Lilac (Syringa oblata Lindl.) is an important eco-economic aromatic shrub in northern China; however, the key volatile organic compounds (VOCs) responsible for its floral aroma and their origin remain largely unexplored. In this study, we conducted an integrated analysis of volatile metabolomics and transcriptomics to elucidate the composition, dynamic changes, and potential regulatory network of VOCs across different floral organs and petal developmental stages. A total of 1440 VOCs were identified in the stamens, pistils, and petals, with petals being the primary contributors to the overall floral aroma. Analysis of different petal developmental stages revealed that the full-bloom stage (S3) is critical for VOCs emission. The floral aroma of S. oblata is primarily composed of terpenoids, alcohols, and aldehydes. By relative odor activity value (rOAV) with multivariate statistical screening, seven key VOCs with high contributions to the floral aroma were identified. Transcriptome analysis identified 69,935 differentially expressed genes (DEGs) across petal developmental stages, which were predominantly enriched in metabolic pathways and the biosynthesis of secondary metabolites. The expression patterns of these DEGs were highly consistent with the accumulation trends of VOCs, increasing at stages S2 and S3 and subsequently declining at stage S4. Integrative analysis of VOCs and gene expression further identified candidate genes significantly correlated with the key aroma volatiles. Specifically, the carotenoid pathway-related genes CYP97A3 and LYC may influence the formation of carotenoid-derived volatiles and floral aroma. Additionally, genes associated with the fatty acid-lipoxygenase pathway, transport-related genes, and transcription factors are potentially involved in the formation and regulation of aldehyde and alcohol volatiles. These findings advance our understanding of the floral aroma formation in S. oblata and provide metabolic basis, candidate gene resources and a theoretical foundation for the genetic improvement of aroma traits and the breeding of new cultivars.

Prunus sibirica L. is a key ecological and economic tree species in northern China that is threatened by habitat degradation due to climate change and human activities. To address the gaps of incomplete historical dynamics and lack of conservation integration in existing studies, we integrated MaxEnt and Zonation v4.0 to predict its suitable habitat across five periods (LIG to 2090s) and three CMIP6 SSP scenarios, identifying key drivers and priority conservation zones. The model showed high prediction accuracy (mean AUC > 0.9). Results indicated that Human Footprint (HFP), Precipitation Seasonality (Bio15), Annual Mean Temperature (Bio1), Elevation (ELEV), and Mean Temperature of the Coldest Quarter (Bio11) were the key environmental factors (cumulative contribution 91.4%), with Bio1, Bio15, Temperature Seasonality (Bio4), and HFP confirmed as major drivers (AUC > 0.8) via jackknife test. Spatiotemporally, the species’ suitable habitat contracted from the Last Interglacial to the Last Glacial Maximum and expanded to the current total suitable area of 506,620.1 km². Under future SSP scenarios, suitable habitats expanded continuously under SSP126 and SSP245 but showed a “first expansion then contraction” trend under SSP585, with a persistent northeastward migration of the habitat centroid. The vertical (altitudinal) distribution of P. sibirica showed a trend of moving to higher elevations under future warming scenarios, especially in the SSP585 scenario. High-priority conservation zones are concentrated in northern China with insufficient existing protection. It is emphasized that this study contributes to improving the adaptive capacity and genetic characterization of P. sibirica almond populations to future climate.