Impact of wildfire on soil characteristics and arbuscular mycorrhizal fungi.

This study explored whether wildfire alters the soil properties and arbuscular mycorrhizal fungi (AMF) community composition when compared with burnt rangeland, non-burnt rangeland and adjacent tilled in mesothermal ecosystems. The study was carried out in August 2020, 1 year later after wildfire. The results of this study showed that the wildfire played a key role in altering soil characteristics and AMF community composition in Bartin Province located in the Western Black Sea Region. Soil samples were made according to standard methods. AMF spores were isolated according to the wet sieving method, and the spores of AMF were identified according to their morphological characteristics. Analysis of variance was performed to determine the differences between the parameters, and correlation analysis was performed to determine the relationships between the parameters. The highest values of soil organic carbon (2.20%), total nitrogen (0.18%), K2O (74.68 kg/da), root colonization (87.5%) and the frequency of occurrence of Funneliformis geosporum (20%), Claroideoglomus claroideum (16%) and Claroideoglomus etunicatum (11%) were found in burnt rangeland. Sporulation of Acaulospora dilatata, Acaulospora morrowiae, Acaulospora tuberculata, Scutellospora castanea, Scutellospora coralloidea, Scutellospora scutata, Glomus coremioides and Glomus multicaule was either decreased or completely inhibited in the burnt rangeland. While species diversity of AMF (12) decreased, the number of AMF spores (325.6 (number/50 gr soil)) increased in burnt areas. In conclusion, the number of spores and root colonization of AMF increased but species diversity of AMF reduced after the wildfire. In ecosystems with high fire risk where AMF transfer is planned, it is suggested that it would be more appropriate to select species with an increase in spore number after fire.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

Evaluation of effects of heat released from SOC decomposition on soil carbon stock and temperature.

Heat released from soil organic carbon (SOC) decomposition (referred to as microbial heat hereafter) could alter the soil's thermal and hydrological conditions, subsequently modulate SOC decomposition and its feedback with climate. While understanding this feedback is crucial for shaping policy to achieve specific climate goal, it has not been comprehensively assessed. This study employs the ORCHIDEE-MICT model to investigate the effects of microbial heat, referred to as heating effect, focusing on their impacts on SOC accumulation, soil temperature and net primary productivity (NPP), as well as implication on land-climate feedback under two CO2 emissions scenarios (RCP2.6 and RCP8.5). The findings reveal that the microbial heat decreases soil carbon stock, predominantly in upper layers, and elevates soil temperatures, especially in deeper layers. This results in a marginal reduction in global SOC stocks due to accelerated SOC decomposition. Altered seasonal cycles of SOC decomposition and soil temperature are simulated, with the most significant temperature increase per unit of microbial heat (0.31 K J-1) occurring at around 273.15 K (median value of all grid cells where air temperature is around 273.15 K). The heating effect leads to the earlier loss of permafrost area under RCP8.5 and hinders its restoration under RCP2.6 after peak warming. Although elevated soil temperature under climate warming aligns with expectation, the anticipated accelerated SOC decomposition and large amplifying feedback on climate warming were not observed, mainly because of reduced modeled initial SOC stock and limited NPP with heating effect. These underscores the multifaceted impacts of microbial heat. Comprehensive understanding of these effects would be vital for devising effective climate change mitigation strategies in a warming world.

Inhibition of microbially mediated total organic carbon decomposition in different types of cadmium contaminated soils with wheat straw addition.

Wheat straw returning is a common agronomic measure in the farmland. Understanding organic carbon transformation is of great significance for carbon budget under the premise of widespread distribution of cadmium (Cd) contaminated soils. An incubation experiment was conducted to assess the influence of Cd contamination on the decomposition and accumulation of total organic carbon (TOC) as well as the composition and abundance of bacterial communities in eight soil types with wheat straw addition. The results showed that inhibition of Cd contamination on microbially mediated organic carbon decomposition was affected by soil types. The lower cumulative C mineralization and higher TOC content could be observed in the acidic soils relative to that in the alkaline soils. The content of Cd in soil exhibits different effects on the inhibition in decomposition of TOC. The high dosage level of Cd had stronger inhibitory impact due to its high toxicity. The decomposition of TOC was restricted by a reduction in soil bacterial abundance and weakening of bacterial activities. Redundancy analysis (RDA) indicated that Proteobacteria and Gemmatimonadetes were abundant in alkaline Cd-contaminated soils with wheat straw addition, while Bacteroidetes dominated cumulative C mineralization in acidic Cd-contamination soils. Moreover, the abundance of predicted functional bacteria indicated that high-dose Cd-contamination and acid environment all inhibited the decomposition of TOC. The present study suggested that pH played an important role on carbon dynamics in the Cd-contaminated soils with wheat straw addition.

Changes in productivity partitioning induced by precipitation extremes increase inaccuracy of grassland carbon estimation.

The fraction of net primary productivity (NPP) allocated to belowground organs (fBNPP) in grasslands is a critical parameter in global carbon cycle models; moreover, understanding the effect of precipitation changes on this parameter is vital to accurately estimating carbon sequestration in grassland ecosystems. However, how fBNPP responds to temporal precipitation changes along a gradient from extreme drought to extreme wetness, remains unclear, mainly due to the lack of long-term data of belowground net primary productivity (BNPP) and the fact that most precipitation experiments did not have a gradient from extreme drought to extreme wetness. Here, by conducting both a precipitation gradient experiment (100-500 mm) and a long-term observational study (34 years) in the Inner Mongolia grassland, we showed that fBNPP decreased linearly along the precipitation gradient from extreme drought to extreme wetness due to stronger responses in aboveground NPP to drought and wet conditions than those of BNPP. Our further meta-analysis in grasslands worldwide also indicated that fBNPP increased when precipitation decreased, and the vice versa. Such a consistent pattern of fBNPP response suggests that plants increase the belowground allocation with decreasing precipitation, while increase the aboveground allocation with increasing precipitation. Thus, the linearly decreasing response pattern in fBNPP should be incorporated into models that forecast carbon sequestration in grassland ecosystems; failure to do so will lead to underestimation of the carbon stock in drought years and overestimation of the carbon stock in wet years in grasslands.

Not all soil carbon is created equal: Labile and stable pools under nitrogen input.

Anthropogenic activities have raised nitrogen (N) input worldwide with profound implications for soil carbon (C) cycling in ecosystems. The specific impacts of N input on soil organic matter (SOM) pools differing in microbial availability remain debatable. For the first time, we used a much-improved approach by effectively combining the 13C natural abundance in SOM with 21 years of C3-C4 vegetation conversion and long-term incubation. This allows to distinguish the impact of N input on SOM pools with various turnover times. We found that N input reduced the mineralization of all SOM pools, with labile pools having greater sensitivity to N than stable ones. The suppression in SOM mineralization was notably higher in the very labile pool (18%-52%) than the labile and stable (11%-47%) and the very stable pool (3%-21%) compared to that in the unfertilized control soil. The very labile C pool made a strong contribution (up to 60%) to total CO2 release and also contributed to 74%-96% of suppressed CO2 with N input. This suppression of SOM mineralization by N was initially attributed to the decreased microbial biomass and soil functions. Over the long-term, the shift in bacterial community toward Proteobacteria and reduction in functional genes for labile C degradation were the primary drivers. In conclusion, the higher the availability of the SOM pools, the stronger the suppression of their mineralization by N input. Labile SOM pools are highly sensitive to N availability and may hold a greater potential for C sequestration under N input at global scale.

Comparative electrochemical study of veterinary drug danofloxacin at glassy carbon electrode and electrified liquid-liquid interface.

This work compares the electroanalytical performance of two electroanalytical systems based on (1) the glassy carbon electrode (GCE), and (2) the electrified liquid-liquid interface (eLLI), for the detection of fluoroquinolone antibiotic-danofloxacin (DANO). Our aim was to define the optimal conditions to detect the chosen analyte with two employed systems, extract a number of electroanalytical parameters, study the mechanism of the charge transfer reactions (oxidation at GCE and ion transfer across the eLLI), and to provide physicochemical constants for DANO. Detection of the chosen analyte was also performed in the spiked milk samples. To the best of our knowledge, this is the first work that directly compares the electroanalytical parameters obtained with solid electrode (in this case GCE) and eLLI. We have found that for DANO the latter provides better electroanalytical parameters (lower LOD and LOQ) as well as good selectivity when the milk was analyzed.

Carbon peaking prediction scenarios based on different neural network models: A case study of Guizhou Province.

Global warming, caused by greenhouse gas emissions, is a major challenge for all human societies. To ensure that ambitious carbon neutrality and sustainable economic development goals are met, regional human activities and their impacts on carbon emissions must be studied. Guizhou Province is a typical karst area in China that predominantly uses fossil fuels. In this study, a backpropagation (BP) neural network and extreme learning machine (ELM) model, which is advantageous due to its nonlinear processing, were used to predict carbon emissions from 2020 to 2040 in Guizhou Province. The carbon emissions were calculated using conversion and inventory compilation methods with energy consumption data and the results showed an "S" growth trend. Twelve influencing factors were selected, however, five with larger correlations were screened out using a grey correlation analysis method. A prediction model for carbon emissions from Guizhou Province was established. The prediction performance of a whale optimization algorithm (WOA)-ELM model was found to be higher than the BP neural network and ELM models. Baseline, high-speed, and low-carbon scenarios were analyzed and the size and time of peak carbon emissions in Liaoning Province from 2020 to 2040 were predicted using the WOA-ELM model.

Bomb radiocarbon evidence for strong global carbon uptake and turnover in terrestrial vegetation.

Vegetation and soils are taking up approximately 30% of anthropogenic carbon dioxide emissions because of small imbalances in large gross carbon exchanges from productivity and turnover that are poorly constrained. We combined a new budget of radiocarbon produced by nuclear bomb testing in the 1960s with model simulations to evaluate carbon cycling in terrestrial vegetation. We found that most state-of-the-art vegetation models used in the Coupled Model Intercomparison Project underestimated the radiocarbon accumulation in vegetation biomass. Our findings, combined with constraints on vegetation carbon stocks and productivity trends, imply that net primary productivity is likely at least 80 petagrams of carbon per year presently, compared with the 43 to 76 petagrams per year predicted by current models. Storage of anthropogenic carbon in terrestrial vegetation is likely more short-lived and vulnerable than previously predicted.

Measurement and spatio-temporal heterogeneity analysis of coupling coordination between development of digital economy and agricultural carbon emission performance.

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness.

Impacts of conservation agriculture on crop yield and soil carbon sequestration: a meta-analysis in the Indian subcontinent.

In the quest of achieving sustainable crop productivity, improved soil health, and increased carbon (C) sequestration in the soil, conservation agriculture (CA) is increasingly being promoted and adopted in the Indian subcontinent. However, because some researchers from different regions of the world have reported reduced crop yield under CA relative to agriculture based on conventional tillage (CT), a meta-analysis has been conducted based on published research from India to evaluate the effects of CA on the yield of crops, accumulation of soil organic C as an index of soil health, and C sequestration in the soil in different regions and soil textural groups in the country. The meta-analysis is based on 544 paired observations under CA and CT from 35 publications from India was carried out using Meta Win 2.1 software. The results showed an overall significant (p < 0.05) reduction of 1.15% crop yield under CA compared to CT. Yearwise data showed a reduction of yields under CA from 2009 to 2016, but an increase from 2017 to 2020. Yield reduction was observed in the eastern, north-eastern, and southern regions of India but in western, northern, and north-western regions of the country, an increase was observed under CA rather than CT. Sandy loam and clayey soils exhibited higher crop yield under CA than under CT. Compared to CT, soil organic C content and soil C sequestration under CA increased by 8.9% and 7.3%, respectively. Also, in all the regions and soil textural groups both soil organic C accumulation and soil C sequestration were higher under CA than under CT. Factors such as rainfall, soil depth, available nitrogen (N), and total N significantly influenced the extent of yield increase/decrease and soil organic C accumulation under CA. Overall, results of the meta-analysis suggest that the promotion of CA in India will have to be location-specific taking into consideration the crops, soil attributes, and climatic conditions.

Testing the EKC hypothesis for ecological and carbon intensity of well-being: The role of forest extent.

The G-20 countries represent a considerable percentage of the global economy and are crucial in matters to do with support for environmental sustainability. The uniqueness of this study lies in determining the effects of forests on human well-being and environmental degradation with respect to G20, offering a unique perspective regarding the efforts to battle climate change. The study analyzed the impact of income, forest extent and education on ecological and carbon intensity of well-being following the Environmental Kuznets Curve (EKC) hypothesis. Based on annual data from 1990 to 2022 and employing the Method of Moments Quantile Regression, the results validate the presence of an inverted U-shaped relationship between GDP and environmental well-being which refers to the existence of EKC. Our results connect improved ecological and carbon intensity of well-being with expanding forest extent and improving education levels. Forest management combined with educational management work as an effective mechanism reducing environmental degradation while also positively contributing to human well-being. In addition, through these informed and rational decisions, policy makers can promote the environmental stability of forests.

Mixed effects of climate and species richness on aboveground carbon stock in subtropical Atlantic forests.

Tropical forests are global biodiversity hotspots and are crucial in the global carbon (C) cycle. Understanding the drivers of aboveground carbon stock (AGC) in a heterogeneous and biodiverse system can shed light on the processes underlying the relationship between biodiversity and carbon accumulation. Here, we investigate how biodiversity, environment, and landscape structure affect AGC. We examined such associations in 349 plots comprising over 95,346 km2 the Atlantic Forest of southern Brazil, encompassing three forest types: Dense Ombrophylous Forest (DF), Mixed Ombrophylous Forest (MF), and Seasonal Deciduous Forest (SF). Each plot was described by environmental variables, landscape metrics, and biodiversity (species richness and functional diversity). We used diversity, environmental, and landscape variables to build generalized linear mixed models and understand which can affect the forest AGC. We found that species richness is associated positively with AGC in all forest types, combined and separately. Seasonal temperature and isothermality affect AGC in all forest types; additionally, stocks are positively influenced by annual precipitation in SF and isothermality in MF. Among landscape metrics, total fragment edge negatively affects carbon stocks in MF. Our results show the importance of species diversity for carbon stocks in subtropical forests. The climate effect was also relevant, showing the importance of these factors, especially in a world where climate change tends to affect forest stock capacity negatively.

Carbon accumulation features in different functional zones of cities in the steppe zone.

This article presents findings on the study of content, profile distribution, and reserves of various carbon forms (organic carbon (TOC) and inorganic carbon (IC)) in Urbic Technosols and Ekranic Technosols within the residential zone of the city, alongside zonal Calcic Chernozems in the recreational zone of Rostov-on-Don, Aksai, and Bataysk. It was revealed that the TOC content in the upper horizons of Urbic Technosols is significantly lower than in the chernozem horizons of fallow areas, registering at 2.59 ± 0.79% and 3.25 ± 0.94%, respectively. IC exhibits an inverse trend, with maximum content observed in the upper horizons of Ekranic Technosols. Down the soil profile, disparities in TOC and IC contents are mitigated. This specificity in TOC accumulation and profile distribution signifies a "bipartite" profile alteration in buried chernozems, affecting solely the upper stratum rather than the entire soil profile. The presence of woody vegetation in the dry-steppe zone positively influences TOC accumulation. Calcic Chernozems beneath woody vegetation showcase the highest TOC reserves within the 30-cm layer (10.61 ± 1.45 kg/m2). Calcic Chernozems of fallow areas under natural steppe vegetation contain 8.94 ± 1.75 kg/m2, Technosols of the residential zone 8.44 ± 2.47 kg/m2. For Technosols of the residential zone, a weakening of the dependence of TOC and IC content on the depth of the soil horizon is observed.

Into the unknown: The role of post-fire soil erosion in the carbon cycle.

Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires and highlights the need for more field data to further validate this initial assessment.

Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow.

Global climate change intensifies the water cycle and makes freshest waters become fresher and vice-versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of Karenia mikimotoi blooms. To examine the physiological linkage between salinity decreases and K. mikimotoi blooms, we compare chemical, physiological and multi-omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO2 release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species. These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like K. mikimotoi, which has important ecological implications for future climate-driven salinity declines in the coastal ocean with respect to HAB outbreaks.

The impact of green bond issuance on carbon emission intensity and path analysis.

Reducing carbon emission intensity is crucial for achieving sustainable development. Carbon emission intensity is expressively affected by the issuance of green bonds. Thus, it is imperative to assess the influence of green bond issuance on carbon emissions and examine their correlation. Such research holds great potential to expedite the overhaul and modernization of businesses and to construct a circular economy system. This paper uses the spatial Durbin model to draw empirical conclusions by using data from 26 provinces in China between 2016 and 2021. Firstly, under different spatial matrices, it has been analyzed that an increase of 1% in the issuance of green bonds leads to a reduction of 0.306% or 0.331% in carbon emission intensity. It shows that green bonds have the potential to substantially reduce carbon intensity. Additionally, the intensity of emissions in the current period is driven by the intensity of emissions in the previous period. Secondly, the analysis of mediated transmission suggests that green bonds can ultimately reduce carbon emission intensity by changing the energy consumption structure or improving the efficiency of green technology innovation. Thirdly, the analysis of heterogeneity shows that the inhibitory effect of green bond issuance on carbon emissions is stronger in less economically developed regions than in economically developed regions. There is a significant inhibitory effect of green bond issuance in neighboring provinces on local carbon emission intensity. This effect is present only in provinces in less economically developed regions and not in economically developed regions.

Embracing the digital revolution: Examining the relationship between ICT adoption and carbon emissions in the Persian Gulf.

In this digital age, promoting economic development through technology innovation and adoption has become a pressing matter, contributing to increased productivity and, in turn, carbon emissions. Consequently, this study employs a novel technique (Newey-West Standard Error Method, Technology Adaptation Model) to quantify information and communication technology (ICT) adoption rates as a proxy indicator for evaluating the Persian Gulf economy's technology development. Moreover, this study investigates the evidence of the environmental Kuznets curve, with trade openness, technological adoption, and innovation as sustainable development controls. The findings reveal that two of three technological innovation instruments, fixed telephone, and internet subscriptions, increase carbon emissions. In contrast, mobile cellular subscriptions simultaneously reduce carbon emissions in the Persian Gulf. Furthermore, measures of technology adoption, high-technology exports, and electricity use contribute to the increase in carbon emissions. Trade openness also raises carbon emissions in the Persian Gulf. These findings suggest that policymakers must develop technological innovation and adoption strategies that effectively promote a greener environment.

Persistence of dissolved organic matter in sediments influenced by environmental factors：Implication for nutrition and carbon cycle.

The persistence of dissolved organic matter (DOM) plays a crucial role in the cycling and distribution of carbon and nutrients. Nonetheless, our understanding of how environmental alterations affect the persistence of sedimentary DOM remains incomplete. Excitation Emission Fluorescence Matrix-Parallel Factor Analysis (EEM-PARAFAC) was used to examine the fluorescence and compositional characteristics of hydrophilic and hydrophobic DOM (separated using XAD-8 resin) within sediments from twelve lakes and reservoirs. Fluorescence analysis indicated that DOM persistence is dependent on the proportions of the three components derived from PARAFAC. The Mantel test showed that climatic factors had the most significant impact on DOM persistence (Mantel's r = 0.46-0.54, Mantel's p = 0.001-0.007), while anthropogenic (Mantel's r = 0.24-0.32, Mantel's p = 0.03-0.05) and hydrological factors (Mantel's r = 0.03-0.22, Mantel's p = 0.06-0.40) had a somewhat lesser influence. Environmental changes resulted in a consistent decline in DOM persistence from Northeast to Southwest China, accompanied by an increase in gross primary productivity (GPP). Reduced DOM persistence due to climate, hydrological, and anthropogenic factors may lead to elevated concentrations of total phosphorus (TP), contributing to deteriorating water quality and events such as algal blooms. The decline in water quality due to reduced DOM persistence in lakes with high GPP can exacerbate the transition from carbon sinks to carbon sources. Consequently, the persistence of sedimentary DOM significantly influences nutrient and carbon cycling in lakes. Investigating DOM persistence in lakes across diverse geographic locations offers a new perspective on lake eutrophication and carbon emissions. Furthermore, it is crucial to develop targeted recommendations for lake restoration and management.

[Characteristics of leaf stoichiometry and the driving factors of Ammopiptanthus mongolicus, China]. / æ²™å†¬é’å¶ç‰‡åŒ–å­¦è®¡é‡ç‰¹å¾åŠå ¶é©±åŠ¨å› ç´ .

The stoichiometric characteristics of leaves can reflect environmental adaptation of plants, and thus the study of the relationship between them is helpful for exploring plant adaptation strategies. In this study, taking the national second-level key protection species, Ammopiptanthus mongolicus, as the research object, we set up 26 plots to collect samples, and measured the content of carbon (C), nitrogen (N), phosphorus (P) and water use efficiency (WUE) of leaves. We analyzed the relationship between leaf stoichiometric characteristics and WUE, and quantified the contributions of soil, climate, and water use efficiency to the variations of leaf stoichiometry. The results showed that C, N, and P contents in the leaves were (583.99±27.93), (24.31±2.09), and (1.83±0.06) mg·g-1, respectively. The coefficients of variation were 4.8%, 8.6%, and 3.2%, respectively, all belonging to weak variability, indicating that foliar contents of C, N and P tended to a certain stable value. The average value of N:P was 13.3, indicating that the growth of A. mongolicus was mainly limited by N. WUE was not correlated with leaf C content, but was significantly positively correlated with leaf N and P contents and N:P, and significantly negatively correlated with C:N and C:P, indicating that there was a linear synergistic trend between WUE and leaf nutrient content. The main factors influencing leaf C content and C:P were climatic factors, the leaf N content and N:P were mainly affected by soil factors, and the water use efficiency mainly affected leaf P content and C:N, indicating that the driving factors of different stoichiometric characteristics were different. The results could help eva-luate the habitat adaptation of desert plants, which would provide a theoretical basis for the conservation and management of A. mongolicus.