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Summary: Savannas cover a significant fraction of the Earth's land surface. In these ecosystems, C3 trees and C4 grasses coexist persistently, but the mechanisms explaining coexistence remain subject to debate. Different quantitative models have been proposed to explain coexistence, but these models make widely contrasting assumptions about which mechanisms are responsible for savanna persistence. Here, we show that no single existing model fully captures all key elements required to explain tree–grass coexistence across savanna rainfall gradients, but many models make important contributions. We show that recent empirical work allows us to combine many existing elements with new ideas to arrive at a synthesis that combines elements of two dominant frameworks: Walter's two‐layer model and demographic bottlenecks. We propose that functional rooting separation is necessary for coexistence and is the crux of the coexistence problem. It is both well‐supported empirically and necessary for tree persistence, given the comprehensive grass superiority for soil moisture acquisition. We argue that eventual tree dominance through shading is precluded by ecohydrological constraints in dry savannas and by fire and herbivores in wet savannas. Strong asymmetric grass–tree competition for soil moisture limits tree growth, exposing trees to persistent demographic bottlenecks. [ABSTRACT FROM AUTHOR]

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network's (NEON's) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON's AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground‐measured LAI (r = 0.32) and AOP Total Biomass and ground‐measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least‐squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation R2of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP‐derived data products should not be used without extensive ground‐based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field‐based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in‐situ field measurements across a diversity of sites. [ABSTRACT FROM AUTHOR]

Roots are key components of terrestrial ecosystems, yet little is known about how root structure and function vary across a broad range of species, functional groups and ecological gradients in situ.We assessed how woody and grass root anatomical traits vary among soil depths and different fire frequencies to better understand the water‐use strategies exhibited by these two functional groups in tallgrass prairie experiencing woody encroachment. Specifically, we asked: (a) Do root anatomical traits differ with fire frequency or soil depth?; (b) Do relationships between anatomical traits that confer hydraulic safety versus efficiency vary by fire frequency or soil depth?; (c) Is root anatomy associated with integrative root traits (e.g. root diameter, specific root length (SRL) and root biomass)?; and (d) When scaled by root biomass, do root water‐use traits impact the capacity for water uptake?We collected grass and woody roots from 10, 30 and 50 cm deep soil in areas burned every 1, 4 and 20 years. We then measured xylem conduit diameter, conduit cell wall thickness, conduit number, conduit mechanical safety (t/b), stele area, endoderm thickness, hydraulic diameter, theoretical hydraulic conductivity and root‐system theoretical hydraulic conductance.We observed: (a) Woody roots had high hydraulic conductance in shallow soils and greater mechanical strength in deeper soils, which may provide a competitive advantage in less frequently burned, more diverse plant communities; (b) shallow grass roots had unique trait combinations at the anatomical and root‐system levels (thinner, more numerous conduits and higher root‐system hydraulic conductance compared to deeper roots) that likely allow these plants to rapidly use water but tolerate dry soils under multiple fire regimes; and (c) hydraulic safety versus efficiency trade‐offs translate between different hierarchical scales (i.e. from anatomical to integrative root traits).These results provide anatomical evidence to explain water‐use dynamics in tallgrass prairie and also provide novel insight regarding functional strategies that may facilitate the conversion from grassland to shrubland in less frequently burned tallgrass prairie. Future work should investigate these dynamics in situ, as they may explain current and future patterns of woody‐grass coexistence in tallgrass prairies. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]