Global climate and nutrient controls of photosynthetic capacity

Species-specific responses of C and N allocation to N addition: evidence from dual 13C and 15N labeling in three tree species

Soil nitrogen (N) availability affects plant carbon (C) utilization. However, it is unclear how various tree functional types respond to N addition in terms of C assimilation, allocation, and storage. Here, a microcosm experiment with dual 13C and 15N labeling was conducted to study the effects of N addition (i.e., control, 0 g N kg-1; moderate N addition, 1.68 g N kg-1; and high N addition, 3.36 g N kg-1 soil) on morphological traits, on changes in nonstructural carbohydrates (NSC) in different organs, as well as on C and N uptake and allocation in three European temperate forest tree species (i.e., Acer pseudoplatanus, Picea abies and Abies alba). Our results demonstrated that root N uptake rates of the three tree species increased by N addition. In A. pseudoplatanus, N uptake by roots, N allocation to aboveground organs, and aboveground biomass allocation significantly improved by moderate and high N addition. In A. alba, only the high N addition treatment considerably raised aboveground N and C allocation. In contrast, biomass as well as C and N allocation between above and belowground tissues were not altered by N addition in P. abies. Meanwhile, NSC content as well as C and N coupling (represented by the ratio of relative 13C and 15N allocation rates in organs) were affected by N addition in A. pseudoplantanus and P. abies but not in A. alba. Overall, A. pseudoplatanus displayed the highest sensitivity to N addition and the highest N requirement among the three species, while P. abies had a lower N demand than A. alba. Our findings highlight that the responses of C and N allocation to soil N availability are species-specific and vary with the amount of N addition.

Optimizing irrigation and nitrogen addition to balance grassland biomass production with greenhouse gas emissions: A mesocosm study

Achieving a balance between greenhouse gas mitigation and biomass production in grasslands necessitates optimizing irrigation frequency and nitrogen addition, which significantly influence grassland productivity and soil nitrous oxide emissions, and consequently impact the ecosystem carbon dioxide exchange. This study aimed to elucidate these influences using a controlled mesocosm experiment where bermudagrass (Cynodon dactylon L.) was cultivated under varied irrigation frequencies (daily and every 6 days) with (100 kg ha-1) or without nitrogen addition; measurements of net ecosystem carbon dioxide exchange, ecosystem respiration, soil respiration, and nitrous oxide emissions across two cutting events were performed as well. The findings revealed a critical interaction between water-filled pore space, regulated by irrigation, and nitrogen availability, with the latter exerting a more substantial influence on aboveground biomass growth and ecosystem carbon dioxide exchange than water availability. Moreover, the total dry matter was significantly higher with nitrogen addition compared to without nitrogen addition, irrespective of the irrigation frequency. In contrast, soil nitrous oxide emissions were observed to be significantly higher with increased irrigation frequency and nitrogen addition. The effects of nitrogen addition on soil respiration components appeared to depend on water availability, with autotrophic respiration seeing a significant rise with nitrogen addition under limited irrigation (5.4 ± 0.6 μmol m-2 s-1). Interestingly, the lower irrigation frequency did not result in water stress, suggesting resilience in bermudagrass. These findings highlight the importance of considering interactions between irrigation and nitrogen addition to optimize water and nitrogen input in grasslands for a synergistic balance between grassland biomass production and greenhouse gas emission mitigation.

Predicted changes in distribution and grazing value of Stipa-based plant communities across the Eurasian steppe

The Eurasian steppe is one of the world's largest continuous areas of grassland and has an important role in supporting livestock grazing, the most ubiquitous land use on Earth. However, the Eurasian steppe is under threat, from irrational grazing utilization, climate change, and resource exploitation. We used an ensemble modeling approach to predict the current and future distribution of Stipa-dominated plant communities in three important steppe subregions; the Tibetan Alpine, Central Asian, and Black Sea-Kazakhstan subregions. We combined this with an assessment of the grazing value of 22 Stipa species, the dominant grassland species in the area, to predict how grazing value might change under future climate change predictions. We found that the effects of changing climates on grazing values differed across the three subregions. Grazing values increased in the Tibetan alpine steppe and to a lesser extent in Central Asia, but there were few changes in the Black Sea-Kazakhstan subregion. The response of different species to changing climates varied with environmental variables. Finally, our trait-based assessment of Stipa species revealed variations in grazing value, and this had major effects on the overall grazing value of the region. Our results reinforce the importance of trait-based characteristics of steppe plant species, how these traits affect grazing value, and how grazing values will change across different areas of the Eurasian steppe. Our work provides valuable insights into how different species will respond to changing climates and grazing, with important implications for sustainable management of different areas of the vast Eurasian steppe ecosystem.

Glucose input profit soil organic carbon mineralization and nitrogen dynamics in relation to nitrogen amended soils

Exogenous carbon (C) inputs stimulate soil organic carbon (SOC) decomposition, strongly influencing atmospheric concentrations and climate dynamics. The direction and magnitude of C decomposition depend on the C and nitrogen (N) addition, types and pattern. Despite the importance of decomposition, it remains unclear whether organic C input affects the SOC decomposition under different N-types (Ammonium Nitrate; AN, Urea; U and Ammonium Sulfate; AS). Therefore, we conducted an incubation experiment to assess glucose impact on N-treated soils at various levels (High N; HN: 50 mg/m2, Low N; LN: 05 mg/m2). The glucose input increased SOC mineralization by 38% and 35% under HN and LN, respectively. Moreover, it suppressed the concentration of NO3--N by 35% and NH4+-N by 15% in response to HN and LN soils, respectively. Results indicated higher respiration in Urea-treated soils and elevated net total nitrogen content (TN) in AS-treated soils. AN-amended soil exhibited no notable rise in C mineralization and TN content compared to other N-type soils. Microbial biomass carbon (MBC) was higher in glucose treated soils under LN conditions than control. This could result that high N suppressed microbial N mining and enhancing SOM stability by directing microbes towards accessible C sources. Our results suggest that glucose accelerated SOC mineralization in urea-added soils and TN contents in AS-amended soils, while HN levels suppressed C release and increased TN contents in all soil types except glucose-treated soils. Thus, different N-types and levels play a key role in modulating the stability of SOC over C input.

Ecophysiological adaptations shape distributions of closely related trees along a climatic moisture gradient

Tradeoffs between the energetic benefits and costs of traits can shape species and trait distributions along environmental gradients. Here we test predictions based on such tradeoffs using survival, growth, and 50 photosynthetic, hydraulic, and allocational traits of ten Eucalyptus species grown in four common gardens along an 8-fold gradient in precipitation/pan evaporation (P/Ep) in Victoria, Australia. Phylogenetically structured tests show that most trait-environment relationships accord qualitatively with theory. Most traits appear adaptive across species within gardens (indicating fixed genetic differences) and within species across gardens (indicating plasticity). However, species from moister climates have lower stomatal conductance than others grown under the same conditions. Responses in stomatal conductance and five related traits appear to reflect greater mesophyll photosynthetic sensitivity of mesic species to lower leaf water potential. Our data support adaptive cross-over, with realized height growth of most species exceeding that of others in climates they dominate. Our findings show that pervasive physiological, hydraulic, and allocational adaptations shape the distributions of dominant Eucalyptus species along a subcontinental climatic moisture gradient, driven by rapid divergence in species P/Ep and associated adaptations.

Does day length matter for nutrient responsiveness?

For over 2500 years, considerable agronomic interest has been paid to soil fertility. Both crop domestication and the Green Revolution shifted photoperiodism and the circadian clock in cultivated species, although this contributed to an increase in the demand for chemical fertilisers. Thus, the uptake of nutrients depends on light signalling, whereas diel growth and circadian rhythms are affected by nutrient levels. Here, we argue that day length and circadian rhythms may be central regulators of the uptake and usage of nutrients, also modulating responses to toxic elements (e.g., aluminium and cadmium). Thus, we suggest that knowledge in this area might assist in developing next-generation crops with improved uptake and use efficiency of nutrients.

Soil nitrogen fertilization reduces relative leaf nitrogen allocation to photosynthesis

The connection between soil nitrogen availability, leaf nitrogen, and photosynthetic capacity is not perfectly understood. Because these three components tend to be positively related over large spatial scales, some posit that soil nitrogen positively drives leaf nitrogen, which positively drives photosynthetic capacity. Alternatively, others posit that photosynthetic capacity is primarily driven by above-ground conditions. Here, we examined the physiological responses of a non-nitrogen-fixing plant (Gossypium hirsutum) and a nitrogen-fixing plant (Glycine max) in a fully factorial combination of light by soil nitrogen availability to help reconcile these competing hypotheses. Soil nitrogen stimulated leaf nitrogen in both species, but the relative proportion of leaf nitrogen used for photosynthetic processes was reduced under elevated soil nitrogen in all light availability treatments due to greater increases in leaf nitrogen content than chlorophyll and leaf biochemical process rates. Leaf nitrogen content and biochemical process rates in G. hirsutum were more responsive to changes in soil nitrogen than those in G. max, probably due to strong G. max investments in root nodulation under low soil nitrogen. Nonetheless, whole-plant growth was significantly enhanced by increased soil nitrogen in both species. Light availability consistently increased relative leaf nitrogen allocation to leaf photosynthesis and whole-plant growth, a pattern that was similar between species. These results suggest that the leaf nitrogen-photosynthesis relationship varies under different soil nitrogen levels and that these species preferentially allocated more nitrogen to plant growth and non-photosynthetic leaf processes, rather than photosynthesis, as soil nitrogen increased.

Coordination of photosynthetic traits across soil and climate gradients

"Least-cost theory" posits that C₃ 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 CO₂ drawdown (lower ratio of leaf internal to ambient CO₂ , 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.

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.

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO₂ globally and warming in cold areas. However, the strength of the response to both CO₂ 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 CO₂ 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.

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.

Leaf nitrogen from the perspective of optimal plant function

Leaf dry mass per unit area (LMA), carboxylation capacity (V _cmax) and leaf nitrogen per unit area (N_area) and mass (N_mass) are key traits for plant functional ecology and ecosystem modelling. There is however no consensus about how these traits are regulated, or how they should be modelled. Here we confirm that observed leaf nitrogen across species and sites can be estimated well from observed LMA and V _cmax at 25°C (V _cmax25). We then test the hypothesis that global variations of both quantities depend on climate variables in specific ways that are predicted by leaf-level optimality theory, thus allowing both N_area to be predicted as functions of the growth environment.A new global compilation of field measurements was used to quantify the empirical relationships of leaf N to V _cmax25 and LMA. Relationships of observed V _cmax25 and LMA to climate variables were estimated, and compared to independent theoretical predictions of these relationships. Soil effects were assessed by analysing biases in the theoretical predictions.LMA was the most important predictor of N_area (increasing) and N_mass (decreasing). About 60% of global variation across species and sites in observed N_area, and 31% in N_mass, could be explained by observed LMA and V _cmax25. These traits, in turn, were quantitatively related to climate variables, with significant partial relationships similar or indistinguishable from those predicted by optimality theory. Predicted trait values explained 21% of global variation in observed site-mean V _cmax25, 43% in LMA and 31% in N_area. Predicted V _cmax25 was biased low on clay-rich soils but predicted LMA was biased high, with compensating effects on N_area. N_area was overpredicted on organic soils. Synthesis. Global patterns of variation in observed site-mean N_area can be explained by climate-induced variations in optimal V _cmax25 and LMA. Leaf nitrogen should accordingly be modelled as a consequence (not a cause) of V _cmax25 and LMA, both being optimized to the environment. Nitrogen limitation of plant growth would then be modelled principally via whole-plant carbon allocation, rather than via leaf-level traits. Further research is required to better understand and model the terrestrial nitrogen and carbon cycles and their coupling.

Using synthetic biology to improve photosynthesis for sustainable food production

Photosynthesis is responsible for the primary productivity and maintenance of life on Earth, boosting biological activity and contributing to the maintenance of the environment. In the past, traditional crop improvement was considered sufficient to meet food demands, but the growing demand for food coupled with climate change has modified this scenario over the past decades. However, advances in this area have not focused on photosynthesis per se but rather on fixed carbon partitioning. In short, other approaches must be used to meet an increasing agricultural demand. Thus, several paths may be followed, from modifications in leaf shape and canopy architecture, improving metabolic pathways related to CO₂ fixation, the inclusion of metabolic mechanisms from other species, and improvements in energy uptake by plants. Given the recognized importance of photosynthesis, as the basis of the primary productivity on Earth, we here present an overview of the latest advances in attempts to improve plant photosynthetic performance. We focused on points considered key to the enhancement of photosynthesis, including leaf shape development, RuBisCO reengineering, Calvin-Benson cycle optimization, light use efficiency, the introduction of the C₄ cycle in C₃ plants and the inclusion of other CO₂ concentrating mechanisms (CCMs). We further provide compelling evidence that there is still room for further improvements. Finally, we conclude this review by presenting future perspectives and possible new directions on this subject.

Interactions between biochar, arbuscular mycorrhizal fungi and photosynthetic processes in potato (Solanum tuberosum L.)

Pyrolyzed biomass, generating biochar for use as soil amendment, is recognized as a promising strategy for carbon sequestration. Current understanding of the interactions between biochar, arbuscular mycorrhizal (AM), and plant photosynthesis, in terms of biochemical processes and CO₂ uptake, is fragmentary. The aim of this study was to investigate the effects on photosynthesis in potato including maximum rate of carboxylation by Rubisco (V_cmax), maximum rate of electron transport rate for RuBP-regeneration (J_max), mesophyll conductance (g_m) and other plant traits. Four types of biochar (wheat or miscanthus straw pellets pyrolyzed at temperatures of either 550 °C or 700 °C) were amended into low phosphorus soil. Potato plants were inoculated with the AM fungus Rhizophagus irregularis (M+) or not (M-). The results showed that four types of biochar generally decreased nitrogen and phosphorus content of potato, especially the biochars pyrolyzed at high temperature. This negative effect of biochar on nutrient content was alleviated by AM. It was found that V_cmax was limited by low plant nitrogen content as well as leaf area and phosphorus content. Plant phosphorus content also limited J_max, which was mutually constrained by V_cmax of leaves. Low g_m was an additional limiting factor for photosynthesis. The g_m was positively correlated to nitrogen content, which influenced the leaf anatomical structure by alteration of leaf mass per area. In conclusion, the influence of interactions between quality of biochar and AM symbiosis on photosynthesis of potato seems to relate to effects on plant nutrient content and leaf structures. Accordingly, a model for the dependence of V_cmax on nitrogen and phosphorus content and their interactive effect exhibited a high correlation coefficient. As potato plants form AM symbiosis under natural field conditions, the extent and interaction with the quality of amended biochar can be a determining factor for plant nutrient content, growth and yield.

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E_t) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (S_t), i.e., the reduction of E_t from its theoretical maximum. Then, we embed the new S_t formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the S_t formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate S_t and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.

Global evaporation is a key climatic process that remains highly uncertain. Here, the authors shed light on this process with a novel hybrid model that integrates a deep learning representation of ecosystem stress within a physics-based framework.

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor.Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016-2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated with regard to jurisdictional claims in good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.