Integrating Microbial Indicators from High-Throughput Sequencing into Soil Quality Index: A Case Study in a Restored Mining Area.

The Soil Quality Index (SQI) is a vital tool for evaluating soil quality; however, traditional approaches seldom integrate microbial data from high-throughput sequencing-commonly used to characterize soil microbial communities-into the SQI framework. This study enhances the SQI by incorporating microbial indicators derived from high-throughput sequencing, establishing a more comprehensive evaluation system. We collected soil samples from mining areas and analyzed their fundamental physicochemical properties and microbial indicators. Four SQI models were constructed using different indicator sets: (1) only physicochemical properties (T-SQI); (2) physicochemical properties and bacterial α-diversity (α-SQI); (3) physicochemical properties, α-diversity, and relative abundances of the top five abundant bacteria (αMA-SQI); and (4) physicochemical properties, α-diversity, and relative abundances of the top five bacteria based on LDA scores (αBM-SQI). Results demonstrated that integrating multi-level microbial indicators improved the rationality of soil quality rankings and significantly strengthened correlations with α-diversity. Gemmatimonadota was consistently selected in the Minimum Data Set (MDS) for both αMA-SQI and αBM-SQI, highlighting its ecological importance. Statistically, microbial indicators at the order and family levels were most suitable for inclusion in the MDS, as their results deviated least from the total dataset. In conclusion, incorporating microbial diversity across taxonomic levels refines the SQI, enabling a more accurate and holistic assessment of soil health.