Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China
Sui, F.; Zang, S.; Fan, Y.; Lu, X.; Hui, H. (2020). Establishment of a diatom-total phosphorus transfer function for lakes on the Songnen Plain in northeast China. Journal of Oceanology and Limnology 38: 1771-1786. https://dx.doi.org/10.1007/s00343-019-9223-5
In: Journal of Oceanology and Limnology. Science Press: Beijing. ISSN 2096-5508; e-ISSN 2523-3521, more
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| Keywords |
Bacillariophyceae [WoRMS]
Fresh water |
| Author keywords |
Songnen Plain; diatom; total phosphorus; transfer function |
| Abstract |
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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