Beijing Botanical Garden

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground- and remote sensing- based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Abstract Canonical analysis, a generalization of multiple regression to multiple‐response variables, is widely used in ecology. Because these models often involve many parameters (one slope per response per predictor), they pose challenges to model interpretation. Among these challenges, we lack quantitative frameworks for estimating the overall importance of single predictors in multi‐response regression models. Here we demonstrate that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single‐response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple‐response models. In this application, we (a) demonstrate the mathematical links between commonality analysis, variation and hierarchical partitioning; (b) generalize these frameworks to allow the analysis of any number of predictor variables or groups of predictor variables as in the case of variation partitioning; and (c) introduce and demonstrate the implementation of these generalized frameworks in the R package rdacca.hp .

A DNA barcode is a DNA fragment used to identify species. For land plants, DNA fragments of plastid genome could be the primary consideration. Unfortunately, most of the plastid candidate barcodes lack species-level resolution. The identification of DNA barcodes of high resolution at species level is critical to the success of DNA barcoding in plants. We searched the available plastid genomes for the most variable regions and tested the best candidates using both a large number of tree species and seven well-sampled plant groups. Two regions of the plastid gene ycf1, ycf1a and ycf1b, were the most variable loci that were better than existing plastid candidate barcodes and can serve as a barcode of land plants. Primers were designed for the amplification of these regions, and the PCR success of these primers ranged from 82.80% to 98.17%. Of 420 tree species, 357 species could be distinguished using ycf1b, which was slightly better than the combination of matK and rbcL. For the well-sampled representative plant groups, ycf1b generally performed better than any of the matK, rbcL and trnH-psbA. We concluded that ycf1a or ycf1b is the most variable plastid genome region and can serve as a core barcode of land plants.

Summary The J anzen– C onnell hypothesis proposes that specialist natural enemies, such as herbivores and pathogens, maintain diversity in plant communities by reducing survival rates of conspecific seeds and seedlings located close to reproductive adults or in areas of high conspecific density. Variation in the strength of distance‐ and density‐dependent effects is hypothesized to explain variation in plant species richness along climatic gradients, with effects predicted to be stronger in the tropics than the temperate zone and in wetter habitats compared to drier habitats. We conducted a comprehensive literature search to identify peer‐reviewed experimental studies published in the 40+ years since the hypothesis was first proposed. Using data from these studies, we conducted a meta‐analysis to assess the current weight of evidence for the distance and density predictions of the J anzen– C onnell hypothesis. Overall, we found significant support for both the distance‐ and density‐dependent predictions. For all studies combined, survival rates were significantly reduced near conspecifics compared to far from conspecifics, and in areas with high densities of conspecifics compared to areas with low conspecific densities. There was no indication that these results were due to publication bias. The strength of distance and density effects varied widely among studies. Contrary to expectations, this variation was unrelated to latitude, and there was no significant effect of study region. However, we did find a trend for stronger distance and density dependence in wetter sites compared to sites with lower annual precipitation. In addition, effects were significantly stronger at the seedling stage compared to the seed stage. Synthesis . Our study provides support for the idea that distance‐ and density‐dependent mortality occurs in plant communities world‐wide. Available evidence suggests that natural enemies are frequently the cause of such patterns, consistent with the J anzen– C onnell hypothesis, but additional studies are needed to rule out other mechanisms (e.g. intraspecific competition). With the widespread existence of density and distance dependence clearly established, future research should focus on assessing the degree to which these effects permit species coexistence and contribute to the maintenance of diversity in plant communities.

Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis of bias patterns or their consequences exists. As such, views of organismal distribution and the ecosystems they make up may be incorrect, undermining countless ecological and evolutionary studies. Using 742 million records of 374 900 species, we explore the global patterns and impacts of biases related to taxonomy, accessibility, ecotype and data type across terrestrial and marine systems. Pervasive sampling and observation biases exist across animals, with only 6.74% of the globe sampled, and disproportionately poor tropical sampling. High elevations and deep seas are particularly unknown. Over 50% of records in most groups account for under 2% of species and citizen‐science only exacerbates biases. Additional data will be needed to overcome many of these biases, but we must increasingly value data publication to bridge this gap and better represent species' distributions from more distant and inaccessible areas, and provide the necessary basis for conservation and management.

Abstract Aim Large trees (d.b.h. ≥ 70 cm) store large amounts of biomass. Several studies suggest that large trees may be vulnerable to changing climate, potentially leading to declining forest biomass storage. Here we determine the importance of large trees for tropical forest biomass storage and explore which intrinsic (species trait) and extrinsic (environment) variables are associated with the density of large trees and forest biomass at continental and pan‐tropical scales. Location Pan‐tropical. Methods Aboveground biomass ( AGB) was calculated for 120 intact lowland moist forest locations. Linear regression was used to calculate variation in AGB explained by the density of large trees. Akaike information criterion weights ( AICc ‐wi) were used to calculate averaged correlation coefficients for all possible multiple regression models between AGB /density of large trees and environmental and species trait variables correcting for spatial autocorrelation. Results Density of large trees explained c . 70% of the variation in pan‐tropical AGB and was also responsible for significantly lower AGB in Neotropical [287.8 (mean) ± 105.0 ( SD ) M g ha −1 ] versus Palaeotropical forests (Africa 418.3 ± 91.8 M g ha −1 ; Asia 393.3 ± 109.3 M g ha −1 ). Pan‐tropical variation in density of large trees and AGB was associated with soil coarseness (negative), soil fertility (positive), community wood density (positive) and dominance of wind dispersed species (positive), temperature in the coldest month (negative), temperature in the warmest month (negative) and rainfall in the wettest month (positive), but results were not always consistent among continents. Main conclusions Density of large trees and AGB were significantly associated with climatic variables, indicating that climate change will affect tropical forest biomass storage. Species trait composition will interact with these future biomass changes as they are also affected by a warmer climate. Given the importance of large trees for variation in AGB across the tropics, and their sensitivity to climate change, we emphasize the need for in‐depth analyses of the community dynamics of large trees.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (Cas) systems have been successfully used as efficient tools for genome editing in a variety of species. We used the CRISPR/Cas9 system to mutate the Gn1a (Os01g0197700), DEP1 (Os09g0441900), GS3 (Os03g0407400), and IPA1 (Os08g0509600) genes of rice cultivar Zhonghua 11, genes which have been reported to function as regulators of grain number, panicle architecture, grain size and plant architecture, respectively. Analysis of the phenotypes and frequencies of edited genes in the first generation of transformed plants (T0) showed that the CRISPR/Cas9 system was highly efficient in inducing targeted gene editing, with the desired genes being edited in 42.5% (Gn1a), 67.5% (DEP1), 57.5% (GS3), and 27.5% (IPA1) of the transformed plants. The T2 generation of the gn1a, dep1, and gs3 mutants featured enhanced grain number, dense erect panicles, and larger grain size, respectively. Furthermore, semi-dwarf, and grain with long awn, phenotypes were observed in dep1 and gs3 mutants, respectively. The ipa1 mutants showed two contrasting phenotypes, having either fewer tillers or more tillers, depending on the changes induced in the OsmiR156 target region. In addition, we found that mutants with deletions occurred more frequently than previous reports had indicated and that off-targeting had taken place in highly similar target sequences. These results proved that multiple regulators of important traits can be modified in a single cultivar by CRISPR/Cas9, and thus facilitate the dissection of complex gene regulatory networks in the same genomic background and the stacking of important traits in cultivated varieties.

Abstract Water lilies belong to the angiosperm order Nymphaeales. Amborellales, Nymphaeales and Austrobaileyales together form the so-called ANA-grade of angiosperms, which are extant representatives of lineages that diverged the earliest from the lineage leading to the extant mesangiosperms 1–3 . Here we report the 409-megabase genome sequence of the blue-petal water lily ( Nymphaea colorata ). Our phylogenomic analyses support Amborellales and Nymphaeales as successive sister lineages to all other extant angiosperms. The N. colorata genome and 19 other water lily transcriptomes reveal a Nymphaealean whole-genome duplication event, which is shared by Nymphaeaceae and possibly Cabombaceae. Among the genes retained from this whole-genome duplication are homologues of genes that regulate flowering transition and flower development. The broad expression of homologues of floral ABCE genes in N. colorata might support a similarly broadly active ancestral ABCE model of floral organ determination in early angiosperms. Water lilies have evolved attractive floral scents and colours, which are features shared with mesangiosperms, and we identified their putative biosynthetic genes in N. colorata . The chemical compounds and biosynthetic genes behind floral scents suggest that they have evolved in parallel to those in mesangiosperms. Because of its unique phylogenetic position, the N. colorata genome sheds light on the early evolution of angiosperms.

Abstract Soil net nitrogen mineralization rate (N min ), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil N min has not been evaluated on the global scale. By compiling 1565 observational data points of potential net N min from 198 published studies across terrestrial ecosystems, we found that N min significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of N min was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced N min through soil microbes. The structural equation models ( SEM ) showed that soil substrates were the main factors controlling N min when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the N min prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in N min contributed the most to global soil NH 4 + ‐N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on N min and highlights the importance of soil microbial biomass in determining N min and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.

The internal transcribed spacer (ITS) of nuclear ribosomal DNA is one of the most commonly used DNA markers in plant phylogenetic and DNA barcoding analyses, and it has been recommended as a core plant DNA barcode. Despite this popularity, the universality and specificity of PCR primers for the ITS region are not satisfactory, resulting in amplification and sequencing difficulties. By thoroughly surveying and analysing the 18S, 5.8S and 26S sequences of Plantae and Fungi from GenBank, we designed new universal and plant-specific PCR primers for amplifying the whole ITS region and a part of it (ITS1 or ITS2) of plants. In silico analyses of the new and the existing ITS primers based on these highly representative data sets indicated that (i) the newly designed universal primers are suitable for over 95% of plants in most groups; and (ii) the plant-specific primers are suitable for over 85% of plants in most groups without amplification of fungi. A total of 335 samples from 219 angiosperm families, 11 gymnosperm families, 24 fern and lycophyte families, 16 moss families and 17 fungus families were used to test the performances of these primers. In vitro PCR produced similar results to those from the in silico analyses. Our new primer pairs gave PCR improvements up to 30% compared with common-used ones. The new universal ITS primers will find wide application in both plant and fungal biology, and the new plant-specific ITS primers will, by eliminating PCR amplification of nonplant templates, significantly improve the quality of ITS sequence information collections in plant molecular systematics and DNA barcoding.

Anthropogenic activities, and in particular the use of synthetic nitrogen (N) fertilizer, have doubled global annual reactive N inputs in the past 50-100 years, causing deleterious effects on the environment through increased N leaching and nitrous oxide (N2 O) and ammonia (NH3 ) emissions. Leaching and gaseous losses of N are greatly controlled by the net rate of microbial nitrification. Extensive experiments have been conducted to develop ways to inhibit this process through use of nitrification inhibitors (NI) in combination with fertilizers. Yet, no study has comprehensively assessed how inhibiting nitrification affects both hydrologic and gaseous losses of N and plant nitrogen use efficiency. We synthesized the results of 62 NI field studies and evaluated how NI application altered N cycle and ecosystem services in N-enriched systems. Our results showed that inhibiting nitrification by NI application increased NH3 emission (mean: 20%, 95% confidential interval: 33-67%), but reduced dissolved inorganic N leaching (-48%, -56% to -38%), N2 O emission (-44%, -48% to -39%) and NO emission (-24%, -38% to -8%). This amounted to a net reduction of 16.5% in the total N release to the environment. Inhibiting nitrification also increased plant N recovery (58%, 34-93%) and productivity of grain (9%, 6-13%), straw (15%, 12-18%), vegetable (5%, 0-10%) and pasture hay (14%, 8-20%). The cost and benefit analysis showed that the economic benefit of reducing N's environmental impacts offsets the cost of NI application. Applying NI along with N fertilizer could bring additional revenues of $163 ha(-1) yr(-1) for a maize farm, equivalent to 8.95% increase in revenues. Our findings showed that NIs could create a win-win scenario that reduces the negative impact of N leaching and greenhouse gas production, while increases the agricultural output. However, NI's potential negative impacts, such as increase in NH3 emission and the risk of NI contamination, should be fully considered before large-scale application.

The microRNA miR393 has been shown to play a role in plant development and in the stress response by targeting mRNAs that code for the auxin receptors in Arabidopsis. In this study, we verified that two rice auxin receptor gene homologs (OsTIR1 and OsAFB2) could be targeted by OsmiR393 (Os for Oryza sativa). Two new phenotypes (increased tillers and early flowering) and two previously observed phenotypes (reduced tolerance to salt and drought and hyposensitivity to auxin) were observed in the OsmiR393-overexpressing rice plants. The OsmiR393-overexpressing rice demonstrated hyposensitivity to synthetic auxin-analog treatments. These data indicated that the phenotypes of OsmiR393-overexpressing rice may be caused through hyposensitivity to the auxin signal by reduced expression of two auxin receptor genes (OsTIR1 and OsAFB2). The expression of an auxin transporter (OsAUX1) and a tillering inhibitor (OsTB1) were downregulated by overexpression of OsmiR393, which suggested that a gene chain from OsmiR393 to rice tillering may be from OsTIR1 and OsAFB2 to OsAUX1, which affected the transportation of auxin, then to OsTB1, which finally controlled tillering. The positive phenotypes (increased tillers and early flowering) and negative phenotypes (reduced tolerance to salt and hyposensitivity to auxin) of OsmiR393-overexpressing rice present a dilemma for molecular breeding.

Summary The Eurasian steppe has long been subject to grazing by domestic ungulates at high levels, resulting in widespread deterioration of biodiversity and ecosystem services. While abundant evidence demonstrates that heavy grazing alters the ecosystem structure and function of grasslands, research on how grazing specifically affects ecosystem functioning and stoichiometry on broad scales is scarce because of a lack of adequate ungrazed reference sites. We examined the effects of grazing on ecosystem functioning and C : N : P stoichiometry across a precipitation gradient along the 700 km C hina– M ongolia transect ( CMT ), covering three community types: meadow steppe, typical steppe and desert steppe. Long‐term grazing has dramatically altered the C , N and P pools and stoichiometry of steppe ecosystems along the CMT . Grazing reduced the C , N and P pools in above‐ground biomass and litter, while the responses in below‐ground biomass and soil C , N and P pools to grazing differed substantially among community types. Grazing increased N content and decreased C : N ratios in all plant compartments, suggesting accelerated N cycling. The altered C : N : P stoichiometry may be explained by changes in the composition of species and functional groups as well as increased foliar N and P contents for the same species in grazed communities. Synthesis and applications . Plant stoichiometric responses to grazing ranged from large in the meadow steppe to small in the typical steppe to generally insignificant in the desert steppe, implying that different underlying mechanisms operated along the regional precipitation gradient. Our findings suggest that reducing the stocking rate and restoring the vastly degraded steppes are essential to sustain native steppe biodiversity, ecosystem functioning and biological capacity for mitigating the impact of climate change in the Inner M ongolia grassland.

Despite decades of research, how climate warming alters the global flux of soil respiration is still poorly characterized. Here, we use meta-analysis to synthesize 202 soil respiration datasets from 50 ecosystem warming experiments across multiple terrestrial ecosystems. We found that, on average, warming by 2 °C increased soil respiration by 12% during the early warming years, but warming-induced drought partially offset this effect. More significantly, the two components of soil respiration, heterotrophic respiration and autotrophic respiration showed distinct responses. The warming effect on autotrophic respiration was not statistically detectable during the early warming years, but nonetheless decreased with treatment duration. In contrast, warming by 2 °C increased heterotrophic respiration by an average of 21%, and this stimulation remained stable over the warming duration. This result challenged the assumption that microbial activity would acclimate to the rising temperature. Together, our findings demonstrate that distinguishing heterotrophic respiration and autotrophic respiration would allow us better understand and predict the long-term response of soil respiration to warming. The dependence of soil respiration on soil moisture condition also underscores the importance of incorporating warming-induced soil hydrological changes when modeling soil respiration under climate change.

Understanding the dynamics and underlying mechanism of carbon exchange between terrestrial ecosystems and the atmosphere is one of the key issues in global change research. In this study, we quantified the carbon fluxes in different terrestrial ecosystems in China, and analyzed their spatial variation and environmental drivers based on the long-term observation data of ChinaFLUX sites and the published data from other flux sites in China. The results indicate that gross ecosystem productivity (GEP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of terrestrial ecosystems in China showed a significantly latitudinal pattern, declining linearly with the increase of latitude. However, GEP, ER, and NEP did not present a clear longitudinal pattern. The carbon sink functional areas of terrestrial ecosystems in China were mainly located in the subtropical and temperate forests, coastal wetlands in eastern China, the temperate meadow steppe in the northeast China, and the alpine meadow in eastern edge of Qinghai-Tibetan Plateau. The forest ecosystems had stronger carbon sink than grassland ecosystems. The spatial patterns of GEP and ER in China were mainly determined by mean annual precipitation (MAP) and mean annual temperature (MAT), whereas the spatial variation in NEP was largely explained by MAT. The combined effects of MAT and MAP explained 79%, 62%, and 66% of the spatial variations in GEP, ER, and NEP, respectively. The GEP, ER, and NEP in different ecosystems in China exhibited 'positive coupling correlation' in their spatial patterns. Both ER and NEP were significantly correlated with GEP, with 68% of the per-unit GEP contributed to ER and 29% to NEP. MAT and MAP affected the spatial patterns of ER and NEP mainly by their direct effects on the spatial pattern of GEP.

Anthropogenic activities have increased nitrogen (N) deposition by threefold to fivefold over the last century, which may considerably affect soil respiration (Rs). Although numerous individual studies and a few meta-analyses have been conducted, it remains controversial as to how N addition affects Rs and its components [i.e., autotrophic (Ra) and heterotrophic respiration (Rh)]. To reconcile the difference, we conducted a comprehensive meta-analysis of 295 published studies to examine the responses of Rs and its components to N addition in terrestrial ecosystems. We also assessed variations in their responses in relation to ecosystem types, environmental conditions, and experimental duration (DUR). Our results show that N addition significantly increased Rs by 2.0% across all biomes but decreased by 1.44% in forests and increased by 7.84% and 12.4% in grasslands and croplands, respectively (P < 0.05). The differences may largely result from diverse responses of Ra to N addition among biomes with more stimulation of Ra in croplands and grasslands compared with no significant change in forests. Rh exhibited a similar negative response to N addition among biomes except that in croplands, tropical and boreal forests. Methods of partitioning Rs did not induce significant differences in the responses of Ra or Rh to N addition, except that Ra from root exclusion and component integration methods exhibited the opposite responses in temperate forests. The response ratios (RR) of Rs to N addition were positively correlated with mean annual temperature (MAT), with being more significant when MAT was less than 15 °C, but negatively with DUR. In addition, the responses of Rs and its components to N addition largely resulted from the changes in root and microbial biomass and soil C content as indicated by correlation analysis. The response patterns of Rs to N addition as revealed in this study can be benchmarks for future modeling and experimental studies.

Summary The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long‐standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity. Here, we conduct an analysis of relationships between tree species richness, biomass and productivity in 25 forest plots of area 8–50 ha from across the world. The data were collected using standardized protocols, obviating the need to correct for methodological differences that plague many studies on this topic. We found that at very small spatial grains (0.04 ha) species richness was generally positively related to productivity and biomass within plots, with a doubling of species richness corresponding to an average 48% increase in productivity and 53% increase in biomass. At larger spatial grains (0.25 ha, 1 ha), results were mixed, with negative relationships becoming more common. The results were qualitatively similar but much weaker when we controlled for stem density: at the 0.04 ha spatial grain, a doubling of species richness corresponded to a 5% increase in productivity and 7% increase in biomass. Productivity and biomass were themselves almost always positively related at all spatial grains. Synthesis . This is the first cross‐site study of the effect of tree species richness on forest biomass and productivity that systematically varies spatial grain within a controlled methodology. The scale‐dependent results are consistent with theoretical models in which sampling effects and niche complementarity dominate at small scales, while environmental gradients drive patterns at large scales. Our study shows that the relationship of tree species richness with biomass and productivity changes qualitatively when moving from scales typical of forest surveys (0.04 ha) to slightly larger scales (0.25 and 1 ha). This needs to be recognized in forest conservation policy and management.

BACKGROUND: Although the effect of salicylic acid (SA) on photosynthesis of plants including grapevines has been investigated, very little is yet known about the effects of SA on carbon assimilation and several components of PSII electron transport (donor side, reaction center and acceptor side). In this study, the impact of SA pretreatment on photosynthesis was evaluated in the leaves of young grapevines before heat stress (25 degrees C), during heat stress (43 degrees C for 5 h), and through the following recovery period (25 degrees C). Photosynthetic measures included gas exchange parameters, PSII electron transport, energy dissipation, and Rubisco activation state. The levels of heat shock proteins (HSPs) in the chloroplast were also investigated. RESULTS: SA did not significantly (P < 0.05) influence the net photosynthesis rate (Pn) of leaves before heat stress. But, SA did alleviate declines in Pn and Rubisco activation state, and did not alter negative changes in PSII parameters (donor side, acceptor side and reaction center QA) under heat stress. Following heat treatment, the recovery of Pn in SA-treated leaves was accelerated compared with the control (H2O-treated) leaves, and, donor and acceptor parameters of PSII in SA-treated leaves recovered to normal levels more rapidly than in the controls. Rubisco, however, was not significantly (P < 0.05) influenced by SA. Before heat stress, SA did not affect level of HSP 21, but the HSP21 immune signal increased in both SA-treated and control leaves during heat stress. During the recovery period, HSP21 levels remained high through the end of the experiment in the SA-treated leaves, but decreased in controls. CONCLUSION: SA pretreatment alleviated the heat stress induced decrease in Pn mainly through maintaining higher Rubisco activation state, and it accelerated the recovery of Pn mainly through effects on PSII function. These effects of SA may be related in part to enhanced levels of HSP21.

Impacts of reactive nitrogen (N) inputs on ecosystem carbon (C) dynamics are highly variable, and the underlying mechanisms remain unclear. Here, we proposed a new conceptual framework that integrates plant, microbial and geochemical mechanisms to reconcile diverse and contrasting impacts of N on soil C. This framework was tested using long-term N enrichment and acid addition experiments in a Mongolian steppe grassland. Distinct mechanisms could explain effects of N on particulate and mineral-associated soil C pools, potentially explaining discrepancies among previous N addition studies. While plant production predominated particulate C changes, N-induced soil acidification strongly affected mineral-associated C through decreased microbial growth and pH-sensitive associations between iron and aluminium minerals and C. Our findings suggest that effects of N-induced acidification on microbial respiration and geochemical properties should be included in Earth system models that predict ecosystem C budgets under future N deposition/input scenarios.