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Article

Road Density Shapes Soil Fungal Community Composition in Urban Road Green Space

by
Shuhong Luo
1,2,3,
Yong Lin
4,
Ruirui Chen
5,
Jigang Han
2,4,* and
Yun Liu
1,3,*
1
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
2
Shanghai Wildlife and Protected Natural Areas Research Center, Shanghai 202162, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China
5
College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China
*
Authors to whom correspondence should be addressed.
Diversity 2025, 17(8), 539; https://doi.org/10.3390/d17080539
Submission received: 11 July 2025 / Revised: 29 July 2025 / Accepted: 29 July 2025 / Published: 31 July 2025
(This article belongs to the Section Microbial Diversity and Culture Collections)
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Abstract

Road density is a key indicator of human activity, causing habitat loss and fragmentation. Soil fungi, essential for ecosystem functioning, are sensitive bioindicators. Yet their responses to road density in urban green spaces are poorly characterized. Here, we analyzed the composition of the dominant fungal community, examined both the direct and indirect effects of road density on soil fungal communities, and identified specialist species. Focusing on Shanghai, China, a rapidly urbanizing city, we considered both edaphic factor and the road network. Through machine learning and Spearman correlation regression analyses, we quantified the relative importance of road density and edaphic factor in shaping fungal community composition and employed occupancy-specificity modeling to identify specialist taxa. Our results revealed that Ascomycota, Basidiomycota, Zygomycota, Rozellomycota, Chytridiomycota, and Glomeromycota were the dominant phyla, accounting for 93% of the retrieved ITS sequences. Road density was found to be the primary driver of fungal community composition, followed by soil lead and potassium concentrations. Notably, opportunistic pathogens (Acremonium spp.) correlated positively with road density (p < 0.001). Specialist species in high-density areas were primarily pathotrophic fungi, while saprotrophic fungi dominated in low-density areas. These findings highlight the need for urban planning strategies to mitigate the ecological impact of road density.

1. Introduction

Urban ecosystems are particularly impacted by anthropogenic activities [1], representing complex socio-ecological systems characterized by a highly varied mosaic of land cover. Over half of the world’s population now live in urban areas due to rapid global urbanization [2]. This rapid urban development has led to numerous environmental challenges, including landscape fragmentation, biodiversity loss, disruptions of natural ecological processes, and degradation of ecosystem services [3,4,5,6]. The soil, a crucial component of the Earth that provides essential functions for human society [7], has primarily been studied in rural and semi-rural areas, with its significance often underestimated in urban ecosystems [8].
Road density is regarded as an appropriate index of human activity and land-use intensity [9,10,11]. Over the past three decades, the field of road ecology has illuminated the impacts of roads and road networks on landscapes and ecosystem stability [10]. Today, very few landscapes remain unaffected by roads [12]. Since 2000, global road networks have expanded by 12 million kilometers, with an expected increase of an additional 25 million kilometers by 2050 [13]. Notably, the expansion of road networks and the associated increase in traffic drastically affects wildlife populations, primarily due to animal-vehicle collisions [14,15]. Previous studies have examined the impact of road density on wildlife, including bat communities and American moose [9].
Green spaces, as integral components of ecosystems, play a vital role in urban environment [16], offering significant ecosystem services, including environmental, aesthetic, recreational, and economic benefits [17,18]. Urban road green spaces play a vital role as ecological corridors, helping to maintain urban biodiversity and ecosystem functionality [19]. However, urban road green spaces are also subject to unique stressors, particularly from vehicle emissions, which introduce heavy metals such as lead (Pb), zinc (Zn), and copper (Cu) into soils [20], along with physical stressors like soil compaction and heat. These contaminants pose risks to soil health, microbial communities [21], and even human health [22]. Given the sensitivity of soil fungi to environmental disturbances [23], understanding how road density influences fungal communities in these spaces is critical.
Fungal communities are vital and diverse components of ecosystems, serving as decomposers, symbionts, and pathogens [24]. For instance, saprotrophic fungi play key roles in nutrient cycling and pedogenesis, with several groups, especially wood-decaying fungi, being the focus of longstanding taxonomic studies [25]. Mycorrhizal fungi colonize the roots of over 90% of land plants, providing essential nutrients and water in exchange for plant carbon and lipids [26], while pathotrophic fungi can cause disease, affecting the survival of their hosts [27]. The composition of these communities can act as an early warning indicator of environmental stress [23], making them particularly valuable for assessing urban ecological health. While there is abundant research on soil fungal community composition across biogeographical, latitudinal, and elevational gradients in agricultural and forest contexts [28,29,30], studies in urban road green spaces remain scarce. Moreover, although previous research has investigated the impact of road density on wildlife, such as bat populations and American moose [9], their impacts on soil fungal communities—especially in urban road green spaces—are poorly understood. This gap is concerning, given that fungal communities mediate key ecosystem processes, including nutrient cycling and plant health, which are essential for urban sustainability.
In this study, high-throughput sequencing technology was used to characterize the community composition of soil fungal communities in urban road green space. Our main aim was to identify the key driving factors shaping soil fungal communities and to investigate specialist species in soils from these spaces. We hypothesized that (1) Road density is a dominant driver in shaping the composition of soil fungal communities in road green spaces; (2) Road density can indirectly influence the composition of soil fungal communities by affecting edaphic factor; and (3) The specialist species differ between areas with high road density and those with low road density.

2. Materials and Methods

2.1. Study Area and Sample Collection

Shanghai, located in eastern China (30°40′–31°53′ N, 120°52′–122°12′ E), covers a total area of 6.34 × 105 hm2. The city is made up of 16 administrative districts. Topographically, Shanghai is characterized by a gentle gradient, with a relative elevation difference of 3 to 4 m, indicative of its flat plain landscape. The region is influenced by a subtropical humid monsoon climate, featuring an average annual temperature of 15.7 °C and an average annual precipitation of 1100 mm.
Soil samples were collected from June to July 2018 from the road green spaces. A road network map of the study area was provided by the Shanghai Highway Management Bureau. Road density (total length of roads per unit area (km/km2)) thresholds of 1 km/km2 and 0.6 km/km2 were used to categorize areas into high (>1 km/km2), middle (0.6–1 km/km2), and low (<0.6 km/km2) road density levels [9,31] (Table S1). A total of 86 topsoil samples (0–20 cm) were collected from the road green space, comprising 22 high road-density sites, 21 medium-density sites, and 43 low-density sites (Figure 1 and Figure S1). Each sampling location was precisely identified using GPS coordinates. For each site, a 2.25 m2 area (1.5 m × 1.5 m) was marked, and five subsamples were placed in sterile bags taken from the four corners and center of the square using a stainless-steel shovel. Approximately 2 kg of soil from the 0–20 cm depth was collected and combined into a single composite sample in a stainless basin. Rocks and debris were removed, and the samples were homogenized thoroughly before being transported to the laboratory. In the laboratory, the samples were air-dried at room temperature (20–23 °C). One portion was sieved through a nylon mesh (<2 mm) for pH analysis, while another was sieved to <0.15 mm for analysis of organic matter and heavy metals. All prepared samples were stored at 4 °C until further analysis.

2.2. Analysis of Edaphic Factor

Soil pH was measured from soil–water suspensions (1:2.5 v/v) following the method described by Kabala et al. [32]. Soil organic matter (SOM) was quantified using the Walkley–Black method, which involves oxidation with potassium dichromate in a sulfuric acid medium [33]. Total nitrogen (TN) was determined by the Kjeldahl method [34]. Total phosphorus (TP) was determined after nitric-perchloric acid digestion, followed by analysis with an inductively coupled plasma optical emission spectrometer [34]. Total potassium (TK) was measured using flame photometry. For heavy metal analysis (Pb, Zn, Cd, and Cu), approximately 100 mg of homogenized soil was digested with HNO3-HClO4-HF in a microwave, filtered, diluted to 25 mL, and analyzed using a graphite furnace atomic absorption spectrophotometer (Varian, Inc., Palo Alto, CA, USA). Reagent blanks, replicated samples, and standard reference materials were used to provide quality assurance and control (QA/QC) [35].

2.3. DNA Extraction and Illumina Sequencing of Fungal ITS Region

Total genomic DNA was extracted from 0.25 g soil material using the FastDNA SPIN Kit for soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s protocol. DNA concentration was determined with a Nanodrop 2000 UV-Vis Spectrophotometer (Thermo Scientific, Waltham, MA, USA). DNA quality was assessed by 1% agarose gel electrophoresis. The extracted DNA was stored at −80 °C until further use.
The fungal ITS1 region was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) [36,37]. Sequencing was performed on the Illumina MiSeq platform (Illumina, San Diego, CA, USA). Sequences were clustered into operational taxonomic units (OTUs) at 97% similarity using Uparse [38], and singletons were removed. Taxonomic identification was conducted using the UNITE reference database and an RDP classifier [39]. OTU tables were normalized to account for sequencing depth variations. Fungal functional groups (pathotrophs, saprotrophs, and symbiotrophs) were identified using the FUNGuild database [40].

2.4. Statistical Analyses

We tested homogeneity of multivariate dispersions using PERMDISP [41] (function betadisper in R package vegan) based on Bray–Curtis. Community composition variation was assessed through non-metric multidimensional scaling (NMDS) of Bray–Curtis dissimilarity matrices, and alpha diversity indices were computed, with both analyses performed using the “vegan” package in R [42]. Machine learning random forest regression analysis [43] was used to identify the most important predictors of fungal community composition (NMDS1). We performed random forest analysis with 500 trees, using the built-in out-of-bag error estimation. Our models included 10 variables: road density, pH, Pb, Zn, Cd, Cu, SOM, TN, TP, and TK. Spearman correlation analysis was conducted to examine the relationships between road density, edaphic factor (e.g., heavy metals, soil nutrients), fungal community composition (NMDS1) and Shannon diversity. Road density and edaphic factor were standardized to a range from 0 to 1 across all sites. For example, the site with the highest road density was assigned a value of 1, while the site with the lowest received a value of 0. We then averaged the values within each edaphic factor (for instance, Pb, Zn, Cd, and Cu values were averaged to determine soil heavy metals). This standardization creates a metric that is intuitively interpretable [44]. We used random forest analysis to identify the dominant genera that were most strongly responsive to road density. Specificity is defined as the mean abundance of species in the samples, while occupancy represents its relative frequency of occurrence of species in the samples. Specificity is operationally defined as the average abundance of a given OTU within a set of habitat samples, while occupancy is characterized as the relative frequency with which OTU occurs within the same set of habitat samples [45,46]. OTUs with specificity and occupancy values ≥0.7 were classified as specialist species, indicating their habitat specificity and widespread occurrence across sampling sites. The statistical tests were two-sided, with p-values adjusted using the false discovery rate (FDR) method. We performed all analyses in R version 4.4.2 [47].

3. Results

3.1. Diversity Patterns of Soil Fungal Communities in Road Green Spaces

Across all 86 soil samples, a total of 983 abundant OTUs were identified, primarily belonging to the phyla Ascomycota, Basidiomycota, Zygomycota, Rozellomycota, Chytridiomycota, and Glomeromycota, which accounted for 93% of the retrieved ITS sequences. At the genus level, Mortierella, Talaromyces, Fusarium, Trichosporon, Aspergillus, Penicillium, Curvularia, Lycoperdon, Guehomyces, Metarhizium, Gibberella, Monographella, and Trichoderma were the dominant genus, which accounted for 34.23% of the retrieved ITS sequences. Our analyses revealed significant differences in fungal community composition across high, middle, and low road-density areas (ANOSIM, R = 0.11, p = 0.008) (Figure 2). Furthermore, fungal richness (observed richness) was higher in areas with low road density compared to those with high and middle road densities (Figures S2 and S3).

3.2. The Factors That Impact the Fungal Communities in Urban Road Green Spaces

We then employed the machine learning random forest algorithm, which revealed that road density and soil Pb and TK were fundamental drivers of soil fungal community composition (Figure 3A). Additionally, soil Zn and Cd were identified as key drivers of the Shannon diversity of soil fungi. A negative relationship was observed between Shannon diversity and soil Zn and Cd in road green spaces (Figure S4).
To comprehensively understand how road density shapes soil fungal communities in urban road green spaces, it is important to examine both the direct and indirect effects of road density on soil fungal community composition. Our results indicate that road density was significantly and negatively correlated with soil fungal community composition (ρ = −0.4, p < 0.01), while being positively correlated with soil heavy metals and negatively correlated with soil nutrients (Figure 3B and Figure S5). Moreover, soil fungal community composition shows a significant negative response to soil heavy metals, particularly soil Pb and Zn (p < 0.05) (Figure 3B). These findings suggest that road density influences soil fungal community composition primarily through its impact on soil heavy metals.

3.3. Specialist Species in Urban Road Green Spaces

Using random forest modeling, we identified the Acremonium as the most responsive genus of road density (Figure 4A), which showed a significant positive correlation with road density (p < 0.01) (Figure 4B). Further analysis of occupancy and specificity revealed that, in areas with high road density, the number of specialist species was dominated by pathotrophic fungi. In contrast, areas with medium and low road density were dominated by the saprotrophic fungi (Figure 5).

4. Discussion

4.1. Direct and Indirect Effect of Road Density on Soil Fungal Community Composition in Road Green Spaces

Our results revealed that road density significantly influenced soil fungal community composition (ρ = −0.4, p < 0.01) (Figure 3), supporting our first hypothesis. Road green spaces are particularly vulnerable to anthropogenic stressors, notably soil compaction from pedestrian traffic and heavy metal deposition from vehicular emissions [48]. Road construction, particularly during the early stages of land use and land cover change, has been shown to have substantial ecological impacts [9]. Additionally, we found that soil fungal diversity was negatively correlated with road density (Figure S3). Roads typically exert a negative effect on native biodiversity and ecological integrity [49]. This decline in diversity is likely attributable to habitat loss and increased disturbances associated with road construction, which disproportionately impact species sensitive to human activity. Moreover, the expansion of road networks leads to landscape fragmentation, with road density positively correlated with the degree of fragmentation. As road systems become denser, landscape fragmentation intensifies. This relationship has also been observed in urban landscapes [31]. Previous studies have shown that habitat fragmentation is a significant predictor of fungal species richness in urban areas [50]. Fragmentation can isolate populations, limiting gene flow and reducing genetic variability, which poses a long-term threat to the survival of certain species [51].
Fungal communities showed significant negative responses to heavy metals, particularly total Pb (ρ = −0.35, p < 0.01) and total Zn (ρ = −0.25, p < 0.05) (Figure 3 and Table S2), supporting our second hypothesis. Our study highlights heavy metals, particularly total Pb, as a more significant driver in this research (p < 0.01), consistent with previous research findings [52]. This is likely due to increased vehicular traffic associated with higher road density, which enhances total Pb deposition from vehicle emissions in urban soils [53,54]. Elevated concentrations of heavy metals such as total Pb and Zn have been shown to reduce the richness of arbuscular mycorrhizal fungi [55], potentially leading to the local extinction of sensitive taxa [56]. In addition, soil nutrient status has been widely recognized as a key factor shaping fungal communities [57], previous studies in North America have shown that nutrient conditions can influence the sensitivity of mycorrhizal fungi to eutrophication [58]. In our research, soil nutrients did impact community composition though just not through road density, suggests that while road density impacts composition both directly and indirectly through heavy metals, soil nutrients as play a role in composition that is independent of road density. To mitigate these negative impacts on soil fungal communities, promoting the use of electric vehicles and other technologies could help reduce soil heavy metal contamination, thus protecting both soil ecosystems and human health.

4.2. Opportunistic Pathogens in Urban Road Green Spaces

Random forest analysis identified Acremonium as the most responsive fungal genus to road density (Figure 4). Acremonium spp. are opportunistic pathogenic fungi, which may have enhanced tolerance to environmental stressors, allowing them to survive in disturbed urban environments, and their relative abundance shows a significant positive correlation with road density (p < 0.001) (Figure 4). This suggests that Acremonium thrives in areas influenced by human activity, potentially due to altered environmental conditions such as increased pollution, temperature, or moisture levels in road adjacent areas.
Acremonium is known to cause infections in humans, including mycetoma, onychomycosis, and keratitis [59], often originating from contaminated environments or water-damaged building materials. Our findings also reveal a significant correlation between road density and elevated soil Pb and Zn concentrations (p < 0.05) (Figure S5). Soils in urban areas near roads tend to accumulate pollutants, which may promote the growth of fungi like Acremonium and Fusarium, while species sensitive to heavy metals may be diminished or even eliminated. This shift in microbial community composition underscores the complex interplay between human activities, environmental stressors, and the dynamics of pathogenic species in urban road green spaces.

5. Conclusions

In summary, our study reveals that road density, directly and indirectly, influences soil fungal community composition in urban road green spaces. We observed significant differences in fungal communities across high, medium, and low road density areas. Road density, soil Pb, and TK were key drivers of fungal community composition, with road density being the most influential factor. Our results suggest that road density shapes fungal communities primarily by altering soil heavy metal concentrations. The opportunistic pathogenic fungi Acremonium and Fusarium showed a positive correlation with road density, which may have implications for human health in urban environments. Additionally, areas with high road density were dominated by pathotrophic fungi, while medium and low-density areas supported more saprotrophic fungi. The main limitations of this study are the absence of traffic density data and the lack of information on bioavailable heavy metals in the soil. Future research should incorporate both traffic density and bioavailable heavy metals to strengthen the analysis. Additionally, studies on soil microorganisms in urban green spaces would benefit from multi-season sampling to provide a more comprehensive understanding. We recommend further research to improve conservation strategies, emphasizing the need for road management and urban planning that account for the direct and indirect effects of road density on soil microbial diversity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17080539/s1. Figure S1: The locations of the sampling sites included in this study along with their respective road density in urban green spaces. Figure S2: Observed species of saprotroph and symbiotroph fungi in high, middle and low road density. Figure S3: Relationships between the road density and soil fungal observed species in road green spaces. ρ means correlation coefficient; P means probability. Figure S4: The importance of variables influencing the soil fungi diversity identified by the Random Forest algorithm. Blue bars indicate significant variables at the level of 0.05. RD, road density. Relationships between the standardized value of soil Cd, Zn and Shannon index in road green spaces. ρ means correlation coefficient; P means probability. Figure S5: Relationships between the soil pH, SOM, TN, TP, TK, Pb, Cu, Cd, Zn and road density in road green spaces. ρ means correlation coefficient; P means probability. Table S1. The number of sampling sites and road density thresholds in urban road green space. Table S2. The average values of the edaphic factor.

Author Contributions

S.L.: writing—original draft, conceptualization, formal analysis, Y.L. (Yong Lin): supervision; R.C.: writing—review and editing; J.H.: funding acquisition; Y.L. (Yun Liu): supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Shanghai Finance Special Project (Soil quality monitoring system for typical urban green spaces in Shanghai) and the Scientific Research Foundation of Shanghai Landscaping & City Appearance Administrative Bureau (G200201) and Science and Technology Innovation Special Foundation for Carbon Peak and Carbon Neutrality of Jiangsu Province (BK20220004).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Dataset available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Sampling sites and spatial distribution of the major road network in urban green spaces.
Figure 1. Sampling sites and spatial distribution of the major road network in urban green spaces.
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Figure 2. (A) Non-metric multidimensional scaling (NMDS) showing the difference of the community composition of fungi across high (>1 km/km2), middle (0.6~1 km/km2) and low road density (<0.6 km/km2) based on Bray–Curtis dissimilarity index (n = 86). The difference of the community composition of fungi between high and low road density is tested by the analysis of similarities. RD, road density. (B) Observed species of pathotrophic fungi in high, middle, and low road density.
Figure 2. (A) Non-metric multidimensional scaling (NMDS) showing the difference of the community composition of fungi across high (>1 km/km2), middle (0.6~1 km/km2) and low road density (<0.6 km/km2) based on Bray–Curtis dissimilarity index (n = 86). The difference of the community composition of fungi between high and low road density is tested by the analysis of similarities. RD, road density. (B) Observed species of pathotrophic fungi in high, middle, and low road density.
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Figure 3. (A) The importance of variables influencing the soil fungal community composition identified by the random forest algorithm. (B) The direct and indirect influence of road density on the soil fungal community composition in urban road green space. RD, road density. * p < 0.05, ** p < 0.01.
Figure 3. (A) The importance of variables influencing the soil fungal community composition identified by the random forest algorithm. (B) The direct and indirect influence of road density on the soil fungal community composition in urban road green space. RD, road density. * p < 0.05, ** p < 0.01.
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Figure 4. (A) The relative importance of dominant genus shaped by the road density identified by the random forest algorithm. (B) Relationships between the road density and the relative abundance of Acremonium, Monographella, Fusarium, and Curvularia across urban road green space. ρ means correlation coefficient; p means probability.
Figure 4. (A) The relative importance of dominant genus shaped by the road density identified by the random forest algorithm. (B) Relationships between the road density and the relative abundance of Acremonium, Monographella, Fusarium, and Curvularia across urban road green space. ρ means correlation coefficient; p means probability.
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Figure 5. The SPEC-OCCU plots show the abundant OTUs (relative abundance > 0.01%) in high, middle and low road density.
Figure 5. The SPEC-OCCU plots show the abundant OTUs (relative abundance > 0.01%) in high, middle and low road density.
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Luo, S.; Lin, Y.; Chen, R.; Han, J.; Liu, Y. Road Density Shapes Soil Fungal Community Composition in Urban Road Green Space. Diversity 2025, 17, 539. https://doi.org/10.3390/d17080539

AMA Style

Luo S, Lin Y, Chen R, Han J, Liu Y. Road Density Shapes Soil Fungal Community Composition in Urban Road Green Space. Diversity. 2025; 17(8):539. https://doi.org/10.3390/d17080539

Chicago/Turabian Style

Luo, Shuhong, Yong Lin, Ruirui Chen, Jigang Han, and Yun Liu. 2025. "Road Density Shapes Soil Fungal Community Composition in Urban Road Green Space" Diversity 17, no. 8: 539. https://doi.org/10.3390/d17080539

APA Style

Luo, S., Lin, Y., Chen, R., Han, J., & Liu, Y. (2025). Road Density Shapes Soil Fungal Community Composition in Urban Road Green Space. Diversity, 17(8), 539. https://doi.org/10.3390/d17080539

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