Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient- and water-limited native Eucalypt woodland

Global critical soil moisture thresholds of plant water stress

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Microbial competition for phosphorus limits the CO2 response of a mature forest

The capacity for terrestrial ecosystems to sequester additional carbon (C) with rising CO2 concentrations depends on soil nutrient availability1,2. Previous evidence suggested that mature forests growing on phosphorus (P)-deprived soils had limited capacity to sequester extra biomass under elevated CO2 (refs. 3-6), but uncertainty about ecosystem P cycling and its CO2 response represents a crucial bottleneck for mechanistic prediction of the land C sink under climate change7. Here, by compiling the first comprehensive P budget for a P-limited mature forest exposed to elevated CO2, we show a high likelihood that P captured by soil microorganisms constrains ecosystem P recycling and availability for plant uptake. Trees used P efficiently, but microbial pre-emption of mineralized soil P seemed to limit the capacity of trees for increased P uptake and assimilation under elevated CO2 and, therefore, their capacity to sequester extra C. Plant strategies to stimulate microbial P cycling and plant P uptake, such as increasing rhizosphere C release to soil, will probably be necessary for P-limited forests to increase C capture into new biomass. Our results identify the key mechanisms by which P availability limits CO2 fertilization of tree growth and will guide the development of Earth system models to predict future long-term C storage.

A new approach to derive productivity of tropical forests using radar remote sensing measurements

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

Digital twins: dynamic model-data fusion for ecology

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

Stomatal behaviour moderates the water cost of CO2 acquisition for 21 boreal and temperate species under experimental climate change

The linkage of stomatal behaviour with photosynthesis is critical to understanding water and carbon cycles under global change. The relationship of stomatal conductance (gs ) and CO2 assimilation (Anet ) across a range of environmental contexts, as represented in the model parameter (g1 ), has served as a proxy of the marginal water cost of carbon acquisition. We use g1 to assess species differences in stomatal behaviour to a decade of open-air experimental climate change manipulations, asking whether generalisable patterns exist across species and climate contexts. Anet -gs measurements (17 727) for 21 boreal and temperate tree species under ambient and +3.3°C warming, and ambient and ~40% summer rainfall reduction, provided >2700 estimates of g1 . Warming and/or reduced rainfall treatments both lowered g1 because those treatments resulted in lower soil moisture and because stomatal behaviour changed more in warming when soil moisture was low. Species tended to respond similarly, although, in species from warmer and drier habitats, g1 tended to be slightly higher and to be the least sensitive to the decrease in soil water. Overall, both warming and rainfall reduction consistently made stomatal behaviour more conservative in terms of water loss per unit carbon gain across 21 species and a decade of experimental observation.

Optimizing sowing window, cultivar choice, and plant density to boost maize yield under RCP8.5 climate scenario of CMIP5

The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30 days from baseline planting date, and raising the sowing density to 9 m^-2 plants. These insights could minimize the potential reduction of climate change-induced yields by 39% by late century.

Integrating plant physiology and community ecology across scales through trait-based models to predict drought mortality

Forests are a critical carbon sink and widespread tree mortality resulting from climate-induced drought stress has the potential to alter forests from a carbon sink to a source, causing a positive feedback on climate change. Process-based vegetation models aim to represent the current understanding of the underlying mechanisms governing plant physiological and ecological responses to climate. Yet model accuracy varies across scales, and regional-scale model predictive skill is frequently poor when compared with observations of drought-driven mortality. I propose a framework that leverages differences in model predictive skill across spatial scales, mismatches between model predictions and observations, and differences in the mechanisms included and absent across models to advance the understanding of the physiological and ecological processes driving observed patterns drought-driven mortality.

Future climate impacts on forest growth and implications for carbon sequestration through reforestation in southeast Australia

Reforestation is identified as one of the key nature-based solutions to deliver carbon dioxide removal, which will be required to achieve the net zero ambition of the Paris Agreement. However, the potential for sequestration through reforestation is uncertain because climate change is expected to affect the drivers of forest growth. This study used the process-based 3-PG model to investigate the effects of climate change on development of above-ground biomass (AGB), as an indicator of forest growth, in regenerating native forests in southeast Australia. We investigated how changing climate affects AGB, by combining historical data and future climate projections based on 25 global climate models (GCMs) for the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways. We found that the ensemble means of 25 GCMs indicated an increase in temperature with large variations in projected rainfall. When these changes were applied in 3-PG, we found an increase in the simulated AGB by as much as 25% under a moderate emission scenario. This estimate rose to 51% under a high emission scenario by the end of the 21st century across nine selected sites in southeast Australia. However, when CO₂ response was excluded, we found a large decrease in AGB at the nine sites. Our modelling results showed that the modelled response to elevated atmospheric CO₂ (the CO₂ fertilization effect) was largely responsible for the simulated increase of AGB (%). We found that the estimates of future changes in the AGB were subject to uncertainties originating from climate projections, future emission scenarios, and the assumed response to CO₂ fertilization. Such modelling simulation improves understanding of possible climate change impacts on forest growth and the inherent uncertainties in estimating mitigation potential through reforestation, with implications for climate policy in Australia.

Increasing aridity will not offset CO2 fertilization in fast-growing eucalypts with access to deep soil water

Rising atmospheric [CO₂ ] (C_a ) generally enhances tree growth if nutrients are not limiting. However, reduced water availability and elevated evaporative demand may offset such fertilization. Trees with access to deep soil water may be able to mitigate such stresses and respond more positively to C_a . Here, we sought to evaluate how increased vapor pressure deficit and reduced precipitation are likely to modify the impact of elevated C_a (eC_a ) on tree productivity in an Australian Eucalyptus saligna Sm. plantation with access to deep soil water. We parameterized a forest growth simulation model (GOTILWA+) using data from two field experiments on E. saligna: a 2-year whole-tree chamber experiment with factorial C_a (ambient =380, elevated =620 μmol mol^-1 ) and watering treatments, and a 10-year stand-scale irrigation experiment. Model evaluation showed that GOTILWA+ can capture the responses of canopy C uptake to (1) rising vapor pressure deficit (D) under both C_a treatments; (2) alterations in tree water uptake from shallow and deep soil layers during soil dry-down; and (3) the impact of irrigation on tree growth. Simulations suggest that increasing C_a up to 700 μmol mol^-1 alone would result in a 33% increase in annual gross primary production (GPP) and a 62% increase in biomass over 10 years. However, a combined 48% increase in D and a 20% reduction in precipitation would halve these values. Our simulations identify high D conditions as a key limiting factor for GPP. They also suggest that rising C_a will compensate for increasing aridity limitations in E. saligna trees with access to deep soil water under non-nutrient limiting conditions, thereby reducing the negative impacts of global warming upon this eucalypt species. Simulation models not accounting for water sources available to deep-rooting trees are likely to overestimate aridity impacts on forest productivity and C stocks.

Tackling unresolved questions in forest ecology: The past and future role of simulation models

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.

Drought by CO2 interactions in trees: a test of the water savings mechanism

Elevated atmospheric CO₂ (eC_a ) may benefit plants during drought by reducing stomatal conductance (g_s ) but any 'water savings effect' could be neutralized by concurrent stimulation of leaf area. We investigated whether eC_a enhanced water savings, thereby ameliorating the impact of drought on carbon and water relations in trees. We report leaf-level gas exchange and whole-plant and soil water relations during a short-term dry-down in two Eucalyptus species with contrasting drought tolerance. Plants had previously been established for 9 to 11 months in steady-state conditions of ambient atmospheric CO₂ (aC_a ) and eC_a , with half of each treatment group exposed to sustained drought for 5 to 7 months. The lower stomatal conductance under eC_a did not lead to soil moisture savings during the dry-down due to the counteractive effect of increased whole-plant leaf area. Nonetheless, eC_a -grown plants maintained higher photosynthetic rates and leaf water potentials, making them less stressed during the dry-down, despite being larger. These effects were more pronounced in the xeric species than the mesic species, and in previously water-stressed plants. Our findings indicate that eC_a may enhance plant performance during drought despite a lack of soil water savings, especially in species with more conservative growth and water-use strategies.

Linking plant hydraulics and the fast-slow continuum to understand resilience to drought in tropical ecosystems

Tropical ecosystems have the highest levels of biodiversity, cycle more water and absorb more carbon than any other terrestrial ecosystem on Earth. Consequently, these ecosystems are extremely important components of Earth's climatic system and biogeochemical cycles. Plant hydraulics is an essential discipline to understand and predict the dynamics of tropical vegetation in scenarios of changing water availability. Using published plant hydraulic data we show that the trade-off between drought avoidance (expressed as deep-rooting, deciduousness and capacitance) and hydraulic safety (P50 - the water potential when plants lose 50% of their maximum hydraulic conductivity) is a major axis of physiological variation across tropical ecosystems. We also propose a novel and independent axis of hydraulic trait variation linking vulnerability to hydraulic failure (expressed as the hydraulic safety margin (HSM)) and growth, where inherent fast-growing plants have lower HSM compared to slow-growing plants. We surmise that soil nutrients are fundamental drivers of tropical community assembly determining the distribution and abundance of the slow-safe/fast-risky strategies. We conclude showing that including either the growth-HSM or the resistance-avoidance trade-off in models can make simulated tropical rainforest communities substantially more vulnerable to drought than similar communities without the trade-off. These results suggest that vegetation models need to represent hydraulic trade-off axes to accurately project the functioning and distribution of tropical ecosystems.

Low phosphorus supply constrains plant responses to elevated CO2 : A meta-analysis

Phosphorus (P) is an essential macro-nutrient required for plant metabolism and growth. Low P availability could potentially limit plant responses to elevated carbon dioxide (eCO₂ ), but consensus has yet to be reached on the extent of this limitation. Here, based on data from experiments that manipulated both CO₂ and P for young individuals of woody and non-woody species, we present a meta-analysis of P limitation impacts on plant growth, physiological, and morphological response to eCO₂ . We show that low P availability attenuated plant photosynthetic response to eCO₂ by approximately one-quarter, leading to a reduced, but still positive photosynthetic response to eCO₂ compared to those under high P availability. Furthermore, low P limited plant aboveground, belowground, and total biomass responses to eCO₂ , by 14.7%, 14.3%, and 12.4%, respectively, equivalent to an approximate halving of the eCO₂ responses observed under high P availability. In comparison, low P availability did not significantly alter the eCO₂ -induced changes in plant tissue nutrient concentration, suggesting tissue nutrient flexibility is an important mechanism allowing biomass response to eCO₂ under low P availability. Low P significantly reduced the eCO₂ -induced increase in leaf area by 14.3%, mirroring the aboveground biomass response, but low P did not affect the eCO₂ -induced increase in root length. Woody plants exhibited stronger attenuation effect of low P on aboveground biomass response to eCO₂ than non-woody plants, while plants with different mycorrhizal associations showed similar responses to low P and eCO₂ interaction. This meta-analysis highlights crucial data gaps in capturing plant responses to eCO₂ and low P availability. Field-based experiments with longer-term exposure of both CO₂ and P manipulations are critically needed to provide ecosystem-scale understanding. Taken together, our results provide a quantitative baseline to constrain model-based hypotheses of plant responses to eCO₂ under P limitation, thereby improving projections of future global change impacts.

Identifying areas at risk of drought-induced tree mortality across South-Eastern Australia

South-East Australia has recently been subjected to two of the worst droughts in the historical record (Millennium Drought, 2000-2009 and Big Dry, 2017-2019). Unfortunately, a lack of forest monitoring has made it difficult to determine whether widespread tree mortality has resulted from these droughts. Anecdotal observations suggest the Big Dry may have led to more significant tree mortality than the Millennium drought. Critically, to be able to robustly project future expected climate change effects on Australian vegetation, we need to assess the vulnerability of Australian trees to drought. Here we implemented a model of plant hydraulics into the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. We parameterized the drought response behaviour of five broad vegetation types, based on a common garden dry-down experiment with species originating across a rainfall gradient (188-1,125 mm/year) across South-East Australia. The new hydraulics model significantly improved (~35%-45% reduction in root mean square error) CABLE's previous predictions of latent heat fluxes during periods of water stress at two eddy covariance sites in Australia. Landscape-scale predictions of the greatest percentage loss of hydraulic conductivity (PLC) of about 40%-60%, were broadly consistent with satellite estimates of regions of the greatest change in both droughts. In neither drought did CABLE predict that trees would have reached critical PLC in widespread areas (i.e. it projected a low mortality risk), although the model highlighted critical levels near the desert regions of South-East Australia where few trees live. Overall, our experimentally constrained model results imply significant resilience to drought conferred by hydraulic function, but also highlight critical data and scientific gaps. Our approach presents a promising avenue to integrate experimental data and make regional-scale predictions of potential drought-induced hydraulic failure.

Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulics

BACKGROUND AND AIMS

Improved modelling of carbon assimilation and plant growth to low soil moisture requires evaluation of underlying mechanisms in the soil, roots, and shoots. The feedback between plants and their local environment throughout the whole spectrum soil-root-shoot-environment is crucial to accurately describe and evaluate the impact of environmental changes on plant development. This study presents a 3D functional structural plant model, in which shoot and root growth are driven by radiative transfer, photosynthesis, and soil hydrodynamics through different parameterisation schemes relating soil water deficit and carbon assimilation. The new coupled model is used to evaluate the impact of soil moisture availability on plant productivity for two different groups of flowering plants under different spatial configurations.

METHODS

In order to address different aspects of plant development due to limited soil water availability, a 3D FSP model including root, shoot, and soil was constructed by linking three different well-stablished models of airborne plant, root architecture, and reactive transport in the soil. Different parameterisation schemes were used in order to integrate photosynthetic rate with root water uptake within the coupled model. The behaviour of the model was assessed on how the growth of two different types of plants, i.e. monocot and dicot, is impacted by soil water deficit under different competitive conditions: isolated (no competition), intra, and interspecific competition.

KEY RESULTS

The model proved to be capable of simulating carbon assimilation and plant development under different growing settings including isolated monocots and dicots, intra, and interspecific competition. The model predicted that (1) soil water availability has a larger impact on photosynthesis than on carbon allocation; (2) soil water deficit has an impact on root and shoot biomass production by up to 90 % for monocots and 50 % for dicots; and (3) the improved dicot biomass production in interspecific competition was highly related to root depth and plant transpiration.

CONCLUSIONS

An integrated model of 3D shoot architecture and biomass development with a 3D root system representation, including light limitation and water uptake considering soil hydraulics, was presented. Plant-plant competition and regulation on stomatal conductance to drought were able to be predicted by the model. In the cases evaluated here, water limitation impacted plant growth almost 10 times more than the light environment.

Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought

Severe drought events are known to cause important reductions of gross primary productivity (GPP) in forest ecosystems. However, it is still unclear whether this reduction originates from stomatal closure (Stomatal Origin Limitation) and/or non-stomatal limitations (Non-SOL). In this study, we investigated the impact of edaphic drought in 2018 on GPP and its origin (SOL, NSOL) using a dataset of 10 European forest ecosystem flux towers. In all stations where GPP reductions were observed during the drought, these were largely explained by declines in the maximum apparent canopy scale carboxylation rate V_CMAX,APP (NSOL) when the soil relative extractable water content dropped below around 0.4. Concurrently, we found that the stomatal slope parameter (G₁, related to SOL) of the Medlyn et al. unified optimization model linking vegetation conductance and GPP remained relatively constant. These results strengthen the increasing evidence that NSOL should be included in stomatal conductance/photosynthesis models to faithfully simulate both GPP and water fluxes in forest ecosystems during severe drought. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake

The magnitude of the nitrogen (N) limitation of terrestrial carbon (C) storage over the 21st century is highly uncertain because of the complex interactions between the terrestrial C and N cycles. We use an ensemble approach to quantify and attribute process-level uncertainty in C-cycle projections by analysing a 30-member ensemble representing published alternative representations of key N cycle processes (stoichiometry, biological nitrogen fixation (BNF) and ecosystem N losses) within the framework of one terrestrial biosphere model. Despite large differences in the simulated present-day N cycle, primarily affecting simulated productivity north of 40°N, ensemble members generally conform with global C-cycle benchmarks for present-day conditions. Ensemble projections for two representative concentration pathways (RCP 2.6 and RCP 8.5) show that the increase in land C storage due to CO₂ fertilization is reduced by 24 ± 15% due to N constraints, whereas terrestrial C losses associated with climate change are attenuated by 19 ± 20%. As a result, N cycling reduces projected land C uptake for the years 2006-2099 by 19% (37% decrease to 3% increase) for RCP 2.6, and by 21% (40% decrease to 9% increase) for RCP 8.5. Most of the ensemble spread results from uncertainty in temperate and boreal forests, and is dominated by uncertainty in BNF (10% decrease to 50% increase for RCP 2.6, 5% decrease to 100% increase for RCP 8.5). However, choices about the flexibility of ecosystem C:N ratios and processes controlling ecosystem N losses regionally also play important roles. The findings of this study demonstrate clearly the need for an ensemble approach to quantify likely future terrestrial C-N cycle trajectories. Present-day C-cycle observations only weakly constrain the future ensemble spread, highlighting the need for better observational constraints on large-scale N cycling, and N cycle process responses to global change.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

Plant profit maximization improves predictions of European forest responses to drought

Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. We tested a new profit maximization model, where photosynthetic uptake of CO₂ is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil-plant continuum k_max (a key model parameter which is not commonly measured) could be predicted from long-term site climate. Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that k_max could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. Although the generalization of this approach is contingent upon determining k_max , it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate

Land surface models (LSMs) typically use empirical functions to represent vegetation responses to soil drought. These functions largely neglect recent advances in plant ecophysiology that link xylem hydraulic functioning with stomatal responses to climate. We developed an analytical stomatal optimization model based on xylem hydraulics (SOX) to predict plant responses to drought. Coupling SOX to the Joint UK Land Environment Simulator (JULES) LSM, we conducted a global evaluation of SOX against leaf- and ecosystem-level observations. SOX simulates leaf stomatal conductance responses to climate for woody plants more accurately and parsimoniously than the existing JULES stomatal conductance model. An ecosystem-level evaluation at 70 eddy flux sites shows that SOX decreases the sensitivity of gross primary productivity (GPP) to soil moisture, which improves the model agreement with observations and increases the predicted annual GPP by 30% in relation to JULES. SOX decreases JULES root-mean-square error in GPP by up to 45% in evergreen tropical forests, and can simulate realistic patterns of canopy water potential and soil water dynamics at the studied sites. SOX provides a parsimonious way to incorporate recent advances in plant hydraulics and optimality theory into LSMs, and an alternative to empirical stress factors.

Towards better representations of carbon allocation in vegetation: a conceptual framework and mathematical tool

The representation of carbon allocation (CA) in ecosystem differs tremendously among models, resulting in diverse responses of carbon cycling and storage to global change. Several studies have highlighted discrepancies between empirical observations and model predictions, attributing these differences to problems of model structure. We analyzed the mathematical representation of CA in models using concepts from dynamical systems theory; we reviewed a representative sample of models of CA in vegetation and developed a model database within the Python package bgc-md. We asked whether these representations can be generalized as a linear system, or whether a more general framework is needed to accommodate nonlinearities. Some of the vegetation systems simulated with the reviewed models have a fixed partitioning of photosynthetic products, independent of environmental forcing. Vegetation is often represented as a linear system without storage compartments. Yet, other structures with nonlinearities have also been proposed, with important consequences on the temporal trajectories of ecosystem carbon compartments. The proposed mathematical framework unifies the representation of alternative CA schemes, facilitating their classification according to mathematical properties as well as their potential temporal behaviour. It can represent complex processes in a compact form, which can potentially facilitate dialog among empiricists, theoreticians, and modellers.

Advancing global change biology through experimental manipulations: Where have we been and where might we go?

This commentary summarizes the publication history of Global Change Biology for works on experimental manipulations over the past 25 years and highlights a number of key publications. The retrospective summary is then followed by some thoughts on the future of experimental work as it relates to mechanistic understanding and methodological needs. Experiments for elevated CO2 atmospheres and anticipated warming scenarios which take us beyond historical analogs are suggested as future priorities. Disturbance is also highlighted as a key agent of global change. Because experiments are demanding of both personnel effort and limited fiscal resources, the allocation of experimental investments across Earth's biomes should be done in ecosystems of key importance. Uncertainty analysis and broad community consultation should be used to identify research questions and target biomes that will yield substantial gains in predictive confidence and societal relevance. A full range of methodological approaches covering small to large spatial scales will continue to be justified as a source of mechanistic understanding. Nevertheless, experiments operating at larger spatial scales encompassing organismal, edaphic, and environmental diversity of target ecosystems are favored, as they allow for the assessment of long-term biogeochemical feedbacks enabling a full range of questions to be addressed. Such studies must also include adequate investment in measurements of key interacting variables (e.g., water and nutrient availability and budgets) to enable mechanistic understanding of responses and to interpret context dependency. Integration of ecosystem-scale manipulations with focused process-based manipulations, networks, and large-scale observations will aid more complete understanding of ecosystem responses, context dependence, and the extrapolation of results. From the outset, these studies must be informed by and integrated with ecosystem models that provide quantitative predictions from their embedded mechanistic hypotheses. A true two-way interaction between experiments and models will simultaneously increase the rate and robustness of Global Change research.

Observed and modelled historical trends in the water-use efficiency of plants and ecosystems

Plant water-use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO₂ concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO₂ . Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long-term observation-based estimates of WUE that will better inform the representation of WUE in vegetation models.

Bridging Drought Experiment and Modeling: Representing the Differential Sensitivities of Leaf Gas Exchange to Drought

Global climate change is expected to increase drought duration and intensity in certain regions while increasing rainfall in others. The quantitative consequences of increased drought for ecosystems are not easy to predict. Process-based models must be informed by experiments to determine the resilience of plants and ecosystems from different climates. Here, we demonstrate what and how experimentally derived quantitative information can improve the representation of stomatal and non-stomatal photosynthetic responses to drought in large-scale vegetation models. In particular, we review literature on the answers to four key questions: (1) Which photosynthetic processes are affected under short-term drought? (2) How do the stomatal and non-stomatal responses to short-term drought vary among species originating from different hydro-climates? (3) Do plants acclimate to prolonged water stress, and do mesic and xeric species differ in their degree of acclimation? (4) Does inclusion of experimentally based plant functional type specific stomatal and non-stomatal response functions to drought help Land Surface Models to reproduce key features of ecosystem responses to drought? We highlighted the need for evaluating model representations of the fundamental eco-physiological processes under drought. Taking differential drought sensitivity of different vegetation into account is necessary for Land Surface Models to accurately model drought responses, or the drought impacts on vegetation in drier environments may be over-estimated.

Physiological acclimation dampens initial effects of elevated temperature and atmospheric CO2 concentration in mature boreal Norway spruce

Physiological processes of terrestrial plants regulate the land-atmosphere exchange of carbon, water, and energy, yet few studies have explored the acclimation responses of mature boreal conifer trees to climate change. Here we explored the acclimation responses of photosynthesis, respiration, and stomatal conductance to elevated temperature and/or CO₂ concentration ([CO₂ ]) in a 3-year field experiment with mature boreal Norway spruce. We found that elevated [CO₂ ] decreased photosynthetic carboxylation capacity (-23% at 25 °C) and increased shoot respiration (+64% at 15 °C), while warming had no significant effects. Shoot respiration, but not photosynthetic capacity, exhibited seasonal acclimation. Stomatal conductance at light saturation and a vapour pressure deficit of 1 kPa was unaffected by elevated [CO₂ ] but significantly decreased (-27%) by warming, and the ratio of intercellular to ambient [CO₂ ] was enhanced (+17%) by elevated [CO₂ ] and decreased (-12%) by warming. Many of these responses differ from those typically observed in temperate tree species. Our results show that long-term physiological acclimation dampens the initial stimulation of plant net carbon assimilation to elevated [CO₂ ], and of plant water use to warming. Models that do not account for these responses may thus overestimate the impacts of climate change on future boreal vegetation-atmosphere interactions.

Mapping local and global variability in plant trait distributions

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment

Multifactor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date, such models have only been tested against single-factor experiments. We applied 10 TBMs to the multifactor Prairie Heating and CO₂ Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor experiments can be used to constrain models and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m^-2  yr^-1 ). Comparison with data highlighted model failures particularly with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single-factors treatments was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the N cycle models, N availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO₂ -induced water savings to extend the growing season length. Observed interactive (CO₂  × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. We outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax ) on global gross primary production

The maximum photosynthetic carboxylation rate (V_cmax ) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V_cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr^-1 , 65% of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V_cmax variation in the field, particularly in northern latitudes.

A roadmap for improving the representation of photosynthesis in Earth system models

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO₂ assimilation (A) to key environmental variables: light, temperature, CO₂ concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.