The Qinghai-Tibet Plateau (QTP), also known as the Earth’s “third pole,” is sensitive to climate change due to its extensive areas at high elevation, which are presently dominated by snow and ice. Based on daily observations from 1960 to 2016 at 94 meteorological stations, we quantified and compared changes in climate extremes on China’s QTP using 16 extreme temperature indices and 11 extreme precipitation indices, which calculated using colder (1961–1990) and warmer (1988–2016) base periods, respectively. The study showed that when a warmer base period is used, the trend magnitude of the cold temperature indices is amplified, while the trend magnitude of the warm temperature indices and the percentile-based precipitation indices are diminished. The regionally averaged temperature index trends were consistent with global warming, namely warmth indices such as the number of warm days, warm nights, summer days, and tropical nights all showed significant increases. However, the cold indices, such as the number of cool days, cool nights, ice days, and frost days, decreased significantly. The number of frost days and ice days decreased the fastest (3.9 and 2.9 days/decade, respectively), while the length of the growing season increased the fastest (2.9 days/decade). The warming trend strengthened with increasing latitude, and the occurrences of daytime extreme temperature events increased with increasing longitude. In addition, cold temperature events increased in frequency and intensity at high elevations, while warm temperature events decreased. We also found that average monthly maximum 1-day precipitation and maximum consecutive 5-day precipitation increased by 0.1 and 0.3 mm per decade, respectively. Extreme precipitation occurrences were more common in places with low latitudes and high longitudes. The strengthening Subtropical High Area (SHA) and Subtropical High Intensity (SHI), Westward movement of Subtropical High Western Ridge Point (SHW) have contributed to the changes in climate extremes on the Qinghai-Tibet Plateau. The study’s findings will provide a more comprehensive reference for predicting the occurrence of extreme events and improving the region’s disaster prevention capability.

Soil extracellular enzyme activity (EEA) and enzyme stoichiometry are indicators of soil nutrient availability and microbial substrate limitation. However, the patterns in soil enzyme activities and stoichiometry, and their key drivers remain unclear during vegetation restoration in degraded sandy land. Here, soil ecosystems at five restoration stages of degraded sandy land were selected for investigation and we quantified the microbial nutrient limitation using EEA stoichiometry. The average C:N:P ratio for the nutrient-acquisition enzymes was 1:0.88:0.86, which differed from the global ratio of 1:1:1, indicating stronger microbial C limitation than N or P limitation. With vegetation restoration, the enzyme N:P ratio decreased, but the angle term used in vector analysis increased, indicating that the restored grassland transitioned from N-restricted (angle < 45°) to P-restricted (angle > 45°). Plant C inputs increased the soil nutrient content and significantly decreased the microbial C and N limitation but increased P limitation due to nutrient competition between plants and soil microorganisms. Decreased soil water levels caused by vegetation transpiration may have shifted the microbial limitation from N to P. The decreased fungi:bacteria ratio strengthened the microbial C and N or P limitations. On the basis of these findings, as measures to alleviate the associated nutrient limitations, we would recommend supplementation with phosphorus in the potential and slight stages of rocky desertification and the supplementary application of nitrogen in the moderate and severe stages during the restoration of degraded sandy ecosystems.

The North China agro–pastoral zone is a large, ecologically fragile zone in the arid and semi-arid regions. Quantitative remote sensing inversion of soil organic carbon (SOC) in this region can facilitate understanding of the current status of degraded land restoration and provide data support for carbon cycling research in the region. Deep learning (DNN) for SOC inversion has been W.a hot topic over the past decade, but there have been few studies at the regional scale in the arid and semi-arid zones. In this study, a DNN model with five hidden layers and five skip connections was established using 644 spatially distributed SOC samples and Landsat 8 OLI imagery. The model was compared with the random forest algorithm in terms of generalization ability. The main conclusions were as follows: 1. The DNN algorithm can establish a high-precision SOC inversion model (R² = 0.52, RMSE = 0.7), with 90% of errors concentrated in the range of −2.5 to 2.5 kg·C/m²; 2. the Boruta variable-screening algorithm can effectively improve the model accuracy of the random forest algorithm, but due to the DNN’s better ability to mine hidden information in the data, the improvement effect on the DNN model accuracy is limited; 3. the SOC samples in arid and semi-arid areas are highly positively skewed, with a significant impact on the modeling accuracy of DNN, and conversion is required to obtain a model with better generalization ability; and 4. in arid and semi-arid regions, SOC has a weak correlation with vegetation indices but a stronger correlation with temperature, elevation, and aridity. This study established a reliable deep learning model for SOC density in a large arid and semi-arid region, providing a reference and framework for the establishment of SOC inversion models in other regions.

A critical issue in microbial ecology is quantifying the relative contributions of deterministic and stochastic processes to microbial community assembly, and predicting ecosystem function by understanding the ecological processes of community composition is an integral part. However, the mechanisms driving microbial community assembly along altitudinal gradients in mountain ecosystems remain largely unexplored. Here, we used high-throughput sequencing to examine the structural characteristics and diversity maintenance mechanisms of soil bacterial and fungal communities along an altitudinal gradient (2632–3661 m) in Mahan Mountain, the highest peak of the Loess Plateau. Proteobacteria, Acidobacteriota and Actinobacteriota dominated the bacterial communities, while Ascomycota, Basidiomycota and Mortierellomycota were the predominant fungal groups. Although elevation did not significantly affect bacterial and fungal alpha diversity, notable shifts in community structure were observed along the altitudinal gradients. Bacterial communities were predominantly shaped by deterministic processes, leading to pronounced structural and compositional differentiation across altitudes. In contrast, fungal community assembly was primarily determined by a combination of deterministic and stochastic processes, leading to small pronounced structural divergence. The interplay of topography, climate, and soil conditions influenced the altitudinal distribution and community structure of soil bacteria in this mountain ecosystem.

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M − K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69 % of vegetation will degrade, that is, NDVI will degrade instead of improvement. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had >70 % explanatory power (73.5, 79.9, 79.0, and 74.9 %, respectively). The explanatory power of edaphic factors was >30 %, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of <30 %. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.