1. Introduction
In tourist areas, infrastructure construction and recreational tourism activities that have a certain impact on the ecological environment is called tourism interference [1,2,3]. One of the environmental factors most affected by tourism disturbance is soil [4,5]. Soil is susceptible to a series of man-made activities, such as tourism development, trampling, and garbage discharge, all of which cause it to gradually degrade [6]. Related research has shown that the surface soil of Dawei Mountain in Hunan province is slightly polluted; microorganisms have played an important role in soil health, especially in regard to the degradation of heavy metals [7]. In recent years, multidisciplinary microbiology has regarded the microbial community structure as the focus of the subject field, mainly due to its important role in maintaining the ecosystemic equipoise and the characteristics and functions of the soil. Therefore, from a soil ecological balance and biodiversity perspective, it is important to understand the impact of tourism disturbance on the soil microbial community structure in ecologically fragile areas.
At present, there are few domestic or foreign studies on the relationship between tourism disturbance and soil microbial communities. Li Sensen et al. [8] used the Songjiang Wetland as their research object. The study found that a large number of tourists trampled on the wetland vegetation, which resulted in a significant change in the structure of the microbial community [8]. With the increase in travel disturbance time, the structure and quantity of microbial community in the tourist area soil changes significantly, which seriously affects soil function performance [9,10,11,12,13,14]. The grazing behavior associated with tourism has a significant impact on the soil microbial activity and microbial community in the Suoxiyu Scenic Area, Zhangjiajie, destroying the microbial diversity of the local soil [15,16]. However, it is unclear how different tourism disturbance intensities affect microorganism population or how the soil microbial species trends change as a result of different disturbance intensities. There is a lack of research analyzing the impact of tourism interference on the structure of the soil microbial community at the micro level. This study chose the Dawei Mountain National Forest Park in China as its research area. The aim is to explore the impact of different degrees of tourism disturbance on soil microbial diversity and structural composition on Dawei Mountain and to analyze the response characteristics of microbial community composition and structure under different interference intensities. This study provides a scientific theoretical basis for soil health restoration and ecological tourism planning in scenic areas.
2. Materials and Methods
2.1. Overview of the Research Area
Our work was approved by the Dawei Mountain National Forest Park Management Office (Supporting Information 1). This study is based at Dawei Mountain National Forest Park (114°01′51″–114°12′52″ E, 28°20′54″–28°28′47″ N, Supporting Information 2), which is located in Hunan province, southern China. It has a mid-subtropical monsoon climate, with an annual average temperature of 11–16 °C and an average annual rainfall of 1800–2000 mm. Qixingling, which is the peak of the scenic area, is 1607.9 m above sea level. It was rated as a national 4A scenic spot in 2007, a national forest park in 2012, and was a national eco-tourism demonstration area in 2014. In 2020, a total of 860,000 tourists were received throughout the year, with the daily passenger flow on weekends and holidays exceeding 4000 people. The peak season (April–October) accounted for about 80% of the total annual tourist numbers. In 2020, it generated 36.8 million yuan in tourism income.
2.2. Methods
2.2.1. Quadrat Setting and Soil Collection
Daweishan National Forest Park is a mountain-type scenic area that attracts many sightseeing tourists. Recreational activities are mainly carried out along the trails and scenic spots. Considering the geographical distribution characteristics of Dawei Mountain National Forest Park, the feasibility of sampling and the principle of representativeness, we chose ecologically fragile rhododendron forests with the same altitude, dominant vegetation, topography, landforms, adjacent regions, and red soil as the test plots. According to the method of dividing tourism interference intensity outlined by ANDRES [17], Wang Shuai [18], and Wang Shutian [19], the plots were divided according to the distance of the tour trails in the core scenic spots. The closer the distance, the greater the degree of interference to the community. Preliminary investigations have shown that the impact of tourism interference on the soil in the Dawei Mountain Scenic Area mainly occurs within 3 m of the edge of the tour route, with obvious boundaries in the interference area, and mainly in heavily and moderately disturbed areas. We ensured as far as possible that there was no obvious difference in the micro-terrain environment, setting up continuous sample squares and different disturbance intensities based on the distance between each sample square and the path. In this study, the intensity of tourism disturbance was divided into three ordinal levels according to the distance from near to far. The three levels were: (1) high disturbance: 1 m away from the edge of the trail, severely trampled, and no deciduous weeds or other shrubs on the surface; (2) middle disturbance: 3 m away from the edge of the trail, with few traces of tourism activities and with the ground covered by a small amount of shrubs and weeds; (3) the control area: 6 m away from the edge of the tour lane, with the ground layer growing vigorously, almost no tourists entering, and no trace of tourism disturbance. Soil collections were taken at Dawei Mountain National Forest Park on 16 August 2019.
To collect the required samples, we removed the litter, leaves, and floating soil, plus other visible impurities from the soil surface, collected the soil from the target depth, and then passed it through a 2 mm mesh screen. We then sun-dried the filtered sample, extracted 5–10 g, stored it in a sterile centrifuge tube, and sealed it immediately. The soil tubes were stored in a sampling box with dry ice and then refrigerated. Another 200 g of soil was placed in a Ziplock bag for physical and chemical properties analyses.
2.2.2. Determination of Soil Physical and Chemical Properties
The physical and chemical soil indicators measured included pH, organic matter, total nitrogen, total phosphorus, total potassium, available phosphorus, available potassium, nitrate nitrogen, ammonium nitrogen, catalase, invertase, and alkaline phosphatase. The samples underwent a range of treatments, including pH testing, the potassium dichromate volumetric method-external heating method, the semimicro Kjeldahl method, the molybdenum antimony colorimetric method, the flame photometer method, the molybdenum antimony anti-colorimetric method, naphthalene ethylene diamine hydrochloride spectrophotometry, and salicylic acid hypochlorite spectrophotometry. Furthermore, each enzyme was determined by the corresponding kit (Suzhou Keming Biotechnology Co., Ltd. Suzhou, China) [20,21,22].
2.2.3. DNA Extraction, PCR Amplification, and Illumina MiSeq Sequencing
As per the instructions of FastDNA® SPIN Kit (MP Biomedicals, Irvine, CA, USA) for the total DNA extraction of microbial communities, 1% agarose gel electrophoresis was used to detect DNA quality, while NanoDrop2000 was used to determine DNA concentration and purity [23,24]. PCR amplification was utilized on the 16S rRNA gene V3–V4 variable regions using 806R and 338F [25,26], with ITS1F and ITS2R used to amplify the ITS1 region of the fungal rDNA gene [27]. There were three replicates for each sample. We mixed the PCR products of each sample, used 2% agarose gel to recover the PCR products, purified the recovered products using AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and detected the products using 2% agarose gel electrophoresis. A Quantus™ Fluorometer (Promega, Madison, WI, USA) was used to detect quantitatively recovered products [28]. The NEXTFLEX Rapid DNA-Seq Kit was used to build a library. Finally, sequencing was performed on the platform (MiSeq PE300, Shanghai Meiji Biomedical Technology Co., Ltd., Shanghai, China).
2.2.4. Data Processing
We employed the following software for data processing: Fastp software (for quality control of the original sequencing sequence), FLASH software (for sequence splicing), UPARSE software (97% similarity) (for performing OTU clustering and chimera removal) [29,30], and RDP classifier software (using a 70% threshold to compare the fungus and bacteria database to annotate the species information for each sequence). Then, Mothur software was used to analyze and evaluate the OTU similarity level of 97% (0.97) and Student’s t-test was used to calculate the difference in the α diversity index between samples. R software was used to analyze the differences in the abundance of the main dominant groups of soil microorganisms and create related charts. SPSS was used for variance analysis and correlation analysis and Canoco (Windows 4.5) was used for redundancy analysis (RDA) and principal component analysis (PCoA).
3. Results
3.1. The Influence of Tourism Disturbance Activities on the Main Physical and Chemical Properties of Soil
The physical and chemical soil properties analyzed under different interference levels showed that there were significant differences in soil bulk density, pH value, ammonium nitrogen, catalase, invertase, alkaline phosphatase, and nitrate nitrogen among different tourism disturbance groups (p < 0.05). There was no significant difference in total potassium and available phosphorus indicators (p < 0.05) (Table 1). It can be seen that tourism activities with different levels of disturbance have a greater impact on the physical and chemical properties of the soil.
3.2. The Influence of Tourism Disturbance Activities on the Composition of Microbial Species
3.2.1. The Number of Microorganism Species at Different Soil Levels
High-throughput sequencing of 18 soil samples showed that the magnitudes of the effective tags (after filtering the chimera, it was finally used for effective tags for subsequent data analysis) of the bacterial and fungal communities were 482,645 and 584,611, respectively. The effective bases (number of bases in effective tags) were 199,333,806 bp and 144,740,450 bp, respectively, and the average effective sequence lengths were 413 bp and 247 bp, respectively. Species annotation of the sequencing results showed that a total of 2495 OTUs in the tested soil bacteria samples belonged to 817 species in 28 phyla, 78 classes, 190 orders, 303 families, and 448 genera. In addition, 1231 OTUs of fungi belonged to 12 phyla, 30 classes, 64 orders, 145 families, and 221 genera. Under different intensities of tourism disturbance, the species composition of the soil microbial communities was different among the groups (Table 2).
At lower tourism interference intensities, there were significantly more bacteria than fungi in the soil of Dawei Mountain, with the bacteria approximately three times as large as the fungi. For example, the control group had the most species of bacteria, with 701 species identified. The middle disturbance group had the fewest species of fungus, with 126 identified. In other words, tourism disturbance had a greater impact on the species attributes of soil microorganisms as it intensified. The number of species in the control group was significantly larger than that in the middle and high disturbance groups. However, there was little difference in species composition between the middle and high disturbance groups (Table 2).
3.2.2. Similarity of Soil Microbial Species under Different OTU Levels
By comparing the species similarity index between plots with different tourism interference levels, this study analyzed the impact of tourism interference on the changes in soil community species composition and visually displayed the unique and shared OTUs among the groups. Venn diagram figures based on the OTU analysis results are shown in Figure 1a,b. For bacterial and fungal communities, there were a total of 1103 and 119 OTUs in the three groups, respectively; with 228 and 526 OTUs unique to the control group, respectively; with 136 and 163 OTUs unique to the middle disturbance group, respectively; and with 296 and 130 OTUs unique to the high disturbance group, respectively. The statistical results of the number of OTUs in the three groups showed that the CK > MD > HD, with the high disturbance group having the least total amount of OTUs. Among the bacterial communities, the similarity index between the CK group and the middle disturbance group was the highest (82.18%), the similarity index between the CK group and the high disturbance group was the lowest (69.10%), and the species similarity index between the different interference levels did not reach the significance level (p > 0.05). For the fungal community, the similarity index between the CK group and the middle disturbance plot was the highest (45.58%), the similarity index between the severe interference group and the middle disturbance plot was the lowest (26.62%), and the species similarity index between the plots with different interference levels reached a significant level (F = 7.122, p = 0.026). In general, compared with the bacterial community, the fungal community species similarity index of different interference groups was smaller, i.e., both less than 0.456, which means that as the degree of interference increases, the composition of the fungal community will show more obvious differences in the succession process.
3.3. The Influence of Tourism Disturbance Activities on the Structure of the Soil Microbial Community
Among the bacterial phyla of soil bacteria, there were mainly Acidobacteria, Chloroflexi, Proteobacteria, and GAL15, which accounted for 28.44%, 25.50%, 24.44%, and 6.07%, respectively. In the class of bacteria, there were mainly Acidobacteria, AD3, and Alphaproteobacteria, which accounted for 26.51%, 12.99%, and 12.85%, respectively. At the level of bacterial phyla, there were significant differences between the control group and severe and moderately disturbed soils in the phyla Chloroflexus, GAL15, Rokubacteria, and Blastomonas (p < 0.05) (Figure 2a).
Among the soil fungi, there were mainly Ascomycota, Basidiomycota, Mortierellomycota, and unclassified k fungi, which accounted for 34.56%, 28.75%, 3.64%, and 32.68%, respectively. In the fungal class, there were mainly Agaricomycetes, unclassified p Ascomycota, and unclassified k fungi, which accounted for 27.38%, 17.46%, and 33.26%, respectively. For phyla, the relative abundance of Ascomycota, Glomus, Basidiomycota, and unidentified fungi showed significant differences between the control group and the severe and middle disturbance groups (p < 0.05) (Figure 2b).
The linear discriminant analysis (LDA) effect size method (LEfSe) supports high-dimensional classification and comparison, and can be used to screen species that are most likely to explain differences between groups. First, the non-parametric Kruskal–Wallis rank sum test was used in multiple samples to screen species with significant differences in abundance among different groups. Then, based on the significantly different species obtained, paired Wilcoxon rank sum tests were used to establish differences between subgroups analysis. Finally, linear discriminant analysis (LDA) was used to evaluate the effect size of each species with a significant difference; using bacteria |LDA| > 3.5, fungus |LDA| > 4, and p < 0.05 as the difference screening threshold, obtaining species with significant differences in abundance between groups, and visualizing the results. The results of the LEfSe analysis of the bacteria and fungi are shown in Figure 3. For bacteria, we selected species with an LDA score greater than 3.5, and for fungi, we selected species with an LDA score greater than 4. These scores served as a biomarker representing each group to estimate the impact of the abundance of each component (species) on the difference effect. Biomarker species with significant differences in bacterial communities included 1 phylum, 2 classes, 12 orders, 14 families, and 17 genera. Biomarker species with significant differences in fungal communities included 4 phyla, 5 classes, 5 orders, 6 families, and 10 genera. Whether it was a bacterial community or a fungal community, microbial species with significant differences in the severe interference group were the most abundant. The different species of bacteria were mainly WPS 2 and Nitrospira phyla in the severe interference group (Figure 3a). At the fungal community phylum level, the different species were mainly Basidiomycota in the severe interference group, unclassified fungi in the middle disturbance group, and Mortierellomycota in the control group (Figure 3b).
Principal coordinate analysis (PCoA), which was based on the Bray–Curtis distance matrix, was used to determine the differences in the structure of soil bacterial communities in different interference groups. The results showed that, no matter whether bacteria or fungi, the distance between the two organisms in soil samples undergoing different treatments is quite different and that the community structure of bacteria and fungi can be significantly separated under different interference methods (p = 0.05). This result showed that tourism disturbance changed the structure of the soil microbial community. Bacterial PC1 contributed 78.85% of the changes in bacterial community structure and PC2 contributed 10.33% of the changes in bacterial community structure (Figure 4a). Fungal PC1 contributed 51.35% of the fungal community structure change and PC2 contributed 42.19% of the fungal community structure change (Figure 4b).
3.4. The Relationship between the Degree of Tourism Disturbance and the Alpha Diversity of Soil Community
The ACE index, the Smith and Wilson index, the Qstat index, and the coverage index, which reflect the abundance, evenness, diversity, and coverage of a community, respectively, were selected successively to determine the α diversity of the samples. The results are shown in Table 3. The sequencing coverage of bacteria and fungi was greater than 98%, which indicated that the sequencing depth was close to saturation. Almost all of the species in the soil were detected. The sequencing results can more accurately reflect the presence of bacteria and fungi in the tested soil. Regardless of bacteria or fungi, the order reflecting the abundance index (ACE) and diversity index (Qstat) of the community was CK > MD > HD, which indicated that as the degree of tourism interference increased, the abundance and diversity of bacterial and fungal communities both declined. There was no significant difference in the diversity index (Qstat) of the bacterial community between the control group and the middle disturbance group. However, there were significant differences between the control and middle disturbance and the high disturbance groups, while the ACE index (ACE) was significantly different in the three groups of plots with different degrees of interference. The ACE index and diversity index (Qstat) of the fungal community control group were significantly higher than those of the other two groups, while there were significant differences in the three groups with different interference levels (Table 3).
3.5. The Influence of Physical and Chemical Indexes of Soil on Microbial Community
Redundant analysis (RDA) was used to evaluate the impact of environmental indicators on the microbial community in tourism-disturbed areas. The results are shown in Figure 5. For the bacterial community, the three soil environmental factors of pH, catalase (CAT), and organic matter (OM) had a greater impact than other factors on the soil microbial bacterial community. The first axis of the redundant analysis reflects 75.96% of all information and the second axis explains 3.54%, for a total of 79.5%. In other words, the first two axes can explain the impact of environmental indicators on the bacterial communities in the tourism interference area, and they have the closest relationship with the first axis. The order of importance of soil factors on the occurrence of bacterial communities was as follows: pH (r2 = 0.9878, p = 0.001) > catalase (CAT) (r2 = 0.976, p = 0.001) > OM (organic matter) (r2 = 0.819, p = 0.028). Among them, catalase was positively correlated with Acidobacteria, Alphaproteobacteria, Planctomycetacia, Gammaproteobacteria, Actinobacteria, and Deltaproteobacteria, while it was negatively correlated with Ktedonobacteria, AD3, norank p GAL15, and NC10. Potential of hydrogen (pH) was negatively correlated with Acidobacteria, Alphaproteobacteria, Planctomycetacia, Gammaproteobacteria, Actinobacteria, and Deltaproteobacteria and positively correlated with Ktedonobacteria, AD3, norank p GAL15, and NC10. OM (organic matter) was negatively correlated with Acidobacteria, Alphaproteobacteria, and Deltaproteobacteria and positively correlated with Ktedonobacteria (Ktedonobacteria) and AD3 (Figure 5a). For the fungal community, the four soil environmental factors, pH, NH (ammonium nitrogen), total phosphorus (TP), and organic matter (OM), had a greater impact on the soil microbial fungal community. RDA showed that the first axis explained 94.37% of all information and the second axis explained 2.50%, for a total of 96.87%. The first two axes can well reflect the relationship between microbial fungal communities and soil factors. The order of importance of soil factors on the occurrence of bacterial communities was as follows: ammonium nitrogen (r2 = 0.987, p = 0.002) > pH (r2 = 0.911, p = 0.007) > total phosphorus (r2 = 0.756, p = 0.01) > organic matter (r2 = 0.748, p = 0.035). Among them, total phosphorus was positively correlated with Agaricomycetes and Archaeorhizomycetes and negatively correlated with unclassified k fungi. Ammonium nitrogen was negatively correlated with Agaricomycetes and Archaeorhizomycetes and positively correlated with Sordariomycetes, Mortierellomycetes, and Dothideomycetes. Organic matter (OM) was positively correlated with Sordariomycetes, Mortierellomycetes, and Dothideomycetes and negatively correlated with Leotiomycetes (Figure 5b).
4. Discussion
4.1. Response Characteristics of Soil Microbial Communities to Disturbance Modes
As a key part of the biogeochemical cycle, the soil microbial community is an important sensitivity index for underground ecosystems to measure their response to environmental disturbances. Tourism disturbance usually changes the composition of the microbial community, which cannot recover immediately afterwards. Changes in the composition of the microbial community often affect changes in the ecosystem and may even be directly related to the balance and stability of the ecosystem [31]. Zhao et al. found that after removing the shrubs and grasses in the lower layer of the forest, the corresponding soil microbial community structure composition changed significantly, especially the fungal community [32].
Li Sensen et al. [8] studied the effects of four disturbance modes of agriculture, industry, tourism, and protection on the structure and function of soil microbial communities in wetlands. The results showed that agricultural, industrial, and tourism disturbance changed the composition and structure of soil microbial communities, significantly reducing microbial biomass and diversity, and leading to poor stability of the wetland ecosystem. It also proved that the soil microbial community structure could be used as a sensitive biological indicator for rapid warning and early indication of the health of the urban wetland ecological environment. Wang Shuai et al. [18] believed that agricultural tourism activities in the low mountain and hilly areas of eastern Sichuan could lead to the decline of microbial abundance and diversity, with the impact on microbes having the following order: bacteria > actinomycetes > fungi. In the impacted soil microbial community, the biomass of bacteria, Gram-positive bacteria, Gram-negative bacteria, and fungi showed a decreasing trend as the distance from the path decreased. Duan Guilan [33] have shown that tourism disturbance significantly reduced soil enzyme activity, microbial abundance, and diversity. The impact of tourism disturbance on the soil ecosystem was negatively correlated with the distance of tourist activity centers such as tour routes and trails. Therefore, in general, tourism interference will have a greater impact on the types, quantity, diversity, abundance, and similarity of soil microorganisms.
Our study found that the predominant phyla of bacteria in the soil were Acidobacteria, Chloroflexus, Proteobacteria, GAL15, and Actinomycetes, while the predominant phyla of fungal communities were Ascomycota, Goncobacteria, and Basidiomycota. These bacterial communities were also detected in other ecosystems and dominated the structure of the soil microbial community [34,35]. This indicated that the above-mentioned taxa may be widely involved in the process of soil ecology regulation.
The analysis of the relative abundance difference of bacterial community populations in this study showed that the dominant class of bacteria in the three groups of soils was the α-Proteobacteria of the Proteobacteria, and that the α-Proteobacteria belonged to soil nitrogen-fixing microorganisms. The relative abundance decreased significantly in the severely disturbed plots, which was significantly different from the moderately disturbed and control plots.
The LEfSe differential species analysis of bacteria showed that the different species of bacteria were mainly the Nitrospira phylum in the high disturbance group and that the action of nitrifying and denitrifying bacteria would cause the loss of soil nitrogen. The analysis of relative abundance differences of fungal community populations showed that the dominant fungal phyla in the three groups of soils were Ascomycota and Basidiomycota. The relative abundances of the two groups were significantly different in the three plots and were mainly manifested as high disturbance zone > control zone > middle disturbance zone. At the same time, the different species of fungal LEfSe showed that the different species of fungi were mainly Ascomycota and Basidiomycota in the severely disturbed group and that the Ascomycota and Basidiomycota corresponding to these two phyla belonged to higher fungi. They play a major role in the decomposition of soil material [36].
In the severely disturbed soil, the input of foreign substances such as garbage and beverages deposited by tourists provided nutrients for Ascomycota. In the control group, plants and litter provided nutrients for the growth of microorganisms in Ascomycota and Basidiomycota. Therefore, the difference in the relative abundance of Ascomycota and Basidiomycota among the three groups was obvious. The microbial similarity index can be used to measure the stability of soil ecosystems [37]. In different tourism disturbance areas, there may be significant differences in the composition of the microbial community structure in the soil. Thus, within the severely disturbed group, the low similarity means that the resistance and stability of the microbial community are weaker [38].
In this study, the characteristics of the soil microbial community changed significantly with the influence of tourism disturbance, which indicates a close correlation between soil microbes and tourism disturbance, especially regarding the significant impact on fungal community structure. The number of microorganisms with important functions was reduced, thereby deteriorating the physical environment of the soil.
4.2. The Influence of Physical and Chemical Indexes of Soil under Tourism Disturbance on Bacteria
As an important part of the ecosystem, soil is easily degraded by a series of man-made activities such as tourism development activities, trampling, garbage discharge, etc., which affects the growth and development of vegetation and may eventually lead to the degradation of soil and vegetation. It affects the entire tourism environment and is not conducive to the sustainable development of eco-tourism [39]. The quality of soil conditions has an important impact on the survival of the soil ecosystem, the plant ecosystem that grows on it, its animal communities, and soil microbial communities, while it also plays an important role in the health and stability of the tourism ecosystem. The impact of tourism disturbance on the soil environment has the characteristics of diverse ways, prominent effects, and wide impacts [40].
Under the effect of tourism disturbance, changes in the soil environment and nutrient input may cause changes in the soil organic matter content and decrease the stability of the microbial community structure, thereby affecting the characteristics of the soil microbial structure. Yang et al. found that the decline in soil-available phosphorus, available nitrogen, total nitrogen, and phosphorus may be the main factors that cause a decline in soil microbial richness [41]. Fierer et al. believed that the main factors for the distribution of soil fungi were affected by soil organic matter and total soil nitrogen; that the distribution of actinomycetes was limited by soil available phosphorus, organic matter, and total phosphorus; and that the distribution of bacteria was mainly affected by soil total phosphorus and pH [42].
In addition, the distribution of microorganisms is affected by catalase in the soil, which is closely related to the intensity of biochemical reactions in the soil. The RDA of the bacterial community population and the physical and chemical properties of the soil showed that pH, catalase (CAT), and organic matter (OM) have a greater impact on the soil microbial bacterial community, while pH, NH (ammonium nitrogen), total phosphorus (P), and organic matter (OM) have a greater impact on soil microbial fungal communities. Alkaline materials such as cement and lime, which are often used during the construction of scenic spots and pavilions, increase the pH in high disturbance areas. RDA showed that pH was negatively correlated with the relative abundance of α-Proteobacteria and Acidophilus, while the abundance of α-Proteobacteria and Acidophilus showed a significant decline with increasing interference.
Studies have found that Blastomonas has a strong denitrification function and its relative abundance decreases with increasing nitrogen levels [43,44]. This study found that the ammonium nitrogen in the high disturbance group was the lowest, with the relative abundance of Blastomonas the highest in the high disturbance group. This study found that soil catalase was closely related to changes in soil microbial bacterial communities. There have also been studies showing that the class Geoglossum can coexist with plants and that its relative abundance is significantly negatively correlated with the amount of organic matter (OM). Therefore, it can be inferred that the symbiosis pattern of the class Geoglossum in soils with high-organic-matter content is weakened, which leads to the significantly reduced relative abundance in high-organic-matter soils [45,46,47].
5. Conclusions
Under the influence of tourism disturbance, the composition of the bacterial community and the similarity of the bacterial community in the soil of different groups of sample plots underwent significant changes, with the alpha diversity of the soil bacterial community in the severe tourism disturbance area significantly reduced. The relative abundances of Chloroflexus, GAL15, Blastomonas, etc., included in the dominant bacterial phyla showed significant differences among the different interference groups. The relative abundance of all major dominant fungal phyla was significantly different after tourism interference. In addition, tourism interference can cause changes in soil environment factors and reduce the availability of soil nutrients, thereby resulting in a decrease in the stability of soil microbial communities and undermining the balance of soil ecosystems.
Conceptualization, Q.L. and M.D.; methodology, F.L.; software, Q.L.; validation, Q.L., M.D. and F.L.; formal analysis, Q.L.; investigation, M.D.; resources, Q.L.; data curation, Q.L.; writing—original draft preparation, Q.L.; writing—review and editing, M.D.; visualization, M.D.; supervision, F.L.; project administration, M.D.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.
This research was funded by [Hunan Provincial Science and Technology Commissioner Serving Rural Revitalization Project] grant number [2021NK4274].
Not applicable.
Not applicable.
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
Without the support of the staff of Daweishan National Forest Park, extensive field work was impossible. We are very grateful for the hard work of the staff at the park.
The authors declare no competing interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Venn diagram denoting the distribution of soil bacteria (a) and fungi (b) under different tourist disturbance levels.
Enlarge this image.Figure 2. Significance tests for the relative abundance of soil bacteria (a) and fungi (b) under different tourist disturbance levels.
Enlarge this image.Figure 2. Significance tests for the relative abundance of soil bacteria (a) and fungi (b) under different tourist disturbance levels.
Enlarge this image.Figure 3. LEfSe analysis of bacterial (a) and fungal (b) communities of soil under different tourism disturbance levels.
Enlarge this image.Figure 4. PCoA cluster analysis of bacterial (a) and fungal (b) communities under different tourism disturbance levels.
Enlarge this image.Figure 5. RDA analyses of soil physical and chemical factors and soil bacterial (a) and fungal (b) microbial communities.
Analysis of the main physical and chemical properties at different soil disturbance levels.
| NO | Moisture |
Bulk |
pH | Organic |
Total N/g·kg−1 | Total P/g·kg−1 | Total K/g·kg−1 |
|---|---|---|---|---|---|---|---|
| CK | 21.50 ± 0.19a | 1.47 ± 0.04b | 7.72 ± 0.29c | 14.44 ± 0.74a | 1.48 ± 0.2 | 0.23 ± 0.06a | 39.46 ± 1.15 |
| MD | 20.47 ± 0.49a | 1.24 ± 0.05c | 8.05 ± 0.21a | 10.14 ± 0.11b | 1.49 ± 0.14 | 023 ± 0.01ab | 39.01 ± 0.8 |
| HD | 6.94 ± 0.60b | 1.60 ± 0.17a | 8.27 ± 0.18b | 12.95 ± 0.80a | 1.48 ± 0.27 | 0.24 ± 0.04a | 38.83 ± 0.49 |
| NO | NH+4-N/mg·kg−1 | Available P/mg·g−1 | Available K/mg·g−1 |
Catalase/
|
Invertase/
|
Alkaline phosphatase/umol·d−1·g−1 | NO−3-Nmg·kg−1 |
| CK | 43.23 ± 0.02b | 0.03 ± 0.01 | 0.06 ± 0.006a | 18.69 ± 0.98a | 11.69 ± 0.52a | 7.40 ± 0.30a | 56.56 ± 0.63a |
| MD | 48.51 ± 0.52a | 0.03 ± 0.01 | 0.04 ± 0.006b | 22.24 ± 0.28b | 6.38 ± 0.64b | 4.55 ± 0.44b | 85.15 ± 2.25b |
| HD | 32.28 ± 0.11c | 0.03 ± 0.01 | 0.03 ± 0.002b | 12.03 ± 0.74c | 4.45 ± 0.22c | 2.47 ± 0.10c | 75.97 ± 1.59c |
Note: Data were expressed as the mean ± SE. The different lowercase letters within the same row indicate a significant difference exists (p < 0.05).
Species notes of soil microorganisms at different classification levels.
| Group | Phylum | Class | Order | Family | Genus | Species | OTU | |
|---|---|---|---|---|---|---|---|---|
| Bacteria | CK | 26 | 73 | 176 | 277 | 396 | 701 | 1904 |
| MD | 27 | 66 | 163 | 256 | 366 | 646 | 1829 | |
| HD | 25 | 71 | 170 | 254 | 359 | 629 | 1700 | |
| Fungi | CK | 12 | 26 | 56 | 120 | 173 | 212 | 896 |
| MD | 9 | 21 | 46 | 89 | 108 | 126 | 530 | |
| HD | 8 | 22 | 42 | 62 | 105 | 127 | 336 |
Alpha diversity indices for bacteria communities in bacterial and fungi communities of soil under different tourism disturbance levels.
| Group | ACE Index | Qstat | Smithwilson Index | Coverage/% | |
|---|---|---|---|---|---|
| Bacteria | CK | 1889.7 ± 51.82a ** | 384.57 ± 16.975a ** | 0.4438 ± 0.002b * | 98.76 ± 0.04a * |
| MD | 1799.7 ± 13.73b ** | 366.47 ± 12.634a ** | 0.4443 ± 0.003b * | 98.84 ± 0.02b ** | |
| HD | 1595.3 ± 69.58c ** | 317.03 ± 22.759b ** | 0.4511 ± 0.002a * | 99.03 ± 0.08c * | |
| Fungi | CK | 615.54 ± 44.29a ** | 154.99 ± 20.94a ** | 0.4810 ± 0.007c ** | 99.84 ± 0.01c * |
| MD | 394.95 ± 18.79b ** | 78.346 ± 2.49b ** | 0.5010 ± 0.006b ** | 99.886 ± 0.01b ** | |
| HD | 218.38 ± 17.82c ** | 38.795 ± 8.11c ** | 0.5259 ± 0.006a ** | 99.951 ± 0.01a * |
Note: **: significantly correlated at the 0.01 level (both sides), *: significantly correlated at the 0.05 level (both sides).
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1 College of Tourism, Central South University of Forestry and Technology, Changsha 410004, China;