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Factors structuring phytoplankton community in a large tropical river: Case study in the Red River (Vietnam).

Authors :
Duong, Thi Thuy
Hoang, Thi Thu Hang
Nguyen, Trung Kien
Le, Thi Phuong Quynh
Le, Nhu Da
Dang, Dinh Kim
Lu, XiXI
Bui, Manh Ha
Trinh, Quang Huy
Dinh, Thi Hai Van
Pham, Thi Dau
Rochelle-newall, Emma
Source :
Limnologica; May2019, Vol. 76, p82-93, 12p
Publication Year :
2019

Abstract

Algal assemblages have been widely used as an ecological indicator of aquatic ecosystem health conditions because of their specific sensitivity to a wide variety of environmental conditions. In turbid rivers, as in other aquatic systems, phytoplankton structure plays an important role in structuring aquatic food webs. Worldwide, phytoplankton is less studied in turbid, large tropical rivers compared to temperate river systems. The present study aimed to describe the phytoplankton diversity and abundance in a turbid tropical river (the Red River, northern part of Vietnam from 20°00 to 25°30 North; from 100°00 to 107°10 East) and to determine the importance of a series of environmental variables in controlling the phytoplankton community composition. Phytoplankton community was composed of 169 phytoplankton taxa from six algal groups including Bacillariophyceae, Chlorophyceae, Cryptophyceae, Euglenophyceae, Dinophyceae and Cyanobacteria. Community composition varied both spatially and with season. Sixteen measurement environmental variables were used as input variables for a three-layer backpropagation neural network that was developed to predict the phytoplankton abundance. Phytoplankton abundance was successfully predicted using the tagsig transfer function and the Levenberg-Marquardt backpropagation algorithm. The network was trained to provide a good overall linear fit to the total data set with a slope (R) and mean square error (MSE) of 0.808 and 0.0107, respectively. The sensitivity analysis and neutral interpretation diagram revealed that total phosphorus (TP), flow discharge, water temperature and P-PO 4 <superscript>3−</superscript> were the significant variables. The results showed that the developed ANN model was able to simulate phytoplankton abundance in the Red River. These findings can help for gaining insight into and the relationship between phytoplankton and environmental factors in this complex, turbid, tropical river. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00759511
Volume :
76
Database :
Supplemental Index
Journal :
Limnologica
Publication Type :
Academic Journal
Accession number :
136660359
Full Text :
https://doi.org/10.1016/j.limno.2019.04.003