Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis.

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Leaf economics fundamentals explained by optimality principles.

The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with diverse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed divergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics.

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.

The global spectrum of plant form and function: enhanced species-level trait dataset.

Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

The China plant trait database version 2.

Plant functional traits represent adaptive strategies to the environment, linked to biophysical and biogeochemical processes and ecosystem functioning. Compilations of trait data facilitate research in multiple fields from plant ecology through to land-surface modelling. Here we present version 2 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a diversity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not sampled previously; (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and turgor loss point; (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments and flagging the reliability of individual trait measurements; and (5) inclusion of standardized templates for systematical field sampling and measurements.

Critical soil moisture thresholds of plant water stress in terrestrial ecosystems.

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θcrit). Knowledge of the spatial distribution of θcrit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θcrit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EFmax), θcrit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θcrit, S, and EFmax is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θcrit and show adaptation to water deficits. The negative relationship between θcrit and S indicates that dryland ecosystems minimize θcrit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

A new method based on surface-sample pollen data for reconstructing palaeovegetation patterns.

Aim: Biomisation has been the most widely used technique to reconstruct past regional vegetation patterns because it does not require an extensive modern pollen dataset. However, it has well-known limitations including its dependence on expert judgement for the assignment of pollen taxa to plant functional types (PFTs) and PFTs to biomes. Here we present a new method that combines the strengths of biomisation with those of the alternative dissimilarity-based techniques. Location: The Eastern Mediterranean-Black Sea Caspian Corridor (EMBSeCBIO). Taxon: Plants. Methods: Modern pollen samples, assigned to biomes based on potential natural vegetation data, are used to characterize the within-biome means and standard deviations of the abundances of each taxon. These values are used to calculate a dissimilarity index between any pollen sample and every biome, and thus assign the sample to the most likely biome. We calculate a threshold value for each modern biome; fossil samples with scores below the threshold for all modern biomes are thus identified as non-analogue vegetation. We applied the new method to the EMBSeCBIO region to compare its performance with existing reconstructions. Results: The method captured changes in the importance of individual taxa along environmental gradients. The balanced accuracy obtained for the EMBSeCBIO region using the new method was better than obtained using biomisation (77% vs. 65%). When the method was applied to high-resolution fossil records, 70% of the entities showed more temporally stable biome assignments than obtained using biomisation. The technique also identified likely non-analogue assemblages in a synthetic modern dataset and in fossil records. Main conclusions: The new method yields more accurate and stable reconstructions of vegetation than biomisation. It requires an extensive modern pollen dataset, but is conceptually simple, and avoids subjective choices about taxon allocations to PFTs and PFTs to biomes.

CO2 fertilization of terrestrial photosynthesis inferred from site to global scales.

SignificanceThe magnitude of the CO2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

Atmospheric dryness reduces photosynthesis along a large range of soil water deficits.

Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates.

Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements.

Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits. We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000-m elevation gradient. Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, vH ). The observed coordination between vH and sapwood hydraulic conductivity (KS ) and photosynthetic capacity (Vcmax ) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between KS and vH . Leaf drought tolerance (inferred from turgor loss point, -Ψtlp ) increased with wood density, but the trade-off between hydraulic efficiency (KS ) and -Ψtlp was weak. Plant trait effects on vH were dominated by variation in KS , while effects of environment were dominated by variation in temperature. This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.

Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China.

Leaf mass per area (Ma), nitrogen content per unit leaf area (Narea), maximum carboxylation capacity (Vcmax) and the ratio of leaf-internal to ambient CO2 partial pressure (χ) are important traits related to photosynthetic function, and they show systematic variation along climatic and elevational gradients. Separating the effects of air pressure and climate along elevational gradients is challenging due to the covariation of elevation, pressure and climate. However, recently developed models based on optimality theory offer an independent way to predict leaf traits and thus to separate the contributions of different controls. We apply optimality theory to predict variation in leaf traits across 18 sites in the Gongga Mountain region. We show that the models explain 59% of trait variability on average, without site- or region-specific calibration. Temperature, photosynthetically active radiation, vapor pressure deficit, soil moisture and growing season length are all necessary to explain the observed patterns. The direct effect of air pressure is shown to have a relatively minor impact. These findings contribute to a growing body of research indicating that leaf-level traits vary with the physical environment in predictable ways, suggesting a promising direction for the improvement of terrestrial ecosystem models.

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.

When and where soil is important to modify the carbon and water economy of leaves.

Photosynthetic 'least-cost' theory posits that the optimal trait combination for a given environment is that where the summed costs of photosynthetic water and nutrient acquisition/use are minimised. The effects of soil water and nutrient availability on photosynthesis should be stronger as climate-related costs for both resources increase. Two independent datasets of photosynthetic traits, Globamax (1509 species, 288 sites) and Glob13C (3645 species, 594 sites), were used to quantify biophysical and biochemical limitations of photosynthesis and the key variable Ci /Ca (CO2 drawdown during photosynthesis). Climate and soil variables were associated with both datasets. The biochemical photosynthetic capacity was higher on alkaline soils. This effect was strongest at more arid sites, where water unit-costs are presumably higher. Higher values of soil silt and depth increased Ci /Ca , likely by providing greater H2 O supply, alleviating biophysical photosynthetic limitation when soil water is scarce. Climate is important in controlling the optimal balance of H2 O and N costs for photosynthesis, but soil properties change these costs, both directly and indirectly. In total, soil properties modify the climate-demand driven predictions of Ci /Ca by up to 30% at a global scale.

The climatic space of European pollen taxa.

Pollen data are widely used to reconstruct past climate changes, using relationships between modern pollen abundance in surface samples and climate at the surface-sample sites as a calibration. Visualization of modern pollen data in multidimensional climate space provides a way to establish that taxon abundances are well behaved before using them in climate reconstructions. Visualization is also helpful for ecological interpretation of variations in pollen abundance in space and time. Here, we present Generalized Additive Models for the distribution of 195 European pollen and pteridophyte spore taxa in a bioclimate space defined by seasonal temperatures (as mean temperature of the coldest month and annual growing degree days) and an annual moisture index. These models can be used to explore the realized climate niche of pollen taxa and to build statistical models for palaeoclimate reconstruction. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article.

Acclimation of leaf respiration consistent with optimal photosynthetic capacity.

Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial ('dark') respiration (Rd ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperature via a link to Vcmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd and Vcmax accessed at growth temperature (Rd,tg and Vcmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax respectively. Data-fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both Rd and Vcmax assessed at 25°C (Rd,25 and Vcmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler-and potentially more reliable-than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.

Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle.

The ratio of leaf internal (ci ) to ambient (ca ) partial pressure of CO2 , defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation model uncertainties for predicting terrestrial carbon uptake and water use. We test a theory based on the least-cost optimality hypothesis for modelling historical changes in χ over the 1951-2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely dated tree-ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, ca and atmospheric pressure. The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the intersite variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model. Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant-available soil water and other site-specific characteristics might improve the predictions.

The validity of optimal leaf traits modelled on environmental conditions.

The ratio of leaf intercellular to ambient CO2 (χ) is modulated by stomatal conductance (gs ). These quantities link carbon (C) assimilation with transpiration, and along with photosynthetic capacities (Vcmax and Jmax ) are required to model terrestrial C uptake. We use optimization criteria based on the growth environment to generate predicted values of photosynthetic and water-use efficiency traits and test these against a unique dataset. Leaf gas-exchange parameters and carbon isotope discrimination were analysed in relation to local climate across a continental network of study sites. Sun-exposed leaves of 50 species at seven sites were measured in contrasting seasons. Values of χ predicted from growth temperature and vapour pressure deficit were closely correlated to ratios derived from C isotope (δ13 C) measurements. Correlations were stronger in the growing season. Predicted values of photosynthetic traits, including carboxylation capacity (Vcmax ), derived from δ13 C, growth temperature and solar radiation, showed meaningful agreement with inferred values derived from gas-exchange measurements. Between-site differences in water-use efficiency were, however, only weakly linked to the plant's growth environment and did not show seasonal variation. These results support the general hypothesis that many key parameters required by Earth system models are adaptive and predictable from plants' growth environments.

Quantifying leaf-trait covariation and its controls across climates and biomes.

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

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.