Tree water uptake patterns across the globe.

Plant water uptake from the soil is a crucial element of the global hydrological cycle and essential for vegetation drought resilience. Yet, knowledge of how the distribution of water uptake depth (WUD) varies across species, climates, and seasons is scarce relative to our knowledge of aboveground plant functions. With a global literature review, we found that average WUD varied more among biomes than plant functional types (i.e. deciduous/evergreen broadleaves and conifers), illustrating the importance of the hydroclimate, especially precipitation seasonality, on WUD. By combining records of rooting depth with WUD, we observed a consistently deeper maximum rooting depth than WUD with the largest differences in arid regions - indicating that deep taproots act as lifelines while not contributing to the majority of water uptake. The most ubiquitous observation across the literature was that woody plants switch water sources to soil layers with the highest water availability within short timescales. Hence, seasonal shifts to deep soil layers occur across the globe when shallow soils are drying out, allowing continued transpiration and hydraulic safety. While there are still significant gaps in our understanding of WUD, the consistency across global ecosystems allows integration of existing knowledge into the next generation of vegetation process models.

Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems.

Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models. Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites. We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures. By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate.

Diagnosing evapotranspiration responses to water deficit across biomes using deep learning.

Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone. Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole-column moisture availability. We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water-unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites. A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice.

Climate change is shifting the growing seasons of plants, affecting species performance and biogeochemical cycles. Yet how the timing of autumn leaf senescence in Northern Hemisphere forests will change remains uncertain. Using satellite, ground, carbon flux, and experimental data, we show that early-season and late-season warming have opposite effects on leaf senescence, with a reversal occurring after the year's longest day (the summer solstice). Across 84% of the northern forest area, increased temperature and vegetation activity before the solstice led to an earlier senescence onset of, on average, 1.9 ± 0.1 days per °C, whereas warmer post-solstice temperatures extended senescence duration by 2.6 ± 0.1 days per °C. The current trajectories toward an earlier onset and slowed progression of senescence affect Northern Hemisphere-wide trends in growing-season length and forest productivity.

Global patterns of water storage in the rooting zones of vegetation.

The rooting-zone water-storage capacity-the amount of water accessible to plants-controls the sensitivity of land-atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth's vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.

Extensive global wetland loss over the past three centuries.

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity1,2. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain3. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km2 (confidence interval 2.9-3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16-23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration4.

Acclimation of phenology relieves leaf longevity constraints in deciduous forests.

Leaf phenology is key for regulating total growing-season mass and energy fluxes. Long-term temporal trends towards earlier leaf unfolding are observed across Northern Hemisphere forests. Phenological dates also vary between years, whereby end-of-season (EOS) dates correlate positively with start-of-season (SOS) dates and negatively with growing-season total net CO2 assimilation (Anet). These associations have been interpreted as the effect of a constrained leaf longevity or of premature carbon (C) sink saturation-with far-reaching consequences for long-term phenology projections under climate change and rising CO2. Here, we use multidecadal ground and remote-sensing observations to show that the relationships between Anet and EOS are opposite at the interannual and the decadal time scales. A decadal trend towards later EOS persists in parallel with a trend towards increasing Anet-in spite of the negative Anet-EOS relationship at the interannual scale. This finding is robust against the use of diverse observations and models. Results indicate that acclimation of phenology has enabled plants to transcend a constrained leaf longevity or premature C sink saturation over the course of several decades, leading to a more effective use of available light and a sustained extension of the vegetation CO2 uptake season over time.

Application of the rapid leaf A-Ci response (RACiR) technique: examples from evergreen broadleaved species.

Using steady-state photosynthesis-intercellular CO2 concentration (A-Ci) response curves to obtain the maximum rates of ribulose-1,5-bisphosphate carboxylase oxygenase carboxylation (Vcmax) and electron transport (Jmax) is time-consuming and labour-intensive. Instead, the rapid A-Ci response (RACiR) technique provides a potential, high-efficiency method. However, efficient parameter settings of RACiR technique for evergreen broadleaved species remain unclear. Here, we used Li-COR LI-6800 to obtain the optimum parameter settings of RACiR curves for evergreen broadleaved trees and shrubs. We set 11 groups of CO2 gradients ([CO2]), i.e. R1 (400-1500 ppm), R2 (400-200-800 ppm), R3 (420-20-620 ppm), R4 (420-20-820 ppm), R5 (420-20-1020 ppm), R6 (420-20-1220 ppm), R7 (420-20-1520 ppm), R8 (420-20-1820 ppm), R9 (450-50-650 ppm), R10 (650-50 ppm) and R11 (650-50-650 ppm), and then compared the differences between steady-state A-Ci and RACiR curves. We found that Vcmax and Jmax calculated by steady-state A-Ci and RACiR curves overall showed no significant differences across 11 [CO2] gradients (P > 0.05). For the studied evergreens, the efficiency and accuracy of R2, R3, R4, R9 and R10 were higher than the others. Hence, we recommend that the [CO2] gradients of R2, R3, R4, R9 and R10 could be applied preferentially for measurements when using the RACiR technique to obtain Vcmax and Jmax of evergreen broadleaved species.

Environmental controls on the light use efficiency of terrestrial gross primary production.

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C3 species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

Towards a unified theory of plant photosynthesis and hydraulics.

The global carbon and water cycles are governed by the coupling of CO2 and water vapour exchanges through the leaves of terrestrial plants, controlled by plant adaptations to balance carbon gains and hydraulic risks. We introduce a trait-based optimality theory that unifies the treatment of stomatal responses and biochemical acclimation of plants to environments changing on multiple timescales. Tested with experimental data from 18 species, our model successfully predicts the simultaneous decline in carbon assimilation rate, stomatal conductance and photosynthetic capacity during progressive soil drought. It also correctly predicts the dependencies of gas exchange on atmospheric vapour pressure deficit, temperature and CO2. Model predictions are also consistent with widely observed empirical patterns, such as the distribution of hydraulic strategies. Our unified theory opens new avenues for reliably modelling the interactive effects of drying soil and rising atmospheric CO2 on global photosynthesis and transpiration.

Photosynthetic acclimation and sensitivity to short- and long-term environmental changes in a drought-prone forest.

Future climate will be characterized by an increase in frequency and duration of drought and warming that exacerbates atmospheric evaporative demand. How trees acclimate to long-term soil moisture changes and whether these long-term changes alter trees' sensitivity to short-term (day to months) variations of vapor pressure deficit (VPD) and soil moisture is largely unknown. Leaf gas exchange measurements were performed within a long-term (17 years) irrigation experiment in a drought-prone Scots pine-dominated forest in one of Switzerland's driest areas on trees in naturally dry (control), irrigated, and 'irrigation-stop' (after 11 years of irrigation) conditions. Seventeen years of irrigation increased photosynthesis (A) and stomatal conductance (gs) and reduced gs sensitivity to increasing VPD and soil drying. Following irrigation-stop, gas exchange decreased only after 3 years. After 5 years, maximum carboxylation (Vcmax) and electron transport (Jmax) rates in irrigation-stop recovered to similar levels as to before the irrigation-stop. These results suggest that long-term release from soil drought reduces the sensitivity to VPD and that atmospheric constraints may play an increasingly important role in combination with soil drought. Moreover, our study indicates that structural adjustments lead to an attenuation of initially strong leaf-level acclimation to strong multiple-year drought.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

The global distribution and environmental drivers of aboveground versus belowground plant biomass.

A poor understanding of the fraction of global plant biomass occurring belowground as roots limits our understanding of present and future ecosystem function and carbon pools. Here we create a database of root-mass fractions (RMFs), an index of plant below- versus aboveground biomass distributions, and generate quantitative, spatially explicit global maps of RMFs in trees, shrubs and grasses. Our analyses reveal large gradients in RMFs both across and within vegetation types that can be attributed to resource availability. High RMFs occur in cold and dry ecosystems, while low RMFs dominate in warm and wet regions. Across all vegetation types, the directional effect of temperature on RMFs depends on water availability, suggesting feedbacks between heat, water and nutrient supply. By integrating our RMF maps with existing aboveground plant biomass information, we estimate that in forests, shrublands and grasslands, respectively, 22%, 47% and 67% of plant biomass exists belowground, with a total global belowground fraction of 24% (20-28%), that is, 113 (90-135) Gt carbon. By documenting the environmental correlates of root biomass allocation, our results can inform model projections of global vegetation dynamics under current and future climate scenarios.

Organizing principles for vegetation dynamics.

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.

Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions.

Leaf unfolding in temperate forests is driven by spring temperature, but little is known about the spatial variance of that temperature dependency. Here we use in situ leaf unfolding observations for eight deciduous tree species to show that the two factors that control chilling (number of cold days) and heat requirement (growing degree days at leaf unfolding, GDDreq) only explain 30% of the spatial variance of leaf unfolding. Radiation and aridity differences among sites together explain 10% of the spatial variance of leaf unfolding date, and 40% of the variation in GDDreq. Radiation intensity is positively correlated with GDDreq and aridity is negatively correlated with GDDreq spatial variance. These results suggest that leaf unfolding of temperate deciduous trees is adapted to local mean climate, including water and light availability, through altered sensitivity to spring temperature. Such adaptation of heat requirement to background climate would imply that models using constant temperature response are inherently inaccurate at local scale.

Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency.

Multiple lines of evidence suggest that plant water-use efficiency (WUE)-the ratio of carbon assimilation to water loss-has increased in recent decades. Although rising atmospheric CO2 has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimating WUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystem-scale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO2-induced reductions in stomatal conductance.

Quantifying soil moisture impacts on light use efficiency across biomes.

Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought-related assessments.

Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition.

Contents Summary 507 I. Introduction 507 II. The return on investment approach 508 III. CO2 response spectrum 510 IV. Discussion 516 Acknowledgements 518 References 518 SUMMARY: Land ecosystems sequester on average about a quarter of anthropogenic CO2 emissions. It has been proposed that nitrogen (N) availability will exert an increasingly limiting effect on plants' ability to store additional carbon (C) under rising CO2 , but these mechanisms are not well understood. Here, we review findings from elevated CO2 experiments using a plant economics framework, highlighting how ecosystem responses to elevated CO2 may depend on the costs and benefits of plant interactions with mycorrhizal fungi and symbiotic N-fixing microbes. We found that N-acquisition efficiency is positively correlated with leaf-level photosynthetic capacity and plant growth, and negatively with soil C storage. Plants that associate with ectomycorrhizal fungi and N-fixers may acquire N at a lower cost than plants associated with arbuscular mycorrhizal fungi. However, the additional growth in ectomycorrhizal plants is partly offset by decreases in soil C pools via priming. Collectively, our results indicate that predictive models aimed at quantifying C cycle feedbacks to global change may be improved by treating N as a resource that can be acquired by plants in exchange for energy, with different costs depending on plant interactions with microbial symbionts.

Response to Comment on "Mycorrhizal association as a primary control of the CO2 fertilization effect".

Norby et al center their critique on the design of the data set and the response variable used. We address these criticisms and reinforce the conclusion that plants that associate with ectomycorrhizal fungi exhibit larger biomass and growth responses to elevated CO2 compared with plants that associate with arbuscular mycorrhizae.

Precipitation and carbon-water coupling jointly control the interannual variability of global land gross primary production.

Carbon uptake by terrestrial ecosystems is increasing along with the rising of atmospheric CO2 concentration. Embedded in this trend, recent studies suggested that the interannual variability (IAV) of global carbon fluxes may be dominated by semi-arid ecosystems, but the underlying mechanisms of this high variability in these specific regions are not well known. Here we derive an ensemble of gross primary production (GPP) estimates using the average of three data-driven models and eleven process-based models. These models are weighted by their spatial representativeness of the satellite-based solar-induced chlorophyll fluorescence (SIF). We then use this weighted GPP ensemble to investigate the GPP variability for different aridity regimes. We show that semi-arid regions contribute to 57% of the detrended IAV of global GPP. Moreover, in regions with higher GPP variability, GPP fluctuations are mostly controlled by precipitation and strongly coupled with evapotranspiration (ET). This higher GPP IAV in semi-arid regions is co-limited by supply (precipitation)-induced ET variability and GPP-ET coupling strength. Our results demonstrate the importance of semi-arid regions to the global terrestrial carbon cycle and posit that there will be larger GPP and ET variations in the future with changes in precipitation patterns and dryland expansion.