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Abstract The relationship between stomatal traits and environmental drivers across plant communities has important implications for ecosystem carbon and water fluxes, but it has remained unclear. Here, we measure the stomatal morphology of 4492 species-site combinations in 340 vegetation plots across China and calculate their community-weighted values for mean, variance, skewness, and kurtosis. We demonstrate a trade-off between stomatal density and size at the community level. The community-weighted mean and variance of stomatal density are mainly associated with precipitation, while that of stomatal size is mainly associated with temperature, and the skewness and kurtosis of stomatal traits are less related to climatic and soil variables. Beyond mean climate variables, stomatal trait moments also vary with climatic seasonality and extreme conditions. Our findings extend the knowledge of stomatal trait–environment relationships to the ecosystem scale, with applications in predicting future water and carbon cycles.

Abstract Given the leveling off in oxidized nitrogen emissions around the world, the atmospheric deposition of reduced nitrogen (NH x = NH3 + NH4 +) has become progressively critical, especially dry deposition, which presents great threats to plant growth. A combination of historical deposition data of measured wet NH x and modeled dry NH x in China suggests that dry NHx deposition has been increasing substantially (4.50% yr−1, p

Forests are chiefly responsible for the terrestrial carbon sink that greatly reduces the buildup of CO2 concentrations in the atmosphere and alleviates climate change. Current predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest age. Here, we predict the role of China’s current forest age in future carbon sink capacity by generating a high-resolution (30 m) forest age map in 2019 over China’s landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 plots. As China’s forests currently have large proportions of young and middle-age stands, we project that China’s forests will maintain high growth rates for about 15 years. However, as the forests grow older, their net primary productivity will decline by 5.0% ± 1.4% in 2050, 8.4% ± 1.6% in 2060, and 16.6% ± 2.8% in 2100, indicating weakened carbon sinks in the near future. The weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved afforestation. This finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.

In the face of accelerated global dryland expansion and grassland degradation, signaling grassland ecosystem state transitions is an ongoing challenge in ecology. However, there is still a lack of effective indicators and understanding of the mechanisms of grassland ecosystem state transitions at the continental scale. Here, we propose a framework that links ecosystem function-based indicators and critical slowing down (CSD) theory to reveal grassland state transitions. Across precipitation gradients, we quantified the statistical characteristics and spatial patterns in ANPP and PUE dynamics (variability, asymmetry, and sensitivity to precipitation and temperature) in Eurasian grasslands. We show that the CVANPP, CVPUE, AANPP, APUE, SPUE-P, and SANPP-P of temperate steppes were significantly higher than those of alpine steppes, while the SPUE-T and SANPP-T were the inverse. In temperate grasslands, AANPP, APUE, and SANPP-P indicated the transition of typical steppes, and CVANPP, APUE, and SPUE-T indicated the transition from meadow to typical steppes. In alpine grasslands, APUE indicated the transition between alpine deserts and alpine steppes, and AANPP and SANPP-P indicated the transition between alpine steppes and meadow steppes. The interannual variability of precipitation strongly affected xerophyte proportion and demographic processes, which control state transitions in low-resilience grasslands. Community structures and limiting factors (nutrient, light, and/or temperature) regulate state transitions in high-resilience grasslands. Our results demonstrate that function-based indicators are predictive of impending state transitions of temperate and alpine grasslands, highlighting the complementation of ANPP and PUE dynamics that have the potential for predicting grassland ecosystem regime shifts and their underlying mechanisms.

Drylands dominate the trend and variability of the land carbon (C) sink. A better understanding of the implications of climate-induced changes in the drylands for C sink-source dynamics is urgently needed. The effect of climate on ecosystem C fluxes (gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP)) in drylands has been extensively explored, but the roles of other concurrently changing factors, such as vegetation conditions and nutrient availability, remain unclear. We used eddy-covariance C-flux measurements from 45 ecosystems with concurrent information on climate (mean annual temperature (MAT) and mean annual precipitation (MAP)), soil (soil moisture (SM) and soil total nitrogen content (soil N)), and vegetation (leaf area index (LAI) and leaf nitrogen content (LNC)) factors to assess their roles in C fluxes. The results showed that the drylands in China were weak C sinks. GPP and ER were positively correlated with MAP, while they were negatively correlated with MAT. NEP first decreased and then increased with increasing MAT and MAP, and 6.6 °C and 207 mm were the boundaries for the NEP response to MAT and MAP, respectively. SM, soil N, LAI, and MAP were the main factors affecting GPP and ER. However, SM and LNC had the most important influence on NEP. Compared with climate and vegetation factors, soil factors (SM and soil N) had a greater impact on C fluxes in the drylands. Climate factors mainly affected C fluxes by regulating vegetation and soil factors. To accurately estimate the global C balance and predict the response of ecosystems to environmental change, it is necessary to fully consider the discrepant effects of climate, vegetation, and soil factors on C fluxes, as well as the cascade relationships between different factors.

Ecologically vulnerable areas (EVAs) are regions with ecosystems that are fragile and vulnerable to degradation under external disturbances, e.g., environmental changes and human activities. A comprehensive understanding of the climate change characteristics of EVAs in China is of great guiding significance for ecological protection and economic development. The ecosystem carbon use efficiency (CUEe) can be defined as the ratio of the net ecosystem productivity (NEP) to gross primary productivity (GPP), one of the most important ecological indicators of ecosystems, representing the capacity for carbon transfer from the atmosphere to a potential ecosystem carbon sink. Understanding the variation in the CUEe and its controlling factors is paramount for regional carbon budget evaluation. Although many CUEe studies have been performed, the spatial variation characteristics and influencing factors of the CUEe are still unclear, especially in EVAs in China. In this study, we synthesized 55 field measurements (3 forestland sites, 37 grassland sites, 6 cropland sites, 9 wetland sites) of the CUEe to examine its variation and influencing factors in EVAs in China. The results showed that the CUEe in EVAs in China ranged from -0.39 to 0.67 with a mean value of 0.20. There were no significant differences in the CUEe among different vegetation types, but there were significant differences in CUEe among the different EVAs (agro-pastoral ecotones < Tibetan Plateau < arid and semiarid areas < Loess Plateau). The CUEe first decreased and then increased with increasing mean annual temperature (MAT), soil pH and soil organic carbon (SOC) and decreased with increasing mean annual precipitation (MAP). The most important factors affecting the CUEe were biotic factors (NEP, GPP, and leaf area index (LAI)). Biotic factors directly affected the CUEe, while climate (MAT and MAP) and soil factors (soil pH and SOC) exerted indirect effects. The results illustrated the comprehensive effect of environmental factors and ecosystem attributes on CUEe variation, which is of great value for the evaluation of regional ecosystem functions.

Ecosystem is a fundamental organizational unit of the biosphere in which biological communities interact with their non-biological environment through energy flows and material cycles. Ecosystem science is the study of patterns, processes, and services of ecosystems. Since the 1990s, rising concerns regarding global climate change, biodiversity loss, ecosystem degradation, and sustainability of the human-dominated biosphere have stimulated the growth of ecosystem science, which is expected to provide systematic solutions to many of these major issues facing human societies. This paper provides a comprehensive review of the current progress in ecosystem science and identifies some key research challenges facing this discipline. We demonstrate that a key feature of the current progress in ecosystem science is its evolution from primarily theoretical explorations toward more systematic, integrative and application-oriented studies. Specifically, five major changes in the discipline over the past several decades can be identified. These include: (1) the expansion of the primary goal from understanding nature to include human activities; (2) the broadening of the research focus from single ecosystem types to macro-ecosystems comprising multiple regional ecosystems; (3) the shifting of research methods from small-scale observations and experiments to large-scale observations, network experiments, and model simulations; (4) the increasing attention to comprehensive integration of ecosystem components, processes, and scales; and (5) the shifting from a primarily biology-oriented focus to an integrated multi-disciplinary scientific field. While ecosystem science still faces many challenges in the future, these directional changes, along with the rapidly enriched research tools and data acquisition capabilities, lay a promising ground for the discipline’s future as a fundamental scientific basis for solving many environmental challenges facing human societies.

Abstract Global warming has significantly altered the distribution and productivity of vegetation owing to shifts in plant functional traits. However, chlorophyll adaptations—good representative of plant production—in grasslands have not been investigated on a large scale, hindering ecological predictions of climate change. Three grassland transects with a natural temperature gradient were designed in the Tibetan, Mongolian, and Loess Plateau to describe the changes in chlorophyll under different warming scenarios for 475 species. In the three plateaus, variations and distributions of species chlorophyll concentration and composition were compared. The results showed that the means of chlorophyll concentration and composition (chlorophyll a/b) increased with the mean annual temperature. Still, their distributions shifted in opposite manners: chlorophyll concentration was distributed in a broader but more differential manner, while chlorophyll composition was distributed in a narrower but more uniform manner. Compared to chlorophyll concentration, chlorophyll composition was more conservative, with a slight shift in distribution. At the regional level, the chlorophyll concentration and composition depend on the limitations of the local climate or resources. The results implied that warming might drive shifts in grassland chlorophyll distribution mainly by alternations in species composition. Large-scale chlorophyll investigations will be useful for developing prediction techniques.

At present, using plant traits to predict ecosystem functions is of great significance as trait-based ecology recasts classic ecological propositions. However, functional traits at the individual level do not predict higher-level aggregated ecosystem processes (e.g., gross primary productivity) accurately because vital contextual information on specific communities or ecosystems is not considered. Here, we integrate individual or species level plant traits with background information, such as total leaf mass, to form plant community traits that explain spatial variability in productivity. We use a systematic dataset of leaf nitrogen (N) content in 73 typical ecosystems of China that were classified based on community weighted mean leaf N content (Ncontent, mg g−1) and the total leaf N reserves per land area (Nquantity, g m−2) to verify their variations at a large scale across biomes and their responses to changing environmental factors. The results showed that using plant community traits, even without factoring in climatic factors, explains 81% and 68% of the spatial variability in annual gross primary productivities and monthly gross primary productivities, respectively. In addition, both Ncontent and Nquantity were significantly affected by environmental factors (climatic and soil factors). Our findings highlight that two-dimensional plant community traits of content vs. quantity on land area can strengthen the predictability of variations of primary productivity and even other ecosystem functions.

Measurement(s) photosynthetically active radiation • biomass • plant litter • leaf area index • soil organic carbon • CO2 flux • carbon cycling • temperature of air • rain • humidity of soil • humidity of air Technology Type(s) Sensor Device • forest inventory • litter trap • soil coring • eddy covariance • data assimilation Factor Type(s) plot Sample Characteristic - Environment forest biome Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13348154

Viruses act as “regulators” of the global carbon cycle because they impact the material cycles and energy flows of food webs and the microbial loop. The average contribution of viruses to the Earth ecosystem carbon cycle is 8.6‰, of which its contribution to marine ecosystems (1.4‰) is less than its contribution to terrestrial (6.7‰) and freshwater (17.8‰) ecosystems. Over the past 2,000 years, anthropogenic activities and climate change have gradually altered the regulatory role of viruses in ecosystem carbon cycling processes. This has been particularly conspicuous over the past 200 years due to rapid industrialization and attendant population growth. The progressive acceleration of the spread and reproduction of viruses may subsequently accelerate the global C cycle.

Precipitation-use efficiency (PUE) is an important ecosystem indicator of the efficiency of carbon–water conversion. The trend and interannual variation of precipitation-use efficiency (PUE) response to climatic factors provide a theoretical foundation for understanding how Eurasian grasslands adapt to climate change. However, the long-term trends and regulating factors of PUE in Eurasian grasslands at the continental scale are still unclear. Here, we integrated long-term Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI), field surveys of aboveground net primary production (ANPP) and meteorological datasets during 1982–2015 to reveal the temporal variations and controls of PUE in Eurasian grasslands. We found that there was an overall uptrend of PUE (3 × 10−3 g C m−2 mm−1/10 yr) in Eurasian grasslands. The greatest increasing trends of PUE was found in forest steppe at the rate of 13 × 10−3 g C m−2 mm−1/10 yr, while greatest decreasing trend presented in alpine steppe at the rate of −2.6 × 10−3 g C m−2 mm−1/10 yr. The PUE showed linearly decreasing patterns with precipitation at the biome and continental scales, while it was uncorrelated with temperature at the continental scale and showed diverse patterns of linear increase, concave-down and no correlation with temperature for different biomes. The temporal variation of PUE was mainly controlled by precipitation in Eurasian grasslands. This result further revealed that climatic factors shaped the temporal pattern of PUE by the cascading effects networks of climatic factors (precipitation and temperature) − vapor pressure deficit (VPD) – leaf area index (LAI) – ANPP – PUE (CVLP-CENet). This study identified the long-term trends, interannual variations and controls of PUE in Eurasian grasslands over the past three decades, and provided crucial insights into understanding grassland ecosystems dynamics and response to climate change.

Soil fungi predominate the forest topsoil microbial biomass and participate in biogeochemical cycling as decomposers, symbionts, and pathogens. They are intimately associated with plants but their interactions with aboveground and belowground plant traits are unclear. Here, we evaluated soil fungal communities and their relationships with leaf and root traits in nine forest ecosystems ranging from tropical to cold temperate along a 3,700-km transect in eastern China. Basidiomycota was the most abundant phylum, followed by Ascomycota, Zygomycota, Glomeromycota, and Chytridiomycota. There was no latitudinal trend in total, saprotrophic, and pathotrophic fungal richness. However, ectomycorrhizal fungal abundance and richness increased with latitude significantly and reached maxima in temperate forests. Saprotrophic and pathotrophic fungi were most abundant in tropical and subtropical forests and their abundance decreased with latitude. Spatial and climatic factors, soil properties, and plant traits collectively explained 45% of the variance in soil fungal richness. Specific root length and root biomass had the greatest direct effects on total fungal richness. Specific root length was the key determinant of saprotrophic and pathotrophic fungal richness while root phosphorus content was the main biotic factor determining ectomycorrhizal fungal richness. In contrast, spatial and climatic features, soil properties, total leaf nitrogen and phosphorus, specific root length, and root biomass collectively explained >60% of the variance in fungal community composition. Soil fungal richness and composition are strongly controlled by both aboveground and belowground plant traits. The findings of this study provide new evidence that plant traits predict soil fungal diversity distribution at the continental scale.

Spatial heterogeneity of soil bacterial community depends on scales. The fine-scale spatial heterogeneity of bacterial community composition and functions remains unknown. We analyzed the main driving factors of fine-scale spatial patterns of soil bacterial community composition and carbon metabolic functions across a 30 m × 40 m plot within a Korean pine forest by combining Illumina 16S rRNA sequencing with Biolog Ecoplates based on 53 soil samples. Clear spatial patterns in bacterial community composition and metabolic functions were observed in the forest soil. The bacterial community composition and metabolic functions both showed distance-decay of similarity within a distance of meters. Structural equation model analysis revealed that environmental variables and geographic distance together explained 37.9% and 63.1% of community and metabolic functions, respectively. Among all environmental factors, soil organic carbon (SOC) and root biomass emerged as the most important drivers of the bacterial community structure. In contrast, soil pH explained the largest variance in metabolic functions. Root biomass explained the second-largest variance in soil bacterial community composition, but root traits made no difference in metabolic functions variance. These results allow us to better understand the mechanisms controlling belowground diversity and plant-microbe interactions in forest ecosystems.

The Qinghai-Tibetan Plateau (TP) accumulated a large amount of organic carbon, while its size and response to environmental factors for the whole area remain uncertain. Here, we synthesized a dataset to date with the largest data volume and broadest geographic coverage over the TP, composing of 7196 observations from multiple field campaigns since the 1980s, and provided a comprehensive assessment of the size and spatial distribution of carbon pools for both plant and soils on the TP using machine learning algorithms. The estimated soil organic carbon (SOC) storage to 1 m depth was ${\text{32}}{\text{.01}}_{{\text{19}}{\text{.69}}}^{{\text{47}}{\text{.9}}}$ Pg ( ${\text{11}}{\text{.72}}_{{\text{7}}{\text{.2}}}^{{\text{17}}{\text{.53}}}$ kg m ^−2 on average), accounting for approximately ${\text{37}}{\text{.2}}_{{\text{22}}{\text{.9}}}^{{\text{55}}{\text{.6}}}$ % of China’s SOC stock on its

Chlorophyll is the molecular basis for the function of photosystems and is also a promising tool for ecological prediction. However, the large-scale patterns of chlorophyll variation in grasslands remain poorly understood. We performed consistent measurements of chlorophyll a, b, a+b, and the a:b ratio (chlorophyll a/b) for 421 species across northern hemisphere grassland transects, recorded their distributions, variations, and influencing factors, and examined their relationships with leaf nitrogen. The results showed that the distributional ranges were 0.52–28.33 (mean 5.49) mg·g−1 dry weight, 0.15–12.11 (mean 1.83) mg·g−1 dry weight, 0.67–39.29 (mean 7.32) mg·g−1 dry weight, and 1.28–7.84 (mean 3.02) for chlorophyll a, b, a+b, and a/b, respectively. The chlorophyll averages differed among regions (higher in the Loess Plateau and the Mongolian Plateau than in the Tibetan Plateau), grassland types (desert grasslands > meadow > typical grasslands), life forms, life spans, and taxonomies. In the entire northern hemisphere grassland, chlorophyll concentrations and chlorophyll a/b were negatively correlated to photosynthetically active radiation and the soil N:P ratio, and positively correlated to the mean annual temperatures. These results implied that chlorophyll in grasslands was shaped by the layered structure of grasses, distinct plateau environments, and phylogeny. The allocation patterns of leaf nitrogen to chlorophyll differed among regions and grassland types, showing that caution is required if simply relating single leaf N or chlorophyll to productivity separately. These findings enhance our understanding of chlorophyll in natural grasslands on a large scale, as well as providing information for ecological predictive models.

Abstract Background The accurate estimation of forest biomass at different scales is the critical step in the assessment of forest carbon stocks. We used three models at increasing scales: allometric model at ecoregional scale (model 1), dummy variable allometric model at both ecoregion and regional scales (model 2), and allometric model at regional scale (model 3) to estimate the aboveground biomass of six subtropical forests in China. Furthermore, we also tested whether wood density can improve the accuracy of the allometric model at regional scale. Results Aboveground biomass estimates for six subtropical forests were significantly affected by the ecoregions (p

Abstract Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO‐C‐C (carbon (C)‐only model), TECO‐CN‐C (TECO‐CN coupled model with only C measurements as assimilating data), and TECO‐CN‐CN (TECO‐CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long‐term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long‐term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process‐induced disequilibrium in the initial value of C pools. However, in the long‐term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C‐N coupling in ecosystem models and highlight critical observational needs for future studies.

Seasonality, an exogenous driver, motivates the biological and ecological temporal dynamics of animal and plant communities. Underexplored microbial temporal endogenous dynamics hinders the prediction of microbial response to climate change. To elucidate temporal dynamics of microbial communities, temporal turnover rates, phylogenetic relatedness, and species interactions were integrated to compare those of a series of forest ecosystems along latitudinal gradients. The seasonal turnover rhythm of microbial communities, estimated by the slope (w value) of similarity-time decay relationship, was spatially structured across the latitudinal gradient, which may be caused by a mixture of both diurnal temperature variation and seasonal patterns of plants. Statistical analyses revealed that diurnal temperature variation instead of average temperature imposed a positive and considerable effect alone and also jointly with plants. Due to higher diurnal temperature variation with more climatic niches, microbial communities might evolutionarily adapt into more dispersed phylogenetic assembly based on the standardized effect size of MNTD metric, and ecologically form higher community resistance and resiliency with stronger network interactions among species. Archaea and the bacterial groups of Chloroflexi, Alphaproteobacteria, and Deltaproteobacteria were sensitive to diurnal temperature variation with greater turnover rates at higher latitudes, indicating that greater diurnal temperature fluctuation imposes stronger selective pressure on thermal specialists, because bacteria and archaea, single-celled organisms, have extreme short generation period compared to animal and plant. Our findings thus illustrate that the dynamics of microbial community and species interactions are crucial to assess ecosystem stability to climate variations in an increased climatic variability era.

Climatic variables not only directly affect the interannual variability (IAV) in net ecosystem exchange of CO2 (NEE) but also indirectly drive it by changing the physiological parameters. Identifying these direct and indirect paths can reveal the underlying mechanisms of carbon (C) dynamics. In this study, we applied a path analysis using flux data from 65 sites to quantify the direct and indirect climatic effects on IAV in NEE and to evaluate the potential relationships among the climatic variables and physiological parameters that represent physiology and phenology of ecosystems. We found that the maximum photosynthetic rate was the most important factor for the IAV in gross primary productivity (GPP), which was mainly induced by the variation in vapour pressure deficit. For ecosystem respiration (RE), the most important drivers were GPP and the reference respiratory rate. The biome type regulated the direct and indirect paths, with distinctive differences between forests and non-forests, evergreen needleleaf forests and deciduous broadleaf forests, and between grasslands and croplands. Different paths were also found among wet, moist and dry ecosystems. However, the climatic variables can only partly explain the IAV in physiological parameters, suggesting that the latter may also result from other biotic and disturbance factors. In addition, the climatic variables related to NEE were not necessarily the same as those related to GPP and RE, indicating the emerging difficulty encountered when studying the IAV in NEE. Overall, our results highlight the contribution of certain physiological parameters to the IAV in C fluxes and the importance of biome type and multi-year water conditions, which should receive more attention in future experimental and modelling research.

Canopy temperature (Tc), one of the most important plant ecophysiological parameters, has been known to respond rapidly to environmental change. However, how environmental factors—especially the temperature and precipitation pattern—impact Tc has been less discussed for forest stands. In this study, we investigated seasonal variations and responses of the Tc and canopy-to-air temperature difference (ΔT) associated with environmental conditions in two subtropical forests with contrasting temperature and precipitation patterns—Dinghushan (DHS) (temperature and precipitation synchronous site: hot and wet in the summer) and Qianyanzhou (QYZ) (temperature and precipitation asynchronous site: hot and arid in the summer). The results showed that Tc exhibits clear diurnal and seasonal variations above air temperature throughout the day and year, suggesting that the canopy of both DHS and QYZ is typically warmer than ambient air. However, the canopy-warming effect was substantially intensified in QYZ, and the difference of ΔT between dry and wet seasons was small (−0.07 °C) in DHS, while it was up to 0.9 °C in QYZ. Regression analysis revealed that this resulted from the combined effects of the increased solar radiation and vapor pressure deficit (VPD), but reduced canopy conductance (gc) caused by drought in the summer in QYZ. Sensitivity analysis further indicated that the responses of ΔT to VPD and gc changes were quite divergent, presenting negative responses to the enhanced VPD and gc in QYZ, while there were positive responses in DHS. The high productivity coupled with low transpiration cooling that occurs in a temperature and precipitation synchronous condition mainly contributes to the positive responses of ΔT in DHS. This study reveals the seasonal variations, environmental responses, and underlying causes of Tc under different temperature and precipitation patterns, providing useful information for the regional assessment of plant responses to future climate change.

Measurements of greenhouse gas fluxes over many ecosystems have been made as part of the attempt to quantify global carbon and nitrogen cycles. In particular, annual flux observations are of great value for regional flux assessments, as well as model development and optimization. The chamber method is a popular approach for soil/ecosystem respiration and CH4 flux observations of terrestrial ecosystems. However, in situ flux chamber measurements are usually made with non-continuous sampling. To date, efficient methods for the application of such sporadic data to upscale temporally and obtain annual cumulative fluxes have not yet been determined. To address this issue, we tested the adequacy of non-continuous sampling using multi-source data aggregation. We collected 330 site-years monthly soil/ecosystem respiration and 154 site-years monthly CH4 flux data in China, all obtained using the chamber method. The data were randomly divided into a training group and verification group. Fluxes of all possible sampling months of a year, i.e., 4094 different month combinations were used to obtain the annual cumulative flux. The results showed a good linear relationship between the monthly flux and the annual cumulative flux. The flux obtained during the warm season from May to October generally played a more important role in annual flux estimations, as compared to other months. An independent verification analysis showed that the monthly flux of 1 to 4 months explained up to 67%, 89%, 94%, and 97% of the variability of the annual cumulative soil/ecosystem respiration and 92%, 99%, 99%, and 99% of the variability of the annual cumulative CH4 flux. This study supports the use of chamber-observed sporadic flux data, which remains the most commonly-used method for annual flux estimating. The flux estimation method used in this study can be used as a guide for designing sampling programs with the intention of estimating the annual cumulative flux.

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

To improve models for accurate projections, data assimilation, an emerging statistical approach to combine models with data, have recently been developed to probe initial conditions, parameters, data content, response functions and model uncertainties. Quantifying how many information contents are contained in different data streams is essential to predict future states of ecosystems and the climate. This study uses a data assimilation approach to examine the information contents contained in flux- and biometric-based data to constrain parameters in a terrestrial carbon (C) model, which includes canopy photosynthesis and vegetation–soil C transfer submodels. Three assimilation experiments were constructed with either net ecosystem exchange (NEE) data only or biometric data only [including foliage and woody biomass, litterfall, soil organic C (SOC) and soil respiration], or both NEE and biometric data to constrain model parameters by a probabilistic inversion application. The results showed that NEE data mainly constrained parameters associated with gross primary production (GPP) and ecosystem respiration (RE) but were almost invalid for C transfer coefficients, while biometric data were more effective in constraining C transfer coefficients than other parameters. NEE and biometric data constrained about 26% (6) and 30% (7) of a total of 23 parameters, respectively, but their combined application constrained about 61% (14) of all parameters. The complementarity of NEE and biometric data was obvious in constraining most of parameters. The poor constraint by only NEE or biometric data was probably attributable to either the lack of long-term C dynamic data or errors from measurements. Overall, our results suggest that flux- and biometric-based data, containing different processes in ecosystem C dynamics, have different capacities to constrain parameters related to photosynthesis and C transfer coefficients, respectively. Multiple data sources could also reduce uncertainties in parameter estimation if these data sources contain complementary information.

Abstract Ecosystem restoration requires considering both above‐ground biomass (AGB) and soils, and the latter is even more essential due to the importance and restoration difficulty of soil organic matter (SOM). Remote sensing studies have shown that AGB has recovered in the grasslands of northern China, but the recovery of soil organic carbon (SOC) or SOM is still unclear. Here, based on the published data between the 1980s and 2014, we used the variation in carbon (C) density observed in the vegetation and soils of four regions across northern China as integrative indicator to explore grassland restoration. Overall, northern Chinese grasslands were a weak carbon source (−14 Tg C), although C density in AGB and below‐ground biomass increased on average by 0.019 and 0.224 kg C/m2, respectively, during the period considered in the present study. Unexpectedly, SOC density in the 0–20 cm soil layer decreased by 0.193 kg C/m2 on average, and all regions registered a decrease in SOC, although values differed among them. Our findings were consistent with previous evaluations using remote sensing and with the idea that vegetation in northern China has been restored. However, SOC has not been restored, and given its importance for sustaining nutrient supply and water conservation, as well as the high difficulty of SOC restoration, grassland restoration in northern China is still far from being achieved.

Rivers play an important role in carbon (C) exchange between terrestrial and oceanic water bodies and the atmosphere. The aim of this study was to systematically quantify fluxes in riverine C export and C exchange in the air–sea interface of marine ecosystems in China. Results show that annual C transport from rivers to coastal ecosystems in China can reach up to 64.35 TgC, which accounts for approximately 4.8%–8.1% of global C transport from river systems. In the Bohai Sea, particulate inorganic carbon is the main form of C influx, and it can reach up to 20.79 TgC/yr. Conversely, dissolved inorganic carbon is the main form of C influx into the East China Sea, and it can reach up to 10.52 TgC/yr, which is 42.6% of the total annual C imported into the East China Sea. China's marine ecosystems including the Yellow Sea, the Bohai Sea, the East China Sea, and the South China Sea can absorb 65.06 TgC/yr from the atmosphere.

Latent heat flux (LE) and the corresponding water vapor lost from the Earth’s surface to the atmosphere, which is called Evapotranspiration (ET), is one of the key processes in the water cycle and energy balance of the global climate system. Satellite remote sensing is the only feasible technique to estimate LE over a large-scale region. While most of the previous satellite LE methods are based on the optical vegetation index (VI), here we propose a microwave-VI (EDVI) based LE algorithm which can work for both day and night time, and under clear or non-raining conditions. This algorithm is totally driven by multiple-sensor satellite products of vegetation water content index, solar radiation, and cloud properties, with some aid from a reanalysis dataset. The satellite inputs and the performance of this algorithm are validated with in situ measurements at three ChinaFLUX forest sites. Our results show that the selected satellite observations can indeed serve as the inputs for the purpose of estimating ET. The instantaneous estimations of LE (LEcal) from this algorithm show strong positive temporal correlations with the in situ measured LE (LEobs) with the correlation coefficients (R) of 0.56−0.88 in the study years. The mean bias is kept within 16.0% (23.0 W/m2) across the three sites. At the monthly scale, the correlations between the retrieval and the in situ measurements are further improved to an R of 0.84−0.95 and the bias is less than 14.3%. The validation results also indicate that EDVI-based LE method can produce stable LEcal under different cloudy skies with good accuracy. Being independent of any in situ measurements as inputs, this algorithm shows great potential for estimating ET under both clear and cloudy skies on a global scale for climate study.

China’s terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China’s terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China’s terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.

It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1) and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

Climate, vegetation, and soil characteristics play important roles in regulating the spatial variation in carbon dioxide fluxes, but their relative influence is still uncertain. In this study, we compiled data from 241 eddy covariance flux sites in the Northern Hemisphere and used Classification and Regression Trees and Redundancy Analysis to assess how climate, vegetation, and soil affect the spatial variations in three carbon dioxide fluxes (annual gross primary production (AGPP), annual ecosystem respiration (ARE), and annual net ecosystem production (ANEP)). Our results showed that the spatial variations in AGPP, ARE, and ANEP were significantly related to the climate and vegetation factors (correlation coefficients, R = 0.22 to 0.69, P < 0.01) while they were not related to the soil factors (R = -0.11 to 0.14, P > 0.05) in the Northern Hemisphere. The climate and vegetation together explained 60% and 58% of the spatial variations in AGPP and ARE, respectively. Climate factors (mean annual temperature and precipitation) could account for 45-47% of the spatial variations in AGPP and ARE, but the climate constraint on the vegetation index explained approximately 75%. Our findings suggest that climate factors affect the spatial variations in AGPP and ARE mainly by regulating vegetation properties, while soil factors exert a minor effect. To more accurately assess global carbon balance and predict ecosystem responses to climate change, these discrepant roles of climate, vegetation, and soil are required to be fully considered in the future land surface models. Moreover, our results showed that climate and vegetation factors failed to capture the spatial variation in ANEP and suggest that to reveal the underlying mechanism for variation in ANEP, taking into account the effects of other factors (such as climate change and disturbances) is necessary.

The principle of enzyme kinetics suggests that the temperature sensitivity (Q10) of soil organic matter (SOM) decomposition is inversely related to organic carbon (C) quality, i.e., the C quality-temperature (CQT) hypothesis. We tested this hypothesis by performing laboratory incubation experiments with bulk soil, macroaggregates (MA, 250-2000 μm), microaggregates (MI, 53-250 μm), and mineral fractions (MF, MF>bulk soil >MI(P

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.

The worldwide development of industry and agriculture has generated noticeable increases in atmospheric nitrogen (N) deposition, significantly altering the global N cycle. These changes might affect the global carbon (C) cycle by enhancing forest C sequestration. Here, we used a series of datasets from eight typical forests along the north–south transect of eastern China (NSTEC). These datasets contained information on community structure, C and N concentrations in the soil and the organs (leaf, branch, stem, and fine-root) of 877 plant species, and atmospheric wet N deposition. Using the biomass weighting method, we scaled up the C:N ratios from the organ level to the ecosystem level, and evaluated the C sequestration rate (CSR _N ) in response to wet N deposition and N use efficiency (NUE) in China’s forests based on the principles of ecological stoichiometry. Our results showed that atmospheric wet N deposition had a modest impact on forest C storage. Specifically, mean CSR _N was estimated as 231 kg C ha ^−1 yr ^−1 (range: 32.7–507.1 kg C ha ^−1 yr ^−1 ), accounting for 2.1% of NPP and 4.6% of NEP at the ecosystem level. The NUE _eco of atmospheric N deposition ranged from 9.6−27.7 kg C kg ^−1 N, and increased with increasing latitude from subtropical to cold-temperate forests in China ( P

The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC) tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R2 of 0.77 and a root mean square error of 0.72 mm·day−1. The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET) in China with the model simulations were assessed.

Understanding the geographic patterns and potential drivers of leaf stoichiometry is critical for modelling the nutrient fluxes of ecosystems and to predict the responses of ecosystems to global changes. This study aimed to explore the altitudinal patterns and potential drivers of leaf C∶N∶P stoichiometry. We measured the concentrations of leaf C, N and P in 175 plant species as well as soil nutrient concentrations along an altitudinal transect (500-2300 m) on the northern slope of Changbai Mountain, China to explore the response of leaf C∶N∶P stoichiometry to plant growth form (PGF), climate and soil. Leaf C, N, P and C∶N∶P ratios showed significant altitudinal trends. In general, leaf C and C∶N∶P ratios increased while leaf N and P decreased with elevation. Woody and herbaceous species showed different responses to altitudinal gradients. Trees had the largest variation in leaf C, C∶N and C∶P ratios, while herbs showed the largest variation in leaf N, P and N∶P ratio. PGF, climate and soil jointly regulated leaf stoichiometry, explaining 17.6% to 52.1% of the variation in the six leaf stoichiometric traits. PGF was more important in explaining leaf stoichiometry variation than soil and climate. Our findings will help to elucidate the altitudinal patterns of leaf stoichiometry and to model ecosystem nutrient cycling.

To date, few studies are conducted to quantify the effects of reduced ammonium (NH4+) and oxidized nitrate (NO3-) on soil CH4 uptake and N2O emission in the subtropical forests. In this study, NH4Cl and NaNO3 fertilizers were applied at three rates: 0, 40 and 120 kg N ha(-1) yr(-1). Soil CH4 and N2O fluxes were determined twice a week using the static chamber technique and gas chromatography. Soil temperature and moisture were simultaneously measured. Soil dissolved N concentration in 0-20 cm depth was measured weekly to examine the regulation to soil CH4 and N2O fluxes. Our results showed that one year of N addition did not affect soil temperature, soil moisture, soil total dissolved N (TDN) and NH4+-N concentrations, but high levels of applied NH4Cl and NaNO3 fertilizers significantly increased soil NO3(-)-N concentration by 124% and 157%, respectively. Nitrogen addition tended to inhibit soil CH4 uptake, but significantly promoted soil N2O emission by 403% to 762%. Furthermore, NH4+-N fertilizer application had a stronger inhibition to soil CH4 uptake and a stronger promotion to soil N2O emission than NO3(-)-N application. Also, both soil CH4 and N2O fluxes were driven by soil temperature and moisture, but soil inorganic N availability was a key integrator of soil CH4 uptake and N2O emission. These results suggest that the subtropical plantation soil sensitively responses to atmospheric N deposition, and inorganic N rather than organic N is the regulator to soil CH4 uptake and N2O emission.

Evapotranspiration (ET) is an important component of the water cycle in terrestrial ecosystems. Understanding the ways in which ET changes with meteorological factors is central to a better understanding of ecological and hydrological processes. In this study, we used eddy covariance measurements of ET from a typical alpine shrubland meadow ecosystem in China to investigate the hysteresis response of ET to environmental variables including air temperature (Ta), vapor pressure deficit (VPD) and net radiation (Rn) at a diel timescale. Meanwhile, the simulated ET by Priestly-Taylor equation was used to interpret the measured ET under well-watered conditions. Pronounced hysteresis was observed in both Ta and VPD response curves of ET. At a similar Ta and VPD, ET was always significantly depressed in the afternoon compared with the morning. But the hysteresis response of ET to Rn was not evident. Similar hysteresis patterns were also observed in the Ta/VPD response curves of simulated ET. The magnitudes of the measured and simulated hysteresis loops showed similar seasonal variation, with relatively smaller values occurring from May to September, which agreed well with the lifetime of plants and the period of rainy season at this site. About 62% and 23% of changes in the strength of measured ET-Ta and ET-VPD loops could be explained by the changes in the strength of simulated loops, respectively. Thus, the time lag between Rn and Ta/VPD is the most important factor generating and modulating the ET-Ta/VPD hysteresis, but plants and water status also contribute to the hysteresis response of ET. Our research confirmed the different hysteresis in the responses of ET to meteorological factors and proved the vital role of Rn in driving the diel course of ET.

We added the stable isotope (15)N in the form of ((15)NH4)2SO4 and K(15)NO3 to forest ecosystems in eastern China under two different N deposition levels to study the fate of the different forms of deposited N. Prior to the addition of the (15)N tracers, the natural (15)N abundance ranging from -3.4‰ to +10.9‰ in the forest under heavy N deposition at Dinghushan (DHS), and from -3.92‰ to +7.25‰ in the forest under light N deposition at Daxinganling (DXAL). Four months after the tracer application, the total (15)N recovery from the major ecosystem compartments ranged from 55.3% to 90.5%. The total (15)N recoveries were similar under the ((15)NH4)2SO4 tracer treatment in both two forest ecosystems, whereas the total (15)N recovery was significantly lower in the subtropical forest ecosystem at DHS than in the boreal forest ecosystem at DXAL under the K(15)NO3 tracer treatment. The (15)N assimilated into the tree biomass represented only 8.8% to 33.7% of the (15)N added to the forest ecosystems. In both of the tracer application treatments, more (15)N was recovered from the tree biomass in the subtropical forest ecosystem at DHS than the boreal forest ecosystem at DXAL. The amount of (15)N assimilated into tree biomass was greater under the K(15)NO3 tracer treatment than that of the ((15)NH4)2SO4 treatment in both forest ecosystems. This study suggests that, although less N was immobilized in the forest ecosystems under more intensive N deposition conditions, forest ecosystems in China strongly retain N deposition, even in areas under heavy N deposition intensity or in ecosystems undergoing spring freezing and thawing melts. Compared to ammonium deposition, deposited nitrate is released from the forest ecosystem more easily. However, nitrate deposition could be retained mostly in the plant N pool, which might lead to more C sequestration in these ecosystems.

We assessed the total nitrogen (N) concentrations of 28 still surface water (lake and pond), and 42 flowing surface water (river), monitoring sites under 29 typical terrestrial ecosystems of the Chinese Ecosystem Research Network (CERN) using monitoring data collected between 2004 and 2009. The results showed that the median total N concentrations of still surface water were significantly higher in the agro- (1.5 mg · L(-1)) and oasis agro- ecosystems (1.8 mg · L(-1)) than in the forest ecosystems (1.0 mg · L(-1)). This was also the case for flowing surface water, with total N concentrations of 2.4 mg · L(-1), 1.8 mg · L(-1) and 0.5 mg · L(-1) for the agro-, oasis agro- and forest ecosystems, respectively. In addition, more than 50% of the samples in agro- and oasis agro- ecosystems were seriously polluted (>1.0 mg · L(-1)) by N. Spatial analysis showed that the total N concentrations in northern and northwestern regions were higher than those in the southern region for both still and flowing surface waters under agro- and oasis agro- ecosystems, with more than 50% of samples exceeding 1.0 mg · L(-1) (the Class III limit of the Chinese National Quality Standards for Surface Waters) in surface water in the northern region. Nitrogen pollution in agro- ecosystems is mainly due to fertilizer applications, while the combination of fertilizer and irrigation exacerbates nitrogen pollution in oasis agro- ecosystems.

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003-2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003-2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May-June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation.

Understanding the variation in stomatal characteristics in relation to climatic gradients can reveal the adaptation strategies of plants, and help us to predict their responses to future climate changes. In this study, we investigated stomatal density (SD) and stomatal length (SL) in 150 plant species along an elevation gradient (540-2357 m) in Changbai Mountain, China, and explored the patterns and drivers of stomatal characteristics across species and plant functional types (PFTs: trees, shrubs, and herbs). The average values of SD and SL for all species combined were 156 mm(-2) and 35 µm, respectively. SD was higher in trees (224 mm(-2)) than in shrubs (156 mm(-2)) or herbs (124 mm(-2)), and SL was largest in herbs (37 µm). SD was negatively correlated with SL in all species and PFTs (P < 0.01). The relationship between stomatal characteristics and elevation differed among PFTs. In trees, SD decreased and SL increased with elevation; in shrubs and herbs, SD initially increased and then decreased. Elevation-related differences in SL were not significant. PFT explained 7.20-17.6% of the total variation in SD and SL; the contributions of CO2 partial pressure (P CO2), precipitation, and soil water content (SWC) were weak (0.02-2.28%). Our findings suggest that elevation-related patterns of stomatal characteristics in leaves are primarily a function of PFT, and highlight the importance of differences among PFTs in modeling gas exchange in terrestrial ecosystems under global climate change.

Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS)). Our findings are summarized as follows. (i) Spatiotemporal variation patterns of sensible heat flux (H) and evapotranspiration (ET) under the land cover scenarios (A2a or B2a) and climate change scenario (A1B) are unanimous. (ii) Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii) Based on the regional interannual variability analysis, H displays a downward trend (10%) and ET presents an increasing trend (15%). (iv) The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change.

Increased nitrogen (N) deposition is common worldwide. Questions of where, how, and if reactive N-input influences soil carbon (C) sequestration in terrestrial ecosystems are of great concern. To explore the potential for soil C sequestration in steppe region under N and phosphorus (P) addition, we conducted a field experiment between 2006 and 2012 in the temperate grasslands of northern China. The experiment examined 6 levels of N (0-56 g N m(-2) yr(-1)), 6 levels of P (0-12.4 g P m(-2) yr(-1)), and a control scenario. Our results showed that addition of both N and P enhanced soil total C storage in grasslands due to significant increases of C input from litter and roots. Compared with control plots, soil organic carbon (SOC) in the 0-100 cm soil layer varied quadratically, from 156.8 to 1352.9 g C m(-2) with N addition gradient (R(2) = 0.99, P < 0.001); and logarithmically, from 293.6 to 788.6 g C m(-2) with P addition gradient (R(2) = 0.56, P = 0.087). Soil inorganic carbon (SIC) decreased quadratically with N addition. The net C sequestration on grassland (including plant, roots, SIC, and SOC) increased linearly from -128.6 to 729.0 g C m(-2) under N addition (R(2) = 0.72, P = 0.023); and increased logarithmically, from 248.5 to 698 g C m(-2)under P addition (R(2) = 0.82, P = 0.014). Our study implies that N addition has complex effects on soil carbon dynamics, and future studies of soil C sequestration on grasslands should include evaluations of both SOC and SIC under various scenarios.

Protecting groundwater from nitrogen contamination is an important public-health concern and a major national environmental issue in China. In this study, we monitored water quality in 29 wells from 2009 to 2010 in a village in Shanghai city, whick belong to typical rural-urban continuum in China. The total N and NO(3)-N exhibited seasonal changes, and there were large fluctuations in NH(4)-N in residential areas, but without significant seasonal patterns. NO(2)-N in the water was not stable, but was present at high levels. Total N and NO(3)-N were significantly lower in residential areas than in agricultural areas. The groundwater quality in most wells belonged to Class III and IV in the Chinese water standard, which defines water that is unsuitable for human consumption. Our health risk assessments showed that NO(3)-N posed the greatest carcinogenic risk, with risk values ranging from 19×10(-6) to 80×10(-6), which accounted for more than 90% of the total risk in the study area.

An understanding of the factors controlling plant community composition will allow improved prediction of the responses of plant communities to natural and anthropogenic environmental change. Using monitoring data from 1980 to 2009, we quantified the changes in community composition in Leymus chinensis and Stipa grandis dominated grasslands in Inner Mongolia under long-term grazing-exclusion and free-grazing conditions, respectively. We demonstrated that the practice of long-term grazing exclusion has significant effects on the heterogeneity, the dominant species, and the community composition in the two grasslands. The community composition of L. chinensis and S. grandis grasslands exhibited directional changes with time under long-term grazing exclusion. Under free grazing, the L. chinensis community changed directionally with time, but the pattern of change was stochastic in the S. grandis community. We attributed the divergent responses to long-term grazing exclusion in the S. grandis and L. chinensis grasslands to litter accumulation and changes in the microenvironment after grazing exclusion, which collectively altered the growth and regeneration of the dominant species. The changes in the grazed grasslands were primarily determined by the selective feeding of sheep during long-term heavy grazing. Overall, the responses of the community composition of the Inner Mongolian grasslands to long-term grazing exclusion and heavy grazing were divergent, and depended primarily on the grassland type. Our findings provide new insights into the role of grazing in the maintenance of community structure and function and therefore have important implications for grassland management.