Abstract

Introduction:

The rubber tree (Hevea brasiliensis) is an important industrial raw material and strategic resource in China. Red root rot, caused by the pathogenic fungus Ganoderma pseudoferreum, is the most severe root disease and poses a serious threat to rubber production. Understanding the differences and correlations in rhizosphere soil microbial communities, environmental factors, and enzyme activities between healthy rubber trees and those infected with red root rot is of great significance for the green prevention and control of rubber tree root diseases and the regulation of soil microecology.

Methods:

In this study, Illumina MiSeq high-throughput sequencing technology was used to analyze the differences in the structure and composition of rhizosphere soil microbial communities between healthy rubber trees and those infected with red root rot. Combined with soil physicochemical properties and enzyme activity indicators, the relationships between microbial ecological characteristics (such as soil nutrients and soil enzyme activities) and the occurrence of red root rot were explored.

Results:

According to the results, the occurrence of red root rot increased the richness and diversity of bacterial and fungal populations in the rhizosphere soil. Fungal community composition has a greater impact on plant disease occurrence: the abundance of Actinobacteria in the rhizosphere soils of diseased plants is significantly lower than that of healthy plants, while the abundance of Ascomycota is significantly higher. Notable genus-level changes revealed a dramatic fungal community shift. The abundance of beneficial genus Termitomyces plummeted from 37.50% in healthy soils to 0.026% in diseased soils. Concurrently, Hygrocybe, Peniophora, unclassified Archaeorhizomycetes, and Ganoderma in diseased soils were significantly increased by 82.13%, 98.59%, 69.74%, and 97.87% compared with healthy soils, respectively. Diseased soils exhibited significantly higher pH, soil water content (SWC), total nitrogen (TN), invertase (INV), and catalase (CAT) activities, but lower nitrate nitrogen (NO₃-N) and acid phosphatase (ACP) activities. In addition, the study found that soil pH, TN, SWC, available phosphorus (AP), CAT, and INV were positively correlated with the relative abundances of Ganoderma and Archaeorhizomyces, while NO₃-N content and AP were positively correlated with the relative abundance of Termitomyces.

Discussion:

In summary, red root rot alters root-zone microbial communities and enzyme activities in Hevea brasiliensis, thereby clarifying the correlations between soil environmental factors and key microbial taxa (particularly Ganoderma spp. and rhizosphere bacteria). This study provides a crucial theoretical basis for advancing our understanding of the disease’s occurrence mechanism and microecological underpinnings, as well as for formulating targeted ecological management strategies for its control.

1 Introduction

Rubber trees (Hevea brasiliensis Muell. Arg.), a perennial deciduous tree belonging to the genus Hevea in the family Euphorbiaceae, are the primary source of natural rubber worldwide. Owing to their high elasticity, insulation properties, and wear resistance, they find extensive applications in aerospace, medical and healthcare, electrical and electronic industries, as well as the automotive sector (Shi et al., 2021). Beyond its role as an irreplaceable industrial feedstock and strategic commodity (He and Huang, 1987), H. brasiliensis also performs essential ecological functions such as carbon sequestration, soil and water conservation, microclimate modulation, and biodiversity preservation, thus emerging as a vital tree species integrating economic and ecological values in tropical areas (Singh et al., 2021). China is a major producer of both natural rubber and rubber products. Currently, its rubber planting area exceeds 11.3 million mu (approximately 1.7 million acres), with an annual output of 800,000 tons, accounting for about 6% of the global total. Ranking third and fourth in the world, respectively (Rong, 2023). Yunnan and Hainan provinces are the two main rubber-producing regions in China, contributing approximately 49.14 and 47.82% of the national planting area (Liu R. J. et al., 2022). However, China’s self-sufficiency rate in natural rubber is less than 15% (Ai and Huang, 2023), underscoring the crucial importance of ensure a secure supply of this resource.

In recent years, alongside the development of the natural rubber industry, root rot diseases in rubber trees have grown increasingly severe. This escalation can be attributed to factors such as long-term continuous cultivation, low economic returns, extensive management practices, and climate variability. These diseases have resulted in large-scale declines in latex production and even tree mortality, significantly undermining both the yield and quality of natural rubber. Among these, red root rot caused by infection with Ganoderma pseudoferreum, stands out as one of the most devastating soil-borne diseases affecting rubber trees. Red root rot is characterized by its high latency, strong infectivity, rapid transmission, and widespread occurrence, making it an extremely destructive pathogen. It often triggers secondary infestations, such as by bark beetles (Scolytidae), and if left untreated, the mortality rate can reach 100% (Bai et al., 2008). Due to its high resistance to control measures and the severe economic losses it incurs, this disease poses a major challenge to rubber plantation management. Currently, conventional control measures for red root rot primarily rely on chemical fungicides (e.g., tridemorph) for root irrigation and trenching to isolate infected roots. While these methods have shown moderate efficacy in mitigating the disease, they suffer from significant drawbacks, including high chemicals and labor costs, which hinder large-scale implementation. Given these limitations, biocontrol technologies have emerged as a promising alternative due to their environmentally friendliness. Notably, endophytic antagonistic bacteria (e.g., Bacillus subtilis) isolated from rubber trees have demonstrated significant suppressive effects against red root rot (Liang et al., 2022, 2023). However, in practice, the antagonistic effects of these beneficial bacteria against pathogens often fail to manifest as fully under field conditions as they do in controlled laboratory settings. This discrepancy arises from complex interactions involving the pathogen, host plant species, indigenous microbial communities, and environmental variables (Maraha et al., 2004; Mazzola and Freilich, 2017). Key challenges include the inability of introduced biocontrol strains to effectively compete with native microorganisms for resources, or unfavorable soil conditions that hinder their survival and colonization, ultimately compromising disease control efficacy. This underscores the need for integrated management strategies that account for multiple influencing factors. Crucially, understanding the relationship between microbial community structure and environmental variables may provide theoretical support for improved disease control (Bulluck and Ristaino, 2002). Therefore, elucidating the dynamic changes in rhizosphere soil microbial communities and enzyme activities during the progression of rubber tree root rot holds significant practical value for both red root disease management and rubber tree quality enhancement.

Red root rot primarily infects rubber trees through root contact with infected or dead root debris and mycelia in the soil (Li and Luo, 2007). As a critical interface between plants and soil, the rhizosphere plays a vital role in nutrient uptake and stress resistance (Li et al., 2025). Microorganisms, as key component of soil ecosystems, are essential for soil nutrient cycling, organic matter transformation, and energy metabolism (Pratscher et al., 2011; Kluge et al., 2015; Petersen et al., 2012). Their sensitivity to environmental changes makes them effective early indicators of soil quality and ecological function. Furthermore, the abundance and activity of rhizosphere microorganisms are closely linked to plant diseases dynamics (Arancon et al., 2004). Currently, the use of high-throughput sequencing technologies to characterize changes in soil microbial community diversity at the molecular level has become a research focus. Root rot pathogenesis is complex, with its occurrence closely associated with microbial community dysbiosis and deterioration of the soil microecological environment. Scholars have shown that there are significant differences in the community structures of bacterial and fungal in the rhizosphere soil between plants affected by diseases such as honeysuckle root rot (Li et al., 2025), flue-cured tobacco bacterial wilt (Wei et al., 2025), white peony root rot (Zhang et al., 2024), wheat head blight (Wang et al., 2024), Sanqi (Panax notoginseng) root rot (Wang et al., 2023), Coptis root rot (Tang et al., 2022), American ginseng root rot (Jiang et al., 2018; Yu et al., 2018), apple root rot (Yang et al., 2020), and cruciferous clubroot (Wu et al., 2020) and their healthy counterparts, with marked disparities in population diversity and richness.

Early research on rubber tree root diseases predominantly emphasized pathogen control agents and antagonistic microorganisms in the soil, overlooking the characteristic microorganisms (bacteria and fungi) of the entire microbial community, their interactions with soil properties, and their role in the onset of root rot. Consequently, a systematic investigation is warranted to examine root rot pathogenesis, shifts in soil microbial community diversity, and enzyme activities dynamics during rubber trees cultivation. This entails an in-depth analysis of the microbial community fluctuations between healthy and diseased soils, soil chemical properties, and the interplay between these factors and enzyme activities changes. Identifying the dominant contributors to red root rot in rubber trees within this region is crucial for advancing our understanding of its occurrence and dissemination, as well as for developing targeted prevention and control strategies. From the perspective of the rhizosphere soil microecology of rubber trees, this study focuses on the rhizosphere soils of both red root rot and healthy rubber trees. This work analyze the alterations in the microbial community structure within the rhizosphere soil, elucidate the relationships among soil microbial communities, rhizosphere environmental factors, and enzyme activities, and uncover the interaction mechanisms between soil microorganisms and environmental conditions in the context of red root rot in Qiongzhong, Hainan.

2 Materials and methods2.1 Sample site and sampling collection

Hevea brasiliensis cultivar “PR107” was cultured in 1998 at Hongxing Team, Xinjin Farm, Qiongzhong County (19°13′53”N, 109°50′21″E), Hainan Province, China. The main climatic conditions of Qiongzhong County are as follows: tropical monsoon climate, annual mean temperature, 22 °C; annual average relative humidity, 80%; annual average precipitation, 2,300 mm; annual sunshine duration, 1700 h; altitude, 200–350 m; the predominant soil type, laterite soil. The planting density ranges from 30 to 33 trees per mu (approximately 666.7 m2), with a spacing arrangement of 2.5 m × 8 m. In November 2019, we conducted a field survey in this area and observed a 10% incidence of red root rot (caused by Ganoderma pseudoferreum) in the rubber trees at Hongxing Team.

Based on the incidence of red root rot in the experimental area, we selected three healthy rubber tree plots and three plots affected by red root rot for soil sample collection. Diseased plants were selected based on clear rubber tree root rot symptoms, including yellowing and stunted leaves of the aboveground parts and necrotic rotting of the belowground roots. Healthy plants were selected that were separated by at least four plants from diseased trees, characterized by vigorous growth with no disease symptoms observed in adjacent plants. Each plot measured 20 m × 20 m.

A cross-shaped sampling pattern was employed to randomly collect root-zone soil from two trees per plot (healthy rubber trees designated as ‘J’, diseased trees as ‘C’). Soil samples were obtained using a 7-cm diameter soil auger within a 1-meter radius from the trunk. Four auger cores (0–20 cm depth) were extracted per tree and composited after removing litter and gravel. Samples were sealed in bags labeled ‘C’ or ‘J’ and stored in an ice box, and transport to the laboratory within 12 h. Six red root rot affected soils (labeled C1-C6) and six healthy root-zone soils (labeled J1-J6) were collected.

The collected soil samples were thoroughly mixed, and sieved through a 2-mm mesh. These soils samples were split into three sections: one part of fresh soil samples were used to determine soil physicochemical properties including nitrate-nitrogen (NO₃-N) and ammonium-nitrogen (NH₄+-N); another part was subjected to basic physicochemical analyses and soil enzyme activities determinates following air drying; the remaining samples were kept at −80 °C for total DNA extraction.

2.2 Soil physicochemical properties and enzyme activities

The analysis of basic physicochemical properties of air-dried soil was performed assessing five key indicators: soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and pH value. Measured using a pH meter (FE28-Standard, Mettler Toledo, Switzerland) with a water: soil ratio of 2.5:1 (v/w) according to the agricultural industry standard of the People’s Republic of China (NY/T 1377–2007) (Wang et al., 2007). Soil total nitrogen (TN) was detected by Kjeldahl nitrogen analyzer (Hanon K9840, Shandong, China). Soil available potassium (AK) was measured by the ammonium acetate extraction-flame photometry method (Lu, 1999). Soil available phosphorus (AP) and soil organic matter (SOM) were analyzed using soil physicochemical determination reagent kits produced by Suzhou Comin Biotechnology Co., Ltd. The activities of five key enzymes involved in the cycles of carbon (C), nitrogen (N), and phosphorus (P) were tested: Urease (URE), Catalase (CAT), Polyphenol oxidase (PPO), Acid phosphatase (ACP), and β-Fructosidase (INV). The activities of these five soil hydrolases were determined using assay kits manufactured by Suzhou Kemin Biotechnology Co., Ltd., China. For fresh soil, the content of ammonium nitrogen (NH₄+-N) and nitrate nitrogen (NO₃-N) were determined using analytical kits produced by Suzhou Comin Biotechnology Co., Ltd., China.

2.3 Soil microbiome diversity analysis

To compare the root-zone soils microbial community structure between healthy and red root rot of H. brasiliensis, we conducted the following analyses: Total DNA was extracted from 0.5 g homogenized soil samples using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, GA, USA). Using diluted genomic DNA (20 ng/μL) as template, we performed PCR amplification targeting: the bacterial 16S rRNA V3-V4 region with primers 515F/806R (Caporaso et al., 2011), and the fungal ITS region with primers ITS1F/ITS2R (Zinger et al., 2009). The PCR reaction mix was 20 μL, consisted of 1 μL DNA template, 0.4 μL each of forward and reverse primers, 10 μL 2 × Taq PCR StarMix and 8.2 μL ddH₂O. The PCR amplification procedure involved initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturing at 95 °C for 30s, annealing at 55 °C for 30s, and extension at 72 °C for 60s, with a final single extension at 72 °C for 10 min, and subsequent cooling at 4 °C. The PCR products were purified using VAHTS™ DNA Clean Beads (Vazyme Biotech Co., Ltd., China), quantified with Qubit 3.0, and verified by 1.5% agarose gel electrophoresis. Afterward, the DNA was sequenced by Sangon Biotech Co., Ltd. (Shanghai, China) using an Illumina MiSeq PE300 platform.

2.4 Statistical analysis

Raw sequences underwent primer/adapter trimming and barcode-based demultiplexing in QIIME 1.9.0 (Caporaso et al., 2010) with stringent quality control (Q-score ≥20, minimum length 200 bp). Chimeric sequences were detected de novo and removed using USEARCH v11.0.667. High-quality reads were clustered into operational taxonomic units (OTUs) at 97% similarity threshold via the UPARSE (Edgar, 2013) algorithm. Taxonomic assignment was performed using the RDP Classifier v2.13 (Wang et al., 2007) against the Greengenes database (v13_8) with an 80% confidence cutoff.

Data were statistically processed using Microsoft Excel 2010 (Microsoft, Redmond, WA, USA) and JMP 10 (SAS Institute, Cary, NC, USA), with graphical presentations created in Origin 2019 (OriginLab, Northampton, MA, USA). Using IBM SPSS Statistics 25.0 (IBM Corp., Armonk, NY, USA), independent samples t-tests identified significant differences (p < 0.05) in soil physicochemical properties, enzyme activities, and microbial indices. Pearson correlation analysis was used to examine relationships between soil microbial community composition and both soil physicochemical properties and enzyme activities. The microbial community’s alpha diversity was quantified using three indices: Chao1 (richness), Shannon (diversity), and Simpson (evenness), while beta diversity was assessed through principal coordinate analysis (PCoA) of Unifrac-based dissimilarity matrices, implemented in MOTHUR v1.44.0 (Schloss et al., 2009).

3 Results3.1 Soil physicochemical properties and enzyme activities

As shown in Table 1, there were significant alterations in root-zone soil properties of red root rot of H. brasiliensis compared to healthy trees (p < 0.05). Diseased trees exhibited 3.43% higher pH, 11.29% greater water content (SWC), and 9.31% increased total nitrogen (NT). Conversely, nitrate nitrogen (NO₃-N) content showed a 58.71% reduction in infected trees. No significant differences were detected in available phosphorus (AP), available potassium (AK), organic matter (SOM), or ammonium nitrogen (NH4+-N) levels between healthy and diseased specimens. Red root rot significantly altered enzyme activities in the root-zone soil of H. brasiliensis (p < 0.05). Compared with healthy plants, the non-rhizosphere soil, red root rot resulted in 23.01% higher catalase (CAT) and 15.19% elevated sucrase (INV) activities, while acid phosphatase (ACP) activity decreased by 4.5%. Urease (URE) and polyphenol oxidase (PPO) activities remained unaffected. These findings suggest that red root rot occurrence correlates with specific soil parameter changes (pH, SWC, TN and NO₃-N) and induces metabolic disturbances affecting energy metabolism, carbon cycling, and phosphorus conversion efficiency.

IndexJCpH4.22 ± 0.05b4.37 ± 0.04aSWC (%)17.13 ± 0.33b19.31 ± 0.77aTN (mg/kg)736.17 ± 1.01b811.79 ± 3.45aNO3-N (mg/kg)7.29 ± 2.25a3.01 ± 0.64bNH4+-N (mg/kg)1.20 ± 0.45a0.85 ± 0.17aAP (mg/kg)1.82 ± 0.27a2.16 ± 0.55aAK (mg/kg)38.58 ± 1.10a39.60 ± 3.76aSOM (%)0.96 ± 0.11a1.07 ± 0.26aURE (μg/d/g)526.47 ± 8.96a508.93 ± 7.02aCAT (μmol/d/g)6.02 ± 0.43b7.82 ± 0.36aINV (mg/d/g)5.47 ± 0.14b6.45 ± 0.15aACP (μmol/d/g)18.64 ± 0.39a17.80 ± 0.34bPPO (mg/d/g)5.18 ± 0.16a5.14 ± 0.05a

Changes physicochemical properties and enzyme activities in root-zone soil of healthy and red root rot plants of rubber trees.

J and C represent healthy and red root rot-affected root-zone soils, respectively. Values followed by different letters within the columns are significantly different according to independent samples t-tests (p < 0.05). The same as below.

3.2 Microbial gene abundance and α diversity in the soil microbiome

The α-diversity indices effectively reflect the abundance and diversity of microbial communities. As shown in Table 2, the bacterial gene copy number in the root-zone soil of healthy rubber trees was significantly higher (16.53% increase) than that in the root-zone soil of diseased plants (p < 0.05). However, the bacterial community richness (Chao1 index) was significantly lower in healthy plants compared to diseased plants root-zone soil (p < 0.05). Other bacterial diversity parameters including observed OTUs, Shannon index (species diversity), and Simpson index (evenness) showed slightly lower values or no significant differences in healthy plants compared to diseased soil samples (p > 0.05). For fungal communities, the gene copy number in the root-zone soil of diseased plants was significantly reduced by 52.63% compared to healthy plants (p < 0.05). In contrast, the fungal Chao1 index (richness), OTUs, and Shannon index were significantly higher in diseased plants, while the Simpson index was significantly lower than in healthy rubber tree root-zone soil (p < 0.05).

ClassificationSoil sampleα-diversity indexGene copy number(×108 copies/ g)OTUsChao1ShannonSimpsonBacteriaJ5.14 ± 1.22a6116.67 ± 272.69a9172.60 ± 325.46b6.57 ± 0.07a0.005 ± 0.001aC4.29 ± 1.15b6622.67 ± 229.78a10056.03 ± 304.69a6.71 ± 0.07a0.004 ± 0.001aFungusJ0.38 ± 0.22a1465.67 ± 18.48b1708.89 ± 33.81b3.96 ± 0.16b0.14 ± 0.02aC0.18 ± 0.13b1627.00 ± 44.84a1928.41 ± 11.78a4.91 ± 0.13a0.03 ± 0.00b

The microbial gene abundance and α-diversity index in root-zone soil of healthy and red root rot plants of rubber trees.

3.3 Structural differences in soil microbial communities

Principal Coordinate Analysis (PCoA) provides a visual representation of differences in microbial community structure between samples, where closer sample distances indicate greater similarity in species composition. PCoA based on the Unifrac distance matrix revealed that for bacterial communities, PCoA1 and PCoA2 accounted for 46.7 and 19.1% of the sample variation, respectively, with a cumulative contribution rate of 65.8%. In fungal communities, PCoA1 and PCoA2 explained 53.6 and 26.0% of the variation, respectively, with a cumulative contribution rate of 79.6%, representing the primary source of differentiation. As illustrated in Figure 1A (bacterial communities), samples J (healthy plants) were distributed in the negative region of PCoA1 and both positive/negative regions of PCoA2, while samples C (red root rot-infected plants) clustered in the positive region of PCoA1 and both positive/negative regions of PCoA2, with relatively small separation distances between groups. In Figure 1B (fungal communities), samples J were scattered across both positive and negative regions of PCoA1 and PCoA2, whereas samples C aggregated distinctly in the positive quadrant of both PCoA1 and PCoA2. Notably, the three biological replicates of C formed a tight cluster in the first quadrant, demonstrating significant spatial separation from J samples. This clear partitioning indicates substantial differences in principal coordinates between healthy and diseased plant microbiomes.

PCoA of soil bacterial community (A) and fungal community (B).

ANOSIM and Adonis analyses of bacterial and fungal communities between healthy and diseased plants revealed no significant differences in overall composition (Table 3). However, the p-value for fungal community composition was smaller than that for bacterial communities, suggesting that fungal community structure may play a more important role than bacterial communities in disease development.

ClassificationANOSIMR2AdonisRPFPBacteria0.520.210.453.250.21Fungus0.810.110.8623.930.11

Comparative analysis of soil microbial community composition in healthy and red root rot plants of rubber trees.

3.4 Soil microbial community composition

Microbial community composition analysis of root-zone soils from healthy and diseased plants of rubber trees at phylum and genus levels. At the phylum level, the dominant bacterial phyla (relative abundance >5%) included Acidobacteria, Proteobacteria, Actinobacteria, and Firmicutes (Figure 2A). Compared with healthy soils, diseased soils showed a significant decrease (0.92%) in Actinobacteria and a significant increase (0.21%) in Gemmatimonadetes, while no significant differences were observed in other bacterial phyla (Figure 2A). For fungal communities, the predominant phyla (relative abundance >5%) were Basidiomycota, Ascomycota, and Mortierellomycota (Figure 2B). Notably, diseased soils exhibited a sharp decline (28.89%) in Basidiomycota and a significant increase (6.28%) in Ascomycota, with no significant variations in other fungal phyla between healthy and diseased soils (Figure 2B).

Taxonomic summary of the relative abundance of the root-zone bacterial phyla (A), fungal phyla (B), bacterial genera (C), and fungal genus (D) in healthy and diseased soil. *p < 0.05, **p < 0.01.

At the genus level, dominant taxa were defined as those with an average relative abundance >5%. In the bacterial communities of rubber tree root-zone soils, Acidobacteria Gp2, Gp1, and Gp3 were identified as the dominant genera in both healthy and diseased soils. Notably, the relative abundances of Rhizomicrobium and Gemmatimonas were significantly higher in diseased soils (p < 0.05), with increases of 0.10 and 0.21%, respectively. Conversely, Aciditerrimonas and Phenylobacterium showed significantly lower abundances in diseased soils (p < 0.05), decreasing by 0.19 and 0.38%, respectively (Figure 2C). No significant differences were observed for other bacterial genera. For fungal communities, Termitomyces, Mortierella, and Archaeorhizomyces were the dominant genera in both healthy and diseased soils. Strikingly, Termitomyces accounted for 37.50% of the fungal community in healthy soils but only 0.026% in diseased soils, while Archaeorhizomyces exhibited a lower relative abundance in healthy soils (3.48%) compared to diseased soils (6.34%). Additionally, the genera Hygrocybe, Peniophora, unclassified Archaeorhizomycetes, and Ganoderma showed markedly higher abundances in diseased soils, with increases of 82.13, 98.59, 69.74, and 97.87%, respectively (Figure 2D). The fungal community exhibited a dramatic decline in the symbiont Termitomyces (from 37.5 to 0.026%), alongside enrichment of saprotrophic genera (e.g., Ganoderma; +97.87%), suggesting a shift from mutualistic to pathogenic taxa in diseased soils. These genera contribute to the observed shifts in fungal community structure and may be associated with the development of red root rot.

3.5 Associations between soil microbial communities and soil physicochemical/enzymatic properties

Genus level correlations between soil microbial communities and physicochemical properties are summarized in Table 4. In the rubber tree root-zone bacterial communities, Acidobacteria subgroup Gp2 showed a significant positive correlation with NH₄+-N (p < 0.05), while the seven other most abundant bacterial genera exhibited no significant associations with any soil properties (Supplementary Table S1). Among the dominant fungal genera, Termitomyces sp. was positively correlated with NO₃-N (p < 0.05) and NH₄+-N (p < 0.05) but negative correlated with pH (p < 0.01), TN (p < 0.01), AP (p < 0.01), SWC (p < 0.05), and SOM (p < 0.05). Archaeorhizomyces sp. displayed positively correlations with pH (p < 0.01), SWC (p < 0.01), TN (p < 0.01) and AP (p < 0.01), while showing a negative correlated with NO₃-N (p < 0.05). Hygrocybe sp. exhibited positively correlated with pH (p < 0.05), TN (p < 0.05), and AP (p < 0.05), but a negative correlation with NH₄+-N (p < 0.05). Ganoderma sp. was positively correlated with SWC (p < 0.01), AP (p < 0.01), pH (p < 0.05), and TN (p < 0.05).

GenerapHSWCTNNO3-NNH4+-NAPAKSOMGp2−0.75−0.46−0.720.610.84*−0.690.01−0.69Gp10.02−0.20−0.090.12−0.05−0.14−0.070.00Gp30.060.130.030.40−0.090.060.350.12Subdivision3_genera0.550.540.58−0.28−0.730.67−0.010.36Burkholderia−0.70−0.67−0.780.590.74−0.80−0.24−0.74Citrobacter−0.07−0.35−0.14−0.070.17−0.28−0.080.20Ktedonobacter0.21−0.040.240.03−0.330.18−0.150.67Rhizomicrobium0.95**0.760.92**−0.79−0.86*0.86*0.530.88*Gemmatimonas0.97**0.84*0.97**−0.92**−0.89*0.94**0.500.82*Gp130.360.040.31−0.48−0.390.24−0.220.35Spartobacteria_genera−0.060.05−0.010.21−0.160.12−0.34−0.22Gemmata−0.44−0.19−0.350.110.53−0.33−0.07−0.41Aciditerrimonas−0.95**−0.79−0.97**0.88*0.89*−0.92**−0.44−0.92**Phenylobacterium−0.97**−0.88*−0.98**0.740.93**−0.97**−0.56−0.88*WPS-2_genera−0.27−0.58−0.340.490.09