The rooting zone water storage capacity (S0) extends from the soil surface to the weathered bedrock (the Critical Zone) and determines land-atmosphere exchange during dry periods. Despite its importance to land-surface modeling, variations of S0 across space are largely unknown as they cannot be observed directly. We developed a method to diagnose global variations of S0 from the relationship between vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) and the cumulative water deficit (CWD). We then show that spatial variations in S0 can be predicted from the assumption that plants are adapted to sustain CWD extremes occurring with a return period that is related to the life form of dominant plants and the large-scale topographical setting. Predicted biome-level S0 distributions, translated to an apparent rooting depth (zr) by accounting for soil texture, are consistent with observations from a comprehensive zr dataset. Large spatial variations in S0 across the globe reflect adaptation of zr to the hydroclimate and topography and implies large heterogeneity in the sensitivity of vegetation activity to drought. The magnitude of S0 inferred for most of the Earthtextquoterights vegetated regions and particularly for those with a large seasonality in their hydroclimate indicates an important role for plant access to water stored at depth - beyond the soil layers commonly considered in land-surface models.Competing Interest StatementThe authors have declared no competing interest.

Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO2) emitted by human activities each year1, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO2 (refs. 2,3). Although plant biomass often increases in elevated CO2 (eCO2) experiments4–6, SOC has been observed to increase, remain unchanged or even decline7. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections8,9. Here we synthesized data from 108 eCO2 experiments and found that the effect of eCO2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO2, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO2 in grasslands (8 textpm 2 per cent) but not in forests (0 textpm 2 per cent), even though plant biomass in grasslands increase less (9 textpm 3 per cent) than in forests (23 textpm 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.

Plants and vegetation play a critical—but largely unpredictable—role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.