The study used camera traps (2946 trap days, 60 sites) to investigate the diversity, habitat use, and activity rhythms of six sympatric ungulates in a montane ecosystem of southwestern China: tufted deer (Elaphodus cephalophus), Chinese goral (Naemorhedus caudatus), Chinese serow (Naemorhedus griseus), sambar (Rusa unicolor), wild boar (Sus scrofa), and blue sheep (Pseudois nayaur). Relative abundance indices indicated that sambar were most frequent, while blue sheep and Chinese goral were least common. Species showed distinct elevational, slope, and vegetation preferences, suggesting spatial niche segregation. Kernel density estimates revealed predominantly diurnal activity, with bimodal patterns for tufted deer, sambar, and Chinese goral, and unimodal peaks for blue sheep, wild boar, and Chinese serow. Temporal overlap was highest between sambar and tufted deer, and lowest between tufted deer and blue sheep. These results demonstrate spatial and temporal partitioning as key mechanisms enabling ungulate coexistence and underscore the importance of conserving heterogeneous montane habitats.

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival.

Understanding how climate and land-use change influence habitat suitability is essential for the conservation of the François’ langur (Trachypithecus francoisi). In this study, climatic, land-use, and topographic variables were integrated to model the current distribution and future dynamics of suitable T. francoisi habitats in southwestern China. The model performed well, climatic factors were the primary determinants of distribution, particularly precipitation of the driest month (BIO14), mean diurnal temperature range (BIO2), and precipitation seasonality (BIO15); additionally, forest cover, slope, and elevation further improved model performance. Suitable habitat currently covers 53,109 km² (10.75% of the study area) and is mainly concentrated in Chongqing and Guizhou, with smaller areas in Guangxi. Future projections indicate substantial habitat redistribution and an overall decline in suitable area under both scenarios. By the 2050s and 2070s, suitable habitats will show strong spatial turnover, with coexistence of retained, newly suitable, and lost areas. Suitable habitat is projected to shift toward northern areas. These results suggest that conservation priorities should shift focus northward under climate warming, with emphasis on protecting mountainous refuges and improving habitat connectivity.

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management.