Snow leopards (Panthera uncia) are a flagship species for global biodiversity conservation, and their effective protection relies on accurate habitat assessment. This study focused on the Chengdu section of the Giant Panda National Park (Pengzhou, Dujiangyan, Chongzhou, Dayi), integrating terrain, climate, vegetation and human disturbance factors. Using the MaxEnt model (AUC = 0.943) and field infrared camera data, we evaluated snow leopard habitat quality. Results showed that: (1) 95.7% of snow leopard records were concentrated in Dayi County; (2) Key drivers included annual mean temperature (peak at −2 °C), annual mean ground temperature (peak at −1 °C) and human population density (>5 km), while NDVI (≈2000) had a significant negative effect; (3) Suitable habitat was 320.98 km² (22.20%), decreasing from Qionglai Mountain to Minshan. This study fills regional survey gaps and provides a scientific basis for snow leopard conservation.

Forest types vary in root exudation, litter quality, microclimate, and soil properties, but their impact on microbial community assembly, especially when mixed with elevation, is unclear. Using high-throughput sequencing, phylogenetic null-model analysis, and co-occurrence network inference, we profiled topsoil microbiomes in four forest types, including coniferous, deciduous, mixed and plantation, in a subtropical mountain. Soil bacterial alpha diversity varied significantly by forest type, being lower in coniferous stands, while fungal diversity showed limited variation. Soil bacterial communities were more distinct across forest types than fungal ones. Co-occurrence network analysis revealed plantation-specific bacterial over-connectivity and deciduous-specific fungal fragmentation. Bacterial assembly varied by forest type, with heterogeneous selection in deciduous forests, homogenizing dispersal in plantations, and mixed processes in coniferous and mixed forests. Fungal assembly was marked by homogenizing selection and dispersal limitation. Soil bacterial structure was linked to phosphorus, moisture, and pH, while fungal differentiation was tied to C:N ratios and spatial factors. Because the results derive from a single sampling and forest type was confounded with elevation, controlled studies are needed to separate vegetation effects from climate and soil influences and to confirm the potential linkages among taxa.

Soil nematodes are integral to soil micro-food webs and serve as sensitive bioindicators of soil ecological condition. However, how forest vegetation and soil properties interact to shape nematode community assembly, network structure, and functional stability remains inadequately understood. Using 18S rRNA gene amplicon sequencing coupled with phylogenetic null modeling, we examined soil nematode communities across four forest types along a succession gradient. Although taxonomic diversity (e.g., Shannon and Pielou indices) differed significantly among forest types, network topology and stochastic assembly processes were more closely associated with community restructuring and co-occurrence patterns. This suggests that, while diversity is not irrelevant, network- and assembly-based metrics provide complementary and often more sensitive indicators of nematode community responses to forest type. Co-occurrence network analysis revealed that mixed forests fostered more complex and potentially stable networks, whereas plantations formed dense but potentially vulnerable networks. Assembly processes were not dominated by strong deterministic selection (|βNTI| ≤ 2 for most comparisons), a pattern consistent with undominated processes (e.g., ecological drift, weak environmental filtering). Dispersal limitation played a negligible role in this system. Partial Least Square Path Modeling identified spatial factors and key soil properties (e.g., pH, electrical conductivity, soil water content, and organic carbon) as primary drivers of community structure. Our findings indicate that assessing soil food web health should integrate network analysis and stochasticity metrics rather than rely solely on taxonomic diversity. For sustainable forest management, mixed-species stands are preferable to monoculture plantations, and maintaining soil physicochemical heterogeneity is critical for community stability.