Hydrological and soil physiochemical variables determine the rhizospheric microbiota in subtropical lakeshore areas

Abstract

Background: Due to intensive sluice construction and other human disturbances, lakeshore vegetation has been destroyed and ecosystems greatly changed. Rhizospheric microbiota constitute a key part of a functioning rhizosphere ecosystem. Maintaining rhizosphere microbial diversity is a central, critical issue for sustaining these rhizospheric microbiota functions and associated ecosystem services. However, the community composition and abiotic factors influencing rhizospheric microbiota in lakeshore remain largely understudied.

Methods: The spatiotemporal composition of lakeshore rhizospheric microbiota and the factors shaping them were seasonally investigated in three subtropical floodplain lakes (Lake Chaohu, Lake Wuchang, and Lake Dahuchi) along the Yangtze River in China through 16S rRNA amplicon high-throughput sequencing.

Results: Our results showed that four archaeal and 21 bacterial phyla (97.04 ± 0.25% of total sequences) dominated the rhizospheric microbiota communities of three lakeshore areas. Moreover, we uncovered significant differences among rhizospheric microbiota among the lakes, seasons, and average submerged depths. The Acidobacteria, Actinobacteria, Bacteroidetes, Bathyarchaeota, Gemmatimonadetes, and Proteobacteria differed significantly among the three lakes, with more than half of these dominant phyla showing significant changes in abundance between seasons, while the DHVEG-6, Ignavibacteriae, Nitrospirae, Spirochaetes, and Zixibacteria varied considerably across the average submerged depths (n = 58 sites in total). Canonical correspondence analyses revealed that the fluctuation range of water level and pH were the most important factors influencing the microbial communities and their dominant microbiota, followed by total nitrogen, moisture, and total phosphorus in soil. These results suggest a suite of hydrological and soil physiochemical variables together governed the differential structuring of rhizospheric microbiota composition among different lakes, seasons, and sampling sites. This work thus provides valuable ecological information to better manage rhizospheric microbiota and protect the vegetation of subtropical lakeshore areas.

Keywords: Hydrology; Lakeshore area; Microbial community; Rhizospheric microbiota.

Figure 1. Location of the sampled three lakes and transects in this study.

Along each transect, five sites numbered I, II, III, IV, and V were sequentially set perpendicular to the lakeshore from the mean annual lowest water level to its highest water level. The elevation differences were equal between any two adjacent sites in each transect. The average submerged depths of sampling sites were showed in Fig. S1(N). The sample group names were formed by combining sampling season, lake, and sampling site. AU, autumn; LD, Lake Dahuchi; hence AULDIII indicated the sample taken from the III site of Lake Dahuchi in autumn, 2016.

Figure 2. Boxplots (A-O) show the differences of environmental factors among seasons, among lakes, and among sampling sites.

OC, organic content; TN, total nitrogen; FRWL, fluctuation range of the water level; TP, total phosphorus; SD, submerged duration; ASD, average submerged depth. Different lower case letters above the boxes indicate there were significant differences between the two groups (p < 0.05).

Figure 3. Dominant phyla (A) and significant dominant phyla (B) among different groups in the lakeshore rhizospheric microbiota.

The sample group names were formed by combining sampling season, lake, and sampling site. SP, spring; SU, summer; AU, autumn; WI, winter; LC, Lake Chaohu; LW, Lake Wuchang; LD, Lake Dahuchi. Therefore, for example, SPLWIII indicated the sample taken from the III site of Lake Wuchang in spring, 2017.

Figure 4. Correspondence analysis (A, B, and C) and canonical correspondence analysis (D) profiles of samples based on OTUs in lakeshore rhizospheric microbiota.

Different colors indicate different seasons (A), lakes (B), and sampling sites (C). The sample group names were formed by combining sampling season, lake, and sampling site. SP, spring; SU, summer; AU, autumn; WI, winter; LC, Lake Chaohu; LW, Lake Wuchang; LD, Lake Dahuchi. Therefore, for example, SPLWIII indicated the sample taken from the III site of Lake Wuchang in spring, 2017.

Figure 5. Correlation of the weighted UniFrac distances between the sampling sites to the geographic distances in spring (A), summer (B), autumn (C), and winter (D).

Figure 6. Canonical correspondence analysis profiles showing the influence of various environmental factors on rhizospheric microbiota (A) and dominant microbiota (B).

Different colors indicate different sampling lakes. The sample group names were formed by combining sampling season, lake, and sampling site. SP, spring; SU, summer; AU, autumn; WI, winter; LC, Lake Chaohu; LW, Lake Wuchang; LD, Lake Dahuchi. Therefore, for example, SPLWIII indicated the sample taken from the III site of Lake Wuchang in spring, 2017. TN, total nitrogen; TP, total phosphorus; SD, submerged duration; FRWL, fluctuation range of water level.