Next Article in Journal
Spatial Pattern of Deadwood Biomass and Its Drivers in a Subtropical Forest
Previous Article in Journal
Influence of Vegetation Types on the C, N, and P Stoichiometric Characteristics of Litter and Soil and Soil Enzyme Activity in Karst Ecosystems
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 4
10.3390/f14040772
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Response of Soil Microbial Communities to Elevation Gradient in Central Subtropical Pinus taiwanensis and Pinus massoniana Forests

by
Kexin Huang
1,
Jian Xiang
1,
Yuying Ma
2,
Jinping Cheng
1,
Jie Gu
1,
Meng Hu
1,3,
Yuan Yang
1,3,
Yanming Fang
1,3,
Genmei Wang
1 and
Huanchao Zhang
1,*
1
Co-Innovation Center for Sustainable Forestry in Southern China, College of Forest, Nanjing Forestry University, Nanjing 210037, China
2
Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
3
College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(4), 772; https://doi.org/10.3390/f14040772
Submission received: 8 March 2023 / Revised: 5 April 2023 / Accepted: 6 April 2023 / Published: 9 April 2023
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
In forest ecosystems, elevation gradient is one of the most influential factors on soil characteristics, vegetation types, and soil microorganisms. However, it remains unclear how the elevation gradient and the soil environment under its influence affect soil microbial communities under two distinct vegetation types. In this study, high-throughput sequencing technology from Illumina was utilized to examine the response of soil microbial communities to elevation and their driving factors in forests of Pinus taiwanensis and Pinus massoniana in various Jiangxi Province locales. The results demonstrated that the elevation gradients of the two pines had significant effects on soil organic carbon (SOC) and total nitrogen (TN), both in unimodal mode as well as on the alpha diversity of soil microbes. The community structure of soil bacteria is more sensitive to elevation than that of soil fungus. At different elevations in the two pine forests, Acidobacteria, Proteobacteria, Chloroflexi, Verrucomicrobia, Bacteroidetes, Patescibacteria, and Thaumarchaeota are the dominant bacterial phyla, and Ascomycota, Basidiomycota, and Mucoromycota are the dominant fungal phyla. This investigation revealed that SOC and TN were the two most influential factors on the alteration of the soil microbial community in two pine forests. In summary, there were substantial changes in soil microbial diversity and community composition across the two different pine forests, with elevation and soil characteristics (SOC and TN) serving as the primary drivers.

1. Introduction

In recent years, there has been a great deal of interest in the effect of environmental influences on soil properties and microbial populations. Soil microorganisms are a crucial component of soil ecosystems, and soil microbial community diversity can be utilized to assess the functional stability of soil ecosystems and their environmental impact [1]. Elevation is an important aspect of mountain topography that can result in the spatial heterogeneity of biotic and abiotic elements. Studies have shown that changes in climatic parameters such as temperature and precipitation are more pronounced along the gradient of altitude than along the gradient of latitude [2]. Research in the past has claimed that geographic distance does not affect the distribution pattern of microorganisms [3], but a growing number of studies have demonstrated that the distribution of soil microorganisms is influenced by both geographical distance and environmental factors [4]. Changes in precipitation and temperature caused by changes in elevation gradients will affect soil conditions and vegetation types, which in turn will affect soil microbial diversity and species composition. However, the response of microbial diversity to elevation gradients has not been studied consistently in different locations. In Mount Fuji, Japan, soil microbial diversity peaked in the middle of the elevation-increasing process [5]; in the Andes Mountains, soil microbial species abundance decreased with elevation, and differences in species composition increased with elevation differences [6]; in the Changbai Mountains of China, soil bacterial communities did not exhibit obvious changes with elevation [7]. This suggests that on a worldwide scale there is no definitive pattern of soil microbial response to elevation gradient, and additional research is required to determine its distribution pattern and response mechanism to elevation.
In mountain ecosystems, interactions between soil physicochemical properties and plant communities due to variations in elevation can also alter microbial communities [8], and numerous studies have demonstrated that soil qualities can significantly affect soil microbial communities. Soil pH is a fundamental determinant of vertical spatial variations in soil microbial diversity and community composition [9,10], and the differences in soil bacterial communities in Changbai Mountain are a result of these changes. Different altitudes of mountain tundra are significantly correlated with soil total carbon (TC), total nitrogen (TN), and carbon–nitrogen (C/N) ratios [11], and soil phosphorus deficiency will cause an increase in the abundance of genes associated with soil microbial communities to adapt to soil nutrient restriction [12]. Vegetation type is also closely related to soil microorganisms [13], and studies have shown that vegetation type can indirectly cause changes in soil bacterial community distribution and function by determining substrate supply, changing soil properties such as carbon and nitrogen content and pH [14,15,16]. Currently, although many studies focus on the response of soil microbial communities to altitude changes under vegetation, such as obvious peaks of fungi and bacteria in tea tree soil at different altitudes, pH is the primary influencing factor [17]; the soil fungal abundance of Pinus armandii decreased with elevation and was significantly negatively correlated with soil organic matter [18], but these studies only involved one type of vegetation, and there were few studies of soil microbial communities under two different types of vegetation in the same mountain ecosystem.
In the central subtropical region of southeast China, Pinus taiwanensis and Pinus massoniana are the most commonly distributed coniferous species. In the red soil region of Jiangxi Province, soil nutrient loss due to soil erosion is severe, and Pinus massoniana can retain soil water and nutrients well, and has a strong ability to adapt to poor soil, so it is a typical reforestation tree species in the red soil region and is widely planted to prevent environmental degradation [19]. China’s unique tree species, Pinus taiwanensis, can establish pure forests on subtropical poor soil, which is helpful for soil and water conservation and restoration [20]. With the intensification of global warming and the influence of human disturbance, Pinus massoniana and Pinus taiwanensis communities have declined in numerous locations, which may result in the loss of biological genes and the destruction of ecosystem stability. However, there are still relatively few systematic studies on soil microbial communities under these two pine forests, so this study focused on the soil microbial communities of Pinus massoniana and Pinus taiwanensis. Sample plots were set up along the elevation in several mountainous areas in Jiangxi Province, China, to examine the changes of soil microbial communities under Pinus massoniana and Pinus taiwanensis forests at different elevations, to comprehensively analyze the response of soil microbial communities to elevation gradients and their driving factors under different vegetation types, and to understand the adaptability of soil microbial communities of Pinus spp. to different elevations, and their response mechanisms.

2. Materials and Methods

2.1. Study Area

The main landform types in Jiangxi Province, China are mountains and hills, with mountains accounting for 36% of the province’s area and hills accounting for 42%. This experiment was carried out on 25 November 2020 in three study areas: Sanqing Mountain (28°50′ N, 117°58′ E), Lu Mountain (29°32′ N, 115°59′ E), and Jinggang Mountain (26°36′ N, 114°7′ E) in Jiangxi Province, China (Figure 1). The sample sites are located at different elevations in the eastern, northern, and southern parts of Jiangxi Province; this is to conduct a more comprehensive study of forest soils in Jiangxi Province and to comprehensively analyze the soil microbial characteristics of different pine forests at different elevations in Jiangxi Province. The study area belongs to the subtropical warm and humid monsoon climate, with annual average temperatures of 10.9 °C, 11.6 °C, and 14.2 °C, and annual rainfall of 1857.7 mm, 2068.1 mm, and 1870.4 mm, respectively. The soil distribution in the study area was dominated by Plinthic Acrisols, followed by Dystric Luvisols [21,22]. Jiangxi Province is relatively rich in forest resources. There are many kinds of vegetation, in large numbers and with wide distribution, and most of the province’s forests are natural secondary forests, with a large proportion of coniferous forests [23]. Among them, the Pinus massoniana forest is mainly distributed below 800 m elevation, and the Pinus taiwanensis forest is mainly distributed above 600 m elevation. The main tree species in the study area are Pinus massoniana or Pinus taiwanensi, which are native natural second growth, 40–60 years old, and less subject to human management.

2.2. Experimental Design and Soil Sampling

In three study regions, we collected understory soils with different elevation gradients of Pinus taiwanensis and Pinus massoniana. Each mountain is outfitted with 2~3 plots (20 m × 50 m) with varying elevation gradients, for a total of 7 plots. Jinggang Mountain (referred to as “J”) has three sample plots of 1300 m, 850 m, and 500 m; Sanqing Mountain (referred to as “S”) has two sample plots of 1180 m and 400 m; Lu Mountain (referred to as “L”) has two plots of 930 m and 300 m. In each sample plot, three sample locations were selected along the diagonal (5–10 m between each plot), and six to seven 0–20 cm soil samples were collected from each sample point using an S-pattern soil sampler with a 2 cm diameter, for a total of 21 samples. The samples were transferred back to the laboratory after being kept in an incubator at −4 °C. The collected soil samples were sieved through a 2 mm mesh. A portion of each well-mixed sample was refrigerated at −80 °C for subsequent DNA extraction, while the remaining amount was air-dried at ambient temperature for evaluating soil physical and chemical parameters. The sampling design illustration can be shown in Figure A1.

2.3. Soil Properties Analysis

The soil samples first underwent air drying treatment. Then a portion of the air-dried soil sample was filtered through a 0.149 mm sieve. Visible plant tissues were selected by hand for analysis in laboratory. A 2 mm soil sample was used for soil pH, available phosphorus (AP), and available potassium (AK) measurement; a 0.149 mm soil sample was used for soil organic carbon (SOC) and total nitrogen (TN) measurement. A pH meter determined the pH value of soil in the extract with soil:water of 1:5. The dichromate oxidation method assisted in analyzing the soil organic carbon (SOC) content. The Kjeldahl digestion method assisted in determining the soil total nitrogen (TN) content [24]. Sodium bicarbonate and ammonium acetate extraction served for confirming the available phosphorus (AP) content and available potassium (AK) content [25].

2.4. Extraction, Amplification and Sequencing of DNA

The HiPure Soil DNA Kits (Magen, Guangzhou, China) were employed for extracting microbial DNA from 0.5 g fresh weight soil as per the protocols of the manufacturer. NanoDrop 2000 spectrophotometer (ThermoFischer Scientific, Waltham, MA, USA) together with agar gel electrophoresis examined DNA concentration and quality. In the 16S rDNA gene region, the primers 341F(CCTACGGGNGGCWGCAG) and 806R(GGACTACHVGGGTATCTAAT) [26] amplified the V3–V4 region. In the ITS gene region, the ITS3_KYO2(GATGAAGAACGYAGYRAA) and ITS4(TCCTCCGCTTATTGATATGC) [27] primers amplified the ITS2 region. We conducted all polymerase chain reaction (PCR) reactions in triplicate 50 μL mixture constituting of 5 × Q5@ Reaction Buffer (10 μL), 2.5 mM dNTPs (1.5 μL), Q5@ High-Fidelity DNA Polymerase (0.2 μL), 5 × Q5@ High GC Enhancer (10 μL), template DNA (50 ng), etc. Reaction conditions: initial 95 °C (5 min), 30 cycles at 95 °C (1 min), 60 °C (1 min), 72 °C (1 min) and a final extension at 72 °C (7 min). Relevant PCR reagents were provided by New England Biolabs.
The PCR products from three replicate amplifications per sample underwent purification by AxyPrep DNA Purification Kit (Axygen Biosciences, Union City, CA, USA), and ABI StepOnePlus Real-Time PCR System assisted in the quantification. We pooled purified amplicons in equal amounts to receive paired-end sequencing (PE250) on an Illumina platform as per standard protocols [28]. We stored the raw data in the NCBI Sequence Read Archive (SRA) database. (BioProject accession number: PRJNA936495).
To obtain credible target sequences for the following analysis, raw data were further filtered by removing low-quality bases with FASTP [29] (0.18.0). FLASH (1.2.11) was used for assembling the paired reads (a minimum overlap: 10 bp; mismatch error rate: 2%). UPARSE [30] (9.2.64) pipeline served for clustering the clean sequences into operational taxonomic units (OTUs) with the similarity no less than 97%. A naive Bayesian model served for classifying the corresponding bacterial and fungal OTU sequences into species under the assistance of RDP classifier [31] (2.2) combining SILVA database [32] (132) and ITS2 database [33] (confidence threshold: 0.8).

2.5. Statistical Analyses

One-way ANOVA together with Tukey’s test assisted in determining the significant difference among elevation gradients in terms of the soil physical and chemical properties, and p < 0.05 reported statistical difference. The Sobs, Chao1, Shannon, and Simpson diversity indices served for examining the alpha diversity exhibited by microbial communities, which were calculated in QIIME [34] (1.9.1). R package ggplot2 [35] (2.2.1) assisted in the visualization of stacked bar plots regarding the soil microbial phylum composition. Muscle [36] (3.8.31) served for sequence alignment. FastTree [37] (2.1) constructed the UPGMA clustering algorithm combining the Bray–Curtis distance matrix computed by R package Vegan [38] (2.5.3), which illustrated the microbial community composition of the 27 samples in terms of altitudinal structure. R package ggplot2 [35] (2.2.1) served for plotting PCoA regarding Bray–Curtis distances. Unlike Pearson correlation analysis, Spearman correlation analysis focuses more on the ranking values based on each variable than the raw data [39]. Therefore, in cases where the specific alpha diversity values are similar, Spearman correlation analysis is more suitable for estimating the effects of soil characteristics on soil microbial alpha diversity. Spearman correlation analysis between soil properties and microbial diversity was conducted using SPSS 24.0. Redundancy analysis (RDA) and Variation partition analysis (VPA) were executed in R package Vegan [38] (2.5.3). R package psych [40] (1.8.4) served for the calculation of the Pearson correlation coefficient for clarifying the way environmental factors impacted the dominant phyla and genera in bacterial and fungal communities composition. Omicsmart (http://www.omicsmart.com accessed on 1 October 2022.) was adopted to generate the heatmap and network regarding the correlation coefficient.

3. Results

3.1. Soil Properties along the Elevation Gradient

Soil pH in Pinus taiwanensis forest initially increased and subsequently fell with elevation; soil pH in Pinus massoniana forest increased significantly at 500 m, and soil pH in Pinus taiwanensis area was lower than that of the entire Pinus massoniana area (Table 1). The soil organic carbon (SOC) content and total nitrogen (TN) of Pinus taiwanensis and Pinus massoniana soils at different elevations increased initially and then decreased with elevation, with the highest SOC and TN content being 61.83 g/kg and 13.06 g/kg in the Pinus taiwanensis area, both at 930 m in Lu Mountain, and 37.99 g/kg and 7.41 g/kg in the Pinus massoniana area, both at 400 m in Sanqing Mountain. The average SOC content in the Pinus taiwanensis area (45.96 g/kg) was 2.1 times higher than that in the Pinus massoniana area (21.59 g/kg), while the average TN content in Pinus taiwanensis area (9.76 g/kg) was 1.9 times higher than that in Pinus massoniana area (5.2 g/kg) (Table 1). In the Pinus taiwanensis zone, soil AP concentration fell with elevation and rebounded around 1300 m, averaging 2.4 mg/kg; in the Pinus massoniana zone, soil AP content decreased and subsequently climbed along the elevation gradient, averaging 2.72 mg/kg (Table 1). The soil AK concentration in the understory of Pinus taiwanensis rose with elevation, with the lowest concentration at 850 m in Jinggang Mountain (43.3 mg/kg) and the highest concentration at 1300 m in Jinggang Mountain. The soil AK in the Pinus massoniana forest showed no obvious pattern (Table 1). The variation of soil properties with elevation is graphically shown in Figure A2.

3.2. Analysis of Soil Microbial Diversity

A total of 2,391,422 bacterial sequences were generated in the 21 samples, and after filtration, 53,628 OTUs were clustered. Additionally, 2,233,973 fungal sequences were generated, and 26,637 OTUs were clustered after filtration. Table 2 displays the alpha diversity index of soil microorganisms. Significant variations were observed in the Sobs index, Chao1 index, and Shannon index of soil bacteria in the Pinus taiwanensis forest. The diversity of bacterial community increased with elevation below 1180 m, but decreased at 1300 m. The Sobs index, Chao1 index, and Shannon index of the soil in the Pinus massoniana forest showed significant differences and increased with elevation. At the overall level, The Sobs and Chao1 index of soil bacteria were lower in the Pinus taiwanensis area than in the Pinus massoniana area. The Shannon index of soil bacteria in the Pinus taiwanensis area were not statistically distinguishable from those in the Pinus massoniana area (Figure 2a).
The Sobs index and Chao1 index of soil fungus differed significantly between the two vegetation stands. The Sobs index and Chao1 index in the Pinus taiwanensis forest grew and then decreased with the elevation gradient, while those in Pinus massoniana forest decreased with the elevation. The Sobs index and Chao1 index of soil fungus were higher in the Pinus taiwanensis area than in the Pinus massoniana area (Figure 2b). Specific values of bacterial and fungal alpha diversity indices can be seen in Table A1 and Table A2.

3.3. Analysis of Soil Microbial Community Composition

UPGMA (Unweighted Pair-Group Method with Arithmetic Mean) is a clustering analysis technique capable of resolving the classification problem [41]. The length of the common branch decreases as sample similarity increases. Upon constructing the clustering tree for the samples, it was discovered that among the seven groups of samples, the bacterial communities were clustered under the Bray distance with J500 and L300 in the Pinus massoniana group, and all samples in the Pinus taiwanensis group were clustered into one group. Additionally, it can be shown that the samples at a closer elevation have closer branch clustering (Figure 3a). Samples from the Pinus massoniana group and the Pinus taiwanensis group clustered into the same fungal community category based on the Bray distance (Figure 3b).
Through PCoA analysis, it was determined that the contribution value of the PCo1 and PCo2 axis samples of the bacterial community were 25.30% and 16.12%, respectively, with a total contribution value of 41.42%. The samples of the Pinus taiwanensis group were closer together and grouped together, but the samples of the three groups of the Pinus massoniana group were further apart and more divergent (Figure 4a). PCo1 and PCo2 axis samples of the fungal community had contribution values of 16.33% and 13.71%, respectively. Additionally, the cumulative contribution value of both axes was 30.04%. The sample of the Pinus taiwanensis group was clustered with the L300 sample of the Pinus massoniana group, which was separated from the other two samples of the Pinus massoniana group by a large distance and had a great difference (Figure 4b).

3.4. Distribution of Major Phyla of Soil Microbial Communities

The bacterial community in this study comprised 36 phyla, 102 classes, 211 orders, 274 families, and 427 genera. There were 16 fungal phyla, 54 classes, 143 orders, 292 families, and 494 genera in the fungal community. In the bacterial community, the dominant bacterial phyla (relative abundance > 1%) from high to low are Acidobacteria (27.53%), Proteobacteria (26.56%), Actinobacteria (11.89%), Planctomycetes (9.88%), Chloroflexi (7.67%), Verrucomicrobia (6.37%), Bacteroidetes (1.14%), Patescibacteria (1.11%) and Thaumarchaeota (1.00%). The main bacterial communities under the two types of vegetation were comparable (Figure 5a). Ascomycota (53.47%), Basidiomycota (34.71%), and Mucoromycota (6.58%) are the three main phyla (relative abundance > 1%) in the fungal community (Figure 5b). In general, the dominating microbial communities were comparable at the two major tree species and at various elevations of the same tree species.

3.5. The Association between Soil Properties and Microbial Communities

Spearman correlation analysis was performed on soil properties and soil microbial diversity (Table 2), revealing that soil bacterial Chao1 index was significantly positively correlated with soil TN (p = 0.033), whereas soil bacterial Shannon index was significantly positively correlated with SOC content and soil TN (p = 0.027; p = 0.013). The Chao1 index and Shannon index of soil fungus do not correlate significantly with soil properties.
With soil microbial dominant phylum community as response variables and soil properties as environmental explanatory variables for redundancy analysis (RDA), soil properties explained 79.56% of the total variation at the level of bacterial community phylum (Figure 6a) and 99.19% of the total variation at the level of fungal community phylum (Figure 6b). SOC, TN, and AK had a substantial impact on the bacterial community, whereas SOC and TN had a substantial impact on the fungal community.
Pearson correlation analysis was used to determine the relationship between each environmental factor and the relative abundance of the top 20 genera level of microorganisms in different pine forests. Variance partitioning analysis (VPA) was used to determine the contribution of each environmental factor variable to the total variation in species distribution in different pine forests. For the bacterial community, the heatmap revealed that in the Pinus taiwanensis area, soil pH was significantly negatively correlated with the genus Mycobacterium (p < 0.001), and SOC content and soil TN content were significantly negatively correlated with the genus Gemmatirosa (p < 0.01) and positively correlated with the genus Gemmata (p < 0.01) (Figure 7a); in the Pinus massoniana area, soil pH was significantly correlated with the genus Singulisphaera (p < 0.001), SOC content and soil TN content were significantly positively correlated with ADurbBin063–1 genus (p < 0.001), and significantly negatively correlated with FCPS473 and Mycobacterium genus (p < 0.001) (Figure 7b). For the fungal communities, in the Pinus taiwanensis area, soil pH was significantly negatively correlated with Sebacina genus (p < 0.01), SOC content was significantly negatively correlated with Aspergillus genus (p < 0.001), soil TN content was significantly negatively correlated with Umbelopsis genus and Aspergillus genus (p < 0.01) (Figure 7c); in the Pinus massoniana area, soil pH was significantly positively correlated with Oidiodendron genus (p < 0.01), SOC content and soil TN content were significantly negatively correlated with Umbelopsis genus, Oidiodendron genus, Penicillium genus, and Geminibasidium genus (p < 0.01), soil AP was significantly correlated with Oidiodendron genus and Geminibasidium genus (p < 0.01) (Figure 7d). The community environmental contribution table revealed that soil properties in the Pinus massoniana zone contributed more to the distribution of microbial community genera than those in the Pinus taiwanensis zone, and that SOC content, soil TN content, and soil AK in the Pinus taiwanensis zone contributed more positively to the structural changes of microbial community genera, while AP predominantly produced negative correlation effects. SOC content and soil TN content were the environmental parameters that contributed most to the change in microbial community genus structure in the Pinus massoniana area, but soil pH, soil AP and soil AK had a greater effect on the variance of bacterial community genus than fungal community genus.

4. Discussion

4.1. Effect of Elevation Gradient on Soil Properties in Different Pine Forests

Overall, soil pH was lower in the Pinus taiwanensis area than in the Pinus massoniana area, and there was no significant pattern of variation in soil pH at different elevations. However, there were significant variances between elevations, which may be caused by a combination of factors. Under both pine forests, SOC content and soil TN content displayed a single-peaked model with increasing elevation, meaning that they first increased and then decreased, and the overall SOC and TN contents were higher in the Pinus taiwanensis area than in the Pinus massoniana area, consistent with the findings of Yu et al. [42]. The decrease in temperature due to elevation will reduce soil reevaporation and soil respiration rate, resulting in the buildup of SOC and TN in soil [43], as well as greater carbon sequestration and lower nitrogen fixation rates due to the higher recalcitrance of coniferous litter [44]. In contrast to soil carbon and nitrogen, soil AP varies considerably with elevation under different pine forests, which may be related to differences in the consumption of effective phosphorus due to differences in understory vegetation cover at different elevations [45]. Soil AK increased with elevation under the Pinus taiwanensis forest, which is consistent with the findings of Hu [22] et al. At low elevation under the Pinus massoniana forest, which is more affected by anthropogenic disturbance, there was no discernible pattern of AK content variation. Shao et al. [46] also found in their research that human activities would cause great disturbance to soil fertility.

4.2. Response Characteristics of Soil Microbial Communities to Elevation Gradient in Different Pine Forests

In this study, the diversity of soil bacteria and fungi in a Pinus taiwanensis forest showed a single peak with increasing elevation. In Pinus massoniana forests, soil bacterial diversity increased with altitude, whereas soil fungal diversity decreased with altitude. It is possible that the change in soil nutrients caused by an increase in altitude indirectly impacts the variety and organization of the soil microbial population. Ren et al. [47] also found that soil bacterial alpha diversity and beta diversity were significantly correlated with soil C, N and p contents. The soil fungal diversity in the Pinus taiwanensis area was significantly greater than that in the Pinus massoniana area. Due to the tougher conditions for microbial survival at higher altitudes, it is anticipated that bacterial and fungal abundance may decrease in higher altitude regions [48]. However, we observed higher bacterial and fungal diversity at higher elevations of both pine forests (except for fungi in the Pinus massoniana area), which may be attributable to the elevated SOC and TN concentrations at these altitudes. Previous studies have shown that microbial activity in the Alpine forest soils also increased with elevation [49]. The higher soil carbon and nitrogen content at higher elevations may promote microbial growth, which may account for the greater microbial abundance at these elevations [50].
The response of microbial community composition structure of Pinus taiwanensis and Pinus massoniana to elevation was analyzed for beta diversity, and it was discovered that Pinus taiwanensis soil microorganisms were able to aggregate better, while sites closer together in elevation had a more similar community composition. This suggests that the soil microbial community structure in the Pinus taiwanensis area changed significantly with elevation gradient, due to the ability of altitude to influence soil temperature and moisture and control the oxygen supply of aerobic microorganisms, thus changing the composition of microbial communities [51]. The soil microbial community composition of Pinus massoniana forests exhibited greater divergence with depth. This means that the microbial community composition varies greatly among different elevations in the Pinus massoniana region. In addition to natural factors such as elevation and temperature, anthropogenic activities at lower altitudes are a significant contributor to this outcome. Numerous studies have examined karst soil microbial community as influenced by management [52], soil erosion [53], and land use changes [54]. PCoA analysis revealed that the soil microbial community structure in areas dominated by Pinus taiwanensis was significantly different from that in areas dominated by Pinus massoniana. This difference was better reflected in the bacterial community, and previous research has shown that aboveground plant biomass and cover can significantly affect soil microbial bacterial beta diversity, while fungi do not respond significantly [8]. These results suggest that differences in elevation affect the species composition and characteristics of aboveground plants, resulting in differences in the structural composition of soil microbial communities in the understory of Pinus taiwanensis and Pinus massoniana, with a greater impact on bacteria.
In this study, Acidobacteria, Proteobacteria, Actinobacteria, and Planctomycetes were the dominant phyla in soil bacterial communities at different elevations under two pine forests. The first two dominant phyla were Acidobacteria (27.53%) and Proteobacteria (26.56%), which is consistent with the results of Wang [55] et al. on the altitudinal distribution pattern of soil bacteria on the Tibetan Plateau. Contrary to the effect of plant species on the composition of soil bacterial communities, plant community richness has a substantial effect on the composition of soil bacterial communities [56]. Acidobacteria, one of the most widespread phyla in forest soil, is sensitive to inorganic and organic nutrient inputs and can play a role in soil restoration, which facilitates soil nutrient cycling and plant growth after severe disturbance [57]. The number of Proteobacteria is proportional to the nutritional concentration of the soil, with higher nutrient content encouraging their growth [58]. The first two prominent fungal phyla in the fungal community were Ascomycota and Basidiomycota, similar to the findings of Zeng et al. [59] in the Loess Plateau. However, in this study, Ascomycota (53.47%) was more prevalent than Basidiomycota (34.71%), which contradicts the results from Taibai Mountain [8], and in the global scale study, Basidiomycota (55.7%) was more prevalent Ascomycota (31.3%) [60]; the relative difference between the two dominant phyla may be related to local soil properties, climate, or vegetation species and vegetation community abundance [61,62]. In this study, the fungal dominant communities were not significantly correlated with the dominant vegetation species. One possible explanation is that climatic factors (precipitation and temperature) are the primary drivers of the composition of soil fungal communities [63]. Studies have shown that the increase in soil temperature promotes the growth and reproduction of soil microorganisms [64], and the arbuscular mycorrhiza (AM) fungi, which form a symbiosis with plant roots, also show seasonal (precipitation and temperature) differences in diversity and spore density [65].

4.3. Driving Factors of Soil Microbial Community Changes in Different Pine Forests

The amount and distribution of soil microbial communities are influenced by the nutritional level of the soil. Changes in soil nutrient content at different elevations will lead to difference in vegetation, which will result in major differences in soil microbial community at different elevations [66]. According to Spearman correlation analysis, SOC and TN have a significant positive association with the Chao1 index and Shannon index of soil bacteria, but have no significant effect on soil fungal diversity, which is consistent with Ding et al. [67]. The decline in bacterial community diversity is attributable to carbon and nitrogen deficiencies in the soil. According to RDA analysis, SOC content, soil TN content, and soil AK have a substantial impact on the bacterial community, whereas SOC content and soil TN content have a big impact on the fungal community at the microbial phylum level. Numerous studies have shown that SOC content and soil TN have considerable effects on the soil microbial community [49,68,69], and that SOC content and soil TN regulate microbial metabolism and alter microbial composition structure and function. According to Pearson correlation heatmap and VPA analysis, at the microbial genus level, soil AP exerts a negative influence on microbial community changes in the Pinus taiwanensis area, but has little correlation with microorganisms in the Pinus massoniana area, as AP content is too low in the Pinus taiwanensis area. Lack of soil phosphorus inhibits the life activities of some soil microorganisms, making soil AP a limiting factor for microbial community expansion and variety [45]. In Pinus massoniana area, soil AP and AK contributed more to the change in bacterial than fungal community structure, because bacteria were more involved in the biochemical processes of soil AP and AK [70].
Changes in soil microbial community structure are not simply a function of soil nutrient availability, but also the interaction of multiple factors. With elevation variation, the diversity of surface vegetation will also contribute to the diversity of litter species and mass composition, resulting in the difference of soil nutrients, hence influencing the soil microbial community and structure with various properties and functions [71]. In this study, the diversity and community composition of soil microorganisms under both pine forests showed significant differences with elevation, which was the combined result of differences in elevation and vegetation. Finally, the major limitation of this study is that the sample size is too small, which will result in narrow observable results when comparing the variations of soil microorganisms in the two pine forests along the elevation gradient. Providing more credible experimental data support at a larger scale is the future work to be carried out.

5. Conclusions

In summary, this paper analyzes the soil microbial diversity and community structure in the soils of Pinus massoniana and Pinus taiwanensis forests in Jiangxi Province, China, along an elevational gradient. The results indicate that SOC and TN under both pine forests responded considerably to the elevation gradient, and that SOC content and TN content significantly influenced the soil microbial community. The variance in soil microbial diversity and community composition is caused by the change in elevation, which also contributes more to the bacterial community. There are significant differences in the soil microbial community composition structure between Pinus massoniana and Pinus taiwanensis, with elevation and soil qualities serving as the primary determinants. These findings can assist in comprehending the relationship between soil microbial communities and forest ecosystems at various elevations, as well as provide a theoretical foundation for proper forest management. Elevation influences the ecological differentiation of microbial communities, and understanding the changes in the composition and diversity of fungal and bacterial communities across the elevation gradient can provide an ecological foundation for manipulating the soil microbiome of forest ecosystems to establish a link with forest tree growth and development. Future research must investigate the structure and spatiotemporal response mechanism of the dominant soil microbial community in conjunction with aboveground litter types and seasonal dynamics.

Author Contributions

Conceptualization, K.H. and J.C.; Formal analysis, K.H. and J.C.; Investigation, K.H., J.C., M.H. and Y.Y.; Project administration, Y.F. and H.Z.; Writing—original draft preparation, K.H.; Writing—review & editing, J.X., Y.M., J.G., G.W. and H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Special Foundation for National Science and Technology Basic Resources Investigation of China (2019FY202300 to Y.F.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

The study did not involve humans.

Data Availability Statement

All data supporting the findings of this study are available within the paper, which are published online.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Forests 14 00772 g0a1 550
Figure A1. Sampling design at sample sites of different elevations. Three rectangles are sampling points set along the diagonal of the 20 m × 50 m sample plot. Red dots show the distribution where mixed sampling was performed.
Figure A1. Sampling design at sample sites of different elevations. Three rectangles are sampling points set along the diagonal of the 20 m × 50 m sample plot. Red dots show the distribution where mixed sampling was performed.
Forests 14 00772 g0a1
Forests 14 00772 g0a2 550
Figure A2. The soil characteristics in Pinus taiwanensi and Pinus massoniana forests soils with the elevations. Yellow for Pinus massoniana and blue for Pinus taiwanensi.
Figure A2. The soil characteristics in Pinus taiwanensi and Pinus massoniana forests soils with the elevations. Yellow for Pinus massoniana and blue for Pinus taiwanensi.
Forests 14 00772 g0a2
Table
Table A1. Alpha diversity of bacterial diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
Table A1. Alpha diversity of bacterial diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
VegetationElevation (m)SobsChao1ShannonSimpson (10−2)
Pinus
taiwanensis		J13002306 ± 71.47 b2554.55 ± 76.16 b8.62 ± 0.08 ab99.25 ± 0.05 a
S11802648.33 ± 73.93 a2926.19 ± 70.7 a8.79 ± 0.06 a99.33 ± 0.05 a
L9302453.67 ± 65.69 ab2687.35 ± 41.28 ab8.54 ± 0.14 ab99.01 ± 0.24 a
J8502324.67 ± 139.87 b2550.47 ± 121.2 b8.33 ± 0.14 b99.05 ± 0.12 a
Pinus
massoniana		J5003160 ± 40 a3307.43 ± 45.61 a9.05 ± 0.11 a99.32 ± 0.12 a
S4002486.67 ± 133.96 b2741.61 ± 115.76 b8.69 ± 0.13 b99.17 ± 0.15 a
L3002496.67 ± 34.27 b2726.66 ± 35.84 b8.76 ± 0.02 ab99.25 ± 0.15 a
Data are mean ± standard deviation (n = 3). Different letters in a column denote statistically significant differences (p < 0.05).
Table
Table A2. Alpha diversity of fungal diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
Table A2. Alpha diversity of fungal diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
VegetationElevation (m)SobsChao1ShannonSimpson (10−2)
Pinus
taiwanensis		J13001372 ± 10.79 a1694.52 ± 34.24 a6.02 ± 0.45 a92.03 ± 4.84 a
S11801386 ± 54.68 a1702.34 ± 32.13 a5.96 ± 0.63 a92.7 ± 3.92 a
L9301448.67 ± 30.6 a1760.94 ± 69.23 a6.21 ± 0.18 a96.03 ± 0.65 a
J8501150.67 ± 45.74 b1391.58 ± 36.66 b6.25 ± 0.26 a96.71 ± 0.53 a
Pinus
massoniana		J5001057.67 ± 15.32 b1268.47 ± 24.26 b5.77 ± 0.44 a92.6 ± 3.21 a
S4001225.67 ± 42.21 a1503.58 ± 59.46 a6.06 ± 0.15 a94.7 ± 1.38 a
L3001238.33 ± 41.57 a1558.44 ± 25.47 a6.21 ± 0.27 a95.01 ± 0.56 a
Data are mean ± standard deviation (n = 3). Different letters in a column denote statistically significant differences (p < 0.05).

References

  1. Cui, Y.; Fang, L.; Guo, X.; Wang, X.; Wang, Y.; Li, P.; Zhang, Y.; Zhang, X. Responses of soil microbial communities to nutrient limitation in the desert-grassland ecological transition zone. Sci. Total. Environ. 2018, 642, 45–55. [Google Scholar] [CrossRef] [PubMed]
  2. Körner, C. The use of ‘altitude’in ecological research. Trends Ecol. Evol. 2007, 22, 569–574. [Google Scholar] [CrossRef]
  3. De Wit, R.; Bouvier, T. ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environ. Microbiol. 2006, 8, 755–758. [Google Scholar] [CrossRef] [PubMed]
  4. Liu, D.; Wang, H.; An, S.; Bhople, P.; Davlatbekov, F. Geographic distance and soil microbial biomass carbon drive biogeographical distribution of fungal communities in Chinese Loess Plateau soils. Sci. Total. Environ. 2019, 660, 1058–1069. [Google Scholar] [CrossRef] [PubMed]
  5. Singh, D.; Takahashi, K.; Kim, M.; Chun, J.; Adams, J.M. A Hump-Backed Trend in Bacterial Diversity with Elevation on Mount Fuji, Japan. Microb. Ecol. 2011, 63, 429–437. [Google Scholar] [CrossRef]
  6. Nottingham, A.T.; Fierer, N.; Turner, B.; Whitaker, J.; Ostle, N.; McNamara, N.P.; Bardgett, R.D.; Leff, J.W.; Salinas, N.; Silman, M.R.; et al. Microbes follow Humboldt: Temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 2018, 99, 2455–2466. [Google Scholar] [CrossRef] [Green Version]
  7. Shen, C.; Xiong, J.; Zhang, H.; Feng, Y.; Lin, X.; Li, X.; Liang, W.; Chu, H. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai Mountain. Soil Biol. Biochem. 2013, 57, 204–211. [Google Scholar] [CrossRef]
  8. Ren, C.; Zhang, W.; Zhong, Z.; Han, X.; Yang, G.; Feng, Y.; Ren, G. Differential responses of soil microbial biomass, diversity, and compositions to altitudinal gradients depend on plant and soil characteristics. Sci. Total Environ. 2018, 610–611, 750–758. [Google Scholar] [CrossRef] [PubMed]
  9. Shen, C.; Liang, W.; Shi, Y.; Lin, X.; Zhang, H.; Wu, X.; Xie, G.; Chain, P.; Grogan, P.; Chu, H. Contrasting elevational diversity patterns between eukaryotic soil microbes and plants. Ecology 2014, 95, 3190–3202. [Google Scholar] [CrossRef] [Green Version]
  10. Yang, Y.; Zhou, Y.; Shi, Z.; Rossel, R.A.V.; Liang, Z.; Wang, H.; Zhou, L.; Yu, W. Interactive effects of elevation and land use on soil bacterial communities in the Tibetan Plateau. Pedosphere 2020, 30, 817–831. [Google Scholar] [CrossRef]
  11. Shen, C.C.; Ni, Y.Y.; Liang, W.J.; Wang, J.J.; Chu, H.Y. Distinct soil bacterial communities along a small-scale elevational gradient in alpine tundra. Front. Microbiol. 2015, 6, 582. [Google Scholar]
  12. Yao, Q.; Li, Z.; Song, Y.; Wright, S.J.; Guo, X.; Tringe, S.G.; Tfaily, M.M.; Paša-Tolić, L.; Hazen, T.C.; Turner, B.L.; et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat. Ecol. Evol. 2018, 2, 499–509. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Trivedi, P.; Leach, J.E.; Tringe, S.G.; Sa, T.; Singh, B.K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 2020, 18, 607–621. [Google Scholar] [CrossRef] [PubMed]
  14. Chong, C.W.; Pearce, D.A.; Convey, P.; Tan, G.A.; Wong, R.C.; Tan, I.K. High levels of spatial heterogeneity in the biodiversity of soil prokaryotes on Signy Island, Antarctica. Soil Biol. Biochem. 2010, 42, 601–610. [Google Scholar] [CrossRef]
  15. Chu, H.; Neufeld, J.D.; Walker, V.K.; Grogan, P. The Influence of Vegetation Type on the Dominant Soil Bacteria, Archaea, and Fungi in a Low Arctic Tundra Landscape. Soil Sci. Soc. Am. J. 2011, 75, 1756–1765. [Google Scholar] [CrossRef] [Green Version]
  16. Xiao, L.; Liu, G.-B.; Xue, S. Effects of vegetational type and soil depth on soil microbial communities on the Loess Plateau of China. Arch. Agron. Soil Sci. 2016, 62, 1665–1677. [Google Scholar] [CrossRef]
  17. Kui, L.; Xiang, G.; Wang, Y.; Wang, Z.; Li, G.; Li, D.; Yan, J.; Ye, S.; Wang, C.; Yang, L. Large-Scale Characterization of the Soil Microbiome in Ancient Tea Plantations Using High-Throughput 16S rRNA and Internal Transcribed Spacer Amplicon Sequencing. Front. Microbiol. 2021, 12, 745225. [Google Scholar] [CrossRef]
  18. Zhao, Y.; Zhou, Y.; Jia, X.; Han, L.; Liu, L.; Ren, K.; Ye, X.; Qu, Z.; Pei, Y. Soil characteristics and microbial community structure on along elevation gradient in a Pinus armandii forest of the Qinling Mountains, China. For. Ecol. Manag. 2021, 503, 119793. [Google Scholar] [CrossRef]
  19. Yao, X.; Yu, K.; Deng, Y.; Zeng, Q.; Lai, Z.; Liu, J. Spatial distribution of soil organic carbon stocks in Masson pine (Pinus massoniana) forests in subtropical China. Catena 2019, 178, 189–198. [Google Scholar] [CrossRef]
  20. Chen, D.; Fang, K.; Li, Y.; Dong, Z.; Zhang, Y.; Zhou, F. Response of Pinus taiwanensis growth to climate changes at its southern limit of Daiyun Mountain, mainland China Fujian Province. Sci. China Earth Sci. 2015, 59, 328–336. [Google Scholar] [CrossRef]
  21. Shi, X.; Yu, D.; Xu, S.; Warner, E.; Wang, H.; Sun, W.; Zhao, Y.; Gong, Z. Cross-reference for relating Genetic Soil Classification of China with WRB at different scales. Geoderma 2010, 155, 344–350. [Google Scholar] [CrossRef]
  22. People’s Government of Jiangxi Province; Provincial Department of Water Resources of Jiangxi. Jiangxi Soil and Water Conservation Plan (2016–2030). 2016. Available online: http://www.jiangxi.gov.cn/col/col472/index.html (accessed on 1 October 2022).
  23. Zhang, C.; Deng, Q.; Liu, A.; Liu, C.; Xie, G. Effects of Stand Structure and Topography on Forest Vegetation Carbon Density in Jiangxi Province. Forests 2021, 12, 1483. [Google Scholar] [CrossRef]
  24. Bangroo, S.; Najar, G.; Rasool, A. Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest Range. Catena 2017, 158, 63–68. [Google Scholar] [CrossRef]
  25. Hu, L.; Xiang, Z.; Wang, G.; Rafique, R.; Liu, W.; Wang, C. Changes in soil physicochemical and microbial properties along elevation gradients in two forest soils. Scand. J. For. Res. 2015, 31, 242–253. [Google Scholar] [CrossRef]
  26. Guo, M.; Wu, F.; Hao, G.; Qi, Q.; Li, R.; Li, N.; Wei, L.; Chai, T. Bacillus subtilis Improves Immunity and Disease Resistance in Rabbits. Front. Immunol. 2017, 8, 354. [Google Scholar] [CrossRef] [Green Version]
  27. Toju, H.; Tanabe, A.; Yamamoto, S.; Sato, H. High-Coverage ITS Primers for the DNA-Based Identification of Ascomycetes and Basidiomycetes in Environmental Samples. PLoS ONE 2012, 7, e40863. [Google Scholar] [CrossRef] [Green Version]
  28. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Huntley, J.; Fierer, N.; Owens, S.M.; Betley, J.; Fraser, L.; Bauer, M.; et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012, 6, 1621–1624. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
  30. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef]
  31. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [Green Version]
  32. Pruesse, E.; Quast, C.; Knittel, K.; Fuchs, B.M.; Ludwig, W.; Peplies, J.; Glöckner, F.O. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007, 35, 7188–7196. [Google Scholar] [CrossRef] [Green Version]
  33. Ankenbrand, M.J.; Keller, A.; Wolf, M.; Schultz, J.; Foerster, F. ITS2 Database V: Twice as Much. Mol. Biol. Evol. 2015, 32, 3030–3032. [Google Scholar] [CrossRef]
  34. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Gonzalez Peña, A.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  35. Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 2011, 3, 180–185. [Google Scholar] [CrossRef]
  36. Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef] [Green Version]
  37. Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2—Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef] [PubMed]
  38. Oksanen, J. Vegan: Community Ecology Package. 2010. Available online: http://cran.R-project.org/package=vegan (accessed on 1 October 2022).
  39. Thirumalai, C.; Chandhini, S.A.; Vaishnavi, M. Analysing the concrete compressive strength using Pearson and Spearman. In Proceedings of the 2017 International Conference of Electronics, Communication and Aerospace Technology, Coimbatore, India, 20–22 April 2017; pp. 215–218. [Google Scholar] [CrossRef]
  40. Revelle, W. Psych: Procedures for Personality and Psychological Research. 2015. Available online: http://CRAN.R-project.org/package=psych (accessed on 1 October 2022).
  41. Gronau, I.; Moran, S. Optimal implementations of UPGMA and other common clustering algorithms. Inf. Process. Lett. 2007, 104, 205–210. [Google Scholar] [CrossRef]
  42. Yu, F.; Zhang, Z.; Chen, L.; Wang, J.; Shen, Z. Spatial distribution characteristics of soil organic carbon in subtropical forests of mountain Lushan, China. Environ. Monit. Assess. 2018, 190, 545. [Google Scholar] [CrossRef] [PubMed]
  43. Müller, M.; Oelmann, Y.; Schickhoff, U.; Böhner, J.; Scholten, T. Himalayan treeline soil and foliar C:N:P stoichiometry indicate nutrient shortage with elevation. Geoderma 2017, 291, 21–32. [Google Scholar] [CrossRef] [Green Version]
  44. Berger, T.W.; Duboc, O.; Djukic, I.; Tatzber, M.; Gerzabek, M.H.; Zehetner, F. Decomposition of beech (Fagus sylvatica) and pine (Pinus nigra) litter along an Alpine elevation gradient: Decay and nutrient release. Geoderma 2015, 251–252, 92–104. [Google Scholar] [CrossRef] [Green Version]
  45. Jiang, L.; He, Z.; Liu, J.; Xing, C.; Gu, X.; Wei, C.; Zhu, J.; Wang, X. Elevation Gradient Altered Soil C, N, and P Stoichiometry of Pinus taiwanensis Forest on Daiyun Mountain. Forests 2019, 10, 1089. [Google Scholar] [CrossRef] [Green Version]
  46. Shao, G.; Ai, J.; Sun, Q.; Hou, L.; Dong, Y. Soil quality assessment under different forest types in the Mount Tai, central Eastern China. Ecol. Indic. 2020, 115, 106439. [Google Scholar] [CrossRef]
  47. Ren, C.; Zhao, F.; Kang, D.; Yang, G.; Han, X.; Tong, X.; Feng, Y.; Ren, G. Linkages of C:N:P stoichiometry and bacterial community in soil following afforestation of former farmland. For. Ecol. Manag. 2016, 376, 59–66. [Google Scholar] [CrossRef]
  48. Margesin, R.; Jud, M.; Tscherko, D.; Schinner, F. Microbial communities and activities in alpine and subalpine soils. FEMS Microbiol. Ecol. 2009, 67, 208–218. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Siles, J.A.; Cajthaml, T.; Minerbi, S.; Margesin, R. Effect of altitude and season on microbial activity, abundance and community structure in Alpine forest soils. FEMS Microbiol. Ecol. 2016, 92, fiw008. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Weng, X.-H.; Sui, X.; Li, M.-S.; Liu, Y.-N.; Zhang, R.-T.; Yang, L.-B. Effects of Simulated Nitrogen Deposition on Soil Microbial Carbon Metabolism in Calamagrostis angustifolia Wetland in Sanjiang Plain. Huan Jing Ke Xue = Huanjing Kexue 2022, 43, 4674–4683. [Google Scholar] [PubMed]
  51. Meng, H.; Li, K.; Nie, M.; Wan, J.-R.; Quan, Z.-X.; Fang, C.-M.; Chen, J.-K.; Gu, J.-D.; Li, B. Responses of bacterial and fungal communities to an elevation gradient in a subtropical montane forest of China. Appl. Microbiol. Biotechnol. 2012, 97, 2219–2230. [Google Scholar] [CrossRef]
  52. Gomoryova, E.; Strelcova, K.; Fleischer, P.; Gomory, D. Soil microbial characteristics at the monitoring plots on wind-throw areas of the Tatra National Park (Slovakia): Their assessment as environmental indicators. Environ. Monit. Assess. 2011, 174, 31–45. [Google Scholar] [CrossRef]
  53. Hamer, U.; Makeschin, F.; An, S.; Zheng, F. Microbial activity and community structure in degraded soils on the Loess Plateau of China. J. Plant Nutr. Soil Sci. 2009, 172, 118–126. [Google Scholar] [CrossRef]
  54. Yang, Y.; Chai, Y.; Xie, H.; Zhang, L.; Zhang, Z.; Yang, X.; Hao, S.; Gai, J.; Chen, Y. Responses of soil microbial diversity, network complexity and multifunctionality to three land-use changes. Sci. Total. Environ. 2023, 859, 160255. [Google Scholar] [CrossRef]
  55. Wang, J.-T.; Cao, P.; Hu, H.-W.; Li, J.; Han, L.-L.; Zhang, L.-M.; Zheng, Y.-M.; He, J.-Z. Altitudinal Distribution Patterns of Soil Bacterial and Archaeal Communities Along Mt. Shegyla on the Tibetan Plateau. Microb. Ecol. 2014, 69, 135–145. [Google Scholar] [CrossRef]
  56. Schlatter, D.C.; Bakker, M.; Bradeen, J.; Kinkel, L.L. Plant community richness and microbial interactions structure bacterial communities in soil. Ecology 2015, 96, 134–142. [Google Scholar] [CrossRef] [Green Version]
  57. Kielak, A.M.; Barreto, C.C.; Kowalchuk, G.A.; Van Veen, J.A.; Kuramae, E.E. The Ecology of Acidobacteria: Moving beyond Genes and Genomes. Front. Microbiol. 2016, 7, 744. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Lv, X.; Yu, J.; Fu, Y.; Ma, B.; Qu, F.; Ning, K.; Wu, H. A Meta-Analysis of the Bacterial and Archaeal Diversity Observed in Wetland Soils. Sci. World J. 2014, 2014, 437684. [Google Scholar] [CrossRef] [Green Version]
  59. Zeng, Q.; Jia, P.; Wang, Y.; Wang, H.; Li, C.; An, S. The local environment regulates biogeographic patterns of soil fungal communities on the Loess Plateau. Catena 2019, 183, 104220. [Google Scholar] [CrossRef]
  60. Tedersoo, L.; Bahram, M.; Põlme, S.; Kõljalg, U.; Yorou, N.S.; Wijesundera, R.; Ruiz, L.V.; Vasco-Palacios, A.M.; Thu, P.Q.; Suija, A.; et al. Global diversity and geography of soil fungi. Science 2014, 346, 1256688. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Thomson, B.C.; Tisserant, E.; Plassart, P.; Uroz, S.; Griffiths, R.I.; Hannula, S.E.; Buee, M.; Mougel, C.; Ranjard, L.; Van Veen, J.A.; et al. Lemanceau, Soil conditions and land use intensification effects on soil microbial communities across a range of European field sites. Soil Biol. Biochem. 2015, 88, 403–413. [Google Scholar] [CrossRef]
  62. Urbanová, M.; Šnajdr, J.; Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil Biol. Biochem. 2015, 84, 53–64. [Google Scholar] [CrossRef]
  63. Bahram, M.; Põlme, S.; Kõljalg, U.; Zarre, S.; Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. N. Phytol. 2011, 193, 465–473. [Google Scholar] [CrossRef]
  64. Donhauser, J.; Niklaus, P.A.; Rousk, J.; Larose, C.; Frey, B. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biol. Biochem. 2020, 148, 107873. [Google Scholar] [CrossRef]
  65. Guadarrama, P.; Castillo, S.; Ramos-Zapata, J.A.; Hernandez-Cuevas, L.V.; Camargo-Ricalde, S.L. Arbuscular my-cor-rhizal fungal communities in changing environments: The effects of seasonality and anthropogenic disturbance in a seasonal dry fores. Pedobiologia 2014, 57, 87–95. [Google Scholar] [CrossRef]
  66. Zimmermann, M.; Leifeld, J.; Conen, F.; Bird, M.; Meir, P. Can composition and physical protection of soil organic matter explain soil respiration temperature sensitivity? Biogeochemistry 2010, 107, 423–436. [Google Scholar] [CrossRef]
  67. Ding, L.; Shang, Y.; Zhang, W.; Zhang, Y.; Li, S.; Wei, X.; Zhang, Y.; Song, X.; Chen, X.; Liu, J.; et al. Disentangling the effects of driving forces on soil bacterial and fungal communities under shrub encroachment on the Guizhou Plateau of China. Sci. Total. Environ. 2019, 709, 136207. [Google Scholar] [CrossRef] [PubMed]
  68. Pei, Z.; Eichenberg, D.; Bruelheide, H.; Kröber, W.; Kühn, P.; Li, Y.; von Oheimb, G.; Purschke, O.; Scholten, T.; Buscot, F.; et al. Soil and tree species traits both shape soil microbial communities during early growth of Chinese subtropical forests. Soil Biol. Biochem. 2016, 96, 180–190. [Google Scholar] [CrossRef]
  69. Zhao, R.; Yang, X.; Tian, Q.; Wang, X.; Liao, C.; Li, C.-L.; Liu, F. Soil microbial community structure, metabolic potentials and influencing factors in a subtropical mountain forest ecosystem of China. Environ. Pollut. Bioavailab. 2020, 32, 69–78. [Google Scholar] [CrossRef]
  70. Zhang, B.; Xue, K.; Zhou, S.; Che, R.; Du, J.; Tang, L.; Pang, Z.; Wang, F.; Wang, D.; Cui, X.; et al. Phosphorus mediates soil prokaryote distribution pattern along a small-scale elevation gradient in Noijin Kangsang Peak, Tibetan Plateau. FEMS Microbiol. Ecol. 2019, 95, fiz076. [Google Scholar] [CrossRef] [Green Version]
  71. Fu, D.; Wu, X.; Duan, C.; Smith, A.R.; Jones, D.L. Traits of dominant species and soil properties co-regulate soil mi-crobial communities across land restoration types in a subtropical plateau region of Southwest China. Ecol. Eng. 2020, 153, 105897. [Google Scholar] [CrossRef]
Forests 14 00772 g001 550
Figure 1. Distribution of sampling sites in Jiangxi Province, China.
Figure 1. Distribution of sampling sites in Jiangxi Province, China.
Forests 14 00772 g001
Forests 14 00772 g002 550
Figure 2. Alpha diversity of bacterial (a) and fungal (b) diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
Figure 2. Alpha diversity of bacterial (a) and fungal (b) diversity in Pinus taiwanensi and Pinus massoniana forests soils with the elevations.
Forests 14 00772 g002
Forests 14 00772 g003 550
Figure 3. UPGMA cluster tree of bacterial (a) and fungal (b) communities in different forest soils at different elevations.
Figure 3. UPGMA cluster tree of bacterial (a) and fungal (b) communities in different forest soils at different elevations.
Forests 14 00772 g003
Forests 14 00772 g004 550
Figure 4. Principal coordinates analysis (PCoA) of bacterial (a) and fungal (b) communities in different forest soils at different elevations.
Figure 4. Principal coordinates analysis (PCoA) of bacterial (a) and fungal (b) communities in different forest soils at different elevations.
Forests 14 00772 g004
Forests 14 00772 g005 550
Figure 5. Relative abundance of bacterial (a) and fungal (b) phyla in different forest soils at different elevations.
Figure 5. Relative abundance of bacterial (a) and fungal (b) phyla in different forest soils at different elevations.
Forests 14 00772 g005
Forests 14 00772 g006 550
Figure 6. Redundancy analysis (RDA) between elevation and soil properties (red arrows) and the dominant (relative abundance) bacterial (a) and fungal (b) phyla in different forest soils. SOC, Soil organic carbon; TN, Total nitrogen; AP, Available phosphorus; AK, Available potassium.
Figure 6. Redundancy analysis (RDA) between elevation and soil properties (red arrows) and the dominant (relative abundance) bacterial (a) and fungal (b) phyla in different forest soils. SOC, Soil organic carbon; TN, Total nitrogen; AP, Available phosphorus; AK, Available potassium.
Forests 14 00772 g006
Forests 14 00772 g007 550
Figure 7. Pearson’s rank correlation coefficients between soil properties and the dominant (relative abundance) bacterial genera in P. taiwanensis (a) and P. massoniana (b) and fungal genera in P. taiwanensis (c) and P. massoniana (d) forest soils. SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; AK, available potassium. The colors represent positive and negative correlations (red: positive; green and blue: negative), and the numbers represent correlation coefficients. *** (p < 0.001); ** (p < 0.01); * (p < 0.05).
Figure 7. Pearson’s rank correlation coefficients between soil properties and the dominant (relative abundance) bacterial genera in P. taiwanensis (a) and P. massoniana (b) and fungal genera in P. taiwanensis (c) and P. massoniana (d) forest soils. SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; AK, available potassium. The colors represent positive and negative correlations (red: positive; green and blue: negative), and the numbers represent correlation coefficients. *** (p < 0.001); ** (p < 0.01); * (p < 0.05).
Forests 14 00772 g007
Table
Table 1. The soil characteristics in Pinus taiwanensi and Pinus massoniana forest soils with the elevations.
Table 1. The soil characteristics in Pinus taiwanensi and Pinus massoniana forest soils with the elevations.
VegetationElevation (m)pHSOC (g/kg)TN (g/kg)AP (mg/kg)AK (mg/kg)
Pinus
taiwanensis		J13003.98 ± 0.03 b40.59 ± 3.45 b8.94 ± 0.73 b1.94 ± 0.06 bc72.16 ± 3.72 a
S11804.33 ± 0.03 a39.55 ± 4.85 b8.49 ± 0.87 b1.78 ± 0.26 c71.13 ± 6.04 a
L9304.25 ± 0.06 a61.83 ± 1.98 a13.06 ± 0.07 a2.62 ± 0.29 ab65.64 ± 3.96 a
J8503.81 ± 0.05 c41.97 ± 3.87 b8.54 ± 0.75 b3.29 ± 0.16 a43.3 ± 5.95 b
Pinus
massoniana		J5004.63 ± 0.09 a16.74 ± 4.07 b4.52 ± 1.04 b3.94 ± 0.88 a104.81 ± 17.42 a
S4004.31 ± 0.01 b37.99 ± 2.91 a7.41 ± 0.66 a1.68 ± 0.2 b37.11 ± 4.65 b
L3004.33 ± 0.03 b10.05 ± 3.65 b3.67 ± 0.63 b2.55 ± 0.45 ab58.08 ± 8.67 b
Data are mean ± standard deviation (n = 3). Different letters in a column denote statistically significant differences (p < 0.05). SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; AK, available potassium.
Table
Table 2. Spearman correlation coefficients (r) between soil bacterial and fungal diversity (Chao1 index, Shannon index) and soil properties.
Table 2. Spearman correlation coefficients (r) between soil bacterial and fungal diversity (Chao1 index, Shannon index) and soil properties.
VariableBacterial Chao1 IndexBacterial Shannon IndexFungal Chao1 IndexFungal Shannon Index
rprprprp
pH−0.3920.079−0.2740.2290.0270.9090.1510.514
SOC0.4220.0570.481 *0.0270.0430.854−0.1780.44
TN0.466 *0.0330.531 *0.0130.1090.638−0.1950.397
AP0.3990.0730.3370.1360.3780.0910.2680.241
AK−0.0420.8580.1890.4110.3410.13−0.0780.737
* indicates significant dissimilarity (p < 0.05). SOC, soil organic carbon; TN, total nitrogen; AP, available phosphorus; AK, available potassium.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, K.; Xiang, J.; Ma, Y.; Cheng, J.; Gu, J.; Hu, M.; Yang, Y.; Fang, Y.; Wang, G.; Zhang, H. Response of Soil Microbial Communities to Elevation Gradient in Central Subtropical Pinus taiwanensis and Pinus massoniana Forests. Forests 2023, 14, 772. https://doi.org/10.3390/f14040772

AMA Style

Huang K, Xiang J, Ma Y, Cheng J, Gu J, Hu M, Yang Y, Fang Y, Wang G, Zhang H. Response of Soil Microbial Communities to Elevation Gradient in Central Subtropical Pinus taiwanensis and Pinus massoniana Forests. Forests. 2023; 14(4):772. https://doi.org/10.3390/f14040772

Chicago/Turabian Style

Huang, Kexin, Jian Xiang, Yuying Ma, Jinping Cheng, Jie Gu, Meng Hu, Yuan Yang, Yanming Fang, Genmei Wang, and Huanchao Zhang. 2023. "Response of Soil Microbial Communities to Elevation Gradient in Central Subtropical Pinus taiwanensis and Pinus massoniana Forests" Forests 14, no. 4: 772. https://doi.org/10.3390/f14040772

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop