Solanum aculeatissimum is a herbaceous to semi-woody perennial plant native to the Brazilian ecosystem. It has naturalized extensively in southwestern China, posing significant threats to local biodiversity. This study systematically screened and integrated 100 distribution records from authoritative databases, including the Chinese Virtual Plant Specimen Database, the Global Biodiversity Information Facility, and Chinese Natural Museums. Additionally, 23 environmental variables were incorporated, comprising 19 bioclimatic factors from the World Climate Dataset, 3 topographic indicators, and the Human Footprint Index. The objectives of this research are as follows: (1) to simulate the plant’s current and future distribution (2050s/2070s) under CMIP6 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5); (2) to quantify changes in the distribution range; and (3) to determine the migration trajectory using MaxEnt 3.4.4 software. The findings reveal that human pressure (contributing 79.7%) and isothermality (bioclimatic factor 3: 10.1%) are the primary driving forces shaping its distribution. The core suitable habitats are predominantly concentrated in the provinces of Yunnan, Guizhou, and Sichuan. By 2070, the distribution center shifts northeastward to Qujing City. Under the SSP5-8.5 scenario, the invasion front extends into southern Tibet, while retreat occurs in the lowlands of Honghe Prefecture. This study underscores the synergistic effects of socioeconomic development pathways and bioclimatic thresholds on invasive species’ biogeographical patterns, providing a robust predictive framework for adaptive management strategies.

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies.

Global high-mountain ecosystems are increasingly subjected to intensified anthropogenic disturbances, which facilitate the spread of invasive alien plants and threaten agricultural sustainability and ecological security. Using Laojun Mountain in Yunnan as the study area, this research investigates the relationship between the distribution patterns of invasive plants and land-use changes, based on data from 38 transect surveys conducted in 2023 and 30-m-resolution land-use data spanning 2003–2023. The analysis incorporates a random forest model and a land-use transition matrix. The key findings are as follows: (1) Variable importance analysis revealed elevation as the most critical factor influencing invasion occurrence (mean decrease in Gini index: 8.0), followed by slope, aspect, and land-use type. (2) Cultivated land exhibited the highest probability of invasion, with high-risk areas (>0.8) concentrated in agricultural zones in the central-southern and northeastern regions. (3) From 2003 to 2023, cultivated land increased by a net area of 20.85 km², primarily due to conversion from forests (19.57 km²) and grasslands, while grassland area decreased by 24.70 km². This study concludes that agricultural expansion has intensified habitat fragmentation and anthropogenic disturbances, creating favorable conditions for invasive plant establishment. It is recommended that invasive species monitoring and ecological restoration efforts be strengthened in agroforestry transition zones to enhance landscape resilience against biological invasions.