Members of the genusPaeonia, which consists of globally renowned ornamentals and traditional medicinal plants with a rich history spanning over 1500 years, are widely distributed throughout the Northern Hemisphere. Since 1900, over 2200 new horticulturalPaeoniacultivars have been created by the discovery and breeding of wild species. However, information pertaining toPaeoniabreeding is considerably fragmented, with fundamental gaps in knowledge, creating a bottleneck in effective breeding strategies. This review systematically introducesPaeoniagermplasm resources, including wild species and cultivars, summarizes the breeding strategy and results of eachPaeoniacultivar group, and focuses on recent progress in the isolation and functional characterization of structural and regulatory genes related to important horticultural traits. Perspectives pertaining to the resource protection and utilization, breeding and industrialization ofPaeoniain the future are also briefly discussed.

Benzenoids/phenylpropanoids, the second most diverse group of plant volatiles, exhibit significant structural diversity and play crucial roles in attracting pollinators and protecting against pathogens, insects, and herbivores. This review summarizes their complex biosynthetic pathways and regulatory mechanisms, highlighting their links to plant growth, development, hormone levels, circadian rhythms, and flower coloration. External factors like light, humidity, and temperature also influence their biosynthesis. Their ecological value is discussed, offering insights for enhancing floral scent, pollinator attraction, pest resistance, and metabolic engineering through genetic modification.

Paeonia (Paeoniaceae), a culturally and economically important plant genus, has an isolated taxonomy while the evolution of this genus is unclear. A plant species endemic to southwest China, Paeonia mairei is precious germplasm for evolution-related research and cultivar improvement, and its conservation is urgent. However, little is known about its patterns of habitat distribution and responses to climate change. Using 98 occurrence sites and data of 19 bioclimatic variables, we conducted principal component analysis and hierarchical cluster analysis to delineate different climatic populations. Maximum entropy algorithm (MaxEnt) was applied to each population to evaluate the importance of environmental variables in shaping their distribution, and to identify distribution shifts under different climate change scenarios. We also applied MaxEnt to all of the P. mairei presence sites (P_Whole) to evaluate the need to construct separate species distribution models for separate populations rather than a common approach by treating them as a whole. Our results show that local adaptation exists within the distribution range of P. mairei and that all presence sites were clustered into a western population (P_West) and an eastern population (P_East). Two variables (precipitation of the driest month and temperature seasonality) are important when shaping the distribution of P_West, and another two variables (mean diurnal range and mean temperature of the wettest quarter) are important for P_East. Both populations are likely to shift upward under climate change, while P_East may lose most current suitable areas while P_West may not. P_Whole produced a narrower area compared to the combination of P_West and P_East but a suitable area (south Chongqing) may have been missed in the prediction. Accordingly, a population-based approach in constructing a species distribution model is needed to provide a detailed appreciation of the distribution of P. mairei, allowing for a population-based conservation strategy. In this case, it could include assisted migration to new and suitable distribution areas for P_West and in situ conservation in high elevation regions for P_East. The results of our study could be a useful reference for implementing the long-term conservation and further research of P. mairei.

Thirty herbaceous peony (section Paeonia of the genus Paeonia ) cultivars were divided into four groups (no fragrance, light fragrance, medium fragrance, or intense fragrance) based on their sensory evaluation scores. Using dynamic headspace sampling (DHS) and automatic thermal desorption–gas chromatography/mass spectrometry (ATD-GC/MS), 130 volatile organic components were detected in these 30 cultivars and a total of 72 compounds were identified as scent components. The main compounds were phenylethyl alcohol, β-caryophyllene, linalool, ( R )-citronellol, and nerol. Selecting α-pinene as the standard, the volatile components of these cultivars were quantitatively analyzed. By combining the sensory evaluation scores and the results of quantitative analysis, we found that ‘Going Bananas’, ‘Cream Delight’, ‘Zhu Sha Pan’, ‘Qiao Ling’, ‘Duchess de Nemours’, and ‘Yang Fei Chu Yu’ displayed an intense fragrance and, thus, had relatively high commercial value for the flower fragrance industry. ‘Red Magic’, ‘Joker’, ‘Fairy Princess’, ‘Lovely Rose’, ‘Carina’, and ‘Etched Salmon’ were excluded from the hierarchical cluster of aromatic compounds and the analysis of fragrance patterns because of the low amount of fragrance they released and poor sensory evaluation results. Based on a cluster analysis, assessment of the major aromatic compounds, and the results of sensory evaluation, the remaining 24 cultivars were divided into five fragrance patterns for the first time: woody scent [cluster I (major fragrance β-caryophyllene)], fruity scent [cluster II (phenylethyl alcohol)], lily scent [cluster III (linalool)], rose scent {cluster IV [( R )-citronellol]}, and an orange blossom scent [cluster V (nerol)].