Ecosystem services (ESs) serve as a fundamental cornerstone for upholding global biodiversity and promoting human well-being. ESs trade-off and synergy are supposed to be significantly affected by climate change (CC) and land use/cover change (LULC). However, the limited availability of finely classified future land-use data and integrated landscape change models incorporating climate change scenarios has hindered our understanding of the trade-off and synergistic patterns and controls of ESs at multiple scales, particularly in arid areas. Here, a future multi-scenario ESs trade-off/collaborative assessment framework (SD-PLUS-InVEST model) for multi-scale conversion and refined land-use classification was developed by coupling the patch-generated land-use simulation (PLUS) model, system dynamics (SD) model, InVEST model, geographically weighted regression (GWR) model, optimal parameter geographical detector (OPGD) model, and structural equation model (SEM). The four ESs, namely carbon storage (CS), habitat quality (HQ), water conservation (WC), and soil conservation (SC), were assessed. Further, multi-scale ESs were evaluated under different climate change and development scenarios (i.e., the SSP1-2.6 and ecological protection scenario, SSP1-2.6-EP; SSP2-4.5 and natural development scenario, SSP2-4.5-ND; SSP5-8.5 and economic growth scenario, SSP5-8.5-EG). The results demonstrated that the arid region of northwest China (ANWC) was experiencing a significant and continuous warming trend accompanied by increased humidity. There will be a significant decrease in the areas occupied by paddy fields, natural forests, and permanent glaciers among the 24 LULC types. Conversely, there will be a substantial increase in dry land, high-coverage grassland, and urban construction land areas. According to the SSP1-2.6-EP, SSP2-4.5-ND, and SSP5-8.5-EG scenarios, the comprehensive land-use dynamic degrees were estimated to reach 2.58%, 4.08%, and 4.74%, respectively. The LULC resulting from CC exacerbates the differences in the four ESs of ANWC. In particular, CS and HQ experience significant reductions in 2100. Conversely, WC and SC show notable increases during the same period. The changes in CS, HQ, WC, and SC reach 11.36 × 10⁸ m³, 1735.25 × 10⁸ t, −1.29 × 10⁸ t, and −0.009, respectively. The four ESs of CS, HQ, WC, and SC in ANWC display a synergistic relationship. This synergy is influenced by the heterogeneous spatial distribution of CS, HQ, WC, and SC, with the strongest synergy observed between CS and HQ and the weakest between CS and WC. Interestingly, the distribution differences in ESs synergy were amplified at watershed, county, and grid scales in mountainous areas, with the most significant detection differentiation occurring at the grid scale. Furthermore, the detection of spatial heterogeneity in the four ESs can be attributed to various factors. These factors include the drought index (q = 0.378), annual average precipitation (q = 0.375), economic density (q = 0.095), vegetation coverage (q = 0.262), and soil bulk density (q = 0.077). Our results highlight the importance of CC in influencing ESs. The spatial variations in ESs trade-offs and coordination at different scales, particularly the pronounced differences observed in mountainous areas, underscore the need to prioritize the conservation of arid mountainous regions in terms of future policy making.

Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R² value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R² = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m⁻²), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m⁻¹) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m⁻², which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.

Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m^–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO₂ emissions in the Qilian Mountains.

In response to the significant ecological damage caused by unsustainable mining practices in the Qilian Mountains, ecological restoration projects have been undertaken in recent years. Analyzing the changes in soil microbial communities during the restoration process of mine meadows helps to reveal the mechanism of the restoration process in alpine mining areas. To explore the characteristics of soil microbial community distribution and their relationships with soil environmental factors during the restoration of alpine grasslands in the Qilian Mountains, we conducted surveys and analyses in two restoration levels low restoration (LR) and high restoration (HR) in the eastern Qilian Mountains, along with an undisturbed natural grassland control (NG). We found that as the degree of high-altitude mining area recovery increases, there were significant increases in vegetation cover, vegetation height, above-ground biomass, vegetation Shannon–Wiener index, soil organic carbon (SOC), soil water content (SWC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available nitrogen (AN). Conversely, soil pH and electrical conductivity (EC) significantly decreased, with soil pH decreasing from 6.93 to 4.13. Restoration of high-altitude mining area grasslands significantly alters the distribution and composition of soil bacteria and fungi, while the impact on soil microbial community changes was not significant. Notably, with increasing recovery level, the dominant bacterial phyla are Acidobacteria and Proteobacteria, while the dominant fungal phyla are Ascomycota and Basidiomycota. These results indicate that changes in vegetation and soil properties both affect the composition of soil microbial communities, with soil properties having a greater influence. Soil fertility and nutrient levels emerge as the primary drivers influencing soil microbial composition communities and the degree of high-altitude mining area grassland recovery.

Understanding the relationships between nutrient content in plant roots and ecological stoichiometry is crucial for elucidating nutrient utilization strategies and material cycling in alpine plant communities. However, data characterizing the stoichiometric characteristics of plant roots in this region remain limited. In this study, we collected fine-root and soil samples from five common alpine shrub species—Salix gilashanica, Potentilla fruticosa, Caragana jubata, Caragana tangutica, and Berberis diaphana—to investigate the carbon (C), nitrogen (N), and phosphorus (P) stoichiometric characteristics of their fine roots and examine the potential nutrient control strategies based on the soil properties. Our analysis revealed that the mean C (541.38 g kg⁻¹) and P (1.10 g kg⁻¹) contents in the shrub fine roots exceeded the average levels of the plant roots in China. However, the mean N content (8.61 g kg⁻¹) was lower than the global average. Notably, the mean C:N ratio (71.3) in these fine roots was significantly higher than the global average, whereas both the mean C:P ratio (527.61) and N:P ratio (8.11) were considerably lower. The N:P ratios in the fine roots of the five shrub species were below 14, indicating nitrogen limitation for growth in the degraded alpine shrub communities. Our findings indicate that soil available phosphorus (33.2%) and pH (20.5%) are the primary factors influencing the eco-stoichiometric characteristics of shrub fine roots in the Qilian Mountains. These findings provide valuable data and theoretical support for a better understanding of the role of shrub roots in nutrient cycling within alpine ecosystems.