This study explores the co-occurrence patterns of microbial taxa in soil using network analysis to gain insights into the structure and assembly of complex microbial communities. The researchers analyzed over 160,000 bacterial and archaeal 16S rRNA gene sequences from 151 soil samples across various ecosystem types. They found that microbial taxa tended to co-occur more frequently than expected by chance, indicating non-random community assembly. Network analysis revealed significant inter-taxa associations, including general non-random co-occurrence, common life history strategies at broad taxonomic levels, and unexpected relationships between community members. The network structure was characterized by high connectivity, modularity, and clustering, suggesting that environmental filtering effects and niche differentiation are evident at broad taxonomic levels. The study also identified habitat generalists and specialists, with generalists forming a less connected and more compartmentalized network compared to specialists. These findings highlight the potential of network analysis in understanding the ecological rules guiding microbial community composition and the mechanisms of community coexistence.This study explores the co-occurrence patterns of microbial taxa in soil using network analysis to gain insights into the structure and assembly of complex microbial communities. The researchers analyzed over 160,000 bacterial and archaeal 16S rRNA gene sequences from 151 soil samples across various ecosystem types. They found that microbial taxa tended to co-occur more frequently than expected by chance, indicating non-random community assembly. Network analysis revealed significant inter-taxa associations, including general non-random co-occurrence, common life history strategies at broad taxonomic levels, and unexpected relationships between community members. The network structure was characterized by high connectivity, modularity, and clustering, suggesting that environmental filtering effects and niche differentiation are evident at broad taxonomic levels. The study also identified habitat generalists and specialists, with generalists forming a less connected and more compartmentalized network compared to specialists. These findings highlight the potential of network analysis in understanding the ecological rules guiding microbial community composition and the mechanisms of community coexistence.