Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (P ) from soil water potential (P ) and vapor pressure deficit (D). Modeled responses to D and P were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and P across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization.

During droughts, leaves are predicted to act as 'hydraulic fuses' by shedding when plants reach critically low water potential (Ψ ), thereby slowing water loss, stabilizing Ψ and protecting against cavitation-induced loss of stem hydraulic conductivity (K ). We tested these predictions among trees in seasonally dry tropical forests, where leaf shedding is common, yet variable, among species. We tracked leaf phenology, Ψ and K in saplings of six tree species distributed across two forests. Species differed in their timing and extent of leaf shedding, yet converged in shedding leaves as they approached the Ψ value associated with a 50% loss of K and at which their model-estimated maximum sustainable transpiration rate approached zero. However, after shedding all leaves, the Ψ value of one species, Genipa americana, continued to decline, indicating that water loss continued after leaf shedding. K was highly variable among saplings within species and seasons, suggesting a minimal influence of seasonal drought on K . Hydraulic limits appear to drive diverse patterns of leaf shedding among tropical trees, supporting the hydraulic fuse hypothesis. However, leaf shedding is not universally effective at stabilizing Ψ , suggesting that the main function of drought deciduousness may vary among species.

Stomatal response to environmental conditions forms the backbone of all ecosystem and carbon cycle models, but is largely based on empirical relationships. Evolutionary theories of stomatal behaviour are critical for guarding against prediction errors of empirical models under future climates. Longstanding theory holds that stomata maximise fitness by acting to maintain constant marginal water use efficiency over a given time horizon, but a recent evolutionary theory proposes that stomata instead maximise carbon gain minus carbon costs/risk of hydraulic damage. Using data from 34 species that span global forest biomes, we find that the recent carbon-maximisation optimisation theory is widely supported, revealing that the evolution of stomatal regulation has not been primarily driven by attainment of constant marginal water use efficiency. Optimal control of stomata to manage hydraulic risk is likely to have significant consequences for ecosystem fluxes during drought, which is critical given projected intensification of the global hydrological cycle.

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking.We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m À2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad-and needleleaf species, and upper-and lower-canopy (i.e. sun and shade) growth environments.Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R 2 = 0.89; root mean square error (RMSE) = 15.45 g m À2 ).Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.