Interactive effects of functional, taxonomic, and structural diversity on aboveground carbon storage in humid subtropical mixed forests

• Functional dominance emerged as the strongest driver of above-ground carbon storage. • Functional divergence, taxonomic, and structural diversity independently enhanced above-ground carbon storage. • A significant three-way interaction supported biodiversity–carbon synergy. • Diversity benefits were amplified in vertically heterogeneous forests. Understanding how interactive effects of forest diversity dimensions influence carbon storage is crucial for biodiversity-based climate mitigation strategies. While previous studies have examined structural and species diversity effects, the interdependence among multiple diversity dimensions remains unclear. This study evaluated the interactive effects of functional dominance, functional divergence, taxonomic diversity, and tree size inequality on aboveground carbon storage (AGC) in oak-pine forests of the southeastern U.S. using data from 946 Forest Inventory and Analysis (FIA) plots across Alabama, Georgia, and Florida (2009–2019). AGC (kg/ha) was extracted directly from FIA, which has pre-calculated the values using species-specific allometric equations. Three hypotheses were tested: H1 (Main Effects): Functional dominance (community-weighted mean of maximum height), functional divergence (trait variance weighted by species’ basal area), and taxonomic diversity (Shannon index), each positively affects AGC. H2 (Two-Way Interaction): The positive effect of functional dominance on AGC is enhanced under high taxonomic diversity. H3 (Three-Way Interaction): Functional divergence, taxonomic diversity, and tree size inequality (coefficient of variation in height) interact to influence AGC. Results supported H1, with significant positive effects of functional dominance (β = 0.318, p < 0.001), functional divergence (β = 0.132, p < 0.001), and taxonomic diversity (β = 0.110, p < 0.001). H2 was not supported (β = −0.0005, p = 0.792). H3 was supported by a significant three-way interaction (β = 0.024, p = 0.022), indicating that functional and taxonomic diversity effects on AGC were enhanced by high structural heterogeneity. The model explained 75 % of AGC variation (R 2 = 0.753). These findings underscore the need to integrate multiple dimensions of forest diversity, particularly dominant traits, trait variation, taxonomic diversity, and structural complexity in forest carbon assessments and offer a transferable framework for sustainable forest management in similar ecosystems globally.