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. 2024 Oct 25;14:25369. doi: 10.1038/s41598-024-74448-8

Effects of urban green space habitats and tree species on ectomycorrhizal fungal diversity

Qian-Cai Lin 1, Ying-Qing Cen 1, Ming Xu 1,2, Dan-Dan Jiang 1, Jian Zhang 1,2,✉
PMCID: PMC11511879  PMID: 39455594

Abstract

Ectomycorrhizal fungi (EMF) are key symbiotic microbial components for the growth and health of trees in urban greenspace habitats (UGSHs). However, the current understanding of EMF diversity in UGSHs remains poor. Therefore, in this study, using morphological classification and molecular identification, we aimed to investigate EMF diversity in three EMF host plants: Cedrus deodara in the roadside green belt, and C. deodara, Pinus massoniana, and Salix babylonica in the park roadside green belt, in Guiyang, China. A total of 62 EMF Operational Taxonomic Units (OTUs) were identified, including 13 EMF OTUs in the C. deodara roadside green belt, and 23, 31, and 9 EMF OTUs in the park green belts. C. deodara, P. massoniana, and S. babylonica were respectively identified in park green belts. Ascomycota and Basidiomycota were the dominant phylum in the EMF communities in roadside and park green habitat, respectively. The Shannon and Simpson indexes of the C. deodara EMF community in the park green belt were higher than those in the roadside green belt. EMF diversity of the tree species in the park green belt was P. massoniana > C. deodara > S. babylonica. Differences in EMF community diversity was observed among the different greening tree species in the UGSHs. UGSHs with different disturbance gradients had a significant impact on the EMF diversity of the same greening tree species. These results can be used as a scientific reference for optimizing the design and scientific management of UGSHs.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-74448-8.

Keywords: Diversity, Ectomycorrhizal fungi, Greening trees, Urban greenspace habitats, Urbanization

Subject terms: Ecology, Ecology, Environmental sciences

Introduction

Urbanization has changed the human living environment and played a “filter” role to microbial diversity1,2. Often, alien plant species are introduced in urban areas to increase the beauty of urban landscapes, leading to ecosystem convergence. This convergence phenomenon leads to biodiversity decline, and homogenization of habitats lead to homogenization of urban soil microorganisms3. A reduction in contact with natural biodiversity weakens the microbiome and immune system of the human body, thereby affecting human health. Thus, rapid urbanization negatively impacts microbial biodiversity and consequently, human health4–7. Exposure to diverse urban greenspace habitats (UGSHs) can reduce blood pressure, relieve pain, and reduce mortality8–10. Although the mechanisms associated with these positive effects remain unclear, the effects may be due to the interaction of plants and soil microbial communities with humans and the soil microbiome can transfer to the residents, altering human microbiome composition, influencing immune function and health outcomes11–13. Therefore, it is necessary to improve our understanding of soil microbial diversity among different UGSHs.

UGSHs allow pollution degradation and remediation for human survival14,15, water resource management, carbon maintenance, nutrient cycling, and a series of basic ecosystem services16–18, such as promoting biochemical cycles and soil processes19,20; therefore, they are key in modern urban ecosystems21. “The microbiome rewilding hypothesis” proposes that microbial diversity in urban green spaces can effectively improve urban population health by providing a “natural” microbiome to urban residents22,23. However, owing to environmental characteristics, artificial management, and maintenance types, different UGSHs differ considerably in structure and function. Urban roadside and park green belts are common UGSHs types. Roadside green belts promote isolation, safety, and ecological protection; these soils become relatively isolated habitats surrounded by concrete fences and are subject to multiple disturbances (for example, automobile exhaust emissions, dust fall, road management, and maintenance), which can influence soil enzyme activity and organic carbon content, affecting soil microbial diversity24,25. Urban parks provide important ecological services such as air purification, climate regulation, environment beautification, and physical and mental health promotion of residents26,27. Parks are often affected by human activities, and the soil surface is trampled and compacted28,29, which affects soil pore connectivity, permeability, air permeability, temperature, rooting space, nutrient flow, and biological activity30–32. Human disturbance is the main factor affecting soil microbial diversity33,34. Soil microbiomes in UGSHs play an important role in the sustainable and stable development of urban ecological environments by alleviating psychological pressure and physical health27. Therefore, strengthening the monitoring, evaluation, and research of soil microorganisms in UGSHs is of great scientific and practical significance.

Ectomycorrhizal fungi (EMF) play an important role in maintaining biodiversity and plant community succession35. Pinaceae, Fagaceae, Salicaceae, and other major tree species are EMF host plants that are widely distributed in most forest ecosystems36. EMF help host plants absorb nutrients (nitrogen, phosphorus, potassium, and other elements) and alleviate heavy metal pollution stress and antagonize diseases37,38. Some EMF can form fruiting bodies during the completion of their life cycle and are often used as indicators of changes in UGSHs39. EMF allows for rapid establishment or promotion of early tree species colonization in disturbed areas40. Considering that many ectomycorrhizal host trees such as Pinus, Populus, and Salix are distributed in cities, UGSHs are characteristic of habitat isolation and long-term artificial perturbations (for example, exposed pollutants, artificial disturbance, and management activities) and obstruct the interspecies interactions of fungi, resulting in lower EMF diversity in UGSHs41,42. Therefore, a deeper understanding of EMF diversity in UGSHs will allow for improved scientific management.

A large majority of studies on EMF diversity has focused on natural ecosystem habitats such as different forest types and grasslands43,44, reporting changes in soil microbial communities45, whereas few studies have focused on EMF diversity, in UGSHs. Therefore, our research focused on the EMF diversity in UGSHs. In this study, we selected C. deodara, P. massoniana, and S. babylonica in the urban park roadside green belt and C. deodara in the urban roadside green belt as the research objects. We compared the effects of different habitats (park and roadside green belt) on the same EMF host plant and the same habitat (park roadside green belt) on the EMF diversity of different host plants. We hypothesized that, under different UGSHs: (1) EMF diversity would differ among different tree species within the same environment; and (2) EMF diversity would vary for the same tree species under different environments. The present study aimed to provide a scientific reference for optimizing the design and scientific management of UGSHs.

Results

EMF infestation rate

In the two UGSHs, significant differences (P < 0.05) (Fig. 1a) in EMF infestation rates were observed among three greening tree species; EMF infestation rate of S. babylonica in park green belts was the lowest, whereas no significant difference (P > 0.05) in the C. deodara EMF infestation rate was observed. The EMF infection rates were as follows: P. massoniana in parks (54.48%) > C. deodara in parks (51.27%) > C. deodara on roads (41.3%) > S. babylonica in parks (36.97%).

Fig. 1.

Fig. 1

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Ectomycorrhizal fungi (EMF) infection rate (a) and dilution curves (b) of different greening tree species. The height of the purple bars in (a) represents the extent of EMF infection in the greening tree species. Inline graphic represents Cedrus deodara in the roadside green belts; Inline graphic represent Cedrus deodara in the park roadside green belts; Inline graphic represent Pinus massoniana in the park roadside green belts; Inline graphic represent Salix babylonica in the park roadside green belts.

EMF community composition and structure

EMF OTUs (Table 1) was as follows: P. massoniana in parks (31) > C. deodara in parks (23) > C. deodara on roads (13) > S. babylonica in parks (9), which indicated that EMF richness was obviously different among the diverse greening trees in the different UGSHs. EMF species dilution curve (Fig. 1b) showed that with an increase in the sampling amount, the number of EMF OTUs gradually increased but did not tend to stabilize, which indicated that the sampling amount did not fully or accurately reflect EMF diversity in the study sites, suggesting large spatial variation in the EMF community in UGSHs. Therefore, increasing the number of EMF samples in subsequent related investigations is necessary.

Table 1.

Ectomycorrhizal fungi colonizing greening tree species root from different green belts association habitats were identified based on morphotyping and sequencing of the internal transcribed spacer (ITS) rDNA.

Number OTUs Genbank ID Sequence length/bp EMF hosts
1 Amphinema sp.1 PQ049667 645 ▽△
2 Amphinema sp.2 PQ049668 682 ▽
3 Archaeorhizomyces borealis PQ049669 530 ▽
4 Basidiomycota sp. PQ049670 680 ▽
5 Cenococcum geophilum PQ049671 511 ▽○
6 Cenococcum sp.1 PQ049672 570 △
7 Ceratobasidiaceae sp. PQ049673 697 ▽
8 Clavulicium delectabile PQ049674 670 ▽
9 Clavulina sp. PQ049675 672 △
10 Clavulina sp.1 PQ049676 618 ▽
11 Clavulina sp.2 PQ049677 706 ▽
12 Clavulinaceae sp.1 PQ049678 672 ▽
13 Cortinarius sp. PQ049679 522 ▽
14 Cortinarius sp.1 PQ049680 634 △
15 Dothideomycetes sp. PQ049681 542 △
16 Eurotiomycetes sp.1 PQ049682 644 ▲
17 Helvellosebacina sp. PQ049683 640 △
18 Hypomyces luteovirens PQ049684 684 ▽
19 Ilyonectria macrodidyma PQ049685 584 △
20 Ilyonectria sp.1 PQ049686 574 △
21 Inocybe pseudoreducta PQ049687 716 ○
22 Lactarius akahatsu PQ049688 597 ○
23 Lactarius hatsudake PQ049689 597 ○
24 Lactarius kesiyae PQ049690 762 ▽
25 Lactarius salmonicolor PQ049691 760 ▽
26 Lophiostoma corticola PQ049692 552 ▲
27 Massarina sp.1 PQ049693 554 ▲
28 Oidiodendron citrinum PQ049694 540 ▲△
29 Oidiodendron maius PQ049695 576 ▲△
30 Oidiodendron sp.1 PQ049696 585 ▲△
31 Oidiodendron sp.2 PQ049697 599 △
32 Otidea bicolor PQ049698 648 ▽
33 Otidea bufonia PQ049699 672 ▽
34 Otidea subpurpurea PQ049700 647 ▽
35 Pseudotomentella sp.1 PQ049701 758 △
36 Russula brevispora PQ049702 608 ▲○
37 Russula catillus PQ049703 648 ▽△
38 Russula cremicolor PQ049704 716 ▽△
39 Russula indocatillus PQ049705 701 ▽
40 Russula sp.1 PQ049706 585 ▽
41 Sebacina dimitica PQ049707 636 ▽
42 Sebacina incrustans PQ049708 534 ▽
43 Sebacina sp. PQ049709 652 ▽
44 Sebacina sp.1 PQ049710 537 ▽○
45 Sebacina sparassoidea PQ049711 652 △
46 Thelephora sp.1 PQ049712 653 ○
47 Thelephoraceae sp.1 PQ049713 685 △
48 Thelephoraceae sp.2 PQ049714 584 ▽
49 Tomentella sp. PQ049715 695 ▽
50 Tomentella sp.1 PQ049716 683 ▽
51 Tomentella sp.2 PQ049717 683 ▽
52 Tomentella sp.3 PQ049718 698 △
53 Tomentella sp.4 PQ049719 686 ○
54 Tomentella stuposa PQ049720 686 ▽○
55 Trichophaea sp. PQ049721 613 ▲
56 Trichophaea sp.1 PQ049722 595 ▲△
57 Trichophaea sp.2 PQ049723 614 ▲△
58 Tylospora sp. PQ049724 463 ▽
59 Venturia sp.1 PQ049725 591 △
60 Wilcoxina sp. PQ049726 616 ▲
61 Wilcoxina sp.1 PQ049727 617 ▲△
62 Wilcoxina sp.2 PQ049728 611 ▲△
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Note: ▲ represents the C. deodara on roads; △ represents the C. deodara in parks; ▽ represents the P. massoniana in parks; ○ represents the S. babylonica in parks.

EMF community in each greening tree showed that the number of specific OTUs was higher than that of common OTUs (Fig. 2), and the common OTUs were mainly Oidiodendron, Wilcoxina, and Russula (Fig. 3). No common OTUs were observed among the three greening trees, including C. deodara and S. babylonica in the park green belt.

Fig. 2.

Fig. 2

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UpSet Venn diagram of Ectomycorrhizal fungi in different greening tree species. Orange-red dots and the height of the column represent the corresponding tree species and the number of unique Operational Taxonomic Units (OTUs) occupied, respectively. The line connected by the black dots indicates that the tree species corresponding at the two endpoints share common OTUs, while the height of the black column represents the number of common OTUs between the corresponding two greening tree species.

Fig. 3.

Fig. 3

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Co-occurrence network of greening tree species ectomycorrhizal fungi communities in different green belt.

Ascomycota and Basidiomycota were the dominant phylum in the roadside and park green belts, respectively (Fig. 4a). The EMF community structures in different UGSHs differed significantly (Fig. 4b). Trichophaea, Wilcoxina, and Oidiodendron were the dominant genus in roadside green belts. Tomentella, Russula, and Genocococcum were the dominant genus in park green belts. Both Wilcoxina and Oidiodendron were the most common dominant genus of C. deodara in the two UGSHs but were more abundant in the roadside green belt (Fig. 4a).

Fig. 4.

Fig. 4

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Ratios of Ascomycota to Basidiomycota fungal phylum (a) and relative abundance at the ectomycorrhizal fungi (EMF) genus level (b) in different greening tree species. The small squares of different colors represent the classification of EMF at the phylum (a) and genus levels (b) with each color corresponding to the color of the column in the figure. The height of the corresponding color column represents the richness or relative abundance of the EMF in the phylum or genus in the greening tree species.

EMF community diversity and similarity

The EMF diversity (Table 2) among greening trees was as follows: P. massoniana in parks > C. deodara in parks > C. deodara on roads > S. babylonica in parks. This shows that UGSHs disturbance tends to reduce EMF diversity in C. deodara, and EMF host plants might have an important influence on EMF diversity. The Jaccard and Sørenson similarity indices of the EMF communities among different host plants was low, and C. deodara and S. babylonica EMF communities differed (Table 3).

Table 2.

EMF diversity of urban greening tree species.

Plots EMF hosts OTUs Shannon index Simpson index Pielou index
Roadside green belt C. deodara 13 2.57 0.92 0.97
Park green belt C. deodara 23 3.18 0.96 0.99
S. babylonica 9 2.20 0.89 0.96
P. massoniana 31 3.43 0.97 0.98
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Table 3.

The Jaccard (lower left) and Sørenson (upper right) similarity index of EMF community of greening tree species in different UGSHs.

Tree species for parks and roads greening habitat Similarity index
C. deodara on roads C. deodara in parks P. massoniana in parks S. babylonica in parks
C. deodara on roads 0.33 0.00 0.09
C. deodara in parks 0.20 0.15 0.00
P. massoniana in parks 0.00 0.08 0.13
S. babylonica in parks 0.05 0.00 0.08
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Discussion

EMF is highly sensitive to soil nutrient and pollutant levels46. EMF plays a vital role in UGSHs and serves as an indicator of environmental quality. In this study, 62 EMF OTUs related to the two UGSHs were identified in three greening tree species. The decline in EMF OTU number showed the following pattern: P. massoniana in parks (31) > C. deodara in parks (23) > C. deodara on roads (13) > S. babylonica in parks (9). Among these, EMF OTU richness of P. massoniana was the highest, which might be related to the fact that P. massoniana is an native tree in China with relatively higher EMF richness (76 ~ 138 OTUs)38 in natural forest habitats. EMF richness in S. babylonica and C. deodara was lower, which is consistent with previous reports (S. babylonica and C. deodara had 7 and 19 EMF OTUs, respectively40,46). In addition, the species dilution curve of the number of samples (Fig. 1b) showed that the sampling amount in this study was insufficient to estimate the true EMF richness of each greening tree species. Therefore, further studies with adequate greening tree species sampling volumes in different UGSHs are required.

Basidiomycota richness in park green belts was significantly higher than that in roadside green belts, which might be due to the higher forest coverage and diversity in the park47, resulting in plant litter rich in lignin and cellulose. Basidiomycota can be effectively degraded and utilized to meet their own growth and reproduction needs48. This reflects the dominant position and important role of Basidiomycota in these habitats. Ascomycota richness in the roadside green belt was higher than that of Basidiomycota, which is consistent with P. sylvestris EMF diversity in sandy land49. Ascomycota EMF species is more adaptable to various environmental stresses50 and is likely the reason for Wilcoxina and Trichophaea dominance. In this study, EMF community composition was different among the different greening tree species. Habitats with high plant richness drives EMF richness25. EMF community structures in park green belts with higher plant diversity were more complex than those in roadside green belts. Although there are more common EMF among greening tree species at the genus or higher classification level, the OTU level is host-specific51,52. The spatial homogeneity of soil and vegetation increases with tourism disturbance53. Therefore, tourist disturbances might promote C. deodara and P. massoniana EMF community composition similarity at the genus level (seven common genus). Russula richness negatively correlated with the soil compaction gradient54. However, Russula was widespread in this study, and its richness was highest in P. massoniana, indicating that P. massoniana in the rhizosphere soil was less compacted and disturbed by tourist activities. The relative abundance of Wilcoxina and Oidiodendron in the roadside green belt was significantly higher than that in the park C. deodara, probably owing to enhanced adaptability to stressful environments, becoming the dominant species in this habitat.

Studying the EMF species diversity in urban greening trees enhance the public understanding of the ecological interactions between urban and natural environments. Such research can also aid in managing and improving urban ecological quality, and promote the diversity of ecological functions55. EMF diversity is mainly affected by the host plant type and soil environment8. EMF diversity in the park green habitat was P. massoniana > C. deodara > S. babylonica, and EMF diversity in park green belts was higher than that in roadside green belts, which might be due to the more complex composition and structure of vegetation communities in parks33 with higher diversity of cultivated non-native species and frequent conservation management (including regular fertilization, tillage, and watering). These human disturbances provide heterogeneous conditions for the greening tree species. The geographical environment and soil matrix of the parks are different and are also affected by the root exudates of adjacent plants56. This might selectively affect specific microbial communities in the rhizosphere of plants57, indirectly increasing nutrient availability for EMF, providing a broader niche for EMF growth and diversity, and shaping EMF diversity. Additionally, diversity was associated with high plant species richness58. A small number of unique EMF groups were observed in the non-native host plants C. deodara. This result is likely attributed to the wide compatibility between non-native host plants and native EMF59,60. Notably, EMF is the only group that tends to lose diversity owing to changes in urban land use61. In our study, C. deodara exhibited varying EMF diversity across different habitats owing to differences in land use and management patterns. This result suggests that the habitats conversion of host plants may be an important driving factor for the loss of EMF diversity. However, EMF in roadside green belts might be limited by other related factors such as single vegetation species (only C. deodara), shallow roots, nitrogen deposition caused by automobile exhaust emissions, and changes in temperature and humidity conditions62,63, which lead to EMF growth inhibition. C. deodara in the roadside greening habitat requires more frequent fertilization and irrigation, which reduces the dependence of plants on the absorption of nutrients and water by mycorrhizal fungi64,65. With the increase in soil compaction and the serious impact of human activities, the soil carbon deposition rate and the soil animals’ species (for example earthworms) content are affected, resulting in decreases in EMF diversity and host root infestation rate of C. deodara in roadside greening habitats38. In this study, the similarity of EMF communities among different greening tree species was low, due to the construction of EMF community dependence on both random and deterministic processes, and the diffusion limitation in the random process is the primary influencing factor; that is, the geographical distance hinders the diffusion of fungal propagules (spores and hyphae)35.

Conclusions

Overall, our study demonstrated significant differences in the composition and diversity of EMF communities among different greening tree species in two UGSHs. In addition, EMF diversity in the same greening tree species (C. deodara) was significantly affected in different UGSHs. UGSHs are urban functional lands and important habitats for urban biodiversity maintenance. Further studies that examine EMF diversity changes of other greening tree species from different perspectives of habitat interference are required to provide a scientific basis for healthy development of UGSHs ecology and the development of EMF agents for urban greening trees management.

Materials and methods

Study area

The study area is located in Huaxi District (106° 27–106° 52′ E, 26°11′–26° 34′ W), Guiyang City, Guizhou Province, China, which terrain is mainly mountainous and hilly, located in the east of Yunnan-Guizhou Plateau and the middle of Miaoling Mountains and belonged to the typical karst landform area. The area belongs to subtropical monsoon humid climate zone, the average annual rainfall approximately is 1178.3 mm, the average annual temperature is 15.7 °C, and the soil type is yellow. Zone vegetation type is evergreen broad-leaved mixed and coniferous forest. The tree species include P. massoniana, C. deodara, S. babylonica, Camphor and Ginkgo.

Sample collection and processing

In April 2023, the following tree species and sample plots were selected for analysis: three greening tree species in the Huaxi park green belt–C. deodara, P. massoniana, and S. babylonica, and one species in the roadside green belts of Jiaxiu South Road–C. deodara. The site conditions information was recorded and presented in Table 4. In the survey site, a total of 40 root samples were collected, 10 healthy host plants with relatively uniform diameters at breast height and separated by a minimum spatial distance of 3 m were randomly selected as sampling objects. For each greening tree, mycorrhizal root tips were collected using a root-cutting knife within a 1–1.5 m radius around the base of the tree trunk. Moreover, samples 15–20 cm in length from a 0–20 cm deep soil layer in two directions were traced from the trunk of the selected trees. Each root sample was placed in a plastic bag, stored on ice at 4 °C and transported to the laboratory. In the laboratory, samples were combined with dry silica gel and stored in a refrigerator at 4 °C until further analysis.

Table 4.

Basic information of different greening tree species in the plot.

Plots EMF hosts Altitude Longitude Latitude Diameter Height
Roadside green belt C. deodara 1093 106° 39′ 39″ E 26° 27′ 4″ N 16.48 7.57
Park green belt C. deodara 1098 106° 40′ 8″ E 26° 26′ 1″ N 20.16 8.24
S. babylonica 1088 106° 40′ 5″ E 26° 26′ 9″ N 19.98 6.97
P. massoniana 1078 106° 40′ 29″ E 26° 26′ 37″ N 20.89 9.64
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The collection of plant material in our study is licensed. This materials not stored in a publicly available herbarium.

Morphological and molecular identification of EMF

The root samples were extracted with tweezers and cut into 3–5 cm segments using scissors. After being washed repeatedly with tap water, the samples were transferred into a clean Petri dish with a small amount of water. Under the stereomicroscope (Mac Audi SMZ-171, Xiamen, China), the morphological classification was based on characteristics such as shape, size, branching, color and presence or absence of mycelium in mycorrhizal root tips66. The morphology and quantity of the mycorrhizal roots were recorded and photographed. Two to three root tips of each mycorrhizal were selected, placed into a centrifuge tube containing 200 µL of sterile water (3 replicates), and stored in a -20 °C freezer. Subsequently, two or three robust root tips were selected for extraction for species identification67. EMF DNA was extracted by modified cetyltrimethyl ammonium bromide (CTAB) method. The primers ITS1-F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS4 (5′-TCCTCCGCTTATTGATATATATGC-3′) were used to amplify the polymerase chain reaction (PCR) products. The PCR system volume was 25 µL68. The PCR reaction conditions were as follows: initial denaturation at 94 °C for 3 min; denaturation at 94 °C for 30 s; annealing at 55 °C for 30 s; and extension at 72 °C for 60 s. This cycle was repeated for a total of 34 cycles, followed by a final extension at 72 °C for 10 min. All PCR amplifications were analyzed using 2 µL of the sample through 1% agarose gel electrophoresis. After passing the test, the amplified products were sent to Sangon Bioengineering (Shanghai, China) Co., Ltd. for sequencing.

Data processing

The obtained DNA sequence was analyzed using Chromas (https://technelysium.com.au), and the low-quality sequence ends were trimmed and corrected. The two effective sequences of the same PCR product were spliced using SeqMan (https://www.dnastar.com). To preliminarily identify the relevant species, the spliced DNA sequence was compared against the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/genbank/) database using BLAST, with a 97% sequence similarity threshold for OTUs classification. Then, phylogenetic trees were constructed using the neighbor-joining method in MEGA-X software (https://www.megasoftware.net), with a bootstrap value of 1000. Phylogenetic analysis was carried out to further determine its OTUs. OriginPro 2022 (https://www.originlab.com) was used to draw the cumulative curve of OTU sampling number and the histogram of mycorrhizal infestation rate, and the relative abundance diagram for the phylum and genus levels was drawn. Co-occurrence network analysis of EMF was conducted at the OTU level using Gephi 0.10.2 software (https://gephi.org), and the diversity index of EMF was calculated with R version 4.3.2. (https://cran.rstudio.com). The Jaccard and Sørenson indexes were used to evaluate the EMF diversity among host plants. The simplified calculation formula is as follows:

graphic file with name M1.gif

J and S represents Jaccard and Sørenson indexes, respectively; a and b represent two sets, respectively, and f represents the intersection between the two sets “a” and “b”. In this study, a and b refer to two greening tree species, respectively, and f represents the OTUs shared between the two greening tree species.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (11.3KB, docx)

Acknowledgements

We wish to thank the editor and anonymous reviewers for their constructive comments and suggestions for improving our manuscript. This work was supported by the National Nature Science Foundation of China (NSFC) project (Grant Numbers 31660150 and 31960234).

Author contributions

Q.C.L. was responsible for article writing and data collation. Y.Q.C. was responsible for data collection. D.D.J. was responsible for experimental and data processing technical guidance. M.X. was responsible for revising the article, approving the final version, and acquiring funding. J.Z. was responsible for conceiving and designing the experiment, analyzing data, writing and revising the article, approving the final version, and acquiring funding.

Data availability

Sequence data that support the findings of this study have been deposited in the National Center for Biotechnology Information (NCBI) with the primary accession code SUB14603063.

Code availability

The code used in this study is available in the supplementary material published online by the paper.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (11.3KB, docx)

Data Availability Statement

Sequence data that support the findings of this study have been deposited in the National Center for Biotechnology Information (NCBI) with the primary accession code SUB14603063.

The code used in this study is available in the supplementary material published online by the paper.

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