The hidden risk: Changes in functional potentials of microbial keystone taxa under global climate change jeopardizing soil carbon storage in alpine grasslands.

Climate change is endangering the soil carbon stock of alpine grasslands on the Qinghai-Tibetan Plateau (QTP), but the limited comprehension regarding the mechanisms that sustain carbon storage under hydrothermal changes increases the uncertainty associated with this finding. Here, we examined the relative abundance of soil microbial keystone taxa and their functional potentials, as well as their influence on soil carbon storage with increased precipitation across alpine grasslands on the QTP, China. The findings indicate that alterations in precipitation significantly decreased the relative abundance of the carbon degradation potentials of keystone taxa, such as chemoheterotrophs. The inclusion of keystone taxa and their internal functional potentials in the two best alternative models explained 70% and 63% of the variance in soil organic carbon (SOC) density, respectively. Moreover, we found that changes in chemoheterotrophs had negative effects on SOC density as indicated by a structural equation model, suggesting that some specialized functional potentials of keystone taxa are not conducive to the accumulation of carbon sink. Our study offers valuable insights into the intricate correlation between precipitation-induced alterations in soil microbial keystone taxa and SOC storage, highlighting a rough categorization is difficult to distinguish the hidden threats and the importance of incorporating functional potentials in SOC storage prediction models in response to changing climate.

A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015.

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai-Tibet Plateau in 1982-2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982-2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

The China plant trait database version 2.

Plant functional traits represent adaptive strategies to the environment, linked to biophysical and biogeochemical processes and ecosystem functioning. Compilations of trait data facilitate research in multiple fields from plant ecology through to land-surface modelling. Here we present version 2 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a diversity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not sampled previously; (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and turgor loss point; (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments and flagging the reliability of individual trait measurements; and (5) inclusion of standardized templates for systematical field sampling and measurements.

Control effect of virtual reality technology on procedural pain in children's wound: A meta-analysis.

BACKGROUND: Children who undergo wound manipulation usually experience pain. Virtual reality technology is a novel and effective non pharmaceutical therapy for reducing pain in children scheduled to undergo wound manipulation. However, the effectiveness of Virtual reality technology in controlling procedural pain in children's wounds has not been evaluated in a systematic review. METHODS: It employed a meta-analysis design. We included studies with randomized controlled trials, reporting children's wound manipulation pain, and published them in English. Two reviewers independently evaluated the methodological quality of the included studies. RESULTS: Of the 108 studies identified, 39 were eligible for the meta-analysis, with a total sample of 273 patients. The use of virtual reality technology has significantly reduced pain intensity during wound manipulation in children. There was a significant difference between the experimental group (virtual reality) and the control group (no virtual reality) in reducing the pain of the children's wound manipulation (P < .05). CONCLUSION: As a distraction method of non drug assisted analgesia intervention, virtual reality technology can reduce children's procedural pain and discomfort symptoms.

Integrating future grassland degradation risk to improve the spatial targeting efficiency of payment for ecosystem services.

Spatial targeting plays a key role in improving the efficiency of payment for ecosystem services (PES). However, the risk of grassland degradation after implementing PES increases uncertainty about the efficiency of PES. Here, we identified the spatial heterogeneity of grassland degradation risk using Future Land Use Simulation (FLUS) model, then incorporated grassland degradation risk as a criterion into PES spatial targeting using cost-benefit analysis and ranking optimization. The framework was applied to a case study of the Three-River-Source National Park, China. We found that grasslands in the study area continued to degrade between 2015 and 2025, and the area of degraded grasslands increased by 26%. Compared with spatial targeting of PES without considering grassland degradation risk, PES spatial targeting that considered grassland degradation risk was significantly different (the overlap area accounted for only 75%, 82%, and 94% of the PES area within 25%, 50%, and 75% of the total protection cost budget). When the grassland degradation risk was considered as a targeting criterion, PES efficiency increased by 154%, 116%, 124%, and 99%, respectively, within 25%, 50%, 75%, and 100% of the total protection cost budget. Our results demonstrate that considering grassland degradation risk in the spatial targeting of PES increases efficiency because it helps to target areas with greater environmental benefits.

Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient.

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea), leaf nitrogen per mass (Nmass) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait-environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions.

Quantifying the Effects of Vegetation Restorations on the Soil Erosion Export and Nutrient Loss on the Loess Plateau.

The transport of eroded soil to rivers changes the nutrient cycles of river ecosystems and has significant impacts on the regional eco-environment and human health. The Loess Plateau, a leading vegetation restoration region in China and the world, has experienced severe soil erosion and nutrient loss, however, the extent to which vegetation restoration prevents soil erosion export (to rivers) and it caused nutrient loss is unknown. To evaluate the effects of the first stage of the Grain for Green Project (GFGP) on the Loess Plateau (started in 1999 and ended in 2013), we analyzed the vegetation change trends and quantified the effects of GFGP on soil erosion export (to rivers) and it caused nutrient loss by considering soil erosion processes. The results were as follows: (1) in the first half of study period (from 1982 to 1998), the vegetation cover changed little, but after the implementation of the first stage of the GFGP (from 1999 to 2013), the vegetation cover of 75.0% of the study area showed a significant increase; (2) The proportion of eroded areas decreased from 41.8 to 26.7% as a result of the GFGP, and the erosion intensity lessened in most regions; the implementation significantly reduce the soil nutrient loss; (3) at the county level, soil erosion export could be avoided significantly by the increasing of vegetation greenness in the study area (R = -0.49). These results illustrate the relationships among changes in vegetation cover, soil erosion and nutrient export, which could provide a reference for local government for making ecology-relative policies.

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China.

Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (N area), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait-climate relationships and employ a Gaussian mixture model (GMM) to project vegetation distributions under different climate scenarios. The results show that (1) the three climatic variables, mean annual temperature (MAT), mean annual precipitation (MAP), and monthly photosynthetically active radiation (mPAR) could capture 65% of the covariations of three functional traits; (2) tropical, subtropical and temperate forest complexes expand while boreal forest, temperate steppe, temperate scrub and tundra shrink under future climate change scenarios; and (3) the GMM classification based on trait covariations should be a powerful candidate for building new generation of DGVM, especially predicting the response of vegetation to future climate changes. This study provides a promising route toward developing reliable, robust and realistic vegetation models and can address a series of limitations in current models.

Quantifying leaf-trait covariation and its controls across climates and biomes.

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants.

Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modeling. The China Plant Trait Database contains information on morphometric, physical, chemical, and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1,215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article, recognizing the hard work that went into collecting the data and the authors' willingness to make it publicly available.

No significant association between PIK3CA mutation and survival of esophageal squamous cell carcinoma: A meta-analysis.

The prognostic value of phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) in patients with esophageal squamous cell carcinoma (ESCC) is controversial. We aimed to investigate the prognostic significance of PIK3CA mutation in patients with ESCC. EMBASE, PubMed, and Web of Science databases were systematically searched from inception through Oct. 3, 2016. The hazard ratios (HRs) and 95% confidence intervals (CI) were calculated using a random effects model for overall survival (OS) and disease-free survival (DFS). Seven studies enrolling 1505 patients were eligible for inclusion of the current meta-analysis. Results revealed that PIK3CA mutation was not significantly associated with OS (HR: 0.90, 95% CI: 0.63-1.30, P=0.591), with a significant heterogeneity (I 2=65.7%, P=0.012). Additionally, subgroup analyses were further conducted according to various variables, such as types of specimen, the sample size, technique and statistical methodology. All results suggested that no significant relationship was found between PIK3CA mutation and OS in patients with ESCC. For DFS, there was no significant association between PIK3CA mutation and DFS in patients with ESCC (HR: 1.00, 95% CI=0.47-2.11, P=0.993, I 2=73.7%). Publication bias was not present and the results of sensitivity analysis were very stable in the current meta-analysis. Our findings suggest that PIK3CA mutation has no significant effects on OS and DFS in ESCC patients. More well-designed prospective studies with better methodology for PIK3CA assessment are required to clarify the prognostic significance of PIK3CA mutation in ESCC patients.

Effects of ecological restoration projects on changes in land cover: A case study on the Loess Plateau in China.

Changes in land cover have become key components of global environmental change and represent the impact of human activity. To better understand the fundamental processes of land transition characteristics before and after the implementation of ecological programmes, we determined the dominant systematic changes in land cover in Yongshou, a hilly-gully region on the Loess Plateau. This was achieved by performing an in-depth analysis of a cross-tabulation matrix and a modified spatial dynamic degree model. Our results indicated that (1) forest land and cultivated land were the most important land cover types in Yongshou and their persistence would greatly affect the landscape pattern of the entire region; (2) the most significant changing signals in the study area during the periods 1992-2000 and 2000-2013 were from immature forest land to forest land, cultivated land to orchards and orchards to construction land; and (3) the region that experienced the most changes during 1992-2000 was the densely populated county seat of Yongshou; however, from 2000-2013, the region of most changes was Changning, a town located in the northcentral region of Yongshou. These findings reveal the main characteristics of the land cover changes in this region and provide insight into the processes underlying these changes.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades.

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation.

The Spatial and Temporal Distribution of Dissolved Organic Carbon Exported from Three Chinese Rivers to the China Sea.

The lateral transport of dissolved organic carbon (DOC) plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. Neglecting the lateral flow of dissolved organic carbon can lead to an underestimation of the organic carbon budget of terrestrial ecosystems. It is thus necessary to integrate DOC concentrations and flux into carbon cycle models, particularly with regard to the development of models that are intended to directly link terrestrial and ocean carbon cycles. However, to achieve this goal, more accurate information is needed to better understand and predict DOC dynamics. In this study, we compiled an inclusive database of available data collected from the Yangtze River, Yellow River and Pearl River in China. The database is collected based on online literature survey and analysed by statistic method. Overall, our results revealed a positive correlation between DOC flux and discharge in all three rivers, whereas the DOC concentration was more strongly correlated with the regional net primary productivity (NPP). We estimated the total DOC flux exported by the three rivers into the China Sea to be approximately 2.73 Tg yr-1. Specifically, the annual flux of DOC from the Yangtze River, Yellow River and Pearl River was estimated to be 1.85 Tg yr-1, 0.06 Tg yr-1 and 0.82 Tg yr-1, respectively, and the average annual DOC concentrations were estimated to be 2.24 ± 0.53 mg L-1, 2.70 ± 0.38 mg L-1 and 1.51 ± 0.09 mg L-1, respectively. Seasonal variations in DOC concentrations are greatly influenced by the interaction between temperature and precipitation. NPP is significantly and positively related to the DOC concentration in the Yangtze River and the Pearl River. In addition, differences in climate and the productivity of the vegetation may influence both the flux and concentrations of DOC transported by the rivers and thus potentially affect estuarine geochemistry.

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China.

Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-Nmass-LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

[Spatiotemporal variations of natural wetland CH4 emissions over China under future climate change].

Based on a new process-based model, TRIPLEX-GHG, this paper analyzed the spatio-temporal variations of natural wetland CH4 emissions over China under different future climate change scenarios. When natural wetland distributions were fixed, the amount of CH4 emissions from natural wetland ecosystem over China would increase by 32.0%, 55.3% and 90.8% by the end of 21st century under three representative concentration pathways (RCPs) scenarios, RCP2. 6, RCP4.5 and RCP8.5, respectively, compared with the current level. Southern China would have higher CH4 emissions compared to that from central and northern China. Besides, there would be relatively low emission fluxes in western China while relatively high emission fluxes in eastern China. Spatially, the areas with relatively high CH4 emission fluxes would be concentrated in the middle-lower reaches of the Yangtze River, the Northeast and the coasts of the Pearl River. In the future, most natural wetlands would emit more CH4 for RCP4.5 and RCP8.5 than that of 2005. However, under RCP2.6 scenario, the increasing trend would be curbed and CH4 emissions (especially from the Qinghai-Tibet Plateau) begin to decrease in the late 21st century.

Relationship between air pollutants and economic development of the provincial capital cities in China during the past decade.

With the economic development of China, air pollutants are also growing rapidly in recent decades, especially in big cities of the country. To understand the relationship between economic condition and air pollutants in big cities, we analysed the socioeconomic indictors such as Gross Regional Product per capita (GRP per capita), the concentration of air pollutants (PM10, SO2, NO2) and the air pollution index (API) from 2003 to 2012 in 31 provincial capitals of mainland China. The three main industries had a quadratic correlation with NO2, but a negative relationship with PM10 and SO2. The concentration of air pollutants per ten thousand yuan decreased with the multiplying of GRP in the provincial cities. The concentration of air pollutants and API in the provincial capital cities showed a declining trend or inverted-U trend with the rise of GRP per capita, which provided a strong evidence for the Environmental Kuznets Curve (EKC), that the environmental quality first declines, then improves, with the income growth. The results of this research improved our understanding of the alteration of atmospheric quality with the increase of social economy and demonstrated the feasibility of sustainable development for China.

[Spatiotemporal variation of vegetation in northern Shaanxi of Northwest China based on SPOT-VGT NDVI].

By using 1998-2010 SPOT-VGT NDVI images, this paper analyzed the spatiotemporal variation of vegetation in northern Shaanxi. In 1998-2010, the NDVI in northern Shaanxi had an obvious seasonal variation. The average monthly NDVI was the minimum (0.14) in January and the maximum (0.46) in August, with a mean value of 0.28. The average annual NDVI presented an overall increasing trend, indicating that the vegetation in this area was in restoring. Spatially, the restoration of vegetation in this area was concentrated in central south part, and the degradation mainly occurred in the north of the Great Wall. Air temperature and precipitation were the important climate factors affecting the variation of vegetation, with the linear correlation coefficients to NDVI being 0.72 and 0.58, respectively. The regions with better restored vegetation were mainly on the slopes of 15 degrees-25 degrees, indicating that the Program of Conversion of Cropland to Forestland and Grassland had a favorable effect in the vegetation restoration in northern Shaanxi.