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Article

The Composition and Assembly of Soil Microbial Communities Differ across Vegetation Cover Types of Urban Green Spaces

by
Yangyi Zhou
and
Jiangping Wang
*
School of Urban Design, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 13105; https://doi.org/10.3390/su151713105
Submission received: 9 July 2023 / Revised: 10 August 2023 / Accepted: 25 August 2023 / Published: 31 August 2023
(This article belongs to the Section Soil Conservation and Sustainability)
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Abstract

:
Soil microorganisms play an important role in urban green spaces by providing ecological functions. However, information on the structure and assembly of microbial communities and the public risk of pathogenic bacteria in urban green spaces remains elusive. Here, we conducted a field survey on soil organisms in different vegetation cover types of urban green spaces (e.g., grasslands, shrublands, and woodlands) based on 16 S rRNA gene amplicon sequencing. We found that soil microbial communities in grasslands were dominated by Pseudomonadota, Acidobacteriota, Actinomycetota, and Chloroflexota. The diversity and niche breadth of the microbial communities in grasslands showed differences compared to shrublands and woodlands. Stochastic processes, which contribute to community assembly in grasslands, were lower compared to shrublands and woodlands, dominating the soil microbial community assembly of urban green spaces. Compared with soil microbial communities in scrublands and woodlands, the network of soil microbial communities in grasslands was simpler and had a weaker stability. Furthermore, the value of the microbial index of pathogenic bacteria in the observed green spaces was 0.01, which means that the risk of potential pathogens in green spaces was low. This study provides crucial information for the sustainable management of urban green spaces by regulating soil microorganisms, offering novel insights into the public health risks associated with potential pathogenic bacteria in these green spaces.

1. Introduction

The United Nations predicts that by 2050, more than two-thirds of the world’s population will reside in cities, which will worsen the environmental and social challenges faced by urban residents [1]. To improve the living environment and comfort of urban residents and to mitigate the negative effects of urbanization on human habitats, urban green spaces have been increasing [2]. Urban green spaces, such as parks, gardens, and street trees, play an essential role in promoting mental and physical health and reducing morbidity and mortality in urban residents. Aside from providing opportunities for recreation and relaxation, green spaces also act as buffers between busy roads and residential areas, reducing noise pollution, improving air quality, and reducing the risks of flooding in cities [3]. Furthermore, urban green spaces not only boost biodiversity by providing habitats for plants, animals, and many invertebrates above and below the ground [4], but also host a diverse community of microbes, such as archaea, bacteria, fungi, and protists, in their soils [5,6]. Soil biodiversity is crucial for maintaining ecosystem services such as nitrogen fixation, organic matter decomposition, carbon dioxide sequestration, and food web support [7,8]. However, the degree of soil microbial diversity and community composition preservation in urban green spaces is still disputed. Some studies observed that soil microbial diversity was higher in urban green spaces than that in rural areas [9,10,11], while others found no or slight differences between urban and rural soils [12]. Moreover, different vegetation restoration can cause significant changes in soil community characteristics [13], and significant variations in alpha and beta diversities across vegetation complexities were also observed [14]. For instance, a higher plant diversity and coverage increased bacterial functional diversity by increasing litter quantity, root exudate, and root activity [15]. Furthermore, the type of vegetation above the soil plays a crucial part in affecting the structure and diversity of soil microbial communities [16]. Nonetheless, the extent to which microbial communities respond to urban green spaces in different vegetation cover types is not well understood.
Environmental changes can affect the ecological processes that determine soil microbial communities, which can cause substantial changes in beta diversity when the environmental conditions are either heterogeneous or homogeneous [17]. Soil microbial processes are mainly impacted by two types of processes: deterministic (niche-based) and stochastic (neutral) [18]. Deterministic processes, such as environmental filters, biotic interactions, and species traits, follow the niche theory and drive the local community composition toward a stable phase. Stochastic processes, such as dispersal, ecological drift, and stochastic diversification, follow the neutral theory and generate random changes in microbial communities that lead to community composition patterns that are indistinguishable from random assemblages. The relative significance of these microbial community assembly processes can be assessed using neutral models, null models, and multivariate analyses [17,18,19]. However, the relevant importance of the two processes remains unclear for soil microbial community assembly processes in the different vegetation cover types of urban green spaces.
Soil microbes have been proven to be associated with human health [20]. Soil microbes affect human health through multiple pathways, including the supply of genetic, medical, and biochemical resources (e.g., antibiotics), the suppression of human, animal, and plant pathogens, and the modulation of human immune responses [21,22]. Moreover, some soil microbes as pathogens have adverse effects on humans, animals, and plants. For instance, Clostridium tetani, Listeria monocytogenes, and Blastomyces dermatitidis can cause serious human diseases such as tetanus, listeriosis, and skin and respiratory tract diseases [23]. The large and widespread free-living soil protist Acanthamoeba spp. may cause serious human infections, including corneal abrasions [24]. Obviously, the presence of pathogenic bacteria in soil cannot be ignored. A previous study has reported that urban greenspaces are sources of pathogens [6]. In contrast, Li et al. [25] confirmed that urban greenness and plant species were key factors in reducing pathogens. However, the research on pathogenic bacteria in urban green spaces is still limited, and the public risk of pathogenic bacteria in urban green spaces with different vegetation coverages has not been assessed.
Thus, the objectives of our study were: (1) to characterize the composition and diversity of soil microbial communities in urban green spaces with different vegetation coverages, (2) to elucidate the interactions and community assembly mechanisms, and (3) to reveal the composition of potential bacterial pathogens and the public risk of pathogenic bacteria in urban green spaces by employing 16 S rRNA high-throughput genetic sequencings. Our findings will provide insights into microbial community assembly and the public risk of pathogenic bacteria in urban green spaces.

2. Materials and Methods

2.1. Study Location and Sample Collection

The sampling sites were located in Guangzhou, Guangdong Province (26°36′40″ N, 106°43′80″ E). Guangzhou is located in Southern China and is the third-largest city in the country. It boasts numerous green infrastructure facilities, including 270 parks that have been built to date (Bureau of Forestry and landscaping of Guangzhou Municipality, http://lyylj.gz.gov.cn). We randomly selected 26 parks throughout the city center and suburbs as sampling points and collected a total of 78 soil samples from the grasslands, shrublands, and woodlands within these parks (Figure S1). Several plots (1 m × 1 m) were established, and five soil cores were collected from each plot at depths of 0–5 cm and mixed. Each soil sample was sifted through a 2 mm sieve to remove visible roots and rock fragments. The sifted soil was divided into two parts. One portion of the composite sample was stored at 80 °C for soil microbial DNA extraction, while the other portion was air-dried for soil physicochemical analyses.

2.2. Soil Physicochemical Analysis

The pH and electrical conductivity (EC) of the soil samples were measured from a soil-water suspension (1:5 m/v) using a portable probe (SG2, Mettler-Toledo, Greifensee, Switzerland). The soil organic matter (SOM) was measured using the dichromate oxidation method [26]. Ammonium (NH4+-N) and nitrate (NO3−-N) concentrations were determined using spectrophotometry after extracting the soil samples with 2 M KCl. Moreover, the soil available phosphorus (AP) was extracted with 0.5 mol/L NaHCO3 and measured with a microplate reader (Infinite M200 PRO, Tecan, Männedorf, Switzerland). The soil’s available potassium (AK) was extracted with neutral ammonium acetate and was measured using a flame spectrophotometer (FP6450, INESA, Shanghai, China).

2.3. DNA Extraction and Sequencing

A TINAamp soil DNA kit (Tiangen, Beijing, China) was used to extract the total DNA from the soil following the manufacturer’s instructions. For the microbial community, the hypervariable V4-V5 region of the 16 S rRNA gene was amplified using a prokaryotic universal primer set with 515 F/909 R [27]. To allow for the multiplexing of samples, an 8-mer barcode specific to each sample was added to the 5′ end of both the forward and reverse primers. The 20 μL PCR mixture contained 10 μL of high-fidelity PCR master mix (Accurate Bio, Changsha, China), 0.2 μM of each primer, and approximately 10 ng of sample DNA. The 16 S rRNA gene DNA amplification process was performed as previously described [9]. Negative controls were included in each batch of DNA extraction and PCR. The PCR products from all samples were combined in equimolar concentrations and purified using a gel extraction kit. The combined PCR products were sent to Azenta (Suzhou, China) for subsequent sequencing on the Illumina HiSeq PE250 platform.

2.4. Sequence Processing and Putative Pathogen Identification

The raw sequences were filtered, dereplicated, merged, chimera identified, and inferred to amplicon sequence variants (ASVs) using the DADA2 package (version 1.6) [28] in R software (version 4.1.2) [29]. The taxonomy was assigned using an RDP naïve Bayesian classifier [30] with the SILVA v132 database [31]. To minimize bias in the sequencing depth, the ASV table was rarefied to 15,900 sequences per sample. The raw sequencing reads were deposited into the EMBL sequence read archive with an accession number of PRJEB6,2679.
To distinguish the roles and contributions of rare and abundant microbial taxa in the community, all ASVs were defined and classified into six exclusive categories based on their range of abundance, as previously described [32], including: (i) rare taxa (RT), ASVs with an abundance ≤ 0.1% in all samples; (ii) abundant taxa (AT), ASVs with an abundance ≥ 1% in all samples; (iii) moderate taxa (MT), the abundance of ASVs was between 0.1 and 1% in all samples; (iv) conditionally rare taxa (CRT), the abundance of ASVs was below 1% in all samples and ≤0.1% in some samples; (v) conditionally abundant taxa (CAT), ASVs with an abundance greater than 0.1% in all samples and ≥1% in some samples but never rare (≤0.1%); and (vi) conditionally rare or abundant taxa (CRAT), ASVs with an abundance varying from rare (≤0.1%) to abundant (≥1%).
To explore putative pathogens in soil microbial communities, multiple bacterial pathogen detection (MBPD), which can detect a broad range of animal, plant, and zoonotic pathogens based on 16 S rRNA gene sequencing, was used for bacterial pathogen detection [33]. To understand the public health risk of environmental microbes in green spaces, the microbial index of pathogenic bacteria (MIP) was calculated as previously described [34] to assess the potential risk.

2.5. Co-Occurrence Network Construction and Community Stability Analysis

In the microbial network analysis, three co-occurrence networks were constructed for the soil microbial communities of grasslands, shrublands, and woodlands. To reduce the complexity of the datasets, only the ASVs that appeared in more than 20% of all the samples were used for further analysis. The network was inferred using SparCC [35], and the significance of the correlations between taxa was tested with 1000 permutations. Only robust (|r| > 0.5) and statistically significant (p < 0.01) correlations were considered. Network visualization was generated using Gephi version 0.10.
For the microbial community stability analysis, cohesion and network robustness were performed. Cohesion is a recently developed method that measures within-microbiome dynamics by quantifying the connectivity of microbial communities based on the association and abundance of taxa [36]. The cohesion values were calculated according to the protocol reported by Herren and McMahon [37]. Less positive cohesion values indicate more stable microbial communities [36,37]
The network robustness test was a powerful method for measuring network stability by examining how natural connectivity changes affect the network when nodes or edges were removed in descending order of their betweenness or weight [38]. The natural connectivity of the network was estimated by “attacking” nodes or edges in the network [39,40].

2.6. Neutral Community Model

In this study, the Sloan neutral model [41] was applied to estimate the contribution of stochastic processes on community assembly in the soil of grasslands, shrublands, and woodlands following the protocol described by Burns et al. [42]. Based on the model, species that were more abundant in the metacommunity were more likely to disperse randomly, whereas species with lesser abundance were more likely to be lost due to ecological drift. The model was fitted to the frequency of ASV occurrence in a group of local communities (e.g., the grassland community of an individual sampling site) and their average relative abundances across the metacommunity (e.g., grassland communities of all the sampling sites). The ASVs were divided into three groups based on their frequency of occurrence relative to the 95% confidence interval of the neutral model: above partition, below partition, and neutral partition. The “Hmisc” package (version 4.2.2) [43] in R was used to calculate the 95% confidence interval. The parameter R2 represents the overall fit to the neutral model, and the parameter m represents the estimated migration rate.

2.7. Diversity, Correlation, and Multivariate Analyses

The observed species and Shannon metrics were calculated for filtered (i.e., without singletons) amplicon libraries using the “phyloseq” (version 3.2.3) package in R software. The turnover versus nestedness components of the beta diversity analyses were calculated using the “betapart” (version 1.5.1) package. Mantel tests were used to compare turnover and nestedness across green spaces. A canonical correspondence analysis (CCA) was performed using “vegan” to investigate the relationships between microorganisms and environmental variables. Before the CCA analysis, the environmental variables with a high variance inflation factor (VIF) larger than 10 were excluded to avoid collinearity among factors, and a forward selection was conducted to select significantly explanatory variables for further analyses [44]. Meanwhile, a multiple regressions analysis [45] was used to evaluate the importance of environmental factors on the observed species of microbial communities in soil. The R package “relaimpo” [46] was used to estimate the importance of the variables using the lmg method. Spearman correlations were calculated to discern the relationship between the major module of the network in each kind of green land and environmental variables using the “picante” package [47].

3. Results

3.1. Composition, Diversity, and Ecological Niche of the Microbial Community in Urban Green Spaces

A total of 6,052,294 high-quality prokaryotic microbial sequences were acquired from 78 soil samples belonging to grasslands, shrublands, and woodlands, which were identified as 79,003 amplicon sequence variants. The soil microbial community was represented by 36 phyla, and was dominated by Pseudomonadota, Acidobacteriota, Actinomycetota, Chloroflexota, Planctomycetota, Nitrospirota, Gemmatimonadota, and Cyanobacteria (Figure 1a). Pseudomonadota, Acidobacteriota, Acidobacteriota, and Actinomycetota were the most dominant phyla, accounting for 64.1–86.2% of the total prokaryotic 16 S rRNA gene sequences in grasslands, 71.4–89.3% in shrublands, and 72.3–88.1% in woodlands. By comparison, the relative abundance of Actinomycetota was significantly lower in grasslands than in woodlands (p < 0.05), whereas Pseudomonadota, Acidobacteriota and Chloroflexota did not vary between each green space (Figure 1a). The alpha diversity (Shannon index) showed that the grassland soil samples had a more diverse prokaryotic community than was found in shrublands and woodlands (p < 0.05, Figure 1b). Similarly, more species were observed in soil samples of grasslands than in shrublands and woodlands (Figure S2a). Moreover, woodland microbial communities displayed a wide range of variation in pairwise measurements of taxonomic beta diversity (Figure S2c). The taxonomic beta diversities were mainly driven by the turnover component, while the contribution of nestedness was minor (Figure S2c). Similar to alpha diversity, both the turnover and nestedness components of the taxonomic beta diversities in grasslands were less than in woodlands.
To study the differences in community structure more in-depth, we classified ASVs into abundant and rare sub-communities and calculated the niche breadth indices of each ASV in the grassland, shrubland, and woodland soil samples. In total, all ASVs were grouped into four sub-communities, including rare taxa (RT), moderate taxa (MT), conditionally rare taxa (CRT), and conditionally rare or abundant taxa (CRAT). The ASVs classified as CRAT were the most numerous, with 33,163, 32,859, and 32,167 in the soil samples of grasslands, shrublands, and woodlands, respectively. The ASVs classified as MT were the least numerous, with only 29, 32, and 31 in the soil samples of grasslands, shrublands, and woodlands, respectively. Based on the result of calculating the niche breadth indices, the taxa in the grassland soil exhibited significantly (p < 0.05) higher niche breadth indices than in the shrubland and woodland soil (Figure 1c). Generalists and specialists were identified when the niche breadth index was higher or lower than simulated chance, respectively. Specialists were found among moderate, conditionally rare, and conditionally rare or abundant subcommunities, whereas generalists were found only in conditionally rare taxa subcommunities (Figure 1d). Specialists in CRAT and MT were found in slightly lower proportions in the grassland soil samples compared to shrublands and woodlands, while the proportions of specialists both in RT and CRT and generalists in CRT showed no significant difference, which meant that specialists were the main drivers of niche breadth variation.

3.2. Factors Related to the Variation in Microbial Communities

To disentangle the drivers of prokaryotic beta diversity, nonmetric multidimensional scaling (NMDS) analyses, canonical correlation analyses (CCA), and multiple linear regression models were used to estimate the relative contributions of environmental parameters to microbial community composition (Figure 2 and Figure S3). The results suggested that the environmental parameters played an important role in structuring the microbial communities, explaining 10.9% of the microbial communities’ variation (Figure S3). Notably, pH and nitrite were the variables most strongly correlated with changes in soil microbial community composition (Figure 2a). Moreover, the results of multiple linear regression models further corroborated the important roles of pH and EC in shaping soil microbial community composition (Figure 2b).

3.3. Microbial Networks and Community Assembly Processes

To explore the relationships between prevalent prokaryotic ASVs (present in at least 80% of samples), co-occurrence networks were constructed for the microbial communities of the grasslands, shrublands, and woodlands. The topological characteristics of the microbial networks were calculated to understand the complex co-occurrence pattern among the taxa. Fewer edges (1649) and nodes (1299) were found in the co-occurrence networks of the grassland soil microbial communities compared to the shrubland (edges: 2160, nodes: 1424) and woodland (edges: 4161, nodes: 1704) communities (Figure 3a). The lowest average degree (2.53) was observed in the co-occurrence network of the grassland prokaryotic community. The modularity of the grassland prokaryotic co-occurrence networks (0.77) was higher than that of the shrublands (0.74) and woodlands (0.55). All of the networks were parsed into four major modules, of which the number of nodes in modules I and II accounted for 12.1% and 9.6% of the grassland’s network, respectively, 16.5% and 12.5% of the shrubland’s network, respectively, and 20.6% and 20.3% of woodland’s network, respectively. In addition, the microbial composition of the modules in the co-occurrence networks of different types of soil microbial communities was different. For example, module I in the grassland network was mainly composed of Pseudomonadota, Acidobacteriota, and Actinomycetota, while the woodland network also included Cyanobacteria and Chloroflexota (Figure 3a).
To understand the main driving factors for the formation of each module of each co-occurrence network, the correlation between the microbial composition of each module and environmental parameters was analyzed (Figure 3b). The results showed that ammonium was the major factor in shaping the microbial composition of module I in the grassland network, while nitrate and AK were in module IV. In contrast, organic matter was the major factor in shaping the microbial composition of module I in the woodland network, while modules II, III, and IV were pH. Obviously, the formation of co-occurrence network modules in the grasslands differed from that in the shrubland and woodland networks. Based on microbial network analyses, the community with highly negative and low positive associations would tend to be more stable [36], and the resistance of microbial networks to disturbances could be tested by changing the degree of connectivity with the removal of the network’s nodes or edges [37]. Therefore, a negative:positive cohesion and robustness analysis of the network was used to evaluate and compare prokaryotic community stability in the grasslands, shrublands, and woodlands. Interestingly, the negative:positive cohesions of water prokaryotic communities in the shrublands and woodlands were comparable, but higher than those in grasslands (Figure 3c). In addition to the robustness analysis of the network, the natural connectivity of prokaryotic networks decreased to a greater degree, with a greater fluctuation in the grasslands than the shrublands and woodlands caused by removing the same proportion of nodes or edges, indicating weakened resistance (Figure 3d). Therefore, compared with the shrublands and woodlands, the grasslands had the worst stability of microbial communities.
The NCM was used to assess the importance of the stochastic process for soil microbial community assembly (Figure 4). The NCM explained a larger fraction of microbial community variation in the soil of woodlands (R2 = 0.649) compared to grasslands (R2 = 0.629) and shrublands (R2 = 0.637) (Figure 4). Moreover, the NCM of the soil microbial communities at supergroup levels exhibited large explained variances for the grasslands (82.9% to 83.8%, mean value 83.2%), shrublands (ranged from 86.7 to 96.5%, mean value 91.3%), and woodlands (87.0% to 96.1%, mean value 90.6%) (Figure S4). Furthermore, the NCM was inclined to keep a large and constant predicted ratio along taxonomic ranks from the kingdom to genus levels, meaning that the same phylogenetical lineages’ taxa exhibited similar responses to stochastic processes. These results showed that stochastic processes played a critical role in affecting the prokaryotic community assembly in urban green space soil.

3.4. Putative Pathogens in Urban Green Land

To grasp the potential public health risks in urban green spaces, putative pathogens were identified in soil prokaryotic communities using a pathogen detection pipeline, multiple bacterial pathogen detection (MBPD), which was able to detect a broad range of animal, plant, and zoonotic pathogens [33]. The results showed that putative pathogens comprised 18.94–20.04% of all prokaryotic communities, among which animal pathogens were the most abundant (Figure 5a). Among the animal pathogens, Ca. Koribacter (2.41–2.80%) had the highest abundance, followed by Saccharopolyspora (1.73–1.84%), and Pannonibacter (1.27–1.35%). Allorhizobium (0.86–0.97%) and Xanthomonas (0.38–0.47%) were found to have the highest abundances among plant pathogens, and Brucella (0.41–0.45%) had the highest abundance among zoonotic pathogens. Except for Pannonibacter in the animal pathogens, which showed a significant difference (p < 0.05) in the grasslands compared with the shrublands and woodlands, the other pathogenic taxa had no significant differences among the grasslands, shrublands, and woodlands. These results indicated the presence of a large number of pathogens in urban green spaces. A canonical correlation analysis was further used to assess the contribution of the environmental parameters to the variation in pathogenic taxa (Figure 5b). Nitrate was a major factor for the abundance variation of Kocuria and Pseudomonas among the animal pathogens. Ca. Koribacter, a major animal pathogen, had a relationship with organic matter. Interestingly, Sphingomonas, a zoonotic pathogen, was mainly affected by ammonia nitrogen, whereas plant pathogens were affected by AP. To evaluate the potential public health risks in urban green spaces, the microbial index of pathogenic bacteria (MIP) was calculated. The MIP value ranged from 0.005 to 0.023, and the mean value was 0.01 ± 0.0004. There were no significant differences in MIP values among the grasslands, shrublands, or woodlands.

4. Discussion

In terrestrial ecosystems, microorganisms are the primary drivers of soil nutrient availability. The impact of soil physicochemical variables on microbial communities has been well documented. However, it remains unknown how soil microorganisms respond to different vegetation cover types in urban green spaces, and what public risks that the potential pathogenic bacteria among the soil microorganisms of urban green spaces pose. In this study, we conducted a survey of soil organisms in 26 urban green spaces in Guangzhou with different vegetation cover types using 16 S rRNA gene amplicon sequencing. It was found that the soil microbial characteristics of grasslands differed from those of shrublands and woodlands. The risk of potential pathogens in green spaces was relatively low, but cannot be ignored. This study provides crucial information for the sustainable management of urban green spaces by regulating soil microorganisms, offering novel insights into the public health risks associated with potential pathogenic bacteria in these spaces.
The structure of microbial communities is of great importance to ecosystem functioning, since microorganisms govern biogeochemical cycling and are more diverse than any other organisms [48]. Soil microbial diversity and community composition are influenced by a range of environmental variables, such as the pH value, above-ground vegetation, nutrient contents, and temperature [49]. Therefore, this study found that soil bacterial communities had different α-diversity indexes among various green spaces (Figure 1b and Figure S2a). Grasslands possessed the highest α-diversity index value regarding bacterial richness and evenness compared to shrublands and woodlands (Figure 1b and Figure S2a). Previous studies have pointed out that soil style was a main factor that affected the distribution patterns of soil microbial communities [6]. Climatic factors (MAT and MAP) were dominant variables that affected the diversity and community composition of bacteria [50]. The bacterial communities shaped in soil are conditioned by different soil styles [51]. In this study, the samples collected were all located in Guangzhou, which had the same climatic conditions and soil types. The reason for this phenomenon may be that grassland soil is covered by grass, which increases species diversity, while shrubland and woodland soil surfaces are bare, leading to low microbial diversity.
The soil microbial communities in different green spaces showed consistent dominance of phyla, including Pseudomonadota, Acidobacteriota, Actinomycetota, and Chloroflexota (Figure 1a). These bacterial phyla are commonly found in soils across the globe [5]. Although the relative abundance of major phyla such as Pseudomonadota showed no significant differences across urban green spaces, Acidobacteriota, Actinomycetota, and Planctomycetota still exhibited significant differences (Figure S5). The relative abundances of Actinomycetota and Acidobacteriota in the grasslands were significantly lower than in the shrublands and woodlands. Actinomycetota can help to decompose the organic matter of dead organisms and plays an important role in promoting the degradation of fallen leaves in shrublands and woodlands [52,53]. Additionally, the relative abundances of Planctomycetota were significantly high in the grasslands. Planctomycetota is a type of slow-growing, aerobic bacteria that exhibits several unique characteristics, including the absence of peptidoglycan in the cell walls, compartmentalization of cells by inner membranes, and large genomes [54]. The phyla are a group of Gram-negative bacteria that exhibit high sensitivity to environmental disturbances and are susceptible to drought stress [55]. This also reflected that the grasslands suffered more disturbances compared to the shrublands and woodlands. In addition, the soil microbial communities in the grasslands had a larger niche breadth compared to those in the shrublands and woodlands. The primary reason for this difference is that the proportion of specialists in CRAT and MT in the grassland soil microbial communities was lower than that in the soil microbial communities of the shrublands and woodlands (Figure 1d). Typically, specialists, which are assumed to use a narrow range of resources and conditions, were primarily influenced by environmental filtering and biotic interactions, and accordingly present smaller realized niche breadths [56].
Understanding how soil microbial communities are assembled is essential for comprehending the generation and maintenance of terrestrial microbial diversity [57,58]. In this study, we found that the assembly of microbial communities in the soil of urban green land was dominated by stochastic processes (Figure 4), which is different from previous findings reporting that soil microorganisms in grasslands and forests are dominated by deterministic processes [59,60]. In addition, we observed that stochastic processes were stronger in the grasslands than in the shrublands and woodlands (Figure 4). Species interactions are deterministic processes based on niche assembly that govern community structure [61]. The results of the co-occurrence network analysis indicated that the grassland network had a low complexity (Figure 3). Its low cohesion and sharply decreased connectivity suggest that the stability of the grassland microbial community is poor, which implies that the interaction between grassland microorganisms is weak. Thus, the contribution of species interactions to determining processes in the grasslands was low. In addition, the grassland soil microbial communities had a high turnover rate (Figure S2c), which further indicates their instability and facilitates the diffusion and migration of microorganisms. Interestingly, the main modules of the grassland microbial co-occurrence network were more strongly correlated with AP, AK, and NO3−. AP and AK mainly originate from human management activities in cities, such as fertilization. This suggests that human management activities in grasslands in cities reduces community stability and increases the stochastic influx and dispersal of microorganisms. Therefore, human activities in cities, such as trampling and human-managed activities, enhanced the stochastic assembly process of soil microbial community in grasslands, supporting previous reports that human activities may increase the stochastic influx and dispersal of microorganisms [62]. Allowing reasonable trampling in the process of urban green space management can help spread soil microorganisms in urban green spaces and maintain soil microbial diversity and ecological functions.
Gaining an understanding of the public risks associated with urban green spaces is a crucial step towards ensuring the health and well-being of city residents. The pathogenic bacteria in soil, an important public risk factor of urban green spaces, is an important hotspot in current research [63]. In this study, the average MIP in green spaces was 0.01, which was much lower than the MIP in habitats such as patient skin (0.27), hospitals (0.21), and mammals (0.02) [34]. This suggests that the potential public health risks in urban green spaces are low. Although the MIP in the urban green space soil was low, the potential risks could not be ignored. For instance, Pannonibacter was one of the most abundant animal putative pathogens among the grasslands, shrublands, and woodlands (Figure 5a), which could be used as a bioremediation agent for the detoxification of heavy metals and polycyclic aromatic compounds (PAHs), while rarely infect healthy populations. However, the infection caused by the opportunistic pathogen Pannonibacter can complicate diagnosis and treatment and pose a serious threat to immunocompromised patients due to its multidrug resistance [64]. In addition to animal pathogens, there were a variety of plant pathogens in the urban green spaces (Figure 5a). Allorhizobium is a bacterium primarily known as a plant pathogen, causing crown gall disease in grapevines [65]. Therefore, although an in-depth analysis was conducted on potential pathogenic bacteria in urban green spaces, more comprehensive research is still needed on pathogenic microorganisms in urban green spaces to obtain comprehensive public risk information on pathogenic microorganisms.

5. Conclusions

Soil microorganisms play a crucial role in providing ecological functions in urban green spaces. It is crucial to understand the structure and assembly of microbial communities and the public risk of pathogenic bacteria in urban green spaces. This study aimed to characterize the composition and diversity of soil microbial communities in urban green spaces with different vegetation coverages, elucidate the interactions and community assembly mechanisms. This study revealed the composition of potential bacterial pathogens and the public risk of pathogenic bacteria in urban green spaces by employing 16 S rRNA gene high-through sequencing. After conducting a survey of soil organisms in 26 urban green spaces in Guangzhou with different vegetation cover types, we found that the soil microbial characteristics in the grasslands differed from those in the shrublands and woodlands. The relative abundances of Actinomycetota and Acidobacteriota in the grasslands were significantly lower than in the shrublands and woodlands, while the relative abundances of Planctomycetota were significantly higher in the grasslands. The assembly of microbial communities in the soil of urban green spaces was dominated by stochastic processes, and the stochastic processes were stronger in the grasslands than in the shrublands and woodlands. The risk of potential pathogens in green spaces is relatively low but cannot be ignored. This study provides valuable information for the sustainable management of urban green spaces through a comprehensive understanding of soil microbes and offers new insights into the public health risks associated with the potential pathogenic bacteria in these spaces.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su151713105/s1, Figure S1: Sampling map of the urban green spaces in Guangzhou, China. Figure S2: The alpha diversity and beta diversity of soil microbial communities in the three types of green land. a, the alpha diversity (observed species) in grassland, shrubland, and woodland; b, Principal coordinates analysis (PCoA) of the soil microbial community composition; c, partitioning beta diversity of species richness of soil microbial community in the three types. Figure S3: Canonical correspondence analysis (CCA) of the environmental variables’ relative contributions to the beta diversity variation. Figure S4: Neutral model fit of soil microbial community evaluated from genus to phylum taxonomic resolutions in grassland, shrubland and woodland, respectively. Figure S5: Relative abundances of prokaryotic most abundant phyla in different green land.

Author Contributions

Conceptualization, Y.Z. and J.W.; formal analysis, Y.Z.; investigation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, J.W.; visualization, Y.Z.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding and the APC was funded by Y.Z.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw sequencing data were deposited into the EMBL sequence read archive with an accession number of PRJEB6,2679.

Acknowledgments

We thank Wangang Zhou for his assistance with the data analysis.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The characteristics of soil microbial communities in urban greenspaces. (a) The composition of soil microbial communities at the phylum level; (b) the alpha diversity of soil microbial communities; (c) the niche breadth of the microbial communities; (d) the generalists and specialists in different categories of taxa.
Figure 1. The characteristics of soil microbial communities in urban greenspaces. (a) The composition of soil microbial communities at the phylum level; (b) the alpha diversity of soil microbial communities; (c) the niche breadth of the microbial communities; (d) the generalists and specialists in different categories of taxa.
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Figure 2. Impact of environmental variables on soil microbial communities. (a) Non-metric multidimensional scaling (NMDS) analysis of the relationship between the environmental variables and microbial communities; (b) the importance of the major parameters assessed using multiple linear regression models of alpha diversity.
Figure 2. Impact of environmental variables on soil microbial communities. (a) Non-metric multidimensional scaling (NMDS) analysis of the relationship between the environmental variables and microbial communities; (b) the importance of the major parameters assessed using multiple linear regression models of alpha diversity.
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Figure 3. Co-occurrence networks of soil microbial taxa and community stability analysis. (a) Co-occurring network of microorganisms in soil communities. Each dot represents an ASV, and each edge correlation, with R > 0.5. The dot size corresponds to the ASV’s relative abundance. (b) the relationship between the major network’s module for the grasslands, shrublands, and woodlands and environmental variables; (c) Network-based negative/positive cohesions in the three types of greenspaces; (d) Robustness analysis displaying the relationship between the proportion of removed nodes and natural connectivity. Larger shifts in the same proportion indicate that there is less robustness or stability within microbial networks. Asterisks indicate statistical significance (** p < 0.01; * p < 0.05).
Figure 3. Co-occurrence networks of soil microbial taxa and community stability analysis. (a) Co-occurring network of microorganisms in soil communities. Each dot represents an ASV, and each edge correlation, with R > 0.5. The dot size corresponds to the ASV’s relative abundance. (b) the relationship between the major network’s module for the grasslands, shrublands, and woodlands and environmental variables; (c) Network-based negative/positive cohesions in the three types of greenspaces; (d) Robustness analysis displaying the relationship between the proportion of removed nodes and natural connectivity. Larger shifts in the same proportion indicate that there is less robustness or stability within microbial networks. Asterisks indicate statistical significance (** p < 0.01; * p < 0.05).
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Figure 4. Soil microbial community assembly in grasslands, shrublands, and woodlands.
Figure 4. Soil microbial community assembly in grasslands, shrublands, and woodlands.
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Figure 5. The composition of putative pathogens and the microbial index of pathogenic bacteria in urban green spaces. (a) Composition of animal, plant, and zoonotic pathogens in urban green spaces; (b) comparison of MIP among grasslands, shrublands, and woodlands; (c) canonical correspondence analysis (CCA) of the environmental variables’ relative contributions to pathogens.
Figure 5. The composition of putative pathogens and the microbial index of pathogenic bacteria in urban green spaces. (a) Composition of animal, plant, and zoonotic pathogens in urban green spaces; (b) comparison of MIP among grasslands, shrublands, and woodlands; (c) canonical correspondence analysis (CCA) of the environmental variables’ relative contributions to pathogens.
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Zhou, Y.; Wang, J. The Composition and Assembly of Soil Microbial Communities Differ across Vegetation Cover Types of Urban Green Spaces. Sustainability 2023, 15, 13105. https://doi.org/10.3390/su151713105

AMA Style

Zhou Y, Wang J. The Composition and Assembly of Soil Microbial Communities Differ across Vegetation Cover Types of Urban Green Spaces. Sustainability. 2023; 15(17):13105. https://doi.org/10.3390/su151713105

Chicago/Turabian Style

Zhou, Yangyi, and Jiangping Wang. 2023. "The Composition and Assembly of Soil Microbial Communities Differ across Vegetation Cover Types of Urban Green Spaces" Sustainability 15, no. 17: 13105. https://doi.org/10.3390/su151713105

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