Soil particle and moisture-related factors determine landward distribution of bacterial communities in a lateral riverside continuum of the Xilin River Basin

Highlights

• The riverbed-oxbow lake bed-floodplain-terrace continuum.

• Dominant bacteria substantially differed along the continuum.

• The highest bacterial diversity in floodplains and the lowest in terraces.

• Soil particle and moisture-related factors determine bacterial communities.

Abstract

Continuous landscape components along the lateral riverside are affected by both hydrologic connectivity and disconnectivity. In recent years, anthropogenic activities and climate changes have caused wetland shrinkage and land degradation along the lateral riverside of many arid and semiarid regions. Since microorganisms are major drivers of soil biochemical cycling, it is essential to examine soil microbial communities along the lateral landscape continuum to understand their ecosystem functioning and predict future land changes. Here, we collected samples along a lateral riverbed center-riverbed edge-oxbow lake-floodplain-terrace continuum in the Xilin River Basin, Inner Mongolia, China. The floodplain had the highest microbial diversity and heterogeneity, with Bacteroidetes, β- and ©-Proteobacteria being the most abundant taxa. In contrast, the terrace had the lowest microbial diversity and heterogeneity, with Acidobacteria, Actinobacteria, Verrucomicrobia, Gemmatimonadetes, and α-Proteobacteria as the most abundant taxa. Silt particle, salinity, and moisture were the most influential factors for bacterial communities along the riverside continuum. Altogether, we demonstrate that dominant bacterial lineages, soil particles, and moisture-related factors are valuable indicators of this continuum, which can be leveraged for early prediction of drought-induced wetland shrinkage and grassland desertification.

Graphical abstract

Keywords

Biogeography / Landward distribution / Lateral riverside continuum / Hydrologic connectivity and disconnectivity / Bacterial communities / Combined environmental factors

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Acknowledgments

This work was supported financially by the National Natural Science Foundation of China to J. Yu (41361053), X. Tanggood (31660724) and Y. Yang (41825016), the Natural Science Foundation of Inner Mongolia to J. Yu (2011MS0603, 2016MS0331) and X. Tanggood (2015MS0306), and the National College Students Innovation and Entrepreneurship Training Program of Inner Mongolia University to C.H. Li, X. Li, W. Z. Wu, W. Dong and Y. Jia (201810126043). The authors thank Huhe, G. Feng, Y. Lu, S.H. Zhang, Z.C. Zhang, Y.B. Shi, Z.Y.Li, W. Wang, Z.H. Bao, and H Qing from Inner Mongolia University and R.X. Zhao from Tsinghua University for assistance in bioinformatics analysis.

Electronic supplementary material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s42832-021-0106-2 and is accessible for authorized users.

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