Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network

Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network Huan Wang, Yilong Feng, Qiaoyong Zhang, Min Zou, Ting Li, Lijiao Ai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244944/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The benefits of urban green space (UGS) for society are widely recognized, as a direct link between plant-microbe interactions and the maintenance of biodiversity, community stability, and ecosystem functioning. There is, however, a lack of knowledge about the factors influencing the microbial communities in urban green spaces, especially those related to phyllosphere epiphytes and stem epiphytes. In this study, we analyzed the microbial community assembly in leaf and stem bark samples collected from Square, Road, Campus, and Park. The α-diversity was higher in the bark epiphytic community, compared to the phyllosphere. Moreover, the types of urban greenspaces altered the way communities gathered. The main factors of the urban greenhouse (soil and air properties) were shaping the characteristics of bacterial communities on the leaf surface and bark epiphytic. In the co-occurrence network analysis, keystone taxa were not mostly observed in abundant species, which may be necessary to maintain ecosystem functions. Our findings provide a deeper understanding of the ecological dynamics and microbial interactions within plant phyllosphere and stem epiphytes microbiomes. Urban green space Biodiversity 16S ribosomal RNA Phyllosphere bacteria bark epiphytic bacterial Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Due to the growing urbanization of human populations, green spaces have become increasingly important for human health 1 , 2 . The evidence for the health benefits of urban green spaces biodiversity is growing, including reduced blood pressure, reduced pain, lower cortisol levels, and reduced mortality rates from all causes 3 – 5 . In the urban ecosystem, microorganisms play an essential role in regulating ecosystem services 6 , 7 , such as pollutant remediation, nutrient cycling, and genetic diversity conservation 8 . Urban ecosystems must perform these functions to reduce the growing global burden of chronic diseases associated with urban environments and to maintain the well-being of citizens living in cities. Human health can be compromised by the loss of contact with a diverse environment microbiome (a specific environment's community of microbes, including bacteria, fungi, archaea, and viruses), as well as key microbial taxa - microbial 'old friends' 9 , 10 . Experiencing environmental microbiomes shapes the human microbiome, which helps educate and maintain a healthy immune system 11 , 12 . It directly influences the development of the immune system 13 and predisposition to infectious and non-infectious diseases 14 , 15 . Human exposure to soil and plant microorganisms in urban green spaces appears to suppress inflammation and reduce immune dysfunction 16 , 17 . Despite the lack of understanding about why these positive health outcomes occur, it is likely a result of human interactions with animals, plants, and microbes. Land uses have changed dramatically over the past several decades due to the massive shift of population from rural to urban areas, potentially changing microorganism habitats 18 . To predict and mitigate the impact of anthropogenic disturbances on microbial communities in urban environments, we must have a better understanding of how these disturbances affect them 19 , 20 . While urban microbial ecosystems are likely to play an important role in human health, they are not well studied in comparison to animal and plant communities 21 . Management practices are intensive in urban ecosystems, The microbial community in urban green spaces is influenced not only by the environment (Factors such as soil properties and climate), but also by human interactions 6 , 22 . To date, a few studies have reported that urbanization can substantially change the microbiota in urban green spaces 22 , 23 , In urban green spaces, however, there is a great deal of variation in microbial community profiles, and it remains unclear what factors contribute to this. The impact of urbanization on soil bacteria has largely been studied. The phyllosphere plays a significant role in microorganisms' habitat, but the phyllosphere and bark epiphytic bacteria have been overlooked. According to previous studies, the phyllosphere provides a great opportunity to test basic ecological principles in microbiology 24 . With an estimated area of more than 1 billion square kilometers, it is one of the world's largest environments 25 , 26 . The phyllosphere microbiome plays a variety of important ecosystem functions besides supporting plant growth 27 . Other ecosystem functions are performed by phyllosphere microbiomes, for example, reducing plant ethanol emissions and fixing nitrogen as part of Earth's biogeochemical cycles 28 . Also, as phyllosphere microbiomes serve as a bridge between environmental and human microbiomes, phyllosphere microbiomes are closely related to human health 29 . Community composition and diversity of epiphytic species are affected not only by air pollution, but also by growth habitat, tree characteristics (e.g., bark properties such as water-holding capacity and bark pH) 30 , tree species 31 , etc. The purpose of this study was to examine patterns of phyllosphere and bark epiphytic bacteria of different plants from urban green spaces in Yongchuan district, Chongqing, China. In our study, we examined bacterial abundance and diversity by high-throughput amplicon sequencing of the small-subunit ribosomal RNA (16S rRNA) gene. In addition, we examine well-replicated plots of four different types of urban green space to integrate these bacterial results with vegetation and environmental surveys (road green space, park green space, square green space, and campus green space). In our analysis, the main aims were to (i) determine the potential differences and links between leaf and bark phyllosphere bacterial communities; (ii) determine the most important factors shaping the bacterial community profiles in urban green spaces, and (iii) understanding community correlation network structure and keystone taxa of samples. 2. Material and methods 2.1. Sampling and experiment design In April 2023, leaf and bark samples were collected at 8 sites in Yongchuan (105°93′E, 29°36′N), Chongqing, China. The sites included road greenspaces (2 sites), parks greenspaces (2 sites), square greenspaces (2 sites), and subsidiary community greenspaces (2 sites), which represent the typical types of greenspaces in urban environments (Fig. 1 ). Square green space is a venue for urban public activities with functions such as recreation, commemoration, assembly, and refuge. Park green spaces meet the leisure needs of urban residents and provide places for rest, excursions, exercise and other collective cultural activities. Road green space refers to the ground used for the cultivation of plants and landscaping within the land area of a road. It is mainly used to purify the air, beautify the environment, prevent noise, and guide the sight of drivers. Campus green space, as an adjunct to the school, provides a comfortable recreational and landscaped site for students and faculty. In order to minimize the effects of weather on sample data, sampling was conducted within one day after seven days without rainfall. In each sampling site, 10 × 10 m squared (m 2 ) were constructed. Leaf and trunk epidermis samples were collected from dominant species of trees and dominant species of shrubs in different types of green spaces. Sterile scissors were used to collect leaves and trunk epidermis from three plants at the same stage of growth and from the same height above ground, and then the leaves and trunk epidermis were placed in labeled sterile bags. Collection of plant material comply with relevant institutional, national, and international guidelines and legislation. The basic information on the sampling points is shown in Table 1 . Three parallel replicates were measured for each sample and a total of 90 samples were collected. The leaves and trunk epidermis samples were cut with sterilized scissors to obtain equal weight, placed in a sterile centrifuge tube with PBS buffer for a while, centrifuged to remove the supernatant, resuspended by adding 1 mL of buffer, snap-frozen in liquid nitrogen, and stored at -80 ℃ for DNA extraction. Data of atmospheric concentration of meteorological parameters and gaseous pollutants (including wind speed, wind direction, atmospheric temperature, soil temperature, TBQ total radiation, radiation accumulation, soil humidity, atmospheric humidity, negative oxygen ions, noise, PM2.5, PM10) were obtained from the hourly monitor by atmospheric fixed-site monitoring stations. Table 1 Information table of plots of different green space types ID Type of green spaces Sample Site Coordinate Altitude (m) Area (hm 2 ) Tree dominant species Shrub dominant species Longitude Latitude 1 Square green space LHGC 105°54′52.72″ 29°21′ 10.55″ 314 1.29 Magnolia denudata Desr Alpinia zerumbet 'Variegata' 2 WQGC 105°53′25.33″ 29°21′26.95″ 316 3.05 Osmanthus fragrans Aucuba japonica var. variegata 3 Road green space WCDL 105°54′49.57″ 29°21′52.09″ 320 0.65 Bauhinia purpurea Loropetalum chinense var. rubrum 4 RMDL 105°55′45.32″ 29°20′48.48″ 304 1.61 Magnolia grandiflora Camellia japonica 5 Campus Green space WLXY 105°56′ 11.43″ 29°21′4.73″ 318 18.28 Eucalyptus robusta Spiraea salicifolia 6 YCZX 105°56′39.33″ 29°20′46. 12″ 326 3.69 Prunus cerasifera 'Atropurpurea' Ligustrum lucidum 7 Park green space ZYGY 105°55′38.71″ 29°21′ 17.46″ 308 6.63 Ficus virens Pittosporum tobira 8 BSGY 105°53′35.16″ 29°21′20.59″ 324 12.12 Osmanthus fragrans 2.2. DNA Extraction, PCR Amplification and Illumina MiSeq Sequencing As directed by the manufacturer, genomic DNA was extracted from each sample using the E.Z.N.A.®Soil DNA Kit. Total DNA was eluted with 50 µL TE buffer (Tris-hydrochloride buffer, pH 8.0, 1.0 mM EDTA contained). In order to determine DNA concentration and purity, a NanoDropTM 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used and samples were stored at -80℃ until PCR amplification. PCR amplification of the V3-V4 region of the 16S ribosomal RNA gene was carried out on bacteria (98℃ for 30 s; with 35 cycles at 98℃ for 10 s, 54℃ for 30 s, and 72℃ for 45 s; and a final extension at 72℃ for 10 min) using a primer set of 341F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’) 32 . Barcodes were added to the 5' ends of the primers and universal primers were used. Each PCR reaction was conducted in a 25 µL mixture containing 12.5 µL of 2× Phusion® Hot Start Flex Master Mix, 2.5 µL of each primer (1 µM), and 50 ng of template DNA. Nuclease-free water served as blank. For the DNA extraction process, ultrapure water was used instead of a sample solution to ensure that there were no false positives. Throughout the DNA extraction process, ultrapure water was used in place of template DNA as a negative control to exclude false-positive PCR results. 2% agarose gel electrophoresis was used to verify the PCR amplicon size. PCR products were purified using AMPure XT beads from Beckman Coulter Genomics in Danvers, MA, USA, and quantified using Qubit from Invitrogen, USA. The amplicon library was sized and quantified using an Agilent 2100 Bioanalyzer (Agilent, USA) and a Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. A NovaSeq PE250 platform at LC-Bio Technology Company (Hangzhou, China) was used to sequence the libraries. 2.3 Sequencing Data Processing and Bioinformatic Analyses Based on the samples' unique barcodes, paired-end reads were assigned, then truncated by cutting off the barcodes and primer sequences, and merged with FLASH 33 . Fqtrim software was used to trim and filter the raw reads, and Vsearch software was used to further filter the chimeric reads 34 . The amplicon sequence variants (ASVs) were generated with the DADA2 package 35 . Through BLAST searches, all of the sequence reads were compared against the Silva rRNA database 36 . Using the average abundance of each group, the relative abundance of each taxon was calculated by normalizing assigned reads to the total number of qualified reads. The rarefaction curves were generated using custom Perl scripts for each sample. The BioVenn software was used to plot Venn diagrams showing the shared and unique features ( http://www.biovenn.nl/index.php accessed on 2 November 2022). To analyze the complexity of species, the alpha diversity indices (Observed species, Shannon diversity, and Simpson evenness) were calculated using QIIME 2 (Quantitative insights into microbial ecology 2) 37 . One-way ANOVA or t-tests was used to test the significance of variance between or among samples using SPSS 22.0 (SAS Institute Inc., Cary, NC, USA). An analysis of beta diversity was used to display and compare bacterial community compositions. With the QIIME 2 plugin, PCA (principal component analysis) was conducted 37 . Using the group average method, hierarchical agglomerative clustering (to group objects similar to each other in clusters) was carried out on the most abundant features according to the groups selected. By using the OmicStudio tool, a heatmap of bacterial communities was generated, with Bray-Curtis similarity calculations used to cluster relative abundance data 38 . Unless otherwise stated, all statistical analyses were conducted at a significance level of 0.05. With the "random Forest" and "rfPermute" R packages, we examined the most important environmental factors driving bacterial Shannon diversity 39 . Venn diagrams showing the shared and unique features were plotted, using BioVenn ( http://www.biovenn.nl/index.php ). Redundancy analysis was performed using the OmicStudio tools at https://www.omicstudio.cn/tool . Correlation network analysis used the igraph (Version 1.2.6) package of R (Version 3.6.3). 3 Result 3.1 Sequence data results and alpha diversity of communities The sequencing process utilizing the 16S rRNA genes yielded a cumulative count of 1,892,424 sequences that underwent quality filtration, amounting to 1.23 Gb of valid data. The sequence count per sample varied between 38,366 and 72,275. Following dereplication using DADA2 within QIIME2, a total of 9,626 prokaryotic operational taxonomic units (OTUs) were acquired. As the quantity of sequencing data increased, all rarefaction curves exhibited saturation, resulting in an average Good's coverage of 88.93% across all samples (Supplementary Table S1 ). Proteobacteria (43.28%) were the most abundant phylum of bacteria, followed by Cyanobacteria (35.51%), Actinobacteriota (5.93%), and Firmicutes (4.37%). At the genus level, Chloroplast_unclassified (35.79%), Sphingomonas (7.28%), Ralstonia (4.11%), Methylobacterium-Methylorubrum (3.30%), and Phyllobacterium (3.20%) were the top 5 abundant genera, and these features accounted for 53.65% of the entire collection, while unclassified represents sequences marked as unclassified bacteria (Fig. 2 ). 3.2 Species composition of bacterial communities among different content Comparing the bark group and leaf groups, there were notable disparities observed in the diversity indices (Shannon diversity and observed otus) (ANOVA, p < 0.05; Fig. 3 a, 3 c). Conversely, no statistically significant distinction was identified in the alpha diversity indices when comparing the shrub and tree groups (Shannon diversity and observed otus) (ANOVA, p > 0.05; Fig. 3 b, 3 d). Regarding the discrepancies in bacterial characteristics between the stem and leaf groups, as depicted in a Venn diagram, it was observed that these two groups shared 1633 bacterial features in common, while the bark and leaf group possessed 6343 and 1650 unique features, respectively (Fig. 4 B). Similarly, the tree and shrub groups exhibited 5102 and 2402 unique features, respectively, with 2102 bacterial features being common to both groups (Fig. 4 C). In the within-group variation analysis of different green space types, road green space contained the most microbial species at 3773. This was followed by parks, plazas, and ancillary (campus) green space at 3640,2987, and 2800, respectively. A total of 580 cooccurring types were found in several types (Fig. 4 D). From samples of all sites, the most abundant microbial phylum was Proteobacteria (43.28%), and other major groups were Cyanobacteria (35.51%), Actinobacteriota (5.93%), Firmicutes (4.37%) and Bacteroidota (4.19%) (Figs. 4 A). Samples were analyzed for clustering using Bray-Curtis distance analysis. Among the different green space types, road green spaces (CZ and RM) had the closest clustering of sample composition and the most similar surface community structure, while the other three green spaces (square green space, campus green space, and park green space) types were not differentiated. The five most abundant phyla were consistent across the tree-shrub and bark-leaf groups, respectively. However, the proportions of distribution of each dominant species varied considerably between sample groups. Proteobacteria and Cyanobacteria accounted for 44.53% and 34.63% of the shrub group, respectively. Similarly, the dominant species in tree groups, Proteobacteria and Cyanobacteria accounted for 41.85% and 37.05% of the tree group, respectively (Figs. 4 F). However, within the leaf and bark, there were large differences in the proportion of dominant species composition. The most two abundant microbial phylum in the bark were Proteobacteria (54.77%) and Cyanobacteria (15.55%). In the leaf group, the dominant species is Cyanobacteria rather than Proteobacteria, with the proportions of Cyanobacteria and Proteobacteria being 56.46% and 31.25%, respectively (Figs. 4 E). 3.3 Environmental factors shaping the bacterial community compositions in urban green spaces For the environment properties, mainly the air temperature, soil temperature, TBQ radiation accumulation, and noise correlated positively with wind speed, while wind direction and atmospheric moisture correlated negatively with wind speed, air temperature, soil temperature, and TBQ radiation accumulation (Fig. 5 A). The partial Mantel test indicated that the diversity indexes of observed-otu, Shannon, and pielou-e index correlated significantly with wind speed. Simpson index correlated significantly with air temperature and soil temperature, and goods coverage correlated significantly with atmospheric moisture. Most other physicochemical contents were significantly negatively correlated with the diversity indexes. The results of the random forest model showed that the rank order of importance of community environmental factors was soil temperature, atmospheric moisture, air temperature, wind speed, negative oxygen ions, PM2.5, and radiation accumulation (Fig. 5 B). The results of RDA showed that the surface bacteria community was regulated by multiple environmental variables (Fig. 5 C). The first axis of RDA explained 12.91% of the variation of species-environment relation, while the two axes together explained 18.44% of variation (p = 0.001). Wind direction, wind speed, and soil moisture appeared to be the three most significant factors affecting the bacteria community. 3.4 Correlation network analysis between microbial community The average changes in network properties were used to define network complexity, and average degrees and edges were the most important parameters 40 . Edges indicate significant correlations between microbial taxa, and average degrees indicate the network's overall connectivity. To determine the correlations among species, the richness information at the genus level was used for the top 30 species based on their richness in the different samples. Correlation networks are presented in Fig. 6 (A-H). There is a correlation between the two species indicated by the connections between the nodes, a positive correlation is shown by the yellow solid line, and a negative correlation is shown by the grey dashed line. A connection's strength is determined by the thickness of the line between any two nodes. The size and color of the dots represent the number of related objects. According to the co-occurrence network analysis of phyllosphere bacteria, the composition of the bacterial community is strongly correlated among whole samples (Fig. 6 A, Spearman’s > 0.7, P < 0.05, average degree:11.52). Abditibacterium , Sphingomonas , Sphingomonadaceae _unclassified, and Acidiphilium dominated the community structure, with node degrees 20, 17, 17 and 17 respectively. Between Burkholderia-Caballeronia-Paraburkholderia and Ralstonia, Mesorhizobium and Phyllobacterium , Actinomycetospora , and Sphingomonas , there were positive correlations, with rho 0.954, 0.938 and 0.935 respectively. Between Chloroplast_unclassified and others, Chloroplast -unclassified and Sphingomonas , Mitochondria _unclassified and others, there were negative correlations, with rho − 0.942, -0.867 and − 0.810 respectively. Between the tree and shrub groups, there were large changes in the major species in the community structure (Fig. 6 B-C). The co-occurrence network for tree samples showed that the network complexity (edge: 112.501; average degree: 11.310) was greater than the shrub samples (edge: 103.267; average degree: 9.742). Abditibacterium , Sphingomonadaceae _unclassified, and Actinomycetospora dominated the community structure in shrub samples with node degrees 20, 18, and 18, while Sphingomonas , Abditibacterium , and Sphingomonadaceae _unclassified in tree samples with node degree 19, 18 and 18. There were positive correlations between Methylobacterium-Methylorubrum and Roseomonas , Mesorhizobium and Ralstonia , and Actinomycetospora and Alphaproteobacteria _unclassified in shrub samples, with rho 0.952, 0.943 and 0.930 respectively. There were negative correlations between Chloroplast _unclassified and Sphingomonas , Chloroplast _unclassified and Others, and Mitochondria _unclassified and Sphingomonas in shrub samples, with rho − 0.921, -0.903 and − 0.886 respectively. In the tree group, there were positive correlations between Mesorhizobium and Phyllobacterium , Mesorhizobium and Ralstonia , and Amnibacterium and Methylobacterium - Methylorubrum , with rho 0.962, 0.941 and 0.932 respectively. Negative correlations were presented between Chloroplast _unclassified and Others, Acetobacteraceae _unclassified and Chloroplast _unclassified, and Chloroplast _unclassified and others, with rho − 0.941, -0.865 and − 0.856 respectively. Similarly, on the bark epiphytic and phyllosphere, there were significant differences in the relationships between microorganisms (Fig. 6 D-E). On the bark epiphytic, Ralstonia , Acidisoma , and Mesorhizobium were the top three species that dominated the community structure, while Abditibacterium , Sphingomonas , and Actinomycetospora in the phyllosphere. The co-occurrence network for leaf samples showed that the network complexity (edge: 101.126; average degree: 10.000) was greater than bark samples (edge: 71.448; average degree: 7.724). A similar analysis also arose between different green space types (Fig. 6 F-I), the network complexity of park green space was greater than the other three green spaces. In the park, square, campus, and road green space types, the distribution of species that account for the most importance in the community was Acetobacteraceae _unclassified, Methylobacterium - Methylorubrum , Sphingomonas , Caulobacteraceae _unclassified respectively. 4 Discussion 4.1 Microbial diversity and community structure in urban green spaces are affected by multiple aspects of urbanization There are unique opportunities for human and natural environmental microbes to interact on the surface of plants in urban greenspaces, which are vital but neglected ecological milieus. As a result of this study, proteobacteria, cyanobacteria, actinobacteria, and firmicutes were abundant in bacterial communities both in trees and shrubs. These phyla have been found to be abundant in the phyllosphere in previous studies 41 , 42 . It has also been reported that Sphingomonas and Methylobacterium (Proteobacteria) are ubiquitously present in the phyllosphere of trees and grasses 43 , 44 . Further, some Sphingomonas strains can protect plants from plant pathogens 45 , while members of the Methylobacterium can utilize methanol released by plants to promote their host's growth 46 . Additionally, Proteobacteria and Cyanobacteria contribute to the cycle of nitrogen in the atmosphere 47 – 49 . To maintain the productivity and resilience of urban ecosystems, especially under the conditions of global climate change, these taxa should be further explored for their features such as plant growth promotion and biocontrol 49 , 50 . A significant impact of plant types and green spaces on alpha and beta diversity of bacteria in bark epiphytic and phyllosphere was observed in this study. Based on the fact that bark and leaves may be the primary sources of airborne microbes, it is reasonable to expect that the microbial communities on the surface of plant bark and leaves are closely related to the air microbiome. There may be a significant role for the mass movement of air in shaping airborne microbiomes 51 , 52 . In our study, trees had more species richness than shrubs, since trees are less affected by air movement than shrubs, and there was a relationship between the lesser wobbling effect of wind action on surface bacteria. A similar situation can be inferred in the grouping of bark and leaves. Our results indicate that microbial diversity and abundance in the bark group exceeded that of the phyllosphere. This may also be since bark are better fixed than leaves and are less affected by air movement. In addition, the morphological structure of the plant surface is also very important in influencing the attachment of bacteria to the surface 53 , 54 . Different microenvironments in which epiphytic bacteria live may explain this difference. Specifically, epiphytic bacteria are subjected to selection by their host plant via physiology, leaf morphology, and nutrients and volatile organic compounds (e.g. methanol) exported to the surface epiphytic bacteria, and the surrounding atmosphere, such as solar humidity, radiation, and temperature 49 , 55 , 56 . Compared to leaves, bark epiphytic have better conditions for providing attachment of microorganisms such as bacteria, which is consistent with our analyses that bark epiphytic retain more species abundance and complex community composition. Some studies have shown that some interleaf microorganisms have important functions such as promoting plant growth, adsorbing and degrading environmental pollutants, and guiding the closure of leaf stomata to prevent the entry of pathogenic bacteria 57 . Plants play an important role in phosphate dissolution, nitrogen fixation, nitrification, and balancing the global carbon and nitrogen cycles 58 . Plant identity significantly affected both the diversity and community structure of phyllosphere epiphytic and endophytic bacteria, as previous studies have shown 44 , 59 , 60 . Various plant species possess various functional traits, such as specific leaf area, nutrient content, osmotic properties, respiration rate, and dust retention efficiency which are significantly correlated with the surface bacteria community of leaf and stem 61 – 63 . In various types of UGS, microbial communities exhibited notable differences, possibly due to their inherent adaptability. Consequently, nutrient disturbances led to a decline in nutrient-tolerant bacteria, and they were then replaced by generalists 64 . There is generally an increase in species richness with the area as illustrated by the species-area relationship 65 , and larger area urban green spaces are less exposed to urbanization and human impacts, and the microbiologic community is relatively less adversely affected. Of our different green space types, campuses and parks have larger areas, however in terms of OTU abundance, parks and roads go on to have greater abundance. 4.2 Various environmental factors as an important factor shaping the bacterial communities in urban green spaces We characterized the relationships between leaf and bark phyllosphere bacterial community compositions in urban green spaces with a variety of factors including soil, air, noise, and radiation properties. Some soil properties identified as key drivers in shaping soil microbial communities have been shown in many studies 66 , 67 , and in this study, surprisingly, soil properties remained also one of the most important factors in shaping the characteristics of bacterial communities on the phyllosphere and bark epiphytic. In addition, air environmental parameters were second only to soil environmental parameters as important microbial community drivers analyzed by random forest. (找土壤、空气与叶际茎影响的文献) Some studies have reported that the more highly urbanized a place is, the more homogenous the community structure of plants 68 , ground animals 69 , birds 70 , and insects 71 . In our study, environmental factors that characterize urbanization were correlated with microbial diversity and also showed that the main factors of the urban greenhouse effect (air and soil temperature and humidity) were significantly correlated with microbial diversity. Species diversity is significantly and positively correlated with both community structure and stability 72 , therefore, the greenhouse effect of urbanization can significantly affect the structure of bacterial communities on the surface of plant bark and leaves. In the RDA analysis, wind speed and direction were very important influences, which were very relevant to the effect of physical air movement on microbial attachment discussed above. Current research lacks in-depth linkages between environmental factors and microbial function, as well as the relationship between microbial function and human health, and future in-depth studies through large-scale and longitudinal analyses may lead to a better understanding of the microbial mechanisms underlying the impact of urban green spaces on human health. In this study, we analyzed the differences in community structure and interspecific differences among different groups by correlation network analysis. Overall, trees have a more complex network structure than shrubs, and leaves have a more complex network structure than bark. An interesting result here is that leaves have less microbial abundance relative to bark, but a complex community structure correlation. Plants' leaves are important organs determining their response to environmental change and highly plastic in long-term evolution 73 , converting energy in ecosystems 74 . Aside from that, leaves are essential for photosynthesis and nutrient uptake in plants, so their role in plant growth is evident 75 . Since plant leaves grow and fall off periodically and are greatly affected by factors such as external air, it is possible to propose the idea that although leaf microbes are not as abundant as bark, they need to be more cohesive in order to keep the leaves growing and to maintain important physiological functions. It has been shown that plants indicate significant differences in microbial communities between urban habitats and that such differences are largely dependent on plant species 76 . In our results, the highest abundance of attached microorganisms and stronger community associations were found in parkland green spaces relative to several other green space types. Some studies have shown that ficus is the most suitable plant species for sustainable urban planting because of its high dust deposition 77 . It can be inferred that Ficus virens , the dominant tree in the parkland in this study, can play an important contribution by adsorbing microorganisms through dust retention. According to analysis of correlation network, Abditibacterium , Sphingomonas , Sphingomonadaceae _unclassified, and Acidiphilium dominated the whole community structure, which not dominant in high abundance. This revealed that the keystone taxa were not mostly observed in abundant species, which may be essential for preserving the ecosystem’s functions. Declarations Data accessibility The sequence data presented in the study were deposited in the Sequence Read Archive (SRA) repository of NCBI at https://www.ncbi.nlm.nih.gov/sra, accession number PRJNA1097078. The environmental parameters datasets used or analysed during the current study available from the corresponding author on reasonable request. Declaration of competing interest This paper's authors declare that they have no known competing financial interests or personal relationships that could have influenced their work. Funding This work was supported by the Postdoctoral Science Foundation of Chongqing Natural Science Foundation (Grant No. CSTB2022NSCQ-BHX0740); the Performance Incentive and Guidance Special Project for Chongqing Scientific Research Institutes (No. cstc2022jxjl20010); the Scientific research project of Chongqing City Administration Bureau (No. CGK 2022-19); the National Natural Science Foundation of China (Grant No. 31670467). Author contributions Conceived and designed the experiments: A.L., W. H & F.Y. Performed the experiments: Z.Q., Z.M. & L.T. Analyzed the data: W.H. Wrote the paper: W.H. All authors reviewed and approved the final manuscript. References Rydin, Y. et al. Shaping cities for health: complexity and the planning of urban environments in the 21st century. The Lancet 379 (2012). Nieuwenhuijsen, M. J., Khreis, H., Mas, M. T., Gascon, M. & Dadvand, P. Fifty Shades of Green. Epidemiology 28 (2017). Raf, A., Olivier, H. & An, V. N. Biodiversity and human health: mechanisms and evidence of the positive health effects of diversity in nature and green spaces. British medical bulletin 127 (2018). Twohig-Bennett, C. & Jones, A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environmental Research 166 (2018). Stanhope, J., Breed, M. F. & Weinstein, P. Exposure to greenspaces could reduce the high global burden of pain. Environmental Research 187 (2020). Nan, H. et al. Soil microbial communities are shaped by vegetation type and park age in cities under cold climate. Environmental microbiology 19 (2017). Kabisch & Nadja. Ecosystem service implementation and governance challenges in urban green space planning—The case of Berlin, Germany. Land Use Policy 42 , 557-567 (2015). Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City's Central Park are similar to those observed globally. Proceedings Biological Sciences 281 , 22 (2014). Leena, v. H., Ilkka, H. & Tari, H. Natural immunity. Biodiversity loss and inflammatory diseases are two global megatrends that might be related. EMBO reports 12 (2011). A, R. G. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proceedings of the National Academy of Sciences of the United States of America 110 (2013). A, G. J. et al. Current understanding of the human microbiome. Nature medicine 24 (2018). Daphna, R. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555 (2018). J, C. E. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nature immunology 17 (2016). Takeshi, I. et al. Microbiota regulates immune defense against respiratory tract influenza A virus infection. Proceedings of the National Academy of Sciences of the United States of America 108 (2011). M, S. M. et al. Innate Immunity and Asthma Risk in Amish and Hutterite Farm Children. The New England journal of medicine 375 (2016). Flies, E. J. et al. Cities, biodiversity and health: we need healthy urban microbiome initiatives. Cities & Health 2 (2018). Flies, E. J. et al. Biodiverse green spaces: a prescription for global urban health. Frontiers in Ecology and the Environment 15 (2017). Li, G., Sun, G. X., Ren, Y., Luo, X. S. & Zhu, Y. G. Urban soil and human health: a review. European Journal of Soil Science 69 (2018). Reese et al. Urban stress is associated with variation in microbial species composition-but not richness-in Manhattan. The ISME journal emultidisciplinary journal of microbial ecology 10 , 751-760 (2016). Falkowski, P. G., Fenchel, T. & Delong, E. F. The Microbial Engines That Drive Earth's Biogeochemical Cycles. Science 320 , 1034-1039 (2008). McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosystems 11 (2008). Haitao, W. et al. Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces. Environmental science & technology 52 (2018). Francini, G. et al. Soil biota in boreal urban greenspace: Responses to plant type and age. Soil Biology and Biochemistry 118 (2018). M, M. K. & J, L. J. H. Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168 (2012). A, V. J. Microbial life in the phyllosphere. Nature reviews. Microbiology 10 (2012). Woodward, F. I. & Lomas, M. R. Vegetation dynamics--simulating responses to climatic change. Biol Rev Camb Philos Soc 79 , 643-670 (2004). L, G. K., W, S. J., Nejc, S., John, G. & Ashley, S. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nature communications 10 (2019). Michael, F. et al. Nitrogen fixation by phyllosphere bacteria associated with higher plants and their colonizing epiphytes of a tropical lowland rainforest of Costa Rica. The ISME journal 2 (2008). Chen, Q.-L., An, X.-L., Zheng, B.-X., Ma, Y.-B. & Su, J.-Q. Long-term organic fertilization increased antibiotic resistome in phyllosphere of maize. Science of the Total Environment 645 (2018). Kubiak, D. O., Piotr. Non-forested vs forest environments: The effect of habitat conditions on host tree parameters and the occurrence of associated epiphytic lichens. Fungal Ecology 47 (2020). Bäcklund, S., Jönsson, M., Strengbom, J., Frisch, A. & Thor, G. A Pine Is a Pine and a Spruce Is a Spruce--The Effect of Tree Species and Stand Age on Epiphytic Lichen Communities. PLoS ONE 11 , e0147004- e0147004 (2017). B, L. J. et al. Experimental insights into the importance of aquatic bacterial community composition to the degradation of dissolved organic matter. The ISME journal 10 (2016). Tanja, M. & L, S. S. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics (Oxford, England) 27 (2011). Torbjørn, R., Tomáš, F., Ben, N., Christopher, Q. & Frédéric, M. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4 (2016). J, C. B. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods 13 (2016). Christian, Q. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research 41 (2013). Evan, B. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology 37 (2019). Polson, S. W. Comparative analysis of microbial community structure associated with acroporid corals during a disease outbreak in the Florida reef tract , Medical University of South Carolina., (2007). Archer, E. rfPermute: Estimate Permutation p-Values for Random Forest Importance Metrics. (2016). Dong, Z. et al. Insights into the roles of fungi and protist in the giant panda gut microbiome and antibiotic resistome. Environment international 155 , 106703-106703 (2021). Bulgarelli, D. et al. structure and functions of the bacterial microbiota of plants further. (2019). Rahim, R. A. et al. Distinctive phyllosphere bacterial communities in tropical trees. (2013). N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proceedings of the National Academy of Sciences (2009). Laforest-Lapointe, I., Messier, C. & Kembel, S. W. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome 4 , 27 (2016). Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against Leaf-Pathogenic Pseudomonas syringae by Sphingomonas Strains in a Controlled Model System. Applied and Environmental Microbiology 77 , 3202-3210 (2011). Abanda-Nkpwatt, D. et al. Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. Journal of Experimental Botany , 4025-4032 (2006). Michael et al. Nitrogen fixation by phyllosphere bacteria associated with higher plants and their colonizing epiphytes of a tropical lowland rainforest of Costa Rica. The ISME journal (2008). Frank et al. Evidence for foliar endophytic nitrogen fixation in a widely distributed subalpine conifer. The New Phytologist (2016). Vacher, C. & Pauvert, C. Microbial associations and interactions at the plant-climate interface. (2017). Tylianakis, J. M. & Coux, C. Tipping points in ecological networks. Trends in Plant Science 19 , 281-283 (2014). Changyi, L. et al. Aboveground plants determine the exchange of pathogens within air-phyllosphere-soil continuum in urban greenspaces. Journal of Hazardous Materials 465 , 133149- (2024). Mu, F. et al. Source identification of airborne bacteria in the mountainous area and the urban areas. Atmospheric Research 231 , 104676-104676 (2020). Barberán, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. Proceedings of the National Academy of Sciences of the United States of America 112 , 5756-5761 (2015). Shao, F. et al. Study on different particulate matter retention capacities of the leaf surfaces of eight common garden plants in Hangzhou, China. The Science of the Total Environment 652 , 939-951 (2019). Knief, C., Delmotte, N., Chaffron, S., Stark, M. & Vorholt, J. A. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. The ISME Journal 6 , 1378-1390 (2011). affSchool of Biological Sciences, U. o. C., Christchurch, New Zealand/raw. Driving factors of epiphytic bacterial communities: A mini-review. Journal of Advanced Research 19 , 57-65 (2019). Ravenscraft, A., Berry, M., Hammer, T., Peay, K. & Boggs, C. Structure and function of the bacterial and fungal gut microbiota of Neotropical butterflies. Ecological Monographs 89 (2019). Rashid, M. M., Hunja, M., Akio, T. & M, K. E. Evaluation of rhizosphere, rhizoplane and phyllosphere bacteria and fungi isolated from rice in Kenya for plant growth promoters. SpringerPlus 2 , 606 (2013). grid.38678.32, Département des Sciences Biologiques, Université du Québec à Montréal, H3C 3P8, Montréal, Québec, Canada et al. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546 , 145-147 (2017). A., G. E. et al. Plant host identity and soil macronutrients explain little variation in sapling endophyte community composition: Is disturbance an alternative explanation? Journal of Ecology 107 , 1876-1889 (2019). Liangmu, W., Meirong, M., Xiaofei, L., Peng, L. & Wenqing, W. Differentiation between true mangroves and mangrove associates based on leaf traits and salt contents. Journal of Plant Ecology 4 , 292-301 (2011). W, K. S. et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proceedings of the National Academy of Sciences of the United States of America 111 , 13715-13720 (2014). Isabelle, L.-L., Christian, M. & W, K. S. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome 4 , 27 (2016). Lemian, L., Shanshan, W. & Jianfeng, C. Transformations from specialists to generalists cause bacterial communities are more stable than micro-eukaryotic communities under anthropogenic activity disturbance. The Science of the total environment 790 , 148141-148141 (2021). Lomolino, M. V. Ecology's Most General, Yet Protean Pattern: The Species-Area Relationship. Journal of Biogeography 27 , 17-26 (2000). The Microbiome Center, D. o. S., University of Chicago , Chicago , Illinois 60637 , United States. et al. Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces. Environmental science & technology 52 , 5115-5124 (2018). Yan, B. et al. Urban-development-induced Changes in the Diversity and Composition of the Soil Bacterial Community in Beijing. Rep 6 , 38811 (2016). Pearse, W. D. et al. Homogenization of plant diversity, composition, and structure in North American urban yards. Ecosphere 9 (2018). Mckinney, M. L. Urbanization as a major cause of biotic homogenization. Biological Conservation , 127 (2006). Proppe, D. S., Sturdy, C. B. & St. Clair, C. C. Anthropogenic noise decreases urban songbird diversity and may contribute to homogenization. Global Change Biology 19 , 1075-1084 (2013). Wang, X., Jens-ChristianLiu, JiajiaZhao, ZhichunZhang, ZhaochenFeng, GangSi, XingfengZhang, Jian. Regional effects of plant diversity and biotic homogenization in urban greenspace - The case of university campuses across China. Urban Forestry & Urban Greening 62 (2021). Ouyang, S., WenhuaGou, MengmengChen, LiangLei, PifengXiao, WenfaDeng, XiangwenZeng, LixiongLi, JiangrongZhang, TaoPeng, ChanghuiForrester, David, I. Stability in subtropical forests: The role of tree species diversity, stand structure, environmental and socio-economic conditions. Global ecology and biogeography 30 (2021). Mao, Q., Lu, X., Mo, H., Gundersen, P. & Mo, J. Effects of simulated N deposition on foliar nutrient status, N metabolism and photosynthetic capacity of three dominant understory plant species in a mature tropical forest. The Science of the Total Environment 610-611 , 555-562 (2018). Wright, I. J. et al. Modulation of leaf economic traits and trait relationships by climate. Glob. Ecol. Biogeogr 15 , 411-421 (2010). Gorné, L. D., Díaz, S., Minden, V., Onoda, Y. & Boenisch, G. The acquisitive-conservative axis of leaf trait variation emerges even in homogeneous environments. Annals of Botany (2020). Jian, L. et al. Plant identity shapes phyllosphere microbiome structure and abundance of genes involved in nutrient cycling. The Science of the total environment 865 , 161245-161245 (2022). Chaudhary, I. J. & Rathore, D. Dust pollution: Its removal and effect on foliage physiology of urban trees. Sustainable Cities and Society 51 , 101696-101696 (2019). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables1.xlsx Table S1 The statistics of obtained sequences data of 16S rRNA gene amplicon sequencing in the present study. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Jun, 2024 Reviews received at journal 13 May, 2024 Reviewers agreed at journal 07 May, 2024 Reviewers invited by journal 26 Apr, 2024 Editor assigned by journal 26 Apr, 2024 Editor invited by journal 17 Apr, 2024 Submission checks completed at journal 17 Apr, 2024 First submitted to journal 10 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4244944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292523721,"identity":"813163f3-4025-48c5-832d-c82286944eea","order_by":0,"name":"Huan Wang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Wang","suffix":""},{"id":292523722,"identity":"07c52f82-aebe-41a5-8784-eb54a5ca5256","order_by":1,"name":"Yilong Feng","email":"","orcid":"","institution":"Chongqing Landscape and Gardening Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yilong","middleName":"","lastName":"Feng","suffix":""},{"id":292523723,"identity":"4120eccd-f8ed-4eef-ae84-124e67025730","order_by":2,"name":"Qiaoyong Zhang","email":"","orcid":"","institution":"Chongqing Landscape and Gardening Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qiaoyong","middleName":"","lastName":"Zhang","suffix":""},{"id":292523724,"identity":"775cbb4c-fa4b-43b3-b470-3fe700abc08a","order_by":3,"name":"Min Zou","email":"","orcid":"","institution":"Chongqing Landscape and Gardening Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zou","suffix":""},{"id":292523725,"identity":"ad720287-19a2-4629-8339-564e4407b33e","order_by":4,"name":"Ting Li","email":"","orcid":"","institution":"Chongqing Landscape and Gardening Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""},{"id":292523726,"identity":"e2d3fcca-c26f-446c-a92c-1e7c870bb584","order_by":5,"name":"Lijiao Ai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3QMWrDMBTG8QcCefnAq9KY9AoyBtPB0Ku8ENCk3KB0KXgyPUuP4FRUWXKADIUYAjlCyBRqe+sieyxU/+2BfkhPRLHYXywhbhkVUtEOo8qmiaB112UmW9Q8EswhIu+qz0r7kdA0Sd/ooti2KLzwD8eXJ1Divj5CRDkyig/fKL3cFNb3D4Mxx+A1A1k3F5SnJj9b2ROFMkgeR3J3KGpoZ+8ziHa00QwHLZGft/UMkrv+kxkGathl+64gp3ZZ7Rve3VA9p7XwS3t9XaWJ8+H1Cfx7luHjQ0k7fSYWi8X+dz+2WEP98XK5FwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Landscape and Gardening Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Lijiao","middleName":"","lastName":"Ai","suffix":""},{"id":292523727,"identity":"70fef367-9518-4624-87e2-a77e7330b484","order_by":6,"name":"Haiyang Wang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-10 04:05:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244944/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55005207,"identity":"7ebc7eb8-8e42-4c85-a8b3-16ed315e5985","added_by":"auto","created_at":"2024-04-19 18:48:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3391644,"visible":true,"origin":"","legend":"\u003cp\u003eSample sites information. (A) Sampling distribution of Yongchuan District, Chongqing City. (B-I) Satellite view of the site of BSGY, ZYGY, LHGC, WQGC, WCDL, RMDL, WLXY, YCZX espectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/917e41543aa1c3470dfc6c88.png"},{"id":55005199,"identity":"a5ac5ab7-871b-4aa2-bc1d-c6f0243413f6","added_by":"auto","created_at":"2024-04-19 18:48:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1132720,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of different phyla (a) and genera (b) in the samples (top 30).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/4d84a0f540f666a09aa71f6f.png"},{"id":55005203,"identity":"7cd36f43-cc4e-475d-9650-42ad672fa2ec","added_by":"auto","created_at":"2024-04-19 18:48:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":447634,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots displaying the alpha diversity measures (including the number of observed operational taxonomic units and Shannon diversity) for the bark vs leaf and shrub vs tree groups (Fig 3a, 3b, 3c, 3d).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/412c6fae1c53fcd87bcb0f81.png"},{"id":55005200,"identity":"9eb28385-61d6-4626-a5ce-7d5e42e33307","added_by":"auto","created_at":"2024-04-19 18:48:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1047091,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Phylogenetic diversity of the bacterial. (B, C) Venn diagram showing the numbers of unique and shared genera in bark and leaf, tree and shrub phyllosphere samples. (D) Upset plots showing variation partitioning models of 4 four urban green space community composition. (E, F) Circos plot of the top 5 abundant bacterial features at the phylum levels in bark and leaf, tree and shrub phyllosphere samples.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/275f89ef926dee0244c2602a.png"},{"id":55005205,"identity":"26fe9e81-e6f0-48c2-8691-37d9bcb70985","added_by":"auto","created_at":"2024-04-19 18:48:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":991328,"visible":true,"origin":"","legend":"\u003cp\u003e(A) A color gradient indicating Pearson’s correlation coefficients was used to depict pairwise comparisons of environmental factors. Partial Mantel tests reveal the relationships between each environmental factor and the diversity indexes. Edge width represents Mantel’s r statistic for the corresponding distance correlations, while edge color indicates the statistical significance. (B) Random forest analysis predicts the important influencing factors for bacterial alpha diversity. (C) Redundancy analysis (RDA) biplot of the distribution of bacterial communities with environmental factors in green space types.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/71d9f08584155c86ae35c530.png"},{"id":55005201,"identity":"d47893f1-63cb-4234-99b3-b0e3b5fef33b","added_by":"auto","created_at":"2024-04-19 18:48:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3564092,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork complexity of community (A) total sample, (B) shrub (C) tree, (D) bark surface, (E) leaf surface, (F) park, (G) square, (H) road, (I) campus.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/034ea687a673c5d26013d280.png"},{"id":55006007,"identity":"110d93f8-efd9-4685-acc2-c2148bd0109d","added_by":"auto","created_at":"2024-04-19 18:56:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6369729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/6634cdc6-50ea-4e40-b985-b8754a0990c1.pdf"},{"id":55005206,"identity":"6d0be710-99de-46f9-b650-08a890012583","added_by":"auto","created_at":"2024-04-19 18:48:12","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e The statistics of obtained sequences data of 16S rRNA gene amplicon sequencing in the present study.\u003c/p\u003e","description":"","filename":"SupplementaryTables1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4244944/v1/649f40d7cd6a9605832f8433.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDue to the growing urbanization of human populations, green spaces have become increasingly important for human health \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The evidence for the health benefits of urban green spaces biodiversity is growing, including reduced blood pressure, reduced pain, lower cortisol levels, and reduced mortality rates from all causes \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In the urban ecosystem, microorganisms play an essential role in regulating ecosystem services \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, such as pollutant remediation, nutrient cycling, and genetic diversity conservation \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Urban ecosystems must perform these functions to reduce the growing global burden of chronic diseases associated with urban environments and to maintain the well-being of citizens living in cities. Human health can be compromised by the loss of contact with a diverse environment microbiome (a specific environment's community of microbes, including bacteria, fungi, archaea, and viruses), as well as key microbial taxa - microbial 'old friends' \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Experiencing environmental microbiomes shapes the human microbiome, which helps educate and maintain a healthy immune system \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. It directly influences the development of the immune system \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and predisposition to infectious and non-infectious diseases \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Human exposure to soil and plant microorganisms in urban green spaces appears to suppress inflammation and reduce immune dysfunction \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Despite the lack of understanding about why these positive health outcomes occur, it is likely a result of human interactions with animals, plants, and microbes.\u003c/p\u003e \u003cp\u003eLand uses have changed dramatically over the past several decades due to the massive shift of population from rural to urban areas, potentially changing microorganism habitats \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. To predict and mitigate the impact of anthropogenic disturbances on microbial communities in urban environments, we must have a better understanding of how these disturbances affect them \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. While urban microbial ecosystems are likely to play an important role in human health, they are not well studied in comparison to animal and plant communities \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eManagement practices are intensive in urban ecosystems, The microbial community in urban green spaces is influenced not only by the environment (Factors such as soil properties and climate), but also by human interactions \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To date, a few studies have reported that urbanization can substantially change the microbiota in urban green spaces \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, In urban green spaces, however, there is a great deal of variation in microbial community profiles, and it remains unclear what factors contribute to this. The impact of urbanization on soil bacteria has largely been studied. The phyllosphere plays a significant role in microorganisms' habitat, but the phyllosphere and bark epiphytic bacteria have been overlooked.\u003c/p\u003e \u003cp\u003eAccording to previous studies, the phyllosphere provides a great opportunity to test basic ecological principles in microbiology \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. With an estimated area of more than 1\u0026nbsp;billion square kilometers, it is one of the world's largest environments \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The phyllosphere microbiome plays a variety of important ecosystem functions besides supporting plant growth \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Other ecosystem functions are performed by phyllosphere microbiomes, for example, reducing plant ethanol emissions and fixing nitrogen as part of Earth's biogeochemical cycles \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Also, as phyllosphere microbiomes serve as a bridge between environmental and human microbiomes, phyllosphere microbiomes are closely related to human health \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Community composition and diversity of epiphytic species are affected not only by air pollution, but also by growth habitat, tree characteristics (e.g., bark properties such as water-holding capacity and bark pH) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, tree species \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, etc.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to examine patterns of phyllosphere and bark epiphytic bacteria of different plants from urban green spaces in Yongchuan district, Chongqing, China. In our study, we examined bacterial abundance and diversity by high-throughput amplicon sequencing of the small-subunit ribosomal RNA (16S rRNA) gene. In addition, we examine well-replicated plots of four different types of urban green space to integrate these bacterial results with vegetation and environmental surveys (road green space, park green space, square green space, and campus green space). In our analysis, the main aims were to (i) determine the potential differences and links between leaf and bark phyllosphere bacterial communities; (ii) determine the most important factors shaping the bacterial community profiles in urban green spaces, and (iii) understanding community correlation network structure and keystone taxa of samples.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling and experiment design\u003c/h2\u003e \u003cp\u003eIn April 2023, leaf and bark samples were collected at 8 sites in Yongchuan (105\u0026deg;93\u0026prime;E, 29\u0026deg;36\u0026prime;N), Chongqing, China. The sites included road greenspaces (2 sites), parks greenspaces (2 sites), square greenspaces (2 sites), and subsidiary community greenspaces (2 sites), which represent the typical types of greenspaces in urban environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Square green space is a venue for urban public activities with functions such as recreation, commemoration, assembly, and refuge. Park green spaces meet the leisure needs of urban residents and provide places for rest, excursions, exercise and other collective cultural activities. Road green space refers to the ground used for the cultivation of plants and landscaping within the land area of a road. It is mainly used to purify the air, beautify the environment, prevent noise, and guide the sight of drivers. Campus green space, as an adjunct to the school, provides a comfortable recreational and landscaped site for students and faculty. In order to minimize the effects of weather on sample data, sampling was conducted within one day after seven days without rainfall. In each sampling site, 10 \u0026times; 10 m squared (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) were constructed. Leaf and trunk epidermis samples were collected from dominant species of trees and dominant species of shrubs in different types of green spaces. Sterile scissors were used to collect leaves and trunk epidermis from three plants at the same stage of growth and from the same height above ground, and then the leaves and trunk epidermis were placed in labeled sterile bags. Collection of plant material comply with relevant institutional, national, and international guidelines and legislation. The basic information on the sampling points is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Three parallel replicates were measured for each sample and a total of 90 samples were collected. The leaves and trunk epidermis samples were cut with sterilized scissors to obtain equal weight, placed in a sterile centrifuge tube with PBS buffer for a while, centrifuged to remove the supernatant, resuspended by adding 1 mL of buffer, snap-frozen in liquid nitrogen, and stored at -80 ℃ for DNA extraction. Data of atmospheric concentration of meteorological parameters and gaseous pollutants (including wind speed, wind direction, atmospheric temperature, soil temperature, TBQ total radiation, radiation accumulation, soil humidity, atmospheric humidity, negative oxygen ions, noise, PM2.5, PM10) were obtained from the hourly monitor by atmospheric fixed-site monitoring stations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation table of plots of different green space types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of green spaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCoordinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAltitude (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTree dominant species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShrub dominant species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSquare green space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLHGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;54\u0026prime;52.72\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime; 10.55\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMagnolia denudata Desr\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAlpinia zerumbet 'Variegata'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWQGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;53\u0026prime;25.33\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime;26.95\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eOsmanthus fragrans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAucuba japonica var. variegata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRoad green space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWCDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;54\u0026prime;49.57\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime;52.09\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eBauhinia purpurea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eLoropetalum chinense var. rubrum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;55\u0026prime;45.32\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;20\u0026prime;48.48\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMagnolia grandiflora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCamellia japonica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCampus\u003c/p\u003e \u003cp\u003eGreen space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWLXY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;56\u0026prime; 11.43\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime;4.73\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEucalyptus robusta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSpiraea salicifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCZX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;56\u0026prime;39.33\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;20\u0026prime;46. 12\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePrunus cerasifera 'Atropurpurea'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eLigustrum lucidum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePark green space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZYGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;55\u0026prime;38.71\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime; 17.46\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eFicus virens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePittosporum tobira\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBSGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026deg;53\u0026prime;35.16\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026deg;21\u0026prime;20.59\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eOsmanthus fragrans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. DNA Extraction, PCR Amplification and Illumina MiSeq Sequencing\u003c/h2\u003e \u003cp\u003eAs directed by the manufacturer, genomic DNA was extracted from each sample using the E.Z.N.A.\u0026reg;Soil DNA Kit. Total DNA was eluted with 50 \u0026micro;L TE buffer (Tris-hydrochloride buffer, pH 8.0, 1.0 mM EDTA contained). In order to determine DNA concentration and purity, a NanoDropTM 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used and samples were stored at -80℃ until PCR amplification. PCR amplification of the V3-V4 region of the 16S ribosomal RNA gene was carried out on bacteria (98℃ for 30 s; with 35 cycles at 98℃ for 10 s, 54℃ for 30 s, and 72℃ for 45 s; and a final extension at 72℃ for 10 min) using a primer set of 341F (5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;) and 805R (5\u0026rsquo;-GACTACHVGGGTATCTAATCC-3\u0026rsquo;) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Barcodes were added to the 5' ends of the primers and universal primers were used. Each PCR reaction was conducted in a 25 \u0026micro;L mixture containing 12.5 \u0026micro;L of 2\u0026times; Phusion\u0026reg; Hot Start Flex Master Mix, 2.5 \u0026micro;L of each primer (1 \u0026micro;M), and 50 ng of template DNA. Nuclease-free water served as blank. For the DNA extraction process, ultrapure water was used instead of a sample solution to ensure that there were no false positives. Throughout the DNA extraction process, ultrapure water was used in place of template DNA as a negative control to exclude false-positive PCR results. 2% agarose gel electrophoresis was used to verify the PCR amplicon size. PCR products were purified using AMPure XT beads from Beckman Coulter Genomics in Danvers, MA, USA, and quantified using Qubit from Invitrogen, USA. The amplicon library was sized and quantified using an Agilent 2100 Bioanalyzer (Agilent, USA) and a Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. A NovaSeq PE250 platform at LC-Bio Technology Company (Hangzhou, China) was used to sequence the libraries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sequencing Data Processing and Bioinformatic Analyses\u003c/h2\u003e \u003cp\u003eBased on the samples' unique barcodes, paired-end reads were assigned, then truncated by cutting off the barcodes and primer sequences, and merged with FLASH \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Fqtrim software was used to trim and filter the raw reads, and Vsearch software was used to further filter the chimeric reads \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The amplicon sequence variants (ASVs) were generated with the DADA2 package \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Through BLAST searches, all of the sequence reads were compared against the Silva rRNA database\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Using the average abundance of each group, the relative abundance of each taxon was calculated by normalizing assigned reads to the total number of qualified reads. The rarefaction curves were generated using custom Perl scripts for each sample. The BioVenn software was used to plot Venn diagrams showing the shared and unique features (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biovenn.nl/index.php\u003c/span\u003e\u003cspan address=\"http://www.biovenn.nl/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed on 2 November 2022). To analyze the complexity of species, the alpha diversity indices (Observed species, Shannon diversity, and Simpson evenness) were calculated using QIIME 2 (Quantitative insights into microbial ecology 2) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. One-way ANOVA or t-tests was used to test the significance of variance between or among samples using SPSS 22.0 (SAS Institute Inc., Cary, NC, USA). An analysis of beta diversity was used to display and compare bacterial community compositions. With the QIIME 2 plugin, PCA (principal component analysis) was conducted \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Using the group average method, hierarchical agglomerative clustering (to group objects similar to each other in clusters) was carried out on the most abundant features according to the groups selected. By using the OmicStudio tool, a heatmap of bacterial communities was generated, with Bray-Curtis similarity calculations used to cluster relative abundance data \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Unless otherwise stated, all statistical analyses were conducted at a significance level of 0.05. With the \"random Forest\" and \"rfPermute\" R packages, we examined the most important environmental factors driving bacterial Shannon diversity \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Venn diagrams showing the shared and unique features were plotted, using BioVenn (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biovenn.nl/index.php\u003c/span\u003e\u003cspan address=\"http://www.biovenn.nl/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Redundancy analysis was performed using the OmicStudio tools at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicstudio.cn/tool\u003c/span\u003e\u003cspan address=\"https://www.omicstudio.cn/tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Correlation network analysis used the igraph (Version 1.2.6) package of R (Version 3.6.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sequence data results and alpha diversity of communities\u003c/h2\u003e \u003cp\u003eThe sequencing process utilizing the 16S rRNA genes yielded a cumulative count of 1,892,424 sequences that underwent quality filtration, amounting to 1.23 Gb of valid data. The sequence count per sample varied between 38,366 and 72,275. Following dereplication using DADA2 within QIIME2, a total of 9,626 prokaryotic operational taxonomic units (OTUs) were acquired. As the quantity of sequencing data increased, all rarefaction curves exhibited saturation, resulting in an average Good's coverage of 88.93% across all samples (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Proteobacteria (43.28%) were the most abundant phylum of bacteria, followed by Cyanobacteria (35.51%), Actinobacteriota (5.93%), and Firmicutes (4.37%). At the genus level, Chloroplast_unclassified (35.79%), Sphingomonas (7.28%), Ralstonia (4.11%), Methylobacterium-Methylorubrum (3.30%), and Phyllobacterium (3.20%) were the top 5 abundant genera, and these features accounted for 53.65% of the entire collection, while unclassified represents sequences marked as unclassified bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Species composition of bacterial communities among different content\u003c/h2\u003e \u003cp\u003eComparing the bark group and leaf groups, there were notable disparities observed in the diversity indices (Shannon diversity and observed otus) (ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Conversely, no statistically significant distinction was identified in the alpha diversity indices when comparing the shrub and tree groups (Shannon diversity and observed otus) (ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the discrepancies in bacterial characteristics between the stem and leaf groups, as depicted in a Venn diagram, it was observed that these two groups shared 1633 bacterial features in common, while the bark and leaf group possessed 6343 and 1650 unique features, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Similarly, the tree and shrub groups exhibited 5102 and 2402 unique features, respectively, with 2102 bacterial features being common to both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In the within-group variation analysis of different green space types, road green space contained the most microbial species at 3773. This was followed by parks, plazas, and ancillary (campus) green space at 3640,2987, and 2800, respectively. A total of 580 cooccurring types were found in several types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom samples of all sites, the most abundant microbial phylum was Proteobacteria (43.28%), and other major groups were Cyanobacteria (35.51%), Actinobacteriota (5.93%), Firmicutes (4.37%) and Bacteroidota (4.19%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Samples were analyzed for clustering using Bray-Curtis distance analysis. Among the different green space types, road green spaces (CZ and RM) had the closest clustering of sample composition and the most similar surface community structure, while the other three green spaces (square green space, campus green space, and park green space) types were not differentiated. The five most abundant phyla were consistent across the tree-shrub and bark-leaf groups, respectively. However, the proportions of distribution of each dominant species varied considerably between sample groups. Proteobacteria and Cyanobacteria accounted for 44.53% and 34.63% of the shrub group, respectively. Similarly, the dominant species in tree groups, Proteobacteria and Cyanobacteria accounted for 41.85% and 37.05% of the tree group, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). However, within the leaf and bark, there were large differences in the proportion of dominant species composition. The most two abundant microbial phylum in the bark were Proteobacteria (54.77%) and Cyanobacteria (15.55%). In the leaf group, the dominant species is Cyanobacteria rather than Proteobacteria, with the proportions of Cyanobacteria and Proteobacteria being 56.46% and 31.25%, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Environmental factors shaping the bacterial community compositions in urban green spaces\u003c/h2\u003e \u003cp\u003eFor the environment properties, mainly the air temperature, soil temperature, TBQ radiation accumulation, and noise correlated positively with wind speed, while wind direction and atmospheric moisture correlated negatively with wind speed, air temperature, soil temperature, and TBQ radiation accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The partial Mantel test indicated that the diversity indexes of observed-otu, Shannon, and pielou-e index correlated significantly with wind speed. Simpson index correlated significantly with air temperature and soil temperature, and goods coverage correlated significantly with atmospheric moisture. Most other physicochemical contents were significantly negatively correlated with the diversity indexes. The results of the random forest model showed that the rank order of importance of community environmental factors was soil temperature, atmospheric moisture, air temperature, wind speed, negative oxygen ions, PM2.5, and radiation accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The results of RDA showed that the surface bacteria community was regulated by multiple environmental variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The first axis of RDA explained 12.91% of the variation of species-environment relation, while the two axes together explained 18.44% of variation (p\u0026thinsp;=\u0026thinsp;0.001). Wind direction, wind speed, and soil moisture appeared to be the three most significant factors affecting the bacteria community.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation network analysis between microbial community\u003c/h2\u003e \u003cp\u003eThe average changes in network properties were used to define network complexity, and average degrees and edges were the most important parameters \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Edges indicate significant correlations between microbial taxa, and average degrees indicate the network's overall connectivity. To determine the correlations among species, the richness information at the genus level was used for the top 30 species based on their richness in the different samples. Correlation networks are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (A-H). There is a correlation between the two species indicated by the connections between the nodes, a positive correlation is shown by the yellow solid line, and a negative correlation is shown by the grey dashed line. A connection's strength is determined by the thickness of the line between any two nodes. The size and color of the dots represent the number of related objects. According to the co-occurrence network analysis of phyllosphere bacteria, the composition of the bacterial community is strongly correlated among whole samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Spearman\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;0.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, average degree:11.52). \u003cem\u003eAbditibacterium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eSphingomonadaceae\u003c/em\u003e_unclassified, and \u003cem\u003eAcidiphilium\u003c/em\u003e dominated the community structure, with node degrees 20, 17, 17 and 17 respectively. Between \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e and \u003cem\u003eRalstonia, Mesorhizobium\u003c/em\u003e and \u003cem\u003ePhyllobacterium\u003c/em\u003e, \u003cem\u003eActinomycetospora\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e, there were positive correlations, with rho 0.954, 0.938 and 0.935 respectively. Between Chloroplast_unclassified and others, \u003cem\u003eChloroplast\u003c/em\u003e-unclassified and \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eMitochondria\u003c/em\u003e_unclassified and others, there were negative correlations, with rho \u0026minus;\u0026thinsp;0.942, -0.867 and \u0026minus;\u0026thinsp;0.810 respectively. Between the tree and shrub groups, there were large changes in the major species in the community structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). The co-occurrence network for tree samples showed that the network complexity (edge: 112.501; average degree: 11.310) was greater than the shrub samples (edge: 103.267; average degree: 9.742). \u003cem\u003eAbditibacterium\u003c/em\u003e, \u003cem\u003eSphingomonadaceae\u003c/em\u003e_unclassified, and \u003cem\u003eActinomycetospora\u003c/em\u003e dominated the community structure in shrub samples with node degrees 20, 18, and 18, while \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eAbditibacterium\u003c/em\u003e, and \u003cem\u003eSphingomonadaceae\u003c/em\u003e_unclassified in tree samples with node degree 19, 18 and 18. There were positive correlations between \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e and \u003cem\u003eRoseomonas\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e, and \u003cem\u003eActinomycetospora\u003c/em\u003e and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e_unclassified in shrub samples, with rho 0.952, 0.943 and 0.930 respectively. There were negative correlations between \u003cem\u003eChloroplast\u003c/em\u003e_unclassified and \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eChloroplast\u003c/em\u003e_unclassified and Others, and \u003cem\u003eMitochondria\u003c/em\u003e_unclassified and \u003cem\u003eSphingomonas\u003c/em\u003e in shrub samples, with rho \u0026minus;\u0026thinsp;0.921, -0.903 and \u0026minus;\u0026thinsp;0.886 respectively. In the tree group, there were positive correlations between \u003cem\u003eMesorhizobium\u003c/em\u003e and \u003cem\u003ePhyllobacterium\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e, and \u003cem\u003eAmnibacterium\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e-\u003cem\u003eMethylorubrum\u003c/em\u003e, with rho 0.962, 0.941 and 0.932 respectively. Negative correlations were presented between \u003cem\u003eChloroplast\u003c/em\u003e_unclassified and Others, \u003cem\u003eAcetobacteraceae\u003c/em\u003e_unclassified and \u003cem\u003eChloroplast\u003c/em\u003e_unclassified, and \u003cem\u003eChloroplast\u003c/em\u003e_unclassified and others, with rho \u0026minus;\u0026thinsp;0.941, -0.865 and \u0026minus;\u0026thinsp;0.856 respectively. Similarly, on the bark epiphytic and phyllosphere, there were significant differences in the relationships between microorganisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). On the bark epiphytic, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eAcidisoma\u003c/em\u003e, and \u003cem\u003eMesorhizobium\u003c/em\u003e were the top three species that dominated the community structure, while \u003cem\u003eAbditibacterium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eActinomycetospora\u003c/em\u003e in the phyllosphere. The co-occurrence network for leaf samples showed that the network complexity (edge: 101.126; average degree: 10.000) was greater than bark samples (edge: 71.448; average degree: 7.724). A similar analysis also arose between different green space types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-I), the network complexity of park green space was greater than the other three green spaces. In the park, square, campus, and road green space types, the distribution of species that account for the most importance in the community was \u003cem\u003eAcetobacteraceae\u003c/em\u003e_unclassified, \u003cem\u003eMethylobacterium\u003c/em\u003e-\u003cem\u003eMethylorubrum\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eCaulobacteraceae\u003c/em\u003e_unclassified respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e4.1 Microbial diversity and community structure in urban green spaces are affected by multiple aspects of urbanization\u003c/p\u003e\n\u003cp\u003eThere are unique opportunities for human and natural environmental microbes to interact on the surface of plants in urban greenspaces, which are vital but neglected ecological milieus. As a result of this study, proteobacteria, cyanobacteria, actinobacteria, and firmicutes were abundant in bacterial communities both in trees and shrubs. These phyla have been found to be abundant in the phyllosphere in previous studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It has also been reported that \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e (Proteobacteria) are ubiquitously present in the phyllosphere of trees and grasses \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Further, some Sphingomonas strains can protect plants from plant pathogens \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while members of the Methylobacterium can utilize methanol released by plants to promote their host\u0026apos;s growth \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Additionally, Proteobacteria and Cyanobacteria contribute to the cycle of nitrogen in the atmosphere \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. To maintain the productivity and resilience of urban ecosystems, especially under the conditions of global climate change, these taxa should be further explored for their features such as plant growth promotion and biocontrol \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA significant impact of plant types and green spaces on alpha and beta diversity of bacteria in bark epiphytic and phyllosphere was observed in this study. Based on the fact that bark and leaves may be the primary sources of airborne microbes, it is reasonable to expect that the microbial communities on the surface of plant bark and leaves are closely related to the air microbiome. There may be a significant role for the mass movement of air in shaping airborne microbiomes \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In our study, trees had more species richness than shrubs, since trees are less affected by air movement than shrubs, and there was a relationship between the lesser wobbling effect of wind action on surface bacteria. A similar situation can be inferred in the grouping of bark and leaves. Our results indicate that microbial diversity and abundance in the bark group exceeded that of the phyllosphere. This may also be since bark are better fixed than leaves and are less affected by air movement. In addition, the morphological structure of the plant surface is also very important in influencing the attachment of bacteria to the surface \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Different microenvironments in which epiphytic bacteria live may explain this difference. Specifically, epiphytic bacteria are subjected to selection by their host plant via physiology, leaf morphology, and nutrients and volatile organic compounds (e.g. methanol) exported to the surface epiphytic bacteria, and the surrounding atmosphere, such as solar humidity, radiation, and temperature \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Compared to leaves, bark epiphytic have better conditions for providing attachment of microorganisms such as bacteria, which is consistent with our analyses that bark epiphytic retain more species abundance and complex community composition. Some studies have shown that some interleaf microorganisms have important functions such as promoting plant growth, adsorbing and degrading environmental pollutants, and guiding the closure of leaf stomata to prevent the entry of pathogenic bacteria \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Plants play an important role in phosphate dissolution, nitrogen fixation, nitrification, and balancing the global carbon and nitrogen cycles \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Plant identity significantly affected both the diversity and community structure of phyllosphere epiphytic and endophytic bacteria, as previous studies have shown \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Various plant species possess various functional traits, such as specific leaf area, nutrient content, osmotic properties, respiration rate, and dust retention efficiency which are significantly correlated with the surface bacteria community of leaf and stem \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. In various types of UGS, microbial communities exhibited notable differences, possibly due to their inherent adaptability. Consequently, nutrient disturbances led to a decline in nutrient-tolerant bacteria, and they were then replaced by generalists \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. There is generally an increase in species richness with the area as illustrated by the species-area relationship \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, and larger area urban green spaces are less exposed to urbanization and human impacts, and the microbiologic community is relatively less adversely affected. Of our different green space types, campuses and parks have larger areas, however in terms of OTU abundance, parks and roads go on to have greater abundance.\u003c/p\u003e\n\u003cp\u003e4.2 Various environmental factors as an important factor shaping the bacterial communities in urban green spaces\u003c/p\u003e\n\u003cp\u003eWe characterized the relationships between leaf and bark phyllosphere bacterial community compositions in urban green spaces with a variety of factors including soil, air, noise, and radiation properties. Some soil properties identified as key drivers in shaping soil microbial communities have been shown in many studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, and in this study, surprisingly, soil properties remained also one of the most important factors in shaping the characteristics of bacterial communities on the phyllosphere and bark epiphytic. In addition, air environmental parameters were second only to soil environmental parameters as important microbial community drivers analyzed by random forest. (找土壤、空气与叶际茎影响的文献)\u003c/p\u003e\n\u003cp\u003eSome studies have reported that the more highly urbanized a place is, the more homogenous the community structure of plants \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, ground animals \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, birds \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, and insects \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In our study, environmental factors that characterize urbanization were correlated with microbial diversity and also showed that the main factors of the urban greenhouse effect (air and soil temperature and humidity) were significantly correlated with microbial diversity. Species diversity is significantly and positively correlated with both community structure and stability \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, therefore, the greenhouse effect of urbanization can significantly affect the structure of bacterial communities on the surface of plant bark and leaves. In the RDA analysis, wind speed and direction were very important influences, which were very relevant to the effect of physical air movement on microbial attachment discussed above. Current research lacks in-depth linkages between environmental factors and microbial function, as well as the relationship between microbial function and human health, and future in-depth studies through large-scale and longitudinal analyses may lead to a better understanding of the microbial mechanisms underlying the impact of urban green spaces on human health.\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed the differences in community structure and interspecific differences among different groups by correlation network analysis. Overall, trees have a more complex network structure than shrubs, and leaves have a more complex network structure than bark. An interesting result here is that leaves have less microbial abundance relative to bark, but a complex community structure correlation. Plants\u0026apos; leaves are important organs determining their response to environmental change and highly plastic in long-term evolution \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, converting energy in ecosystems \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Aside from that, leaves are essential for photosynthesis and nutrient uptake in plants, so their role in plant growth is evident \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Since plant leaves grow and fall off periodically and are greatly affected by factors such as external air, it is possible to propose the idea that although leaf microbes are not as abundant as bark, they need to be more cohesive in order to keep the leaves growing and to maintain important physiological functions. It has been shown that plants indicate significant differences in microbial communities between urban habitats and that such differences are largely dependent on plant species \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. In our results, the highest abundance of attached microorganisms and stronger community associations were found in parkland green spaces relative to several other green space types. Some studies have shown that ficus is the most suitable plant species for sustainable urban planting because of its high dust deposition \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. It can be inferred that \u003cem\u003eFicus virens\u003c/em\u003e, the dominant tree in the parkland in this study, can play an important contribution by adsorbing microorganisms through dust retention. According to analysis of correlation network, \u003cem\u003eAbditibacterium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eSphingomonadaceae\u003c/em\u003e_unclassified, and \u003cem\u003eAcidiphilium\u003c/em\u003e dominated the whole community structure, which not dominant in high abundance. This revealed that the keystone taxa were not mostly observed in abundant species, which may be essential for preserving the ecosystem\u0026rsquo;s functions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData accessibility\u003c/h2\u003e\n\u003cp\u003eThe sequence data presented in the study were deposited in the Sequence Read Archive (SRA) repository of NCBI at https://www.ncbi.nlm.nih.gov/sra, accession number PRJNA1097078. The environmental parameters datasets used or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Declaration of competing interest\u003c/p\u003e\n\u003cp\u003eThis paper\u0026apos;s authors declare that they have no known competing financial interests or personal relationships that could have influenced their work.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Postdoctoral Science Foundation of Chongqing Natural Science Foundation (Grant No. CSTB2022NSCQ-BHX0740); the Performance Incentive and Guidance Special Project for Chongqing Scientific Research Institutes (No. cstc2022jxjl20010); the Scientific research project of Chongqing City Administration Bureau (No. CGK 2022-19); the National Natural Science Foundation of China (Grant No. 31670467).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eConceived and designed the experiments: A.L., W. H \u0026amp; F.Y. Performed the experiments: Z.Q., Z.M. \u0026amp; L.T. Analyzed the data: W.H. Wrote the paper: W.H. \u0026nbsp;All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRydin, Y.\u003cem\u003e et al.\u003c/em\u003e Shaping cities for health: complexity and the planning of urban environments in the 21st century. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e379\u003c/strong\u003e (2012).\u003c/li\u003e\n\u003cli\u003eNieuwenhuijsen, M. J., Khreis, H., Mas, M. T., Gascon, M. \u0026amp; Dadvand, P. Fifty Shades of Green. \u003cem\u003eEpidemiology\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e (2017).\u003c/li\u003e\n\u003cli\u003eRaf, A., Olivier, H. \u0026amp; An, V. N. Biodiversity and human health: mechanisms and evidence of the positive health effects of diversity in nature and green spaces. \u003cem\u003eBritish medical bulletin\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eTwohig-Bennett, C. \u0026amp; Jones, A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. \u003cem\u003eEnvironmental Research\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eStanhope, J., Breed, M. F. \u0026amp; Weinstein, P. Exposure to greenspaces could reduce the high global burden of pain. \u003cem\u003eEnvironmental Research\u003c/em\u003e \u003cstrong\u003e187\u003c/strong\u003e (2020).\u003c/li\u003e\n\u003cli\u003eNan, H.\u003cem\u003e et al.\u003c/em\u003e Soil microbial communities are shaped by vegetation type and park age in cities under cold climate. \u003cem\u003eEnvironmental microbiology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e (2017).\u003c/li\u003e\n\u003cli\u003eKabisch \u0026amp; Nadja. Ecosystem service implementation and governance challenges in urban green space planning\u0026mdash;The case of Berlin, Germany. \u003cem\u003eLand Use Policy\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 557-567 (2015).\u003c/li\u003e\n\u003cli\u003eRamirez, K. S.\u003cem\u003e et al.\u003c/em\u003e Biogeographic patterns in below-ground diversity in New York City\u0026apos;s Central Park are similar to those observed globally. \u003cem\u003eProceedings Biological Sciences\u003c/em\u003e \u003cstrong\u003e281\u003c/strong\u003e, 22 (2014).\u003c/li\u003e\n\u003cli\u003eLeena, v. H., Ilkka, H. \u0026amp; Tari, H. Natural immunity. Biodiversity loss and inflammatory diseases are two global megatrends that might be related. \u003cem\u003eEMBO reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2011).\u003c/li\u003e\n\u003cli\u003eA, R. G. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e (2013).\u003c/li\u003e\n\u003cli\u003eA, G. J.\u003cem\u003e et al.\u003c/em\u003e Current understanding of the human microbiome. \u003cem\u003eNature medicine\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eDaphna, R.\u003cem\u003e et al.\u003c/em\u003e Environment dominates over host genetics in shaping human gut microbiota. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e555\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eJ, C. E.\u003cem\u003e et al.\u003c/em\u003e The cellular composition of the human immune system is shaped by age and cohabitation. \u003cem\u003eNature immunology\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e (2016).\u003c/li\u003e\n\u003cli\u003eTakeshi, I.\u003cem\u003e et al.\u003c/em\u003e Microbiota regulates immune defense against respiratory tract influenza A virus infection. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e (2011).\u003c/li\u003e\n\u003cli\u003eM, S. M.\u003cem\u003e et al.\u003c/em\u003e Innate Immunity and Asthma Risk in Amish and Hutterite Farm Children. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e \u003cstrong\u003e375\u003c/strong\u003e (2016).\u003c/li\u003e\n\u003cli\u003eFlies, E. J.\u003cem\u003e et al.\u003c/em\u003e Cities, biodiversity and health: we need healthy urban microbiome initiatives. \u003cem\u003eCities \u0026amp; Health\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eFlies, E. J.\u003cem\u003e et al.\u003c/em\u003e Biodiverse green spaces: a prescription for global urban health. \u003cem\u003eFrontiers in Ecology and the Environment\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2017).\u003c/li\u003e\n\u003cli\u003eLi, G., Sun, G. X., Ren, Y., Luo, X. S. \u0026amp; Zhu, Y. G. Urban soil and human health: a review. \u003cem\u003eEuropean Journal of Soil Science\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eReese\u003cem\u003e et al.\u003c/em\u003e Urban stress is associated with variation in microbial species composition-but not richness-in Manhattan. \u003cem\u003eThe ISME journal emultidisciplinary journal of microbial ecology\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 751-760 (2016).\u003c/li\u003e\n\u003cli\u003eFalkowski, P. G., Fenchel, T. \u0026amp; Delong, E. F. The Microbial Engines That Drive Earth\u0026apos;s Biogeochemical Cycles. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e320\u003c/strong\u003e, 1034-1039 (2008).\u003c/li\u003e\n\u003cli\u003eMcKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. \u003cem\u003eUrban Ecosystems\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2008).\u003c/li\u003e\n\u003cli\u003eHaitao, W.\u003cem\u003e et al.\u003c/em\u003e Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces. \u003cem\u003eEnvironmental science \u0026amp; technology\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eFrancini, G.\u003cem\u003e et al.\u003c/em\u003e Soil biota in boreal urban greenspace: Responses to plant type and age. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eM, M. K. \u0026amp; J, L. J. H. Microbiology of the phyllosphere: a playground for testing ecological concepts. \u003cem\u003eOecologia\u003c/em\u003e \u003cstrong\u003e168\u003c/strong\u003e (2012).\u003c/li\u003e\n\u003cli\u003eA, V. J. Microbial life in the phyllosphere. \u003cem\u003eNature reviews. Microbiology\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2012).\u003c/li\u003e\n\u003cli\u003eWoodward, F. I. \u0026amp; Lomas, M. R. Vegetation dynamics--simulating responses to climatic change. \u003cem\u003eBiol Rev Camb Philos Soc\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 643-670 (2004).\u003c/li\u003e\n\u003cli\u003eL, G. K., W, S. J., Nejc, S., John, G. \u0026amp; Ashley, S. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. \u003cem\u003eNature communications\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2019).\u003c/li\u003e\n\u003cli\u003eMichael, F.\u003cem\u003e et al.\u003c/em\u003e Nitrogen fixation by phyllosphere bacteria associated with higher plants and their colonizing epiphytes of a tropical lowland rainforest of Costa Rica. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e (2008).\u003c/li\u003e\n\u003cli\u003eChen, Q.-L., An, X.-L., Zheng, B.-X., Ma, Y.-B. \u0026amp; Su, J.-Q. Long-term organic fertilization increased antibiotic resistome in phyllosphere of maize. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e645\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eKubiak, D. O., Piotr. Non-forested vs forest environments: The effect of habitat conditions on host tree parameters and the occurrence of associated epiphytic lichens. \u003cem\u003eFungal Ecology\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e (2020).\u003c/li\u003e\n\u003cli\u003eB\u0026auml;cklund, S., J\u0026ouml;nsson, M., Strengbom, J., Frisch, A. \u0026amp; Thor, G. A Pine Is a Pine and a Spruce Is a Spruce--The Effect of Tree Species and Stand Age on Epiphytic Lichen Communities. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e0147004- e0147004 (2017).\u003c/li\u003e\n\u003cli\u003eB, L. J.\u003cem\u003e et al.\u003c/em\u003e Experimental insights into the importance of aquatic bacterial community composition to the degradation of dissolved organic matter. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2016).\u003c/li\u003e\n\u003cli\u003eTanja, M. \u0026amp; L, S. S. FLASH: fast length adjustment of short reads to improve genome assemblies. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e (2011).\u003c/li\u003e\n\u003cli\u003eTorbj\u0026oslash;rn, R., Tom\u0026aacute;\u0026scaron;, F., Ben, N., Christopher, Q. \u0026amp; Fr\u0026eacute;d\u0026eacute;ric, M. VSEARCH: a versatile open source tool for metagenomics. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e (2016).\u003c/li\u003e\n\u003cli\u003eJ, C. B.\u003cem\u003e et al.\u003c/em\u003e DADA2: High-resolution sample inference from Illumina amplicon data. \u003cem\u003eNature methods\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2016).\u003c/li\u003e\n\u003cli\u003eChristian, Q.\u003cem\u003e et al.\u003c/em\u003e The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e (2013).\u003c/li\u003e\n\u003cli\u003eEvan, B.\u003cem\u003e et al.\u003c/em\u003e Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. \u003cem\u003eNature biotechnology\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e (2019).\u003c/li\u003e\n\u003cli\u003ePolson, S. W. \u003cem\u003eComparative analysis of microbial community structure associated with acroporid corals during a disease outbreak in the Florida reef tract\u003c/em\u003e, Medical University of South Carolina., (2007).\u003c/li\u003e\n\u003cli\u003eArcher, E. rfPermute: Estimate Permutation p-Values for Random Forest Importance Metrics. (2016).\u003c/li\u003e\n\u003cli\u003eDong, Z.\u003cem\u003e et al.\u003c/em\u003e Insights into the roles of fungi and protist in the giant panda gut microbiome and antibiotic resistome. \u003cem\u003eEnvironment international\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 106703-106703 (2021).\u003c/li\u003e\n\u003cli\u003eBulgarelli, D.\u003cem\u003e et al.\u003c/em\u003e structure and functions of the bacterial microbiota of plants further. (2019).\u003c/li\u003e\n\u003cli\u003eRahim, R. A.\u003cem\u003e et al.\u003c/em\u003e Distinctive phyllosphere bacterial communities in tropical trees. (2013).\u003c/li\u003e\n\u003cli\u003eN.\u003cem\u003e et al.\u003c/em\u003e Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e (2009).\u003c/li\u003e\n\u003cli\u003eLaforest-Lapointe, I., Messier, C. \u0026amp; Kembel, S. W. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 27 (2016).\u003c/li\u003e\n\u003cli\u003eInnerebner, G., Knief, C. \u0026amp; Vorholt, J. A. Protection of Arabidopsis thaliana against Leaf-Pathogenic Pseudomonas syringae by Sphingomonas Strains in a Controlled Model System. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 3202-3210 (2011).\u003c/li\u003e\n\u003cli\u003eAbanda-Nkpwatt, D.\u003cem\u003e et al.\u003c/em\u003e Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, 4025-4032 (2006).\u003c/li\u003e\n\u003cli\u003eMichael\u003cem\u003e et al.\u003c/em\u003e Nitrogen fixation by phyllosphere bacteria associated with higher plants and their colonizing epiphytes of a tropical lowland rainforest of Costa Rica. \u003cem\u003eThe ISME journal\u003c/em\u003e (2008).\u003c/li\u003e\n\u003cli\u003eFrank\u003cem\u003e et al.\u003c/em\u003e Evidence for foliar endophytic nitrogen fixation in a widely distributed subalpine conifer. \u003cem\u003eThe New Phytologist\u003c/em\u003e (2016).\u003c/li\u003e\n\u003cli\u003eVacher, C. \u0026amp; Pauvert, C. Microbial associations and interactions at the plant-climate interface. (2017).\u003c/li\u003e\n\u003cli\u003eTylianakis, J. M. \u0026amp; Coux, C. Tipping points in ecological networks. \u003cem\u003eTrends in Plant Science\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 281-283 (2014).\u003c/li\u003e\n\u003cli\u003eChangyi, L.\u003cem\u003e et al.\u003c/em\u003e Aboveground plants determine the exchange of pathogens within air-phyllosphere-soil continuum in urban greenspaces. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e \u003cstrong\u003e465\u003c/strong\u003e, 133149- (2024).\u003c/li\u003e\n\u003cli\u003eMu, F.\u003cem\u003e et al.\u003c/em\u003e Source identification of airborne bacteria in the mountainous area and the urban areas. \u003cem\u003eAtmospheric Research\u003c/em\u003e \u003cstrong\u003e231\u003c/strong\u003e, 104676-104676 (2020).\u003c/li\u003e\n\u003cli\u003eBarber\u0026aacute;n, A.\u003cem\u003e et al.\u003c/em\u003e Continental-scale distributions of dust-associated bacteria and fungi. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 5756-5761 (2015).\u003c/li\u003e\n\u003cli\u003eShao, F.\u003cem\u003e et al.\u003c/em\u003e Study on different particulate matter retention capacities of the leaf surfaces of eight common garden plants in Hangzhou, China. \u003cem\u003eThe Science of the Total Environment\u003c/em\u003e \u003cstrong\u003e652\u003c/strong\u003e, 939-951 (2019).\u003c/li\u003e\n\u003cli\u003eKnief, C., Delmotte, N., Chaffron, S., Stark, M. \u0026amp; Vorholt, J. A. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. \u003cem\u003eThe ISME Journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1378-1390 (2011).\u003c/li\u003e\n\u003cli\u003eaffSchool of Biological Sciences, U. o. C., Christchurch, New Zealand/raw. Driving factors of epiphytic bacterial communities: A mini-review. \u003cem\u003eJournal of Advanced Research\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 57-65 (2019).\u003c/li\u003e\n\u003cli\u003eRavenscraft, A., Berry, M., Hammer, T., Peay, K. \u0026amp; Boggs, C. Structure and function of the bacterial and fungal gut microbiota of Neotropical butterflies. \u003cem\u003eEcological Monographs\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e (2019).\u003c/li\u003e\n\u003cli\u003eRashid, M. M., Hunja, M., Akio, T. \u0026amp; M, K. E. Evaluation of rhizosphere, rhizoplane and phyllosphere bacteria and fungi isolated from rice in Kenya for plant growth promoters. \u003cem\u003eSpringerPlus\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 606 (2013).\u003c/li\u003e\n\u003cli\u003egrid.38678.32, D\u0026eacute;partement des Sciences Biologiques, Universit\u0026eacute; du Qu\u0026eacute;bec \u0026agrave; Montr\u0026eacute;al, H3C 3P8, Montr\u0026eacute;al, Qu\u0026eacute;bec, Canada\u003cem\u003e et al.\u003c/em\u003e Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e546\u003c/strong\u003e, 145-147 (2017).\u003c/li\u003e\n\u003cli\u003eA., G. E.\u003cem\u003e et al.\u003c/em\u003e Plant host identity and soil macronutrients explain little variation in sapling endophyte community composition: Is disturbance an alternative explanation? \u003cem\u003eJournal of Ecology\u003c/em\u003e \u003cstrong\u003e107\u003c/strong\u003e, 1876-1889 (2019).\u003c/li\u003e\n\u003cli\u003eLiangmu, W., Meirong, M., Xiaofei, L., Peng, L. \u0026amp; Wenqing, W. Differentiation between true mangroves and mangrove associates based on leaf traits and salt contents. \u003cem\u003eJournal of Plant Ecology\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 292-301 (2011).\u003c/li\u003e\n\u003cli\u003eW, K. S.\u003cem\u003e et al.\u003c/em\u003e Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 13715-13720 (2014).\u003c/li\u003e\n\u003cli\u003eIsabelle, L.-L., Christian, M. \u0026amp; W, K. S. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 27 (2016).\u003c/li\u003e\n\u003cli\u003eLemian, L., Shanshan, W. \u0026amp; Jianfeng, C. Transformations from specialists to generalists cause bacterial communities are more stable than micro-eukaryotic communities under anthropogenic activity disturbance. \u003cem\u003eThe Science of the total environment\u003c/em\u003e \u003cstrong\u003e790\u003c/strong\u003e, 148141-148141 (2021).\u003c/li\u003e\n\u003cli\u003eLomolino, M. V. Ecology\u0026apos;s Most General, Yet Protean Pattern: The Species-Area Relationship. \u003cem\u003eJournal of Biogeography\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 17-26 (2000).\u003c/li\u003e\n\u003cli\u003eThe Microbiome Center, D. o. S., University of Chicago , Chicago , Illinois 60637 , United States.\u003cem\u003e et al.\u003c/em\u003e Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces. \u003cem\u003eEnvironmental science \u0026amp; technology\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 5115-5124 (2018).\u003c/li\u003e\n\u003cli\u003eYan, B.\u003cem\u003e et al.\u003c/em\u003e Urban-development-induced Changes in the Diversity and Composition of the Soil Bacterial Community in Beijing. \u003cem\u003eRep\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 38811 (2016).\u003c/li\u003e\n\u003cli\u003ePearse, W. D.\u003cem\u003e et al.\u003c/em\u003e Homogenization of plant diversity, composition, and structure in North American urban yards. \u003cem\u003eEcosphere\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e (2018).\u003c/li\u003e\n\u003cli\u003eMckinney, M. L. Urbanization as a major cause of biotic homogenization. \u003cem\u003eBiological Conservation\u003c/em\u003e, 127 (2006).\u003c/li\u003e\n\u003cli\u003eProppe, D. S., Sturdy, C. B. \u0026amp; St. Clair, C. C. Anthropogenic noise decreases urban songbird diversity and may contribute to homogenization. \u003cem\u003eGlobal Change Biology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1075-1084 (2013).\u003c/li\u003e\n\u003cli\u003eWang, X., Jens-ChristianLiu, JiajiaZhao, ZhichunZhang, ZhaochenFeng, GangSi, XingfengZhang, Jian. Regional effects of plant diversity and biotic homogenization in urban greenspace - The case of university campuses across China. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eOuyang, S., WenhuaGou, MengmengChen, LiangLei, PifengXiao, WenfaDeng, XiangwenZeng, LixiongLi, JiangrongZhang, TaoPeng, ChanghuiForrester, David, I. Stability in subtropical forests: The role of tree species diversity, stand structure, environmental and socio-economic conditions. \u003cem\u003eGlobal ecology and biogeography\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eMao, Q., Lu, X., Mo, H., Gundersen, P. \u0026amp; Mo, J. Effects of simulated N deposition on foliar nutrient status, N metabolism and photosynthetic capacity of three dominant understory plant species in a mature tropical forest. \u003cem\u003eThe Science of the Total Environment\u003c/em\u003e \u003cstrong\u003e610-611\u003c/strong\u003e, 555-562 (2018).\u003c/li\u003e\n\u003cli\u003eWright, I. J.\u003cem\u003e et al.\u003c/em\u003e Modulation of leaf economic traits and trait relationships by climate. \u003cem\u003eGlob. Ecol. Biogeogr\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 411-421 (2010).\u003c/li\u003e\n\u003cli\u003eGorn\u0026eacute;, L. D., D\u0026iacute;az, S., Minden, V., Onoda, Y. \u0026amp; Boenisch, G. The acquisitive-conservative axis of leaf trait variation emerges even in homogeneous environments. \u003cem\u003eAnnals of Botany\u003c/em\u003e (2020).\u003c/li\u003e\n\u003cli\u003eJian, L.\u003cem\u003e et al.\u003c/em\u003e Plant identity shapes phyllosphere microbiome structure and abundance of genes involved in nutrient cycling. \u003cem\u003eThe Science of the total environment\u003c/em\u003e \u003cstrong\u003e865\u003c/strong\u003e, 161245-161245 (2022).\u003c/li\u003e\n\u003cli\u003eChaudhary, I. J. \u0026amp; Rathore, D. Dust pollution: Its removal and effect on foliage physiology of urban trees. \u003cem\u003eSustainable Cities and Society\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 101696-101696 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban green space, Biodiversity, 16S ribosomal RNA, Phyllosphere bacteria, bark epiphytic bacterial","lastPublishedDoi":"10.21203/rs.3.rs-4244944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe benefits of urban green space (UGS) for society are widely recognized, as a direct link between plant-microbe interactions and the maintenance of biodiversity, community stability, and ecosystem functioning. There is, however, a lack of knowledge about the factors influencing the microbial communities in urban green spaces, especially those related to phyllosphere epiphytes and stem epiphytes. In this study, we analyzed the microbial community assembly in leaf and stem bark samples collected from Square, Road, Campus, and Park. The α-diversity was higher in the bark epiphytic community, compared to the phyllosphere. Moreover, the types of urban greenspaces altered the way communities gathered. The main factors of the urban greenhouse (soil and air properties) were shaping the characteristics of bacterial communities on the leaf surface and bark epiphytic. In the co-occurrence network analysis, keystone taxa were not mostly observed in abundant species, which may be necessary to maintain ecosystem functions. Our findings provide a deeper understanding of the ecological dynamics and microbial interactions within plant phyllosphere and stem epiphytes microbiomes.\u003c/p\u003e","manuscriptTitle":"Urban greenspace types and climate factors jointly drive the microbial community structure and co-occurrence network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:47:58","doi":"10.21203/rs.3.rs-4244944/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-14T05:01:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T17:37:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228386843901720302172932312121454318073","date":"2024-05-07T15:06:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-26T15:51:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-26T15:43:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-17T12:00:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-17T11:54:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-10T04:03:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8155c1d5-2168-4805-a306-3e13264dd572","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T12:00:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:47:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4244944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244944","identity":"rs-4244944","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}