnology, 2020, 38(6): 1771-1786.
Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China[J]. 海洋湖沼学报(英文), 2020,38(6):1771-1786. DOI: 10.1007/s00343-019-9223-5.
Fengyang SUI, Shuying ZANG, Yawen FAN, et al. Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China[J]. Journal of Oceanology and Limnology, 2020, 38(6): 1771-1786. DOI: 10.1007/s00343-019-9223-5.
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 (
R
2
_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 (
R
2
_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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