Fraxinus angustifolia is a type of street tree and shade tree with ornamental value. It has a beautiful shape and yellow or reddish purple autumn leaves, but its leaf color formation mechanism and molecular regulation network need to be studied. In this study, we integrated the metabolomes and transcriptomes of stage 1 (green leaf) and stage 2 (red-purple leaf) leaves at two different developmental stages to screen differential candidate genes and metabolites related to leaf color variation. The results of stage 1 and stage 2 transcriptome analysis showed that a total of 5,827 genes were differentially expressed, including 2,249 upregulated genes and 3,578 downregulated genes. Through functional enrichment analysis of differentially expressed genes, we found that they were involved in flavonoid biosynthesis, phenylpropanoid biosynthesis, pigment metabolism, carotene metabolism, terpenoid biosynthesis, secondary metabolite biosynthesis, pigment accumulation, and other biological processes. By measuring the metabolites of Fraxinus angustifolia leaves, we found the metabolites closely related to the differentially expressed genes in two different periods of Fraxinus angustifolia, among which flavonoid compounds were the main differential metabolites. Through transcriptome and metabolomics data association analysis, we screened nine differentially expressed genes related to anthocyanins. Transcriptome and qRT-PCR results showed that these nine genes showed significant expression differences in different stages of the sample, and we speculate that they are likely to be the main regulatory factors in the molecular mechanism of leaf coloration. This is the first time that we have analyzed the transcriptome combination metabolome in the process of leaf coloration of Fraxinus angustifolia, which has important guiding significance for directional breeding of colored-leaf Fraxinus species and will also give new insights for enriching the landscape.

China is implementing an extensive urban forestry plan in Xiongan New Area (XNA), a new city in Hebei province. The city has been designated to serve Beijing’s noncapital functions and promote the integration of the broader Beijing–Tianjin–Hebei city-region. As part of a green initiative to minimize environmental impacts and its carbon footprint, a massive urban forestry system has been planned on an unprecedented scale, expected to cover over 600 km² by 2030. Using science to inform policy, one major goal is to simultaneously minimize impacts of invasive species, while making urban forests more resilient to potential invasive species threats. In this review, we introduce these urban forestry plans such as basic concepts and principles for afforestation, tree species to be planted, delineation of existing pests already established, and expected forest invasive species of concern threatening the new area. Finally, we introduce a framework for invasive pest management strategies in XNA based on a “big data” approach and decision system to minimize impacts of invasive species. This new approach to urban forestry has the potential to become an exemplary global model for urban forestry planning, one that integrates research activities focused on forest health surveys and monitoring with sustainable forestry management. Finally, we provide an overview of the forest health policy required for the design of an unprecedentedly large new urban forest from initial planning to full implementation of an integrated forest management program.

This study takes City S, a mega-city in North China, as the research object. Based on a thorough review of relevant literature and theoretical foundations, it employs the Principal Component Analysis (PCA) method to construct a multidimensional indicator system encompassing population, economy, society, and ecology. Using statistical data from 2013 to 2020, the study quantitatively analyzes the degree of influence exerted by various driving factors on urban landscape changes. The results show that natural factors, population factors, economic development factors, and social policy factors are the primary drivers of landscape change. Social development and ecological constraints also play a role in the adjustment of urban spatial structure to a certain extent. The study further reveals the comprehensive driving mechanism underlying urban landscape evolution and provides a theoretical basis and methodological support for urban land use optimization and landscape planning. PCA demonstrates strong applicability in identifying multifactor coupling mechanisms and can serve as a scientific reference for the formulation of urban sustainable development strategies.