Differences in Soil Microbial Communities across Soil Types in China’s Temperate Forests

Abstract

Soil microorganisms are a crucial component of forest ecosystems because of their involvement in the decomposition of organic matter and nutrient cycling and their influence on plant growth and development. Soil type is a fundamental characteristic of soil. In the transitional forest regions from subtropical to temperate zones in China, various soil types can be found, including yellow-brown soils, brown soils, and cinnamon soils. However, the composition and distribution patterns of soil bacterial and fungal communities in different soil types remain uncertain. This study selected a 4.8-hectare plot in Baiyun Mountain Forest National Park, China. To explore the spatial distribution and ecological processes of soil microbial communities across three different soil types, Illumina sequencing was conducted. Results showed that the composition and assembly of bacterial and fungal communities varied substantially among different soil types. Bacteria were more influenced by environmental factors than fungi. Fungal communities consistently demonstrated greater stability compared to bacterial communities across the three soil types. Light was the main environmental factor driving the variation in the assembly of microbial communities among different soil types. This study demonstrates that there are differences in the composition and structure of soil microbial communities among different soil types, providing important insights into the management and sustainable development of soil microorganisms in temperate forests.

1. Introduction

Soil microbial communities play a critical role in forest ecosystems [1]. They are involved in the decomposition of organic matter and nutrient cycling and affect plant growth and development [2,3]. As the foundation of the ecosystem, the diversity of microbial communities is directly related to soil fertility and ecological stability in forests [4]. Forest soils are diverse, and each soil type forms a unique soil microbial community, due to its formation conditions, pedogenesis, and physicochemical properties [5]. The temperate forest regions of China boast rich biodiversity, complex ecological environments, and various typical soil types. Brown soils are primarily distributed in the northern temperate forest areas of China, mainly influenced by temperate broadleaf and coniferous forests, and characterized by neutral to slightly acidic pH and moderate organic matter content [6,7]. Yellow-brown soils are mainly found in the transitional areas between subtropical and temperate zones, influenced by evergreen broadleaf forests, and characterized by high acidity and low organic matter content [8]. Cinnamon soils are widely distributed in temperate humid and semihumid regions, often develop under deciduous broadleaf forests, are rich in organic matter, and have relatively neutral pH [9]. Environmental factors, including topography, organic matter, pH, and other soil physicochemical properties, significantly influence soil bacterial and fungal communities [10,11,12,13]. Therefore, exploring the diversity and ecological functions of microbial communities in different soil types is of great significance for understanding soil microbial ecology and provides a scientific basis for forest soil management and ecosystem protection.

The co-occurrence patterns of soil microorganisms are crucial aspects of microbial interactions, which include mutualistic symbiosis, co-occurrence predation, and parasitic symbiosis; these interactions play key roles in soil ecosystems [14]. Network analysis of soil communities can be employed to examine potential processes underlying species distribution [15]. Soil microbial network stability can be used to predict the succession of microbial communities [16]. The co-occurrence patterns of soil bacteria and fungi are affected by environmental conditions [17], and many environmental factors significantly impact the symbiotic patterns of microbial communities [18,19,20]. Soil bacterial and fungal communities can be constructed into highly interconnected microbial modules. This modularity indicates phylogenetic relationships, niche overlap, and habitat heterogeneity [21]. However, the symbiotic patterns of spatial fungal and bacterial communities in different soil types of temperate forests are not yet fully understood.

Community assembly studies are important in explaining species coexistence and maintaining species diversity. The aggregation patterns of soil microbial communities mainly consist of stochastic and deterministic processes. The assembly process of microbial communities is influenced by various environmental factors, including soil physicochemical properties, light, and other environmental factors [22,23]. However, further investigation is required to fully understand the assembly of microbial communities across varying soil types. Soil microbial communities exhibit spatial dependencies and are highly responsive to environmental factors [24]. Moreover, the conducive environmental conditions for fungal proliferation differ significantly from those favoring bacterial growth and reproduction. The growth and turnover rates are high for bacteria and low for fungi [25], and fungal communities coexist differently from bacterial communities [26,27]. Whether their coexistence varies across different soil types remains unclear.

The Baiyun Mountain National Forest Park is located in Henan Province, China. Renowned for its abundant mountains and diverse plant species, it forms a temperate forest ecosystem. Owing to the influence of forest type, topography, and other environmental factors, the park’s soil types are mainly yellow-brown soils, brown soils, and cinnamon soils. Therefore, this study selected a 4.8-hectare forest plot with significant variation in soil types. The research objectives are as follows: (i) to reveal the distribution differences of soil microbial communities under different soil types, (ii) to explore the coexistence modes and community assembly differences of soil microbes in different soil types, and (iii) to compare the different response mechanisms of fungi and bacteria in different soil types.

2. Materials and Methods

2.1. Study Area

This study was conducted in a 4.8-hectare permanent monitoring plot located at Mount Baiyun, Henan Province, China. Baiyunshan National Forest Park is located in the transitional zone from warm temperate to northern subtropical climates (latitude 33°38′–33°34′ N, longitude 111°48′–111°52′ E), with an average altitude of 1800 m [28]. It encompasses 37 peaks with elevations exceeding 1500 m, and the forest coverage exceeds 98.5% [29].

The 4.8-hectare plot primarily consists of broad-leaved forests dominated by Quercus aliena var. acutiserrata. The elevation of the plot ranges from 1538 m to 1660 m, with slopes ranging from 4.3° to 55.5°. All woody plants or shrubs with a diameter at breast height greater than or equal to 1 cm were carefully tagged, identified, measured, and geographically recorded within the 4.8-hectare plot. A total of 17,963 woody plants representing 34 families and 93 species were identified. The soil types in the plot included brown soils, yellow-brown soils, and cinnamon soils.

2.2. Sampling Design

The 4.8-hectare plot was divided into 120 small plots measuring 20 m × 20 m each. Three soil samples were collected from each small plot at a 10 cm depth using a soil auger and subsequently sieved through a 2 mm mesh to eliminate debris and stones. Finally, the three soil samples from each small plot were evenly mixed to form composite soil samples. We gathered a total of 120 soil samples.

We stored a portion of the collected soil samples at −80 °C for subsequent DNA analysis and refrigerated the remainder at −4 °C for transportation to the laboratory. In the lab, we analyzed available nitrogen (N), soil moisture content (SMC), available phosphorus (P), soil organic matter (SOM), and soil pH.

We recorded the elevation of each small plot using a total station and calculated the slope (SLO), aspect (ASP), and convexity (CON) based on the elevation data [30].

Light data in the forest were also collected using a canopy analyzer (Delta-T Devices Co., Ltd., Britain) [31]. The light intensity of each subplot was measured using a DSLR camera (EOS60D, Canon, Japan) equipped with a fisheye lens. The camera was mounted on a tripod at a height of 1.3 m. Three consecutive photos were taken using different exposure times, selecting the photo with the least exposure and best contrast. The data were analyzed using HemiView SR5 (version 2.1), Gap Light Analyzer (version 2.0), and Sidelook software (version 1.1.01). The light data included total radiation (TR), light transmittance (LT), canopy cover (CC), leaf area index (LAI), scattered radiation (SR), average leaf angle (ALA), and direct radiation (DR). Woody plant richness (WR) for each sample plot was calculated based on the recorded plant quantities. The data on soil physicochemical properties (Table S1), topography (Table S3), sunlight (Table S2), and plants for the three soil types are included in the Supplementary Materials.

2.3. Determination of Soil Microbial Communities

Total DNA was extracted from 0.5 g of fresh soil samples using the Fast DNA SPIN Extraction Kit (Mobio Laboratories, Carlsbad, CA, USA) for both bacterial and fungal analyses. DNA concentration was measured with a spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and samples were stored at −80 °C. Subsequently, PCR amplification and sequencing of the 16S rDNA V3–V4 region were performed using primers 515F and 806R [32]. For fungal analysis, ITS fragments were amplified using primers ITS1 and ITS2 [33]. PCR products were separated on a 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) [29]. Sequencing was conducted on the Illumina HiSeq platform (Illumina Inc., San Diego, CA, USA) using a paired-end (2 × 150 bp) strategy. Shenzhen Huada Gene Technology Co., Ltd., Shenzhen, China, performed sequencing and subsequent bioinformatic analyses.

2.4. Determination of the Physical and Chemical Properties of Soil

We dried the soil samples indoors until they reached a constant weight and then sieved them through a 60-mesh sieve. For the potentiometric determination of soil pH, we used a mixture of water and potassium chloride (1 M of KCl) in a ratio of 1:2.5 [34]. Soil organic matter (SOM) was determined using the potassium dichromate oxidation method [35], available nitrogen (N) was assessed using the alkaline hydrolysis diffusion method [35], available phosphorus (P) was measured using the molybdenum blue method [35], and soil moisture content (SMC) was determined by overnight oven-drying at 105 °C.

2.5. Classification of Soil Types

We observed and recorded the humus condition of the surface soil and the color of the subsoil at each sampling point. The wet sieving method was employed to calculate the proportion of different particle sizes in the soil samples [36]. Based on the measured physicochemical properties of the soil, we characterized different soil types according to the Chinese Soil Taxonomy [37]. The total of 120 plots were classified into 20 yellow-brown soil sites, 61 brown soil sites, and 39 cinnamon soil sites. Figure 1 illustrates the distribution.

2.6. Statistical Analyses

A Venn diagram was constructed using the VennDiagram package in R to display the counts of unique and shared operational taxonomic units (OTUs) across different soil habitats [38]. Significant differences between microbial communities in various soil types were identified using the Kruskal–Wallis test, and these differences were visualized with box plots. The vegan package in R (version 4.2.1) was used to perform Principal Coordinates Analysis (PCoA) to assess the impact of different soil types on fungal and bacterial α-diversity [39]. Additionally, a stacked percentage plot was generated using R (version 4.2.1) to illustrate the relative abundance of major bacterial and fungal communities at the phylum, class, and family levels across different soil types [40].

Redundancy analysis (RDA) was used to analyze the relationship between environmental factors and soil microorganisms for each soil type [41]. Pearson’s test was used to examine the autocorrelation among environmental variables, and Mantel test was applied to explore the relationship between the environmental variable matrix and microbial (OTU) matrix [42]. To investigate the relationships and interactions between bacterial and fungal species in different soils, a co-occurrence network analysis was performed in R, based on Spearman rank correlation and the abundance tables of microbial species. The co-occurrence patterns were examined based on significant correlations (p < 0.01) and strong correlations (Spearman’s ρ > |0.8|) [43]. The network was then visualized using Gephi, and various network topological features were computed [44,45].

Evolutionary diversity was analyzed using a null model approach to investigate the relative significance of deterministic and stochastic processes in shaping the fungal and bacterial communities within different soil types in the forest [46]. This analysis was grounded in the Beta Nearest Taxon Index (βNTI) and the taxonomic diversity index RCBray, quantifying alterations or turnover in phylogenetic and taxonomic diversity [47]. βNTI was utilized to depict the replacement of community phylogenetic composition over space and time [47]. When |βNTI| > 2, the observed bMNTD significantly deviates from the bMNTD null distribution, indicating that the replacement between two communities is predominantly driven by deterministic processes. βNTI > 2 and <−2 signify heterogeneous and homogeneous choices, respectively. When |βNTI| < 2, the observed developmental differences in the system result from a random process. This phenomenon encompasses homogeneous diffusion (RCBray < 0.95), diffusion-limited (RCBray > 0.95), and undominated (|βNTI| < 0.95) processes [48]. A random forest analysis was conducted to further assess the impact of environmental factors on the assembly processes of bacterial and fungal communities in different soil types [49]. This analysis utilized the βNTI values of each environmental variable and the Euclidean distance matrix, involving the community assembly of 999 decision trees and 99 permutations [50], and was implemented using the “randomForest” package in the R programming language.

3. Results

3.1. Relationship between Soil Microorganisms and Environment in Different Soil Types

The three soil types were classified as yellow-brown soils, brown soils, and cinnamon soils detected with 9705, 12,097, and 11,639 bacterial OTUs, respectively, and 2593, 4781, and 4884 fungal OTUs, respectively (Figure 2a). The Kruskal–Wallis rank sum test indicated significant differences in the richness of bacterial and fungal species among the three soil habitats (p < 0.01) (Figure 2b). Analysis of variance after Betadisper revealed significant differences in the community composition of bacteria (F = 14.47, p = 0.01) and fungi (F = 4.67, p = 0.03) among the different soil types (Figure 2c).

At the phylum level, Acidobacteria, Proteobacteria, and Verrucomicrobia were the dominant bacteria in all three soil types. Compared with that in yellow-brown soils, the proportions of Acidobacteria and Verrucomicrobia decreased and that of Proteobacteria increased in brown soils and cinnamon soils. For fungi, Ascomycota, Mortierellomycota, and Basidiomycota were dominant across the three soil types. Compared with that in yellow-brown soils, the proportion of Ascomycota decreased, and those of Mortierellomycota and Basidiomycota increased in brown soils and cinnamon soils (Figure 3).

At the class level, Spartobacteria, DA052, and Alphaproteobacteria were predominant among the bacterial communities across the three soil types. In brown and cinnamon soils, there was an increase in the proportions of Spartobacteria and DA052 compared to yellow-brown soils, while the proportion of Alphaproteobacteria decreased. Regarding fungi, Mortierellomycetes and Agricomycetes were the dominant classes across the three soil types. In brown and cinnamon soils, the proportion of Mortierellomycetes increased, while that of Agricomycetes decreased compared to yellow-brown soils (Figure 3).

At the family level, Chthoniobacteraceae predominated among bacteria across the three habitats. Its proportion decreased in brown and cinnamon soils compared to yellow-brown soils. Among fungi, Russulaceae, Mortierellaceae, and Sebacinaceae were the dominant families. In brown and cinnamon soils, the proportion of Sebacinaceae increased, while those of Russulaceae and Mortierellaceae decreased compared to yellow-brown soils (Figure 3).

3.2. Influence of Environmental Factors on Soil Microbial Communities

RDA results indicated that environmental factors accounted for 23.7% of the variation in bacterial species distribution and 20.41% of the variation in fungal species distribution in yellow-brown soils. In brown soils, environmental factors explained 24.68% of the bacterial distribution variation and 19.14% of the fungal distribution variation. In cinnamon soils, environmental factors accounted for 29.07% of the variation in bacterial species distribution and 19.72% of the variation in fungal species distribution. Among the soil types studied, cinnamon soils exhibited the most significant environmental influence on both bacteria and fungi (Figure 4a). According to the Mantel test results, pH and SLO were the environmental factors significantly affecting bacterial communities, and pH, SLO, and P were the environmental factors significantly affecting fungal communities in yellow-brown soils. In brown soils, SMC and SLO were the environmental factors significantly affecting bacterial communities, and pH and SOM were the environmental factors significantly affecting fungal communities. In cinnamon soils, SLO, pH, and P were the environmental factors significantly affecting bacterial community distribution, and N and LAI were the environmental factors significantly affecting fungal community distribution (Figure 4b).

3.3. Co-Occurrence Patterns of Soil Microbial Communities in Different Soil Types

Differences in the symbiotic networks of bacterial and fungal communities were found across the different soil types. The modularity index of bacterial and fungal communities was significantly higher in brown soils than in cinnamon soils and yellow-brown soils, indicating that the microbial community stability in brown soils is better. Meanwhile, the average degree of bacterial and fungal communities was significantly higher in cinnamon soils than in yellow-brown soils and brown soils, indicating that the diversity and complexity of microbial communities in cinnamon soils are higher. This finding aligns with the previous results on species richness (Figure 5b).

3.4. Assembly Processes of Soil Microbial Communities across Different Soil Types

Figure 6a indicates that deterministic processes (homogeneous selection and heterogeneous selection) and stochastic processes (homogenizing dispersal, undominated processes, and dispersal limitation) are the two major processes shaping the assembly of the forest’s soil microbial communities. From yellow-brown soils to brown soils to cinnamon soils, the proportion of stochastic processes in bacterial community assembly increased. Although the assembly of fungal communities also changed, the variation was relatively small (Figure 6a). Random forest analysis revealed that the SR of CC, DR, and TR were the main factors driving the assembly of bacterial and fungal communities (Figure 6b). This finding indicates that light primarily drives the community assembly of bacteria and fungi.

4. Discussion

4.1. Distribution of Microbial Communities in Different Soil Types and Their Influencing Factors

Soil types are one of the fundamental characteristics of soil and influenced by various environmental factors including vegetation, topography, sunlight, and soil physicochemical properties [51]. The habitats within different soil types can vary significantly. Results showed that the species richness in cinnamon soils and brown soils is higher than that in yellow-brown soils. Previous studies have shown that the nutrients in soil, such as organic matter, nitrogen, and phosphorus, directly affect the growth and metabolic activities of microorganisms [10,12,52]. The significant differences among the soil types may be due to the relatively low nutrient content in yellow-brown soils. Rich soil nutrients promote the growth and development of soil bacteria and fungi [53]. Rich nutrients support the proliferation and functionality of soil microorganisms [54]. Brown soils are rich in organic matter, and the decomposition of organic matter forms colloidal substances that help bind soil particles, thus creating a good soil structure. This soil structure facilitates water infiltration and drainage and gas exchange, providing good aeration that offers a favorable living environment for microorganisms [55]. RDA revealed that the number of significant factors influencing bacterial and fungal communities in brown soils is significantly higher compared with that in the other two soil types. This finding indicates that environmental factors have a pronounced and complex influence on the structure and composition of bacterial and fungal communities in brown soils. Environmental factors have a greater influence on the three soil types than on fungi, suggesting that bacterial communities are highly sensitive to environmental influences [56]. In general, fungal communities are more stable than bacterial communities in the face of environmental changes. Owing to their different metabolic pathways, fungi and bacteria are influenced by distinctly different driving forces [54]. Mantel test results showed that the bacteria in the three soil habitats are most affected by slope. This finding may suggest that the primary reason for the differences in bacterial communities among the three soil types is the influence of slope. In addition to slope, pH significantly affects soil bacterial communities, which is consistent with previous findings [57]. For fungal communities, the driving factors vary across different soil types, indicating that fungi are highly tolerant to environmental changes and can adapt to their environment [58].

4.2. Microbial Community Networks and Assembly in Different Soil Types

Soil microbial communities participate in a diverse and intricate network of interactions, including negative dynamics such as competition, predation, and parasitism and positive interactions including symbiosis [59]. This study found that bacterial and fungal communities exhibit the highest modularity in brown soils, suggesting that the microbial communities in brown soils have high stability, disturbance resistance, and ecological functionality diversity. It also implies that brown soils may promote the development of functional modules or communities where species show potential differentiation in ecological niches [60,61], which may be related to the changes in soil physicochemical properties, thereby stimulating the ecological niche diversification of soil microorganisms [62]. The bacterial and fungal communities have the highest average degree in cinnamon soils. Bacteria have the highest average path coefficient in brown soils, and fungi have the lowest in yellow-brown soils. This finding indicates that bacteria and fungi employ distinct strategies to adapt to various soil types. Although fungi are the least abundant in yellow-brown soils, their communities show close connections [63]. Meanwhile, bacteria show the opposite. This trend indicates that fungi have higher tolerance to environmental changes compared with bacteria, which is consistent with the above conclusions. This study also emphasizes that bacterial and fungal communities exhibit different strategies in their ecological networks across different soil types. Basing on the zero-sum model framework, we elucidated that deterministic and stochastic processes jointly drive the assembly of bacterial communities. In the three soil types, stochastic processes dominate the bacterial community assembly in brown soils but are the least dominant in yellow-brown soils. In contrast, fungal communities exhibit less variation in their assembly processes across different soil types, highlighting the greater responsiveness of bacterial communities to soil types. This phenomenon may be due to the relatively fast generation rate and high dispersal ability of bacteria [64,65]. For fungi, the dominance of stochastic processes may be due to their strong local characteristics and limited dispersal ability [66,67]. Some studies suggest that the contributions of deterministic and stochastic processes may also depend on the specific microbial communities and ecosystems being studied [67]. Random forest analysis indicates that compared with topography and soil physicochemical properties, light is a more significant driver of soil bacterial and fungal communities. Previous research has shown that forest canopy light is a major factor influencing microbial communities [68,69], and the results of this study emphasize this point. Studies suggest that the pathways through which light affects soil microbes can be diverse. Plants transfer nutrients to the soil food web through litter and root exudates, while light influences plant community composition through asymmetric resource competition [70]. Consequently, differences in plant communities can affect the nutrient supply for soil microbial communities [71]. Furthermore, other studies have shown that different light conditions can alter the temperature and humidity under the canopy, thereby changing the microhabitat and impacting soil microbial communities [72]. These factors may all influence how light shapes the assembly of soil microbial communities.

5. Conclusions

This study provides important insights into the patterns of bacterial and fungal communities in different soil types in the temperate forest of Baiyun Mountain Forest Park. Results show that the community composition of bacteria and fungi differs significantly among different soil types, with bacteria being more affected by environmental factors compared with fungi. Fungal communities are more stable than bacterial communities across different soil types. The assembly processes of bacterial and fungal communities across different soil types are primarily driven by light.

Author Contributions

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

Data Availability Statement

For privacy reasons, the data presented in this study are available upon request to the corresponding authors.

Acknowledgments

Special thanks to Wang Nan and other senior students for their contributions in the preliminary experiment.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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.

Figures

Figure 1. (A) The red dot indicates the location of the sampling site in China. Maps of the soil habitats in 20 m × 20 m subplots within the Baiyunshan permanent plot. The three colors represent the three soil types. The black solid line is the contour map of the plot. (B) Pictures of the three soil types.

Figure 2. (a) The number of OTUs and their overlap between soil bacteria and fungi across the three soil types. (b) Box plots of species richness of bacteria and fungi across three soil types. (c) PCoA analysis of bacteria and fungi across three soil types.

Figure 3. Composition of bacteria and fungi at the phylum, class, and family levels across the three soil types.

Figure 4. (a) Redundancy Analysis (RDA) of bacterial and fungal community composition and environmental factors in the three soil types. The red arrows indicate significant environmental factors (p < 0.05). (b) Environmental associations of bacteria and fungi in the three soil types. Color gradients represent correlation coefficients, and the thickness of the lines indicates the strength of the correlations.

Figure 5. (a) The co-occurrence networks of soil bacteria and fungi communities under different soil types. The color of each node represents a bacterial or fungal phylum. Each edge represents the correlation between two nodes (p < 0.05), with red lines indicating positive correlations and green lines indicating negative correlations. (b) Network topological characteristic parameters of bacterial and fungal communities in the three soil types.

Figure 6. (a) Depiction of the contributions of both stochastic and deterministic processes to community assembly. The inner circle illustrates these contributions, while the outer circle delineates the specific ecological processes associated with each. (b) The significance of environmental factors on βNTI.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15071110/s1, Table S1: Soil physical and chemical property information for three Soil Types. Table S2: Illumination information for three soil types. Table S3: Topographic information for three soil types.

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