Spatial variations of bacterial community and its relationship with water chemistry in Sanya Bay, South China Sea as determined by DGGE fingerprinting and multivariate analysis

Abstract

Bacteria play important roles in the structure and function of marine food webs by utilizing nutrients and degrading the pollutants, and their distribution are determined by surrounding water chemistry to a certain extent. It is vital to investigate the bacterial community's structure and identifying the significant factors by controlling the bacterial distribution in the paper. Flow cytometry showed that the total bacterial abundance ranged from 5.27 × 10(5) to 3.77 × 10(6) cells/mL. Molecular fingerprinting technique, denaturing gradient gel electrophoresis (DGGE) followed by DNA sequencing has been employed to investigate the bacterial community composition. The results were then interpreted through multivariate statistical analysis and tended to explain its relationship to the environmental factors. A total of 270 bands at 83 different positions were detected in DGGE profiles and 29 distinct DGGE bands were sequenced. The predominant bacteria were related to Phyla Protebacteria species (31 %, nine sequences), Cyanobacteria (37.9 %, eleven sequences) and Actinobacteria (17.2 %, five sequences). Other phylogenetic groups identified including Firmicutes (6.9 %, two sequences), Bacteroidetes (3.5 %, one sequences) and Verrucomicrobia (3.5 %, one sequences). Conical correspondence analysis was used to elucidate the relationships between the bacterial community compositions and environmental factors. The results showed that the spatial variations in the bacterial community composition was significantly related to phosphate (P = 0.002, P < 0.01), dissolved organic carbon (P = 0.004, P < 0.01), chemical oxygen demand (P = 0.010, P < 0.05) and nitrite (P = 0.016, P < 0.05). This study revealed the spatial variations of bacterial community and significant environmental factors driving the bacterial composition shift. These results may be valuable for further investigation on the functional microbial structure and expression quantitatively under the polluted environments in the world.

Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

“…Our research in Jinan also shows that NH 3 ‐N is an important factor affecting the structure index of food web. In the study of the impact of COD on aquatic ecosystems, Ling et al's (2015) research in Sanya Bay, China, obtained a significant correlation between COD and the spatial variability of bacterial communities through a large amount of data analysis. Yang et al (2012) collected a large number of data of aquatic organisms in the Taihu Lake Basin of China and found that COD affected the structure and community of Microcystis in Taihu Lake.…”

“…This is consistent with the conclusion that the NH 3 -N can promote the maturity of the food web structure in our study. In the study of the impact of COD on the ecosystem, Ling et al (2015) found that COD promotes bacterial abundance in the study of the spatial variability of bacterial communities and their relationship with water chemistry in South China Sea Sanya Bay. The result is different from the conclusion that in our study, mainly because COD has a positive effect on the abundance of the bacterial community, but our study focuses on the entire food web organization.…”

Compositional variations and the environmental responses of bacterioplankton in the Pengxi River of the Three Gorges Reservoir, China

“…Water that was filtered through 0.45 lm pore size cellulose acetate filters (GE Whatman, Sigma-Aldrich Inc., MO, USA) was used to analyze the concentrations of total nitrogen (TN), total phosphorus (TP) ammonium (NH 4 þ ), nitrate (NO 3 À ) and phosphate (PO 4 3À ) via continuous flow analysis on a San þþ automated wet chemistry analyzer (Skalar Analytical B.V., Breda, Dutch) at wavelengths of 540, 880, 660, 540 and 880 nm, respectively, based on protocols provided by the manufacturer. All other physiochemical parameters, including chlorophyll a (Chl-a), chemical oxygen demand (COD), turbidity (Turb), alkalinity (ALK) and water transparency (SD), were determined by the methods as described previously (Ling et al 2015).…”