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Article

Effects of Vegetation Succession on Soil Microbial Communities on Karst Mountain Peaks

by
Wenyu Wang
1,2,
Peiqin Peng
1,
Jiangnan Li
2,
Xionghui Liao
2,
Wei Zhang
2,3,4,
Kelin Wang
2,4 and
Jie Zhao
2,3,4,*
1
College of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
2
Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
3
Guangxi Industrial Technology Research Institute for Karst Rocky Desertification Control, Nanning 530012, China
4
Huanjiang Agriculture Ecosystem Observation and Research Station of Guangxi, Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang 547100, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(4), 586; https://doi.org/10.3390/f15040586
Submission received: 27 January 2024 / Revised: 28 February 2024 / Accepted: 13 March 2024 / Published: 24 March 2024
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Soil microorganisms are vital components of the karst terrestrial ecosystem. However, their responses to the vegetation succession on karst mountain peaks remain unclear as to whether soil microbial diversity and community compositions change with vegetation succession. We investigated the diversity and community compositions of soil bacteria and fungi and associated environmental factors along a vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks. The results indicated that soil organic carbon and total nitrogen generally increased, and soil pH changed in the range of 8.19–8.44 and slightly declined with vegetation succession. Overall, there was an increase in microbial biomass along the vegetation succession, with the dominant phyla of bacteria, including Proteobacteria, Acidobacteria, Actinobacteria, Choroflexi, Gemmatiomnadates, Bacteroidetes, and Planctomycetes, and the dominated phyla of fungi, including Basidiomycota and Ascomycota. Notably, both the bacterial and fungal community compositions were different among the three successional stages. Spearman’s correction analysis showed that soil organic carbon and total nitrogen had stronger and more significant influences on the soil microbial community compositions compared to soil water content, pH, and C:N ratio. Overall, our results provide evidence for the changes and influencing factors of the microbial community with the succession vegetation on karst mountain peaks.

1. Introduction

Vegetation succession is a critical ecological process that not only modifies plant communities but also influences biogeochemical cycles and energy transformations within soil ecosystems [1,2,3]. Soil microorganisms are the main active components of the terrestrial ecosystem and play a crucial role in nutrient cycling and maintaining the stability of the soil food web [4,5]. Microbes initially colonized exposed rock surfaces, initiating weathering, and were involved in soil formation processes during primary succession [6,7,8]. Biological weathering by microbes accelerated the release of mineral elements from rocks and provided rich nutrients for soil and vegetation formation [9]. In addition, the emergence of mycorrhizal fungi was conducive to plant growth and reproduction with vegetation succession [6,10]. Therefore, the vegetation, soil, and microbes may be co-existing [11,12,13]. For example, soil nutrient and microbial communities changed simultaneously along with vegetation succession in arid environments [14,15]. Furthermore, it is widely recognized that soil microorganisms influence vegetation growth by altering soil nutrient availability [16,17]. In turn, vegetation drives changes in soil physico-chemical properties, which also affect soil microbial communities [18,19,20].
Bacteria and fungi are vital microorganisms in the soil [21]. Their diversity and community compositions are greatly impacted by vegetation succession [22,23]. However, the interactions between soil microbial communities and vegetation are fundamentally mediated by the soil [13,24]. For instance, Tedersoo et al. reported that soil water content significantly affected fungal community diversity in a late successional stage [25]. Previous research has found that nitrate and plant productivity were the most important environmental variables explaining the variations in soil bacterial and fungal communities, respectively, in grazed grasslands [26]. Moreover, different microbial taxa may decompose distinct forms of organic carbon in soil; therefore, changes in carbon pools may impact soil microbial community compositions [27]. Soil pH is also a critical factor affecting microbial functional groups and structures with vegetation succession [28].
Karst ecosystems account for roughly fifteen percent of the earth’s surface [3,29]. The subtropical monsoon climate in this region creates the most mature exposed karst mountainous terrain, and the peak-cluster landscape is one of the most typical karst landscapes in southwest China [29,30]. Its peaks are characterized by frequent wet and dry alternations and high temperatures [31,32]. Soil community compositions in such mountain peaks may be unusual [33]. However, it is poorly known about the soil microbial communities on karst mountain peaks.
Our study aimed to evaluate the effects of vegetation succession on the microbial communities of karst mountain peaks in southwest China. Vegetation succession from moss crusts to moss crusts with sparse grasses to sparse grasses, representing a primary succession that began with bare rocks, was selected for this study. We hypothesize that (1) soil microbial biomass and diversity increase along the vegetation succession, and (2) the limiting factors of the soil bacteria and fungi community might be different on karst mountain peaks.

2. Materials and Methods

2.1. Study Site

The experimental site was located in the Huanjiang Observation and Research Station for Karst Ecosystems (24°44′–25°33′ N, 107°51′–108°43′ E), Chinese Academy of Sciences (CAS), Guangxi Province, China. The climate in this region is subtropical monsoon, the mean annual temperature (MAT) is 18.5 °C, and the mean annual precipitation (MAP) is 1389 mm. The calcareous soil developed from a dolostone base. The watershed is a typical karst peak-cluster depression in southwest China [34,35]. The karst mountain peaks typically have steep slopes, exposed bedrock, thin soils, and low and fragile vegetation cover. In summer (May–September), the soils experience frequent wet and dry alternations and high temperatures due to high solar radiation and frequent rainfalls (Figure S1) [33]. A distinct vegetation succession sequence was identified in this extreme environment, on karst mountain peaks; specifically, the primary stage is moss crusts, the second stage is moss crusts with sparse grasses, and the third stage is sparse grasses.

2.2. Experimental Design and Soil Sampling

A randomized block design was employed for this study. Particularly, three sample mountain peaks were selected. The shallow soils were naturally separated by exposed bedrock. Three types of vegetation, separately named moss crusts (MC), moss with sparse grasses (MCG), and sparse grasses (G), could be found in soil patches on each mountain peak. Soil sampling was conducted in June 2018. Each soil sample was collected from five randomly selected soil patches with the same vegetation on each mountain peak at a depth of 0 to 2 cm using a 5 cm inner diameter soil auger. The soil thickness on the karst mountain peaks is generally less than 2 cm. The soil samples were immediately sent to the laboratory, and plant debris and rocks were manually removed from each sample. During soil sampling and preparation, nitrile gloves were changed to avoid cross-contamination between soil samples. Three experiments were performed on each homogenized sample: one was stored in a −80 °C refrigerator for the determination of soil microbial communities (i.e., phospholipid fatty acid analysis and high-throughput sequencing), the second was fresh soil for the determination of water content (SWC), and the third was air-dried at room temperature for the determination of SOC, TN, and pH.

2.3. Soil Analysis

Soil water content (SWC) was determined by drying 20 g of soil at 105 °C for 48 h. Soil organic carbon (SOC) was analyzed by the dichromate oxidation approach. Total nitrogen (TN) was determined using the Kjeldahl technique. The soil pH was measured using the electrode potential method. Soil phospholipid fatty acids (PLFAs) were extracted from 8 g of freeze-dried soil and analyzed as described in Bossio and Scow (1995) [36]. Bacterial biomass was represented by 10 PLFAs (i15:0, a15:0, 15:0, i16:0, 16:1u7, i17:0 [37], a17:0, 17:0, cy17:0, and cy19:0); fungal biomass was represented by the PLFA 18:2u6,9; and microbial biomass was represented by the 10 bacterial PLFAs, the one fungal PLFA, and PLFA 16:0 [37,38,39].

2.4. DNA Extraction, PCR Amplification, and Sequencing

The DNA was extracted from 0.5 g each soil sample using the Fast DNA SPIN Kit for soil (MP Biomedicals, Irvine, CA, USA). Subsequently, the DNA was quantified using a NanoDrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA), and the quality of the extracted DNA was confirmed through 1% agarose gel electrophoresis. We conducted amplification of the V3–V4 region of the total bacteria with primers 338F/806R [40], and the ITS1 region of the total fungi was amplified using primers ITS1F/ITS2R [41]. To perform amplicon sequencing, an Illumina HiSeq2500 platform (Biomarker Technologies, Beijing, China) was employed. Raw sequences were processed by the QIIME 2 pipeline to discard low-quality sequences. UPARSE was utilized to cluster tags at the 97% similarity level, resulting in the identification of operational taxonomic units (OTUs) [42]. Subsequently, taxonomic annotation of the OTUs was performed using the SILVA database for bacteria and the UNITE database for fungi [43]. The raw sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under the accession numbers PRJNA1046589 and PRJNA1046384 for bacteria and fungi, respectively.

2.5. Statistical Analyses

Data were checked for normal distribution and homogeneity of variance and transformed (natural log, square root, or rank) if data were not normally distributed and variances were not homogenous. The Chao1 index and Shannon index were calculated using Mothur [44]. Soil physico-chemical properties, alpha diversity indices, and relative abundances of microbes were assessed using one-way ANOVAs and LSD tests at p < 0.05 (SPSS Inc., Chicago, IL, USA). Spearman’s correlation analysis was used to investigate correlations between microbial alpha diversity indices and soil physico-chemical properties. Microbial beta diversity was calculated by principal coordinate analysis (PCoA) using the ‘vegan’ package in the Bray–Curtis distance matrix in R [45]. Significant differences were evaluated by PERMANOVA at p < 0.05. Redundancy analysis (RDA) was used to assess the relationship between soil properties and the composition of soil microbial communities, RDA was calculated by CANOCO 5.

3. Results

3.1. Soil Physico-Chemical Properties

SOC and TN generally increased with vegetation succession. In particular, SOC in G and MCG was significantly higher than that in MC (Table 1). TN in G was significantly higher than in MC (Table 1). The soil was alkaline; soil pH was not significantly different and slightly declined with vegetation succession. In addition, SWC and the C:N ratio did not change significantly with vegetation succession.

3.2. Microbial Biomass and Diversity

There was a significant increase in microbial biomass, bacterial biomass, and fungal biomass with vegetation succession. In addition, the bacterial Shannon index was significantly higher in G than in MC, and the fungal Chao1 index and Shannon index were significantly higher in G than in MC and MCG (Table 2). A significant positive correlation existed between microbial biomass (bacterial and fungal biomass) and soil physico-chemical properties (SWC, SOC, and TN) (Figure 1).

3.3. Microbial Community Compositions

The main phyla of bacteria (relative abundance > 1%) in the three vegetation successional stages were Proteobacteria (32.1%), Acidobacteria (22.6%), Actinobacteria (22.1%), Choroflexi (5.7%), Gemmatiomnadates (5.8%), Bacteroidetes (5.5%), and Planctomycetes (1.1%) (Table 3). Proteobacteria and Choroflexi increased significantly with vegetation succession. Actinobacteria decreased significantly at the early successional stage, and Acidobacteria decreased significantly at the late successional stage. At the class level, Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria were the dominant Proteobacteria. The main phyla of fungi (relative abundance > 1%) were Basidiomycota (69.9%) and Ascomycota (19.1%) (Table 4). Basidiomycota decreased significantly, and Ascomycota increased significantly with vegetation succession. Agaricomycetes were the most dominant class of Basidiomycota, and their relative abundances significantly decreased with vegetation succession. Sordariomycetes, Dothideomycetes, and Eurotiomycetes were the most dominant classes of Ascomycota. The relative abundance of Sordariomycetes and Dothideomycetes varied consistently, increasing significantly with vegetation succession. The principal coordinate analysis significantly distinguished the bacterial and fungal communities among three successional stages (MC, MCG, and G) (p < 0.01). The first two axes explained, respectively, 48.76% and 26.09% of the variance for the bacterial community and 63.14% and 29.93% for the fungal community (Figure 2).

3.4. Correlation between Microbial Community Compositions and Soil Environmental Variables

Redundancy analysis (RDA) showed the contribution of the examined soil physico-chemical properties to bacterial and fungal community compositions at the taxonomic levels (phylum and class) in the three vegetation stages. These physico-chemical properties explained 39.74% (RD1 27.87% and RD2 11.87%) and 75.31% (RD1 73.16% and RD2 2.21%) of the variances at the bacterial and fungal phylum levels, respectively (Figure 3a,b, Table S1). Actinobacteria, Choroflexi, and Basidiomycota were positively correlated with pH but negatively correlated with SOC and TN (p < 0.05, Table S2). In contrast, Ascomycota was negatively correlated with pH but positively correlated with SOC and TN (p < 0.05, Table S2). At the bacterial and fungal class levels, the physico-chemical properties explained 62.52% (RD1 48.90% and RD2 13.62%) and 82.85% (RD1 72.51% and RD2 10.34%) of the variance for them, respectively (Figure 3c,d, Table S1). Sordariomycetes, Dothideomycetes, and Eurotiomycetes were negatively correlated with pH but positively correlated with SOC and TN (Table S3).

4. Discussion

4.1. Effect of Vegetation Succession on Soil Microbial Biomass and Diversity

The biomass and diversity of soil microorganisms are vital indicators in evaluating ecosystem functions [46]. In line with our hypothesis, this study observed a significant increase in soil microbial biomass and α-diversity with vegetation succession. Previous studies have demonstrated that the increase in microbial diversity was possibly associated with the variations in vegetation communities [24], because enhanced plant biomass and the existence of herbaceous species improved soil nutrients (mainly organic matter), especially in the topsoil. And soil nutrients provided abundant resources and an ecological niche for soil microorganisms [47,48]. In our study, the biomass and diversity of soil microbes were positively correlated with SOC and TN. And the SOC and TN were generally improved from moss crusts to moss crusts with sparse grasses to sparse grasses (Table 1). This confirms that the increase in vegetation cover along vegetation succession enhances the input of organic matter, such as litter and roots, into the soil [46], which contributes to the accumulation of SOC [49,50]. On the karst mountain peaks, we suggested that plant diversity was the greatest in moss crusts with sparse grasses, but the roots were more developed, and the amount of litter was the highest in sparse grasses, and the environment of sparse grasses was conducive to microbial growth and colonization. Therefore, soil formation as well as soil organic matter and nutrient accumulations along the primary succession were likely the main reasons for the increase in soil microbial biomass and diversity on karst mountain peaks.

4.2. Effect of Vegetation Succession on Microbial Community Compositions

Changes in plant communities can alter the abundance and composition of soil microorganisms directly and influence the soil microenvironment through root exudates [51], resulting in specific microbial populations colonizing around plants [52,53]. Our results demonstrated that the bacterial and fungal community structures altered along vegetation succession. Regarding the community composition, the dominant bacterial and fungal phyla on the karst mountain peaks were not exactly the same as those in the karst depression areas [54]. In the present study, Ascomycota and Proteobacteria increased and were positively related to SOC and TN during vegetation succession (Table S2). Previous studies have found that Ascomycota participated in the decomposition of stubborn organic carbon [55]. In our study, the stubborn organic carbon concentrations in litters improved across cession of karst mountain peaks. Therefore, the late successional stages were beneficial to Ascomycota growth [56]. In our study, the increase in Proteobacteria could be due to multitudinous subgroups and their life strategies [57,58]. In particular, Alphaproteobacteria were the dominant Proteobacteria, which have a biological nitrogen–fixing ability [59]. In addition, previous research had shown that Alphaproteobacteria were more active beneath grasses than in other environments [60,61]. However, Acidobacteria, Actinobacteria, Choroflexi, Basidiomycota and their dominant class decreased and were negatively correlated with SOC and TN. These microbial taxa may have adapted to resource-limited conditions in severe environments such as dry–wet cycles or extremely high temperature ecosystems [62,63,64], which may play important roles in regulating soil processes and functions on karst mountain peaks. In this study, soil bacterial and fungal community structures were also influenced by soil pH. Previous findings have found soil pH to be a significant predictor of microbial community compositions in extreme environments [63,65] or dryland soils [53]. However, it cannot be ruled out that other soil physio-chemical factors may also play a substantial role in determining soil microbial community compositions [66,67]. Overall, we suggest that the effects of SOC and TN on microbial community compositions were greater than SWC, pH, and the C:N ratio, and the responses of different microbial taxa to SOC and TN were different.

5. Conclusions

In summary, the study observed an overall increase in microbial biomass and diversity with vegetation succession on karst mountain peaks. In addition, soil bacterial and fungal community structures altered completely among the three successional stages. Additionally, our results showed that soil organic carbon and total nitrogen exerted a greater influence on microbial community compositions compared to soil water content, pH, and C:N ratio. The responses of different microbial taxa to soil organic carbon and total nitrogen were different. The results of the present research can provide a theoretical basis for the relationships between soil biota and vegetation succession in harsh environments.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/f15040586/s1, Figure S1: the soil temperature at 2 cm depth under moss crust from 18 April 2014 to 20 September 2014; Table S1: RDA ordination summary for dominant phyla and environmental variables; Table S2: pearson correlation between dominant phyla (i.e., bacterial and fungal compositions) and soil physico-chemical properties; Table S3: pearson correlation between dominant class (i.e., bacterial and fungal compositions) and soil physico-chemical properties.

Author Contributions

Conceptualization, J.Z. and K.W.; methodology, W.Z.; software, P.P.; validation, X.L., P.P. and J.Z.; formal analysis, J.L.; investigation, X.L.; resources, J.Z.; data curation, J.L.; writing—original draft preparation, W.W.; writing—review and editing, J.Z.; visualization, P.P.; supervision, P.P.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundations of China (U21A20189 and 42377284); the Science and Technology Innovation Program of Hunan Province (2023RC1076); and the Guangxi Bagui Young Scholars Special Funding given to Jie Zhao.

Data Availability Statement

Data available in a publicly accessible repository.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 15 00586 g001
Figure 1. Heat maps showing significant correlations between microbial properties and soil physico-chemical properties (* p < 0.05, ** p < 0.01, and *** p < 0.001).
Figure 1. Heat maps showing significant correlations between microbial properties and soil physico-chemical properties (* p < 0.05, ** p < 0.01, and *** p < 0.001).
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Figure 2. Principal coordinate analysis (PCoA) based on Bray–Curtis distance matrix show the distribution patterns of bacteria (a) and fungi (b) among vegetation successional stages. MC: moss crusts MCG: moss crusts with sparse grasses G: sparse grasses.
Figure 2. Principal coordinate analysis (PCoA) based on Bray–Curtis distance matrix show the distribution patterns of bacteria (a) and fungi (b) among vegetation successional stages. MC: moss crusts MCG: moss crusts with sparse grasses G: sparse grasses.
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Figure 3. Redundancy analysis (RDA) of the relationships between dominant bacterial phyla (a) and class (b) and environmental factors, the relationships between fungal phyla (c) and class (d) and environmental factors. The blue arrows represent dominant phyla and class, and red arrows represent environmental factors. SWC: soil water content; TN: total nitrogen; Prot: Proteobacteria; Acid: Acidobacteria; Acti: Actinobacteria; Chlo: Chloroflexi; Gemm: Gemmatimonadetes; Bact: Bacteroidetes; Plan: Planctomycetes; Basi: Basidiomycota; Asco: Ascomycota Alph: Alphaproteobacteria; Beta: Betaproteobacteria; Delt: Deltaproteobacteria; Gamm: Gammaproteobacteria; Blas: Blastocatellia; Subg: Subgroup_6 Ther: Thermoleophilia; Agar: Agaricomycetes; Sord: Sordariomycetes; Doth: Dothideomycetes; Euro: Eurotiomycetes.
Figure 3. Redundancy analysis (RDA) of the relationships between dominant bacterial phyla (a) and class (b) and environmental factors, the relationships between fungal phyla (c) and class (d) and environmental factors. The blue arrows represent dominant phyla and class, and red arrows represent environmental factors. SWC: soil water content; TN: total nitrogen; Prot: Proteobacteria; Acid: Acidobacteria; Acti: Actinobacteria; Chlo: Chloroflexi; Gemm: Gemmatimonadetes; Bact: Bacteroidetes; Plan: Planctomycetes; Basi: Basidiomycota; Asco: Ascomycota Alph: Alphaproteobacteria; Beta: Betaproteobacteria; Delt: Deltaproteobacteria; Gamm: Gammaproteobacteria; Blas: Blastocatellia; Subg: Subgroup_6 Ther: Thermoleophilia; Agar: Agaricomycetes; Sord: Sordariomycetes; Doth: Dothideomycetes; Euro: Eurotiomycetes.
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Table 1. Soil water content (SWC), pH, soil organic carbon (SOC), total nitrogen (TN), and the C:N ratio as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks.
Table 1. Soil water content (SWC), pH, soil organic carbon (SOC), total nitrogen (TN), and the C:N ratio as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks.
PropertyVegetation Succession
MCMCGG
SWC (%)8.98 ± 2.1315.02 ± 4.0213.28 ± 2.85
SOC (g·kg−1)31.99 ± 4.32 b47.81 ± 5.71 a52.89 ± 6.03 a
TN (g·kg−1)2.62 ± 0.41 b4.07 ± 0.35 ab4.32 ± 0.67 a
pH8.44 ± 0.058.22 ± 0.108.19 ± 0.08
C:N12.34 ± 0.3511.68 ± 0.4512.51 ± 1.10
Values represent means ± standard errors (n = 3). Different letters indicate significant differences among vegetation successional stages at p < 0.05. MC: moss crusts, MCG: moss crusts with sparse grasses, G: sparse grasses.
Table 2. Soil microbial community properties as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks.
Table 2. Soil microbial community properties as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks.
VariableVegetation Successional Stages
MCMCGG
Microbial biomass18.87 ± 0.49 b24.98 ± 2.37 a25.93 ± 2.68 a
Bacterial biomass15.12 ± 0.47 b19.92 ± 1.78 a20.54 ± 1.72 a
Fungal biomass3.75 ± 0.31 b5.26 ± 0.49 a5.39 ± 1.24 a
Bacterial Chao1 index2054 ± 322083 ± 52100 ± 17
Bacterial Shannon index9.39 ± 0.04 b9.47 ± 0.01 a9.48 ± 0.02 a
Fungal Chao1 index302 ± 12 b300 ± 5 b419 ± 20 a
Fungal Shannon index3.99 ± 0.09 b4.38 ± 0.02 b5.06 ± 0.21 a
Values represent means ± standard errors (n = 3). Different letters indicate significant differences among vegetation successional stages at p < 0.05 by the LSD test.
Table 3. Relative abundances (mean ± SE) of bacterial community compositions across taxonomical classifications (phyla and class) as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G).
Table 3. Relative abundances (mean ± SE) of bacterial community compositions across taxonomical classifications (phyla and class) as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G).
PhylaClassVegetation Successional StagesANOVA
MCMCGGFp
Prot 29.48 ± 0.57 c32.01 ± 0.09 b35.02 ± 0.41 a46.32<0.001
Alph18.38 ± 0.33 a17.13 ± 0.07 b19.15 ± 0.37 a12.74<0.01
Beta3.83 ± 0.22 c6.63 ± 0.11 a5.84 ± 0.10 b88.08<0.001
Delt5.31 ± 0.10 b6.10 ± 0.03 a6.09 ± 0.12 a23.91<0.01
Gamm1.96 ± 0.05 b2.15 ± 0.12 b3.93 ± 0.08 a166.81<0.001
Acid 22.61 ± 0.91 ab24.23 ± 0.11 a21.1 ± 0.29 b8.00<0.05
Acid3.04 ± 0.163.05 ± 0.112.88 ± 0.140.450.65
Blas12.42 ± 0.77 a11.57 ± 0.04 a8.77 ± 0.04 b18.46<0.01
Subg12.42 ± 0.77 a6.54 ± 0.12 b7.30 ± 0.21 a47.26<0.001
Acti 24.27 ± 0.75 a21.32 ± 0.1 b20.64 ± 0.87 b8.34<0.05
Actin8.87 ± 0.38 a5.97 ± 0.07 b6.23 ± 0.21 b39.35<0.001
Chlo 6.27 ± 0.10 a5.61 ± 0.04 b5.43 ± 0.18 b13.52<0.01
Ther11.07 ± 0.5910.18 ± 0.3110.32 ± 0.471.030.41
Gemm 5.84 ± 0.12 b6.32 ± 0.13 a5.46 ± 0.15 b10.40<0.05
Gemm4.22 ± 0.10 b4.53 ± 0.07 a3.90 ± 0.08 c14.35<0.01
Bact 5.92 ± 0.985.08 ± 0.085.56 ± 0.350.480.64
Plan 1.08 ± 0.051.18 ± 0.051.15 ± 0.041.260.35
Phyla level: Prot: Proteobacteria; Acid: Acidobacteria; Acti: Actinobacteria; Chlo: Chloroflexi; Gemm: Gemmatimonadetes; Bact: Bacteroidetes; Plan: Planctomycetes Class level: Alph: Alphaproteobacteria; Beta: Betaproteobacteria; Delt: Deltaproteobacteria; Gamm: Gammaproteobacteria; Acid: Acidimicrobiia; Blas: Blastocatellia; Subg: Subgroup_6 Ther: Thermoleophilia; Gemm: Gemmatimonadetes; MC: moss crusts; MCG: moss crusts with sparse grasses; G: sparse grasses. Different lowercase letters denote significant differences (p < 0.05), and no letters indicate no significant differences among different successional stages.
Table 4. Relative abundances (mean ± SE) of fungal community compositions across taxonomical classification (Phyla and Class) as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G).
Table 4. Relative abundances (mean ± SE) of fungal community compositions across taxonomical classification (Phyla and Class) as affected by vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G).
PhylaClassVegetation Successional StagesANOVA
MCMCGGFp
Basi 72.31 ± 0.83 a73.24 ± 0.32 a64.39 ± 2.59 b9.47<0.05
Agar72.09 ± 0.83 a73.07 ± 0.31 a64.28 ± 2.63 b9.05<0.05
Asco 12.93 ± 0.08 b20.05 ± 0.56 a24.12 ± 2.40 a15.82<0.01
Sord3.68 ± 0.11 b4.87 ± 0.38 b7.10 ± 0.71 a13.63<0.01
Doth1.94 ± 0.06 c2.76 ± 0.18 b4.52 ± 0.13 a97.80<0.001
Euro1.65 ± 0.112.35 ± 0.203.71 ± 1.461.510.29
Phyla level: Basi: Basidiomycota; Asco: Ascomycota Class level: Agar: Agaricomycetes; Sord: Sordariomycetes; Doth: Dothideomycetes; Euro: Eurotiomycetes; MC: moss crusts; MCG: moss crusts with sparse grasses; G: sparse grasses. Different lowercase letters denote significant differences (p < 0.05), and no letters indicate no significant differences among different successional stages.
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Wang, W.; Peng, P.; Li, J.; Liao, X.; Zhang, W.; Wang, K.; Zhao, J. Effects of Vegetation Succession on Soil Microbial Communities on Karst Mountain Peaks. Forests 2024, 15, 586. https://doi.org/10.3390/f15040586

AMA Style

Wang W, Peng P, Li J, Liao X, Zhang W, Wang K, Zhao J. Effects of Vegetation Succession on Soil Microbial Communities on Karst Mountain Peaks. Forests. 2024; 15(4):586. https://doi.org/10.3390/f15040586

Chicago/Turabian Style

Wang, Wenyu, Peiqin Peng, Jiangnan Li, Xionghui Liao, Wei Zhang, Kelin Wang, and Jie Zhao. 2024. "Effects of Vegetation Succession on Soil Microbial Communities on Karst Mountain Peaks" Forests 15, no. 4: 586. https://doi.org/10.3390/f15040586

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