Hierarchical taxonomic constraints shape bacterial nitrogen cycling across functional and molecular scales

Microbes are essential for global nitrogen cycling, yet the extent to which taxonomic identity constrains functional potential remains poorly quantified. Using 73,472 representative bacterial genomes, we establish a multi-scale quantitative framework revealing systematic, hierarchical relationships between taxonomic identity and nitrogen cycling functional potential. Hierarchical variance decomposition reveals that taxonomy explains 20–46% of functional variation across six nitrogen pathways, with the strongest constraints for dissimilatory nitrite reduction to ammonium (46.1%) and nitrogen fixation (44.2%), both negatively correlated with functional prevalence. K-means clustering identifies four class-level functional archetypes (Functionally Inactive, Diversified, Integrated, and Specialized) among 77 bacterial classes. 1,281 genera resolve into five ecological strategies differentiated by their nitrogen retention versus loss capabilities, exhibiting strong phylogenetic signals. Cross-scale validation demonstrates 78.7% genus-level conformity to class-level archetypes, confirming hierarchical functional organization. Molecular evolutionary analysis of 13 genes reveals sequence conservation as a dimension partially independent of pathway-level functional constraints. Paradoxically, the functionally most constrained pathway exhibits only moderate sequence conservation ( nrfA : 9th/13 genes), while a moderately constrained pathway shows exceptional conservation ( napA ). Four constraint-conservation patterns demonstrate that gene-specific structural and ecological factors generate evolutionary rate variation independently of taxonomic associations. Our results establish a hierarchical framework in which taxonomic constraints set baseline functional potential, ecological trade-offs shape strategy diversification, and molecular evolution modulates gene-level conservation patterns across biological scales. This framework establishes quantitative baselines that enable probabilistic inference of nitrogen cycling capabilities from taxonomic composition, with potential applications in amplicon-based community analysis, targeted cultivation, and biogeochemical modeling.