The key factors on the composition of phytoplankton functional groups in different watersheds in the Huanghe River basin

• Published in: Journal of oceanology and limnology Vol. 42; no. 6; pp. 1909 - 1927
• Main Authors: Dong, Jing, Wang, Feihu, Zhang, Shuwen, Yuan, Huatao, Gao, Xiaofei, Zhang, Jingxiao, Li, Xuejun
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.11.2024; Springer Nature B.V; College of Fisheries,Henan Normal University,Xinxiang 453007,China
• Subjects: Aquatic ecosystems; Chemical oxygen demand; Climate change; Composition; Correlation analysis; Earth and Environmental Science; Earth Sciences; Functional groups; Lakes; Oceanography; Oxygen requirement; Phytoplankton; Plankton; Potassium; Potassium permanganate; Research Paper; Reservoirs; riparian areas; River basins; Rivers; Total nitrogen; Total phosphorus; Turbidity; Venn diagrams; Water temperature; Watersheds; Yellow River; Huanghe River; Yellow River; phytoplankton functional group; lake-reservoir region; driving factor; riverine watershed; Huanghe River
• ISSN: 2096-5508; 2523-3521
• DOI: 10.1007/s00343-024-3136-7

To understand the distribution of phytoplankton functional groups (PFGs) and key factors on their compositions in different watersheds of the Huanghe (Yellow) River basin, 25 river sites and 25 lake-reservoirs sites were selected. The contents of nephelometric turbidity (NTU), total nitrogen (TN), and total phosphorus (TP) were significantly higher in rivers than that in lakes or reservoirs, whereas the pH and COD Mn (chemical oxygen demand or potassium permanganate index) were lower. Results show that, 27 PFGs, namely, assemblages A, B, C, D, E, F, G, H, J, K, LM, Lo, M, MP, N, P, S1, S2, T, TC, W1, W2, X1, X2, X3, XPh, and Y, were identified. Additionally, ANOSIM correlation analysis demonstrated significant differences in PFG composition between the riverine and lake-reservoir sections in the Huanghe River basin. In the riverine watersheds, the group MP was dominant, while assemblages B and J were prevalent in lakes and reservoirs. The Mantel correlation tests and RDA analysis showed that environmental variables, such as NTU, water temperature (WT), conductivity (Cond), and TP, were key driving factors of shaping the dominant PFGs of the study area. Using the Venn diagram based on variation partitioning analysis, PFGs were mainly influenced by WT and TP in lake-reservoir sites, while in the river sites were affected mainly by geo-climatic variables. This study helps understanding the PFGs in river ecosystems, and unraveling the key driving factors in different watersheds, which shall be important for the protection and management of entire Huanghe River basin.