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Abstract Grasslands cover approximately a third of the Earth’s land surface and account for about a third of terrestrial carbon storage. Yet, we lack strong predictive models of grassland plant biomass, the primary source of carbon in grasslands. This lack of predictive ability may arise from the assumption of linear relationships between plant biomass and the environment and an underestimation of interactions of environmental variables. Using data from 116 grasslands on six continents, we show unimodal relationships between plant biomass and ecosystem characteristics, such as mean annual precipitation and soil nitrogen. Further, we found that soil nitrogen and plant diversity interacted in their relationships with plant biomass, such that plant diversity and biomass were positively related at low levels of nitrogen and negatively at elevated levels of nitrogen. Our results show that it is critical to account for the interactive and unimodal relationships between plant biomass and several environmental variables to accurately include plant biomass in global vegetation and carbon models. 

Abstract Forbs (“wildflowers”) are important contributors to grassland biodiversity but are vulnerable to environmental changes. In a factorial experiment at 94 sites on 6 continents, we test the global generality of several broad predictions: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment. (2) Forb cover and richness increase under herbivory by large mammals. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. (4) Forb families will respond differently to nutrient enrichment and mammalian herbivory due to differences in nutrient requirements. We find strong evidence for the first, partial support for the second, no support for the third, and support for the fourth prediction. Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs, but grazing under high herbivore intensity can offset these nutrient effects. 

Abstract Background and AimsDynamic global vegetation models (DGVMs) are essential for quantifying the role of terrestrial ecosystems in the Earth’s climate system, but struggle with uncertainty and complexity. Eco-evolutionary optimality (EEO) theory provides a promising approach to improve DGVMs based on the premise that leaf carbon gain is optimized with resource costs. However, the timescales at which plant traits can adjust to environmental changes have not yet been systematically incorporated in EEO-based models. Our aims were to identify temporal constraints on key leaf photosynthetic and leaf functional traits, and develop a conceptual framework for incorporation of temporal leaf trait dynamics in EEO-based models. MethodsWe reviewed the scientific literature on temporal responses of leaf traits associated with stomata and hydraulics, photosynthetic biochemistry, and morphology and lifespan. Subsequent response times were categorized from fast to slow considering physiological, phenotypic (acclimation) and evolutionary (adaptation) mechanisms. We constructed a conceptual framework including several key leaf traits identified from the literature review. We considered temporal separation of dynamics in the leaf interior to atmospheric CO2 concentration (ci:ca) from the optimal ci:ca ratio [χ(optimal)] and dynamics in stomatal conductance within the constraint of the anatomical maximum stomatal conductance (gsmax). A proof-of-concept was provided by modelling temporally separated responses in these trait combinations to CO2 and humidity. Key ResultsWe identified 17 leaf traits crucial for EEO-based modelling and determined their response mechanisms and timescales. Physiological and phenotypic response mechanisms were considered most relevant for modelling EEO-based trait dynamics, while evolutionary constraints limit response ranges. Our conceptual framework demonstrated an approach to separate near-instantaneous physiological responses in ci:ca from week-scale phenotypic responses in χ(optimal), and to separate minute-scale physiological responses in stomatal conductance from annual-scale phenotypic responses in gsmax. ConclusionsWe highlight an opportunity to constrain leaf trait dynamics in EEO-based models based on physiological, phenotypic and evolutionary response mechanisms. 

Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities.We explored relationships between five leaf P fractions (orthophosphate P, P_i; lipid P, P_L; nucleic acid P, P_N; metabolite P, P_M; and residual P, P_R) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities.We found that plants exhibiting conservative strategies increased the proportions of P_Nand P_M, but decreased the proportions of P_iand P_L, thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations (R² = 0.50–0.88 vs 0.34–0.74) or proportions (R² = 0.43–0.70 vs 0.06–0.45).These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models. 

Summary It has been 60 years since the discovery of C₄photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C₄photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C₄CO₂‐concentrating mechanism supports a general C₄plant phenotype capable of fast growth and high resource‐use efficiencies. The fast‐efficient C₄phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C₄fast‐efficient phenotype near the acquisitive pole of the world‐wide leaf economic spectrum, but with a pathway‐specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait‐based perspective of C₄photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands. 

Summary Natural selection favors growth by selecting a combination of plant traits that maximize photosynthetic CO₂assimilation at the lowest combined carbon costs of resource acquisition and use. We quantified how soil nutrient availability, plant nutrient acquisition strategies, and aridity modulate the variability in plant costs of nutrient acquisition relative to water acquisition (β).We used an eco‐evolutionary optimality framework and a global carbon isotope dataset to quantify β.Under low soil nitrogen‐to‐carbon (N : C) ratios, a mining strategy (symbioses with ectomycorrhizal and ericoid mycorrhizal fungi) reduced β by mining organic nitrogen, compared with a scavenging strategy (symbioses with arbuscular mycorrhizal fungi). Conversely, under high N : C ratios, scavenging strategies reduced β by effectively scavenging soluble nitrogen, compared with mining strategies. N₂‐fixing plants did not exhibit reduced β under low N : C ratios compared with non‐N₂‐fixing plants. Moisture increased β only in plants using a scavenging strategy, reflecting direct impacts of aridity on the carbon costs of maintaining transpiration in these plants. Nitrogen and phosphorus colimitation further modulated β.Our findings provide a framework for simulating the variability of plant economics due to plant nutrient acquisition strategies in earth system models. 

Abstract Plants respond to increasing atmospheric CO2 concentrations by reducing leaf nitrogen content and photosynthetic capacity—patterns that correspond with increased net photosynthesis and growth. Despite the longstanding notion that nitrogen availability regulates these responses, eco-evolutionary optimality theory posits that leaf-level responses to elevated CO2 are driven by leaf nitrogen demand for building and maintaining photosynthetic enzymes and are independent of nitrogen availability. In this study, we examined leaf and whole-plant responses of Glycine max L. (Merr) subjected to full-factorial combinations of two CO2, two inoculation, and nine nitrogen fertilization treatments. Nitrogen fertilization and inoculation did not alter leaf photosynthetic responses to elevated CO2. Instead, elevated CO2 decreased the maximum rate of ribulose-1,5-bisophosphate oxygenase/carboxylase (Rubisco) carboxylation more strongly than it decreased the maximum rate of electron transport for ribulose-1,5-bisphosphate (RuBP) regeneration, increasing net photosynthesis by allowing rate-limiting steps to approach optimal coordination. Increasing fertilization enhanced positive whole-plant responses to elevated CO2 due to increased below-ground carbon allocation and nitrogen uptake. Inoculation with nitrogen-fixing bacteria did not influence plant responses to elevated CO2. These results reconcile the role of nitrogen availability in plant responses to elevated CO2, showing that leaf photosynthetic responses are regulated by leaf nitrogen demand while whole-plant responses are constrained by nitrogen availability. 

ABSTRACT 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. 

Summary Interactions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO₂and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf‐level photosynthetic capacity. Whole‐plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO₂also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO₂fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections. 

Abstract Plants with the C₄photosynthesis pathway typically respond to climate change differently from more common C₃-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C₄and C₃vegetation distributions. However, current C₄vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C₄vegetation. We find that global C₄vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C₄natural grass cover due to elevated CO₂favoring C₃-type photosynthesis, and an increase in C₄crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C₄vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C₄plants in the contemporary global carbon cycle.