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Forest canopy structural complexity (CSC), an emergent ecosystem property, plays a critical role in controlling ecosystem productivity, resource acquisition and resource use‐efficiency; yet is poorly characterized across broad geographic scales and is difficult to upscale from the plot to the landscape.Here, we show that the relationship between canopy height and CSC can be explained using power laws by analysing lidar‐derived CSC data from 17 temperate forest sites spanning over 17 degrees of latitude. Across three plant functional types (deciduous broadleaf, evergreen needleleaf and mixed forests), CSC increases as an approximate power law of forest height. In evergreen needleleaf forests, increases in canopy height do not result in increases in complexity to the same magnitude as in other forest types.We attribute differences in the slope of height:complexity relationships among forest types to: (a) the limited diversity of crown architectures among evergreen conifer trees relative to broadleaf species; (b) differences in how vertical forest layering develops with height; and (c) competitive exclusion by needleleaf species. We show support for these potential mechanisms with an analysis of 4,324 individual trees from across 18 National Ecological Observatory Network sites showing that crown geometry‐to‐tree height relationships differ consistently between broadleaf and needleleaf species.Power law relationships between forest height and CSC have broad implications for modelling, scaling and mapping forest structural attributes. Our results suggest that forest research and management should consider the nonlinearity in scaling between forest height and CSC and that the nature of these relationships may differ by forest type. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions. • Structural explained carbon storage better than did species diversity. • Structural diversity outperformed stand age, soil, and climate variables. • Influences of diversity on carbon storage vary across natural and planted forests. • Plot-based structural diversity showed stronger correlations than GEDI-based metrics. • GEDI-based metrics of forest structure will help enable large scale carbon mapping. [ABSTRACT FROM AUTHOR]