Microbial data from remediated rare earth tailings
Description
Research Hypothesis: We hypothesized that the application of different composite amendments would significantly alter the diversity and composition of sulfate-reducing bacterial (SRB) communities in ion-type rare earth tailings (IRET), and that specific environmental factors such as soil organic matter (SOM), pH, and SO₄²⁻ content would be the main drivers of these changes. What the Data Shows: The dataset provides OTU-based taxonomic profiles of SRB communities across four treatments (CK, T1, T2, T3) after six months of in-situ remediation. The data include relative abundance of SRB at phylum and genus levels, alpha diversity metrics (Chao1, Shannon), beta diversity distances (Bray-Curtis, UniFrac), and differential taxa identified through LEfSe analysis. Notable Findings: Amendments T2 (P-Ca-Mg-based) and T3 (zeolite-based) significantly enhanced SRB diversity and phylogenetic richness.T1 (biochar-based) suppressed SRB communities, showing dominance of a single genus (Desulfobulbus).SOM, pH, and SO₄²⁻ were identified by RDA as the most influential environmental variables shaping SRB community structure. How the Data Was Gathered: Soil samples were collected from field plots treated with different amendments. DNA was extracted from the 0–20 cm topsoil layer and sequenced targeting the dsrB gene, a functional marker for SRB. Sequence processing and OTU clustering were performed using QIIME2 (v2022.2), and taxonomy was assigned using a custom SRB reference database. Downstream statistical analyses (e.g., PCoA, heatmaps, LEfSe) were conducted in R (v4.2.0). How to Interpret and Use the Data: The dataset can be used to assess how SRB communities respond to environmental restoration efforts in acidic, sulfate-rich mining tailings. It can also support comparative studies on microbial community assembly, functional redundancy, and microbial indicators for tailing remediation. Researchers can reproduce diversity and ordination analyses or integrate this data into meta-analyses of SRB ecology.