Introduction: Under global climate change and intensified human activities, species distributions are undergoing significant shifts. Marmota baibacina , a representative keystone species among Central Asian high-altitude species, exacerbates vegetation degradation and soil erosion through herbivory and burrowing activities. As the primary reservoir of Yersinia pestis, it poses a significant public health threat. Methods: This study integrated five machine learning models (XGBoost, RF, SVM, LogBoost) and the MaxEnt model to predict the current (1970–2000) and future (2041–2100) distribution of Marmota baibacina under three climate scenarios (SSP126, SSP370, SSP585), utilizing 111 occurrence records and 29 environmental variables spanning climatic, topographic, edaphic, and vegetation dimensions. Results: The results indicated that (1) All five models demonstrated high predictive accuracy with AUC values exceeding 0.9. After screening 29 environmental variables, machine learning models identified 10 key variables with high feature importance, while MaxEnt selected 16 environmental variables; (2) Dominant drivers revealed that Bio18 (warmest quarter precipitation), Bio2 (diurnal temperature range), Bio11 (coldest quarter temperature), and Bio15 (precipitation seasonality) collectively contributed >70% to machine learning models, whereas MaxEnt prioritized slope, NDVI, and Bio18; (3) Under current climatic conditions, the potential suitable habitats of Marmota baibacina in Xinjiang are primarily concentrated in the central Tianshan Mountains, with core distribution centers in Bayingolin Mongolian Autonomous Prefecture (Hejing County), Ili Kazakh Autonomous Prefecture, and the western part of Bortala Mongolian Autonomous Prefecture, The total suitable habitat area estimated by the five models ranged from 2.75 × 10 ⁴ km² to 13.59 × 10 ⁴ km² under the current climate; (4) Future projections under all scenarios indicated an overall decreasing trend in suitable habitat area, with habitat contraction particularly pronounced in the southern Tianshan under SSP585. Discussion: Such distributional shifts may intensify competition between marmots and livestock, accelerate alpine meadow degradation, and elevate zoonotic plague transmission risks due to population aggregation. This study provides critical insights for balancing alpine ecosystem conservation and plague prevention strategies, offering actionable guidance for safeguarding ecological security and public health in Xinjiang’s ethnically diverse pastoral regions.

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R² and RMSE across all grassland types. For instance, the RF model achieved an R² of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R² of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management.

The rapid spread of invasive plants presents significant challenges for the management of grasslands. Uncrewed aerial vehicles (UAVs) offer a promising solution for fast and efficient monitoring, although the optimal methodologies require further refinement. The objective of this research was to establish a rapid, repeatable, and cost-effective computer-assisted method for extracting Pedicularis kansuensis (P. kansuensis), an invasive plant species. To achieve this goal, an investigation was conducted into how different backgrounds (swamp meadow, alpine steppe, land cover) impact the detection of plant invaders in the Bayanbuluk grassland in Xinjiang using Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) with three feature combinations: spectral band, vegetation index (VI), and spectral band + VI. The results indicate that all three feature combinations achieved an overall accuracy ranging from 0.77 to 0.95. Among the three models, XGBoost demonstrates the highest accuracy, followed by Random Forest (RF), while Support Vector Machine (SVM) exhibits the lowest accuracy. The most significant feature bands for the three field plots, as well as the invasive species and land cover, were concentrated at 750 nm, 550 nm, and 660 nm. It was found that the green band proved to be the most influential for improving invasive plant extraction while the red edge 750 nm band ranked highest for overall classification accuracy among these feature combinations. The results demonstrate that P. kansuensis is highly distinguishable from co-occurring native grass species, with accuracies ranging from 0.9 to 1, except for SVM with six spectral bands, indicating high spectral variability between its flowers and those of co-occurring native background species.

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions.

Examining the long-term spatiotemporal distribution of grassland types and their transitions is crucial for better understanding regional and global changes. Most research in this field has examined the spatial distribution, temporal dynamics of grasslands, and their causes as a unified entity. This study predicted the distribution of nine major grassland types in Xinjiang under three climate change scenarios from 2041 to 2100 based on 1980s grassland maps, field data in 2023, and 28 factors. The total area of the nine grassland types showed a decreasing trend from 2041 to 2100. The lowland meadow (LM), temperate meadow steppe (TMS), temperate steppe desert (TSD), temperate desert steppe (TDS), and mountain meadow (MM) expanded, while significant declines occurred in alpine meadow (AM), alpine steppe (AS), temperate desert (TD), and temperate steppe (TS). Among cumulative contribution rate of the 28 factors examined in this study, NDVI, vegetation type, slope, elevation, soil_symbol, soil_ph, Bio1, Bio5, Bio8, Bio9, Bio10, Bio12, Bio13, Bio15, and Bio18 played important roles in most grassland types. LM, TD, and AS grassland were found to be more sensitive to E (environment), while AM, TDS, and TSD were more influenced by T (temperature). The distributions of MM and TMS are significantly influenced by the combined effects of all three categories of factors. For TS, the impacts of both temperature and environmental factors are substantial. These findings provided a robust foundation for conservation planning and the sustainable management of grassland ecosystems in temperate and alpine regions.