ABSTRACT Soil bacteria play a crucial role in soil processes, such as carbon sequestration and nutrient cycling. While soil bacterial communities and their interactions with pedo‐climatic factors have been well documented, most studies typically focus on broad taxonomic levels, leaving distribution and responses at the genus level unexplored. This study optimized machine learning models to predict the distribution of dominant bacterial genera across Australia on a comprehensive dataset of 1971 topsoil samples. Our high‐resolution digital maps (~1 km resolution) reveal four distinct distribution patterns for the dominant bacterial genera: coastal or inland enriched patterns and latitude‐related patterns. Each genus exhibited unique responses to critical factors, including temperature, precipitation, soil organic carbon (SOC), and pH. Notably, our findings highlight the importance of genus‐level analysis, as bacterial genera within the same phylum can respond markedly differently to pedo‐climatic conditions. Intensive land use significantly homogenized bacteria composition and increased the relative abundance of Rubrobacter, RB41, Microvirga, and Sphingomonas. Overall, this study enhances our understanding of bacterial macroecological trends and offers insights for more precise interventions to improve soil health and resilience against environmental changes.
Du et al. (2025) studied this question.