Network Structure Explained the Differences in the Response of Soil Bacterial Community Structure and Functional Structure to Afforestation Types

This study used 16S rDNA high-throughput sequencing and Faprotax functional prediction to analyze the effects of different artificial forests (coniferous forest, conifer–broad-leaved mixed forest, broad-leaved forest) in the Fanggan ecological restoration area of North China on soil bacterial community composition and functional characteristics and, based on network topology features, analyzed the potential influencing pathways. Planting broad-leaved forests significantly increased soil bacterial α-diversity indices (ACE, Chao1, Shannon) and induced the greatest heterogeneity in both community and functional composition. Soil bacteria exhibit significant differences in taxonomic structure across forest types but not in functional structure. The classification network and functional network of broad-leaved forests are more complex than those of coniferous and mixed forests, with the former having more nodes and edges, as well as higher weighted degree and betweenness centrality. Zi-Pi analysis indicates that high-abundance taxa involved in carbon and nitrogen cycles dominate the keystone taxa of the taxonomic network, while low-abundance pathogenic, urea-decomposing, and trace element metabolism functional groups dominate the keystone groups of the functional network. Redundancy analysis further revealed that soil available potassium concentration, pH, and tree species composition (importance values of Pinus tabulaeformis and Populus davidiana) were the principal determinants of bacterial functional structure. Collectively, broad-leaved forests achieve higher network robustness via elevated network complexity and functional redundancy, whereas coniferous forests might rely on functional convergence and modular integration to cope with resource limitation. These results indicate that network traits mediate the distinct responses of bacterial communities and their functional potentials, offering practical references for vegetation restoration in limestone mountain areas.