Bacterial and fungal communities exhibit contrasting assembly processes during native bamboo expansion through coupled vegetation-soil modifications across elevation gradients

Effects of native species expansion on microbial assembly processes remain unquantified. While such expansions alter vegetation-soil systems, the role of edaphic factors such as pH, ionic composition, and nutrient dynamics in shaping microbial responses is unknown. Here, we examined how moso bamboo ( Phyllostachys edulis ) expansion influences microbial communities by studying a natural transition gradient (from pure bamboo to non-expanded forests) across four elevational sites (358-1176 m) in subtropical China. Through high-throughput sequencing in both topsoil (0-20 cm) and subsoil (20-40 cm) layers, we found that bacterial communities showed stronger responses to expansion than fungal communities across both soil depths. Homogeneous selection dominated bacterial assembly in both topsoil (60.60-66.67%) and subsoil (50.00-95.45%), while fungal communities exhibited stochastic patterns driven by dispersal limitation (topsoil: 60.61-81.82%; subsoil: 48.48-63.64%). These contrasting patterns reflect bamboo's uniform conditions filtering resource-responsive bacteria deterministically, whereas fungal hyphal networks maintain stochastic dispersal across environmental gradients. Network analysis revealed that bamboo expansion increased bacterial network complexity while simplifying fungal networks. Soil properties, particularly pH and base cations, emerged as key drivers of community composition, explaining up to 34.73% of bacterial and 25.23% of fungal community variation in topsoil, with similar patterns in subsoil. Notably, fungal communities exhibited stronger associations with geographic distance and elevation gradients than bacteria, highlighting their greater sensitivity to spatial and climatic factors. These findings reveal how native species expansion reshapes soil microbial communities through coupled vegetation-soil modifications, providing crucial insights for predicting ecosystem responses to vegetation change.