Spatial variability of cyanobacterial community composition in Sanya Bay as determined by DGGE fingerprinting and multivariate analysis

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Abstract

The cyanobacterial communities in the surface and bottom waters of Sanya Bay were investigated on April 24 and 25, 2010. Flow cytometry showed that the total cyanobacterial abundance in the surface and bottom layers ranged from 0. 7×104 to 2. 38×104 cells mL-1 and from 1×104 to 1. 8×104 cells mL-1, respectively. Cyanobacterial diversity was analyzed using a molecular fingerprinting technique called denaturing gradient gel electrophoresis (DGGE), followed by DNA sequencing. The results were then interpreted through multivariate statistical analysis. Differences in the compositions of cyanobacterial communities were observed in the surface and bottom waters at the same station, with some bands obtained from both the surface and bottom layers, whereas some bands were present only in one layer. The predominant cyanobacterial species of the excised DGGE bands were related to Synechococcus or Synechococcus-like species (56. 2%). Other phylogenetic groups identified included Chroococcidiopsis (6. 3%), Cyanobium (6. 3%) and some unclassified cyanobacteria (31. 2%). A redundancy analysis (RDA) was conducted to reveal the relationships between the cyanobacterial community composition and environmental factors. Analysis results showed that the spatial variations in the cyanobacterial community composition in surface waters was significantly related to chlorophyll a (Chla), the biochemical oxygen demand (BOD), nitrate and phosphate (P<0. 05). Meanwhile, the spatial variations in the bottom waters was significantly affected by nitrate, nitrite, and phosphate (P<0. 05). Environmental parameters could explain 99. 3% and 58. 3% of the variations in the surface and bottom layers, respectively. © 2012 The Author(s).

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Ling, J., Zhang, Y. Y., Dong, J. D., Wang, Y. S., Huang, H., Chen, L., … Zhang, S. (2013). Spatial variability of cyanobacterial community composition in Sanya Bay as determined by DGGE fingerprinting and multivariate analysis. Chinese Science Bulletin, 58(9), 1019–1027. https://doi.org/10.1007/s11434-012-5424-4

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