Comparison of bacterial community structure between wetland and dryland in Xianshan Lake National Wetland Park

Abstract:

[Background] The microbial community structure of wetland soil will adjust itself according to the environmental changes. Therefore, different wetland environments will shape soil microbial communities with different structures and distribution characteristics. [Objective] To investigate the impacts of land use changes on soil bacteria in in dryland and wetland, we collected soil samples of two land use patterns (0−20 cm) from the wetland and dryland of Xianshan Lake National Wetland Park for analysis. [Methods] High-throughput sequencing was employed to study the composition, α-diversity, and abundance of soil bacterial communities, and the soil physical and chemical factors affecting bacterial community structure were analyzed. [Results] The bacteria in the soil of Xianshan Lake Wetland Park belonged to 147 classes of 49 phyla. The dominant phyla (OTUs>1%) in the dryland soil were Acidobacteria, Proteobacteria, Chloroflexi, Crenarchaeota, Actinobacteria, Cyanobacteria, Gemmatimonadetes, Planctomycetes, and Bacteroidetes, and those in the wetland soil were Proteobacteria, Cyanobacteria, Bacteroidetes, Chloroflexi, Acidobacteria, Firmicutes, and Nitrospirae. The dominant genus (OTUs>0.3%) were Candidatus Solibacter, Candidatus Koribacter, Rhodoplanes, Burkholderia, and Mycobacterium in the dryland and Geobacter, Bacteroides, Bradyrhizobium, Dok59, Candidatus Methylomirabilis, and Flavobacterium in the wetland. The dominant OTUs of wetland soil and dryland soil were positively correlated with soil carbon content and negatively correlated with soil water content and pH. The LEfSe results revealed the relationship between soil biomarkers and soil physical and chemical properties in the dryland and wetland. [Conclusion] Land use changes could vitally alter the bacterial community structure in the wetland soil of Xianshan Lake Wetland Park, it can provide theoretical basis for wetland soil protection.

Key words:wetland;dryland;16S rRNA gene;high-throughput sequencing