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Article

Assessment of Artificial Forest Restoration by Exploring the Microbial Community Structure and Function in a Reclaimed Coal Gob Pile in a Loess Hilly Area of Shanxi, China

by
Shuang Liu
1,
Jiuping He
1,
Yuewei Ning
1,
Junjian Li
1,
Hong Zhang
2 and
Yong Liu
1,*
1
Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China
2
College of Environment and Resources, Shanxi University, Taiyuan 030006, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(9), 1888; https://0-doi-org.brum.beds.ac.uk/10.3390/f14091888
Submission received: 20 July 2023 / Revised: 12 September 2023 / Accepted: 13 September 2023 / Published: 17 September 2023
(This article belongs to the Special Issue Assessments and Restoration Technique of Forest Land Development in Hilly Areas)
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Abstract

:
In this study, soil obtained from a reclaimed coal gob pile was expected to be rapidly improved with the use of artificial vegetation restoration practices, such as artificial forests, which increase the taxonomic variety in the soil microbial community and its functions. In order to successfully identify the effect of artificial forest restoration project on the soil’s quality, a field study was conducted on soil reclaimed from a coal gob pile in a loess hilly area located in Shanxi to assess the effects of five commonly used artificially restored coniferous forest species (i.e., Platycladus orientalis: PO, Sabina chinensis: SC, Pinus sylvestris: PS, Picea asperata: PA and Pinus tabuliformis: PT) on the soil’s physico-chemical properties, the bacterial community and functional gene attributes. The results showed that significant differences were observed in the bacterial community’s diversity and structure, as well as in functional genes, among the different artificial tree species. PS and PA presented lower pH and bulk density levels and higher soil alkaline protease (PRO), alkaline phosphatase (ALP) and urease (URE) activities, in comparison to other tree species. The bacterial community’s diversity and functional genes were noticeably higher in both PS and PA. In addition, soil bulk density and pH can directly affect the soil keystone bacteria and microbial functions and can indirectly affect the soil keystone genus and microbial functions by affecting the soil nutrient elements and enzyme activity. Moreover, soil bacterial keystone bacteria significantly affect these functions. Finally, compared to the other coniferous tree species, PS and PA presented a significantly higher integrated fertility index (IFI) score. Therefore, PS and PA might be more suited to the forest restoration project using reclaimed soil obtained from a coal gob pile located in Shanxi’s mining region. The present research contributes to the understanding of how various tree species affect microbial populations and functions in similar mining zones and/or hilly terrains.

1. Introduction

At present, China produces the highest amount of coal than any other country in the world. However, highly mechanized mining practices invariably result in serious environmental and land problems [1,2,3]. Shanxi Province has an abundance of mineral resources; however, the long-term excessive exploitation of these resources has caused serious damage to the ecosystem of coal mining areas, especially those located to the north of the Shanxi coal mining area, in the semi-arid loess hilly area of China [4]. For example, the exploitation of resources has led to subsidence and land fissures, the destruction of vegetation, desertification, environmental pollution, and biodiversity loss [5]. Therefore, coal mining is becoming one of the most prominent examples of ecosystem function degradation created by human activity [6]. Approximately 17,100 km2 of land were destroyed by coal mining practices conducted in Shanxi province. The accumulated actions of subsidence, destruction, and land occupation have reached 7560 km2 and have been increasing by 50 km2 every year, severely restricting the development of the regional economy, society, and ecology in the area [7].
Coal mining practices usually result in the gradual dumping of coal gobs in and/or close to a mined region, which eventually accumulates into large gob piles that occupy part of the land. A coal gob consists of solid waste combined with invaluable coal and waste rock materials, which account for 25 percent of the total industrial solid waste produced [5]. This excessive coal gob dumping behavior inevitably leads to the coal gob-occupied land near the mine area, especially in Shanxi province, and transforms into a special landscape in many regions [8,9]. In addition to occupying land, coal gob dumping and accumulation in mining areas can cause serious environmental problems, such as water and soil erosion and pollution from toxic metals, and air pollution from the toxic gases produced during the spontaneous combustion of coal gobs [10,11,12]. These factors produce serious environmental problems and threaten the lives and health of residents living close to the mining area.
Vegetation restoration is an effective method to solve these problems concerning gob piles [13,14]. However, it is extremely difficult to perform natural vegetation restorations on coal gob piles in the short term, because of the poor quality of the soil in the mine, i.e., poor soil structure, hydrological regimes, and soil nutrient [4,5,15]. The restoration of natural vegetation, in contrast to artificial forests, is a process that requires a very long period of time, when attempting to restore ecosystem functions [5]. This process would require approximately 50–100 years for the successful development of suitable vegetation types, and approximately 100–1000 years to develop productive soil by natural vegetation succession [16,17]. Therefore, a number of artificial vegetation restoration projects were conducted in coal mine waste dumps by covering the tops of coal gob piles with soil and artificially replanting vegetation, through tree planting, agricultural reclamation projects, and the establishment of botanical gardens [16,18]. These are the most effective and useful implementation methods to biologically enhance soil quality and restore vegetation [19,20,21,22]. However, at present, ecological restoration projects remain in the initial stage in the majority of coal mining areas located in Shanxi province, and vegetation reconstruction and soil remediation practices are both difficult tasks in the field [5].
For the purpose of maintaining and enhancing the stability of artificially restored plants and soil ecosystems, appropriate vegetation restoration practices are presently of the utmost importance. A crucial issue, particularly in mining areas, is the selection of suitable plant species for the rapid formation of a self-sustaining revegetation ecosystem [23]. Different soil types may have suitable plant restoration types, because different vegetation types or plant species produce different types of organic matter and carbon contents, which results in changes in the physical structure, chemical composition, and microbiological characteristics of the soil [24,25,26]. For example, the soil bulk density decreases when soil porosity increases due to root penetration [27,28]; thus, soil infiltration [29] and field capacities [30] increase. The soil microbial and functional characteristics of soil, which connect the soil and plants, are crucial to controlling the succession and recovery processes of mining vegetation [31,32,33]. Researchers in the field are increasingly using microbial properties as an ecological indicator of the effects of soil recovery efforts in mined areas [34,35]. This is due to the fact that the activity of soil organisms offers useful information when examining the soil’s quality following extensive coal mining activities [3,36]. However, it remains unclear in the literature how soil bacterial communities and functions respond to vegetation restoration practices performed on reclaimed coal gob piles in the loess areas of Shanxi, China. Therefore, the knowledge concerning soil microbial properties and functions during soil reclamation and vegetation recovery processes is especially crucial to guide the ecological restoration of mining areas in the future.
The Jinhuagong coal mine restoration park is located in the Datong mining area in Shanxi province, which has a long coal mining history and has a positive regional representation. In this study, we used high-throughput and Illumina sequencing technology to sequence the soil bacteria communities and functional gene properties to assess their influence on different tree species restoration projects conducted in the coal mine restoration area. The aims of this study are to (i) assess the influence of different coniferous tree species on the characteristics of the soil bacterial community and potential impact factors that contribute to the occurrence of bacterial communities; (ii) explore the relationships among soil physico-chemical factors, bacterial communities, and functions; and (iii) suggest suitable tree species that can be used for the restoration of coal mine soil properties and provide a scientific basis for vegetation restoration management in the coal mine restoration areas located in the loess areas of China.

2. Materials and Methods

2.1. Study Location and Soil Sampling

The research area is located in Jinhua Palace National Mine Park (40°10′ N, 113°13′ E), which is located in Datong, northern Shanxi province, China (Figure 1). This area is situated on a reclaimed coal mining site. This region has a temperate continental semi-arid monsoon climate. The mean annual temperature is 6.5 °C, and the mean annual precipitation is 400 mm, with 70% of the annual precipitation occurring between June and September. Chestnut soil, which is categorized as Arenosols by the FAO/UNESCO soil classification system, characterized by its low organic matter content and poor structure, predominated in the study area [4,5]. This soil type can be well restored by artificially forest vegetation, including conifer trees [4]. Therefore, five coniferous tree species were planted in the reclaimed coal mining area in 2005, including Platycladus orientalis (PO), Sabina chinensis (SC), Pinus sylvestris (PS), Picea asperata (PA) and Pinus tabuliformis (PT) (Figure 1). The distance between the plots was over 6 m and the spacing of the samplings was 3 m × 3 m. Three repeated plots of 10 m×10 m were set for each vegetation restoration type.
Soil samples were collected in July of 2019. The rhizosphere soil samples were collected using the method of shaking the root [37,38]. The bulk soil samples were collected at 0–10 cm soil depth. All samples were obtained at six random sampling sites with three replicates for each vegetation restoration type, and all 18 subsamples were mixed into one composite sample and placed in polyethylene (PE) bags. Each soil sample was divided into two parts: the fresh half was kept in an icebox and transported to the laboratory within 24 h and then kept at −80 °C for DNA extraction; and the other part was air-dried, crushed, and passed through a 2.00 mm sieve for determination of soil chemical properties.

2.2. Analysis of Soil Physical and Chemical Properties

Soil bulk density was measured using the cutting ring method [39]. The soil pH was measured with a pH meter with 10 g soil at a water-to-soil ratio of 1:2.5 [40]. The contents of soil total carbon (TC), total nitrogen (TN) and total sulfur (TS) using the vario MACRO cube (Elementar, Germany) was measured using 0.1 g soil. Alkaline phosphatase (ALP) activity was determined using the phenyl disodium phosphate colorimetric method with 0.1 g soil [41]; alkaline protease (PRO) activity was determined using the Folin–Ciocalteu method with 0.1 g soil [42]; urease (URE) activity was determined using the sodium phenolate sodium hypochlorite colorimetric method with 0.25 g soil [43]; and dehydrogenase activity (DHA) was determined using the TTC (2,3,5-triphenyltetrazolium chloride) method with 0.1 g soil [44].

2.3. Soil DNA Extraction and Quantitative PCR Analysis of Bacterial 16S rRNA

According to the manufacturer’s instructions, 0.5 g of soil was weighed to extract the total DNA from the soil using a TIANnamp Soil DNA Kit (TIANGEN, Biotech Co., Ltd., Beijing, China). The barcode primers 338 F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to amplify the V3–V4 hypervariable region of 16S rRNA gene in bacteria [45]. Each sample was then subjected to the following amplification process: initial denaturation at 95 °C for 3 min followed by 27 rounds of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min. The PCR products were purified and high-throughput paired-end sequencing was performed on the Illumina MiSeq PE300 platform. Sequencing and bioinformatics analyses were conducted by Majorbio Bio-pharm Technology Co., Ltd., Shanghai, China.

2.4. Functional Genes Analysis

In order to check reproducibility, each qPCR reaction was performed in triplicate. In order to evaluate the functional potential of the microbial community, soil functional genes were examined using HTqPCR technology (Mega Gene Technology Co., Ltd., Guangzhou, China). Functional genes related to C, N, P, and S were quantified using QMEC [46]. According to the MagaBio soil DNA extraction kit method, 0.25 g of the soil sample was obtained to extract soil DNA, and the Qubit 4.0 instrument (Thermo Fisher Scientific, Waltham, MA, USA) was used to determine the total quantity and purity of the extracted soil DNA. The sample and primer reagents were added to the micro-pores of the high-throughput qPCR chip using a SmartChip MyDesign Kit (Takara Biomedical Technology, Clontech, Beijing, China), and the DNA concentration was diluted with 20 ng uL−1. For the detection of the qPCR reaction and fluorescence signal, SmartChip Real-Time PCR System (WaferGen Biosystems, Alameda, CA, USA) was employed. Target genes whose amplification efficiencies exceeded the 1.8–2.2 range were eliminated. The detection limit was 31 cycle thresholds (CTs). Each qPCR reaction was conducted in triplicate to assess the reproducibility outcomes [46].

2.5. Statistical Analysis

The analysis of variance (one-way and two-way ANOVA) test was employed to determine the effects of tree species on the soil’s physico-chemical properties, enzyme activity, alpha diversity of the soil bacterial community, and functional genes. Statistical analyses were performed with SPSS 26.0 software (SPSS IBM Corp, New York, NY, USA). A Duncan’s test was performed to detect the significant differences at the 0.05 level. A redundancy analysis (RDA) was used to evaluate the effect of the soil physico-chemical properties of bulk and rhizoshpere soils on bacterial phylogeny and soil C-, N-, P-, and S-cycling functional genes’ structure, and R environment version 3.6.1 (http://www.r-project.com, accessed on 1 August 2022) was used to perform a Monte Carlo permutation test to detect significant differences. The co-occurrence network analyses, which represented the possible pairwise correlations between the keystone bacterial taxa and functional genes, were visualized using Gephi 0.9.2. The correlation was accepted when a Pearson’s coefficient (p) >0.72 and a p-value of 0.05 were obtained in order to reduce any redundant information. Structural equation modeling (SEM) was conducted to determine the potential direct and indirect edaphic factors of the keystone taxa and functional potential with a maximum likelihood estimation in Amos 26.0. The fitness of SEM was assessed by a chi-square test (p > 0.05), goodness-of-fit index (GFI > 0.9), and root mean square error of approximation (RMSEA < 0.05) [47].
The mine soil integrated fertility index (IFI), which utilizes PCA to select the soil physico-chemical and microbial characteristics, was described by the principal components (PCs) with an eigenvalue ≥ 1, which explained at least 5% of the variation in the data [48,49,50]. The integrated fertility index (IFI) value can be calculated as follows:
IFI = ∑ i = 1 n W i S i
where, W is the ith PC eigenvalue, S is the ith indicator contribution rate for each variable, and i = 1, 2, 3 … n.

3. Results

3.1. Soil Physico-Chemical and Enzyme Activity Properties

Tree species, soil, and tree species by soil all presented a significant effect on the TC, TN and TS variables. Tree species also had a significant effect on soil bulk and pH levels; however, no significant effect was observed in the species-by-soil interaction (Table 1). The results showed that there were significant differences evident in the soil elements, soil bulk, and pH level among tree species. For example, soil bulk density for PS was significantly lower than the other tree species (p < 0.05). No significant difference in the pH levels between PS and PA were evident, which was lower than the other tree species. The TN, TC, and TS contents for PS and SC were higher than the other tree species in both bulk and rhizosphere soils (Table 1). The two-way ANOVA results showed that both tree species and soil had a significant (p < 0.05) effect on all of the response variables (Table 2), which indicated that there were significant differences for four soil enzyme activities occurring between the bulk and rhizosphere soils, as well as among different tree species. For example, the PRO, ALP, and URE activities for PS and PA presented the higher values (p < 0.05) than those for SC and PO, and PS and PA presented the highest values for DHA, although no significant statistical differences were observed in the rhizosphere soils. The ALP values for PS and PT were significantly higher than the other tree species in the bulk soil (p < 0.05). URE showed significantly higher values for PS and PA than PO and PT in the bulk soil (p < 0.05). PS had the highest alkaline protease and dehydrogenase activity levels than the other tree species in the bulk soil (Table 2).

3.2. Soil Microbial Community Composition and Diversity

The composition and structure of the bacterial community in bulk and rhizosphere soils for five tree species were elucidated in this study by high-throughput sequencing analysis; a total of 91,713 high quality sequences were obtained. The dominant community groups were Proteobacteria (19.6%–28.8%), Acidobacteria (11.3%–25.5%), Bacteroidetes (9.5%–21.7%), Actinobacteria (9.8%–25.7%), Chloroflexi (4.29%–9.04%), Gemmatimonadetes (2.78%–10.06%), Planctomycetes (3.96%–5.65%), Verrucomicrobia (1.22%–5.32%), Thaumarchaeota (0.86%–2.47%) and Cyanobacteria (0.04%–6.65%); the remaining samples consisted of rare community groups (Figure 2). Tree species, soil, and tree species by soil all presented significant effects on the four most dominant phyla: Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria (Table S1). The results indicated that there were significant differences present in the bulk and rhizosphere soils for the four most dominant phyla, e.g., the average relative abundances of Proteobacteria and Actinobacteria in the rhizoshpere soil were 25.2% and 17.9%, respectively, which were higher than those in the bulk soil (23.0% and 14.3%, respectively), and the average relative abundances of Acidobacteria (18.2%) and Bacteroidetes (12.5%) in the rhizoshpere soil were lower than those in the bulk soil (23.0% and 14.3%, respectively) (Figure 2). Furthermore, significant differences in the four most dominant phyla were also observed among the different tree species in both the bulk and rhizosphere soil samples (Figure 2A,B). For example, PA significantly enriched the relative abundances (p < 0.05) of Proteobacteria and Bacteroidota in the bulk soil compared to the other tree species; and PT and PO significantly enriched the relative abundances of Acidobacteria and Actinobacteria (p < 0.05) in the bulk soil, respectively, compared to the other tree species (Figure 2A).
Tree species, soil, and tree species-by-soil interactions all showed significant effects (p < 0.001) for the diversity index variables (Table 3). e.g., PS and PO presented significantly higher Chao1 richness than the other tree species in the bulk soil, and PS and PA presented higher Shannon, Chao1, and operational taxonomic unit (OTU) values than the other tree species in rhizoshpere soils. PS presented the highest significant Shannon and OTU scores compared to the other tree species in both the bulk and rhizosphere soils. Moreover, the dissimilarity test conducted, based on three analysis methods (MRPP, ANOSIM, and PERMANOVA), showed that bacterial community structure considerably changed among the tree species in both bulk and rhizosphere soils (Table 4).

3.3. Soil C-, N-, P-, and S-Cycling Genes

A total of 53 gene nodes and 801 edges were discovered by functional molecular ecological networks in the bulk soil, which was comparable to the rhizosphere soil (55 gene nodes and 783 edges) (Figure 3). The result implies that the correlation between the functional genes presents no significant difference in the bulk and rhizosphere soil samples. The average degree of networks was 30.23 and 28.47 for the bulk and rhizosphere soils, respectively, suggesting the presence of a slightly more complex network in the bulk soil. Moreover, most genes were positively (blue edges) connected with each other in both the bulk and rhizosphere soil samples (Figure 3), suggesting that artificially forest vegetation restoration activity could promote the cooperative behavior of microbial functional communities.
Additionally, for C-cycling genes, both total C-degradation and C-fixation genes for PS and PA were observed to be significantly higher than the PT present in the bulk soil. However, PO and PA showed significantly higher values than PS in the rhizosphere soil (Figure 4A,B), which showed a trend similar to the total genes for different tree species. Significantly higher values for the total N-cycling genes were also observed for PA, compared to the other tree species in bulk soil; however, PO was significantly higher (p < 0.05) than the other tree species in the rhizosphere soil (Figure 4C). There were no statistical differences in total P-cycling genes among the tree species in bulk soil (Figure 4D). For total S-cycling genes, PS and PO had significantly higher values than PA and PT in the bulk soil; however, there were no statistical differences among tree species in the rhizosphere soil (Figure 4E). Meanwhile, the dissimilarity test showed that the structure of functional genes was highly changed among the tree species in both the bulk and rhizosphere soils, based on three analytical methods (Table 4).

3.4. The Effect of Edaphic Factors on Microbial Community and Function

In order to assess the connections between edaphic factors and microbial communities present in bulk and rhizosphere soils, a redundancy analysis (RDA) was performed (Figure 5A,B). The results showed that soil TS, pH, and bulk density levels all exhibited significant impacts on the microbial community present in the bulk soil (Figure 5A), and TC, TN, TS, and bulk density significantly impacted the microbial community in the rhizosphere soil (Figure 5B). Meanwhile, the results of the relationships that exist between the edaphic factors and functional genes showed that bulk density exhibited remarkable impacts on functional genes present in the bulk soil (Figure 5C), while TS and bulk density showed significant impacts on the functional genes in the rhizosphere soil (Figure 5D).
The relationships that exist between the keystone microbes and functional genes were analyzed by using the co-occurrence network to calculate the potential connectivity at the generic level (Figure 6A). A total of 391 nodes were evident in the microbial co-occurrence network, among which 63 functional genes and 328 bacterial genera were observed. The lines indicate that the nodes have significant Pearson’s correlation values, with 45% negatively correlated and 55% positively correlated (Figure 6A). Seven genera, namely, Emticicia, Thiobacillus, Terrimicrobium, Turicibacter, Planomonospora, Microlunatus and Pedomicrobium, presented the greatest connectivity behavior with the functional genes, which were considered keystone genera that play important roles in network construction and stability activities. Emticicia and Terrimicrobium presented the most connected degrees, which exhibited a significant correlation with more than 40 functional genes (Figure 6B). All the keystone genera belong to rare genera, except pedomicrobium. The Mantel test further showed that Pedomicrobium and Thiobacillus were significantly correlated with carbon fixation functional genes (p < 0.05) (Figure 6C). The significant positive correlation evident between keystone bacteria and microbial function was further supported by the structural equation model (SEM). Moreover, soil bulk density and pH levels directly negatively affected the keystone bacteria and functions. Soil bulk density and pH levels can also significantly affect the soil elements (TC, TN, and TS), which then significantly positively affect the keystone bacteria (Figure 6D). Additionally, the higher integrated fertility index (IFI) values of PS (i.e., 0.579 and 0.830) and PA (i.e., 0.749 and 0.730) were observed in both the bulk and rhizosphere soils (Table 5).

4. Discussion

4.1. Changes in Soil Physico-Chemical Properties

The soil’s physical and chemical properties can be changed for different types of vegetation or tree species [24,25,51]. In our study, we observed significant differences in the bulk density, soil element, and pH levels among tree species., i.e., the soil bulk density level was significantly decreased for PS compared to the other tree species. Previous studies also observed that the soil bulk density level decreased following vegetation restoration practices [24,28,52]. Due to more roots penetrating the soil and increased porosity levels, the bulk density of the soil decreased [27,53]. Moreover, the TN, TC, and TS contents for PS and SC were higher than the other tree species. Previous studies proved that restoration activities can increase TN and SOC in the soil [54]. This result was mostly caused by the buildup of litter, leaves, roots, and other aboveground biomass, which aided in the production of humus [14,55]. The PS and PA values in this study showed significant lower pH levels than the other tree species. Previous studies also proved the significant changes in soil pH levels by restoration activities [56].

4.2. Changes in Microbial Community and Functional Characteristics

According to research previously conducted, different microbial diversity and composition characteristics were observed among restored tree species [57]. Our study results also indicate significant differences among coniferous tree species for both the alpha diversity and the relative abundance values of dominant bacterial phyla in the soil. Our study observed that the most prevalent bacterial phyla in the soil were Proteobacteria, Acidobacteria, Actinobacteria and Bacteroidetes. These results are generally consistent with previous research conducted on collected soils from mining area in northern China [33]. According to previous studies [58,59], Proteobacteria were the most abundant phylum in the soil, and previous studies also indicated that soil bacteria obtained from coniferous forests were dominated by Proteobacteria [60]. In addition, the community composition was similar for all phyla among different tree species in our study; however, the structure of soil bacteria at the phyla level significantly changed among different coniferous tree species. e.g., PA significantly enriched Proteobacteria and Bacteroidota relative abundance levels; however, PT and PO significantly enriched Acidobacteria and Actinobacteria relative abundance levels, respectively, in the bulk soil. The higher bacterial abundance and diversity outcomes in PS and PA in our study might have resulted from the relatively high contents of carbon and nitrogen in the soil [61,62]. The ultimate goal of soil reclamation processes in mining regions is the restoration of soil ecosystem functions. Therefore, it is important to study the reclamation of microbial community functions. In our study, PS and PA generally had a high abundance of C-, N-, P-, and S-cycling genes relative to the other tree species, and the structure of the functional genes was significantly impacted among different tree species. Furthermore, increased significant differences between tree species were observed in the bacterial taxonomic structure, compared to the functional structure. This indicates that the presence of bacterial community taxa are important and support microbiomes with ecological functions.

4.3. Relationships among Edaphic Factors and Microbial Community Function

The edaphic factors including soil physical, chemical and biological properties are interlinked to impact vegetation restoration activity. The diversity, structure, and activity of the microbial community in forest soils can be influenced by the physico-chemical parameters of the soil during the restoration process [58,61,63]. In our study, the diversity and structure of the bacterial communities across different tree species were mostly influenced by bulk density, pH, TC, TN, and TS factors. Soil bulk density and TS presented significant impacts on functional genes. This might have occurred due to the presence of different tree species resulting in different rates of litter decomposition, release, and availability of nutrients, which impact microorganism structure, diversity, and function [25,26,64,65,66]. Soil nutrient elements showed greater impacts on the keystone bacteria than the functions, which is evidenced by the previous results in Table 4. Other studies also reported that the effects of soil environmental factors on the microbial community result from their synergism, rather than being governed by a single component [58,67]. In addition, the interactions occurring among the soil physico-chemical factors, bacterial community, and functions were evaluated by SEM, which demonstrated that the soil bulk density and pH levels could directly negatively affect the keystone bacteria and microbial functions. Soil bulk density and pH levels can significantly affect the soil elements (TC, TN, and TS), which then significantly positively affect keystone bacteria, which coincides with the results of previous studies that determined that soil nutrition could affect microbial community and functions [68,69]. Soil enzyme activity negatively affected microbial functions, which was in accordance with the results of a previous study [33]. Additionally, our study discovered that the keystone bacteria had a remarkable impact on the microbial functional potential. This suggests that rare bacteria play an important role in the construction of microbial community functions.

5. Conclusions

In the present study, we provided insights into the assessment of the artificial forest restoration practices of a reclaimed coal gob pile by exploring the characteristics of microbial taxonomic structures and functions in a loess hilly area in Shanxi. Our results demonstrate that differences can be observed in the soil’s physico-chemical characteristics, bacterial community diversity, structure, and function genes among different artificial tree species. Meanwhile, artificial tree species restoration activity changed the bacterial taxonomic structure more than functional genes in both bulk and rhizosphere soils. PS and PA presented relatively higher bacterial community diversity and microbial functions. Moreover, the soil bulk density and pH levels can directly negatively affect the soil keystone bacteria and microbial function by influencing the soil nutrient elements and enzyme activity. In addition, bacterial communities positively affected these functions, and the bacterial functional potential was mainly affected by the relative abundance of keystone bacteria that belong to a rare group. Finally, PS and PA had significantly higher IFI values compared to the other coniferous tree species in both bulk and rhizosphere soils. Therefore, PS and PA are more suitable for use in the reclaimed coal gob pile soil in the mining area located in Shanxi, China.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/f14091888/s1, Table S1: Relative abundance of dominant phyla of different tree species in bulk and rhizosphere soils.

Author Contributions

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

Funding

This study was supported by National Natural Science Foundation of China (grant numbers U1910207, U22A20557 and 41401618), and the Key R&D program (202202090301008).

Data Availability Statement

Summarized data are presented and available in this manuscript and rest of the data used and/or analyzed are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the research area and the soil sampling sites in the Jinhuagong coal mining area of Shanxi province.
Figure 1. Location of the research area and the soil sampling sites in the Jinhuagong coal mining area of Shanxi province.
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Figure 2. Soil bacterial dominant phyla in bulk soil (A) and rhizosphere soil (B) for different tree species. Error bars represent standard error of triplicate. Columns marked by the same small letter do not vary significantly (p > 0.05). Relative abundance less than 0.1% are included as others.
Figure 2. Soil bacterial dominant phyla in bulk soil (A) and rhizosphere soil (B) for different tree species. Error bars represent standard error of triplicate. Columns marked by the same small letter do not vary significantly (p > 0.05). Relative abundance less than 0.1% are included as others.
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Figure 3. The networks revealing correlations among detected genes in bulk and rhizosphere soils for different tree species and, accordingly, topological properties. The node color indicates different functional groups, and the node size is proportional to the degree. The blue edges represent the positive associations, and the red edges represent the negative associations.
Figure 3. The networks revealing correlations among detected genes in bulk and rhizosphere soils for different tree species and, accordingly, topological properties. The node color indicates different functional groups, and the node size is proportional to the degree. The blue edges represent the positive associations, and the red edges represent the negative associations.
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Figure 4. Average relative abundances of total C degradation (A), C fixation (B), N cycling (C), P cycling (D), S cycling (E), and total functional genes (F) are compared among different tree species for bulk soil and rhizosphere, respectively. Error bars represent the standard deviation. Different lowercase letters on the column for bulk and rhizosphere soils column indicate the significant difference among tree species (p < 0.05). * p < 0.05, ** p < 0.01, *** p < 0.001; ns, indicates no significant effect.
Figure 4. Average relative abundances of total C degradation (A), C fixation (B), N cycling (C), P cycling (D), S cycling (E), and total functional genes (F) are compared among different tree species for bulk soil and rhizosphere, respectively. Error bars represent the standard deviation. Different lowercase letters on the column for bulk and rhizosphere soils column indicate the significant difference among tree species (p < 0.05). * p < 0.05, ** p < 0.01, *** p < 0.001; ns, indicates no significant effect.
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Figure 5. Redundancy analyses (RDA) between environmental factors (red arrows) and microbial composition at the phylum level (relative abundance > 0.1%, blue arrows) in bulk soil (A) and rhizosphere soil (B), and between environmental factors (red arrows) and C, N, P and S functional genes (based on gene data) in bulk soil (C) and rhizosphere soil (D). Significant differences were identified by the Monte Carlo permutation test, * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 5. Redundancy analyses (RDA) between environmental factors (red arrows) and microbial composition at the phylum level (relative abundance > 0.1%, blue arrows) in bulk soil (A) and rhizosphere soil (B), and between environmental factors (red arrows) and C, N, P and S functional genes (based on gene data) in bulk soil (C) and rhizosphere soil (D). Significant differences were identified by the Monte Carlo permutation test, * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 6. Co-occurrence pattern between microbial community at the genera level and functional genes involved C, N, P and S cycling, the connections represent a strong Pearson’s correlations (|r| > 0.72 and p < 0.05), nodes in red and black are functional gene and soil bacteria, respectively (A). Connectivity of key bacterial genera. Five key bacterial genera were selected according to their close association with functional genes (B). The correlation between key bacterial genera and functional gene groups was determined by mantel test (C). The structural equation model (SEM), continuous and dashed arrows indicate positive and negative relationships, respectively. The standardized path coefficients are adjacent to the arrows (D). * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 6. Co-occurrence pattern between microbial community at the genera level and functional genes involved C, N, P and S cycling, the connections represent a strong Pearson’s correlations (|r| > 0.72 and p < 0.05), nodes in red and black are functional gene and soil bacteria, respectively (A). Connectivity of key bacterial genera. Five key bacterial genera were selected according to their close association with functional genes (B). The correlation between key bacterial genera and functional gene groups was determined by mantel test (C). The structural equation model (SEM), continuous and dashed arrows indicate positive and negative relationships, respectively. The standardized path coefficients are adjacent to the arrows (D). * p < 0.05, ** p < 0.01, *** p < 0.001.
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Table 1. Soil physicochemical properties of different tree species in bulk and rhizosphere soils.
Table 1. Soil physicochemical properties of different tree species in bulk and rhizosphere soils.
SoilTree SpeciesBulk Density (g∙cm−3)pHTotal N (g∙kg−1)Total C (g∙kg−1)Total S (g∙kg−1)
PO1.47 ± 0.02 a8.17 ± 0.02 a0.50 ± 0.18 b12.39 ± 2.73 d0.64 ± 0.04 c
SC1.43 ± 0.03 b8.13 ± 0.02 ab1.38 ± 0.22 a29.91 ± 2.94 a1.82 ± 0.15 a
Bulk soilPS1.36 ± 0.01 c8.09 ± 0.01 c1.18 ± 0.02 a25.86 ± 0.44 b1.13 ± 0.05 b
PA1.42 ± 0.02 b8.08 ± 0.01 c0.80 ± 0.02 b17.53 ± 0.83 c0.33 ± 0.03 d
PT1.41 ± 0.03 b8.12 ± 0.02 bc0.80 ± 0.02 b23.65 ± 0.63 b0.86 ± 0.09 c
PO1.47 ± 0.02 a8.12 ± 0.01 a0.62 ± 0.20 b13.20 ± 2.20 d0.64 ± 0.03 c
SC1.43 ± 0.03 b8.10 ± 0.02 a1.43 ± 0.20 a29.85 ± 3.20 a1.78 ± 0.10 a
Rhizosphere soilPS1.36 ± 0.01 c8.05 ± 0.01 b1.22 ± 0.03 a26.53 ± 1.23 a1.18 ± 0.04 b
PA1.42 ± 0.02 b8.05 ± 0.01 b0.88 ± 0.03 b17.80 ± 0.72 c0.35 ± 0.02 d
PT1.41 ± 0.03 b8.10 ± 0.02 a0.90 ± 0.02 b23.56 ± 0.55 b0.85 ± 0.06 c
Summary of ANOVA test
Tree species***************
Soilns************
Tree species × soilnsns*********
Note: Platycladus orientalis (PO), Sabina chinensis (SC), Pinus sylvestris (PS), Picea asperata (PA), Pinus tabuliformis (PT). Values are mean ± standard deviation (n = 3). Statistical significance was assessed by ANOVA followed by a Duncan’s test. Different lowercase letters per column for bulk and rhizosphere soil indicate the significant difference (p < 0.05). *** p < 0.001; ns, indicates no significant effect.
Table 2. Soil enzyme activity of different tree species in bulk and rhizosphere soils.
Table 2. Soil enzyme activity of different tree species in bulk and rhizosphere soils.
SoilTree SpeciesPRO μmol∙(d∙g)−1URE mg∙(d∙g)−1DHA mL∙(h∙g)−1ALP μmol∙(d∙g)−1
PO0.133 ± 0.019 d0.175 ± 0.024 d1.067 ± 0.146 b11.778 ± 1.257 b
SC0.193 ± 0.014 c0.413 ± 0.0113 b0.658 ± 0.341 b10.618 ± 0.809 b
Bulk soilPS0.576 ± 0.036 a0.382 ± 0.011 bc0.667 ± 0.420 b20.796 ± 1.019 a
PA0.039 ± 0.007 e1.0565 ± 0.032 a1.958 ± 0.260 a13.208 ± 0.409 b
PT0.406 ± 0.005 b0.335 ± 0.008 c1.183 ± 0.350 ab19.478 ± 1.247 a
PO0.107 ± 0.001 c0.356 ± 0.0116 d1.283 ± 0.458 a7.115 ± 0.379 d
SC0.051 ± 0.008 d0.429 ± 0.011 c1.250 ± 0.198 a2.454 ± 0.551 e
Rhizosphere soilPS0.175 ± 0.007 b0.753 ± 0.015 b1.425 ± 0.263 a11.070 ± 0.584 c
PA0.197 ± 0.006 b0.934 ± 0.029 a1.725 ± 0.152 a20.947 ± 1.322 a
PT0.271 ± 0.039 a0.463 ± 0.008 c1.233 ± 0.628 a16.732 ± 1.313 b
Summary of ANOVA test
Tree species***********
Soil**********
Tree species × soil******ns***
Note: PRO: Alkaline protease, URE: urease, DHA: dehydrogenase, ALP: alkaline phosphatase. Values are mean ± standard deviation (n = 3). Statistical significance was assessed by one-way ANOVA followed by a Duncan’s test. Different lowercase letters per column indicate the significant difference (p < 0.05). * p < 0.05, ** p <0.01, *** p < 0.001; ns, indicates no significant effect.
Table 3. Alpha diversity of the soil bacterial community for different tree species.
Table 3. Alpha diversity of the soil bacterial community for different tree species.
SoilTree SpeciesChao1ShannonOperational Taxonomic Units (OTUs)
PO3895.5 ± 24.0 a9.49 ± 0.25 b3849.0 ± 19.97 b
SC3565.1 ± 24.9 b9.57 ± 0.02 b3594.0 ± 16.82 c
Bulk soilPS3863.9 ± 36.1 a10.07 ± 0.28 a3939.3 ± 34.85 a
PA3092.6 ± 23.4 d9.25 ± 0.26 b3062.3 ± 13.32 e
PT3359.3 ± 27.2 c9.30 ± 0.11 b3326.7 ± 14.29 b
PO2893.9 ± 47.0 d8.72 ± 0.16 c2915.3 ± 22.90 e
SC3422.1 ± 43.1 b9.34 ± 0.13 b3351.7 ± 25.38 c
Rhizosphere soilPS3624.2 ± 21.1 a9.89 ± 0.23 a3627.0 ± 19.70 a
PA3587.5 ± 18.2 a9.38 ± 0.14 b3559.3 ± 35.08 b
PT3166.0 ± 28.3 c8.99 ± 0.22 c3191.0 ± 15.13 d
Summary of ANOVA test
Tree species*********
Soil*********
Tree species × soil*********
Note: Values are mean ± standard deviation (n = 3). Statistical significance was assessed by ANOVA followed by a Duncan’s test. Different lowercase letters in the same column indicate significant difference (p < 0.05). *** p < 0.001.
Table 4. Dissimilarity test of bacterial community and functional gene structure for forest restoration in bulk and rhizosphere soils.
Table 4. Dissimilarity test of bacterial community and functional gene structure for forest restoration in bulk and rhizosphere soils.
SoilMethodBacterial Community StructureFunctional Genes Structure
Bulk soilMRPPδ = 0.475δ = 0.115
ANOSIMR = 0.998R = 0.575
PERMANOVAR2 = 0.775R2 = 0.660
Rhizosphere soilMRPPδ = 0.485δ = 0.163
ANOSIMR = 1.000R = 0.947
PERMANOVAR2 = 0.788R2 = 0.758
Note: Bray–Curtis distance of microbial community structure and functional are calculated based on the OTUs defined at the 97% similarity level and SmartChip probes data, respectively.
Table 5. Integrated fertility index (IFI) for different forest vegetation types in bulk soil and rhizosphere soil.
Table 5. Integrated fertility index (IFI) for different forest vegetation types in bulk soil and rhizosphere soil.
SoilTree SpeciesF1F2F3F4IFI
PO−2.017−1.082−1.063−0.569−1.137
SC0.400−2.0580.5150.153−0.265
Bulk soilPS2.974−0.504−1.9680.9180.579
PA−0.6042.8981.0110.8330.749
PT0.799−0.187−1.235−0.481−0.042
PO−3.380−0.270−0.4001.380−1.090
SC0.1200.190−0.220−0.2300.030
Rhizosphere soilPS1.8400.5101.600−1.6600.830
PA0.5802.550−0.170−0.3700.730
PT−0.550−1.9101.750−0.570−0.390
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MDPI and ACS Style

Liu, S.; He, J.; Ning, Y.; Li, J.; Zhang, H.; Liu, Y. Assessment of Artificial Forest Restoration by Exploring the Microbial Community Structure and Function in a Reclaimed Coal Gob Pile in a Loess Hilly Area of Shanxi, China. Forests 2023, 14, 1888. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091888

AMA Style

Liu S, He J, Ning Y, Li J, Zhang H, Liu Y. Assessment of Artificial Forest Restoration by Exploring the Microbial Community Structure and Function in a Reclaimed Coal Gob Pile in a Loess Hilly Area of Shanxi, China. Forests. 2023; 14(9):1888. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091888

Chicago/Turabian Style

Liu, Shuang, Jiuping He, Yuewei Ning, Junjian Li, Hong Zhang, and Yong Liu. 2023. "Assessment of Artificial Forest Restoration by Exploring the Microbial Community Structure and Function in a Reclaimed Coal Gob Pile in a Loess Hilly Area of Shanxi, China" Forests 14, no. 9: 1888. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091888

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