Abstract
Aim Empirical biodiversity – forest structure relationships (BSRs) underlie the use of forest structure as a remotely sensible proxy of biodiversity. However, little is known about how BSRs generalize to continental scales or how climate interacts with structure to drive local patterns in plant diversity. Resolving these research gaps in macrosystems ecology will strengthen our understanding of the biogeography of plant diversity, with implications for global‐scale biodiversity mapping. Location USA. Time period Contemporary. Major taxa studied Vascular plants and trees. Methods We combined climate variables with field measurements and airborne lidar from all forest and woodland plots in the National Ecological Observatory Network (NEON) to characterize the role of climate in constraining BSRs across the United States. Spatial generalized linear mixed models were used to quantify the individual and joint effects of structure and climate on vascular plant and tree diversity. Results These findings provide evidence for broad‐scale BSRs across the USA; namely, between plant/tree diversity and forest structural metrics in the vertical and horizontal planes. Vascular plant diversity was positively related to horizontally heterogeneous and bottom‐skewed canopies, whereas tree diversity was positively associated with canopy cover and structural heterogeneity. In addition, climate variables related to stress (negatively), energy (positively) and seasonality (negatively) affected broad‐scale patterns in diversity, with water availability (but not temperature) exerting significant effects on structural conditions. Importantly, climate and structure interact to explain variance in BSRs along coldness, mean temperature and evapotranspiration gradients. Main conclusions Our findings reinforce the importance of local context dependence in assessing non‐stationary biogeographical patterns in forest biodiversity, as well as the unique ways that forest structure and climate interact to constrain plant diversity. Beyond these theoretical insights, this study provides an empirical foundation for generalizing remotely sensed estimates of climate and forest structure to model biodiversity over vast extents.