Global Assessment of Environmental and Plant‐Trait Influences on Root: Shoot Biomass Ratios - Ruijie Ding; Rodolfo L. B. Nóbrega; Iain Colin Prentice (Global Change Biology, 2025)
The distribution of assimilated carbon (C) among roots, stems, and leaves is a central process in terrestrial ecosystem dynamics. Yet the biomass allocation schemes used in current global vegetation and land surface models pre‐date the existence of large plant‐trait data sets and remain largely untested. Here we formulate hypotheses on the controls of root: shoot biomass ratios (R:S), based on eco‐evolutionary optimality principles, and assess them quantitatively by analysing data on nearly 30,000 observations of R:S. We analysed global R:S patterns using multiple linear regression models for woody and herbaceous species separately, considering as candidate predictors growing‐season mean temperature ( T g ), gross primary production (GPP), a measure of root‐zone water capacity (RZ WC ), soil pH, sand content, aridity index (AI), and plant traits: vegetation height (H), leaf thickness (LT), leaf dry matter content (LDMC), specific leaf area (SLA), specific root length (SRL), and rooting depth (RRD). R:S was systematically greater in herbaceous plants. R:S decreased with T g , GPP, and height but increased with sand content, RRD, and LDMC in both woody and herbaceous plants. However, AI and leaf thickness had opposing effects on R:S. RZ WC and SLA were important in woody plants, while pH and SRL played a larger role in herbaceous plants. The models explained 13% (woody) and 31% (herbaceous) of R:S variation. The lower explanatory power for woody plants is likely influenced by unmeasured variations in (for example) forest age and canopy position. These empirical findings provide a step towards a quantitative theory of plant C allocation responses to resource availability and an improved C allocation scheme for ecosystem models.

Soil Nitrogen Supply Exerts Largest Influence on Leaf Nitrogen in Environments with the Greatest Leaf Nitrogen Demand - Alissar Cheaib; Elizabeth F. Waring; Risa McNellis; Evan A. Perkowski; Jason P. Martina; Eric W. Seabloom; Elizabeth T. Borer; Peter A. Wilfahrt; Ning Dong; Iain Colin Prentice; Ian J. Wright; Sally A. Power; et al. (Ecology Letters, 2025)
Accurately representing the relationships between nitrogen supply and photosynthesis is crucial for reliably predicting carbon–nitrogen cycle coupling in Earth System Models (ESMs). Most ESMs assume positive correlations amongst soil nitrogen supply, leaf nitrogen content, and photosynthetic capacity. However, leaf photosynthetic nitrogen demand may influence the leaf nitrogen response to soil nitrogen supply; thus, responses to nitrogen supply are expected to be the largest in environments where demand is the greatest. Using a nutrient addition experiment replicated across 26 sites spanning four continents, we demonstrated that climate variables were stronger predictors of leaf nitrogen content than soil nutrient supply. Leaf nitrogen increased more strongly with soil nitrogen supply in regions with the highest theoretical leaf nitrogen demand, increasing more in colder and drier environments than warmer and wetter environments. Thus, leaf nitrogen responses to nitrogen supply are primarily influenced by climatic gradients in photosynthetic nitrogen demand, an insight that could improve ESM predictions.

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency - Sungchan Jeong; Youngryel Ryu; Pierre Gentine; Xu Lian; Jianing Fang; Xing Li; Benjamin Dechant; Juwon Kong; Wonseok Choi; Chongya Jiang; Trevor F. Keenan; Sandy P. Harrison; et al. (Remote Sensing of Environment, 2024)

Global leaf‐trait mapping based on optimality theory - Ning Dong; Benjamin Dechant; Han Wang; Ian J. Wright; Iain Colin Prentice (Global Ecology and Biogeography, 2023)
Aim Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine‐learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco‐evolutionary optimality theory, to yield predictions of spatio‐temporal patterns in leaf traits that can be independently evaluated. Innovation Global patterns of community‐mean specific leaf area (SLA) and photosynthetic capacity ( V cmax ) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area ( N area ) and mass ( N mass ) are inferred using their (previously derived) empirical relationships to SLA and V cmax . Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf‐level measurements and/or remote‐sensing methods, which are an increasingly important source of information on spatio‐temporal variation in plant traits. Main conclusions Model predictions evaluated against site‐mean trait data from > 2,000 sites in the Plant Trait database yielded R 2 = 73% for SLA, 38% for N mass and 28% for N area . Declining species‐level N mass , and increasing community‐level SLA, have both been recently reported and were both correctly predicted. Leaf‐trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf‐trait responses to environmental change.

Towards a universal evapotranspiration model based on optimality principles - Shen Tan; Han Wang; Iain. Colin Prentice; Kun Yang; Rodolfo. L.B. Nóbrega; Xiaomang Liu; Yong Wang; Yuting Yang (Agricultural and Forest Meteorology, 2023)

Coordination of photosynthetic traits across soil and climate gradients - Andrea C. Westerband; Ian J. Wright; Vincent Maire; Jennifer Paillassa; Iain Colin Prentice; Owen K. Atkin; Keith J. Bloomfield; Lucas A. Cernusak; Ning Dong; Sean M. Gleason; Caio Guilherme Pereira; Hans Lambers; et al. (Global Change Biology, 2023)
“Least‐cost theory” posits that C 3 plants should balance rates of photosynthetic water loss and carboxylation in relation to the relative acquisition and maintenance costs of resources required for these activities. Here we investigated the dependency of photosynthetic traits on climate and soil properties using a new Australia‐wide trait dataset spanning 528 species from 67 sites. We tested the hypotheses that plants on relatively cold or dry sites, or on relatively more fertile sites, would typically operate at greater CO 2 drawdown (lower ratio of leaf internal to ambient CO 2 , C i : C a ) during light‐saturated photosynthesis, and at higher leaf N per area (N area ) and higher carboxylation capacity ( V cmax 25 ) for a given rate of stomatal conductance to water vapour, g sw . These results would be indicative of plants having relatively higher water costs than nutrient costs. In general, our hypotheses were supported. Soil total phosphorus (P) concentration and (more weakly) soil pH exerted positive effects on the N area – g sw and V cmax 25 – g sw slopes, and negative effects on C i : C a . The P effect strengthened when the effect of climate was removed via partial regression. We observed similar trends with increasing soil cation exchange capacity and clay content, which affect soil nutrient availability, and found that soil properties explained similar amounts of variation in the focal traits as climate did. Although climate typically explained more trait variation than soil did, together they explained up to 52% of variation in the slope relationships and soil properties explained up to 30% of the variation in individual traits. Soils influenced photosynthetic traits as well as their coordination. In particular, the influence of soil P likely reflects the Australia's geologically ancient low‐relief landscapes with highly leached soils. Least‐cost theory provides a valuable framework for understanding trade‐offs between resource costs and use in plants, including limiting soil nutrients.

Optimality principles explaining divergent responses of alpine vegetation to environmental change - Ziqi Zhu; Han Wang; Sandy P. Harrison; Iain Colin Prentice; Shengchao Qiao; Shen Tan (Global Change Biology, 2023)
Recent increases in vegetation greenness over much of the world reflect increasing CO 2 globally and warming in cold areas. However, the strength of the response to both CO 2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite‐observed peak season absorbed photosynthetically active radiation ( F max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year −1 in drier regions and a browning of 0.12 ± 0.08% year −1 in wetter regions). Using an eco‐evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F max with a correlation coefficient ( r ) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year −1 ) and browning (0.07 ± 0.06% year −1 ). We also predicted the observed reduced sensitivities of F max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO 2 stimulates vegetation growth by enhancing water‐use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde - Catherine Morfopoulos; Jean‐François Müller; Trissevgeni Stavrakou; Maite Bauwens; Isabelle De Smedt; Pierre Friedlingstein; Iain Colin Prentice; Pierre Regnier (Global Change Biology, 2022)
Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short‐lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote‐sensing data are applied here to satellite‐derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.

Global decadal variability of plant carbon isotope discrimination and its link to gross primary production - Aliénor Lavergne; Deborah Hemming; Iain Colin Prentice; Rossella Guerrieri; Rebecca J. Oliver; Heather Graven (Global Change Biology, 2022)
Carbon isotope discrimination (Δ 13 C) in C 3 woody plants is a key variable for the study of photosynthesis. Yet how Δ 13 C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ 13 C modelling capability in the land‐surface model JULES incorporating both photorespiratory and mesophyll‐conductance fractionations. We test the ability of four leaf‐internal CO 2 concentration models embedded in JULES to reproduce leaf and tree‐ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ 13 C values, and to underestimate interannual variability in Δ 13 C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post‐photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR‐derived Δ 13 C values. Nonetheless, the “least‐cost” (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy‐level carbon and water fluxes. Overall, modelled Δ 13 C trends vary strongly between regions during the recent (1979–2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ 13 C trend by 0.0015 ± 0.005‰ and –0.0006 ± 0.001‰ ppm −1 , respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ 13 C and GPP tend to be negatively correlated in wet‐humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ 13 C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ 13 C in those forests. Our results demonstrate that the combined analysis of Δ 13 C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.

Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: Insights from stable carbon isotope data - Aliénor Lavergne; David Sandoval; Vincent J. Hare; Heather Graven; Iain Colin Prentice (Global Change Biology, 2020)
Atmospheric aridity and drought both influence physiological function in plant leaves, but their relative contributions to changes in the ratio of leaf internal to ambient partial pressure of CO 2 ( χ ) – an index of adjustments in both stomatal conductance and photosynthetic rate to environmental conditions – are difficult to disentangle. Many stomatal models predicting χ include the influence of only one of these drivers. In particular, the least‐cost optimality hypothesis considers the effect of atmospheric demand for water on χ but does not predict how soils with reduced water further influence χ , potentially leading to an overestimation of χ under dry conditions. Here, we use a large network of stable carbon isotope measurements in C 3 woody plants to examine the acclimated response of χ to soil water stress. We estimate the ratio of cost factors for carboxylation and transpiration ( β ) expected from the theory to explain the variance in the data, and investigate the responses of β (and thus χ ) to soil water content and suction across seed plant groups, leaf phenological types and regions. Overall, β decreases linearly with soil drying, implying that the cost of water transport along the soil–plant–atmosphere continuum increases as water available in the soil decreases. However, despite contrasting hydraulic strategies, the stomatal responses of angiosperms and gymnosperms to soil water tend to converge, consistent with the optimality theory. The prediction of β as a simple, empirical function of soil water significantly improves χ predictions by up to 6.3 ± 2.3% (mean ± SD of adjusted‐ R 2 ) over 1980–2018 and results in a reduction of around 2% of mean χ values across the globe. Our results highlight the importance of soil water status on stomatal functions and plant water‐use efficiency, and suggest the implementation of trait‐based hydraulic functions into the model to account for soil water stress.

Plant respiration: Controlled by photosynthesis or biomass? - Alessio Collalti; Mark G. Tjoelker; Günter Hoch; Annikki Mäkelä; Gabriele Guidolotti; Mary Heskel; Giai Petit; Michael G. Ryan; Giovanna Battipaglia; Giorgio Matteucci; Iain Colin Prentice (Global Change Biology, 2020)
Two simplifying hypotheses have been proposed for whole‐plant respiration. One links respiration to photosynthesis; the other to biomass. Using a first‐principles carbon balance model with a prescribed live woody biomass turnover, applied at a forest research site where multidecadal measurements are available for comparison, we show that if turnover is fast the accumulation of respiring biomass is low and respiration depends primarily on photosynthesis; while if turnover is slow the accumulation of respiring biomass is high and respiration depends primarily on biomass. But the first scenario is inconsistent with evidence for substantial carry‐over of fixed carbon between years, while the second implies far too great an increase in respiration during stand development—leading to depleted carbohydrate reserves and an unrealistically high mortality risk. These two mutually incompatible hypotheses are thus both incorrect. Respiration is not linearly related either to photosynthesis or to biomass, but it is more strongly controlled by recent photosynthates (and reserve availability) than by total biomass.

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems - Aliénor Lavergne; Heather Graven; Martin G. De Kauwe; Trevor F. Keenan; Belinda E. Medlyn; Iain Colin Prentice (Global Change Biology, 2019)
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 CO 2 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 CO 2 . 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.