Microbiota associated with urban forests

Study sites and experimental design

Located at the Zhuyu Bay Scenic Area, Guangling District, Yangzhou, Jiangsu Province, the main site of the National Positioning Observation and Research Station of Urban Ecosystem was selected for this study. The climate in this area transits from subtropical humid monsoon type to temperate monsoon type, with an average annual temperature of 15.8 ^∘C and an average annual precipitation of 864 mm. This region has an average altitude of 2 m, covered with zonal secondary forest vegetation. The well-grown forest stands represent the plain water network area in the lower reaches of the Yangtze River. The forest resources here are abundant, and the main types of vegetation include Cedrus deodara, Metasequoia glyptostroboides, and Phyllostachys pubescens.

Sample collection

We selected six stands for research, including Phyllostachys edulis (ZL), Metasequoia glyptostroboides (SSL), Cornus officinalis (SZY), Populus, Camellia oleifera and Clerodendrum trichotomum (ZKG), Cedrus deodara and Cupressus funebris (SBL), Yulania denudate, Eriobotrya japonica, Osmanthus (ZKQ). The bulk soil (BS) not in the vicinity of any roots near the selected plant was collected for investigation. Two plots of the same area were selected for each stand. The plots are as follows: ZL (8 m ×10 m), SSL (8 m ×8 m), SZY (10 m ×5 m), ZKG (8 m ×8 m), SBL (10 m ×8 m), ZKQ (8 m ×8 m). Five sampling points were set at the four corners and centers of each plot. After removing the superficial humus layer, a soil auger was employed to collect the soil layer at a depth of 0–20 cm. After picking out litter, fine roots, small stones, and other impurities, the sample was mixed thoroughly to minimize the impact of heterogeneity. Each sample was separately placed in a sterile plastic bag, sealed, and transported to the laboratory on ice. For microbial sequencing, the samples were stored in a refrigerator at −80 ^∘C. The remaining part of the sample was used for the determination of soil physical and chemical properties. After air-drying, BS samples were ground and transmitted through a 1.0 mm sieve. Then, they were stored in a cool and dry place for soil physical and chemical property determination.

Soil physical and chemical properties

According to the previous study on Dendrobium (Yuan et al., 2020), the properties of air-dried BS samples were measured, including pH, total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), and available potassium (AK). All chemical analyses were repeated three times.

DNA extraction, amplification and sequence analysis

Microbial DNA from 60 samples (from the six forest stands with ten replicates of each plot) was extracted using the CTAB/SDS method (Niemi et al., 2001). DNA concentration and purity were identified on 1% agarose gel. Depending on the concentration, DNA was diluted to 1 ng/ μL with sterile water. The primer sets 515F (5′-GTGCCAGCMGCCGCGG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to generate bacterial libraries with a unique 6-nt barcode at 5′ of the forward primer to amplify the V4 region of the 16S rRNA gene for each sample. Similar to the above process, the ITS1 region of the fungus was amplified using ITS1-1F-F (CTTGGTCATTTAGGAAGTAA) and ITS1-1F-R (GCTGCGTTCTTCATCGATGC) (Caporaso et al., 2012) to generate sequencing libraries. Index codes were added with the TruSeq®DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA), complying with the manufacturer’s recommendations. Library quality was evaluated on a Qubit@ 2.0 fluorometer (Thermo Scientific, Waltham, MA, USA) and an Agilent Bioanalyzer 2100 system. After purification and quantification, the libraries were sequenced on an Illumina NovaSeq 6000 platform according to standard protocols. To treat all raw data from the 16S V4 bacterial region and the fungal ITS1 region (V1.9.1, http://qiime.org/scripts/split libraries fastq.html), QIIME and FLASH were adopted for quality-controlled process and paired reads, respectively (V1.2.7, http://ccb.jhu.edu/software/FLASH/) (Bolyen et al., 2019; Magoč & Salzberg, 2011). For bacteria and fungi, Silva sequences were matched with the UCHIME algorithm and Unite database for annotation (ITS: https://unite.ut.ee/) (UCHIME, http://www.drive5.com/usearch/manual/uchime_algo.html) (Koljalg et al., 2013; Quast et al., 2012).

Statistical analysis

The analysis of alpha diversity adopted four indices, including ACE, Chao1, Shannon, and Pielou evenness (Chao & Yang, 1993; Chao, 1984; Shannon, 1948). These indices were calculated using QIIME (Version 1.9.1) and visualized with R package ggplot2 (Version 2.15.3). One-way ANOVA was used to determine significant differences with a P-value of 0.05. If significant differences were detected, the outlier data were identified using Duncan’s post hoc test. The differences in species complexity among samples were derived from the beta diversity analysis. Moreover, the beta diversity on Bray-Curtis was obtained using QIIME software (version 1.9.1). The R language labdsv package was employed to calculate the indicators of each species in each treatment in the comparison group (Roberts & Roberts, 2016). Cross-validation was used for statistical testing to obtain the P value, which is displayed in a bubble chart.

Alpha and beta diversity of urban forest stands microbial communities

Alpha diversity is an important index reflecting the number and relative abundance of species in a community. Among the four indices of alpha diversity, Chao1 and ACE are classified as richness indices; Shannon comprehensively considers the number of species and the evenness of the community; Pielou’s evenness is used the most commonly. They were calculated in this study to compare the diversity and evenness of BS microorganisms in diverse forest stands. The results exhibit that the ACE and Chao1 of the six forest stands have the same trend, with SZY being the highest and SBL being the lowest, indicating that SZY has the highest abundance of BS bacteria (Fig. 1A–Fig. 1B). In terms of Pielou’s evenness, ZL has the highest value, and SBL has the lowest, representing that the evenness of BS bacteria is the highest in ZL and the lowest in SBL (Fig. 1C). The value of Shannon in ZL is the highest, implying that ZL has the highest BS bacterial diversity (Fig. 1D). The One-way ANOVA analysis shows significant differences in the richness of BS bacteria among the six forest stands, while the differences in evenness and diversity are not significant. The alpha diversity analysis of BS fungi was performed on the six forest stands. The results show that the ACE and Chao1 of ZKQ are the highest, indicating that ZKQ has the highest abundance in BS fungi (Fig. 1E–Fig. 1F). The Pielou’s evenness and Shannon of the six forest stands present the same trend. Meanwhile, these values of ZKQ are the highest, indicating that ZKQ has the highest evenness and diversity of BS fungi (Fig. 1G–Fig. 1H). One-way ANOVA among the six forest stands discloses significant differences in the diversity and evenness of BS fungi.

In the PCA figure of bacteria, PC1 and PC2 account for 23.33% and 17.18% of the total variance, respectively (Fig. 2A). Meanwhile, ZL, SZY, and ZKG are far from the other samples, demonstrating that their bacterial communities are more different. In the PCA of fungi, the ratios of PC1 and PC2 are 21.80% and 16.90%, respectively (Fig. 2B). The differences among the bacterial communities of the six forest stands are greater than those among the fungal communities.

Microbial community composition in urban forest stands

According to the OTU annotation results, the BS bacteria of the six forest stands contain 45 phyla, 138 classes, 303 orders, 447 families, and 776 genera (Fig. 3). Among them, Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Planctomycetes, Bacteroidetes, Verrucomicrobia, Firmicutes, Gemmatimonadet, and Patescibacteria are the dominant bacterial phyla with a relative abundance greater than 1%. The results manifest that the differences among the six forest species are increasingly evident below the phylum level. At the phylum level, Proteobacteria has the highest relative abundance in SZY. Proteobacteria and Acidobacteria in SSL, ZKG, SBL, and ZKQ have similar relative abundance. At each level, although the top ten species with higher relative abundance among the six BS bacteria are the same, their relative abundances are still somewhat different. In addition, BS fungi include 22 phyla, 54 classes, 144 orders, 290 families, and 513 genera (Fig. 4). Among them, the dominant bacterial phyla with a relative abundance greater than 1% are Ascomycota, Basidiomycota, Mortierellomycota, Anthophyta, Mucoromycota, Chlorophyta, Ciliophora, Glomeromycota, Nematoda, and Rozellomycota. The results verify that, at the phylum level, Ascomycota dominates the BS fungi of the six forest stands. Its relative abundance is the largest in ZL and ZKG and exceeds 60% in ZL and ZKG. At all levels, the relative abundance of the top-ranked bacterial group in the six forest stands exceeds 40%.

Differences in the abundance of OTUs in different forest stands of bulk soil microbial communities

To detect abundance differences in bacterial communities, DESeq2 was used to perform differential analysis on pairwise comparisons of BS bacteria in the six forest stands (Fig. 5). The results show that at the phylum and class levels, only SZY has unique bacterial groups. At the order level, only ZKG does not have unique bacterial groups. There are no common bacterial groups at the family level. ZL, SSL, SZY, ZKQ, and SBL have 16, 5, 22, 3, and 4 unique bacterial groups, respectively. At the genus level, common and unique bacterial groups bacterial groups both exist in the six forest stands. Additionally, differential analysis was performed on fungi. There are fifteen bacterial groups at the phylum level, of which 13 are common bacterial groups, and the remaining two are unique bacterial groups in ZKQ and SZY. At the class and order levels, the six forest stands have both common and unique bacterial groups. At the family and genus levels, there are no common bacterial groups, and the six forest stands all have unique bacterial groups.

Identification of biomarker taxa in the bulk soil of different urban forest stands

Biomarkers refer to species or communities within a certain area that can indicate the growth environment or certain environmental conditions. Indicator analysis is generally used to screen indicator species for each group. The bacteria results represent that among the top ten biomarkers at the phylum level, there is no significant difference in the relative abundance of different BS indicator species in ZL, SSL, and ZKQ (Fig. 6). The relative abundance of various BS indicator species in SZY, ZKG, and SBL is not significant. The difference in abundance is significant. Among them, the relative abundance of Firmicutes in ZKG is the lowest. Moreover, below the class level, the relative abundance of different indicator species in each forest stand is significantly different. The indicator analysis of fungi shows that their relative abundance and bacterial community differ significantly (Fig. 7). The results indicate that the relative abundance of indicator species of BS fungi is generally low at all levels. At the phylum level, relatively abundant indicator species include Nematoda and Anthophyta in SZY, Mucoromycota in SBL, and Glomeromycota in ZKQ. Furthermore, SZY and SBL have indicator species with relatively high abundance at all levels. The relative abundance of Nematoda is only high in SZY and low in the other five forest stands

Relationships between bulk soil microbiomes and soil factors

In order to analyze the relationship between samples, microbial communities, and environmental factors, the redundancy analysis (RDA) was performed on BS samples (Fig. 8). The results exhibit that SOM, AP, and pH change greatly in BS microbial communities. Among them, SOM is positively correlated with the changes in the BS bacterial community and fungal community of SSL. The changes in the BS bacterial communities and fungal communities of ZL, SBL, and ZKQ are affected by AP. Meanwhile, soil pH influences the BS of SZY and ZKG. Important factors change in bacterial and fungal communities. In addition, Anthophyta and Basidiomycota impact changes in the BS fungal communities of SSL and SZY. Environmental factors have evident effects on BS bacterial communities and fungal communities. The determination of soil physical and chemical properties shows that the values of SOM, TN, AK, and AP in SZY are relatively low (Table 2). There is no significant difference in the values of soil SOM of SZY, ZKG, and SBL, all lower than those of ZL, SSL, and ZKQ, and the latter three have significant differences. The values of TP of the six forest stands have no significant difference. The difference in TN between ZKQ and SZY is significant, while there is no significant difference in TN between ZL, SSL, ZKG, and SBL (Table 2). Moreover, the differences in pH, AK, and AP among the six forest stands are significant, indicating that factors affecting the soil environments of the six forest stands are different.