Next Article in Journal
The Potential Role of Tumor Microorganisms in Remodeling Tumor Biomechanical Signals and Application Strategies in Tumor Therapy
Next Article in Special Issue
Research Progress on Rhizosphere Microbiota for Controlling Soil-Borne Diseases: Mechanisms, Applications, and Challenges
Previous Article in Journal
Role of Stress Response Genes in Resistance and Germination of Bacillus subtilis Spores
Previous Article in Special Issue
Diversity and Community Structure of Soil Bacteria of Different Vegetation Types in Volcanic Lava of Wudalianchi, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 14
Issue 4
10.3390/microorganisms14040806
microorganisms-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

Soil Microbial Diversity and Its Environmental Drivers in the Rhizosphere Profile of Camellia reticulata

by
Fu-Jun Yan
1,
Chong Ma
1,
Hong-Xing Xiao
1,
Yu-Jia Zeng
1,
Yuan-Yuan Huang
1,
Zhi-Yu Zhang
1,
Zhong-Lang Wang
2,
Long-Qing Chen
1 and
Fang Geng
1,*
1
College of Landscape Architecture and Horticulture, Yunnan Key Laboratory of Landscape Plant Resource Cultivation and Application, Southwest Forestry University, Kunming 650224, China
2
Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China
*
Author to whom correspondence should be addressed.
Microorganisms 2026, 14(4), 806; https://doi.org/10.3390/microorganisms14040806
Submission received: 5 March 2026 / Revised: 21 March 2026 / Accepted: 25 March 2026 / Published: 1 April 2026
(This article belongs to the Special Issue Advances in Soil Microbial Ecology, 3rd Edition)
Browse Figures
Versions Notes

Abstract

To investigate the main drivers of rhizosphere soil microbial community structure and diversity in Camellia reticulata, this study collected rhizosphere soil samples from six regions at two soil depths (0–30 cm and 30–60 cm). Using high-throughput sequencing, we systematically analyzed the effects of soil environmental factors on microbial communities. The results showed that the dominant bacterial phyla were Proteobacteria, Acidobacteriota, Chloroflexi, Actinobacteriota, and Bacteroidota, while the dominant fungal phyla were Ascomycota, Basidiomycota, and Mortierellomycota. Alpha diversity of both bacterial and fungal communities was higher in surface soils (0–30 cm) than in deeper layers (30–60 cm), although the differences were not statistically significant (p > 0.05). Soil pH, potassium content (K), and catalase activity (S-CAT) were identified as the main environmental factors significantly correlated with microbial community structure. Network analysis identified Acidobacteriota and Ascomycota as highly connected taxa within microbial networks, suggesting their potential importance in maintaining network structure. This study reveals the vertical differentiation characteristics of rhizosphere microbial communities in C. reticulata and their responses to environmental factors, providing a theoretical basis for cultivation management and rhizosphere microecological regulation.

1. Introduction

Camellia reticulata is an evergreen tree or shrub belonging to the family Theaceae and the genus Camellia. As an important ornamental plant endemic to Yunnan, China, it is characterized by large flower size, diverse coloration, and varied floral forms, conferring high horticultural value [1,2]. In terms of natural distribution, C. reticulata exhibits pronounced regional specificity, being primarily concentrated in parts of southwestern China [3], with a cultivation history spanning over 1300 years [4]. Despite its significant ornamental and economic value, C. reticulata has relatively strict soil requirements, making its cultivation challenging. Therefore, how to overcome the difficulty in cultivating C. reticulata by improving the soil or cultivation substrate has always been a key concern for its growers and researchers and is of great significance to the development of the C. reticulata industry.
As a key medium for plant growth and development, soil not only supplies water and nutrients but also profoundly influences root architecture, microbial interactions, and physiological-metabolic processes [5]. Rhizosphere microorganisms are widely recognized as key mediators of plant–soil interactions, playing crucial roles in nutrient cycling, stress resistance maintenance, and microecological balance [6,7,8]. Due to the gradients in carbon availability, nutrient limitation, and oxygen content with increasing soil depth, microbial communities exhibit significant heterogeneity in their vertical distribution [9].
In Camellia plants, existing studies have confirmed that agricultural management practices and host characteristics are key drivers shaping rhizosphere microecology. Gu et al. [10] demonstrated that fertilizer type not only significantly alters the diversity of soil microbial communities in C. sinensis but also reshapes their microbial network structure. Tang et al. [11] further revealed that although the interaction between cultivar characteristics and management practices leads to specific differentiation in the rhizosphere community structure of C. oleifera, core community similarity is maintained across different habitats. Jia et al. [12] revealed a strong correlation between available nutrient content in the rhizosphere of C. sinensis germplasm resources and bacterial community composition, confirming the key role of rhizosphere microorganisms in nutrient mobilization. Beyond nutrient supply. Chen et al. [13] elucidated the mechanism by which microorganisms enhance stress resistance in tea plants through upregulating antioxidant enzyme activities, and Shao et al. [14] demonstrated that nitrogen-cycling functional microorganisms in mountain tea plantations regulate nitrogen cycling through specific response mechanisms. Collectively, these studies indicate that the structural and functional plasticity of rhizosphere microbial communities constitutes an important basis for the adaptation of Camellia plants to complex environments. In contrast, C. reticulata has received little research attention. Its rhizosphere microbial communities, including their vertical distribution characteristics and environmental response mechanisms, remain completely unexplored.
Thus, to address the effects of soil microbial diversity and the environmental drivers on the growth of C. reticulata, we hypothesize that (1) the microbial community composition, diversity, and co-occurrence network topology differ significantly in different soil layers; and (2) the microbial community structure and diversity exhibit significant vertical variations along the soil profile, and these variations are primarily driven by key environmental factors. To test these hypotheses, soil profile samples were collected from two depths (0–30 cm and 30–60 cm) across six wild C. reticulata populations in Yunnan Province. Soil physicochemical properties and enzyme activities were measured, and the composition of bacterial and fungal communities was analyzed using high-throughput sequencing (Illumina (San Diego, CA, USA) MiSeq platform). By identifying the key environmental factors associated with rhizosphere microbial community assembly, this study provides a scientific basis for understanding rhizosphere microecological processes and optimizing cultivation management of C. reticulata.

2. Materials and Methods

2.1. Research Area

The study area encompasses Kunming City, Chuxiong Yi Autonomous Prefecture, Dali Bai Autonomous Prefecture, and Baoshan City in Yunnan, China. The regional elevation ranges from 1951 to 2489 m above sea level, with mean annual temperatures between 15.0 and 20.0 °C and annual precipitation ranging from 800 to 1500 mm. The specific geographic coordinates and elevation data of each sampling site are detailed in Table 1.

2.2. Collection and Analysis of Samples

The sampling sites were all wild habitats without human management interventions, with consistent red soil across all locations. In October 2024, rhizosphere soil samples were collected from the distribution sites of C. reticulata listed in Table 1. Within each sampling site, soil samples were collected from two soil layers (0–30 cm and 30–60 cm) using an S-shaped multi-point sampling method with a soil auger (3.5 cm inner diameter). Multiple subsamples from the same plot and the same soil layer were thoroughly mixed to form a composite soil sample. Three independent biological replicates were established per sampling site, and a total of 36 soil samples were collected. The specific procedure was as follows: soil blocks containing intact root systems (representing the main root distribution zone) were excavated and gently shaken to remove loosely attached bulk soil. Subsequently, the soil firmly adhering to the root surfaces (0–5 mm from the root surface) was carefully brushed off and defined as rhizosphere soil. During the collection process, special care was taken to minimize damage to plant roots and to carefully remove any inadvertently included root fragments. To minimize exogenous contamination, the entire sampling and storage process was carried out with sterile tools. Personnel wore protective suits and sterile gloves, and direct hand contact with samples was strictly prohibited. Before each sampling, any residual material on the tool surfaces was wiped with sterile tissue. The collected composite soil samples were divided into two parts on site: one portion was placed in pre-labeled sterile resealable bags for physicochemical analysis, while the other was transferred into sterile centrifuge tubes, flash-frozen in liquid nitrogen, and subsequently stored at −80 °C to suppress microbial metabolic activity and chemical reactions, thereby preserving the original state of the samples as much as possible. Air-dried samples were ground and sieved through a 20-mesh sieve for the determination of soil physicochemical properties, while frozen samples were reserved for subsequent high-throughput sequencing analysis of soil microbial community structure [15].

2.3. Soil Physicochemical Properties and Enzyme Activities

Soil physicochemical parameters were determined following the methods described by Bao et al. [16]. Soil pH was measured using a pH meter with a soil-to-water ratio of 1:10 (mass basis). Soil organic matter (SOM) content was determined by the potassium dichromate-sulfuric acid (K2Cr2O7-H2SO4) oxidation method, with results expressed as g·kg−1. Total nitrogen (TN) was quantified using the Kjeldahl nitrogen determination method, with results expressed as g·kg−1. Total phosphorus (TP) was measured using the sodium oxide melting Jinkang colorimetric method, with results expressed as g·kg−1. Total potassium (TK) was determined via the sodium oxide melting flame photometric method, with results expressed as g·kg−1. Alkaline nitrogen (AN) was determined using the alkaline diffusion method, with results expressed as mg·kg−1. Available potassium (AK) was measured using the acetic acid extraction flame photometric method, with results expressed as mg·kg−1. Soil available phosphorus (AP) content was determined through the hydrochloric acid ammonium chloride extraction method, with results expressed as mg·kg−1.
Soil enzyme activities were assessed using chromogenic methods: phosphatase activity was determined by p-nitrophenyl phosphate colorimetry, urease activity by sodium phenolate-sodium hypochlorite colorimetry, and sucrase activity by 3,5-dinitrosalicylic acid colorimetry [17,18].

2.4. DNA Extraction, PCR Amplification, and Sequencing of Rhizosphere Soil

Total microbial community DNA was extracted from soil samples using the CretMag™ Power Soil DNA Kit (Cretaceous Biotechnology Co., Ltd., Suzhou, China) following the manufacturer’s instructions. After extraction, DNA integrity was verified by 1% agarose gel electrophoresis, and concentration and purity (A260/A280 ratio) were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). For bacterial community analysis, the V3–V4 variable region of the 16S rRNA gene was amplified using primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). For fungal community analysis, the internal transcribed spacer (ITS) region of ribosomal RNA genes was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′). PCR amplification was performed on an A200 PCR thermocycler (Hangzhou LongGene, Hangzhou, China) with the following program: initial denaturation at 94 °C for 2 min; 30 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s; followed by a final extension at 72 °C for 10 min, and hold at 4 °C. Each 50 μL PCR reaction contained 25 μL of 2 × ES Taq MasterMix (Dye), 2 μL each of forward and reverse primers (10 μM), template DNA (10 ng), and ddH2O to final volume. Three technical replicates were performed per sample to ensure amplification reliability.
PCR products from the same sample were then pooled, separated by 2% agarose gel electrophoresis, and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). The purified products were examined by 2% agarose gel electrophoresis and quantified using a Quantus™ Fluorometer (Promega Corporation, Madison, WI, USA). Libraries were constructed using the VAHTS® Universal Plus DNA Library Prep Kit for MGI V2 (Vazyme Biotech Co., Ltd., Nanjing, China) and sequenced on the DNBSEQ-G99 PE300 platform (BGI Group, Shenzhen, China) with paired-end mode. Raw sequencing reads were quality-filtered using fastp v0.20.0 (https://github.com/OpenGene/fastp, accessed on 15 February 2025) and merged using FLASH v1.2.7 (http://www.cbcb.umd.edu/, accessed on 15 February 2025). Operational taxonomic units (OTUs) were clustered at 97% similarity threshold using UPARSE v7.1 (http://drive5.com/uparse/, accessed on 15 February 2025), and chimeric sequences were removed during the clustering process. Taxonomic assignment of representative sequences was performed using the RDP classifier v2.14 (https://sourceforge.net/projects/rdp-classifier/, accessed on 16 February 2025) against the Silva database (https://www.arb-silva.de/, accessed on 16 February 2025) for bacterial 16S rRNA genes and the UNITE database (https://unite.ut.ee/, accessed on 16 February 2025) for fungal ITS sequences, with a confidence threshold of 70%.

2.5. Statistical Analysis

Statistical analysis was performed using SPSS 22.0 software. One-way analysis of variance (ANOVA) followed by Duncan’s multiple range test was used to determine the significance of differences (p < 0.05). Microbial community abundance plots were generated using Origin 2021 software. Based on the optimized sequence data, alpha diversity indices including Shannon, Simpson, and Chao1 were calculated using an online analysis platform (http://www.pronetscloud.com, accessed on 5 January 2026). Paired-sample t-tests were then performed to compare the differences in alpha diversity indices and the relative abundances of dominant microbial taxa between the surface (0–30 cm) and subsurface (30–60 cm) soil layers. Non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances was also performed using the same platform to evaluate differences in microbial community structure among samples.
Data analysis was conducted using R software (version 4.30): Mantel test was performed to analyze correlations between key microbial communities and soil environmental factors [19]; Redundancy analysis (RDA) was used to explore the effects of environmental factors on microbial communities, with environmental factors standardized (z-score transformation) prior to analysis. Variance inflation factor (VIF) was employed to diagnose multicollinearity, with redundant variables (VIF > 10) being excluded [20]. To investigate the coexistence patterns among microbial taxa, this study constructed co-occurrence networks based on Spearman’s correlation coefficients. For each soil layer, phyla with an average relative abundance >1% and OTUs with an average relative abundance >0.01% were selected for correlation analysis. A stringent threshold (adjusted p < 0.05, |r| > 0.6) was applied to ensure the reliability of the associations. The networks were constructed based on a modified random matrix theory approach. Topological indices, including average connectivity, average clustering coefficient, and average path distance, were calculated using the igraph package to quantify network characteristics. Based on within-module connectivity (Zi) and among-module connectivity (Pi), nodes were classified into four functional categories: peripheral nodes (Zi < 2.5 and Pi < 0.62), module hubs (Zi ≥ 2.5 and Pi < 0.62), connectors (Zi < 2.5 and Pi ≥ 0.62), and network hubs (Zi ≥ 2.5 and Pi ≥ 0.62). Network graphs were visualized using Gephi (version 10.60), and Zi-Pi plots were generated using GraphPad Prism 10 to further analyze the topological features [21].

3. Results and Analysis

3.1. Soil Physicochemical Properties and Soil Enzyme Activities

The physicochemical properties of soil across different sampling sites and depth layers of C. reticulata are presented in Table 2. Soil pH in ZWY ranged from 6.10 to 6.22, indicating a near-neutral condition, while other sampling sites exhibited acidic soil with pH increasing with depth. Organic matter showed significant differences (p < 0.05) between ZSZ and YB in both 0–30 cm and 30–60 cm layers. Total nitrogen differed significantly (p < 0.05) only between ZSZ and YB in the 0–30 cm layer, and between MHC and YB in the 30–60 cm layer. Additionally, significant differences (p < 0.05) were observed across depths at site YB. Total phosphorus varied significantly (p < 0.05) only between ZWY and SDS in the 0–30 cm layer. Total potassium showed significant differences (p < 0.05) between the 0–30 cm and 30–60 cm layers. Alkali-hydrolyzable nitrogen exhibited significant differences (p < 0.05) among ZWY, ZSZ, and YB in the 0–30 cm layer, and between ZWY and YB in the 30–60 cm layer, with significant differences across depths observed at all sampling sites except SDS and HMC. Available phosphorus showed significant differences (p < 0.05) among all sampling sites in both 0–30 cm and 30–60 cm layers, as well as across depths in all sites except ZSZ and HMC. Available potassium exhibited significant differences (p < 0.05) among different sampling sites and soil layers.
Soil enzyme activities at different soil depths and sampling sites of C. reticulata are shown in Table 3. Urease activity in the 0–30 cm layer showed no significant difference between ZWY, ZSZ, and SDS, while ZWY and ZSZ differed significantly from HMC (p < 0.05). MHC and YB also exhibited significant differences (p < 0.05). In the 30–60 cm layer, ZSZ, HMC, and YB displayed significant differences (p < 0.05), with ZSZ and YB showing significant variations between different depths (p < 0.05). Acid phosphatase activity differed significantly among all sampling sites in the 0–30 cm layer (p < 0.05). In the 30–60 cm layer, MHC differed significantly from other sites (p < 0.05), and ZWY, SDS, and MHC showed significant variations between depths (p < 0.05). Sucrase activity exhibited significant differences between ZWY and ZSZ in the 0–30 cm layer (p < 0.05). In the 30–60 cm layer, ZWY and YB differed significantly from other sampling sites (p < 0.05), while ZSZ and MHC showed significant variations between depths (p < 0.05). Catalase activity in both the 0–30 cm and 30–60 cm layers showed significant differences between SDS and other sampling sites (p < 0.05).

3.2. Soil Microbial Composition and Diversity

The Venn diagram (Figure 1) shows that the number of bacterial operational taxonomic units (OTUs) shared across all samples in the 0–30 cm soil layer was 990, while in the 30–60 cm soil layer, the number of shared bacterial OTUs was 854. For fungi, the number of OTUs shared across all samples was 97 in the 0–30 cm layer and 77 in the 30–60 cm layer.
Alpha diversity analysis showed that the Ace, Chao, Shannon, and Simpson indices of both bacterial and fungal communities were generally higher in the 0–30 cm layer than in the 30–60 cm layer. Independent samples t-tests (n = 18 per depth) revealed that these differences were not statistically significant (p > 0.05 for all indices) (Figure 2).
Based on the Bray–Curtis distance algorithm, non-metric multidimensional scaling (NMDS) was used to evaluate differences in diversity reflected by OTU abundance across sampling sites and soil depths (Figure 3). For bacterial communities, the HMC sample site was relatively distant from other sampling sites at both soil depths (0–30 cm and 30–60 cm). For fungal communities, the MHC sample site was distinctly separated from other sites in the 0–30 cm soil layer.

3.3. Compositional Characteristics of Rhizosphere Soil Microbial Communities at Phylum and Genus Levels

The analysis of the relative abundance of bacterial phyla and genera in the rhizosphere soil of C. reticulata across different habitats (Figure 4). The dominant bacterial and fungal groups were largely similar across different sampling sites, though slight variations were observed between sites and between soil depths within the same site.
At the bacterial phylum level (Figure 4A), the dominant groups were Proteobacteria, Acidobacteria, Chloroflexi, Actinobacteria, and Bacteroidota, in descending order, the result aligns with the typical distribution patterns of bacterial communities in most forest soils and also indicates that these phyla are core groups maintaining the rhizosphere microecological functions of C. reticulata. At the bacterial genus level (Figure 4B), the dominant genera were Subgroup-2, AD-3, Burkholderia-Caballeronia-Paraburkholderia, and Massilia. The relative abundance of Vicinamibacteraceae was higher in the two soil layers at the ZWY site compared to other sites.
Analysis of the relative abundance of bacterial phyla and genera in rhizosphere soils of C. reticulata across different habitats. At the fungal phylum level (Figure 4C), the dominant groups were Ascomycota, Basidiomycota, and Mortierellomycota in descending order, constituting the core of the rhizosphere fungal community of C. reticulata. These three phyla collectively dominated the ecological functions of the fungal community, the dominant genera were Podila, Russula, Saitozyma, and Linnemannia. The relative abundance of Solicoccozyma was higher in both soil layers at the ZWY site compared to other genera.

3.4. Correlation Analysis Between Soil Environmental Factors and Soil Microbial Communities in C. reticulata

As shown in the heatmap (Figure 5), the analysis of bacterial and fungal communities in the rhizosphere soil of C. reticulata revealed significant correlations with environmental variables. In the 0–30 cm soil layer (Figure 5A), pH was significantly positively correlated with Actinobacteriota, Bacteroidota, Gemmatimonadota, Latescibacterota, Myxococcota, and Entotheonellaeota. SOM was significantly positively correlated only with Bacteroidota. TN and TP were significantly positively correlated with Actinobacteriota and Bacteroidota. AN was significantly positively correlated with Bacteroidota and Desulfobacterota. AP and AK were significantly positively correlated with Gemmatimonadota, Myxococcota, and Entotheonellaeota. S-UE was significantly positively correlated with Nitrospirota, Gemmatimonadota, Latescibacterota, and Myxococcota. S-ACP was significantly positively correlated with WPS-2 and RCP254. S-SC was significantly positively correlated with Bacteroidota and Proteobacteria. S-CAT was significantly positively correlated with Actinobacteriota, Bacteroidota, and Proteobacteria. In the 30–60 cm soil layer (Figure 5B), pH was significantly positively correlated with Myxococcota, Entotheonellaeota, Planctomycetota, Bacteroidota, Patescibacteria, Actinobacteriota, and Gemmatimonadota. SOM was significantly positively correlated only with Proteobacteria. TN and AN were also significantly positively correlated with Proteobacteria. TP was significantly positively correlated with Patescibacteria, while TK was significantly positively correlated with Acidobacteriota. AP was significantly positively correlated with WPS-2. AK was significantly positively correlated with Myxococcota, Bacteroidota, Patescibacteria, Actinobacteriota, and Bdellovibrionota. S-UE was significantly positively correlated with Chloroflexi and GAL15. S-ACP was significantly positively correlated with Firmicutes and RCP254. Additionally, S-SC was significantly positively correlated with Bacteroidota, Patescibacteria, Proteobacteria, and Actinobacteriota, while S-CAT was significantly positively correlated with Proteobacteria, Actinobacteriota, and Bdellovibrionota.
In the 0–30 cm soil layer (Figure 5C), pH, available phosphorus (AP), and available potassium (AK) were all significantly positively correlated with Ascomycota. Total potassium (TK) was significantly positively correlated with Kickxellomycota, Calcarisporiellomycota, Blastocladiomycota, and Monoblepharomycota. Sucrase activity (S-UE) was significantly positively correlated with Chytridiomycota and Fungi_phy_Incertae_sedis, while acid phosphatase activity (S-ACP) was significantly positively correlated with Basidiomycota and Mucoromycota. In the 30–60 cm soil layer (Figure 5D), pH was significantly positively correlated with Olpidiomycota, Ascomycota, and Basidiobolomycota. TK was significantly positively correlated with Blastocladiomycota, Monoblepharomycota, and Calcarisporiellomycota. AK was significantly positively correlated with Ascomycota. Additionally, S-UE was significantly positively correlated with Calcarisporiellomycota and Rozellomycota, while S-ACP was also significantly positively correlated with Basidiomycota and Mucoromycota.

3.5. Soil Environmental Factors Influencing the Microbial Community Structure in the Rhizosphere of C. reticulata

To investigate the effects of soil physicochemical factors and enzyme activities on microbial community structure, redundancy analysis (RDA) was employed to further elucidate their relationships. The RDA results indicated that the first two ordination axes (RDA1 and RDA2) effectively reflected the extent to which environmental factors explained the variation in microbial communities.
The first two axes explained 24.00% and 22.33% of the total variation in the bacterial community of the 0–30 cm rhizosphere soil (Figure 6A), cumulatively accounting for 46.33%. In the 30–60 cm layer, the first two axes explained 30.74% and 18.48%, cumulatively accounting for 49.22% (Figure 6B). In the 0–30 cm soil layer, sucrase activity (S-SC), catalase activity (S-CAT), total nitrogen (TN), pH, available potassium (AK), and urease activity (S-UE) were significantly positively correlated with Proteobacteria, Actinobacteriota, and Bacteroidota. In contrast, acid phosphatase activity (S-ACP) was significantly positively correlated with Acidobacteriota. Additionally, S-SC, S-CAT, and TN were significantly negatively correlated with Chloroflexi, while pH, AK, and S-UE showed significant negative correlations with Acidobacteriota. In the 30–60 cm soil layer, S-CAT, S-SC, AK, and pH were similarly significantly positively correlated with Actinobacteriota, Proteobacteria, and Bacteroidota, whereas total potassium (TK) was significantly positively correlated with Chloroflexi and Acidobacteriota. Conversely, S-CAT, S-SC, AK, and pH were significantly negatively correlated with Chloroflexi and Acidobacteriota, while TK showed a significant negative correlation with Actinobacteriota, Proteobacteria, and Bacteroidota.
In the 0–30 cm soil layer, the first two RDA axes explained 23.24% and 15.74% of the variation in the fungal community, cumulatively accounting for 38.98% (Figure 6C). In the 30–60 cm layer, the first two axes explained 21.47% and 18.96%, cumulatively accounting for 40.43% (Figure 6D). In the 0–30 cm soil layer, S-UE and TK were significantly positively correlated with Glomeromycota, Rozellomycota, and Ascomycota, while acid phosphatase activity (S-ACP), catalase activity (S-CAT), and sucrase activity (S-SC) were significantly positively correlated with Basidiomycota and Mortierellomycota. Conversely, S-UE and TK were significantly negatively correlated with Basidiomycota and Mortierellomycota, whereas S-ACP, S-CAT, and S-SC showed significant negative correlations with Glomeromycota, Rozellomycota, and Ascomycota. In the 30–60 cm soil layer, TK was significantly positively correlated with Mortierellomycota, Rozellomycota, and Glomeromycota; pH and S-SC were significantly positively correlated with Ascomycota; and S-ACP and S-CAT were significantly positively correlated with Basidiomycota. TK exhibited a significant negative correlation with Basidiomycota; pH and S-SC were significantly negatively correlated with Mortierellomycota, Rozellomycota, Glomeromycota, and Basidiomycota; while S-ACP and S-CAT showed significant negative correlations with Mortierellomycota, Rozellomycota, Glomeromycota, and Ascomycota.
Mantel test analysis of soil microbial communities (at the phylum level) with enzyme activities and soil physicochemical properties revealed that (Figure 7): pH exhibited significant positive correlations with TP, AP, and AK; SOM showed significant positive correlations with TN, AN, and S-CAT; TN was significantly positively correlated with AN; TP demonstrated significant positive correlations with AP and AK; AN was significantly positively correlated with S-CAT; and AP showed a significant positive correlation with AK. Additionally, S-SC was significantly positively correlated with S-CAT.
Regarding bacterial communities (Figure 7A,B): At 0–30 cm soil depth, bacterial community abundance was significantly positively correlated with S-UE and S-SC (Figure 7A). At 30–60 cm soil depth, bacterial community abundance exhibited significant positive correlations with SOM, TP, and S-CAT, while bacterial community diversity was significantly positively correlated with pH and S-ACP (Figure 7B).
Regarding fungal communities (Figure 7C,D): At 0–30 cm soil depth, fungal community abundance was significantly positively correlated with pH, AP, AK, S-UE, S-ACP, and S-SC, whereas fungal community diversity showed significant positive correlations with AP, AK, and S-UE (Figure 7C). At 30–60 cm soil depth, fungal community abundance was significantly positively correlated with pH, AK, S-ACP, and S-SC, while fungal community diversity was significantly positively correlated with pH, TN, AP, AK, and S-ACP (Figure 7D).

3.6. Network Analysis of Fungal and Bacterial Communities in Different Habitats

To elucidate the interaction patterns and ecological niche-sharing characteristics of keystone microorganisms in the rhizosphere soil of C. reticulata across different sampling sites and soil depths, microbial co-occurrence networks were constructed (Figure 8). The topological structures of bacterial and fungal networks exhibited significant differences between the 0–30 cm and 30–60 cm soil layers. In terms of network associations (Table 4), network analysis of both soil layers revealed that connectivity, number of edges, average degree, clustering coefficient, and path length of bacterial networks markedly decreased with increasing soil depth. In contrast, fungal network connectivity, number of edges, average degree, clustering coefficient, and path length showed a slight increasing trend with soil depth.
In the 0–30 cm soil layer, the bacterial network comprised 3823 positive correlations and 824 negative correlations, whereas in the 30–60 cm soil layer, the numbers of positive and negative correlations were 2152 and 233, respectively. For the fungal network, the 0–30 cm soil layer exhibited 1013 positive correlations and 25 negative correlations, while the 30–60 cm soil layer showed 1056 positive correlations and 13 negative correlations.
According to the Zi-Pi plot based on network topological analysis (Table 5 and Table 6; Figure 9). In the bacterial co-occurrence network of the 0–30 cm soil layer, seven module hubs were identified, predominantly belonging to the following phyla: Acidobacteriota (4 OTUs), Proteobacteria (2 OTUs), and Actinobacteriota (1 OTU). In the 30–60 cm soil layer network, two module hubs were identified, both assigned to Chloroflexi (2 OTUs). The compositional differences in keystone taxa between soil layers may reflect the influence of resource availability on microbial niche differentiation: resource-rich surface soils support multiple phyla cooperating to serve as network backbones, while in resource-limited deeper soils, specific phyla such as Chloroflexi assume core network functions.
Regarding connectors, the 0–30 cm soil layer network contained seven keystone connector nodes, taxonomically classified as follows: Acidobacteriota (3 OTUs), Chloroflexi (2 OTUs), Proteobacteria (1 OTU), and Actinobacteriota (1 OTU). In the 30–60 cm soil layer network, six connector nodes were identified, belonging to Actinobacteriota (3 OTUs), Acidobacteriota (2 OTUs), and Proteobacteria (1 OTU), respectively. In the fungal co-occurrence network, only three module hubs and one connector were identified in the 0–30 cm soil layer, all assigned to Ascomycota. In the 30–60 cm soil layer network, two module hubs were identified, also entirely belonging to Ascomycota. Ascomycota, as the sole keystone phylum in the fungal networks, underscores its core ecological role in organic matter decomposition and nutrient mineralization.

4. Discussion

4.1. Variations in Soil Microbial Community Composition Along the Soil Profile Depth

Soil microbial communities are key components of soil ecosystems, primarily composed of bacteria and fungi [22]. They drive essential ecological processes such as organic matter decomposition, nutrient cycling, and gas fluxes, while regulating soil environmental factors that indirectly influence plant nutrient uptake and stress resistance [23]. Although plants can fix carbon through photosynthesis, the nutrients required for plant growth are predominantly derived from microbial-mediated transformation and cycling processes in the soil, which directly determine the relationship between soil and plant growth [24].
In this study, the alpha diversity of both bacterial and fungal communities exhibited a decreasing trend with increasing soil depth (Figure 2), which is consistent with patterns commonly observed in forest [25] and agricultural [26] ecosystems. This vertical decline can be primarily attributed to the following ecological mechanisms: First, with increasing soil depth, the input of plant litter and root exudates decreases, leading to a significant reduction in carbon source availability, which limits the support for copiotrophic microbial taxa (e.g., Proteobacteria and Actinobacteriota) [27,28]. Second, nutrient limitation intensifies, as indicated by the decline in available nitrogen and phosphorus contents in deeper soil layers (Table 1), which selects for oligotrophic microorganisms adapted to nutrient-poor environments (e.g., Acidobacteriota and Chloroflexi) [29]. Additionally, poor aeration and enhanced anaerobic conditions in deeper soil layers further shape the functional metabolic types of microbial communities [30,31].
At the community composition level, the dominant bacterial phyla in this study were Proteobacteria, Acidobacteriota, Chloroflexi, Actinobacteriota, and Bacteroidota, while the dominant fungal phyla were Ascomycota, Basidiomycota, and Mortierellomycota (Figure 4). This compositional pattern is similar to that of soil microbial communities associated with C. sinensis [32,33]. Different phyla exhibited distinct responses to soil depth: copiotrophic groups such as Proteobacteria and Actinobacteriota showed higher relative abundances in surface soils, whereas oligotrophic groups such as Acidobacteriota and Chloroflexi increased in deeper layers (Figure 4). This vertical differentiation pattern is consistent with observations by Lv et al. [34] in forest soils, supporting the theoretical hypothesis that “resource gradients drive microbial niche differentiation.” Notably, the enrichment of Acidobacteriota in deeper soils aligns with their ecological characteristics as “K-strategists”—microorganisms that grow slowly but efficiently utilize scarce resources and maintain survival under environmental stress [35].

4.2. Correlation Between Soil Microbial Communities and Soil Environmental Factors

Integrated analysis of Spearman’s correlation, RDA, and Mantel test results (Figure 5, Figure 6 and Figure 7) revealed that pH, potassium (K) content, and catalase activity (S-CAT) are the core environmental drivers regulating the assembly of rhizosphere microbial communities in C. reticulata. This finding is consistent with the results reported by Zong [36]. Total potassium (TK) was identified as the most important environmental factor influencing soil fungal communities across soil layers, which aligns with the findings of Luo [37] and Wan et al. [38].
Soil pH variation was found to exert a significant impact on microbial diversity [39]. Changes in pH may not only lead to denaturation and inactivation of key soil enzymes, thereby reducing nutrient solubility and plant availability, but also exacerbate soil acidification, promoting the dissolution of aluminum ions and certain heavy metals. This process enhances their direct phytotoxicity and suppresses soil microbial activity and diversity [40]. In the present study, soil pH exhibited an increasing trend with increasing rhizosphere soil depth (Table 1). This pattern may be attributed to two main factors: first, the application of nitrogen fertilizers such as urea and ammonium sulfate increases the concentration of exchangeable Al3+ and H+ in the soil, thereby intensifying soil acidification [41]; second, plant roots are more densely distributed in surface layers, where organic acids and alcohols secreted by roots accumulate in the plow layer, further lowering local pH [42].
Potassium (K) emerged as the most influential nutrient factor shaping the rhizosphere microbial community of C. reticulata in this study, and its role should not be underestimated. Soil potassium not only activates multiple enzyme systems and enhances nutrient transformation efficiency but also plays a critical role in regulating the rhizosphere microenvironment, facilitating the colonization of beneficial microorganisms, and reshaping the structure of rhizosphere microbial communities [43].
Regarding soil enzyme activities, catalase activity (S-CAT) was identified as a key factor influencing the rhizosphere soil of C. reticulata. In the study by Chen [44], catalase activity was also reported as a major environmental determinant affecting Proteobacteria. Collectively, soil pH, potassium, and S-CAT serve as core drivers co-regulating the assembly of rhizosphere microbial communities in C. reticulata.

4.3. Shifts in Soil Microbial Co-Occurrence Networks Along the Soil Depth Gradient

Microbial co-occurrence network analysis can reveal previously unrecognized patterns of microbial coexistence, thereby elucidating the complex relationships among different microbial taxa. In this study, we found that the average clustering coefficient of both fungal and bacterial networks decreased with increasing soil depth (Table 3), indicating that the network complexity of bacterial and fungal communities declined as soil depth increased. This suggests a higher degree of habitat heterogeneity in surface soils [45]. Similarly, the higher average clustering coefficient observed in surface soils indicates more connections within modules than between modules, implying that soil bacteria occupying similar ecological niches are more closely associated than those in different niches [46]. This study also found that the average path distance of fungal networks increased with soil depth, whereas that of bacterial networks decreased. Average path distance characterizes the tightness of connections between species (nodes) in a network; a smaller average path distance indicates closer interactions among species within the network. However, a more tightly connected community also implies faster propagation of external disturbances through the network, potentially making it more sensitive to perturbations [47]. This suggests that deep-layer fungal networks are more stable in response to disturbances, while the opposite is true for bacteria. Additionally, with increasing soil depth, the modularity of bacterial networks increased, while that of fungal networks decreased. Network modularity is commonly used to indicate the resistance of a system to external disturbances; higher modularity confers greater resistance to such disturbances [48]. Therefore, in the soils of different C. reticulata habitats, bacterial networks were more stable in deeper layers, whereas fungal networks exhibited greater stability in surface layers.
Based on Zi-Pi analysis, Acidobacteriota and Ascomycota were identified as highly connected taxa within the rhizosphere microbial networks of C. reticulata (Table 5 and Table 6). It should be noted that such “module hubs” and “connectors,” identified based on network topological features, represent nodes with high connectivity within ecological networks, suggesting their potential importance in maintaining network structure; however, they are not directly equivalent to ecologically defined “keystone functional taxa.” In this study, highly connected taxa within the bacterial network were identified across both soil layers. Among fungi, Ascomycota was the sole highly connected phylum within the fungal networks. These results highlight the importance of Acidobacteriota and Ascomycota in maintaining network structure along the deep soil profile in the restored ecosystem under investigation. Within the C. reticulata soil ecosystem, members of Acidobacteriota are considered to contribute to nutrient cycling and organic matter decomposition [49,50]. They are known for their tolerance to a wide range of soil pH and are particularly abundant in acidic soils. Some members of Acidobacteriota are classified as K-strategists, meaning they can thrive in nutrient-limited environments with slow growth rates and high tolerance to toxic compounds [35]. The majority of Ascomycota taxa are saprophytic fungi, driving carbon and nitrogen cycling in plant–soil systems through the degradation of substances such as cellulose, thereby playing an important role in soil nutrient mobilization [51]. As highly connected taxa identified within the bacterial and fungal networks, Acidobacteriota and Ascomycota may play important roles in maintaining the stability of the microbial network in the C. reticulata soil ecosystem. Their potential involvement in nutrient cycling and organic matter transformation could contribute to the ecological functioning of deep soil layers, though experimental validation is needed to confirm their specific functional roles.

5. Conclusions

This study investigated the vertical distribution patterns of rhizosphere soil microbial communities in C. reticulata across the 0–30 cm and 30–60 cm soil layers, revealing the responses of microbial community composition, diversity, and co-occurrence networks to soil depth gradients. Soil depth significantly influenced microbial community structure: the alpha diversity of both bacterial and fungal communities decreased with increasing soil depth, and community composition exhibited distinct vertical differentiation, with copiotrophic phyla (e.g., Proteobacteria and Actinobacteriota) showing higher relative abundances in surface soils, while oligotrophic phyla (e.g., Acidobacteriota and Chloroflexi) were enriched in deeper layers. Co-occurrence network analysis revealed that bacterial network complexity decreased with increasing soil depth, whereas fungal networks exhibited the opposite trend, suggesting divergent ecological strategies of bacteria and fungi in response to environmental gradients. Environmental factor analysis indicated that soil pH, potassium (K) content, and catalase activity (S-CAT) were significantly correlated with microbial community structure, representing major environmental factors influencing rhizosphere microbial assembly in C. reticulata. Network topological analysis further identified Acidobacteriota and Ascomycota as highly connected taxa within the microbial networks, suggesting their potential importance in maintaining network structure. This study provides fundamental data for understanding the vertical differentiation patterns of rhizosphere microbial communities in C. reticulata and their environmental response mechanisms, and offers a theoretical reference for the targeted regulation of rhizosphere microecology in C. reticulata cultivation management.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: F.G. Data collection and analysis: F.-J.Y., C.M., H.-X.X., Y.-J.Z., Y.-Y.H., Z.-Y.Z., Z.-L.W. and L.-Q.C. Draft manuscript preparation: F.-J.Y. and F.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Foundation of China (32260416), Yunnan Province Xingdian Talent Project “Youth Talent Special Project” Support Project (YNQR-QNRC-2-19-104), and Yunnan Agricultural Basic Research Joint Special General Project (202101BD070001-095). Funders have no role in the design and publication of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict interests.

References

  1. Yan, F.-J.; Huang, Y.-Y.; Zhang, Z.-Y.; Xiao, H.-X.; Wu, X.-Q.; Wu, Y.-L.; Wang, Z.-L.; Geng, F. Effects of drought stress on physiological-biochemical characteristics and anatomical structure of Camellia reticulata and C. sasanqua seedlings: A comprehensive evaluation. Front. Plant Sci. 2025, 16, 1727605. [Google Scholar] [CrossRef] [Scilit]
  2. Xiao, H.; Nie, R.; Zeng, Y.; Chen, S.; Yan, F.; Wu, X.; Wang, Z.; Geng, F. The physiological mechanism of the flower color formation of Camellia reticulata ‘Tongzimian’. Ornam. Plant Res. 2025, 5, e024. [Google Scholar] [CrossRef] [Scilit]
  3. Fan, Z.; Xu, X.; Wang, Q.; Zheng, W. Comparative chloroplast genomes and phylogenetic analyses shed new insights on the phyloevolution of different ploidy in Camellia reticulata. BMC Plant Biol. 2025, 25, 321. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  4. Xu, X.; Zheng, W.; Harris, A.; Wang, W.; Shao, W.; Wen, J. Assessing the maternal origin in the polyploid complex of Camellia reticulata based on the chloroplast rpl 16 intron sequences: Implications for camellia cross breeding. Mol. Breed. 2018, 38, 123. [Google Scholar] [CrossRef] [Scilit]
  5. Rengel, Z.; Djalovic, I. The Root Systems in Sustainable Agricultural Intensification; Wiley Online Library: Hoboken, NJ, USA, 2021. [Google Scholar]
  6. Xu, H.; Liu, W.; He, Y.; Zou, D.; Zhou, J.; Zhang, J.; Bai, Y. Plant-root microbiota interactions in nutrient utilization. Eng. Agric. 2025, 12, 16–26. [Google Scholar]
  7. Philippot, L.; Chenu, C.; Kappler, A.; Rillig, M.C.; Fierer, N. The interplay between microbial communities and soil properties. Nat. Rev. Microbiol. 2024, 22, 226–239. [Google Scholar] [CrossRef] [Scilit]
  8. Liu, W.S.; Li, H.Q.; He, Y.; Huang, Y.Y.; Qiu, K.Y.; Xie, Y.Z. Research progress on the interaction regulation of plant and soil by rhizosphere microbiome. Soil Fertil. Sci. China 2021, 5, 318–327. [Google Scholar]
  9. Jenkinson, D.; Poulton, P.; Bryant, C. The turnover of organic carbon in subsoils. Part 1. Natural and bomb radiocarbon in soil profiles from the Rothamsted long-term field experiments. Eur. J. Soil Sci. 2008, 59, 391–399. [Google Scholar] [CrossRef] [Scilit]
  10. Gu, S.; Hu, Q.; Cheng, Y.; Bai, L.; Liu, Z.; Xiao, W.; Gong, Z.; Wu, Y.; Feng, K.; Deng, Y.; et al. Application of organic fertilizer improves microbial community diversity and alters microbial network structure in tea (Camellia sinensis) plantation soils. Soil Tillage Res. 2019, 195, 104356. [Google Scholar] [CrossRef] [Scilit]
  11. Tang, W.; Chen, L.; Chen, Y.; Peng, Y.; Liu, C. Analysis of microbial community structure in rhizosphere of Camellia oleifera. Nonwood For. Res. 2021, 39, 51–59. [Google Scholar]
  12. Jia, X.; Lin, S.; Wang, Y.; Zhang, Q.; Jia, M.; Li, M.; Wang, Z.; Wang, H. Recruitment and aggregation capacity of tea trees to rhizosphere soil characteristic bacteria affects the quality of tea leaves. Plants 2024, 13, 1686. [Google Scholar] [CrossRef] [Scilit]
  13. Chen, Y.; Fu, W.; Xiao, H.; Zhai, Y.; Luo, Y.; Wang, Y.; Li, X.; Huang, J. A review on rhizosphere microbiota of tea plant (Camellia sinensis L.): Recent insights and future perspectives. J. Agric. Food Chem. 2023, 71, 19165–19188. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  14. Shao, S.; Li, Y.; Li, Z.; Ma, X.; Zhu, Y.; Luo, Y.; Wang, Y.; Li, Q. Impact of tea tree cultivation on soil microbiota, soil organic matter, and nitrogen cycling in mountainous plantations. Agronomy 2024, 14, 638. [Google Scholar] [CrossRef] [Scilit]
  15. Hu, B.; Zheng, Y.; Wang, D.; Guo, Y.; Dong, Y. Managing faba bean wilt disease through intercropping with wheat and reasonable nitrogen application: Enhancing nutrient absorption and biochemical resistance in faba beans. Physiol. Mol. Biol. Plants 2024, 30, 1029–1046. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. Bao, S.D.; Lu, R.K.; Jiang, S.H. Analytical Methods for Soil and Agro-Chemistry; China Agriculture Press: Beijing, China, 2000. (In Chinese) [Google Scholar]
  17. Dempsey, M.A.; Fisk, M.C.; Yavitt, J.B.; Fahey, T.J.; Balser, T.C. Exotic earthworms alter soil microbial community composition and function. Soil Biol. Biochem. 2013, 67, 263–270. [Google Scholar] [CrossRef] [Scilit]
  18. Yao, X.-H.; Min, H.; Lü, Z.-H.; Yuan, H.-P. Influence of acetamiprid on soil enzymatic activities and respiration. Eur. J. Soil Biol. 2006, 42, 120–126. [Google Scholar] [CrossRef] [Scilit]
  19. Lu, J.; Li, J.; Peng, P.; Chen, L.; Li, Z.; Li, Y.; Peng, S. Effects of different varieties of Camellia oleifera on soil microbial community structure and stability. J. Soil Sci. Plant Nutr. 2025, 25, 4183–4196. [Google Scholar] [CrossRef] [Scilit]
  20. Tang, S.; Zhou, J.; Pan, W.; Sun, T.; Liu, M.; Tang, R.; Li, Z.; Ma, Q.; Wu, L. Effects of combined application of nitrogen, phosphorus, and potassium fertilizers on tea (Camellia sinensis) growth and fungal community. Appl. Soil Ecol. 2023, 181, 104661. [Google Scholar] [CrossRef] [Scilit]
  21. Xu, Y.; Bi, Z.; Zhang, Y.; Wu, H.; Zhou, L.; Zhang, H. Impact of wine grape pomace on humification performance and microbial dynamics during pig manure composting. Bioresour. Technol. 2022, 358, 127380. [Google Scholar] [CrossRef] [Scilit]
  22. Wagg, C.; Schlaeppi, K.; Banerjee, S.; Kuramae, E.E.; van der Heijden, M.G. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 2019, 10, 4841. [Google Scholar] [CrossRef] [Scilit]
  23. Hartmann, M.; Six, J. Soil structure and microbiome functions in agroecosystems. Nat. Rev. Earth Environ. 2023, 4, 4–18. [Google Scholar] [CrossRef] [Scilit]
  24. Jibola-Shittu, M.Y.; Heng, Z.; Keyhani, N.O.; Dang, Y.; Chen, R.; Liu, S.; Lin, Y.; Lai, P.; Chen, J.; Yang, C. Understanding and exploring the diversity of soil microorganisms in tea (Camellia sinensis) gardens: Toward sustainable tea production. Front. Microbiol. 2024, 15, 1379879. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Pei, J.; Li, J.; Luo, Y.; Rillig, M.C.; Smith, P.; Gao, W.; Li, B.; Fang, C.; Nie, M. Patterns and drivers of soil microbial carbon use efficiency across soil depths in forest ecosystems. Nat. Commun. 2025, 16, 5218. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  26. Pan, Z.; Cai, X.; Cai, L.; Dong, B.; Haider, F.U.; Bo, Y.; Hu, Z.; Li, A.; Xue, Q. Soil and Microbial Biomass Response to Land-Use Changes in the Loess Plateau. Sustainability 2024, 16, 10496. [Google Scholar] [CrossRef] [Scilit]
  27. Hernandez, J.C.; Ribeiro, H.M.; Bayne, E.; MacKenzie, M.; Lanoil, B. Impact of stockpile depth and storage time on soil microbial communities. Appl. Soil Ecol. 2024, 196, 105275. [Google Scholar] [CrossRef] [Scilit]
  28. Liu, J.; Li, C.; Ma, W.; Wu, Z.; Liu, W.; Wu, W. Exploitation alters microbial community and its co-occurrence patterns in ionic rare earth mining sites. Sci. Total Environ. 2023, 898, 165532. [Google Scholar] [CrossRef] [Scilit]
  29. Xiong, J.; Liu, Y.; Lin, X.; Zhang, H.; Zeng, J.; Hou, J.; Yang, Y.; Yao, T.; Knight, R.; Chu, H. Geographic distance and pH drive bacterial distribution in alkaline lake sediments across Tibetan Plateau. Environ. Microbiol. 2012, 14, 2457–2466. [Google Scholar] [CrossRef] [Scilit]
  30. Zhuxiu, L.; Junjie, L.; Xiaojing, H.; Jian, J.; Guanghua, W. Research progress on the distribution of microbial community in soil profile. Soils Crops 2022, 11, 129–138. [Google Scholar]
  31. Murawska-Wlodarczyk, K.; Kushwaha, P.; Stokes, O.; Rasmussen, C.; Neilson, J.W.; Maier, R.M.; Babst-Kostecka, A. Depth-dependent heterogeneity in topsoil stockpiles influences plant-microbe interactions and revegetation success in arid mine reclamation. Sci. Total Environ. 2025, 1003, 180673. [Google Scholar] [CrossRef] [Scilit]
  32. Zheng, X.; Wu, Y.; Xu, A.; Lin, C.; Wang, H.; Yu, J.; Ding, H.; Zhang, Y. Response of soil microbial communities and functions to long-term tea (Camellia sinensis L.) planting in a subtropical region. Forests 2023, 14, 1288. [Google Scholar] [CrossRef] [Scilit]
  33. Kui, L.; Xiang, G.; Wang, Y.; Wang, Z.; Li, G.; Li, D.; Yan, J.; Ye, S.; Wang, C.; Yang, L. Large-scale characterization of the soil microbiome in ancient tea plantations using high-throughput 16S rRNA and internal transcribed spacer amplicon sequencing. Front. Microbiol. 2021, 12, 745225. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  34. Lv, H.; Yang, J.; Su, S.; Liu, Y.; Feng, J.; Sheng, Y.; Wang, T.; Pan, J.; Tang, L.; Chen, L.; et al. Distribution of genes and microbial taxa related to soil phosphorus cycling across soil depths in subtropical forests. Forests 2023, 14, 1665. [Google Scholar] [CrossRef] [Scilit]
  35. Kielak, A.M.; Cipriano, M.A.; Kuramae, E.E. Acidobacteria strains from subdivision 1 act as plant growth-promoting bacteria. Arch. Microbiol. 2016, 198, 987–993. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  36. Zong, D.; Quan, Y.; Penttinen, P.; Qi, L.; Wang, J.; Tang, X.; Xu, K.; Chen, Y. Organic Amendments Drive Soil Organic Carbon Sequestration and Crop Growth via Microorganisms and Aggregates. Agronomy 2025, 15, 2919. [Google Scholar] [CrossRef] [Scilit]
  37. 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. [Google Scholar] [CrossRef] [Scilit]
  38. Wan, P.; Peng, H.; Ji, X.; Chen, X.; Zhou, H. Effect of stand age on soil microbial communities of a plantation Ormosia hosiei forest in southern China. Ecol. Inform. 2021, 62, 101282. [Google Scholar] [CrossRef] [Scilit]
  39. Naz, M.; Dai, Z.; Hussain, S.; Tariq, M.; Danish, S.; Khan, I.U.; Qi, S.; Du, D. The soil pH and heavy metals revealed their impact on soil microbial community. J. Environ. Manag. 2022, 321, 115770. [Google Scholar] [CrossRef] [Scilit]
  40. Hu, Z.; Delgado-Baquerizo, M.; Fanin, N.; Chen, X.; Zhou, Y.; Du, G.; Hu, F.; Jiang, L.; Hu, S.; Liu, M. Nutrient-induced acidification modulates soil biodiversity-function relationships. Nat. Commun. 2024, 15, 2858. [Google Scholar] [CrossRef] [Scilit]
  41. Tripathi, A.; Pandey, M.; Sharma, P. A review: Effects of nitrogenous fertilizers on soil (PH, microbial community, greenhouse gases emission and carbon pool). Environ. Contam. Rev. 2022, 5, 44–48. [Google Scholar] [CrossRef] [Scilit]
  42. Yan, K.; Fu, X.; Xiong, H.; Dong, J.; Luo, K.; Sun, Y.; Li, Y. Effects of intercropping system on phytoremediation of cadmium contaminated farmland soil in the central Hanjiang River Basin. Front. Sustain. Food Syst. 2025, 9, 1655220. [Google Scholar] [CrossRef] [Scilit]
  43. Zhang, Z.; Wang, Y.; Ai, J.; Dao, J.; Li, R.; Yang, S.; Liu, J.; Deng, J.; Zhao, Y. The role of K fertilizer in shaping bacterial and fungal communities in the sugarcane rhizosphere. Trop. Plants 2025, 4, e028. [Google Scholar] [CrossRef] [Scilit]
  44. Chen, F.; Yu, G.; Sun, Y.-B.; Zhang, H.-L.; Tian, X.; Xia, B. Characteristics of microbial community structure in the surrounding farmlands of a mercury mining area and its environmental driving factors. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 4342–4352. [Google Scholar]
  45. Ji, L.; Shen, F.; Liu, Y.; Yang, Y.; Wang, J.; Purahong, W. Contrasting altitudinal patterns and co-occurrence networks of soil bacterial and fungal communities along soil depths in the cold-temperate montane forests of China. Catena 2022, 209, 105844. [Google Scholar] [CrossRef] [Scilit]
  46. Ji, L.; Yang, Y.; Yang, L. Seasonal variations in soil fungal communities and co-occurrence networks along an altitudinal gradient in the cold temperate zone of China: A case study on Oakley Mountain. Catena 2021, 204, 105448. [Google Scholar] [CrossRef] [Scilit]
  47. Deng, Y.; Jiang, Y.H.; Yang, Y.; He, Z.; Luo, F.; Zhou, J. Molecular ecological network analyses. BMC Bioinform. 2012, 13, 113. [Google Scholar] [CrossRef] [Scilit]
  48. Carpenter, S.R.; Arrow, K.J.; Barrett, S.; Biggs, R.; Brock, W.A.; Crépin, A.S. General resilience to cope with extreme events. Sustainability 2012, 4, 3248–3259. [Google Scholar] [CrossRef] [Scilit]
  49. Gai, X.; Chen, B.; Xiong, S.; Zhou, Q.; Yuan, Y.; Mou, Y. Impacts of different types of straw returning on soil physicochemical properties, microbial community structure, and pepper quality. Front. Plant Sci. 2025, 16, 1620502. [Google Scholar] [CrossRef] [Scilit]
  50. Chen, L.-F.; He, Z.-B.; Zhao, W.-Z.; Kong, J.-Q.; Gao, Y. Empirical evidence for microbial regulation of soil respiration in alpine forests. Ecol. Indic. 2021, 126, 107710. [Google Scholar] [CrossRef] [Scilit]
  51. Zhang, J.; Zhang, W.; Wu, X.; Fu, W.; Yang, C.; Long, N. Insights into the Mycosphere Fungal Community and Its Association with Nucleoside Accumulation in Ophiocordyceps sinensis. J. Fungi 2025, 11, 696. [Google Scholar] [CrossRef] [Scilit]
Microorganisms 14 00806 g001
Figure 1. Venn diagrams showing shared and unique OTUs of bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths. Numbers indicate OTUs shared across all samples within each depth.
Figure 1. Venn diagrams showing shared and unique OTUs of bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths. Numbers indicate OTUs shared across all samples within each depth.
Microorganisms 14 00806 g001
Microorganisms 14 00806 g002
Figure 2. Comparison of bacterial (A–D) and fungal (E–H) alpha diversity indices between 0–30 cm (T) and 30–60 cm (S) soil depths. No significant differences were detected between depths for any index (independent samples t-test, p > 0.05, n = 18 per depth).
Figure 2. Comparison of bacterial (A–D) and fungal (E–H) alpha diversity indices between 0–30 cm (T) and 30–60 cm (S) soil depths. No significant differences were detected between depths for any index (independent samples t-test, p > 0.05, n = 18 per depth).
Microorganisms 14 00806 g002
Microorganisms 14 00806 g003
Figure 3. NMDS ordination of bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths based on Bray–Curtis distances.
Figure 3. NMDS ordination of bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths based on Bray–Curtis distances.
Microorganisms 14 00806 g003
Microorganisms 14 00806 g004
Figure 4. Relative abundance of soil bacteria (A,B) and fungi (C,D) at phylum (A,C) and genus (B,D) levels across sampling sites.
Figure 4. Relative abundance of soil bacteria (A,B) and fungi (C,D) at phylum (A,C) and genus (B,D) levels across sampling sites.
Microorganisms 14 00806 g004
Microorganisms 14 00806 g005
Figure 5. Spearman correlation heatmap showing relationships between soil environmental factors and microbial communities at different soil depths. (A) Bacteria at 0–30 cm; (B) Bacteria at 30–60 cm; (C) Fungi at 0–30 cm; (D) Fungi at 30–60 cm. Red and blue indicate positive and negative correlations, respectively. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 5. Spearman correlation heatmap showing relationships between soil environmental factors and microbial communities at different soil depths. (A) Bacteria at 0–30 cm; (B) Bacteria at 30–60 cm; (C) Fungi at 0–30 cm; (D) Fungi at 30–60 cm. Red and blue indicate positive and negative correlations, respectively. * p < 0.05, ** p < 0.01, *** p < 0.001.
Microorganisms 14 00806 g005
Microorganisms 14 00806 g006
Figure 6. Redundancy analysis (RDA) of soil microbial communities (at the phylum level) and soil environmental factors. (A) Bacterial community at phylum level (0–30 cm soil depth); (B) Bacterial community at phylum level (30–60 cm soil depth); (C) Fungal community at phylum level (0–30 cm soil depth); (D) Fungal community at phylum level (30–60 cm soil depth). * p < 0.05, ** p < 0.01, *** p < 0.001. Red arrows represent quantitative environmental parameters, while blue arrows represent species. The length of the arrows indicates the magnitude of the influence of environmental factors on species data. Positive correlations are represented by acute angles, negative correlations by obtuse angles, and right angles indicate no correlation. The relative influence of environmental factors on the distribution of community composition across sampling sites is represented by the projection distance from sampling site points to the origin along the direction of the quantitative environmental factor arrow.
Figure 6. Redundancy analysis (RDA) of soil microbial communities (at the phylum level) and soil environmental factors. (A) Bacterial community at phylum level (0–30 cm soil depth); (B) Bacterial community at phylum level (30–60 cm soil depth); (C) Fungal community at phylum level (0–30 cm soil depth); (D) Fungal community at phylum level (30–60 cm soil depth). * p < 0.05, ** p < 0.01, *** p < 0.001. Red arrows represent quantitative environmental parameters, while blue arrows represent species. The length of the arrows indicates the magnitude of the influence of environmental factors on species data. Positive correlations are represented by acute angles, negative correlations by obtuse angles, and right angles indicate no correlation. The relative influence of environmental factors on the distribution of community composition across sampling sites is represented by the projection distance from sampling site points to the origin along the direction of the quantitative environmental factor arrow.
Microorganisms 14 00806 g006
Microorganisms 14 00806 g007
Figure 7. Partial Mantel tests based on Bray–Curtis distances were performed to analyze the correlations between soil microbial communities and environmental factors, with results visualized as color gradients based on Spearman’s correlation coefficients. The results for different soil layers and microbial taxa are shown in (A) (0–30 cm bacteria), (B) (30–60 cm bacteria), (C) (0–30 cm fungi), and (D) (30–60 cm fungi), respectively. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 7. Partial Mantel tests based on Bray–Curtis distances were performed to analyze the correlations between soil microbial communities and environmental factors, with results visualized as color gradients based on Spearman’s correlation coefficients. The results for different soil layers and microbial taxa are shown in (A) (0–30 cm bacteria), (B) (30–60 cm bacteria), (C) (0–30 cm fungi), and (D) (30–60 cm fungi), respectively. * p < 0.05, ** p < 0.01, *** p < 0.001.
Microorganisms 14 00806 g007
Microorganisms 14 00806 g008
Figure 8. Co-occurrence networks of soil bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths across different habitats. Node size represents relative abundance and connectivity (number of connections); red and blue lines indicate positive and negative correlations, respectively.
Figure 8. Co-occurrence networks of soil bacterial (A,B) and fungal (C,D) communities at 0–30 cm (A,C) and 30–60 cm (B,D) soil depths across different habitats. Node size represents relative abundance and connectivity (number of connections); red and blue lines indicate positive and negative correlations, respectively.
Microorganisms 14 00806 g008
Microorganisms 14 00806 g009
Figure 9. Zi-Pi plot analysis of topological features of soil bacterial and fungal communities across different soil depths. (A) Bacterial community at 0–30 cm soil depth; (B) Bacterial community at 30–60 cm soil depth; (C) Fungal community at 0–30 cm soil depth; (D) Fungal community at 30–60 cm soil depth. Each symbol represents an individual OTU. The horizontal dashed line indicates the threshold for within-module connectivity (Zi = 2.5), and the vertical dashed line indicates the threshold for among-module connectivity (Pi = 0.62). Nodes are classified into four categories: peripheral nodes (Zi < 2.5, Pi < 0.62), module hubs (Zi ≥ 2.5, Pi < 0.62), connectors (Zi < 2.5, Pi ≥ 0.62), and network hubs (Zi ≥ 2.5, Pi ≥ 0.62).
Figure 9. Zi-Pi plot analysis of topological features of soil bacterial and fungal communities across different soil depths. (A) Bacterial community at 0–30 cm soil depth; (B) Bacterial community at 30–60 cm soil depth; (C) Fungal community at 0–30 cm soil depth; (D) Fungal community at 30–60 cm soil depth. Each symbol represents an individual OTU. The horizontal dashed line indicates the threshold for within-module connectivity (Zi = 2.5), and the vertical dashed line indicates the threshold for among-module connectivity (Pi = 0.62). Nodes are classified into four categories: peripheral nodes (Zi < 2.5, Pi < 0.62), module hubs (Zi ≥ 2.5, Pi < 0.62), connectors (Zi < 2.5, Pi ≥ 0.62), and network hubs (Zi ≥ 2.5, Pi ≥ 0.62).
Microorganisms 14 00806 g009
Table 1. The list of the location of soil samples.
Table 1. The list of the location of soil samples.
No.Study AreaLongitude (E)Latitude (N)Elevation (m)
1Kunming Botanical Garden
(Chinese Academy of Sciences)		102°74′25°14′1951
2Zhongshan Town, Chuxiong101°10′24°90′2342
3Shidong Temple, Fengqing100°20′24°27′2073
4Hemu Village, Tengchong98°30′25°10′1967
5Meihua Village, Yongping99°38′25°30′2456
6Yangbi Yi Autonomous County, Dali99°51′25°38′2489
Table 2. Physicochemical properties of rhizosphere soil at different soil depths.
Table 2. Physicochemical properties of rhizosphere soil at different soil depths.
Soil DepthSampling SitespHSoil Organic MatterTotal NTotal pTotal KAlkali-
Hydrolyzable N		Available pAvailable K
0–30 cmZWY6.10 ± 0.13 Aa63.18 ± 10.98 Ab4.93 ± 0.04 Ab2.46 ± 0.46 Aa7.58 ± 0.31 Ae350.45 ± 36.57 Ac60.24 ± 2.05 Aa457.64 ± 35.15 Aa
ZSZ5.59 ± 0.48 Ab114.91 ± 6.41 Aa7.59 ± 1.94 Aa1.29 ± 0.08 Abc17.16 ± 3.50 Ac623.00 ± 7.47 Aa9.40 ± 1.70 Ade220.79 ± 6.58 Ab
SDS5.30 ± 0.09 Abc29.69 ± 11.24 Ac2.64 ± 0.86 Ab1.10 ± 0.08 Ac29.05 ± 1.22 Aa212.61 ± 54.29 Ad45.18 ± 6.34 Ab254.58 ± 30.43 Ab
HMC5.41 ± 0.09 Bb60.43 ± 17.42 Ab4.60 ± 2.20 Ab1.31 ± 0.20 Abc13.18 ± 1.04 Ad231.20 ± 40.20 Ad3.94 ± 1.94 Ae100.29 ± 29.11 Bc
MHC4.97 ± 0.08 Ac46.63 ± 8.32 Abc2.91 ± 0.38 Ab1.30 ± 0.44 Abc12.27 ± 1.11 Ad253.79 ± 12.21 Ad14.61 ± 3.65 Bd100.37 ± 27.84 Ac
YB5.41 ± 0.12 Ab109.16 ± 9.67 Aa7.49 ± 0.83 Aa1.94 ± 0.69 Aab21.69 ± 1.20 Ab550.85 ± 38.58 Ab32.38 ± 1.88 Ac425.62 ± 12.95 Aa
30–60 cmZWY6.22 ± 0.20 Aa38.26 ± 24.30 Ab2.82 ± 1.52 Aab1.71 ± 0.64 Aa8.82 ± 1.56 Ad249.00 ± 2.00 Bc15.26 ± 0.11 Bb511.00 ± 11.00 Aa
ZSZ5.70 ± 0.18 Ab38.18 ± 1.04 Bb4.89 ± 2.20 Aab1.09 ± 0.32 Aa16.25 ± 3.69 Abc293.33 ± 7.77 Bb5.42 ± 3.24 Ac166.30 ± 6.70 Bc
SDS5.40 ± 0.12 Ac31.78 ± 6.40 Ab2.83 ± 0.65 Aab1.14 ± 0.24 Aa26.27 ± 3.56 Aa225.00 ± 5.56 Ad20.78 ± 5.89 Bab163.70 ± 3.20 Bc
HMC5.60 ± 0.08 Abc43.31 ± 10.61 Aab3.84 ± 1.13 Aab1.47 ± 0.54 Aa13.88 ± 3.14 Acd289.33 ± 4.47 Ab6.14 ± 4.53 Ac500.33 ± 34.00 Aa
MHC5.09 ± 0.11 Ad43.93 ± 4.12 Aab2.63 ± 0.33 Ab1.24 ± 0.48 Aa12.35 ± 1.70 Acd215.00 ± 10.44 Bd26.19 ± 5.98 Aa77.07 ± 22.44 Ad
YB5.59 ± 0.09 Abc61.98 ± 1.31 Ba 5.20 ± 0.52 Ba1.85 ± 0.55 Aa19.65 ± 2.81 Ab396.33 ± 25.79 Ba20.25 ± 4.87 Bab231.33 ± 3.00 Bb
Note: Data are presented as mean ± standard deviation. Different uppercase letters indicate significant differences among different soil depths at the same sampling site, while different lowercase letters indicate significant differences among different sampling sites at the same soil depth (p < 0.05).
Table 3. Enzyme activities in rhizosphere soil under different habitats.
Table 3. Enzyme activities in rhizosphere soil under different habitats.
Soil DepthSampling SitesUreasePhosphataseSucraseCatalase
0–30 cmZWY344.63 ± 26.57 Aa15.42 ± 3.57 d15.61 ± 3.15 Ab23.83 ± 1.00 Aa
ZSZ359.23 ± 2.68 Aa21.71 ± 0.20 Ab12.58 ± 1.69 Abc23.59 ± 1.52 Aa
SDS348.51 ± 9.74 Aa16.67 ± 1.98 Bcd9.80 ± 1.90 Ac19.99 ± 0.29 Ab
HMC290.18 ± 24.37 Ab20.35 ± 1.72 Abc9.71 ± 3.59 Ac22.94 ± 1.59 Aa
MHC307.10 ± 6.42 Ab44.21 ± 2.22 Aa23.03 ± 4.13 Aa23.23 ± 1.63 Aa
YB298.73 ± 20.31 Ab20.45 ± 3.93 Abc26.48 ± 2.26 Aa25.25 ± 1.16 Aa
30–60 cmZWY321.80 ± 13.44 Aa21.48 ± 0.74 b22.98 ± 4.74 Aa22.72 ± 0.62 Aa
ZSZ210.75 ± 5.63 Bc21.60 ± 1.12 Ab8.23 ± 2.06 Bb21.67 ± 1.15 Aa
SDS345.15 ± 26.42 Aa19.81 ± 3.95 Ab8.76 ± 1.79 Ab18.48 ± 2.17 Ab
HMC309.58 ± 47.86 Aa18.23 ± 5.35 Ab11.59 ± 5.64 Ab22.24 ± 1.08 Aa
MHC291.81 ± 57.42 Aab32.62 ± 7.98 Ba13.45 ± 6.34 Bb22.84 ± 1.77 Aa
YB232.90 ± 20.21 Bbc21.98 ± 1.22 Ab24.57 ± 3.02 Aa23.07 ± 0.90 Aa
Note: Data are presented as mean ± standard deviation. Different uppercase letters indicate significant differences among different soil depths at the same sampling site, while different lowercase letters indicate significant differences among different sampling sites at the same soil depth (p < 0.05).
Table 4. Topological characteristics of soil bacterial and fungal co-occurrence networks in different habitats.
Table 4. Topological characteristics of soil bacterial and fungal co-occurrence networks in different habitats.
Network IndexesBacteriaFungi
0–30 cm30–60 cm0–30 cm30–60 cm
Node517395247248
Link4647238510381069
positive correlation3823215210131056
negative correlation8242332513
average clustering
coefficient		0.5490.5110.6190.595
Average path distance3.7823.7784.2264.408
Modularity0.3580.3860.6500.591
Table 5. Distribution of module hubs and connectors in soil bacterial networks across different soil depths.
Table 5. Distribution of module hubs and connectors in soil bacterial networks across different soil depths.
Classification of
Bacteria (Phylum)		Number of Modular HubNumber of Connection Node
0–30 cm30–60 cm0–30 cm30–60 cm
Proteobacteria2011
Acidobacteriota4032
Actinobacteriota1013
Chloroflexi0210
Values represent the number of OTUs identified as module hubs or connectors within each phylum.
Table 6. Distribution of module hubs and connectors in soil fungal networks across different soil depths.
Table 6. Distribution of module hubs and connectors in soil fungal networks across different soil depths.
Classification of
Fungal (Phylum)		Number of Modular HubNumber of Connection Node
0–30 cm30–60 cm0–30 cm30–60 cm
Ascomycota3210
Values represent the number of OTUs identified as module hubs or connectors within each phylum.
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

Yan, F.-J.; Ma, C.; Xiao, H.-X.; Zeng, Y.-J.; Huang, Y.-Y.; Zhang, Z.-Y.; Wang, Z.-L.; Chen, L.-Q.; Geng, F. Soil Microbial Diversity and Its Environmental Drivers in the Rhizosphere Profile of Camellia reticulata. Microorganisms 2026, 14, 806. https://doi.org/10.3390/microorganisms14040806

AMA Style

Yan F-J, Ma C, Xiao H-X, Zeng Y-J, Huang Y-Y, Zhang Z-Y, Wang Z-L, Chen L-Q, Geng F. Soil Microbial Diversity and Its Environmental Drivers in the Rhizosphere Profile of Camellia reticulata. Microorganisms. 2026; 14(4):806. https://doi.org/10.3390/microorganisms14040806

Chicago/Turabian Style

Yan, Fu-Jun, Chong Ma, Hong-Xing Xiao, Yu-Jia Zeng, Yuan-Yuan Huang, Zhi-Yu Zhang, Zhong-Lang Wang, Long-Qing Chen, and Fang Geng. 2026. "Soil Microbial Diversity and Its Environmental Drivers in the Rhizosphere Profile of Camellia reticulata" Microorganisms 14, no. 4: 806. https://doi.org/10.3390/microorganisms14040806

APA Style

Yan, F.-J., Ma, C., Xiao, H.-X., Zeng, Y.-J., Huang, Y.-Y., Zhang, Z.-Y., Wang, Z.-L., Chen, L.-Q., & Geng, F. (2026). Soil Microbial Diversity and Its Environmental Drivers in the Rhizosphere Profile of Camellia reticulata. Microorganisms, 14(4), 806. https://doi.org/10.3390/microorganisms14040806

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