Next Article in Journal
Predicting Fungemia in the ICU: Unveiling the Value of Weekly Fungal Surveillance and Yeast Colonisation Monitoring
Previous Article in Journal
The Combination of Morphological and Phylogenetic Evidence Reveals Four New Gymnopus Species and New Distribution
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JoF
Volume 10
Issue 10
10.3390/jof10100673
jof-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain’s Elevation Gradients, China

by
Qinglin Zuo
1,2,
Keke Dang
1,2,*,
Jing Yin
1,2,
Dandan Yuan
1,2,
Jing Lu
1,2 and
Xingjia Xiang
1,2,3,*
1
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2
Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Hefei 230601, China
3
International Collaborative Research Center for Huangshan Biodiversity and Tibetan Macaque Behavioral Ecology, Hefei 230601, China
*
Authors to whom correspondence should be addressed.
J. Fungi 2024, 10(10), 673; https://doi.org/10.3390/jof10100673
Submission received: 13 August 2024 / Revised: 13 September 2024 / Accepted: 25 September 2024 / Published: 27 September 2024
(This article belongs to the Section Fungal Evolution, Biodiversity and Systematics)
Browse Figures
Versions Notes

Abstract

Elevation gradients strongly influence the diversity pattern of soil microorganisms. To date, many studies have elucidated the response of soil microbes to changes in elevation gradients. However, the effects of these gradients on the assembly mechanisms and network complexity of rhizospheric microbial communities remain underexplored. To bridge this knowledge gap, this study assessed the response of rhizospheric fungal communities of Pinus hwangshanensis along different elevation gradients in the Huangshan Mountain scenic area with regard to diversity, community composition, and assembly mechanisms using high-throughput amplicon sequencing. The results revealed significant differences in rhizospheric fungal community composition across three elevation gradients. The soil organic matter and pH were the most relevant factors influencing the changes in rhizospheric fungal community composition. The rhizospheric fungal diversity was significantly lower at both low and high elevations compared to the medium elevation. The rhizospheric fungal community assembly showed a more deterministic process at low and high elevations than at the medium elevation, indicating that stronger environmental filtering contributed to reduced fungal diversity at the extremes of the elevation gradient. In addition, rhizospheric pathogens, particularly Dermateaceae, acted as keystone taxa, diminishing the stability of co-occurrence networks at the medium elevation. This study contributes to a more comprehensive understanding of rhizospheric fungal community patterns and their ecological functions along elevation gradients in mountainous regions.

1. Introduction

As an important component of terrestrial ecosystems, mountains are critical for maintaining biodiversity and ecosystem functions [1]. Microorganisms significantly contribute to mountain ecosystems, and they are closely related to soil functional diversity and nutrient cycling within these environments [2,3]. Soil microbial communities are spatially dynamic and complex, exhibiting different structures and functions in the soil at different elevations [4]. In addition, environmental heterogeneity induced by elevation affects microbial turnover, which has indirect effects on ecosystem function [5,6]. A number of environmental factors, such as soil total nitrogen (TN) and pH, have been shown to vary with elevation gradients, thereby influencing microbial community composition [7]. These variations in soil properties shape the microbial distribution pattern along elevation gradients. Therefore, investigating the effects of elevation gradients on soil fungal community distribution may enhance our understanding of the mechanisms underlying microbial spatial pattern formation and maintenance.
To date, numerous studies have elucidated the responses of soil microbial communities to changes along elevation gradients. However, the impact of elevation gradients on the characteristics of rhizospheric microbial communities remains largely unexplored. The plant rhizosphere is regarded as one of the most active and complex ecosystems, enhancing plants’ ability to withstand both abiotic and biotic pressures [8]. Rhizospheric microorganisms are important drivers that directly or indirectly influence the ecological health of the plant rhizosphere, and the robustness of microbial communities colonizing the plant rhizosphere contributes to nutrient uptake for their hosts and promotes plant growth [9]. Microorganisms also play integral roles in soil remediation by breaking down some of the harmful organic matter [10,11]. In addition, rhizospheric microorganisms promote plant survival under unfavorable conditions by enhancing the plant’s ability to resist attack by pathogens in the soil [12]. However, the fungal community’s ecological function and diversity in the rhizosphere are strongly impacted by multiple environmental factors [13]. Therefore, the study of the rhizosphere’s microbial composition is a very important guide to promoting plant health and resilience in mountainous regions.
Mountain soil microorganisms undergo ecological succession influenced by the interplay of environmental dispersal and environmental filtration due to the geological conditions of mountain ranges [14]. A related study found that the assembly process has different effects on microbial communities at different elevations [15]. Currently, with the continuous development of high-throughput sequencing technology, an improved understanding of fungal diversity, community composition, and potential functions has been gained at different elevations. The well-preserved ecosystem of Huangshan Mountain, with noticeable vertical geographic differences, serves as a natural laboratory for studying the elevation distribution pattern of soil microbial communities [16]. As one of the “three wonders” of Huangshan Mountains, Pinus hwangshanensis is the most common and important tree species in the Huangshan Mountain area, and it is also an endemic conifer species in China, possessing considerable economic and ecological value [17]. Despite the recognized importance of rhizospheric fungi for plants, knowledge regarding the distribution of rhizospheric fungal communities along elevation gradients remains limited.
To understand the ecological functions of the rhizospheric fungal community of P. hwangshanensis, this study used high-throughput amplicon sequencing technology to analyze the fungal spatial distribution, symbiotic networks, assembly mechanisms, and effects of environmental factors on the rhizospheric fungal communities at three different elevations (800 m, 1000 m, and 1200 m) in Huangshan Mountain. The following two hypotheses were tested: (1) elevation gradients lead to differences in the diversity and structure of rhizospheric fungal communities; (2) the assembly processes and potential functions of rhizospheric fungal communities vary across elevation gradients.

2. Materials and Methods

2.1. Site Selection and Soil Sampling

The study area was located on Huangshan Mountain (118°01′–118°14′ E, 30°01′–30°18′ N), and sample collection took place in September 2022. Firstly, we selected three elevation gradients: 800 ± 20 masl, 1000 ± 20 masl, and 1200 ± 20 masl. At each elevation, eight adult Pinus hwangshanensis trees were selected with an inter-distance of more than 20 m. For each tree, a sample was collected by taking five random rhizospheric soil samples tightly attached to the roots in the root system of P. hwangshanensis and mixing them into one sample, with eight soil samples at each elevation [18]. The rhizospheric soil samples were preserved on ice. After removing prominent leaves and dead branches, the soil samples were divided into two portions. One portion was stored at 4 °C for soil chemical analysis, and the other was stored at −80 °C for DNA extraction.

2.2. Determination of Soil Chemical Properties

The suspension was shaken well at a soil–water mass ratio of 1:2.5 (v/v) and left to stand for 30 min, after which the soil pH was measured using a pH meter. Soil moisture (SM) was determined by drying. The soil organic matter (SOM) and total nitrogen (TN) contents were determined using an elemental analyzer (EA3000, Euro Vector, Pavia, Italy). The total phosphorus (TP), available phosphorus (AP), and alkaline nitrogen (AN) were determined using standard methods [19,20,21].

2.3. DNA Extraction and PCR Amplification

Soil DNA was extracted from 24 separate soil samples using the FastDNA Spin Kit (MP Biomedicals, Santa Ana, CA, USA)for Soil according to the manufacturer’s instructions. The extracted DNA was identified using 1% agarose gel electrophoresis and quantified with ultraviolet spectrophotometry (Eppendorf, Germany) [22]. The ITS1 region of the fungal ITS gene was amplified using the ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) primers to obtain fungal data [23]. The PCR products were extracted from 2% agarose gel and then purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA).

2.4. Sequence Data Processing

We used QIIME1 (v1.9) for the quality control (average quality score > 30) of raw data from the MiSeq platform and USEARCH (version 8.0.1623) to perform operational taxonomic unit (OTU) assignment using >97% as the shared identity threshold [24]. The most abundant sequence within each OTU was selected as a representative sequence identified by the ribosomal database project Classifier with the UNITE (7.2) database. The randomly selected subsets of 61,000 sequences per sample were used for further analysis (Figure S1). The raw data have been submitted to the NCBI (National Center for Biotechnology Information) under project number SRP507372.

2.5. Statistical Analysis

A one-way ANOVA (analysis of variance) was performed to compare the differences between groups at the p < 0.05 level in SPSS 20.0. The differences in community composition were characterized using the non-metric multidimensional scaling analysis (NMDS) method. The effect of environmental variables on fungal community composition was analyzed using Mantel’s test, the variance inflation factor (VIF), and redundancy analysis (RDA) using the R (4.4.1) package vegan (2.6-4). The effects of nesting and turnover on fungal community structure were investigated based on the decomposition of β-diversity by Jaccard variability in the adespatial package (0.3-23) in the R (4.4.1) software [25]. The network parameters were calculated through the R packages “psych” (2.4.3) and “igraph” (2.0.3) and finally visualized with Gephi (0.9.2). Neutral community modeling predicted the relationship between the frequency of OTU occurrence and its relative abundance in fungal communities from different elevations [26]. The β-null model was used to distinguish fungal community assembly processes [27]. Rhizospheric fungal potential functions were predicted using FUNGuild analysis [28].

3. Results

3.1. Soil Chemical Properties

Different elevation gradients had significant effects on the rhizospheric soil properties of Pinus hwangshanensis. Soil pH gradually decreased along the elevation gradients (Table 1). TN, AN, and TP were significantly higher at a high elevation than at low and medium elevations (p < 0.05; Table 1). AP and SOM showed an increasing trend along the elevation gradients (Table 1).

3.2. Fungal Alpha Diversity

A total of 2,965,667 filtered fungal sequences and 8521 fungal OTUs were detected in all soil samples. There were 348 fungal OTUs (10.65%) shared in the rhizosphere of P. hwangshanensis among the three different elevations (Figure S2). The unique fungal OTUs were 2407, 2608, and 2424 for low, medium, and high elevations, respectively (Figure S2). The rhizospheric fungal α-diversity (OTU richness and Shannon index) showed a unimodal pattern with the highest values measured at mid-elevation compared to low and high elevations (p < 0.05) (Figure 1).

3.3. Fungal Community Composition

The predominant fungal phyla in the rhizosphere were Ascomycota (75.27%), Basidiomycota (20.21%), Zygomycota (4.37%), and Chytridiomycota (0.15%) (Figure S3). The relative abundance of Ascomycota was higher in the mid-elevation soil samples compared to the other samples. The relative abundance of Basidiomycota was higher in the high-elevation soil samples (Figure S3). The low-elevation soil samples had the highest relative abundance of Zygomycota and the lowest relative abundance of Chytridiomycota (Figure S4). Fungal community composition (β-diversity) showed significantly different values between the elevation points (Figure 2A). The LEfSe results showed that fungi were enriched in three orders (Venturiales, Boletales, and Sporidiobolales) in the low-elevation rhizospheric soil samples; the higher fungal relative abundance was found in one phylum (Ascomycota), two classes (Leotiomycetes and Saccharomycetes), and five orders (Myriangiales, Helatiales, Saccharomycetales, Ophiostomatales, and Thelephorales) in the medium-elevation rhizospheric soil samples; two classes (Agaricomycetes and Tremellomycetes) and two orders (Boliniales and Russulales) showed enrichment in high-elevation rhizospheric soil samples (Figure 2B). The indicator analysis showed that there were three (Russula, Elaphomyces, and Clitocybula), seven (Oidiodendron, Hypomyces, Umbelopsis, Tomentella, Lactarius, Lachnum, and Hymenoscyphus), and six (Hypocrea, Mortierella, Russula, Hypomyces, Cadophora, and Hymenoscyphus) enriched fungal genera at low, medium, and high elevations, respectively (Table 2).
The results of RDA combined with the variance partitioning analysis (VPA) showed that the differences in fungal community structure in rhizospheric soils at different elevations were mainly influenced by SOM (6.19%), pH (6.15%), TP (5.75%), and SM (5.64%) (Figure 3A). Mantel’s test showed that fungal community composition had the highest correlation with AN (r = 0.501, p < 0.001), TN (r = 0.494, p < 0.001), and SOM (r = 0.477, p < 0.001) (Figure 3B). Furthermore, the dominant fungal genera (Umbelopsis, Chloridium, Cryptococcus, and Alatospora) were strongly associated with these soil properties according to the correlation analysis (Figure S5). To uncover possible intrinsic mechanisms that maintain the structural stability of fungal communities at different elevations, this study further decomposed the structural differences among the communities into turnover and nested components. Overall, taxonomically based differences in community structure were dominated by species turnover (>50%), whereas the contribution of nesting was relatively lower, especially at the medium elevation (6.91%) (Figure 4).

3.4. Potential Functional Fungi in Rhizospheric Soil

The FUNGuild function prediction analysis provided valuable insights into the functional roles of different groups of fungi present in the rhizospheric soil of P. hwangshanensis forests. The results revealed the presence of plant saprotrophs, plant pathogens, endophytes, and ectomycorrhizal fungi in the collected samples, with variations between different elevations. Specifically, the analysis identified 2 plant saprotrophic OTUs, 10 plant pathogenic OTUs, 15 endophyte OTUs, and 77 ectomycorrhizal OTUs across the samples. The relative abundance of plant saprotrophs and pathogens was higher at a medium elevation (Figure 5A,B). In contrast, endophytes and ectomycorrhizal fungi were more abundant at higher elevations (Figure 5C,D).

3.5. Community Assembly Mechanisms of Rhizospheric Fungi

The mobility of fungi in the soil was estimated using the neutral model, which revealed that the mobility of rhizospheric fungi was greater in the mid-elevation soil samples (M = 0.44) than in the low-elevation (M = 0.36) and high-elevation soil samples (M = 0.37), showing that the fungal community was more restricted to dispersal in low- and high-elevation soils than in mid-elevation soil (Figure 6A). The β-null deviation values in the fungal communities differed significantly across elevations, with lower values observed in the mid-elevation group, showing that stochastic processes drove fungal community assembly in mid-elevation soil, while deterministic processes mainly shaped fungal community assembly in low- and high-elevation soils (Figure 6B). Niche width is an important measure to evaluate microbial environmental adaptation. Based on the calculated niche width results, the niche width was wider for mid-elevation than for low- and high-elevation rhizospheric fungal communities (Figure 6C). In addition, the main environmental variables were selected to determine the correlation between the assembly process of fungal communities and environmental factors. The abundance β-null deviation values were significantly and positively correlated mainly with SOM (r = 0.335, p < 0.05) and TP (r = 0.340, p < 0.05) (Figure S6).

3.6. Analysis of Rhizospheric Fungal Networks

The co-occurrence networks of rhizospheric fungi at different elevations were established using Spearman’s correlation analysis. The network complexity was highest at low elevations as the network had the highest edges, average density, and clustering coefficients and the shortest average path lengths compared to medium and high elevation (Table S1). Network stability, assessed by natural connectivity, was also highest at low elevations and lowest at a medium elevation (Figure S7). Ascomycota was the predominant phylum in the rhizospheric fungal network across the three elevations (Figure 6D). Keystone species play important roles in determining the function and interactions of fungal communities, and species with a high degree (>19) and low betweenness centrality (<150) values in the fungal network are considered to be keystone species. The key species found in this study at each elevation are listed in Table S2.

4. Discussion

Understanding the effect of elevation on microbial community patterns and soil chemical properties is important for clarifying fungal-driven ecosystem functioning [29]. Soil nutrient contents, especially SOM, increased significantly along the elevation gradients, possibly because a lower temperature triggered the slow growth of plants and the accumulation of nutrients at higher elevations (Table 1). However, the soil pH showed a decrease along the elevation gradients, as the accumulation of organic matter might have increased the content of humic acid [30]. Environmental heterogeneity strongly influenced the spatial distribution of fungal communities [31,32], consistent with the findings of the present study (Figure 3A). Different soil environments along the elevation gradients created a certain niche, affecting fungal community composition [33,34]. In this study, the SOM content showed a significant increase along the elevation gradients. The SOM, as an important source of nutrients for fungal growth, was closely related to the fungal community composition [35]. Changes in the SOM content might drive a shift in fungal community composition along elevation gradients.
Differences in the spatial turnover rates of fungal communities, driven by dispersal constraints and niche selection, resulted in alterations in the spatial distribution patterns of fungal community composition at different elevations [36]. The contribution of nesting was higher at both low and high elevations relative to the medium elevation (Figure 4). Higher nesting promoted biotic homogenization, leading to a loss of alpha diversity [37,38], which may be an important factor contributing to the lower fungal diversity at low and high elevations (Figure 1). Furthermore, the neutral model and null model analyses suggested that stochastic processes were the dominant mechanism driving the assembly process of soil fungal communities at the medium elevation, whereas deterministic processes dominated the soil fungal communities at the low and high elevations (Figure 6B,C), further demonstrating that higher environmental filtering decreases soil fungal diversity at low and high elevations. Moreover, the soil samples collected at low and high elevations showed a lower fungal niche width than the soil samples collected at a medium elevation (Figure 6C). Previous studies have shown that niche width decreases as the environment becomes hostile, and inter-competition increases among taxa [39], thereby decreasing microbial diversity.
The predicted fungal functions were investigated in this study. The medium-elevation soil had higher relative abundance for plant saprotrophs group. The relative abundance of Ascomycota was highest at the medium elevation (Figure S4). Previous studies have shown the prominent role of Ascomycota in decomposing lignocellulose [40], which is consistent with the predicted function result. The greater abundance of plant saprotroph indicated a strong cycling of organic matter, which might have been induced by the greater fungal species turnover mentioned above at the medium elevation. However, the relative abundance of plant pathogens was higher at the medium elevation. A greater abundance of pathogens might be unfavorable to the growth of Pinus hwangshanensis. The soil samples collected at a high elevation had the highest relative abundance of endophyte and ectomycorrhizal fungi. Both endophyte and ectomycorrhizal fungi are important plant-beneficial fungi (PBF). Endophyte and ectomycorrhizal fungi have proven to be extremely important plant companions that can increase the resilience of the host and promote plant survival in soil [41,42]. As the environmental conditions at high elevations are harsher, P. hwangshanensis might rely more on their rhizospheric PBF to growth.
Fungal co-occurrence network complexity and stability are closely related to topological parameters, including nodes, edges, clustering coefficients, average variation degree (AVD), and natural connectivity [43,44]. The stability of the fungal networks at the mid-elevation was lower than that of the networks at the low and high elevations (Figure S7 and Table S1). Keystone taxa are essential for maintaining network function and structure. For instance, the Trichocomaceae family is likely to play a significant role in soil aggregation through its adhesive properties, and some species in the genus Oidiodendron have been shown to secrete growth hormones and enzymes that have a variety of beneficial effects on host plants (Table S2) [45,46,47,48]. The functional diversity provided by these low-elevation network species may be crucial for enhancing the overall stability of an ecological network. In this study, one of the keystone taxa (Dermateaceae) in the mid-elevation network was found to be a plant pathogen that causes the necrosis of plant leaves (Table S2) [49,50]. It has been demonstrated that pathogens decrease the stability of networks [51,52]. In this study, the mid-elevation rhizospheric soil was invaded by a large number of fungal plant pathogens (Figure 5B). Thus, the lower stability of the mid-elevation network might have been due to the presence of antagonism between pathogens and other fungal taxa. When the stability of fungal symbiotic networks is low, it may reduce the stability and sustainability of soil functions [53]. Therefore, there is a potential risk of pathogen outbreaks in mid-elevation environments that may impact plant health and ecosystem function, and attention should be paid to preventing the spread of plant pathogens in the rhizospheric soil of P. hwangshanensis.

5. Conclusions

In conclusion, our findings highlight significant differences in the fungal community structures within the rhizospheric soils of Pinus hwangshanensis across varying elevation gradients. The observed changes in soil properties likely drive the distinct fungal community patterns associated with these gradients. Notably, we found that the lower fungal diversity at both low and high elevations is attributed to a higher degree of fungal homogenization and more intense environmental filtering compared to mid-elevation areas. At high elevations, the relative abundance of putative endophytic and ectomycorrhizal fungi was markedly higher, indicating that P. hwangshanensis may increasingly depend on these beneficial fungi to withstand the challenging environmental conditions found in such habitats. Conversely, the mid-elevation zone exhibited a greater abundance of rhizospheric plant pathogens, which could pose a threat to the growth and health of P. hwangshanensis. This study enriches our understanding of the complex dynamics of rhizospheric fungal communities and their ecological functions within mountain ecosystems along elevation gradients. By elucidating these relationships, our research not only contributes to the broader field of microbial ecology but also has important implications for the conservation and management of P. hwangshanensis forests in the face of environmental changes.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jof10100673/s1, Table S1: Co-occurrence network topological features statistics; Table S2: Keystone species in co-occurrence network; Figure S1: Rarefaction curves of the number of operational taxonomic units (OTUs) at 97% similarity boxplot for each of 24 samples; Figure S2: Venn diagrams showing endemic and shared fungal OTUs in three different elevation rhizosphere; Figure S3: The relative abundance of rhizosphere fungal phyla at each sampling site; Figure S4: The differences of dominant rhizosphere fungal phyla at different elevations; Figure S5: Spearman rank correlations between the relative abundance of dominant fungal taxa and soil properties; Figure S6: Linear regression analysis of soil physicochemical and β-null deviation; Figure S7: The stability of co-occurrence network. Reference [54] is cited in the Supplementary Materials.

Author Contributions

Q.Z. and X.X. designed the experiment; K.D., J.Y., D.Y. and J.L. collected the samples; Q.Z., X.X. and K.D. performed the data analysis; Q.Z. wrote the manuscript; and X.X. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the Outstanding Youth Research Project of Anhui Province for Xingjia Xiang (2022AH030015).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data were submitted to the NCBI (National Center for Biotechnology Information) under project number SRP507372.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Tian, Q.; Jiang, Y.; Tang, Y.; Wu, Y.; Tang, Z.; Liu, F. Soil PH and Organic Carbon Properties Drive Soil Bacterial Communities in Surface and Deep Layers Along an Elevational Gradient. Front. Microbiol. 2021, 12, 646124. [Google Scholar] [CrossRef] [PubMed]
  2. Gentilin-Avanci, C.; Pinha, G.D.; Ratz Scoarize, M.M.; Petsch, D.K.; Benedito, E. Warming Water and Leaf Litter Quality but Not Plant Origin Drive Decomposition and Fungal Diversity in an Experiment. Fungal. Biol. 2022, 126, 631–639. [Google Scholar] [CrossRef] [PubMed]
  3. Yang, Y.; Qiu, K.; Xie, Y.; Li, X.; Zhang, S.; Liu, W.; Huang, Y.; Cui, L.; Wang, S.; Bao, P. Geographical, Climatic, and Soil Factors Control the Altitudinal Pattern of Rhizosphere Microbial Diversity and Its Driving Effect on Root Zone Soil Multifunctionality in Mountain Ecosystems. Sci. Total Environ. 2023, 904, 166932. [Google Scholar] [CrossRef] [PubMed]
  4. Farrer, E.C.; Porazinska, D.L.; Spasojevic, M.J.; King, A.J.; Bueno de Mesquita, C.P.; Sartwell, S.A.; Smith, J.G.; White, C.T.; Schmidt, S.K.; Suding, K.N. Soil Microbial Networks Shift Across a High-Elevation Successional Gradient. Front. Microbiol. 2019, 10, 2887. [Google Scholar] [CrossRef] [PubMed]
  5. Stegen, J.C.; Lin, X.; Konopka, A.E.; Fredrickson, J.K. Stochastic and Deterministic Assembly Processes in Subsurface Microbial Communities. ISME J. 2012, 6, 1653–1664. [Google Scholar] [CrossRef]
  6. Wagg, C.; Bender, S.F.; Widmer, F.; van der Heijden, M.G.A. Soil Biodiversity and Soil Community Composition Determine Ecosystem Multifunctionality. Proc. Natl. Acad. Sci. USA 2014, 111, 5266–5270. [Google Scholar] [CrossRef]
  7. Liu, D.; Wu, X.; Shi, S.; Liu, H.; Liu, G. A Hollow Bacterial Diversity Pattern with Elevation in Wolong Nature Reserve, Western Sichuan Plateau. J. Soils Sediments. 2016, 16, 2365–2374. [Google Scholar] [CrossRef]
  8. Afridi, M.S.; Fakhar, A.; Kumar, A.; Ali, S.; Medeiros, F.H.V.; Muneer, M.A.; Ali, H.; Saleem, M. Harnessing Microbial Multitrophic Interactions for Rhizosphere Microbiome Engineering. Microbiol. Res. 2022, 265, 127199. [Google Scholar] [CrossRef]
  9. Qu, Q.; Zhang, Z.; Peijnenburg, W.J.G.M.; Liu, W.; Lu, T.; Hu, B.; Chen, J.; Chen, J.; Lin, Z.; Qian, H. Rhizosphere Microbiome Assembly and Its Impact on Plant Growth. J. Agric. Food Chem. 2020, 68, 5024–5038. [Google Scholar] [CrossRef]
  10. Khan, M.F.; Chowdhary, S.; Koksch, B.; Murphy, C.D. Biodegradation of Amphipathic Fluorinated Peptides Reveals a New Bacterial Defluorinating Activity and a New Source of Natural Organofluorine Compounds. Environ. Sci. Technol. 2023, 57, 9762–9772. [Google Scholar] [CrossRef]
  11. Wang, X.; Chi, Y.; Song, S. Important Soil Microbiota’s Effects on Plants and Soils: A Comprehensive 30-Year Systematic Literature Review. Front. Microbiol. 2024, 15, 1347745. [Google Scholar] [CrossRef] [PubMed]
  12. Wang, Z.; Hu, X.; Solanki, M.K.; Pang, F. A Synthetic Microbial Community of Plant Core Microbiome Can Be a Potential Biocontrol Tool. J. Agric. Food Chem. 2023, 71, 5030–5041. [Google Scholar] [CrossRef]
  13. Praeg, N.; Illmer, P. Microbial Community Composition in the Rhizosphere of Larix Decidua under Different Light Regimes with Additional Focus on Methane Cycling Microorganisms. Sci. Rep. 2020, 10, 22324. [Google Scholar] [CrossRef] [PubMed]
  14. Ortiz-Álvarez, R.; Cáliz, J.; Camarero, L.; Casamayor, E.O. Regional Community Assembly Drivers and Microbial Environmental Sources Shaping Bacterioplankton in an Alpine Lacustrine District (Pyrenees, Spain). Environ. Microbiol. 2020, 22, 297–309. [Google Scholar] [CrossRef] [PubMed]
  15. Kang, Y.; Wu, H.; Zhang, Y.; Wu, Q.; Guan, Q.; Lu, K.; Lin, Y. Differential Distribution Patterns and Assembly Processes of Soil Microbial Communities under Contrasting Vegetation Types at Distinctive Altitudes in the Changbai Mountain. Front. Microbiol. 2023, 14, 1152818. [Google Scholar] [CrossRef] [PubMed]
  16. Yao, L.; Hu, L.H.; Zhang, H.C.; Fang, Y.M.; Wang, G.M. Elevational distribution characteristics of soil bacterial community and enzyme activities in Mount Huangshan. Huan Jing Ke Xue 2019, 40, 859–868. (In Chinese) [Google Scholar] [PubMed]
  17. Hu, M.; Yang, Y.; Fan, M.; Huang, K.; Wang, L.; Lv, T.; Yi, X.; Chen, L.; Fang, Y. Inter- and Intra-Population Variation of Foliage Calcium and Magnesium in Two Chinese Pine Species. Plants 2023, 12, 562. [Google Scholar] [CrossRef] [PubMed]
  18. Smalla, K.; Wieland, G.; Buchner, A.; Zock, A.; Parzy, J.; Kaiser, S.; Roskot, N.; Heuer, H.; Berg, G. Bulk and Rhizosphere Soil Bacterial Communities Studied by Denaturing Gradient Gel Electrophoresis: Plant-Dependent Enrichment and Seasonal Shifts Revealed. Appl. Environ. Microbiol. 2001, 67, 4742–4751. [Google Scholar] [CrossRef]
  19. Ståhlberg, S. A New Extraction Method for Estimation of Plant-Available P, K and Mg. Acta Agric. Scand. 1980, 30, 93–107. [Google Scholar] [CrossRef]
  20. Ai, Z.; He, L.; Xin, Q.; Yang, T.; Liu, G.; Xue, S. Slope Aspect Affects the Non-Structural Carbohydrates and C:N:P Stoichiometry of Artemisia Sacrorum on the Loess Plateau in China. Catena 2017, 152, 9–17. [Google Scholar] [CrossRef]
  21. Bowman, R.A. A Rapid Method to Determine Total Phosphorus in Soils. Soil Sci. Soc. Am. J. 1988, 52, 1301–1304. [Google Scholar] [CrossRef]
  22. Keshri, J.; Pradeep Ram, A.S.; Sime-Ngando, T. Distinctive Patterns in the Taxonomical Resolution of Bacterioplankton in the Sediment and Pore Waters of Contrasted Freshwater Lakes. Microb. Ecol. 2018, 75, 662–673. [Google Scholar] [CrossRef] [PubMed]
  23. Edgar, R.C. Search and Clustering Orders of Magnitude Faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [Google Scholar] [CrossRef] [PubMed]
  24. Adams, R.I.; Miletto, M.; Taylor, J.W.; Bruns, T.D. Dispersal in Microbes: Fungi in Indoor Air Are Dominated by Outdoor Air and Show Dispersal Limitation at Short Distances. ISME J. 2013, 7, 1262–1273. [Google Scholar] [CrossRef] [PubMed]
  25. Baselga, A. Partitioning the Turnover and Nestedness Components of Beta Diversity. Glob. Ecol. Biogeogr. 2010, 19, 134–143. [Google Scholar] [CrossRef]
  26. Sloan, W.T.; Lunn, M.; Woodcock, S.; Head, I.M.; Nee, S.; Curtis, T.P. Quantifying the Roles of Immigration and Chance in Shaping Prokaryote Community Structure. Environ. Microbiol. 2006, 8, 732–740. [Google Scholar] [CrossRef]
  27. Luan, L.; Liang, C.; Chen, L.; Wang, H.; Xu, Q.; Jiang, Y.; Sun, B. Coupling Bacterial Community Assembly to Microbial Metabolism across Soil Profiles. mSystems 2020, 5, e00298-20. [Google Scholar] [CrossRef]
  28. Nguyen, N.H.; Song, Z.; Bates, S.T.; Branco, S.; Tedersoo, L.; Menke, J.; Schilling, J.S.; Kennedy, P.G. FUNGuild: An Open Annotation Tool for Parsing Fungal Community Datasets by Ecological Guild. Fungal. Ecol. 2016, 20, 241–248. [Google Scholar] [CrossRef]
  29. Zeng, Q.; Liu, D.; An, S. Decoupled Diversity Patterns in Microbial Geographic Distributions on the Arid Area (the Loess Plateau). Catena 2021, 196, 104922. [Google Scholar] [CrossRef]
  30. Fakhry, A.; Osman, O.; Ezzat, H.; Ibrahim, M. Spectroscopic Analyses of Soil Samples Outside Nile Delta of Egypt. Spectroc. Acta Pt. A-Molec. Biomolec. Spectr. 2016, 168, 244–252. [Google Scholar] [CrossRef]
  31. Cissé, G.; Essi, M.; Kedi, B.; Nicolas, M.; Staunton, S. Accumulation and Vertical Distribution of Glomalin-Related Soil Protein in French Temperate Forest Soils as a Function of Tree Type, Climate and Soil Properties. Catena 2023, 220, 106635. [Google Scholar] [CrossRef]
  32. Zhang, M.; Shi, Z.; Yang, M.; Lu, S.; Cao, L.; Wang, X. Molecular Diversity and Distribution of Arbuscular Mycorrhizal Fungi at Different Elevations in Mt. Taibai of Qinling Mountain. Front. Microbiol. 2021, 12, 609386. [Google Scholar] [CrossRef] [PubMed]
  33. Fierer, N.; McCain, C.M.; Meir, P.; Zimmermann, M.; Rapp, J.M.; Silman, M.R.; Knight, R. Microbes Do Not Follow the Elevational Diversity Patterns of Plants and Animals. Ecology 2011, 92, 797–804. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, X.; Wang, Z.; Liu, W.; Liu, H.; Zhang, Q.; Zeng, J.; Ren, C.; Yang, G.; Zhong, Z.; Han, X. Abundant and Rare Fungal Taxa Exhibit Different Patterns of Phylogenetic Niche Conservatism and Community Assembly across a Geographical and Environmental Gradient. Soil Biol. Biochem. 2023, 186, 109167. [Google Scholar] [CrossRef]
  35. Soukupová, L.; Hršelová, H.; Gryndlerová, H.; Merhautová, V.; Gryndler, M. Alkali-Extractable Soil Organic Matter: An Important Factor Affecting the Mycelial Growth of Ectomycorrhizal Fungi. Appl. Soil Ecol. 2008, 40, 37–43. [Google Scholar] [CrossRef]
  36. Wang, Y.; Lu, G.; Yu, H.; Du, X.; He, Q.; Yao, S.; Zhao, L.; Huang, C.; Wen, X.; Deng, Y. Meadow Degradation Increases Spatial Turnover Rates of the Fungal Community through Both Niche Selection and Dispersal Limitation. Sci. Total Environ. 2021, 798, 149362. [Google Scholar] [CrossRef]
  37. Lee, S.; Klinger, R.; Brooks, M.L.; Ferrenberg, S. Homogenization of Soil Seed Bank Communities by Fire and Invasive Species in the Mojave Desert. Front. Ecol. Evol. 2024, 12, 1271824. [Google Scholar] [CrossRef]
  38. Zorzal-Almeida, S.; Bartozek, E.C.R.; Bicudo, D.C. Homogenization of Diatom Assemblages Is Driven by Eutrophication in Tropical Reservoirs. Environ. Pollut. 2021, 288, 117778. [Google Scholar] [CrossRef]
  39. Hunting, E.R.; Barmentlo, S.H.; Schrama, M.; van Bodegom, P.M.; Zhai, Y.; Vijver, M.G. Agricultural Constraints on Microbial Resource Use and Niche Breadth in Drainage Ditches. PeerJ 2017, 5, e4175. [Google Scholar] [CrossRef]
  40. Heeger, F.; Bourne, E.C.; Wurzbacher, C.; Funke, E.; Lipzen, A.; He, G.; Ng, V.; Grigoriev, I.V.; Schlosser, D.; Monaghan, M.T. Evidence for Lignocellulose-Decomposing Enzymes in the Genome and Transcriptome of the Aquatic Hyphomycete Clavariopsis Aquatica. J. Fungi. 2021, 7, 854. [Google Scholar] [CrossRef]
  41. Ruytinx, J.; Miyauchi, S.; Hartmann-Wittulsky, S.; de Freitas Pereira, M.; Guinet, F.; Churin, J.-L.; Put, C.; Le Tacon, F.; Veneault-Fourrey, C.; Martin, F.; et al. A Transcriptomic Atlas of the Ectomycorrhizal Fungus Laccaria Bicolor. Microorganisms 2021, 9, 2612. [Google Scholar] [CrossRef] [PubMed]
  42. Card, S.; Johnson, L.; Teasdale, S.; Caradus, J. Deciphering Endophyte Behaviour: The Link between Endophyte Biology and Efficacious Biological Control Agents. FEMS Microbiol. Ecol. 2016, 92, fiw114. [Google Scholar] [CrossRef] [PubMed]
  43. Xun, W.; Liu, Y.; Li, W.; Ren, Y.; Xiong, W.; Xu, Z.; Zhang, N.; Miao, Y.; Shen, Q.; Zhang, R. Specialized Metabolic Functions of Keystone Taxa Sustain Soil Microbiome Stability. Microbiome 2021, 9, 35. [Google Scholar] [CrossRef] [PubMed]
  44. Yuan, M.M.; Guo, X.; Wu, L.; Zhang, Y.; Xiao, N.; Ning, D.; Shi, Z.; Zhou, X.; Wu, L.; Yang, Y.; et al. Climate Warming Enhances Microbial Network Complexity and Stability. Nat. Clim. Chang. 2021, 11, 343–348. [Google Scholar] [CrossRef]
  45. Ya, T.; Huang, Y.; Wang, K.; Wang, J.; Liu, J.; Hai, R.; Zhang, T.; Wang, X. Functional Stability Correlates with Dynamic Microbial Networks in Anammox Process. Bioresour. Technol. 2023, 370, 128557. [Google Scholar] [CrossRef]
  46. Daynes, C.N.; Zhang, N.; Saleeba, J.A.; McGee, P.A. Soil Aggregates Formed in Vitro by Saprotrophic Trichocomaceae Have Transient Water-Stability. Soil Biol. Biochem. 2012, 48, 151–161. [Google Scholar] [CrossRef]
  47. Mikheev, V.S.; Struchkova, I.V.; Churkina, L.M.; Brilkina, A.A.; Berezina, E.V. Several Characteristics of Oidiodendron Maius G.L. Barron Important for Heather Plants’ Controlled Mycorrhization. J. Fungi 2023, 9, 728. [Google Scholar] [CrossRef]
  48. Pescie, M.A.; Montecchia, M.; Lavado, R.S.; Chiocchio, V.M. Inoculation with Oidiodendron Maius BP Improves Nitrogen Absorption from Fertilizer and Growth of Vaccinium Corymbosum during the Early Nursery Stage. Plants 2023, 12, 792. [Google Scholar] [CrossRef]
  49. Peng, J.; Rojas, J.A.; Sang, H.; Proffer, T.J.; Outwater, C.A.; Vilgalys, R.; Sundin, G.W. Draft Genome Sequence Resource for Blumeriella Jaapii, the Cherry Leaf Spot Pathogen. Phytopathology 2020, 110, 1507–1510. [Google Scholar] [CrossRef]
  50. Severns, P.M.; Guzman-Martinez, M. Plant Pathogen Invasion Modifies the Eco-Evolutionary Host Plant Interactions of an Endangered Checkerspot Butterfly. Insects 2021, 12, 246. [Google Scholar] [CrossRef]
  51. Wu, Y.; Li, Z.; Zhao, J.; Chen, Z.; Xiang, X. Significant Differences in Intestinal Fungal Community of Hooded Cranes along the Wintering Periods. Front. Microbiol. 2022, 13, 991998. [Google Scholar] [CrossRef] [PubMed]
  52. Zhang, L.; Fang, S.; Hong, W.; Shen, Z.; Li, S.; Fang, W. Differences in Pathogenic Community Assembly Processes and Their Interactions with Bacterial Communities in River and Lake Ecosystems. Environ. Res. 2023, 236, 116847. [Google Scholar] [CrossRef] [PubMed]
  53. Zeng, Q.; Mei, T.; Wang, M.; Tan, W. Linking Phosphorus Fertility to Soil Microbial Diversity and Network Complexity in Citrus Orchards: Implications for Sustainable Agriculture. Appl. Soil Ecol. 2024, 200, 105441. [Google Scholar] [CrossRef]
  54. Chen, Z.; Zheng, Y.; Ding, C.; Ren, X.; Yuan, J.; Sun, F.; Li, Y. Integrated metagenomics and molecular ecological network analysis of bacterial community composition during the phytoremediation of cadmium-contaminated soils by bioenergy crops. Ecotoxicol. Environ. Saf. 2017, 145, 111–118. [Google Scholar] [CrossRef]
Jof 10 00673 g001
Figure 1. Rhizospheric fungal richness and Shannon index of Pinus hwangshanensis at different elevations. Letters represent significant differences from the one-way ANOVA with Duncan’s comparisons (p < 0.05).
Figure 1. Rhizospheric fungal richness and Shannon index of Pinus hwangshanensis at different elevations. Letters represent significant differences from the one-way ANOVA with Duncan’s comparisons (p < 0.05).
Jof 10 00673 g001
Jof 10 00673 g002
Figure 2. (A) NMDS of fungal community composition based on Bray–Curtis distance and differences in community composition between different elevations tested using ANOSIM, ** p < 0.01, *** p < 0.01. (B) The LEfSe analyses show rhizospheric soil fungal biomarkers associated with each elevation (effect sizes greater than 2 and α values less than 0.05). LE: low elevation; ME: medium elevation; and HE: high elevation.
Figure 2. (A) NMDS of fungal community composition based on Bray–Curtis distance and differences in community composition between different elevations tested using ANOSIM, ** p < 0.01, *** p < 0.01. (B) The LEfSe analyses show rhizospheric soil fungal biomarkers associated with each elevation (effect sizes greater than 2 and α values less than 0.05). LE: low elevation; ME: medium elevation; and HE: high elevation.
Jof 10 00673 g002
Jof 10 00673 g003
Figure 3. (A) Redundancy analysis (RDA) showing the effects of major environmental factors on rhizospheric fungal communities at different elevations. (B) Spearman’s correlation analysis was used for correlation analyses between major environmental factors and indices of fungal community diversity (OTU richness and Shannon index), and Mantel’s test was used to test for correlation between the composition of fungal communities and environmental factors. The size of the square corresponds to the value of the correlation coefficient, the width of the line corresponds to the Mantel distance correlation r statistic, and the color of the line indicates the Mantel p-value, * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 3. (A) Redundancy analysis (RDA) showing the effects of major environmental factors on rhizospheric fungal communities at different elevations. (B) Spearman’s correlation analysis was used for correlation analyses between major environmental factors and indices of fungal community diversity (OTU richness and Shannon index), and Mantel’s test was used to test for correlation between the composition of fungal communities and environmental factors. The size of the square corresponds to the value of the correlation coefficient, the width of the line corresponds to the Mantel distance correlation r statistic, and the color of the line indicates the Mantel p-value, * p < 0.05, ** p < 0.01, *** p < 0.001.
Jof 10 00673 g003
Jof 10 00673 g004
Figure 4. Triangulation of β-diversity patterns based on Jaccard differences in fungal community composition at different elevations. Each point is determined by the values in the Jaccard dissimilarity, nestedness, and turnover matrices.
Figure 4. Triangulation of β-diversity patterns based on Jaccard differences in fungal community composition at different elevations. Each point is determined by the values in the Jaccard dissimilarity, nestedness, and turnover matrices.
Jof 10 00673 g004
Jof 10 00673 g005
Figure 5. The fungal predicted function. (A) Potential plant saprophytes in the rhizosphere at different elevations. (B) Potential plant pathogen in the rhizosphere at different elevations. (C) Potential endophyte in the rhizosphere at different elevations. (D) Potential ectomycorrhizal fungi in the rhizosphere at different elevations. Letters represent significant differences derived from the one-way ANOVA with Duncan’s comparisons (p < 0.05).
Figure 5. The fungal predicted function. (A) Potential plant saprophytes in the rhizosphere at different elevations. (B) Potential plant pathogen in the rhizosphere at different elevations. (C) Potential endophyte in the rhizosphere at different elevations. (D) Potential ectomycorrhizal fungi in the rhizosphere at different elevations. Letters represent significant differences derived from the one-way ANOVA with Duncan’s comparisons (p < 0.05).
Jof 10 00673 g005
Jof 10 00673 g006
Figure 6. (A) Neutral community model. OTUs that occur more frequently than the model-predicted values are shown in green, OTUs that occur less frequently than the predicted values are shown in red, and OTUs that occur within the predicted range are shown in black. The dashed line indicates the 95% confidence interval around the model-predicted value (blue line). R2 represents the overall goodness of fit of the neutral community model. The M-value quantifies the migration rate at the community level, with a smaller M-value indicating a more restricted dispersal of species throughout the community and a higher M-value indicating a less restricted dispersal. (B) The abundance-based β-null model distinguishes the relative importance of deterministic and stochastic processes by the value of zero deviation. the closer the value of β-diversity deviation is to zero, the greater the stochasticity; the closer the value of β-diversity deviation is to one, the greater the determinism. (C) Niche width was used to show the utilization of resources by fungi in each elevation. Letters represent significant differences from the one-way ANOVA with Duncan’s comparisons (p < 0.05). (D) The co-occurrence network structure of rhizospheric fungal community with OTU’s relative abundance >0.01% at the phylum level among the three elevations. Null is an unrecognized phylum of fungi. LE: low elevation; ME: medium elevation; and HE: high elevation.
Figure 6. (A) Neutral community model. OTUs that occur more frequently than the model-predicted values are shown in green, OTUs that occur less frequently than the predicted values are shown in red, and OTUs that occur within the predicted range are shown in black. The dashed line indicates the 95% confidence interval around the model-predicted value (blue line). R2 represents the overall goodness of fit of the neutral community model. The M-value quantifies the migration rate at the community level, with a smaller M-value indicating a more restricted dispersal of species throughout the community and a higher M-value indicating a less restricted dispersal. (B) The abundance-based β-null model distinguishes the relative importance of deterministic and stochastic processes by the value of zero deviation. the closer the value of β-diversity deviation is to zero, the greater the stochasticity; the closer the value of β-diversity deviation is to one, the greater the determinism. (C) Niche width was used to show the utilization of resources by fungi in each elevation. Letters represent significant differences from the one-way ANOVA with Duncan’s comparisons (p < 0.05). (D) The co-occurrence network structure of rhizospheric fungal community with OTU’s relative abundance >0.01% at the phylum level among the three elevations. Null is an unrecognized phylum of fungi. LE: low elevation; ME: medium elevation; and HE: high elevation.
Jof 10 00673 g006
Table 1. Soil chemical properties at different elevations.
Table 1. Soil chemical properties at different elevations.
Soil PropertiesSM(%)pHTN(g/kg)AN (mg/kg)AP (mg/kg)TP (g/kg)SOM (g/kg)
LE32.54 ± 9.95 b4.87 ± 0.19 a2.60 ± 0.77 b331.51 ± 105.55 b3.28 ± 0.79 c0.18 ± 0.06 b109.20 ± 14.06 c
ME39.67 ± 14.61 b4.44 ± 0.14 b3.51 ± 1.03 b380.31 ± 157.79 b11.63 ± 4.02 b0.16 ± 0.05 b153.85 ± 24.58 b
HE56.81 ± 17.13 a4.18 ± 0.08 c7.47 ± 1.68 a700.86 ± 118.30 a24.38 ± 10.67 a0.41 ± 0.07 a281.93 ± 64.47 a
The values in brackets represent the standard deviation of the mean. Letters represent significant differences from the one-way ANOVA with Duncan’s comparisons (p < 0.05).
Table 2. Indicator analyses showing genera with a relative abundance greater than 0.1% at each elevation. LE: low elevation; ME: medium elevation; and HE: high elevation.
Table 2. Indicator analyses showing genera with a relative abundance greater than 0.1% at each elevation. LE: low elevation; ME: medium elevation; and HE: high elevation.
TreatmentIndicator
Value		pTaxonomyRelative
Abundance (%)
LE1.7710.026g__Russula2.193
0.9750.03g__Elaphomyces0.278
0.8300.005g__Clitocybula0.247
ME2.6750.055g__Oidiodendron0.582
0.9210.029g__Hypomyces0.910
1.5820.002g__Umbelopsis0.639
1.6590.006g__Tomentella0.474
0.8380.023g__Lactarius0.389
0.9120.001g__Lachnum0.301
0.8280.013g__Hymenoscyphus0.293
HE1.6180.004g__Hypocrea2.221
0.6790.009g__Mortierella1.145
0.9330.029g__Russula0.616
0.8870.013g__Hypomyces0.509
0.8550.005g__Cadophora0.211
0.8940.027g__Hymenoscyphus0.206
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zuo, Q.; Dang, K.; Yin, J.; Yuan, D.; Lu, J.; Xiang, X. Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain’s Elevation Gradients, China. J. Fungi 2024, 10, 673. https://doi.org/10.3390/jof10100673

AMA Style

Zuo Q, Dang K, Yin J, Yuan D, Lu J, Xiang X. Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain’s Elevation Gradients, China. Journal of Fungi. 2024; 10(10):673. https://doi.org/10.3390/jof10100673

Chicago/Turabian Style

Zuo, Qinglin, Keke Dang, Jing Yin, Dandan Yuan, Jing Lu, and Xingjia Xiang. 2024. "Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain’s Elevation Gradients, China" Journal of Fungi 10, no. 10: 673. https://doi.org/10.3390/jof10100673

APA Style

Zuo, Q., Dang, K., Yin, J., Yuan, D., Lu, J., & Xiang, X. (2024). Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain’s Elevation Gradients, China. Journal of Fungi, 10(10), 673. https://doi.org/10.3390/jof10100673

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop