Although the importance of the soil bacterial community for ecosystem functions has long been recognized, there is still a limited understanding of the associations between its community composition, structure, co-occurrence patterns, and soil physicochemical properties. The objectives of the present study were to explore the association between soil physicochemical properties and the composition, diversity, co-occurrence network topological features, and assembly mechanisms of the soil bacterial community. Four typical forest types from Liziping Nature Reserve, representing evergreen coniferous forest, deciduous coniferous forest, mixed conifer-broadleaf forest, and its secondary forest, were selected for this study. The soil bacterial community was analyzed using Illumina MiSeq sequencing of 16S rRNA genes. Nonmetric multidimensional scaling was used to illustrate the clustering of different samples based on Bray–Curtis distances. The associations between soil physicochemical properties and bacterial community structure were analyzed using the Mantel test. The interactions among bacterial taxa were visualized with a co-occurrence network, and the community assembly processes were quantified using the Beta Nearest Taxon Index (Beta-NTI). The dominant bacterial phyla across all forest soils were Proteobacteria (45.17%), Acidobacteria (21.73%), Actinobacteria (8.75%), and Chloroflexi (5.06%). Chao1 estimator of richness, observed ASVs, faith-phylogenetic diversity (faith-PD) index, and community composition were distinguishing features of the examined four forest types. The first two principal components of redundancy analysis explained 41.33% of the variation in the soil bacterial community, with total soil organic carbon, soil moisture, pH, total nitrogen, carbon/nitrogen (C/N), carbon/phosphorous (C/P), and nitrogen/phosphorous (N/P) being the main soil physicochemical properties shaping soil bacterial communities. The co-occurrence network structure in the mixed forest was more complex compared to that in pure forests. The Beta-NTI indicated that the bacterial community assembly of the four examined forest types was collaboratively influenced by deterministic and stochastic ecological processes.

The Chinese red panda (Ailurus styani) is a rare and endangered animal in China; the increase in global temperature and the interference of human activities have caused irreversible effects on the suitable habitat of wild red pandas and threatened their survival. Therefore, it is necessary to carry out scientific research and protection for Chinese red pandas. In this study, the MaxEnt model was used to predict and analyze the suitable habitats of Chinese red pandas in the large and small Xiangling Mountains. The results showed that the main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Daxiangling Mountains are the average slope (45.6%, slope), the distance from the main road (24.2%, road), and the average temperature in the coldest quarter (11%, bio11). The main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Xiaoxiangling Mountains are bamboo distribution (67.4%, bamboo), annual temperature range (20.7%, bio7), and the average intensity of human activities (8.7%, Human Footprint). The predicted suitable habitat area of the Daxiangling Mountains is 123.835 km², and the predicted suitable habitat area of the Xiaoxiangling Mountains is 341.873 km². The predicted suitable habitat area of the Daxiangling Mountains accounts for 43.45% of the total mountain area, and the predicted suitable habitat area of the Xiaoxiangling Mountains accounts for 71.38%. The suitable habitat area of the Xiaoxiangling Mountains is nearly three times that of the Daxiangling Mountains, and the proportion of suitable habitat area of the Xiaoxiangling Mountains is much higher than that of the Daxiangling Mountains. The suitable habitat of Chinese red pandas in the Daxiangling Mountains is mainly distributed in the southeast, and the habitat is coherent but fragmented. The suitable habitat of Chinese red panda in Xiaoxiangling Mountains is mainly distributed in the east, and the habitat is more coherent. The results of this study can provide a scientific basis for the protection of the population and habitat of Chinese red pandas in Sichuan.

Antibiotic resistance has emerged as a critical global public health challenge. In this study, we employed metagenomic sequencing to analyze fecal samples from giant pandas (Ailuropoda melanoleuca) across three distinct stages—semi-wild, released, and wild populations—to investigate shifts in antibiotic resistance genes (ARGs) and virulence factors (VFs) during the reintroduction process. Our findings revealed significant variations in the composition of ARG and VF across different stages, with released and wild giant pandas exhibiting similar ARG and VF profiles. Further analyses identified that the increased abundance of ARGs and VFs in both released and wild individuals compared to semi-wild individuals was mainly from Pseudomonas. We hypothesized that the same geographic environment in which ARGs and VFs are transmitted between a host and the environment via mobile genetic elements (MGEs) may be responsible for the similar structure of ARGs and VFs in released and wild giant pandas. Additionally, diet may modulate the gut microbial community, thereby influencing the distributions of ARG and VF. This study elucidated the impact of geographic and dietary factors on ARGs and VFs dynamics in giant pandas, offering valuable insights for mitigating antibiotic resistance and virulence gene dissemination.

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.

Sympatric species may share a landscape while differing in their environmental associations and daily activity. We examined these dimensions in golden snub-nosed monkeys (Rhinopithecus roxellana) and Tibetan macaques (Macaca thibetana) using 110 field-informed analytical units derived from 118 physical camera stations in Lushan County, Sichuan Province, China. Across 35,529 effective camera-days and an analytical-unit elevational range of 1395–2771 m, we recorded 20 independent unit-level camera-detection events of golden snub-nosed monkeys at 12 units and 1681 events of Tibetan macaques at 94 units. Ten golden snub-nosed monkey-positive units also recorded Tibetan macaques, but overall unit-level similarity was low because Tibetan macaque detections were more widespread. After adjustment for camera-days, golden snub-nosed monkey unit-level detection was positively associated with elevation (OR = 2.007, 95% CI = 1.051–3.833), whereas Tibetan macaques showed a negative estimated association with an interval including one (OR = 0.516, 95% CI = 0.256–1.041). Both species were predominantly diurnal and showed substantial diel activity overlap (Δ₁ = 0.908, 95% CI = 0.696–0.923). Frequent unit-level co-use and substantial diel overlap therefore coexisted with contrasting estimated elevational associations, indicating that shared detections in one ecological dimension need not imply similar patterns in another.

Human activities, together with associated livestock and domestic dog activity, can alter resource use and behavioural decisions in small- and medium-sized carnivores. However, long-term evidence at a common landscape scale remains limited regarding whether different disturbance sources drive the temporal and spatial responses of leopard cats (Prionailurus bengalensis) in multifunctional national parks. Using camera-trap data collected in the Yingjing section of Giant Panda National Park from October 2017 to April 2024, we examined leopard cat activity rhythms, potential habitat distribution, and the spatiotemporal relationships between leopard cats and broadly defined anthropogenic disturbance. The study area included 474 effective camera-trap stations and accumulated 283,279 camera-trap days. In total, 687 independent leopard cat detections were recorded at 181 stations. Leopard cats were predominantly nocturnal, with the highest daily activity occurring between 02:00 and 06:00, while annual activity was mainly concentrated from May to November. Comparing leopard cat activity patterns between disturbed and undisturbed sites revealed a high temporal overlap (Δ4 = 0.91, 95% CI: 0.84–0.97), with no statistically significant difference between the two groups (P = 0.763). The conditional two- species occupancy model likewise detected no spatial association between leopard cats and humans (SIF = 1.00). In contrast, distance to settlements, distance to water sources, and elevation were the main predictors of potential leopard cat habitat, contributing 29.67%, 24.18%, and 19.04%, respectively. Potentially suitable habitat covered 328.03 km², accounting for 39.60% of the Yingjing section. These results indicate that leopard cats showed no clear overall avoidance under the current anthropogenic disturbance conditions, although their habitat distribution was closely associated with distance from human settlements. Because the current definition of anthropogenic disturbance combines records of humans, livestock, and domestic dogs, future analyses should distinguish among multiple disturbance sources at the camera station–year–season scale to identify differentiated spatiotemporal responses that may be obscured by aggregated analyses.