Michelia ‘Xin’ is an evergreen rare ornamental tree species that undergoes FBD only once but blooms twice a year. However, the molecular mechanisms controlling its FBD process remain largely unknown. This study characterized the FBD process and delved into the key molecular regulatory mechanisms through transcriptomic and metabolomic analyses of developing flower buds. FBD in Michelia ‘Xin’ was characterized into five stages, including vegetative (T1), floral meristem transition (T2), tepal primordia differentiation (T3), stamen primordia differentiation (T4), and pistil primordia differentiation (T5). Analyses revealed a stage-specific metabolic and transcriptional regulation of FBD, with increasing numbers of differential metabolites and a decreasing number of DEGs from T1 to T5. Most phytohormone and transcription factor-related DEGs were highly induced from T2. The down-regulation of dormancy-associated protein homologs and CONSTANS-LIKE proteins associated with significant induction of flowering-promoting factor, CLAVATA3, trichome birefringence-like, and GRAVITROPIC IN THE LIGHT proteins was essential for the induction and reproductive organs’ development. Porphyrin biosynthesis, chlorophyll a-b binding proteins, DNA replication, flavonoid biosynthesis, and starch and sucrose metabolism were also significantly induced from T2. Key pivotal candidate genes were screened out. Our results provide fundamental resources for dissecting the molecular network regulating FBD and molecular-assisted flowering control in Michelia ‘Xin’.

Wildfire regimes in mountain forest landscapes are shaped by complex interactions among topography, climate, vegetation, and human activity. However, predicting and interpreting fire occurrence in topographically heterogeneous regions remains challenging because fire–environment relationships vary strongly across elevation gradients and temporal scales. This study developed a hybrid machine-learning framework integrating an Information Value Model (IVM), Random Forest (RF), and Convolutional Neural Network (CNN) to decode elevation-mediated wildfire regimes in western Sichuan, China, a mountainous forest region characterized by strong vertical environmental gradients and high ecological conservation value. Multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) burned-area records, topographic variables, monthly meteorological data, vegetation indices, land-cover information, and human-accessibility proxies, were integrated at a 500 m spatial resolution. Environmentally comparable non-fire samples were generated from unburned vegetated pixels, and model training, RF-based feature selection, hyperparameter tuning using Particle Swarm Optimization (PSO), and performance evaluation were conducted within a nested spatial block cross-validation framework. The model produced continuous wildfire occurrence probabilities and showed strong discriminatory performance under the adopted validation protocol, with AUC values exceeding 0.95 across temporal datasets and low probability-error metrics. RF importance and correlation analyses identified mean temperature, elevation, and precipitation as the dominant predictors of wildfire probability. Spatial analyses revealed pronounced elevation-mediated differentiation in wildfire regimes: low-elevation valleys showed higher fire probability and stronger associations with human-accessibility proxies, whereas high-elevation plateau areas exhibited lower and more scattered fire patterns associated with climatic constraints. Seasonal and monthly analyses further showed that winter and spring fires dominated the regional fire regime, with risk intensifying during the pre-monsoon dry period. By combining probabilistic fire-risk mapping, spatial-context learning, and elevation-gradient interpretation, this study provides a transferable framework for understanding wildfire regimes in complex mountain forest landscapes. The findings support adaptive forest fire management, targeted monitoring, and risk zoning in mountainous regions where forest ecosystems, human activities, and conservation values intersect.