Data Sheet 3_Soil bacterial community diversity, composition, and species specificity across different geographical landscapes in the Mu Us Sandy Land.zip

The Mu Us Sandy Land represents a typical region for ecological restoration in China, characterized by the development of diverse landscapes including desert, meadow patches, arbor forests, and mixed arbor-shrub forests. This study aimed to investigate the diversity, composition, and differential taxa of soil bacterial communities across these distinct geographical landscapes, thereby elucidating the driving mechanisms of vegetation restoration on the sandy land soil microbiome. Soil samples were collected from four typical landscapes in the Mu Us Sandy Land: desert (B), meadow patch (D), arbor forest (T), and mixed arbor-shrub forest (C). High-throughput sequencing of the 16S rRNA gene was performed using the Illumina NextSeq 2000 platform. Our results revealed distinct patterns of bacterial community composition: Actinobacteria dominated the desert (37.42%), while Proteobacteria were more abundant in meadow patches and mixed arbor-shrub forests, and Bacillota were significantly enriched in arbor forests (20.32%). Beta diversity analysis combined with the ANOSIM test (R = 0.7168, P = 0.001) revealed significant divergence in bacterial community structure among the different landscapes. LEfSe analysis further identified specific biomarkers for each landscape, such as Rubrobacter and Streptomyces in the desert, and taxa associated with Acidobacteria and Proteobacteria in the mixed arbor-shrub forests. The research demonstrates that the different geographical landscapes in the Mu Us Sandy Land shape distinct soil bacterial communities. The mixed arbor-shrub forest exhibited a more complex community structure compared to the pure arbor forest, indicating its potential as a more sustainable and resilient ecological restoration model. These findings provide a baseline understanding of microbial community shifts associated with vegetation restoration, which may inform future studies integrating soil physicochemical drivers.