Abstract

Soil microbes play critical roles in regulating ecosystem-level functioning and responding to environmental changes after disturbance events. However, a systematic understanding of soil microbial community assembly processes and responses in the context of severe anthropogenic disturbance remains lacking. Here, microbial communities collected from the soil in the mining reclamation and surrounding grassland of the Baiyinhua #1 open-pit coal mine were profiled and compared by three-generation PacBio sequencing to identify the response characteristics of the soil microbial communities to mining-induced disturbance, microbial community assembly and their relevance to environmental variables. The results suggested that the five locations surveyed had heterogeneous microbial community structures, distribution patterns and network topological characteristics, especially exhibiting contrasting differences between the mining reclamation and the grassland surrounding the mine, and different survey sites supported the enrichment of different and adaptative bacterial and fungal taxa and biomarkers. In comparison to the bacterial community, the fungal community was more susceptible to the current mining disturbances, and the stability of the core fungal interaction network, fungal diversity and resistance index were relatively low. Redundancy analysis showed that the differences in microbial communities were driven by the joint impacts of soil alkaline phosphatase and total nitrogen, and differences in bacterial communities were also driven by pH. Stochastic processes dominated the microbial assemblies at the entire site across the mining reclamation and grasslands around the coal mine, and dispersal limitation and drift were the dominant processes of the bacterial and fungal assemblies, respectively, at most individual sites. The relative influences of deterministic and stochastic processes in terms of shaping microbial community assemblies shifted with soil enzyme activity and nutrients. These findings provide critical insights into the prediction of soil ecosystem recovery after disturbance and deepen our understanding of the responses and assembly rules associated with severe anthropogenic disturbance.

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