Abstract
INTRODUCTION: Acid mine drainage (AMD) systems are extreme acidic environments characterized by low pH and high metal concentrations that shape unique microbial ecosystems. While acidophilic microorganisms are known to drive AMD biogeochemistry, the ecological processes governing their community assembly, niche partitioning, and long-term stability remain incompletely resolved. METHODS: To investigate microbial diversity, community structure, and assembly mechanisms, we integrated high-resolution 16S rRNA gene amplicon sequencing with community ecology analyses and null modeling approaches in an AMD system located in Zhejiang Province, China. We examined microbial communities across water and sediment habitats, assessing the influence of environmental variables (e.g., pH, metal concentrations, redox potential) on community composition. Null models were used to quantify the relative roles of deterministic and stochastic processes in community assembly. RESULTS AND DISCUSSION: Community structure was primarily shaped by pH and habitat type (water vs. sediment), with low-pH conditions selecting for persistent, abundant taxa dominated by specialized acidophiles. Within this group, we identified significant fine-scale niche partitioning and intra-genus microdiversity, both of which were associated with greater persistence across heterogeneous conditions. Co-occurrence and niche analyses revealed that closely related taxa often occupy distinct ecological niches structured by gradients of metals, redox potential, and oxygen availability. Ecological assembly modeling indicated that deterministic homogeneous selection and stochastic drift dominate under the harshest conditions. In contrast, dispersal limitation becomes more important in less chemically-stressed sites, indicating that spatial constraints gain importance when environmental filtering weakens. Our findings reveal that AMD microbial communities are shaped by a dynamic interplay between strong environmental selection, stochasticity, and spatial factors. These insights advance fundamental understanding of microbial community organization in extreme habitats and have practical implications for predicting ecosystem responses to environmental change and optimizing bioremediation strategies in contaminated systems.