The rhizosphere bacteria play crucial roles in plant health and growth as they are involved in assimilating nutrients and resisting adverse conditions such as nutrient stress, drought, and wind erosion. Agriophyllum squarrosum (L.) Moq. is a pioneer plant used in sand fixation due to its strong resistance to drought and wind erosion. However, the bacterial community characteristics and ecological function in the rhizosphere of A. squarrosum are poorly understood. In this study, soil samples were collected from different developmental stages (seedling stage, vegetative stage, reproductive stage, and withering stage) of A. squarrosum. Illumina Miseq sequencing was used to detect differences in soil bacterial abundance. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) program was used to predict bacterial functions, and the relationships among bacteria, functional populations, and soil nutrients were examined using a heatmap analysis. The results showed that the Shannon and Sobs indices of rhizosphere bacteria were significantly higher during the reproductive stage than during the other stages. Pantoea sp. (7.03%) was the dominant genus during the seedling stage; Arthrobacter sp. was the dominant genus during the vegetative (13.94%), reproductive (7.57%), and withering (12.30%) stages. The relative abundances of Chloroflexi, Acidobacteria, and Gemmatimonadetes were significantly high during the reproductive stage. According to the PICRUSt analysis, membrane transport, signal transduction, and environmental adaptation of the bacterial functional population occurred during the seedling stage. Carbohydrate metabolism increased during the vegetative stage, while energy metabolism, lipid metabolism, and biosynthesis of other secondary metabolites of the bacterial functional population significantly increased during the reproductive stage. The abundances of bacterial communities, functional genes, and soil nutrients were synergistically altered during various developmental stages. Our findings suggest that the developmental stages of A. squarrosum play a significant role in defining the composition and structure of bacterial communities in the rhizosphere. The results will provide a basis for better prediction and understanding of soil bacterial metabolic potential and functions of A. squarrosum rhizosphere in sandy areas.

Desertification is a global crucial ecological and environmental issue, and China is among the countries most seriously affected by desertification. In recent decades, numerous independent studies on desertification dynamics have been carried out using remote sensing technology, but there has been a lack of systematic research on desertification trends in China. This study employed the meta-analysis to integrate the findings of 140 published research cases and examined the dynamics of desertification in the eight major deserts, four major sandy lands, and their surrounding areas in China from 1970 to 2019, with a comparative analysis of differences between the eastern (including the Mu Us Sandy Land, the Otindag Sandy Land, the Hulunbuir Sandy Land, the Horqin Sandy Land, and the Hobq Desert) and western (including the Taklimakan Desert, the Gurbantunggut Desert, the Kumtagh Desert, the Ulan Buh Desert, the Qaidam Basin Desert, the Badain Jaran Desert, and the Tengger Desert) regions. The results revealed that from 1970 to 2019, desertification first expanded and then reversed in the whole region. Specifically, desertification expanded from 1980 to 1999 and reversed after 2000. The desertification trend exhibited distinct spatio-temporal variations between the eastern and western regions. From 1970 to 2019, the western region experienced relatively minor changes in desertified land area compared to the eastern region. In the context of global climate change, beneficial climatic conditions and ecological construction projects played a crucial role in reversing desertification. These findings provide valuable insights for understanding the development patterns of desertification in the most representative deserts and sandy lands in China and formulating effective desertification control strategies.

The Turpan-Hami (Tuha) Basin of China, a critical region on the Silk Road Economic Belt and a major national energy base, occupies a significant position in energy security and in the major industrial clusters in Xinjiang Uygur Autonomous Region, China. Understanding spatial and temporal evolution of human activities in this area is essential for harmonizing ecological protection with energy development, safeguarding the ecological security of the Silk Road Economic Belt, and promoting the sustainable development of the area. However, despite rapid socioeconomic advances, the trajectories of human activity intensity and the principal driving mechanisms over the past three decades remain inadequately understood. To address these gaps, this study constructed a land use dataset for the Tuha Basin from 1990 to 2020, utilizing Google Earth Engine (GEE) and random forest classification algorithm. We assessed the intensity of human activities and their spatial autocorrelation patterns and further identified key drivers influencing spatial and temporal variations using the Geodetector model. Our findings indicated that the intensity of human activities in the Tuha Basin has exhibited a "first decline and then recovery" trend over the past 30 a, accompanied by significant spatial clustering. In recent years, the aggregation of hot spots has diminished, while clustering of cold spots has intensified, suggesting a dispersion of human activity centers. Nevertheless, urban areas in the Hami and Turpan cities, along with their surrounding areas, continued to serve as core areas of human activities. Topographic features (slope gradient and aspect) and their interactions with economic variables emerged as dominant determinants shaping the spatial patterns and temporal dynamics of human activity intensity. This result provides critical insights into fostering sustainable regional development and ecological conservation in the Tuha Basin and offers valuable methodological and empirical references for studies on land use dynamics and human activity intensity in similar arid areas.

Water quality is a pressing issue affecting the sustainable development of lakes. To elucidate the spatial and temporal characteristics of water quality in Bosten Lake, China, this study constructed a comprehensive water quality index (CWQI) based on key water quality indicators, utilizing water quality data collected from 17 sampling sites spaning from 2011 to 2019. Key water quality indicators were determined using factor analysis, and the spatial and temporal characteristics of key water quality indicators and the CWQI were examined using multivariate statistical analysis. The key water quality indicators included pH, chemical oxygen demand (COD), water transparency (SD), NO₃^-, total dissolved solids (TDS), Cl^-, SO₄^2-, and electrical conductivity (EC). Furthermore, the contribution rates of all water quality indicators to the water quality were quantitatively elucidated using the SHapley Additive exPlanations (SHAP) values, thereby validating the factor analysis outcomes. Among the eight key water quality indicators, the COD had the most significant influence on the water quality of Bosten Lake. The water quality condition of Bosten Lake has remained at Class III from 2011 to 2019 (CWQI ranging from 3.19 to 3.90). The water quality of Bosten Lake was characterized by distinct regional differences that arose from hydrodynamic processes within the lake and upstream water quality. The southwestern region exhibited the best water quality (mean CWQI of 3.47), whereas the northwestern region exhibited the worst (mean CWQI of 3.58). It is crucial to acknowledge that alongside the increase in industrial and agricultural effluent discharge monitoring, a series of ecological restoration projects for the lake basin have been initiated. Over time, the water quality of Bosten Lake showed gradual improvement (improvement rate of CWQI at 0.05/a). This study provides a critical scientific basis for enhancing the understanding and effective management of water quality in the Bosten Lake Basin through a comprehensive analysis of its spatial and temporal evolution and driving mechanisms.

Increasing human disturbance and climate change have threatened ecological connectivity and structural stability, especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation. Large-scale ecological restoration, such as adding ecological sources or corridors, is difficult in such environments and often faces poor operability and high implementation costs in practice. Taking the southern slope of the Qilian Mountains in China as the study area and 2020 as the baseline, this study integrated weighted complex network theory into the "ecological source-resistance surface-corridor" framework to construct a heterogeneous ecological network (EN). Circuit theory was integrated with weighted betweenness to identify critical barrier points for locally differentiated restoration, followed by assessment of the network optimization effects. The results revealed that 494 ecological sources and 1308 ecological corridors were identified in the study area. Fifty-one barrier points with restoration potential were identified along key ecological corridors and locally restored. After optimization, the network gained 11 additional ecological corridors, and the total ecological corridor length increased by approximately 1143 km. Under simulated attacks, the decline rates of maximum connected subgraph (MCS) and network efficiency (Ne) slowed compared with pre-restoration conditions, indicating improved robustness. These findings demonstrate that targeted local restoration can enhance network connectivity and stability while minimizing disturbance to the overall landscape pattern, providing a practical pathway for ecological restoration and sustainable management in semi-arid mountain areas.

Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets, although the drivers of net ecosystem productivity (NEP) under combined human and environmental pressures remain poorly understood. In this study, we analyzed the spatiotemporal evolution of NEP in the Horqin Sandy Land, China from 2000 to 2020, and observed the variation in NEP across different land use types. We further identified and quantified the effects of human activities, topographical features, climatic conditions, and soil properties on NEP through the application of structural equation modeling (SEM) and boosted regression trees (BRT). The results showed that the multi-year average NEP ranged from -137.79 to 461.96 g C/m² in the Horqin Sandy Land, with 88.21% of the area showing a significant increasing trend. Among different land use types, forestland exhibited the highest NEP values, followed by cropland, grassland, impervious land, and unused land. The NEP in carbon sink areas was primarily regulated by potential evapotranspiration (negatively correlated) and precipitation (positively correlated). Slope was identified as the most significant positive determinant in carbon source areas. Forestland exhibited climate-topography interactions driving NEP, whereas cropland and grassland relied on temperature; unused land and impervious land were susceptible to land use/cover change and human footprint. This study has significant implications for maintaining the carbon sink function and promoting ecological engineering programs that aim to enhance the capacity of terrestrial carbon sinks in the semi-arid agro-pastoral ecotone.

Central Asia is characterized by an arid climate and widespread desert distribution, with its sustainable development severely constrained by dust events. An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area. Based on the meteorological observations from 2000 to 2020, we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries (Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan) via Theil-Sen trend analysis and Geodetector modeling method, quantitatively revealing the influence of environmental factors, such as temperature, precipitation, and vegetation, on the frequency of dust weather. The results showed that: (1) dust weather in Central Asia was mainly distributed in a large ''dust belt'' extending from west to east from northern part of the Caspian lowland desert, and concentrated in basins, plains, and other low-altitude areas. Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia, with a maximum annual frequency of 21.9%; (2) strong dust weather in Central Asia has fluctuated and slightly decreased since 2001. The highest frequency (1.1%) occurred in spring (from March to June); (3) from 2000 to 2020, changes such as spot shifting and shrinking occurred in the four main source areas (north of the Aral Sea, Kyzylkum Desert, Karakum Desert, and Garabogazköl Bay region), where sandstorms occurred in Central Asia, and northern Caspian lowland desert became the most important low-emission dust source in Central Asia; and (4) the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia. This study provides a theoretical basis for sand prevention and sand control in Central Asia. In the future, Central Asia should focus on the rational utilization of land and water resources, and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.

The diversity and discontinuity of plant communities in the oasis-desert ecotone are largely shaped by variations in groundwater depth, yet the relationships between spatial distribution patterns and ecological niches at a regional scale remain insufficiently understood. This study examined the oasis-desert ecotone in Qira County located in the Tarim Basin of China to investigate the spatial distribution of plant communities and groundwater depth as well as their relationships using an integrated approach that combined remote sensing techniques, field monitoring, and numerical modeling. The results showed that vegetation distribution exhibits marked spatial heterogeneity, with coverage ranked as follows: Tamarix ramosissima>Phragmites australis>Populus euphratica>Alhagi sparsifolia. Numerical simulations indicated that groundwater depths range from 2.00 to 65.00 m below the surface, with the system currently in equilibrium, sustaining an average annual recharge of 1.06×10⁸ m³ and an average annual discharge of 1.01×10⁸ m³. Groundwater depth strongly influences vegetation composition and structure: Phragmites australis dominates at average groundwater depth of 5.83 m, followed by Populus euphratica at average groundwater depth of 7.05 m. As groundwater depth increases, the community is initially predominated by Tamarix ramosissima (average groundwater depth of 8.35 m), then becomes a mixture of Tamarix ramosissima, Populus euphratica, and Karelinia caspia (average groundwater depth of 10.50 m), and finally transitions to Alhagi sparsifolia (average groundwater depth of 14.30 m). These findings highlight groundwater-dependent ecological thresholds that govern plant community composition and provide a scientific basis for biodiversity conservation, ecosystem stability, and vegetation restoration in the arid oasis-desert ecotone.

The Wuding River Basin, situated in the Loess Plateau of northern China, is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service (ES) functions. However, the mechanisms driving the spatiotemporal evolution of ES functions, as well as the trade-offs and synergies among these functions, remain poorly understood, constraining effective watershed-scale management. To address this challenge, this study quantified four ES functions, i.e., water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC) in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, and proposed an innovative integration of InVEST with a Bayesian Belief Network (BBN) to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference. A trade-off and synergy index (TSI) was developed to assess the spatial interaction intensity among ES functions, while sensitivity and scenario analyses were employed to determine key driving factors, followed by spatial optimization to delineate functional zones. Results revealed distinct spatiotemporal variations: WY increased from 98.69 to 120.52 mm; SC rose to an average of 3.05×10⁴ t/hm²; CS remained relatively stable (about 15.50 t/km²); and HQ averaged 0.51 with localized declines. The BBN achieved a high accuracy of 81.9% and effectively identified strong synergies between WY and SC, as well as between CS and HQ, while clear trade-offs were observed between WY and SC versus CS and HQ. Sensitivity analysis indicated precipitation (variance reduction of 9.4%), land use (9.8%), and vegetation cover (9.1%) as key driving factors. Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin, while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins, highlighting strong spatial heterogeneity. Overall, this study advances ES research by combining process-based quantification with probabilistic modeling, offering a robust framework for studying nonlinear interactions, driving mechanisms, and optimization strategies, and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.

Climate change in High Mountain Asia (HMA) is characterized by elevation dependence, which results in vertical zoning of vegetation distribution. However, few studies have been conducted on the distribution patterns of vegetation, the response of vegetation to climate change, and the key climatic control factors of vegetation along the elevation gradient in this region. In this study, based on the Normalized Difference Vegetation index (NDVI), we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) methods. Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient. Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient. The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020, and the rate of increase abruptly slowed down after 2017. NDVI showed a fluctuating increase at elevation zones 1-2 (<2500 m) and then decreased at elevation zones 3-9 (2500-6000 m) with the increase of elevation. NDVI was most sensitive to precipitation and temperature at a 1-month lag. With the increase of elevation, the positive response relationship of NDVI with precipitation gradually weakened, while that of NDVI with temperature was the opposite. The total effect coefficient of precipitation (0.95) on vegetation was larger than that of temperature (0.87), indicating that precipitation is the dominant control factor affecting vegetation growth. Spacially, vegetation growth is jointly influenced by precipitation and temperature, but the influence of precipitation on vegetation growth is dominant at each elevation zone. The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.

Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development, accelerating the achievement of the "dual carbon" goals, and promoting the high-quality development of ecological environment. The carbon sequestration capacity within the mountain-desert-oasis system (MDOS), a unique landscape pattern, exhibits significant gradient characteristics, and its carbon sink potential can be substantially improved through multi-scale spatial optimization. This study employed the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model to estimate carbon storage and sequestration (CSS) in the Gansu section of Heihe River Basin, China, a representative MDOS, based on land use/land cover (LULC) data from 1990 to 2020. The Patch-level Land Use Simulation (PLUS) model was coupled to simulate LULC and estimate carrying CSS under natural development (ND), ecological protection (EP), water constraint (WC), and economic development (ED) scenarios for 2035. Furthermore, the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits, exploring the guiding significance of different scenarios for pattern optimization. The results showed that CSS spatial distribution is closely correlated with LULC pattern, and CSS is expected to improve in the future. CSS showed an overall increase across subsystems during 1990-2020, but varied across LULC types. CSS of construction land in all subsystems exhibited an increasing trend, while CSS of unused land showed a decreasing trend, with specific changes of 1.68×10³ and 3.43×10⁵ t, respectively. Regional CSS dynamics were mainly driven by conversions among unused land, cultivated land, and grassland. The CSS pattern of MDOS was divided into carbon sink functional region (CSFR), low carbon conservation region (LCCR), low carbon economic region (LCER), and economic development region (EDR). Water resources coordination served as the basis of pattern optimization, while the four dimensions—ecological carbon sink, low-carbon maintenance, agricultural carbon reduction and sink enhancement, and urban carbon emission reduction—framed the optimization framework. ND, EP, WC, and ED scenarios provided guidance as the basic reference, optimal benefit, "dual carbon" baseline, and upper development limit, respectively. Additionally, the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems, demonstrating the applicability of these sub-partitions. These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.

Oasis soils in Tunisia are characterized by low soil organic carbon (SOC) stocks, primarily due to their coarse texture and intensive irrigation practices. In the Gataaya Oasis, soils receive 3.000 to 4.000 L/m² annually through submersion irrigation, leading to a rapid decline in SOC stocks. Despite their sandy texture, which promotes good water infiltration, these soils are enriched with clay, dissolved materials, and fertilizers in deeper horizons. This study aimed to assess SOC content in the Gataaya Oasis soils, investigate the transport of labile carbon in drainage water, and clarify the destiny of this transported carbon. Soil samples were collected systematically at three depths (0-10, 10-20, and 20-30 cm), focusing on the top 30 cm depth, which is most affected by amendments. Two sampling points (P1 and P2) were selected, i.e., P1 profile near the trunk of date palms (with manure input) and P2 profile between two adjacent date palms (without manure input). Water samples were collected from drainage systems within the oasis (W1, W2, and W3) and outside the oasis (W4). A laboratory experiment simulating manure application and irrigation was conducted to complement field observations. Physical-chemical analyses revealed a significant decrease in SOC stocks with soil depths. In P1 profile, SOC stocks declined from 17.71 t/hm² at the 0-10 cm depth to 7.80 t/hm² at the 20-30 cm depth. In P2 profile, SOC stocks were lower, decreasing from 6.73 t/hm² at the 0-10 cm depth to 3.57 t/hm² at the 20-30 cm depth. Labile carbon content in drainage water increased outside the oasis, with chemical oxygen demand (COD) values rising from 73 mg/L in W1 water sample to 290 mg/L in W4 water sample, indicating cumulative leaching effects from surrounding oases. The laboratory experiment confirmed field observations, showing a decline in soil organic matter (SOM) content from 3.27% to 2.62% after 12 irrigations, highlighting the vulnerability of SOC stocks to intensive irrigation. This study underscores the low SOC stocks in the Gataaya Oasis soils and their rapid depletion under successive irrigations. The findings provide insights into the dynamics of labile carbon transport and its contribution to regional carbon cycling, offering valuable information for sustainable soil management and ecological protection in arid ecosystems.

Land use in arid and semi-arid regions has a substantial effect on climate, environment, and biodiversity, thereby projecting the spatiotemporal changes in land use and the subsequent effects. This study employed the locally calibrated Future Land Use Simulation (FLUS) model, which coupled system dynamics with cellular automata and integrated an artificial neural network algorithm and a roulette wheel selection mechanism. We projected future land use (2020-2100) dynamics of Lanzhou, a typical river valley city in Northwest China, under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The simulation results were validated and subsequently reclassified using the International Geosphere Biosphere Programme (IGBP) system to produce a dataset suitable for driving climatic and environmental models. Under the SSP1-2.6 scenario, urban and built-up land expanded consistently, whereas irrigated cropland and pasture as well as grassland contracted continuously. Conversely, the SSP5-8.5 scenario was characterized by a contraction of urban and built-up land, and relative stability of irrigated cropland and pasture as well as grassland. The SSP2-4.5 scenario presented a more complex trade-off, where urban and built-up land and grassland increased first and then decreased, whereas irrigated cropland and pasture followed an opposite trajectory. A significant inverse relationship between urban and built-up land and irrigated cropland and pasture was observed under all scenarios, underscoring the fundamental spatial competition that prevailed in this land-constrained valley city. Furthermore, the negative correlation of grassland with urban and built-up land, coupled with the positive correlation of grassland with irrigated cropland and pasture under both the SSP1-2.6 and SSP5-8.5 scenarios, indicated an evolution from broad confrontation to intricate internal trade-offs within the urban-agricultural-ecological system. This study underscored the critical influence of regional topographic and hydrological constraints on land-use evolution in arid regions, providing guidance for water resource management and ecosystem protection in Lanzhou, with applications for sustainable land-use planning in other arid and semi-arid river valley cities.

Sand and dust storms (SDSs) are natural disasters that frequently occur during spring in arid and semi-arid areas., causing serious impacts on human health, air quality, transportation, and agricultural production. Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures. In this study, numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia. Using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) data, the most severe SDS events in the spring of 2023 in East Asia were numerically simulated. The simulated results were compared and validated using meteorological observations and multisource remote sensing data. The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events. The primary dust sources in spring 2023 were identified as the western desert of Mongolia, the Gobi Desert, and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China. Peak dust load and maximum wind speed occurred almost simultaneously, indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events. Increased surface vegetation covers partially mitigated wind-driven dust emissions. In April, strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways. Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China. In contrast, their impact on the northwestern regions of China remains relatively limited. These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.

Climate change significantly affects vegetation dynamics. Thus, understanding interactions between vegetation and climatic factors is essential for ecological management. This study used kernel Normalized Difference Vegetation Index (kNDVI) and climatic data (temperature, precipitation, humidity, and vapor pressure deficit (VPD)) of China from 2000 to 2022, integrating Geographic Convergent Cross Mapping (GCCM) causal modeling, Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) nonlinear threshold identification, and Geographical Simulation and Optimization Systems-Future Land Use Simulation (GeoSOS-FLUS) spatial prediction modeling to investigate vegetation spatiotemporal characteristics, driving mechanisms, nonlinear thresholds, and future spatial patterns. Results indicated that from 2000 to 2022, China's kNDVI showed an overall increasing trend (annual average ranging from 0.29 to 0.33) with distinct spatial differentiation: 52.77% of areas locating in agricultural and ecological restoration regions in the central-eastern plain) experienced vegetation improvement, whereas 2.68% of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation. The coefficient of variation (CV) of kNDVI at 0.30-0.40 (accounting for 10.61%) was significantly higher than that of NDVI (accounting for 1.80%). Climate-driven mechanisms exhibited notable library length (L) dependence. At short-term scales (L<50), vegetation-driven transpiration regulated local microclimate, with a causal strength from kNDVI to temperature of 0.04-0.15; at long-term scales (L>100), cumulative temperature effects dominated vegetation dynamics, with a causal strength from temperature to kNDVI of 0.33. Humidity and kNDVI formed bidirectional positive feedback at long-term scales (L=210, causal strength>0.70), whereas the long-term suppressive effect of VPD was particularly pronounced (causal strength=0.21) in arid areas. The optimal threshold intervals identified were temperature at -12.18°C-0.67°C, precipitation at 24.00-159.74 mm, humidity of lower than 22.00%, and VPD of <0.07, 0.17-0.24, and >0.30 kPa; notably, the lower precipitation threshold (24.00 mm) represented the minimum water requirements for vegetation recovery in arid areas. Future kNDVI spatial patterns are projected to continue the trend of "southeastern optimization and northwestern delay" from 2025 to 2040: the area proportion of high kNDVI value (>0.50) will rise from 40.43% to 41.85%, concentrated in the Sichuan Basin and the southern hills; meanwhile, the proportion of low-value areas of kNDVI (0.00-0.10) in the arid northwestern areas will decline by only 1.25%, constrained by sustained temperature and VPD stress. This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.

In recent years, many studies have focused on the effects of global climate warming and increased nitrogen deposition on the structure and function of grassland ecosystem. However, there are still significant uncertainties in the response mechanism of stability of plant community biomass in alpine meadows of the Qinghai-Xizang Plateau, China to these two major climate factors. Given this, based on field control experiments, this study systematically evaluated the effects of different levels of climate warming (W0 (no warming), W1 (air temperature increased by 0.47°C or soil temperature increased by 0.61°C), W2 (air temperature increased by 0.92°C or soil temperature increased by 1.09°C), W3 (air temperature increased by 1.44°C or soil temperature increased by 1.95°C)), nitrogen deposition ((N0 (0 kg N/(hm²•a), N16 (16 kg N/(hm²•a), and N32 (32 kg N/(hm²•a)), and their interactions on plant community biomass and its temporal stability, and explored its potential regulatory mechanisms. The results showed that the biomass of total community, Gramineae, and dominant species increased significantly with increasing temperature, but the biomass of common and rare species decreased significantly. Nitrogen deposition also significantly promoted the biomass accumulation of community and gramineous plants. Under the treatment of W3N32, the biomass of plant community, Gramineae, and dominant species reached the highest values, indicating that there was a synergistic effect under this treatment. Structural equation model showed that increasing temperature significantly decreased the stability of plant community biomass by reducing the stability of grass and dominant species biomass and weakening species asynchronism. Interaction of increased nitrogen deposition and temperature increased the biomass fluctuation of grass functional group, thus amplifying its negative influence on community stability. More attention should be paid to the response and regulatory mechanisms of dominant species and functional groups under global climate change. This study provides a theoretical basis for revealing the stability maintenance mechanism of alpine grassland and also provides scientific support for the development of future grassland ecosystem management and assessment.

Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification, yet where net primary productivity (NPP) degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China. We integrated multi-source remote sensing with the Carnegie-Ames-Stanford Approach (CASA) model to estimate NPP during 2000-2020, assessed trend persistence using the Hurst exponent, and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020, while regional mean NPP rose from 138.97 to 160.69 g C/(m²•a). Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall. Although NPP increased across 64.11% of the region during 2000-2020, persistence analysis suggested that 53.93% of the Tianshan Mountains was prone to continued NPP decline, including 36.41% with significant projected decline and 17.52% with weak projected decline; these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains. In contrast, potential improvement was limited (strong persistent improvement: 4.97%; strong anti-persistent improvement: 0.36%). Driver attribution indicated that land use dominated NPP variability (mean absolute SHAP value=29.54%), followed by precipitation (16.03%) and temperature (11.05%). SHAP dependence analyses showed that precipitation effects stabilized at 300.00-400.00 mm, and temperature exhibited an inverted U-shaped response with a peak near 0.00°C. These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion, highlighting the need for threshold-informed, spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.

Continuous cropping can lead to soil environment deterioration, cause plant health problems, and reduce crop productivity. However, the response mechanisms of soil microbial co-occurrence patterns to the duration of continuous melon cropping remain poorly understood. Here, we employed the metagenomic techniques to comparatively investigate the bulk and rhizosphere soil microbial communities of major melon-producing regions (where the duration of continuous melon cropping ranges from 1 to 30 a) in the eastern and southern parts of Xinjiang Uygur Autonomous Region, China. The results showed that soil pH clearly decreased with increasing melon cropping duration, while soil electrical conductivity (EC) and the other soil nutrient indices increased with increasing melon cropping duration (with the exception of AN and TK in the southern melon-producing region). The most dominant bacterial phyla were Proteobacteria and Actinobacteria, and the most abundant fungal phyla were Ascomycota and Mucoromycota. Redundancy analysis (RDA) indicated that soil pH and EC had no significant effects on the bacterial communities. However, after many years of continuous melon cropping in the southern melon-producing region, fungal communities were significantly negatively correlated with soil pH and significantly positively correlated with soil EC (P<0.050). Co-occurrence network analysis showed that continuous melon cropping increased the complexity but decreased the connectivity of the cross-domain microbial networks. Moreover, the enrichment patterns of microorganisms in the main microbial network modules varied significantly with the duration of continuous melon cropping. Based on the analysis of keystone taxa, we found that continuous melon cropping increased some plant pathogens (e.g., Fusarium and Stagonospora) but decreased beneficial bacteria (e.g., Mesorhizobium and Pseudoxanthomonas). In conclusion, this study has greatly enhanced the understanding of the effects of continuous melon cropping on alterations in the microbial community structure and ecological networks in Xinjiang.

With ongoing global climate change, drought has become the primary threat constraining food security in China. Traditional assessment frameworks based on administrative boundaries or macro-climatic zoning overlook variation in vulnerability affected by key agronomic practices, such as crop phenology and cropping systems, thereby limiting their accuracy. To address this research gap, this study developed and validated a novel drought risk assessment framework based on agricultural cropping zones (single-, double-, and triple-cropping zones). The framework coupled a Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model for forecasting future crop vegetation dynamics with the Standardized Precipitation Evapotranspiration Index (SPEI) to assess drought risk under historical (2001-2020) and projected future (2021-2100) scenarios. The GTNNWR model achieved R² values ranging from 0.72 to 0.82 and RMSE values between 0.11 and 0.14 for NDVI prediction, significantly outperforming conventional models. Historical drought risk assessment revealed that drought events were most frequent during summer and concentrated in single-cropping and double-cropping zones. Future projections indicate a substantial intensification of drought risk. Under the Shared Socioeconomic Pathway (SSP)126 scenario, drought risk is projected to increase in the triple-cropping zones of the middle and lower reaches of the Yangtze River Plain. Under the SSP245 scenario, the frequency of spring and winter droughts is anticipated to rise markedly. Under the SSP585 scenario, drought intensity is projected to intensify in central-eastern single-cropping zones and southwestern double-cropping zones. This assessment framework based on agricultural cropping zones can precisely identify drought risks and facilitate adaptation in agricultural management, such as optimizing irrigation systems and adjusting crop structures.

Over the past six decades, the implementation of soil and water conservation measures has significantly reduced soil erosion and sediment yield on the Loess Plateau, China. However, while the overall reduction is well-documented, the dynamic interplay between soil erosion potential and sediment connectivity, specifically how they spatially covary under land use/cover changes, remains insufficiently understood. To address this gap, this study established a model framework by integrating the revised universal soil loss equation (RUSLE), index of connectivity (IC), and sediment delivery ratio (SDR) to evaluate the spatio-temporal variations in soil erosion and sediment yield in the Hantaichuan Watershed, northern Loess Plateau, China, from 1995 to 2020 and to estimate the effects of land use/cover changes and check dam construction on sediment yield. The results revealed that the soil erosion in the Hantaichuan Watershed decreased by 43.90% from 1995 to 2020 and the sediment yield decreased by 69.28% under the combination of land use/cover changes and check dam construction. The IC and soil erosion (IC-SE) map revealed both the coupling and decoupling covariation relationships between sediment connectivity and soil erosion. By 2020, areas with high connectivity and high erosion (I-E) covered only 18.67% of the watershed, while contributed more than 40.00% to the total erosion. The I-E zones were mainly located in the central part of the watershed where aeolian sands derived from the Hobq Desert are concentrated and were identified as critical areas for soil and water conservation. This study provides support for priority management of watershed conservation measures as well as a valuable reference for future studies.

The Balkhash Lake Basin (BLB), a vital Central Asian watershed, faces hydrological uncertainty under climate warming. This study integrated multi-source remote sensing data (Sentinel-1 snow depth, Randolph Glacier Inventory (RGI) v.7.0 glacier inventory, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mass balance) with a degree-day model to reconstruct decadal snow and ice dynamics across 13 sub-basins and analyzed their hydrological impacts from 1950 to 2014. The results showed that: (1) while flows from the downstream river of the BLB decreased from 1950 to 1982 due to land surface changes, runoff increased significantly after 1982 in the Ili River (18.0%) and moderately increased in most rivers in the east (1.3%-8.3%), driven by increased precipitation and glacier melt. Runoff in the Ayaguz catchment (no glaciers with the highest climate warming) declined (10.5%); (2) climate warming reduced precipitation falling as snow caused snow melt water to decline (0.03-0.22 mm/a) across the BLB, leading to downward shifts in runoff and runoff coefficient, especially in the rivers in the east. However, snow melt during April-June positively correlated with runoff coefficient, contributing to an upward shift in the Ili River Basin; and (3) meltwater from glacierized areas (<5.0% of basin area) contributed to 14.3% of total ablation water. Net glacier melt provided substantial excess flows (11.6 m³/s in the Ili River and <1.0 m³/s in the rivers in the east), generally counterbalancing the negative effect of rising potential evaporation at decadal scales and positively correlating with the runoff coefficient. Therefore, water stress in the BLB may be more severe in the future due to the accelerating glacier melt after the abrupt increase in air temperature in 2000, the continuing decline in snow melt, and the significant inter-annual variations in precipitation.

In dryland ecosystems, nitrogen (N) is the primary limiting factor after water availability, constraining both plant productivity and organic matter decomposition while also regulating ecosystem function and service provision. However, the distributions of different soil N fraction stocks in drylands and the factors that influence them remain poorly understood. In this study, we collected 2076 soil samples from 173 sites across the drylands of northern China during the summers of 2021 and 2022. Using the best-performing eXtreme Gradient Boosting (XGBoost) model, we mapped the spatial distributions of the soil N fraction stocks and identified the key drivers of their variability. Our findings revealed that the stocks of total nitrogen (TN), inorganic nitrogen (IN), and microbial biomass nitrogen (MBN) in the top 30 cm soil layer were 1020.4, 92.2, and 40.8 Tg, respectively, with corresponding mean densities of 164.6, 14.9, and 6.6 g/m². Climate variables—particularly mean annual temperature and aridity—along with human impacts emerged as the dominant drivers of soil N stock distribution. Notably, increased aridity and intensified human impacts exerted mutually counteracting effects on soil N fractions: aridity-driven moisture limitation generally suppressed N accumulation, whereas anthropogenic activities (e.g., fertilization and grazing) promoted N enrichment. By identifying the key environmental and anthropogenic factors shaping the soil N distribution, this study improves the accuracy of regional and global N stock estimates. These insights provide a scientific foundation for developing more effective soil N management strategies in dryland ecosystems, contributing to sustainable land use and long-term ecosystem resilience in drylands.

Grassland shrub encroachment is a phenomenon that is prevalent in arid and semi-arid regions worldwide, impacting grassland ecosystems in several ways. In the context of escalating climate change and human activities, examining the nutrient and stoichiometric characteristics of Spiraea shrubs in grassland ecosystems, along with their relationships with environmental factors, can yield valuable insights into the nutrient utilization and survival strategies of these shrubs. This, in turn, offers a scientific foundation for developing future conservation measures. This study was conducted in July 2023 in the Altay Mountains, Northwest China, where Spiraea shrubs thrive across five grassland types: temperate steppe desert, temperate desert steppe, temperate steppe, temperate meadow steppe, and mountain meadow. Leaf and soil samples were collected from each grassland type to analyze the concentrations of carbon (C), nitrogen (N), and phosphorus (P), as well as the stoichiometric characteristics of both the leaves and soil. Subsequently, correlation analysis and redundancy analysis (RDA) were conducted to investigate the variations in leaf C, N, and P concentrations and leaf stoichiometry of Spiraea shrubs as well as their influencing factors. The results indicated the presence of significant or highly significant differences (P<0.050) in the leaf C, N, and P concentrations and leaf stoichiometry (C:N, C:P, and N:P ratios) of Spiraea shrubs across the five grassland types. The N:P ratios of Spiraea shrub leaves in the five grassland types ranged from 7.37 to 11.77, suggesting that N availability generally limits the growth of Spiraea shrubs. Results of RDA revealed that the most significant contributors to the C, N, and P concentrations and stoichiometric characteristics of Spiraea shrub leaves were in the following order: soil total N>mean annual precipitation>elevation>soil pH>soil organic C>mean annual temperature. These factors had contribution rates of 35.32%, 13.19%, 10.20%, 8.82%, 8.34%, and 6.48%, respectively. It was determined that soil nutrients have a greater impact on the growth and nutrient accumulation of Spiraea shrubs compared to climatic factors. This study makes an important contribution to the theoretical basis and data support, enabling a deeper understanding of the response mechanisms of shrub species in the grassland ecosystems of the Altay Mountains to climate change.

Extreme temperature events have intensified across Jordan over the past 40 a, increasing risks to agriculture, water availability, urban infrastructure, and public health. The purpose of this study is to assess the long-term spatial trends and regime shifts in extreme temperature indicators across Jordan's climate zones to explore climate adaptation strategies. This study presents a high-resolution and spatially explicit assessment of thermal extremes using daily data from 1982 to 2024 across 45 grid-based study points in Jordan. Thirteen temperature indices, including percentile-based thresholds, duration metrics, and absolute extremes, were computed using RClimDex and analyzed across four Köppen climate zones: hot desert (BWh), hot semi-arid (BSh), cold desert (BWk), and Mediterranean (Csa) climates. The analysis confirmed a statistically significant warming trend: annual mean maximum temperatures increased by 2.198°C, while annual mean minimum temperatures rose by 2.035°C. Cold extremes have sharply declined, with cold days (TX10p) decreasing by 70.0%-80.0%, and the cold spell duration indicator (CSDI) dropping from 12.6 to 4.0 d/a, particularly in the BWk zone. Heat indices intensified across all zones, with warm days (TX90p) increasing by over 300.0% in BWh, warm nights (TN90p) rising by 38.1%, and the warm spell duration indicator (WSDI) extending fourfold, indicating prolonged exposure to heatwaves. Mean value of maximum temperature (TXx) reached 45.600°C in most arid areas, while minimum temperature (TNx) exceeded 31.600°C, highlighting increased nocturnal heat stress. Change-point analysis indicated that 1998 was a pivotal year, marking a structural transition in both cold and warm temperature indices. Subsequent intensifications after 2010 in TN90p, TNx, and mean of daily maximum temperature (T_maxmean) reflected an ongoing trend toward sustained thermal extremes. In addition to time-series trends, the study employed network-based correlation analysis to explore the coherence among climate indices. Strong positive correlations were observed among TXx, TX90p, and mean of daily minimum temperature (T_minmean) (r≥0.94), as well as among TN90p, T_minmean, and TNx (r≥0.87), indicating a tightly clustered heat subsystem. Duration metrics like the WSDI showed a close alignment with percentile extremes (between WSDI and TX90p; r=0.88), suggesting integrated heatwave behavior. In contrast, cold indices (TX10p, TN90p, frost days, and CSDI) exhibited weak or negative correlations and displayed peripheral positioning in the climate network, indicating their limited role under a warming regime. Absolute extremes showed weak internal linkages, suggesting episodic rather than systemic response characteristics. This structural realignment indicated a shift from a previously balanced thermal profile to a heat-dominated climate system. Regional variations revealed that BWh and BSh were experiencing the steepest warming, while Csa was transitioning more slowly but was showing signs of reduced winter cooling and increased irrigation demands. The findings establish a robust climate baseline for Jordan and offer actionable insights for climate adaptation planning. Recommended measures include precision irrigation, the development of heat-resilient crops, improvements to urban cooling infrastructure, and early warning systems for thermal extremes. By integrating spatial climate zoning, regime shift analysis, and inter-index correlation structures, this study provides a replicable framework for monitoring climatic transformations and informing resilience strategies in arid and semi-arid areas.

Desert lakes are an important link in the water cycle and an important reservoir of water resources in arid and semi-arid areas, playing an important role in maintaining the stability of the regional natural environment. However, studies on the hydrochemical evolution and transformation relationships between desert lake groups and potential water sources are limited. Taking the Qixing Lake, the only lake group within the Hobq Desert in China, as the area of interest, this study collected samples of precipitation water, Yellow River water, lake water, and groundwater at different burial depths in the Qixing Lake region from July 2023 to October 2024. The hydrochemistry of different water bodies was analyzed using a combination of Piper diagrams, Gibbs diagrams, ratio of ions, and MixSIAR mixing models to reveal the transformational relationships of lake water with precipitation, groundwater, and Yellow River water. Results showed that both groundwater and surface water in the study area are weakly-to-strongly alkaline, with HCO₃^- as the dominant anion and Na⁺, Ca²⁺, and K⁺ as the main cations. The hydrochemical type of groundwater and some lakes was dominated by HCO₃^--Na⁺, whereas that of other lakes was dominated by Cl^--Na⁺ and HCO₃^--Mg²⁺. The hydrochemistry of groundwater and Yellow River water in the Qixing Lake region was controlled mainly by a combination of evaporite saline and silicate rock mineral dissolution. The local meteoric water line (LMWL) of the study area proved that regional water bodies are strongly affected by evaporative fractionation. The MixSIAR model revealed that shallow groundwater is the main recharge source of the lake group in the Qixing Lake region, accounting for 59.0%-64.2% of the total. The findings can provide references for the identification of water sources in desert lakes and the development and utilization of water resources in desert lake regions.

Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas. Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and improving the efforts of ecosystem restoration. However, the applicability of various vegetation indices (VIs) in these arid areas remains uncertain. Evaluating the applicability of multiple VIs for vegetation monitoring can elucidate the variability of VIs performance at regional scale. Therefore, this study selected the Zuli River Basin (ZLRB), a typical loess hilly watershed in the semi-arid areas of China. Using Landsat data, we calculated the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel NDVI (kNDVI) for the ZLRB from 1990 to 2020. We analyzed the spatiotemporal variations of these VIs using trend analysis and the Mann-Kendall test, and quantified the contributions of climate change (considering time-lag effects) and human activities to VIs changes through wavelet and residual analyses. Results indicated that VIs generally exhibited an upward trend in the ZLRB, with significant improvements observed in 54.91% of the area for NDVI, 31.69% for EVI, and 33.71% for kNDVI. Among them, NDVI outperformed EVI and kNDVI in capturing vegetation changes in the semi-arid area. VIs responded to precipitation with 1-month time lag and no time lag to temperature during growing season. Moreover, precipitation had a stronger positive correlation with VIs than temperature. Climate change was identified as the dominant driver of vegetation dynamics in the ZLRB, accounting for 93.12% of NDVI variation, while human activities contributed only 6.88%. Comparative analysis of VIs suggests that NDVI was more suitable for describing vegetation changes in the typical arid area of the ZLRB. Our findings underscore the importance of selecting appropriate VIs for targeted ecological restoration and sustainable land management.

Belowground bud banks are essential for the regeneration of plant population in arid desert areas, and their response to environmental changes could reflect adaptive strategies of plants to desert habitats. However, the size and composition of belowground bud banks and their response to environmental factors in the desert steppe zone remain poorly understood, challenging desertification control efforts in arid desert areas. This study examined the density and vertical distribution of horizontal and vertical rhizome buds of a rhizomatous legume herb Sophora alopecuroides L., its population characteristics, and soil physical-chemical properties in three habitats (interdune lowland (IL), flat sandy land (FSL), and desert steppe (DS)) in a desert steppe zone, northern China. Our findings revealed that: (1) total and horizontal rhizome bud densities of S. alopecuroides differed significantly among the three habitats (P<0.05), with the largest total rhizome bud density (177 buds/m²) in IL and the smallest (63 buds/m²) in DS; (2) horizontal rhizome buds distributed in the deep soil layer were dominant in IL, while vertical rhizome buds in the top soil layer were predominant in DS; and (3) soil coarse sand, nutrient content, and population density were the primary factors affecting bud bank density of S. alopecuroides. Specifically, horizontal rhizome buds were dependent largely on soil coarse sand content, and vertical rhizome buds tended to be more related to soil organic matter content and population density. Our results indicated that horizontal rhizome buds were more important in IL with frequent aeolian disturbance, whereas vertical rhizome buds were more important in DS with abundant water and nutrient resources. The plastic responses and survival strategies of S. alopecuroides bud bank to different habitats provide valuable information for the effective implementation of desertification control measures and the management of desert steppe ecosystems.

Drought is a natural disaster that significantly impacts the Earth's ecological environment, especially in arid and semi-arid areas. However, drought at a large watershed scale, which plays an important role in sustainable environmental development, has received limited attention. In this study, we analyzed the spatial and temporal variations in drought in the Yellow River Basin, China from 2002 to 2022 and its driving factors using a vegetation health index (VHI). Results showed that average VHI in the Yellow River Basin from 2002 to 2022 was 0.581, with the most severe drought occurring in summer and autumn. The basin showed a slow decreasing trend in drought during the study period. Regarding spatial distribution of monthly drought frequency and trend of VHI, the mean of the frequency was 13.00%, and 78.00% had a drought frequency of 10.00%-20.00%, with moderate drought generally prevailing. Regarding land use types, forest land, grassland, agricultural land, construction land, water body, and wasteland showed a descending order for the annual average VHI. VHI of each land use type was the lowest in summer and autumn, with pronounced seasonal characteristics. The uneven distribution of drought in the Yellow River Basin was primarily influenced by annual precipitation, solar-induced chlorophyll fluorescence, and relative humidity. VHI effectively quantified drought conditions at a regional scale and proved to be highly applicable in the Yellow River Basin. The results clarify the effectiveness of VHI for drought monitoring in the Yellow River Basin and can provide a reference for drought monitoring across the basin.

Accurately revealing the spatial heterogeneity in the trade-offs and synergies of land use functions (LUFs) and their driving factors is imperative for advancing sustainable land utilization and optimizing land use planning. This is especially critical for ecologically vulnerable inland river basins in arid regions. However, existing methods struggle to effectively capture complex nonlinear interactions among environmental factors and their multifaceted relationships with trade-offs and synergies of LUFs, especially for the inland river basins in arid regions. Consequently, this study focused on the middle reaches of the Heihe River Basin (MHRB), an arid inland river basin in northwestern China. Using land use, socioeconomic, meteorological, and hydrological data from 2000 to 2020, we analyzed the spatiotemporal patterns of LUFs and their trade-off and synergy relationships from the perspective of production, living, ecological functions. Additionally, we employed an integrated Extreme Gradient Boosting (XGBoost)-SHapley Additive exPlanations (SHAP) framework to investigate the environmental factors influencing the spatial heterogeneity in the trade-offs and synergies of LUFs. Our findings reveal that from 2000 to 2020, the production, living, and ecological functions of land use within the MHRB exhibited an increasing trend, demonstrating a distinct spatial pattern of ''high in the southwest and low in the northeast''. Significant spatial heterogeneity defined the trade-off and synergistic relationships, with trade-offs dominating human activity-intensive oasis areas, while synergies prevailed in other areas. During the study period, synergistic relationships between production and living functions and between production and ecological functions were relatively robust, whereas synergies in living-ecological functions remained weaker. Natural factors (digital elevation model (DEM), annual mean temperature, Normalized Difference Vegetation Index (NDVI), and annual precipitation) emerged as the primary factors driving the trade-offs and synergies of LUFs, followed by socioeconomic factors (population density, Gross Domestic Product (GDP), and land use intensity), while distance factors (distance to water bodies, distance to residential areas, and distance to roads) exerted minimal influence. Notably, the interactions among NDVI, annual mean temperature, DEM, and land use intensity exerted the most substantial impacts on the relationships among LUFs. This study provides novel perspectives and methodologies for unraveling the mechanisms underlying the spatial heterogeneity in the trade-offs and synergies of LUFs, offering scientific insights to inform regional land use planning and sustainable natural resource management in inland river basins in arid regions.

The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets. Conventional vegetation indices, specifically the Normalized Difference Vegetation Index (NDVI), perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils. This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index (MSAVI) for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery (30 m resolution). A spectral index-based classification framework was developed, combining MSAVI with the Normalized Difference Water Index (NDWI) and Salinity Index 1 (SI1) to reduce spectral confusion between vegetation, saline soils, and surface water. The MSAVI-based classification achieved an overall accuracy of 77.96% (Kappa coefficient=0.71), supported by 313 field-collected validation points from 2023. While the multi-index approach enabled finer discrimination of ecologically important classes, particularly separating salt pans from solonchak soils, it resulted in a lower overall accuracy (73.80%), highlighting a trade-off between class separability and classification performance. Land cover change analysis revealed a highly dynamic landscape, with 52.96% of the study area transitioning between classes over the decade. Transformed areas (16,893 km²) exceeded stable zones (15,004 km²), driven primarily by rapid desiccation and salinization. Solonchak soils increased at an annual rate of 5.58%, while surface water bodies declined by 4.83% per year. Concurrently, sparse or distressed vegetation increased by 1.43% annually, reflecting ongoing afforestation efforts. This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail. The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.

Soil bacteria are integral to ecosystem functioning, significantly contributing to nutrients cycling and organic matter decomposition, and enhancing soil structure. This research considered the composition and dynamics of soil bacterial communities under different vegetation types (native Quercus brantii Lindl. and Amygdalus scoparia Spach, and non-native Pinus eldarica Medw. and Cupressus arizonica Greene.) in Zagros mountain area of Iran. This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring (wet season) and summer (dry season) to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms. Soil samples were randomly collected under the canopies of various tree species and a control area, yielding a total of 48 composite samples analyzed for bacterial composition. Results indicated that 11 Gram-negative (e.g., Citrobacter freundii, Enterobacter cloacae, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, etc.) and 2 Gram-positive (Staphylococcus epidermidis and Staphylococcus aureus) bacteria were identified, showing significant seasonal variation. Specifically, 53.85% of bacterial species were common to both seasons, with notable shifts in community composition observed between spring and summer, highlighting a higher abundance of Gram-negative species in spring. Bacterial community structure was significantly influenced by vegetation type, with various tree species shaping distinct microbial assemblages. Moreover, Pearson's correlations revealed that soil properties, particularly pH, phosphorus, and moisture content, were critical drivers of bacterial diversity and abundance. Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes, emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity. Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience, particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role. This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.

Tree growth is extremely vulnerable to climate change, especially in semi-arid areas. Although the response of stem radial growth (SRG) to climate change has been extensively studied, the intra-annual regulatory mechanisms of SRG in trees with different water use strategies and life types remain poorly understood. This study calculated the SRG of four native species in the semi-arid area of the Loess Plateau, China, including two isohydric species (Pinus tabuliformis Carrière and Populus × hopeiensis Hu & Chow) and two anisohydric species (Prunus sibirica L. and Platycladus orientalis (L.) Franco). The results revealed that the intra-annual SRG of all the four tree species exhibited a single peak, and greater SRG was found in anisohydric species. Principal component analysis and structural equation model revealed that atmospheric water, particularly relative humidity, was the main factor affecting the SRG of coniferous species (P. tabuliformis and P. orientalis), whereas the SRG was mainly affected by soil water content in broadleaf species (P. sibirica and P. × hopeiensis). These findings suggested that water use strategies and life types play important roles in SRG and environmental response of trees in semi-arid area. Considering the high climate sensitivity of wood formation in trees, our results highlight the importance of water use strategies and life types of trees in SRG prediction in the context of future climate change in arid and semi-arid areas.

Global grassland degradation necessitates the identification of sustainable grazing management strategies. In semi-arid regions, grazing exclusion (GE), cold-season grazing (CG), and free grazing (FG) represent common practices in grassland ecosystems, yet the long-term ecological consequences of these patterns on plant community structure and soil aggregate stability remain inadequately elucidated. In this study, we evaluated the effects of GE, CG, and FG on soil organic carbon, soil water content, soil bulk density, soil aggregates, and vegetation indicators in Xilamuren steppe, a semi-arid grassland in northern China through field sampling and laboratory analyses in 2024. Our findings revealed that, compared to CG and FG, GE significantly enhanced aboveground and belowground biomass, species diversity, and soil physical-chemical properties in the 0-30 cm layer. The dominant plant species in GE and CG sites were Stipa krylovii, Leymus chinensis, and Agropyron cristatum, whereas Stipa krylovii, Artemisia frigida, and Leymus chinensis were predominant in FG site. Different grazing patterns led to distinct soil aggregate distributions, with >2.00 and <0.25 mm aggregates exhibiting the highest content in different soil layers depending on the grazing patterns. All grazing management strategies significantly improved soil aggregate stability, with the overall stability following the order: GE>CG>FG. Furthermore, random forest modeling identified plant species diversity, plant growth traits, and grazing patterns as the primary determinants of soil aggregate stability. Collectively, these results offer valuable insights into the sustainable management and ecological restoration of semi-arid grasslands under different grazing pressures.

Due to the arid and sandy surface of the Taklimakan Desert (TD) in China, the turbulence structure and vertical distribution of ozone exhibit unique and complex characteristics. However, few studies have focused on these issues. To reveal the variation characteristics of summertime atmospheric turbulence and ozone concentration over the TD, we conducted joint detection experiments in July 2016 and July 2021 at Tazhong in the hinterland of the TD using an eddy covariance detection system, a GPS (Global Positioning System) sounding system, and a meteorological gradient tower. Using methods such as statistical analysis, nonlinear fitting, and Fast Fourier Transform, this study analyzed and processed parameters including temperature, relative humidity, wind speed, turbulence parameters, turbulence spectra, and ozone concentration. The high average temperature is accompanied by low relative humidity over the TD, showing a negative correlation between the two. The temperature of the 10.0-cm-deep sand layer lags the near-surface air temperature by nearly 4 h. From 09:30 to 21:00 (Beijing Time), under conditions where the sensible heat flux is positive but stability parameter (z/L, where z is the height and L is the Obukhov length) is negative, the atmosphere is heated by the land surface, with the occurrence of unstable stratification; however, the conditions are the opposite (sensible heat flux is negative and z/L is positive) after 22:00, which are accompanied with the cooling of the surface radiation, occurrence of temperature inversion in the lower atmosphere, and stable stratification. A positive correlation is identified between the diurnal variation of turbulent kinetic energy (TKE) and the atmospheric boundary layer (ABL) height, with significant contributions from both the buoyancy and shear terms during the daytime. Under unstable stratification, the normalized standard deviations of the three-dimensional wind speed, temperature, and humidity conform to the Monin-Obukhov Similarity Theory (MOST). As the stability parameter z/L transitions from strongly unstable to strongly stable, the energy of the dimensionless turbulent velocity spectra gradually decreases and conforms to the -2/3 power law within the inertial subrange. In the hinterland of the TD, the summertime tropospheric ozone concentration remains below approximately 0.70×10^-6 (volume concentration). Above the troposphere, within the range of 16,500.0-30,000.0 m, a significant increasing trend is identified in the ozone concentration with altitude. At an altitude of 30,000.0 m, the maximum ozone concentration can reach up to 7.50×10^-6. The research findings provide both theoretical and data foundations for future in-depth studies of turbulent motion and ozone concentration distribution in the TD, as well as in the similar areas around the world.

Soil erosion and shallow landslides in the upper reaches of the Yellow River, China, are increasing due to extreme climate events and human disturbances. The biomechanical properties of vegetation roots play an important role in soil stabilization and fixation, as they resist soil erosion and shallow landslides in this area. However, the biomechanical properties of the roots of dominant herbs and their influencing factors in this area remain poorly understood. Therefore, we selected two dominant herbs in this area, Stipa aliena Keng and Poa crymophila Keng, and carried out a series of uniaxial tensile tests on the roots of the two herbs under different treatments. Meanwhile, the effects of root diameter, plant species, gauge length, root water content, and loading rate on the biomechanical properties of the two herbs' roots were analyzed. The results showed that root diameter was the most significant factor affecting the root biomechanical properties (P<0.010), and root tensile force displayed a positive power law relationship with root diameter, whereas root tensile strength and Young's modulus followed negative power law correlations with root diameter, and fracture strain increased linearly with root diameter. Root tensile force, tensile strength, and fracture strain of S. aliena were significantly greater than those of P. crymophila (P<0.001), which was mainly due to the higher lignin content and lignin:cellulose ratio of S. aliena roots. During uniaxial tensile process, hydrated roots exhibited elastic-plastic-brittle behavior, whereas dried roots exhibited elastic-brittle behavior. Root fracture strain of the two herbs was significantly lower under 100 mm gauge length than under 50 mm gauge length (P<0.001), and the Young's modulus was significantly greater (P<0.050). Tensile strength and fracture strain of hydrated roots of the two herbs were significantly greater than those of dried roots (P<0.050), whereas the Young's modulus was significantly lower (P<0.001). Root tensile force, tensile strength, and fracture strain of S. aliena were significantly greater under 20 mm/min loading rate than under 200 mm/min loading rate (P<0.050), whereas loading rate had no significant effect on the root biomechanical properties of P. crymophila (P>0.050). Fibrous roots of the two herbs were well developed, with relatively high tensile strengths and Young's moduli of 78.498 and 837.901 MPa for S. aliena, and 67.541 and 901.184 MPa for P. crymophila, respectively. The two herbs can stabilize soil and prevent soil erosion and can be used as pioneer species for ecological restoration in the upper reaches of the Yellow River. These results provide a theoretical basis for soil erosion and shallow landslide control in the giant landslide area of the upper reaches of the Yellow River.

Net ecosystem productivity (NEP) is a key indicator for estimating carbon sink dynamics in terrestrial ecosystems. Existing studies on carbon sink dynamics in Northwest China have uncertainties in quantifying spatiotemporal variations of NEP and their driving factors. This study estimated NEP across ecosystems in Northwest China during 2000-2020 using multi-model integration, and analyzed its spatiotemporal patterns and drivers. Results showed that the annual average NEP was 97.98 g C/(m²∙a), with higher values at eastern and western margins and lower values in central hinterland. Strong carbon sink areas included the Yili River Basin and northern slope of Tianshan Mountains, while low carbon sink areas concentrated in eastern Xinjiang Uygur Autonomous Region (Eastern Xinjiang) and Alxa-Ejin Plateau. NEP trended upward from 79.22 g C/(m²∙a) in 2000 to 109.03 g C/(m²∙a) in 2020 with low variability and strong persistence, suggesting continuous growth. NEP significantly and positively correlated with near-infrared reflectance of vegetation (NIRv), weakly with climate factors, and negatively with socio-economic density indicators. Topographically, NEP peaked at 2.0-2.4 km elevation, 15°-25° slopes, and north-facing aspects. Changes in ecosystem type significantly influenced NEP, with bare land conversion into grassland/cropland enhancing carbon sinks. Results of this study highlight the need for ecological restoration and rational land use to boost carbon sequestration in this ecologically sensitive region.

Rising temperatures and increased droughts caused by climate change significantly reduce crop yields. Halophytes with different photosynthetic metabolism types have specific mechanisms for resistance to climatic factors. This study analyzed the morphophysiological, biochemical, and molecular-genetic mechanisms of tolerance and adaptation in halophytes, promising candidates for the restoration of salt affected lands in arid and semi-arid areas. Experiments under drought (D) and elevated temperature (eT), as well as their combined action (eT+D), were performed on Atriplex verrucifera M. Bied. (C₃ plant) and Climacoptera crassa (M. Bieb.) Botsch. (C₄-NAD-ME plant) with different types of photosynthesis. The activity of photosystem I (PSI) and the efficiency of photosystem II (PSII) were measured, along with the expression of genes involved in the light (psaA, psaB, psbA, CAB, Fd1, PGR5, and ndhH) and dark (rbcL, Ppc2, and PPDK) reactions of photosynthesis. The content of key carboxylating enzymes ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and phosphoenolpyruvate carboxylase (PEPC), as well as the photorespiration enzyme glycine decarboxylase (GDC), were assessed. Plant growth and water-salt balance parameters, and activity of enzymes in the malate dehydrogenase (MDH) system nicotinamide adenine dinucleotide (phosphate) (NAD(P))-MDH and NAD(P)-malic enzyme (ME) were also examined. A multivariate analysis of the experimental results revealed that A. verrucifera and C. crassa were both resistant to the effects of these climatic stressors. The tolerance mechanisms of both species were significantly influenced by a high level of photosynthetic plasticity. Nevertheless, differences were observed in the protective mechanisms underlying tolerance. In the C₃ species, dissipative processes associated with non-photochemical quenching (NPQ) of PSII and MDH system enzymes (malate valves) were activated, particularly under osmotic stress. The negative effects in the C₃ plants were caused by the combined action of eT+D, which was compensated by an increased expression of rbcL, psaA, CAB, and especially PGR5, i.e., genes encoding Rubisco large subunit and PSI components: apoproteins A, chlorophyll a/b-associated protein (CAB) of light-harvesting complex, and proton gradient regulation 5 (PGR5) protein of the main pathway of cyclic electron transport (CET) around PSI. In C₄ species, the protective MDH complex was expressed to a lesser extent, but activation of the C₄ carbon-concentrating mechanism (CCM) and upregulation of PGR5 expression were observed, particularly under the individual action of the factors. Under the combined stress of eT+D, C. crassa exhibited a synergistic effect, where the increase in NPQ level and NAD-ME activity, as well as decrease in NADP-ME activity was less pronounced compared with the effect of singular factors. Comparative physiological, biochemical, and molecular analyses of how C₃ and C₄ species response to individual and combined climatic factors provide new insights into sustainable plant adaptation strategies in the face of global climate change. Considering the high nutritional value of these two fodder species, a technological approach could be developed to improve the productivity of salt affected lands.

Land desertification severely compromises the core function of ecosystem and significantly disrupts biodiversity. Caragana korshinskii Kom. plays a pivotal role as a critical plant resource in the restoration and ecological reconstruction of desertified areas in Northwest China. Kytorhinus immixtus Motschulsky is the primary pest responsible for causing substantial damage to the seeds of C. korshinskii. In this study, field surveys were utilized in three distinct desertified types (lightly, moderately, and severely desertified areas) in north central Ningxia Hui Autonomous Region, Northwest China. This research was focused on investigating the population dynamics and damage rates of K. immixtus, with an emphasis on examining the relationships among K. immixtus distribution, levels of soil desertification, and associated environmental factors. The results revealed marked variations in the population distribution and abundance of K. immixtus across habitats with different degrees of desertification. Due to the sand-fixing ability of C. korshinskii, the severity of soil desertification decreased progressively from severe to moderate and light with C. korshinskii establishment. This reduction in desertification, along with habitat restoration and an increase in plant diversity, was correlated with a gradual increase in K. immixtus population size and damage rate. Generalized linear mixed model analysis revealed significantly positive correlations of soil total potassium, C. korshinskii height, maximum temperature during the survey, precipitation, and the plant species richness index with K. immixtus population. In contrast, the soil total phosphorus content, organic matter content, minimum temperature during the survey, C. korshinskii canopy width, and branch number were significantly and negatively correlated with K. immixtus population. Due to the sand-fixing capacity of C. korshinskii, the plant mitigated soil desertification, but as desertification severity decreased, habitat restoration and increased plant diversity drove a gradual increase in the population and damage rate of K. immixtus. Both biotic and abiotic factors in the habitat significantly influenced K. immixtus occurrence. To achieve the sustainable restoration of desert ecosystem, optimization of plant community structure with soil nutrient management in ecological rehabilitation is necessary to balance the benefits of sand fixation with pest risks.

In recent years, intensive human activities have increased the intensity of desertification, driving continual desertification process of peripheral meadows. To investigate the effects of restoration on soil microbial communities, we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land, northern China. Through the use of high-throughput sequencing, we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration. Different slope positions were analyzed and spatial heterogeneity was assessed. The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration, and the following order was as follows: bottom slope>middle slope>crest slope. During the restoration in the Hulun Buir Sandy Land, the bacterial communities were dominated by Proteobacteria, Actinobacteria, and Acidobacteria, whereas the fungal communities were dominated by Ascomycota and Basidiomycota. Eutrophic bacterial abundance increased with the restoration duration, whereas oligotrophic bacterial and fungal abundance levels decreased. The soil bacterial abundance significantly increased with the increasing restoration duration, whereas the fungal diversity decreased after 11 a of restoration, except that at the crest slope. Redundancy analysis showed that pH, soil moisture content, total nitrogen, and vegetation-related factors affected the bacterial community structure (45.43% of the total variance explained). Canonical correspondence analysis indicated that pH, total phosphorus, and vegetation-related factors shaped the bacterial community structure (31.82% of the total variance explained). Structural equation modeling highlighted greater bacterial responses (R²=0.49-0.79) to changes in environmental factors than those of fungi (R²=0.20-0.48). The soil bacterial community was driven mainly by pH, soil moisture content, electrical conductivity, plant coverage, and litter dry weight. The abundance and diversity of the soil fungal community were mainly driven by plant coverage, litter dry weight, and herbaceous aboveground biomass, while there was no significant correlation between the soil fungal community structure and environmental factors. These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.

Crude oil pollution is a significant global environmental challenge. The eastern Gansu Province on the Loess Plateau, an important agricultural region containing the Changqing Oilfield, is facing increasing crude oil contamination. Understanding how microbial communities respond to varying pollution levels is critical for developing effective bioremediation strategies. This study examined how different concentrations of crude oil affect soil properties and microbial communities in Qingyang City, eastern Gansu Province, China by comparing lightly polluted (1895.84-2696.54 mg/kg total petroleum hydrocarbons (TPH)), heavily polluted (4964.25-7153.61 mg/kg TPH), and uncontaminated (CK) soils. Results revealed that petroleum contamination significantly increased total organic carbon (TOC), pH, C:N:P ratio, and the activities of dehydrogenase (DHA) and polyphenol oxidase (PPO), while reducing total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), soil organic matter (SOM), soil water content (SWC), the activities of urease (URE) and alkaline phosphatase (APA), and microbial alpha diversity (P<0.050). Light pollution (LP) soils demonstrated an increase in culturable microorganisms, whereas heavy pollution (HP) soils exhibited increased hydrocarbon-degrading microbes and higher expression of key functional genes, such as alkane monooxygenase (AlkB), cytochrome P450 alkane hydroxylases (P450), catechol 2,3-dioxygenase (C23O), and naphthalene dioxygenase (Nah) (P<0.050). Non-metric multidimensional scaling (NMDS) and redundancy analysis (RDA) indicated evident variations in microbial community structure across different oil contamination levels. LP soils were dominated by bacterial genera Pseudoxanthomonas and Solimonadaceae, whereas Pseudomonas, Nocardioides, and hydrocarbon-degrading genera (Marinobacter, Idiomarina, and Halomonas) were predominant in HP soils. The fungal genus Pseudallescheria exhibited the most pronounced abundance shift between LP and HP soils (P<0.050). Environmental factor analysis identified AN, SWC, TN, SOM, and alpha diversity indices (Shannon index and Chao1 index) as the key differentiators of CK soils, whereas the pollutant levels and metal content were characterized in HP soils. Hydrocarbon-degrading microbial abundance was a defining trait of HP soils. Metabolic pathway analysis revealed enhanced aromatic hydrocarbon degradation in HP soils, indicating microbial adaptation to severe contamination. These findings demonstrated that crude oil pollution suppressed soil nutrients while reshaping the structure and function of microbial communities. Pollution intensity directly affected microbial composition and degradation potential. This study offers valuable insights into microbial responses across contamination gradients and supports the development of targeted bioremediation strategies for oil-contaminated loess soils.