Dryland ecosystems, encompassing arid to semi-arid regions, impose strong climatic and edaphic constraints that profoundly shape plant functional traits and secondary metabolism. Understanding how environmental factors regulate phytochemical biosynthesis is essential for biodiversity conservation and sustainable resource management under increasing aridity. Oliveria decumbens Vent., an endemic medicinal species of the drylands of Fars Province, Iran, provides an excellent model for exploring the ecological determinants of metabolite variability in water-limited habitats. We integrated ecological predictors with machine learning to model the spatial variation of thymol and carvacrol concentrations across 59 georeferenced populations of O. decumbens. Three predictive models—Random Forest (RF), Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel (SVR-RBF), and a hybrid ensemble (RF+SVR-RBF)—were developed and evaluated. Model performance was quantified using root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and the concordance correlation coefficient (CCC). Generalized Linear Model (GLM) was applied to identify key environmental variables regulating metabolite biosynthesis. The hybrid ensemble consistently outperformed individual model, achieving the highest predictive accuracy (R2=0.82 for thymol and R2=0.80 for carvacrol). Spatial mapping revealed pronounced heterogeneity in metabolite distribution, with distinct functional hotspots in the northern and western semi-arid regions of Fars Province. GLM analysis indicated that mean annual temperature, slope aspect, slope degree, and sand content were strong positive predictors of thymol and carvacrol accumulation, whereas high soil potassium, clay percentage, and alkaline pH constrained metabolite production. This study shows that hybrid ensemble modeling effectively captures how environmental gradients regulate secondary metabolism in dryland plants. The proposed framework, combining RF with SVR-RBF, is transferable across arid environments. These findings support trait-based ecological predictions and offer practical insights for conservation and sustainable cultivation of high-value medicinal plants in water-limited ecosystems.
Although salinity functions as a key environmental filter for soil microorganisms, its comprehensive effects on soil microbial community structure and assembly processes remain poorly understood, particularly in arid regions prone to salinization. In this study, soil bacterial communities along a natural salinity gradient at the edge of Ebinur Lake in Northwest China were investigated. Using 16S ribosomal RNA (16S rRNA) sequencing, we examined variations in soil bacterial community structure, co-occurrence patterns, and assembly mechanisms across different soil salinity groups, including lightly salinized soils (LSS), moderately salinized soils (MSS), and heavily salinized soils (HSS). The results showed that soil bacterial diversity varied significantly among salinity groups, with the highest value observed in LSS. Community dissimilarity increased notably with greater variations in salinity. Notably, a systematic shift in soil bacterial composition occurred along the salinity gradient, with salt-sensitive bacterial phyla (e.g., Acidobacteria and Gemmatimonadetes) being progressively replaced by salt-tolerant ones (e.g., Firmicutes and Bacteroidetes). Network analysis underscored that increased salinity led to reduced soil bacterial network complexity and stability. More positive correlations among soil bacteria occurred in HSS, suggesting a potential shift toward cooperative microbial strategies under severe salt stress. Moreover, the assembly processes governing soil bacterial communities transitioned from stochastic process, predominantly in LSS (71.43%) and MSS (80.00%), to deterministic process in HSS (66.66%). In summary, the results emphasize the multifaceted role of soil salinity in shaping soil bacterial communities in arid ecosystems, thereby enhancing the understanding of the impacts of soil salinization on soil microbial dynamics.
The middle and lower reaches of the Irtysh River form a transboundary ecological corridor across the Republic of Kazakhstan and Russia and represent an environmentally sensitive region in arid Central Asia. Understanding long-term vegetation dynamics in this region is essential for evaluating ecological stability and supporting cross-border ecosystem management. However, existing studies are mostly confined to individual administrative units, breaking the eco-hydrological integrity of transboundary basins; meanwhile, most analyses rely on single annual-mean vegetation metrics, failing to reveal the differentiated variation patterns and long-term persistence of LAI across different vegetation growth states. This study used the Global Inventory Modeling and Mapping Studies Leaf Area Index 4g (GIMMS LAI4g) dataset and Climate Research Unit (CRU) precipitation data from 1982 to 2020, to investigate the spatiotemporal dynamics of leaf area index (LAI) across 14 transboundary subregions in the middle and lower reaches of the Irtysh River Basin. Specifically, this study applied Mann-Kendall trend analysis and Theil-Sen median slope estimator to identify long-term trends, employed rescaled range analysis evaluate long-term persistence, conducted Pearson correlation analysis to quantify the relationship between LAI and precipitation, and used spatial pattern analysis to characterize regional heterogeneity. The results showed significant increases in annual mean and maximum LAI, with Sen's slopes of 0.0025/a and 0.0077/a, respectively, whereas annual minimum LAI exhibited a significant decreasing trend (–0.0011/a). Pronounced spatial heterogeneity was observed among mountainous and piedmont regions, steppe–riparian transition zones, and downstream forest–wetland landscapes. Rolling-window analysis revealed relatively stable vegetation dynamics during the early period, followed by enhanced spatial divergence and increased interannual variability after the early 2000s. All LAI indicators exhibited strong persistence, with Hurst exponents exceeding 0.7000 across the study area, indicating that the observed vegetation trajectories are likely to persist in the future. Precipitation showed significant positive correlations with annual maximum and mean LAI in 64.29% and 57.14% of the study area, respectively, whereas annual minimum LAI showed generally weak responses to precipitation variability. These findings improve understanding of vegetation dynamics and future persistence in transboundary arid river basins and provide scientific support for ecological conservation and sustainable watershed management in Central Asia.
The Yellow River Basin (YRB), located in the mid-latitude region of China, encompasses diverse ecosystem types and is highly sensitive to climate change. However, the temporal patterns and environmental drivers of net ecosystem CO2 exchange (NEE) across multiple time scales remain poorly understood. Using eddy covariance observations from the ChinaFLUX network collected between 2003 and 2020, this study investigated the temporal dynamics of NEE and its primary environmental controls in five representative ecosystem types within the YRB and its adjacent 100-km buffer zone: cropland, forest, grassland, shrubland, and wetland ecosystems. The results showed that all five ecosystems exhibited a generally U-shaped diurnal pattern from May to September, characterized by net CO2 uptake during the daytime and net CO2 release at night. At the daily scale, cropland displayed a typical bimodal carbon uptake pattern, whereas forest ecosystem exhibited the greatest day-to-day variability in NEE. In contrast, grassland, shrubland, and wetland ecosystems showed relatively smooth daily fluctuations. The net CO2 source-sink functions derived from NEE differed substantially among ecosystem types. Forest ecosystems acted as the most stable and persistent carbon sinks, whereas croplands exhibited short-term but high-intensity carbon uptake. Wetlands showed pronounced interannual variability, including an extreme net CO2 release event at the Haibei wetland site in 2007. Grassland and shrubland ecosystems were more susceptible to environmental stress and could shift from net CO2 sinks to net CO2 sources during drought years. The environmental controls on NEE exhibited clear time-scale dependence. At the half-hourly scale, photosynthetically active radiation (PAR) was the dominant driver of NEE variability, the influence of temperature increased progressively from the daily to monthly scales. These findings improve the understanding of regional carbon dynamics in the YRB, provide insights into the net CO2 source-sink status of different ecosystem types, and elucidate the mechanisms regulating ecosystem CO2 exchange across multiple temporal scales.
High false alarm rates (FARs) in snowmelt flood forecasting persist, largely due to an insufficient understanding of the coupled effects of multi-source hydro-meteorological drivers and their inherent time lags. This study addressed this gap by developing a hybrid snowmelt-flood forecasting framework that combined multiple linear regression (MLR) and backpropagation neural network (BPNN) models, with a simulated annealing (SA) algorithm employed to optimize the ensemble weighting. The developed hybrid model that incorporated daily hydro-meteorological inputs and temporal factor was validated using data from 1978 to 2011 for the Hutubi River Basin, an arid inland basin on the northern slope of the Tianshan Mountains, China. The findings demonstrated that the hybrid model achieved a specificity of 0.8549, significantly outperforming standalone MLR (0.6024) and BPNN (0.6436) models. Correspondingly, the FAR was reduced to 0.1451, which was substantially lower than that of MLR (0.3976) and BPNN (0.3564). Upon integrating temporal factor, the FAR was further reduced to 0.0959, markedly enhancing overall predictive robustness. Collectively, this study offers a robust methodological framework for optimizing snowmelt flood forecasting by effectively integrating multi-source data and temporal dependencies.
As a critical ecological barrier and carbon reservoir on the eastern Qinghai-Xizang Plateau, the Qilian Mountains (QLMs) play a vital role in maintaining regional ecological security. This study employed an enhanced Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with dynamic interannual carbon density parameterization to evaluate carbon storage (CS) variations in the QLMs during the historical period of 1990–2020 and future period of 2030–2100 under climate scenarios (SSP126, SSP245, and SSP585, where SSP is the Shared Socio-economic Pathway). By integrating GeoDetector analysis, random forest modeling, and scenario simulations, we further assessed climate-human interactions and their impacts on CS. The QLMs had a multi-year average CS of 1.49×109 t during 1990–2020, with higher values in the southeast and lower values in the northwest. The spatial heterogeneity of CS was primarily driven by warming–wetting climatic gradients. Multi-scenario projections revealed divergent trajectories: under the SSP126 scenario, ecological restoration measures (e.g., mountain closure and afforestation) increased CS, whereas CS under SSP245 and SSP585 scenarios exhibited inverted U-shaped patterns. Notably, SSP585 resulted in 8.00×107 t losses due to extensive conversions from grassland to urban area during 2050–2100. Grassland and cropland CS, together with precipitation, emerged as the key determinants of regional CS dynamics, with a synergistic interaction between precipitation and temperature. This study introduced a century-scale, multi-scenario framework that captures temporal interactions and dynamically parameterizes carbon density coefficients for era-specific accuracy. The findings suggest optimizing vegetation coverage, managing urban expansion, and implementing climate adaptation strategies to enhance the total CS of this ecologically sensitive region.
River ecosystems experience biodiversity loss due to intensifying anthropogenic disturbance and land use change. Although macroinvertebrate community plays a critical role in sustaining biogeochemical cycling and river ecosystem stability, the extent to which anthropogenic activities and land use changes affect macroinvertebrate biodiversity and sediment ecological health remains insufficiently resolved. This study investigated the effects of anthropogenic activities and land use changes on macroinvertebrate community diversity in the Beiluo River Basin, China. An entropy-based framework was developed to evaluate sediment ecological health. A total of 31 phyla were identified, with Annelida (1.26%–71.95%) and Arthropoda (8.25%–24.17%) as the dominant groups. The entropy values indicated that macroinvertebrate community diversity across the 18 sampling sites was the highest in the middle reaches. Mineral extraction and excessive fertilizer application in the upper and middle reaches were notably associated with sediment degradation. The expansion of riparian cropland and industrial pollution reduced benthic macroinvertebrate abundance, whereas a higher proportion of riparian water bodies buffered pollutant inputs and provided habitat refuges, thereby supporting higher macroinvertebrate abundance. In addition, hydraulic engineering and wetland conservation measures increased the diversity of Mollusca and Rotifera, reflecting positive ecological responses to policy-driven restoration. Overall, the entropy-based sediment ecological quality index (SEQI) integrating sediment chemistry with deoxyribonucleic acid (DNA) metabarcoding diversity indicated that the sediment eco-quality in the Beiluo River Basin was primarily constrained by riparian land use and catchment-scale anthropogenic pressure. Partial Least Squares-Path Modeling (PLS-PM) explained 69.30% of the variance in SEQI (goodness of fit (GOF)=0.519) and revealed that land use exerted a stronger effect on SEQI than socioeconomic factors. These findings highlight the need to curb the expansion of riparian cropland and built-up land, reduce pollutant discharges, sustain the protection of water bodies and wetlands including continued water, conservation investment, and improve wastewater treatment to protect sediment quality and benthic biodiversity. Collectively, these results provide an important basis for assessing river ecological health and informing sustainable regional socioeconomic development.
Remote sensing-based soil salinity inversion serves as a crucial approach for monitoring and assessment in arid regions. However, most existing models rarely account for the spatial autocorrelation (SAC) of soil salinity, which limits both their predictive accuracy and ability to capture spatial patterns. To address this gap, this study investigated the Minqin Oasis and its adjacent desert–oasis transition zone in Northwest China. Based on collected field soil samples and concurrently acquired Landsat-8 OLI remote sensing images in 2024, we incorporated characteristic bands reflecting SAC into conventional spectral indices. Through multi-band combination optimization and comparison of different models' predictive performance, we constructed an optimal soil salinity inversion model for the Minqin Oasis and its adjacent desert–oasis transition zone. The results demonstrated that incorporating SAC of soil salinity markedly improved model performance, with the Gradient Boosting Regression Trees (GBRT) model incorporating SAC (GBRT_SAC) achieving the best accuracy. Compared with the traditional spectral index-based GBRT model, the coefficient of determination (R2) increased by 7.320%, the root mean square error (RMSE) decreased by 20.230%, and the mean absolute percentage error (MAPE) decreased by 121.01% using the GBRT_SAC model. The soil salinity distribution derived from the GBRT_SAC model revealed pronounced spatial heterogeneity, with salinized areas covering approximately 1256.75km2 (36.170% of the total area). Soil salinity was jointly influenced by natural and anthropogenic factors. At the regional scale, soil type and vegetation type emerged as the dominant drivers shaping soil salinity patterns. In contrast, within the oasis interior, soil salinity was primarily driven by groundwater table regulated by irrigation, leading to surface salt accumulation through capillary rise. In the 1000m desert–oasis transition zone, the explanatory power (q-value) of all environmental factors for spatial variation of soil salinity significantly increased, indicating a sensitive interface where hydrological and aeolian processes interact. Notably, although soil salinity was relatively lower in sandy areas, sand content emerged as the most influential factor in this region (q-value=0.483), effectively serving as a key indicator of the transitional environment. By introducing SAC-based features into soil salinity inversion models, this study provides a robust methodological framework and valuable data to support understanding and management of soil salinization in arid desert–oasis ecotone systems.
Accurate estimation of gross primary production (GPP) is crucial for understanding terrestrial carbon cycling, yet the regional performance of existing GPP products remains insufficiently quantified. This study evaluated four widely used GPP products, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS, e.g., MOD17), Global OCO-2-based Solar-Induced Chlorophyll Fluorescence (SIF) product (GOSIF), Global Land Surface Satellite (GLASS), and Penman-Monteith-Leuning Version 2 (PML_V2), across five representative ecosystems in the Heihe River Basin (HRB), northwestern China using 18 eddy covariance (EC) sites during 2007–2022. Multi-scale validation revealed pronounced spatial and ecosystem-dependent differences. Although all products captured the general basin-scale gradient, an analysis of the spatial coefficient of variation (CV) revealed distinct differences in their ability to resolve spatial heterogeneity: PML_V2 and GLASS reasonably captured the observed spatial variability, whereas MOD17 and GOSIF tended to smooth over fine-scale details. Furthermore, a systematic compression of the productivity gradient was evident across products, characterized by considerable underestimation in high-productivity ecosystems (forest land and cropland) and general overestimation in grassland. Temporally, all products performed more reliably in capturing seasonal dynamics than in reproducing inter-annual variations. At the growing season scale, GLASS and GOSIF achieved the highest explanatory power (r>0.95 at several sites), whereas MOD17 exhibited the lowest error (root mean square error (RMSE)=25.26gC/m2 at the Jingyangling site (JYL)). However, inter-annual performance declined markedly, with MOD17 showing weak correlations (r<0.30) at most sites. Ecosystem-specific results identified GOSIF as superior for cropland and wetland ecosystems, while PML_V2 offered the best performance in grassland and desert ecosystems by minimizing systematic bias. Notably, all products consistently failed to establish meaningful correlations (r<0.21) with observations in desert areas due to the low ratios of signal to noise. Consequently, we recommend an ecosystem-dependent application strategy—specifically prioritizing GOSIF for cropland and wetland and PML_V2 for grassland—and urge extreme caution when applying single remote-sensing GPP products in arid desert areas.
In arid regions,saline subgrades are highly susceptible to deterioration caused by evaporation-driven water-salt migration,which can induce salt accumulation,cracking,and long-term loss of stability.To investigate the role of sand replacement layers in regulating thermo-hydro-saline(THS)migration under evaporation,we conducted indoor soil-column experiments in combination with microstructural observations.The effects of sand type(coarse,medium,and fine sand)and replacement ratio(0.00%,20.00%,35.00%,and 50.00%)were systematically examined under simulated high-temperature and strong-evaporation conditions typical of northwestern China,with continuous monitoring of temperature,relative humidity(RH),and electrical conductivity(EC).The results show that sand replacement effectively inhibited capillary rise,reduced surface salt accumulation,and alleviated shrinkage cracking.Among the tested sand types,coarse sand exhibited the strongest inhibitory effect on upward water-salt migration,whereas fine sand showed the weakest effect because its smaller pores and stronger capillary continuity facilitated upward water-salt migration.Under the medium sand condition,increasing the replacement ratio was associated with stronger suppression of surface salt accumulation,with the 50.00%replacement ratio showing the strongest effect.However,the influence of replacement ratio was not monotonic across all response indicators.A replacement ratio of approximately 35.00%maintained relatively continuous pathways for heat and moisture transfer,whereas higher replacement ratios produced a looser soil skeleton and weaker capillary continuity.Microstructural observations further revealed that salt crystals mainly accumulated near the evaporation front and the lower replenishment zone,while coarse sand tended to form larger pores and reduce matric suction,thereby disrupting upward migration pathways.These findings provide a theoretical basis and technical support for optimizing saline subgrade design and mitigating salt-related damage in arid regions.
Soil salinization is a critical factor influencing nitrogen dynamics and crop productivity in arid irrigated areas. In salt-affected areas, a significant mismatch exists between high nitrogen fertilizer input and low nitrogen use efficiency (NUE). This research quantitatively evaluates the influence of soil salinity on the fate and distribution pathways of nitrogen, as well as the total nitrogen balance and NUE in maize (Zea mays L.) fields. A two-year field experiment was conducted in the Hetao Irrigation District, located in the upper reaches of the Yellow River basin, China, to monitor nitrogen loss pathways (N2O emissions, NH3 volatilization, and nitrogen leaching) and crop physiological responses under three salinity gradients (non-saline, slightly saline, and moderately saline). The results demonstrated that salinity stress significantly intensified the loss of reactive nitrogen, increasing the cumulative N2O emissions by 55.61%–283.05% and NH3 volatilization by up to 80.61%. Notably, nitrate leaching increased by 97.44%–248.93% in slightly saline fields, posing a higher environmental risk than in moderately saline fields. The accelerated loss of reactive nitrogen, coupled with suppressed maize growth and grain yield, led to a substantial decline in the partial factor productivity of nitrogen fertilizer. Therefore, tailored water and fertilizer practices (e.g., precision irrigation, split fertilization or urease inhibitor application) are required in a salt-affected field to reduce reactive nitrogen losses and enhance NUE.
Accelerated global climate change and intensified human activities profoundly alter landscape patterns and ecosystem services (ESs), making the quantitative evaluation of their dynamic interactions essential for advancing regional sustainable development. This study focused on Qilian Mountain National Park and employed FRAGSTATS 4.2 to analyze the landscape pattern evolution from 2000 to 2020. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to assess five key ESs: water yield (WY), carbon storage (CS), water quality purification (ND), soil retention (SR), and habitat quality (HQ). Ecosystem service bundles (ESBs) were identified using a self-organizing map (SOM) approach, and nonlinear relationships between landscape pattern indices and ESs were examined using the Boosted Regression Tree (BRT) model combined with spearman correlation and clustered heatmap analyses. The results indicated that the landscape pattern of Qilian Mountain National Park exhibits a clear east to west gradient. The spatiotemporal dynamics of ESs showed divergent trends, with CS and ND consistently improving, whereas WY exhibited pronounced nonlinear fluctuations. ESBs were classified into four types: ESB I (ecosystem transition bundle), ESB II (ecosystem regulation and protection bundle), ESB III (ecosystem degradation and protection bundle), and ESB IV (ecosystem restoration bundle), reflecting a shift from single function dominance toward multifunctional synergies. A nonlinear coupling relationship existed between landscape pattern indices and total ecosystem services (TES), characterized by a notable decline in TES and continued degradation of ES performance and stability. Together, this study provides a robust scientific foundation for developing differentiated zoning management strategies. The findings deliver valuable scientific insights for the management of ESs in the Qilian Mountain National Park and similar mountain ecosystems, while offering a reference for promoting sustainable development in fragile ecological regions worldwide.
Understanding the drivers of gross primary production(GPP)is essential for assessing vegetation productivity dynamics under climate change,particularly across regions with strong climatic heterogeneity.China spans diverse climate zones and ecosystems,yet the relative importance of climatic,environmental,and anthropogenic factors regulating GPP has remained poorly resolved.In this study,we investigated the spatiotemporal patterns of GPP across China from 2001 to 2020 and quantified the contributions of multiple driving factors across different climate zones.We combined ridge regression with an interpretable machine learning framework based on Extreme Gradient Boosting(XGBoost)and SHapley Additive exPlanations(SHAP)to disentangle the long-term linear controls on and short-term nonlinear responses driving GPP.Ridge regression was employed to address multicollinearity among predictors and to quantify their interannual contributions,while SHAP analysis was used to quantify feature contributions in nonlinear model predictions.Our results indicated that leaf area index(LAI)and human footprint dominated the long-term variability of GPP in most climate zones,whereas temperature and solar radiation exerted stronger influences on instantaneous GPP responses.The relative importance of drivers varied markedly among climate zones,reflecting region-specific climatic constraints and vegetation physiological characteristics.In addition,the contribution of atmospheric CO2 to GPP variability was notably limited in the alpine climate zone and showed a declining fertilization effect nationally,suggesting increasing constraints imposed by water availability and nutrient limitations.By integrating linear attribution and nonlinear interpretability,this study provides a comprehensive assessment of the controls on GPP dynamics across China and highlights the importance of accounting for climatic heterogeneity and temporal scales when evaluating vegetation productivity responses to environmental change.
The exploration of sustainable water resource development is pivotal for ensuring regional economic and social advancement,as well as maintaining ecological balance.However,scant research has holistically evaluated ecological,living,and production water uses through the"community of life"lens.This study developed a sustainable water resource utilization(SWRU)evaluation index system for water resources in the Gansu region of Qilian Mountains from 2000 to 2023.We adopted the"three-life"water use approach within the"community of life"and conducted a complete evaluation of the existing state of SWRU in the study area.We further developed a simulation model using system dynamics(SD)approaches for status quo,economic,and comprehensive multi-scenario forecasting,and employed the obstacle degree model to determine the parameters influencing SWRU.The findings revealed that the SWRU in the Gansu section of Qilian Mountains has advanced from a basic phase(0.438)to a commendable level(0.614),with a spatial distribution of"high in the west and low in the east".The SD simulation results indicated that the comprehensive scenario achieves the highest SWRU value(0.638),outperforming the economic(0.636)and status quo(0.630)scenarios.In the short term,a comprehensive scenario can support regional sustainable development;however,it has the potential to exacerbate the supply-demand conflict in the long term,necessitating additional refinement of the water resource allocation system.Proportion of ecological water consumption(obstacle degree of 14.388%),gross domestic product(GDP;12.475%),and total water resources(12.019%)have been highlighted as the primary obstacle factors on SWRU.Future strategies should focus on optimizing resource allocation to provide a high-quality ecological product supply,as well as merging ecological preservation with industrial advancement to support the long-term synergistic development of the living community.
The Shaanxi-Gansu-Ningxia (SGN) border region, as a typical transitional zone between the East Asian monsoon and the westerlies, features a fragile ecological environment. In this study, the spatiotemporal evolution and multiscale driving mechanisms of surface winds in this ecologically fragile transition zone were investigated by integrating trend analysis, empirical orthogonal function (EOF) decomposition, and geographic detector method. The results indicated that from 1980 to 2022, the annual mean surface wind speeds in the SGN border region significantly increased, with a linear growth rate of 0.003m/(s•a). However, the anomaly series revealed a clear interdecadal transition: surface wind speed anomalies were predominantly negative from 1980 to 1999 and shifted to persistently positive and increasing anomalies after 2000. Consistent strengthening was observed in summer, autumn, and winter, with the most pronounced increase occurring in autumn. The spatial distribution generally followed a pattern of higher values in the northwest and lower values in the southeast. Spring presented the strongest surface wind speeds and the most extensive areas with high values. EOF analysis revealed two dominant spatial modes: the first mode (variance contribution>73.40%) reflected regionally consistent changes, and its temporal coefficients increased continuously, corresponding to the overall strengthening trend of surface wind speed; and the second mode exhibited an east‑west dipole oscillation pattern, dominated by interannual fluctuations. The geographic detector results revealed that fractional vegetation cover (FVC), temperature, and topographic elevation were key factors influencing the spatial differentiation of surface wind speeds, with all the factors exhibiting enhanced interactive effects—especially the synergistic effect between vegetation cover and temperature. Background circulation analysis indicated that enhanced westerlies and decreased geopotential height in the mid‑ to upper‑troposphere provided favourable dynamic conditions for increased surface wind speeds. This study advances the understanding of surface wind speed changes in climate transition zones, providing a scientific basis for regional wind energy planning, ecological protection, and wind erosion control.
Desert steppe ecosystems are highly sensitive to variations in water and nitrogen (N) levels. Soil microarthropods serve as crucial indicators of belowground ecological processes, yet their responses to long-term water–N interactions remain unclear. This study investigated the combined effects of long-term N deposition and rainfall variation on the microarthropod community in the desert steppe soil, as well as their potential driving mechanisms. Utilizing a field control experimental platform for global change in the desert steppe of Inner Mongolia Autonomous Region, China, researchers had established a multigradient two-factor (water-N) experiment since 2015. The experiment employed a split-plot design with three water levels (natural rainfall (NR), 30.00% rainfall enhancement (RE), and 30.00% rainfall reduction (RR)) and four N addition levels (0 (N0), 30 (N30), 50 (N50), and 100 (N100) kg N/(hm2•a)), resulting in 12 treatment combinations. After the experimental treatments had been conducted for 5 a and treatment effects had reached a long-term steady state, we collected the soil samples to analyze the variations of soil microarthropod communities. The results revealed that at varying water conditions, N addition increased the abundance, number of taxa, and diversity of soil microarthropods. In the RE treatment, the total abundance and total number of taxa of soil microarthropods were significantly greater than those in the NR and RR treatments. Water-N interactions had a significant effect on soil microarthropod community structure, with the N30 treatment coupled with water variation having the strongest effect. Moreover, the influence of N addition on soil microarthropod communities depended on water changes; both the RR and RE treatments amplified the effect of N addition, with the RR treatment resulting in the greatest amplification. N deposition and changes in rainfall shape the soil microarthropod community by altering key environmental factors. N addition and water variation positively affect the abundance of soil microarthropods by increasing the ammonium nitrogen (NH4+-N) content, litter fall (LF), and soil moisture (SM) content. The interaction between water and N primarily promotes soil microarthropod abundance by reducing the NH4+-N content and increasing the biomass of perennial grass. In summary, this study not only reveals the key pathways through which water and N drive changes in the soil microarthropod community in desert steppes but also provides a scientific basis for understanding soil biodiversity maintenance and ecosystem management in arid regions under global change.
Sustaining surface water in arid mountain-valley corridors has become increasingly difficult under a warming-wetting climate and urbanization.Focusing on the Huangshui River Basin in the Xining-Haidong Corridor of China,this study used Google Earth Engine(GEE)to process Landsat 5/7/8/9 surface reflectance imagery to reconstruct open-water dynamics within the riparian corridor.A three-expert ensemble voting framework integrated spectral water indices,Dynamic Surface Water Extent rules,and a Random Forest classifier,and the extraction results were validated using 1200 manually interpreted points.Trend analysis,Random Forest attribution,and GeoDetector were then applied to assess temporal changes and the combined effects of climate,topography,and human activity.The extraction achieved an overall accuracy of 96.17%and a Kappa coefficient of 0.923.The mapped open-water area increased from 74.33 km2 in 2000 to 121.67 km2 in 2024,corresponding to a net gain of 47.34 km2(63.68%).The Mann-Kendall test indicated a significant upward trend(Z=6.66;P<0.001),with a Theil-Sen slope of 1.45 km2/a.Sequential Mann-Kendall analysis identified no robust year of abrupt change,although the increasing trend became significant after 2007.Attribution results showed that topography remained the dominant spatial regulator of water persistence,as low-elevation valley floors concentrate both runoff accumulation and human land use.Population density(PD)and nighttime light(NTL)signals were stronger in urbanized reaches,where high impervious-surface values and mapped water co-occurred around managed water environments,including regulated channels,impoundments,and reservoir storage.Overlay analysis of barriers and reservoirs further suggested that engineering regulation may account for part of the persistent water patches along the corridor.These findings reveal a coupled mechanism involving climate,topography,and human regulation in shaping surface water change in a water-limited plateau river corridor,and provide evidence for water-resource management and ecological restoration in the upper Yellow River Basin.
The frequent conversion between cropland and grassland in agropastoral ecotones poses severe challenges to the protection of grassland biodiversity, and a systematic understanding of the relationship between these two aspects is urgently needed. In this study, the West Liaohe River Basin, which is a typical agropastoral ecotone in northern China, was chosen as an example. Using the field investigation data from 2023 and 2024, we calculated various biodiversity indices at both α and β scales for grassland vegetation and soil bacteria, and then analyzed the effects of the interactions at the cropland–grassland interface on grassland above-ground biodiversity, below-ground biodiversity, and their interrelationships. Moreover, we explored the driving factors of grassland biodiversity at the cropland–grassland interface. Notably, interactions at the cropland–grassland interface adversely affected grassland above- and below-ground biodiversity. Compared with the sampling points that were farther from the cropland–grassland interface (25 and 50m), the sampling points located very close to the interface (5 and 10m) had a decrease in species richness of more than 5.00%. This effect was jointly determined by various vegetation and soil attribute indicators and the regional environment. The litter and soil organic carbon played a prominent role in modulating the relationships between grassland above- and below-ground biodiversity at the cropland–grassland interface. The results suggested that intensive management of cropland and grassland should be enhanced in areas where agriculture and animal husbandry alternate, the disorderly reclamation and random abandonment of cropland should be prohibited, and the policy of returning cropland to grassland should be promoted systematically. These findings could provide reference data for related studies and promote the protection of grassland biodiversity in agropastoral ecotones.
