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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 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. 

The efflux of carbon dioxide (CO₂) from woody stems, a proxy for stem respiration, is a critical carbon flux from ecosystems to the atmosphere, which increases with temperature on short timescales. However, plants acclimate their respiratory response to temperature on longer timescales, potentially weakening the carbon-climate feedback. The magnitude of this acclimation is uncertain despite its importance for predicting future climate change. We develop an optimality-based theory dynamically linking stem respiration with leaf water supply to predict its thermal acclimation. We show that the theory accurately reproduces observations of spatial and seasonal change. We estimate the global value for current annual stem CO₂efflux as 27.4 ± 5.9 PgC. By 2100, incorporating thermal acclimation reduces projected stem respiration without considering acclimation by 24 to 46%, thus reducing land ecosystem carbon emissions. 

ABSTRACT Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose‐1,5‐bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon‐nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO₂. Plants acclimate to elevated CO₂by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO₂can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO₂conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO₂. 

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. 

Abstract Many plant species form symbiotic associations with nitrogen-fixing bacteria. Through this symbiosis, plants allocate photosynthate belowground to the bacteria in exchange for nitrogen fixed from the atmosphere. This symbiosis forms an important link between carbon and nitrogen cycles in many ecosystems. However, the economics of this relationship under soil nitrogen availability gradients is not well understood, as plant investment toward symbiotic nitrogen fixation tends to decrease with increasing soil nitrogen availability. Here, we used a manipulation experiment to examine how costs of nitrogen acquisition vary under a factorial combination of soil nitrogen availability and inoculation with Bradyrhizobium japonicum in Glycine max L. (Merr.). We found that inoculation decreased belowground biomass carbon costs to acquire nitrogen and increased total leaf area and total biomass, but these patterns were only observed under low fertilization and were the result of increased plant nitrogen uptake and no change in belowground carbon allocation. These results suggest that symbioses with nitrogen-fixing bacteria reduce carbon costs of nitrogen acquisition by increasing plant nitrogen uptake, but only when soil nitrogen is low, allowing individuals to increase nitrogen allocation to structures that support aboveground growth. This pattern may help explain the prevalence of plants capable of forming these associations in less fertile soils and provides useful insight into understanding the role of nutrient acquisition strategy on plant nitrogen uptake across nitrogen availability gradients. 

Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration.